They started by first building technology to double the gross profit of parking lots. Then, they ran into a wall selling it to customers (100+ year old real estate operators).

In Alex’s words “We had unit economic fit. We had product market fit. But we didn’t have go-to-market fit.”

So they decided to raise billions of dollars to acquire parking lot operators, including a publicly traded company. And then they implemented the product themselves.

This has emerged as a legitimate option for AI-native businesses, and I talked to Alex to unpack their venture growth buyout / AI rollup playbook.

We also get into why parking never institutionalized as an asset class (despite being 15%+ of most cities real estate footprint), why parking lots are perfectly positioned to be hubs for robots and autonomous vehicles, and his advice for using technology to grow revenue, not just cut costs.

Thanks to Jamie Siminoff, Adam Bain, Will Quist, and Yoni Rechtman for helping out with topics for Alex on this one!

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👉 Stream on Spotify and Apple

Timestamps to jump in:

• 1:10 Helping 50m Americans park

• 4:00 Building “Buy Now” for the physical world

• 9:02 Real-world checkout technology that works

• 16:07 Why parking never institutionalized as an asset class

• 18:34 Using tech to make parking assets more valuable

• 21:53 Parking lots as autonomous robotics hubs

• 29:07 Going to film school, working at MTV

• 30:55 Starting his first parking data company

• 33:47 Culture of pranking each other

• 36:27 A Fortune 500 CEO convinced him to start a 2nd parking company

• 42:55 Realizing they couldn’t sell to real estate operators

• 46:09 Acquiring a company 10x their size to jumpstart GTM

• 50:20 How to do a successful AI growth buyout

• 54:48 Revenue growth must be driven by technology

• 1:00:33 Why companies should do growth buyouts

• 1:03:55 Being legible to capital

• 1:09:16 You need creativity to take risks

• 1:13:30 AI is the first ever disruption to skilled labor

• 1:19:14 CEO challenges growing zero to 23,000 employees

• 1:24:31 Alex’ personal AI stack

Referenced:

• Metropolis

• Careers at Metropolis

• Roy Amara (Amara’s Law)

Find Alex on X / Twitter and LinkedIn

👉 Stream on YouTube, Spotify, and Apple

Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Alex, welcome to the show.

Alex Israel:

Thanks, Turner. It’s great to be here.

Turner Novak:

I’m excited. I talked to a bunch of people before doing this. I know you told me to talk to Jamie at Ring. I talked to Adam Bain, the guys at Slow Ventures, and Yoni. So I got a bunch of different perspectives, and I think you gave me some ideas too. I think we have a pretty fun conversation coming up.

Alex Israel:

I’m looking forward to it. Now I have to be nervous or intrigued as to what they said.

Turner Novak:

Honestly, it was better than I would’ve come up with by myself. There are a lot of interesting perspectives and narratives to hit on. Probably one of the big ones is that you’re the first, or maybe the most prominent, to pull off this kind of venture growth buyout AI rollup strategy at scale. I’m most interested in hearing how the sauce is made.

Alex Israel:

I was wondering if they took credit for it.

Turner Novak:

They did. They’re like, “Make sure we’re in the lead position for how much work we did here.” But I think the most interesting thing is that we were talking about how something like 50 million Americans have in some way interacted with or used the Metropolis product. That’s kind of massive. And yet a lot of people have never even heard of the company. So what is Metropolis?

Alex Israel:

You’re right, a lot of people haven’t heard of the company. At this point, over 24 million Americans have signed up on the platform, and probably north of 50 million people have actually used our product or our services.

There are two fundamental ways to think about the company. On one side, this idea of an applied AI company. We talk about ourselves as artificial intelligence for the real world. The question is: how do you take these services and products we’re so used to having in the palm of our hand and extend them into the real world?

We’ve leveraged computer vision and artificial intelligence to create seamless experiences everywhere around you. We started with parking. The idea that you could just drive into any Metropolis-enabled facility anywhere in the United States, get a text message when you arrive, and be seamlessly charged when you leave.

That’s one side of our business. The other side is that we’ve rolled up legacy businesses to accelerate our go-to-market. As a result, our services consist of businesses that have been around for almost a hundred years. In just the last few years, those businesses have touched the lives of north of 50 million Americans.

Turner Novak:

That’s insane. So when I’m interacting with a Metropolis product for the first time and I drive my car into a lot, what happens to make the product work?

Alex Israel:

Our goal is to drive what would qualify as a magical experience. You scan a QR code, you enter your credit card, your license plate, and your phone number. From that moment on, you’re a member on the Metropolis platform, which means you have access to the entire Metropolis network.

You pull into any Metropolis-enabled facility anywhere in the United States, drive out the gate automatically, get a text message welcoming you back, and when you leave, you get a text message with your charges. You never have to fumble with tickets. None of the traditional pain points associated with parking.

Turner Novak:

So you do have to sign up the very first time?

Alex Israel:

You do have to sign up for the first time. That’s where we’ve crossed the Rubicon of 24 million Americans signing up on the platform to create those seamless experiences. We started in parking, and now we’re scaling past parking into both mobility and non-mobility based experiences.

Turner Novak:

What are those, for example? I saw some things on the website like restaurants, hospitals. Are these all parking, or is it like placing an order where you walk into Wendy’s and a burger just shows up at the counter?

Alex Israel:

More on that in the future. What you’ll see right now is we’re extending very deeply into the mobility landscape. We’re going deep, and that depth manifests itself in gas stations, car washes, and quick-serve retail. The idea that you can go through a drive-through and you’re welcomed back, your order history is on file, so you can say, “Do you want your same Big Mac or Quarter Pounder?”

There’s a sense of belonging everywhere you go. You’re recognized, your order history is on file, and you can seamlessly pay. At the same time, we’re extending past the mobility landscape and moving further into experiences in your day-to-day life. How can we leverage what we call the recognition economy? How can we leverage biometrics, the kind you’d see with Clear or other organizations, to create seamless experiences everywhere you go?

Whether that’s an office building where you no longer have to check in, or a doctor’s office where you never have to fill out the same forms over and over again. A sense of belonging, a sense of personalization everywhere you go.

Turner Novak:

It’s almost like Stripe for the real world, or Toast for the physical world. Some kind of automated, frictionless checkout experience for things that don’t have a point-of-sale system or a buy-now button on the internet.

Alex Israel:

It’s the buy-now button in the real world. You’re right. We’re so used to online experiences where we just click and it’s seamless, but then we enter the real world. We go into a restaurant, a doctor’s office, a classroom, an office building. We try to check out at a grocery store. We have to show our ID. We have to hand our credit card to a complete stranger.

Think about how payment technology has evolved over time. Most people paid with cash. Then you’d slide a credit card through one of those old readers. Then you’d swipe. Now we tap. And just recently we tap a cell phone. It’s a linear expansion of technology, not exponential, not disruptive.

But why do you need a credit card? Why do you need to prove your identity everywhere you go? Why can’t you just belong? How do we leverage your identity, leverage recognition, leverage the technology that already exists to create these seamless experiences? First starting with the car, then extending past the car and moving with you everywhere you go.

Turner Novak:

How does the actual technology work? I almost get two opinions on this. On one hand, parking seems like it’s just computer vision reading numbers on a license plate. It seems super simple, like anyone could do that with technology from a decade ago or more. On the other hand, you’re talking about biometrics, eye scans, fingerprints. It sounds both really easy and really hard at the same time.

Alex Israel:

Let’s start with the car. I’d say the technology you’re describing has been around since the seventies. Think of it as optical character recognition. You identify a license plate, look up a file in a table, match it with a profile, and charge the person. Relatively easy.

Fortunately, or unfortunately, that’s not what we do. The reason is twofold. One, it’s not very accurate. Those traditional models get to about 70% accuracy. Two, what happens if you can’t see the license plate? What if you need absolute accuracy? What if you’re granting access to a secured government building or a residential building with limited access?

You need near-perfect information, and it needs to be actionable in real time. I need to recognize you, match you with your account, know you’re authorized to pull in, and do all of that in fractions of a second. And I need to maintain that level of accuracy even if your license plate is obstructed.

That’s how we think about biometrics in the context of a vehicle. How do we create a fingerprint of your vehicle? How do we identify it independent of whether your license plate is covered in mud or snow, or you’ve got a bike rack on the back? We’ve captured, I believe, over 250 million images. We’ve trained our data set to identify a vehicle independent of the license plate, where the license plate becomes one variable, not the single most important one.

Turner Novak:

So one of my cars is a beat-up minivan with a dent and a scrape on the side. Would that be in the database?

Alex Israel:

I’d just search “beat-up minivan with dent inside.” But that’s a perfect example. It could be dents, scratches, a bumper sticker, colors. All of these heuristics make up a profile, a fingerprint of your minivan that identifies your vehicle as distinct from another car that’s the same make, model, and color pulling in right after you.

Turner Novak:

I feel like some people listening to this are thinking, “Oh my God, this sounds like a privacy nightmare.” There are tons of potential concerns. How do you think about making sure all this stuff is safe and secure?

Alex Israel:

Privacy is at the forefront of Metropolis. We talk about it at the executive level, the leadership level, the board level, consistently.

On one side, there’s how we secure the data. We use best practices, and we don’t license that data to any third parties. That data is retained by Metropolis and the member. You have your data, and we have a license to use it.

On the other side, there’s what I’d call the fair exchange of value. Why is someone willing to give Metropolis their information? Think about online checkout. You give someone your address and credit card because there’s a fair exchange of value. We carry around these little devices all day. Why do we do that? We see a fair exchange of value.

Think about two concentric circles. One is convenience, the other is privacy. They don’t really overlap. Where a consumer gets involved is where they decide how much privacy they’re comfortable giving up in exchange for something else. You and I are very comfortable giving up privacy in a security context, like going through an airport. That same dynamic presents itself with Metropolis.

If you can’t present a fair exchange of value, it doesn’t work. But if someone is giving you their license plate and credit card, and you’re giving them back time, you’re giving them back what matters most in their lives. Not waiting in line to get out of a parking garage. Getting to the event they want to be at. Getting back to their family for dinner. No one wants to be waiting in line to get out of a parking facility.

Turner Novak:

Waiting in line at a stadium is the worst. You get up between periods, go to the bathroom, you’re waiting in line for a hot dog for 30 minutes.

Alex Israel:

You want to be sitting in your seats with your kids having a wonderful experience. You don’t want to be waiting to get into the bathroom, waiting to buy a Dodger dog, waiting to pay for parking. All of these little pain points erode our most precious asset, which is time. You have to give people something back. There has to be a fair exchange of value.

Turner Novak:

It reminds me of those Amazon Go stores. I’ve used them at stadiums. You walk up, grab your water or hummus or whatever, and walk out. They’re pretty quick and nice to have.

Alex Israel:

Amazon called that “Just Walk Out” technology.

Turner Novak:

You’re almost like “Just Drive Up” technology.

Alex Israel:

Exactly.

Turner Novak:

So what was the pitch when you’d go to a parking lot operator? I feel like parking is notorious for being resistant to technology changes. It just works, and they make money. What’s the pitch to say, “You should use this technology and it’ll do X for your business”?

Alex Israel:

You’re right. The industry as a whole hasn’t really evolved for about a hundred years. Traditional parking was a land play. A guy with a wad of cash, or a box you’d put cash into.

Turner Novak:

Maybe a sign: “$50, Super Bowl parking.”

Alex Israel:

It’s so old world. It’s actually shocking. Parking represents 15% of the surface area of our cities, yet it’s almost not an institutional asset class. We talk about Class A office, multi-family, single-family. When do we talk about parking?

Turner Novak:

You’d think the cash flow is pretty sticky. It’s low maintenance, low CapEx. It’s just a lot with a gate. But you’d also think about developing it into a 50-story complex and making a ton more money on the land. Why has it not really institutionalized?

Alex Israel:

I think there are a few reasons, but this sounds remarkably simplistic: it’s unsexy. Think about it. “What did you do?” “I created the biggest Class A building in New York City.” versus “I buy parking lots.” It’s dirty, it’s gritty, it’s a piece of pavement. There are weird smells and weird associations. It’s the last bastion of non-institutional real estate in the United States.

When we started, we found we could create technology that facilitated a better mousetrap in a few ways. One, it created an amenitized, truly differentiated experience for the consumer. The idea of just driving in and driving out. Two, it reduced the cost to operate these facilities. Three, we captured significantly more revenue. The net result is that we actually increased the value of the underlying property.

So while at first it was a complicated sales cycle, what we found is the product sells itself. It sells itself because of that differentiated consumer experience, and because of the value it drives to the real estate owner by increasing the value of their underlying dirt, which is their primary objective.

Turner Novak:

How does that happen? Are you able to better optimize use of space, charge better prices, use surge pricing? Do they lose less money because of reduced fraud?

Alex Israel:

It’s all of those things. Think about what you’re comparing: a company that’s invested hundreds of millions building this technology, versus the guy with a wad of cash. We founded Metropolis as a technology company from day one. We brought together a group of technologists to build a solution from the ground up, not as a history of parking operators.

We started looking at parking as the first vertical where we could deploy robust applied artificial intelligence to create seamless experiences, but also build a profoundly differentiated product for real estate owners. We reduce fraud, we capture more revenue, and more people choose our locations because they know it’s a Metropolis location.

Turner Novak:

So if there are two parking lots on opposite sides of the street, and you know one is Metropolis and you can just drive in, you’ll always choose Metropolis.

Alex Israel:

That’s the goal. A hundred percent.

Turner Novak:

Where is this going? It hasn’t changed much in a hundred years. When I think about self-driving cars, I wonder: do they even need to park? Does parking go away? Could this either be terrible or really good for you depending on what you say next?

Alex Israel:

We have 23,000 employees in those parking lots, and those Metropolians are driving remarkable value today. At the same time, we see those experiences evolving where that human capital can drive even more value, whether as valets or shuttle drivers.

I’ll also tell you: while Metropolis is the 800-pound gorilla in US parking, we represent 6% of the market. We’re tiny. The question is how quickly we can get from 6 to 12 to 24 percent. That’s about continuing to prove out this value proposition to real estate owners and scaling into new verticals.

There’s nothing more powerful than a real estate owner who realizes they can pay for parking with the same application they use to pay for fast food, get into their office building, and get through tolling. As we expand into new verticals, that becomes self-reinforcing.

On autonomous vehicles, let me dissuade you of any assumption right away: I’m hugely bullish on artificial intelligence in vehicles and on bringing AVs to the forefront of our daily lives. I’m an early adopter, a huge proponent. I have an 8-year-old daughter and I genuinely believe she’ll never have a driver’s license. Around 50,000 Americans lose their lives every year in automobile accidents. I want to take that number to zero.

Now, will it happen tomorrow? No, for a few reasons. Americans’ personal vehicles have a useful life of over 11 years. If you buy a car today, you’re probably going to have it for 11 years. You’re not going to switch immediately to an autonomous vehicle. You might supplement your personal travel with an AV, but the question is temporal.

Here’s why Metropolis is so exciting in this context: we’re the only player at scale that can convert parking into mobility hubs. Where do all these vehicles need to go to be cleaned, serviced, charged, deployed, and to offload data? Without those services, we don’t have level-five autonomy. What you won’t see is some dystopian future where cars just circle the block endlessly waiting for their next ride. Cars are going to need to go somewhere. They need to be stored, deployed, cleaned, serviced.

We have 4,600 locations across the United States that were traditionally archaic infrastructure and are now connected. The question is how you bridge next-generation autonomous vehicles with old-world infrastructure. We’re that API layer. That pragmatism puts us at the forefront of autonomy and allows us to bring autonomous vehicles to reality at a much faster pace.

Turner Novak:

So if I was distilling the Metropolis business model into the simplest, most compelling description, it’s almost like a profitable, cash-flowing, real-world self-checkout business that also happens to be the autonomous robotics hub of cities in the future. Is that a fair way to describe it?

Alex Israel:

We have significant depth and breadth in the business. On one side we have this recognition platform. On the other side, we have verticals, one being mobility and others outside of mobility. How does this technology travel with you everywhere you go?

You’re absolutely right about the convergence from personal car ownership to fleet ownership to individual AV ownership, and how those modalities coalesce into the future of mobility. Metropolis is perfectly positioned to convert parking into mobility hubs. That depth provides defensibility.

And we’ve done something that almost no company in the applied AI or AI space has done: we have a defensible service layer, a traditional business layer. It’s almost as if, almost as if we planned it, this idea of blending next-generation artificial intelligence with traditional managed services.

Turner Novak:

Was it all planned? This is your second parking company. You’d gone to film school, you were working at MTV, and then you became this parking business magnet. How did this all start and evolve?

Alex Israel:

I was very excited to go into a career in film. Starting companies and creating film or television are actually very similar in that you’re taking something from abstraction and bringing it to fruition. You’re taking an idea on paper and building it. The difference is that in film and television, it’s normally a year and then you’re done. With technology startups, you build for a decade and keep building.

When we founded Metropolis, we set out with the ambition that this would be our last company. The four co-founders said: we will build a culture, a company that stands the test of time, and one that we’d all want to remain at. That was an objective from early on.

Turner Novak:

What did you produce at MTV? Were there specific shows or segments you worked on?

Alex Israel:

I was a contract producer working on international segments for international MTV. Red carpet segments, behind-the-scenes content. I loved it. I loved producing shorts. Maybe one day I’ll get back there.

Turner Novak:

So how do you go from that to starting a parking company?

Alex Israel:

It was happenstance. We actually started out building what I’d call a data company. My co-founder and I were late for a movie in Santa Monica. We couldn’t find parking and couldn’t believe it. We realized we could build a reservation and data platform that told people where parking was available in real time, both on and off the street, all over the world.

We pooled our Bar Mitzvah money, raised some family-and-friends money, about $150,000, and flushed it down the toilet. We had no idea what we were doing. We were non-technical. We had no real basis for starting a startup.

Then we started to get traction. We raised a little more money and started getting licensing deals. Large automotive companies and dashboard navigation systems wanted to license our data. We started licensing data to the largest navigation companies globally, from Waze to Google to dashboard systems like TomTom and Tele Atlas, and then most of the largest automotive companies from Ford to Porsche.

Turner Novak:

So I could click a button and see the closest open parking spots?

Alex Israel:

When you get into your car today and navigate to downtown, you’ll see all these little blue Ps pop up on the map. Most likely, that data is still coming from the company I founded and sold.

Turner Novak:

Fascinating. Does that show up on Google Maps, Apple Maps?

Alex Israel:

Exactly. If you go into Google Maps right now and look at all those little parking icons that come up all over the world, that data comes from a company I sold to a Microsoft spinout called Inrix.

Turner Novak:

And do they keep that data cleaned and updated, selling it to Google and these map providers?

Alex Israel:

That business is still up and running and thriving as a division of a larger company. There are executives I hired who are still with that business, almost 20 years later.

Turner Novak:

I know you had a pretty big culture of pranking people internally. What’s the story there?

Alex Israel:

Pulling pranks definitely fades as you get older. At the company I co-founded, ParkMe, we’d pull pranks on one another. These startups are so stressful. You have the highest highs and lowest lows in a single day. We wanted to keep things humor-filled.

I would pull a pretty elaborate prank on my co-founder Sam every birthday. One year we had these glass offices with a door and a glass panel. Everyone went home around eight o’clock and I teamed up with four engineers. We went to Home Depot, bought drywall and molding, and drywalled over his door and window. When he came in the next morning, there was just a wall where his office used to be. It was so well done that he couldn’t figure out where his office was, and he eventually had to Kool-Aid Man his way through the wall.

Turner Novak:

He actually smashed through it?

Alex Israel:

There was no other way to get into his office.

Turner Novak:

Are you still pulling pranks at Metropolis today?

Alex Israel:

Not as much. At the executive leadership team level, we troll each other a lot, mostly via Slack. I asked one of my co-founders for a deliverable on the ELT Slack yesterday, and he responded by saying it was “in that little box under your screen.” We use humor to bring levity. We push each other.

Turner Novak:

You mentioned you sold ParkMe to this Microsoft spinout, and I think I remember you saying you were ready to stop doing parking altogether before starting Metropolis. How did that go?

Alex Israel:

There are two components to that story. When we founded Metropolis, we weren’t really focused on parking. We were focused on this intersection of artificial intelligence, computer vision, and autonomous vehicles. We were thinking about whether we could build what we now call a recognition platform and create seamless experiences.

We thought about grocery stores, we thought about car washes and tolling. We ended up settling on parking because it’s everywhere, because I had some experience in the space, and because we realized that if we could tackle parking, we could build a flywheel that affected tens of millions of people’s lives on a daily basis. That was always meant to be a jumping-off point into so many other verticals.

The other side of the narrative is I was still pushing back. I sat down with a friend of mine, the former CEO of Mattel, who has since passed away, and I told him I had no interest in going after parking again. He basically called me an idiot. He talked about how I had built this idiosyncratic knowledge set, that I knew something deeply about an old-world industry with a defensible, unique data set, and that I should exploit it, go after it, do something truly remarkable in the space. That’s why we settled on parking as our first vertical, and it’s still our most important one. But over the next five years, you’ll see parking become a minority of our total platform.

Turner Novak:

It comes back to: what do you have a competitive advantage in? Just lean into that, because starting a company is hard enough on its own.

Alex Israel:

Completely agree. Brian gave me great advice, and that was exactly it. All great startups, if you go back to Silicon Valley lore of Google, Apple, etc., they all started in a garage. We started in a garage in Santa Monica. Four of us, whiteboards, post-its on the wall. Both of my startups started in a garage. I don’t know, there’s something about a garage. It’s inhospitable but also homey. You can make it your own.

Turner Novak:

So you had this advantage in parking, I’m assuming you hit the ground running, everything worked well, customers were signing up left and right. Is that what happened?

Alex Israel:

Yeah, it was just like a fairy tale. We wrote it down and it played out day by day exactly as planned. No.

Turner Novak:

So what happened?

Alex Israel:

We ended up raising a two-tranche seed round, about $20 million total. The first tranche was $7.5 million, which, compared to my first seed round of $350,000, felt like an exorbitant amount of money. We raised the capital, started to build the team, hired technologists and engineers, started building out our AI platform. This was late 2017, early 2018.

We started putting cameras on the side of the road and testing whether we could catch vehicles. Then we got our first location, which we called M1, Metropolis One, in Venice. Eight years later, we have just under 4,600 locations. But it was definitely non-linear.

We scaled to about 50 locations in Southern California and quickly realized that real estate is just as old-world as parking. Real estate investors like cash flow, they like picks-and-shovels plays, they like low-risk value propositions.

Turner Novak:

They want to go touch the building, feel the brick, pick up the dirt.

Alex Israel:

And they don’t like to take risks. No one in real estate wants to be the first to try something new. You’d sit down with a Class A office developer and say, “We’d like to take the keys to your $250 million building. We’d like to be the first touchpoint for your visitors. We’d like to start with parking and scale past it.” And you’d get something along the lines of, “Cute startup, come back in 50 years.”

The legacy providers these real estate companies work with, construction companies, property management companies, parking companies, many of them have been around for over 50 years. So we shifted strategies. We were going to do something that technology companies do not do: pioneer what we call a growth buyout.

The idea was to take a traditional venture-backed technology company developing artificial intelligence, and actually acquire an old-world business. We were going to buy EBITDA, which is almost a dirty word in technology. Remember Silicon Valley, the show? That scene about being pre-revenue? Tech investors are similar about EBITDA. “No no no, don’t have profitability.” But we went out and acquired EBITDA. We acquired these old-world staffing businesses.

We bought our first parking operator, a company based in Nashville called Premier Parking, probably the 10th or 11th largest parking operator in the United States. We used it to scale our technology to 400 locations overnight. We went from this mom-and-pop technology company to running one of the largest parking operators in the United States.

Turner Novak:

You went from 50 locations to about 450?

Alex Israel:

About 450. Exactly.

Turner Novak:

And Premier was basically a staffing and property management company for parking?

Alex Israel:

That’s exactly how you think about it.

Turner Novak:

How did you acquire them? You’d raised about $20 million, but this company was doing $10-12 million of EBITDA. That’s a pretty big acquisition relative to your size.

Alex Israel:

The company was doing around $10-12 million of EBITDA, and we acquired it for a little north of $120 million. So we had to raise money. We raised from Silver Lake, Dragoneer, and a lot of the usual suspects, including a16z and others. Tier-one Silicon Valley firms. We raised the capital, raised some venture debt as well, and acquired Premier.

We went from just under 200 employees to just over 2,200 employees overnight, from about 50 locations to about 450 locations overnight. We took a company doing a few hundred transactions a day to a few thousand transactions a day. At this point, a new member signs up every one to two seconds, 24 hours a day, seven days a week. Transactions are a multiple of that.

Turner Novak:

You couldn’t just go to Google or Facebook and spend $10 million on ads to acquire profitable customers. You literally had to put things up around hundreds of locations. So it actually made more sense, and probably sped up implementation, to just acquire the existing footprint.

Alex Israel:

At the core, we had unit economic fit, we had product-market fit, but we didn’t have go-to-market. The go-to-market was flawed because we kept hitting a wall with traditional real estate investors. They wanted an institutional operator with proof points, and we needed to use Premier to build that foundation.

What we didn’t know at the time is that the acquisition of Premier was going to be a remarkable success. We didn’t realize we were going to take this traditional business and roughly double its gross profit relatively quickly. So we went back to the venture capital community and said, “We should do this again.” They were pushing for it.

Instead of a slow organic ground war, we could scale inorganically. We raised $1.6 billion as our Series C to acquire the largest operator in the space, SP Plus, which was publicly traded with just over 20,000 employees and just over 3,600 locations. We acquired them almost two years ago now.

Turner Novak:

Were they the biggest parking company in the US?

Alex Israel:

Metropolis is now the largest parking company globally.

Turner Novak:

From garage to largest in the world in a decade or less. That’s crazy.

Alex Israel:

It’s been exciting. What the team has built in a short period of time is remarkable.

Turner Novak:

I feel like this strategy has become pretty common in services businesses. You see it with accounting firms, a dozen AI rollup plays in various categories. What has to be true for this strategy to work? Or does it only work in parking?

Alex Israel:

We pioneered this strategy, and you’re seeing a lot of people now implement it, or realize the value in it, especially through the application of AI. You’re seeing General Catalyst deploy it for hospitals. You’re seeing others deploy it in accounting. It’s becoming a significant component of the future of venture capital.

What makes this strategy successful comes down to one thing, and I couldn’t be more direct about this: people focus on cost synergy. It’s the traditional mantra of private equity, LBOs. Everything is about cost takeout. But cost synergy doesn’t create durable growth.

Any application of AI to an old-world business that focuses only on cost takeout and the reduction of human capital will succeed to a degree, but not at a technology company multiple. They’ll build private equity companies. That’s not a bad thing, but it’s not how you build a next-generation technology company.

In order to succeed with a growth buyout, you need to focus on revenue synergies. You need to drive more revenue through the deployment of technology, not simply the elimination of cost. The simple test: through the deployment of technology, are you seeing more gross profit? Not just, are you seeing costs come out?

Turner Novak:

It’s almost like cost cutting with AI is table stakes. You really should be doing it, but it only truly works if you’re also using it to grow the business much faster than non-technology-native companies in your industry.

Alex Israel:

What you’re finding right now is there are partners and analysts all over the world sitting in investment committees with memos about how they can buy an old-world business and deploy AI. In this model, they’ll be taking technical risk, which private equity firms don’t traditionally want to take, but they’ll have confidence in the underwriting because it all comes down to cost takeout. It’s just not that interesting. It’s not going to be the next trillion-dollar company. It’ll generate decent middle-of-the-road returns, but it’s not how you build a truly compelling business.

Turner Novak:

So what has to be true on the revenue generation side? Because if I’m trying to roll up accounting firms, law firms, or creative agencies, a customer can just leave and go to another firm. Maybe parking is different because once I’m in your lot, I’m committed. Are there certain things that need to be true for the revenue opportunity to play out?

Alex Israel:

You’re right that we’ve built a tremendous moat in parking. But to your comment, the technology itself needs to be what drives the revenue. Take your accounting analogy. The natural inclination with any rollup is: combine firm A and firm B, you don’t need two CEOs, cut all overhead, save 10% of costs. That’s fine, but it’s not going to create durable growth. It’s going to create an uptick in EBITDA but not multiple expansion.

To get multiple expansion, you need to broaden the product and actually drive more revenue from your customers. You need to create a platform that, after rolling up these accounting companies, allows you to generate more revenue from existing relationships. What you often get instead is clients saying, “You’ve reduced costs by 40%, can you share some of that savings with us?”

You need to create durable growth through revenue synergy, through the unique nature of your technology with your partners and customers. It almost reminds me of the Amazon aggregators that popped up during COVID. The model could have worked, but most of them were just cutting costs and aggregating brands. To your point, it might have worked better if they’d expanded those brands into Shopify, Walmart, Target. But a lot of them were also just mediocre products. You need a good underlying asset that actually enables you to lean into higher revenue generation, with or without technology.

Alex Israel:

Look, it could be luxury, it could be premium, but it needs to drive value. The purpose of a technology company is to create products that generate value for customers and partners. If it’s not generating value, you’re engaged in financial engineering. A lot of these rollups are inherently that.

If you look at any company that performs well in public markets, you can distill their value to durable growth. DCF analysis, future value, it’s all tied to how the market perceives the viability of their durable growth over the next five to ten years. Companies trading at a discount? The market is basically saying it doesn’t believe that company is going to grow. That’s why private equity companies are compelling, but they’re not how you build a business that stands the test of time. They’re not how you build the next trillion-dollar company.

Turner Novak:

Do you think more people should be using this growth venture rollup strategy, or is it going to end in disaster if the whole industry adopts it in the wrong ways?

Alex Israel:

I’m a huge advocate, a huge proponent. I think it’s the future. The idea of four co-founders in a garage building something de novo is definitively not dead, especially with the advent of AI. Do I think we’ll have the first $1 billion one-person company? Yeah, I think that’s inevitable. But there are also really compelling solutions to be deployed leveraging a growth buyout, and I think it’s the future.

You’re going to see every major venture capital firm deploying these strategies, and private equity firms moving into a higher-risk venture profile. But people are a little too narrow right now, too simplistic. They’re really thinking about cost synergies, even if they frame them as revenue synergies. The firms that can capture genuine revenue synergy on top of the cost synergy downside protection will be remarkably successful.

Look at our cap table. You have a blend of traditional private equity investors, credit investors, and venture investors all in the same round. You don’t see that. You don’t see those players together in one company. That’s because we have all the downside risk mitigation of a private equity play and all the upside potential of a venture play. That idiosyncratic return profile is why we can work with players like Eldridge and BDT & MSD, but also with Dragoneer and Silver Lake, and also with Temasek and Vista. You don’t see those names on the same cap table.

Turner Novak:

Is that where having the pure private equity guide who wants to cut lines on a spreadsheet, alongside someone like Adam at a16z who wants to grow revenue, helps you triangulate those two aspects?

Alex Israel:

A hundred percent. When we founded the company, I was probably naive about how the capital markets work. I thought: if you can give an investor an opportunity to make money, they should seize on it. You quickly learn that the capital markets operate in pretty narrow boxes. Private equity funds invest here. Venture debt invests here. Private credit invests here. Infrastructure funds. Real estate funds. Very narrow niches.

What we found is Metropolis didn’t sit in any of them.

Turner Novak:

That could be bad. How do you raise money then?

Alex Israel:

It can be very bad. It takes brilliant investors to see the vision and move past those hurdles, and really creative minds to understand how they can play across the capital stack. People like Todd Boehly and Tony Minella at Eldridge have fund structures and capital bases that allow them to invest in venture and also in private credit. They can look at unique businesses and operate outside the box.

I’ll tell you: we’ve had limited success at the VP or analyst level raising money. Historically, if I got on the phone with a VP or principal, almost nothing happened. It took a partner with a significant level of investment experience to understand the vision of Metropolis and where we’re going. So we stopped talking to junior team members, not because we undervalued them, but because they weren’t going to be able to sell their investment committee on the model. Now it’s changed because we have the track record. We have a business generating over $2.1 billion of revenue. But early on, it was very complicated.

Turner Novak:

Have you heard of this concept of being legible to capital?

Alex Israel:

No, but tell me.

Turner Novak:

It’s basically what you just described. How familiar would an investment committee be with this pitch? A memo shows up in the meeting, there are 10 people sitting there, mostly finance people reading the same things, doing the same things, investing in the same businesses. A pretty legible pitch right now is, “We worked at OpenAI for four years, trained some of these models, and now we’re leaving to start this new thing.” That’s the most legible pitch you could possibly have. Versus something like: “It’s kind of real estate, there are some cameras, it’s payments in the real world.” That’s not as legible. Even if they kind of got the idea, they’d think: what can I actually get through IC? They’d just do the thing that’s more trendy, the thing they can talk about at a happy hour and look good at investor networking events.

It’s gotten worse as funds have gotten bigger and capital flows have gotten more pronounced. A lot of investors think about positioning themselves at the beginning of capital flows. Where’s capital going to flow? Robotics? Humanoids? Space? I need to deploy capital into this theme and ride the narrative of this sector, whether or not the individual company makes sense. It’s more about exposure to narratives or themes. There’s a lot of alpha, I think, in just understanding a core new stream of cash flow that’s opening up, whether it’s an individual business or not.

Alex Israel:

For me, if you think about human behavior and decision theory, what you find is that people are inherently risk averse, especially about their careers. If you think about government bureaucracy or a venture capital shop or a private equity firm, people not only stay in their lane, they don’t want to be creative. Creativity exposes them to career risk, personal risk. No one’s going to get fired for investing in OpenAI.

Turner Novak:

You’re going to get promoted because you got allocation in the hot deal.

Alex Israel:

Exactly. Ninety-plus percent of investment is FOMO. There are market forces at work and people are investing out of fear of missing out. There’s no creativity, no unique thought. “This firm is investing, so we should invest.” People like to justify their existence and say that’s not the case, that they’re different. It’s the same way people like to think about AI. “I’m not going to be affected, my job is safe.” Your job is not safe. Everyone’s job is going to be affected.

Most people in venture and private equity globally are lemmings. Most people at startups are lemmings. Most people don’t focus on creativity and don’t want to take the risk. That’s okay. It’s fine to be risk averse. But I think it’s not okay to be a hypocrite. It’s not okay to claim you’re going to be creative and do idiosyncratic things and then not do them.

There are a small handful of investors who are actually doing unique, creative things with capital that haven’t been done before. It’s rare. It takes a level of risk tolerance and creativity that most people don’t want to deploy, even if they’re capable of it.

Turner Novak:

It’s almost riskier to take risks as an investor than as a founder. As a founder, you’re acting on it, you’re doing it. As an investor, you’re a middleman deploying someone else’s capital. It’s almost like the asset class has gotten too institutionalized.

Alex Israel:

Most investors are in the business of raising capital, not deploying capital. Most generate more personal return off their management fees than their carry. So they’re focused on fundraising constantly.

Then you get the rare investor who is truly focused on doing innovative things, on building not only a unique firm but investing in unique companies or building those companies from the ground up. You look at what Todd Boehly and Tony Minella have done at Eldridge. What Robert Smith has done at Vista. What Byron Trott has done at BDT & MSD and Michael Dell. Really interesting entrepreneurs, not just investors. They’re building entrepreneurial firms and injecting that spirit into their organizations. That’s really interesting. But it’s rare.

Turner Novak:

One thing you mentioned earlier is that AI is going to change a lot of skilled, white-collar labor. It’s one of the first times a technological revolution is hitting that end of the spectrum. How should someone position for that in their career? Is the key just to get really good at AI, become the AI expert at your organization?

Alex Israel:

On the personal level, you need to move as quickly toward being an evangelist as possible. Otherwise, the disruption to your personal career is going to be profound.

On the macro level, I think people are getting this wrong and not talking about it. We talk about industrial revolutions historically as the repurposing or retraining of the low-skilled workforce. The Uber driver can be retrained. The factory worker can learn to operate an automated line. It’s an adjustment, an upskilling.

What people are not talking about right now is what happens with the combination of artificial intelligence and the advancements in robotics. We’re affecting both ends of the spectrum. What gets really interesting is what happens when you take a designer, a lawyer, an accountant, a doctor who has trained and worked for 15 years, and say, “You’re no longer needed.” And at the same time, you tell the plumber, the janitor, the chef, the line cook, and the Uber driver the same thing.

We’ve never seen an industrial revolution affect both sides of the bell curve simultaneously. We’re not talking about it because it’s scary and it could seem like fearmongering. But we need to embrace it, understand it, and address it as a society. That’s why you have people like Elon Musk jumping to UBI. He’s arriving at the conclusion without fully diagnosing the cause. I think we need to do both.

I like to think about Amara’s Law: we have a natural inclination to overestimate the impact of technology in the short run and underestimate it in the long run. That’s exactly what we’re doing with AI, especially in combination with robotics.

Turner Novak:

Amara’s Law. A-M-A-R-A. I’ve definitely heard that. It’s like: we overestimate what’s going to happen in the next two years and underestimate what happens in the next ten. Do you remember peak Web3 in 2021? Everyone thought every flight ticket would be an NFT, every meal at Wendy’s would come with an NFT. And then the same thing with autonomous vehicles. Ten years ago people said it would be here in two years. We kind of forgot about it. And now Waymos are quietly working really well in some cities and most people don’t even talk about it.

Alex Israel:

We go from science fiction to entitlement overnight as a society. Ten years ago we didn’t have Wi-Fi on planes. Now if you don’t have satellite internet, you’re panicked. We’re entitled instantly. And yeah, I think we’re grossly underestimating the long-run impact.

Speaking of impact: you mentioned I went from a couple hundred employees to 20,000. The scale of that change may be one of the largest magnitude of CEO transitions ever. How did you do that? Or how are you doing it?

Turner Novak:

How has the CEO role changed as the company has scaled that quickly? What does your day-to-day look like?

Alex Israel:

The role shifts dramatically over time. It goes from heads down, sweeping the floors every day, to doing podcasts, press interviews, and government affairs. What remains the same is that you’re always focused on the biggest problems facing the company at any given moment.

You don’t get the great news. You don’t get the good news. You get the biggest problems, the things your best people couldn’t solve. Something rises to your desk because it’s a nightmare. So you’re constantly battling the most difficult challenges facing your organization at any given moment.

As a CEO, you have to step back and ask: what are actually the greatest imperatives facing the organization right now? Make sure your attention is focused there, not just on the single fire that’s in front of you. You need to balance those two things. And I think that holds from four people in a garage to 23,000 employees across the world.

Am I succeeding at it? Am I failing at it? I’m learning every single day, and I’m humbled by the team willing to put me in this position, our investors, our board, my co-founders. I like to say we’re “failing up”: you’re running into walls, failing, falling down, and getting right back up. I’m very excited about what we’ve built, and even more excited for the future. The true sign that this is your last job is that you wake up, not every day, but almost every day, more excited about the business than the day before. That’s absolutely the case with Metropolis.

Turner Novak:

There’s this concept from Sam Harris called “Begin Again.” I’ve never heard of this before. What is it exactly?

Alex Israel:

Sam Harris talks about “Begin Again” in the context of meditation. The idea is to take a moment, take a breath, and begin again. Being a founder can be so emotional. We talked about the rollercoaster of highs and lows, sometimes in the same day. Just closing your eyes, breathing, and beginning again. I find it very calming and reassuring.

So many things happen to you as a founder that are outside your control, but your breath is always inside your control. And this idea of beginning again keeps you from ruminating on the past, which can be so unhealthy. It allows you to learn from the past while focusing on the future. I may be bastardizing what Sam Harris is actually teaching, but I love the concept.

Turner Novak:

Do you have a personal AI stack? How do you use AI day-to-day? Are you pretty vanilla, mostly ChatGPT? Any cool new tools you’re using?

Alex Israel:

On the spectrum of CEOs across the United States in terms of total capability with the technology, I’d say I’m in the bottom tier. This technology is capable of so much and I’m just scratching the surface. We’re a technology company doing profound things. I’d say we’re definitely on the cutting edge in terms of how our team is leveraging AI internally, and also in terms of what we’re building, namely computer vision. But personally, I’m probably leveraging Claude and Anthropic more than anything else, while also using Gemini and OpenAI every single day.

Turner Novak:

When you talk about interesting things you’ve done internally, anything that another founder might find useful?

Alex Israel:

One thing we’ve focused on is where AI innovation comes from. Is it top-down, bottom-up, or dispersed through the organization? We’re spinning up a Chief AI Officer role right now. We’ve hired that person and placed them in the role. Their job is to streamline and build tools for our organization directly tied to artificial intelligence, and to balance top-down and bottom-up adoption. The goal is to deploy AI at scale to all 23,000 employees, not just a limited set.

Turner Novak:

What does the background of that person look like? Because it could be almost anything.

Alex Israel:

This individual has experience in both large-scale organizational transformation and technology. They can manage engineers, and they’ve been involved in large-scale transformation on the consultancy side. Critically, this person already works at Metropolis today and is highly trusted by all of the department leaders across the organization. They have that foundation of internal trust, a background in driving large-scale transformation, and the technical background to adopt technology at scale with speed.

Turner Novak:

So the idea is to build tools and processes that the rest of the company uses to get work done faster, more efficiently, more profitably.

Alex Israel:

A hundred percent. It comes back to revenue synergy. How do we focus our team members on their highest and best use? We have 23,000 employees. What is the best way for each of them to drive value for our partners at any given moment?

Turner Novak:

Cool. Alex, this was a lot of fun. Thanks for coming on the show.

Alex Israel:

Of course, Turner. Thank you for hosting. This was great.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

Karthik co-leads the University of Michigan’s newly created Institute of Agentic Computing. This is Karthik’s first public conversation on the new institute, which will serve researchers and developers building and applying agentic AI to advance scientific discovery, engineering, and beyond. It will also address broader issues of responsible development and governance, and serve as a central node for managing developers and maintainers of the OpenClaw platform.

We talk about how AI is transforming scientific research, two exciting scientific discoveries made with AI that were demoed at ClawCon in Ann Arbor on April 16th, how universities actually work, why some academics think AI is just a fad, how AI has changed education, and the code red advice Karthik gave his students a few weeks ago.

This is the first time I’ve had an academic on The Peel. Let me know what you think and if I should have more on the show!

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👉 Stream on Spotify and Apple

Timestamps to jump in:

• 0:25 The Institute for Agentic Computing

• 4:27 OpenClaw Foundation and Lobster Compute Company

• 8:19 How Universities actually work

• 12:33 ClawCon in Ann Arbor

• 15:24 Two scientific discoveries made with ScienceClaw

• 20:06 How AI is speeding up scientific discovery

• 25:42 Supporting AI and OpenClaw development

• 29:55 Why universities function like VC funds

• 34:29 How universities get money from the government

• 40:55 Why some academics believe AI is a fad

• 46:17 Biggest bottlenecks in AI today

• 49:26 How AI will change the world

• 53:10 Karthik's Code Red advice for students

• 59:19 Separating learning and doing

• 1:03:10 Ways COVID and AI impacted college students

• 1:14:53 How the role of universities is changing

• 1:23:21 Why college classes suffered from grade inflation

• 1:26:05 How AI is actually impacting the job market

• 1:32:49 Karthik’s advice for students

• 1:39:16 Winning two NCAA basketball national championships

• 1:43:04 Almost dying in the Grand Teton National Park

Referenced:

• Karthik’s bio at UMich

• Institute for Agentic Computing

• OpenClaw

• ScienceClaw

• MIT OpenCourseWare

• ASU iPhone video

Find Karthik and LinkedIn

👉 Stream on YouTube, Spotify, and Apple

Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Karthik, welcome to the show.

Karthik Duraisamy:

Happy to be here.

Turner Novak:

Yeah, thanks for coming on. I think it’s going to be fun. You guys just announced a bunch of things at the University of Michigan. We’re going to talk about that and then also talk a little bit about how AI is changing research, education, finding a job, the economy. But to kick things off, what did you guys just announce at the university?

Karthik Duraisamy:

So first of all, I think it’s an incredibly exciting time to be alive given everything that’s happening, not just around AI, but in science and research more broadly. At ClawCon, we announced the Institute for Agentic Computing, which is a partnership between the OpenClaw Foundation and the University of Michigan. The goal is to develop responsibly powerful agentic frameworks that people can use for a wide range of things. There’ll be a core team developing agentic infrastructure, and then a large number of people working with those developers to apply those frameworks across many different fields. Any field humans have ever touched, I think, will be agentified.

Turner Novak:

And how did this come about? The creation and the thinking around starting this?

Karthik Duraisamy:

Yeah. So first of all, OpenClaw is one of the most popular software frameworks out there right now.

Turner Novak:

And what is OpenClaw for someone who doesn’t know?

Karthik Duraisamy:

So Peter Steinberger and his team introduced OpenClaw in November. At its core it reduces friction, it basically gives you a very strong personal AI assistant that you can use to automate a wide range of tasks. A lot of the initial use was people running it on their laptops to automate manual things: emails, communications, files, that kind of stuff. But that’s scratching the surface.

Then people started using it as a social agent. I use OpenClaw to train my own agent, and now my agent can talk to your agent. They start doing interesting things, some of which we control and some of which the agents do on their own. The way I think about it: you had chatbots, which came on the scene about three years ago. Over the last year they’ve become extremely powerful. But those are just giving you ideas. You type something, it gives you an idea, you use it, you go do something.

Turner Novak:

It’s like better Google, sort of.

Karthik Duraisamy:

Yeah. Much better Google that can also give you cognitively powerful things, put ideas together. Those are chatbots. And then you have agents that act. It’s not passive, not just giving you information. Agents act upon information.

Turner Novak:

It’s like saying, “Hey, go order me a pizza.” And it goes and calls Domino’s on the website, places your order, and you get a pizza.

Karthik Duraisamy:

Exactly. Maybe it’ll deliver the pizza too. Soon.

Turner Novak:

The robots. Yeah, the autonomous robots.

Karthik Duraisamy:

Yeah. And that’s another thing, agents are not restricted to software. They act in the physical world too. People are already controlling their own personal robots with OpenClaw.

Turner Novak:

Oh, really? I didn’t know that was happening already.

Karthik Duraisamy:

Yeah. So anyway, if you think about the progression: personal chatbots, then personal agents, and I think the next evolution is social. The social and economic infrastructure around which our society is organized, all of that may now be agentified. I could have a few agents of my own that encode some of my skills and expertise. You could have a few of your own. They can talk to each other, collaborate, and it can be very decentralized. If you want one sentence: OpenClaw is like an operating system for the agentic world.

Turner Novak:

And OpenClaw is open source, correct? And there’s a foundation attached to it.

Karthik Duraisamy:

Correct.

Turner Novak:

How does this all relate to the institute and the university?

Karthik Duraisamy:

OpenClaw is an open source project and that’s really why it caught fire. There are probably more than three million users right now, and it happened in almost no time. The OpenClaw Foundation was formed to make sure OpenClaw stays open source. And many of the core developers, like Peter Steinberger’s team, will be in the institute as well. Think of that as the core layer, and then there’s a layer around them: people at the University of Michigan, collaborators, pretty much anyone around the world who’s interested in taking these frameworks and adapting them to their specific domains.

Turner Novak:

And I know we got connected through Dan in the investment office at the University of Michigan. They’re involved in this a little bit, they funded something, I think they funded a company that’s sort of a third party also helping. Can you explain what’s going on there?

Karthik Duraisamy:

Right. So the University of Michigan’s fund is heavily involved in the OpenClaw Foundation. And they also formed a new company called the Lobster Compute Company.

Turner Novak:

Lobster Compute. That’s awesome.

Karthik Duraisamy:

The OpenClaw Foundation is purely doing open source, basically supporting the developers. And Lobster Compute is the investment wing. That’s where money goes in for startups and other things.

Turner Novak:

It might be interesting then for people to understand how universities work. I had no idea this is how it works until we talked the other day. Can you explain the whole setup, university, institutes, departments, all of this?

Karthik Duraisamy:

Yeah. Even without the Institute for Agentic Computing, universities are fascinatingly organized places because they do many different things.

Turner Novak:

Yeah. It’s not just teaching classes.

Karthik Duraisamy:

It’s not just teaching classes.

Turner Novak:

It’s like a small percentage.

Karthik Duraisamy:

Yeah. Advancing research, innovation, startups.

Turner Novak:

You’ve got sports teams.

Karthik Duraisamy:

Sports teams. Hospitals, especially at Michigan. These are very complex organizations. But if you want to break it down, the core of the university lives in departments. The department of physics, aerospace engineering, sociology. That’s where students get admitted, take courses, get their degrees, mainly from a teaching perspective but also research. And research is not siloed. If you’re in aerospace engineering, you don’t just work on aerospace engineering. A lot of the interesting problems come when areas intersect.

Turner Novak:

So it could be the intersection of ethics in aerospace or something like that.

Karthik Duraisamy:

In fact, we have a research area in space ethics.

Turner Novak:

Really?

Karthik Duraisamy:

So you’re not far off. The engineering school and the business school have a joint program, for instance. Think of departments as the low-level units where faculty are hired, tenure is given, students are educated. On top of departments we have institutes. Institutes bring together different departments, different researchers, different students. The institute I direct is called MICDE, Michigan Institute for Computational Discovery and Engineering. We have faculty and students from around 40 different departments, all exploring different aspects of computing for science. Institutes sit on top of departments and bring people together to do interdisciplinary research.

Turner Novak:

So how many departments and institutes are there at the University of Michigan?

Karthik Duraisamy:

That’s a hard question. I don’t think anyone knows.

Turner Novak:

Okay.

Karthik Duraisamy:

I’m just kidding. The rough number is about 200 departments. And one of the amazing things about Michigan is pretty much all 200 would be in the top 10 of any ranking you can imagine. It covers all areas of human activity. Institutes would number in the dozens. There’s the institute I direct, the Institute for Agentic Computing that we just announced, the Institute for Firearm Safety, the Institute for Social Research, which is the largest social science organization in the entire world.

Turner Novak:

Yeah. My mother-in-law actually worked there for probably about a decade.

Karthik Duraisamy:

Wonderful. So institutes of various sizes and scopes bring together faculty from many disciplines to go after some grand challenge problems.

Turner Novak:

So you just announced some research at this thing called ClawCon, by the time someone’s listening to this it’s already happened. What is ClawCon for someone who’s never heard of it?

Karthik Duraisamy:

Think of it as a gathering place with a spectrum of people, core developers and heavy users of OpenClaw, but also people who are just genuinely curious about what’s happening. Typical tech meetups cater to a very specific audience. ClawCon is more democratic. It doesn’t distinguish between an ordinary person interested in AI and somebody who’s a big developer. You have powerful keynote demos that show how these things can change science. You also have very basic things like, what is OpenClaw? How do I install it? It runs the whole gamut.

Turner Novak:

So it’s a meetup conference kind of thing, a bunch of enthusiasts and experts and heavy users coming together to spend time, do some demos, do some presentations.

Karthik Duraisamy:

Correct. Mainly focused on getting people to meet, getting people to talk, showing some demos, maybe sparking some ideas.

Turner Novak:

So you just announced some new research. What did you announce?

Karthik Duraisamy:

Yeah, this is pretty amazing. Some of our colleagues at MIT and our group have been working together on scientific discovery using new AI agents. The MIT collaborators developed something called ScienceClaw, the big question it’s trying to answer is: how can science change when you’re combining humans, agents, and powerful tools? And all of it runs completely decentralized. It’s the next generation of collaborative science.

We showed a couple of examples at ClawCon. The first one: what do cricket wings, baroque choral music, and composite materials have in common? We had agents in biology, agents that understand material properties, and agents that understand music, all collaborating in a decentralized way, finding a part of design space that was unexplored and ending up discovering some incredibly useful material resonators.

Think of those agents as experts who know a lot about their own topic but not about the synergies between topics. When you bring them together, interesting things happen.

Turner Novak:

What could you use it for? What’s something someone might actually make with this?

Karthik Duraisamy:

Think about resonance. If you have a material excited by a disturbance, you want it oscillating at a certain frequency. The operational use is very broad. It’s not like this was discovered today and I’m going to use it tomorrow. But if you can now design material resonators for any property you want, and agents are finding the process to achieve that design, that’s a real step forward.

The second application we showed is superconductivity, which is more straightforward to understand. If you can pass energy across a medium with zero loss, no dissipation, that’s a superconductor. The problem is pretty much all known superconductors only work at extremely low temperatures, close to absolute zero. Minus 100, minus 200 degrees. Just not practical for most applications. We’re using agents to search for superconductors that work at much higher temperatures, room temperature, ideally. I don’t want to give the impression that we ran the agent and won a Nobel Prize. This is the first step in a long chain. Next, you have to build the material, test it, get information, come back. But this first step is actually the hard one, because the possible space of superconducting materials and configurations is enormous. Without these newer techniques, searching that space would take a very, very long time.

Turner Novak:

So it might be interesting to talk about the technique, but even before that, the old technique. How would you discover a new hypothesis in science? And how is that changing with AI?

Karthik Duraisamy:

Let’s break down the scientific process. You make observations in nature. You write down a theoretical model that explains the observation. Then you manipulate that model to get the property you want. These are fairly simple models at first, still the mental map.

Turner Novak:

This is the scientific method we probably learned about in high school.

Karthik Duraisamy:

Exactly. Theory, observations, experiments. Then you go to more detailed models, the kind that a lot of people in the institute I direct actually run. Very detailed models of a particular physical process. You gain more insight, you run an optimization. But computation is different from reality, so then you go build whatever you’re designing, do the experiments, take measurements, and iterate. All of these steps still have to be followed in the age of AI.

Until recently, all of these steps were done in sequence by different people. A theoretician might take years on the first part. Then a specialist in computation runs the models and optimizations. Then a specialist in measurement handles the experiments. Then you put it together and maybe you have an outcome.

Turner Novak:

Are you waiting for other people to be done with their phase while you’re working on multiple things at a time?

Karthik Duraisamy:

Generally, yes, it was sequential. Even before AI, some of that had been made more simultaneous. But what AI has done, and promises to do in more areas, is make all of it run at the same time. Especially with specialized agents, you can do decentralized science. You don’t need to know everything about every domain. I still think expertise matters, but it accelerates the whole process.

Turner Novak:

So how does that actually work? Did you need AI to do this? What does it unlock?

Karthik Duraisamy:

That’s the thing. AI is an enabler. Think about AI having access to skills and tools. Skills is my scientific expertise, encoded as a set of rules, literally in a text file.

Turner Novak:

So you’re an expert on aerospace physics. You know everything about how aerospace intersects with the world. And that’s all you know, and you don’t know anything else.

Karthik Duraisamy:

Yeah. Plus I have a certain kind of insight, a certain flavor of the scientific method, that I can also build into that. My approach might differ from yours because we have different tastes, different perceptions, different fields. All of that can be encoded as skills. Agents have access to skills.

And then there are tools. A simulation software that takes a real-world problem, turns it into a computation, helps you understand and manipulate reality virtually. Tools could also be experimental facilities, a lab setting, robots doing repeated experiments, all connected and coordinated by AI. Human expertise plus AI plus tools is what makes this happen. Sometimes the reasoning capabilities of these models are so powerful, especially the last six months or so, that AI seems to be identifying some of the human skills on its own. But I still think human expertise matters. Short answer: AI with access to tools and skills is what makes all of this possible.

Turner Novak:

And this intersects with the institute. What is the institute going to be doing practically on a daily basis?

Karthik Duraisamy:

At least at the beginning, the core job is making sure the open source development of OpenClaw is done well and maintained properly. There are so many users, and they need confidence that this is going to stay open source and keep being developed.

Turner Novak:

And there are two other people running the institute with you.

Karthik Duraisamy:

Yeah. For now the leadership team is myself, Professor Brad Orr from the University of Michigan, and Kurt Skipstad from engineering. That’s the initial team. If you think about the core piece, there are the core developers and maintainers. Then there’s the next layer, like my students or colleagues’ students who want to apply this to specific problems, not just using the software but adapting it for specific use cases. And the reason it makes sense to center this in a university is we have people in every possible discipline, economics, sociology, medicine, engineering.

Turner Novak:

So you can just say, “Hey, we need legal experts,” and there’s just a person who works for the school who’s the world expert on that thing.

Karthik Duraisamy:

In many fields, the world expert is sitting right here. But the other important point is it’s not just about developing and applying software. There’s also the physical piece, robotics is important and we have an amazing robotics department. But also developing this responsibly, and in a sense preparing society for what’s coming. Because a lot of the apprehensions people have about AI aren’t even about the technology, it’s about how fast it’s moving and how it disrupts existing structures.

Can we design institutions that are ready for this? Can we do this responsibly? And I want to make it clear: this isn’t just the OpenClaw Foundation and the University of Michigan. That’s the core, the central node. For this to succeed, we need representation from everywhere in the world. Think of it as a meeting place for that.

Turner Novak:

So could I get involved, as a person who doesn’t work at the university or OpenClaw in any way?

Karthik Duraisamy:

Absolutely. There are three million users of OpenClaw right now, probably around 500,000 GitHub repositories. In a sense they’re already part of the ecosystem. This centralizes some of the most important effort, but it’s a window to developers and contributors around the world. And the other thing I want to emphasize: nobody can predict with any confidence how the technology is going to evolve in the next year or two. So we’ll be very adaptive to the changing scenario, but the goals are clear.

Turner Novak:

Yeah, this might be completely outdated in six months.

Karthik Duraisamy:

I don’t think the core methods will be outdated in six months. A particular piece of software might change, but things do last longer than that. Many other things will change in unpredictable ways, though.

Turner Novak:

You mentioned that these institutes are almost like a VC fund in a way. Can you explain that?

Karthik Duraisamy:

When people ask what a professor does, they think our entire job is teaching. It’s certainly not. There’s teaching in the classroom, mentoring research students, mentoring PhD students to do original research, leading a research group, running a lab doing world-leading work in your particular domain.

One way to think about almost every professor at a top research university is like a startup founder. You’re recruiting some of the best students in the world. I just completed my PhD admissions, I got 200 applications and accepted maybe two. And you’re competing with MIT and Stanford. Just like a startup, you’re fighting other places to recruit top talent. And all of that costs money, so you raise it. Just like every startup founder does.

Turner Novak:

It doesn’t just show up?

Karthik Duraisamy:

It does not show up, unfortunately.

Turner Novak:

So how does the funding work at the University of Michigan?

Karthik Duraisamy:

Normally the largest portion comes from the federal government, NASA, National Science Foundation, Department of Energy, Department of Defense, NIH, etc. We write proposals on research ideas. It’s very competitive, maybe one in five proposals gets accepted on average. At Michigan it’s higher, but one in five is the norm.

Turner Novak:

And this is basically saying, “Hey, government, I have this idea. Here’s the impact it could have on the world. Give me $10 million to work on figuring this thing out.”

Karthik Duraisamy:

Pretty much. You present evidence: here’s my past work, here’s some preliminary results that show a new direction is promising, here’s my five-year plan. Then you ask for funding. Very competitive.

Turner Novak:

What makes it so competitive?

Karthik Duraisamy:

There are so many excellent universities in the world. It’s not just Michigan. There are so many other research groups pushing the envelope. Anyway, coming back to the original question, being a professor is like being a startup founder. And institutes that bring together different faculty can be thought of as incubators or VCs. In MICDE, the institute I direct, we often identify an interesting direction of research that isn’t mainstream yet. We bring together faculty, students and postdocs, build critical mass around that area, and give some seed funding. We have something called the Catalyst Grants Program.

Turner Novak:

So this is without going to the government.

Karthik Duraisamy:

Exactly. Smaller amounts, not $10 million, but say $100,000. People can use that to explore an idea, and then the institute helps those professors put together a bigger proposal. We recently won a $20 million center from the Department of Energy, which we’re very proud of.

Turner Novak:

What was that?

Karthik Duraisamy:

It’s called the Predictive Science Program. A new center called CPRIME, Center for Prediction, Reasoning and Intelligence for Multiphysics Explorations. Expertise, computation, and AI coming together to address a real problem. So yeah, institutes as incubators, that’s a pretty good analogy.

Turner Novak:

What does a good research proposal look like? How do you know if something is worth spending time on?

Karthik Duraisamy:

We don’t send proposals in a completely blue-sky sense. Some foundations say “give me your best idea,” but that’s rare. Normally the federal government has specific requirements around a topic.

Turner Novak:

So they have like a request for research they put out.

Karthik Duraisamy:

Yeah, it’s called an RFP, request for proposals. Someone may be interested in nuclear fusion, propose ideas that can improve the efficiency of fusion.

Turner Novak:

So there’s someone at the federal government level who’s in charge of dishing out these grants. They might say, “We want work done in nuclear fusion,” and “We have $2 billion earmarked and we might fund 20 projects.”

Karthik Duraisamy:

Generally it’s not $2 billion, but something like that. The right order of magnitude: funding a PhD student for a year costs about $100,000. A full PhD is around $500,000 over five years. Faculty members typically have five to ten students. So median research budget per faculty member is probably around $500,000 a year.

To give you a sense: Michigan has the third-largest research program in the US by dollars. Total research activity is about $2.2 billion per year. Not all of it is federal, I’d say about 60% is. The university itself puts in around $700 million a year, which I believe is more than any other university spends on its own research. Then state funding, industry funding. A whole range.

Turner Novak:

This is basically like a corporation outsourcing its R&D to a university, saying, “Do this for us, we want to make products from it”?

Karthik Duraisamy:

Sometimes they’re looking for a specific solution, like outsourced R&D. Sometimes they’re looking for good ideas. Sometimes they’re looking for due diligence because we’re experts and we know how to judge things. It’s a combination.

Turner Novak:

And what’s the benefit for the university? What do you get out of doing research that some other company benefits from?

Karthik Duraisamy:

Well, first of all, they pay us to do it.

Turner Novak:

Okay, fair.

Karthik Duraisamy:

That’s a start. But beyond that, after Stanford and MIT, Michigan is the third-largest producer of spinout startups from university research. About 32 or 33 startups per year. So it’s not like money comes in and we just produce papers and graduate students. Innovation is happening, and some of our colleagues have done really well in that area. But we don’t take money just because it is money. Every faculty member is interested in furthering the boundaries of knowledge in their area. If the funding is aligned with that, that’s the way of having impact. Training students is impact. Solving problems that somebody cares about is impact. Pushing the boundaries of research for the sake of it is also impact.

Turner Novak:

So with a startup, the university owns some of the equity, and when there’s an exit, it goes back to the university? How does that work?

Karthik Duraisamy:

If it’s a startup spinning out of research that happened at Michigan, funded by the government or otherwise, yes, the university takes some equity and some part of the IP, royalties, etc. But I have a startup myself, and the university is actually very fair. They’re not here to make money off it. They genuinely want to help innovation grow, want the impact of faculty and students to be higher. There’s certainly some economic benefit too, but that’s not the primary thing.

Turner Novak:

So I think it might be interesting to talk about how the world is changing because of AI. You mentioned there are a lot of academics who almost don’t believe in AI. It seems like you’re all in on it. You’ve basically created initiatives to lean into it and you’re using it to do research. But some people think it’s a fad. What’s going on there?

Karthik Duraisamy:

I think with something like AI, multiple things that seem contradictory can actually both be true at the same time.

Turner Novak:

Okay. How so?

Karthik Duraisamy:

Some very learned professors say, “I asked AI this question and it hallucinated a nonsensical answer, therefore it doesn’t work.” But they did that two years ago. A lot has happened since then. The core thing is you have to separate the marketing and hype from the actual model capabilities and the scientific value. Sometimes people mix those issues. In some domains, AI is already incredibly useful, coding, mathematics, theoretical physics. In others, it’s still catching up. Both things are true.

Turner Novak:

So how is it good in those things? In your domain, where are you seeing AI being extremely useful?

Karthik Duraisamy:

Why are coding and math more favorable for AI? Because if the AI does something wrong in those domains, you know immediately. If it produces wrong code, it won’t compile. Or you run it and get the wrong answer. The feedback from the output of the model back to its thought process is very direct. These are called objective metrics. That’s where AI is already very powerful.

And the other thing that’s been remarkable about the latest frontier models is their ability to compose ideas from different fields and bring them together seamlessly. Half-baked idea here, half-baked idea there, they see patterns and merge them. That’s genuinely powerful. And many people use AI only in chatbot mode. They don’t take advantage of the tools that can be built around these models. In mathematics, for instance, there are things called interactive theorem provers. AI can suggest something, it goes into the theorem prover, and the theorem prover gives important feedback back to the AI. If you’re just using it as a chatbot, you’re missing a lot. We come back to the same formula: human expertise plus AI plus tools. That’s when you see the real benefit.

Turner Novak:

So it’s not just, “AI, cure cancer,” and it goes and does it?

Karthik Duraisamy:

Not possible. In some domains, AI is proving mathematical theorems that people hadn’t touched before, either for lack of attention or lack of patience. But cancer, fusion energy, most hard practical problems, those have to go through the full scientific process. If AI identifies a drug candidate, that has to be verified computationally, then verified through trials.

Think about bottlenecks. When the bottleneck is purely cognitive, like math, which is all cognitive, AI can probably go all the way soon. But in many problems the bottleneck isn’t just cognitive. Physics has to agree. You have to build an expensive experiment to test your idea. In those cases, you still need human plus AI plus tools. There’s something called Amdahl’s Law: if there are 10 units of work and AI completes 8 instantly, the remaining 2 are still going to slow you down. Just because you’ve handled 90% of the work doesn’t mean the rest happens on its own.

Turner Novak:

So what are some of the bigger bottlenecks we’re running into?

Karthik Duraisamy:

I don’t want to give the impression that everything cognitive has been solved. Current AI models still have many limitations. They’re good at language and reasoning, pretty good at math and computation. They’re not as good as humans at spatial reasoning or physical reasoning, that’s where physical AI and robotics lags behind language and math. The bigger bottleneck for truly hard problems, like actually discovering a superconductor I can use tomorrow, is when you interact with the real world. You have to build something and test it. AI doesn’t do that on its own. Not yet, anyway.

Turner Novak:

So what are some of the big ways you think the world is going to change as AI gets better?

Karthik Duraisamy:

It’s a very broad question, and if anyone answers with absolute certainty, they’re probably not being honest. You can only talk about likelihoods. Here’s how I think about it: we’ve built the entire economy around scarcity and friction.

Before the internet, information was scarce. It was a commodity. Think about the major inflection points in the history of intelligence, language, writing, printing, the internet. Each one broke down information barriers and made certain things less scarce. But until large-scale AI, knowledge and cognition were still scarce. You had to basically learn from age five to age 22 to become competent in a field. And now something you can get for the cost of a Netflix subscription gives you not just information, but knowledge and intelligence. The scarcity of certain things is getting wiped out.

The other piece is friction. A lot of the economy was built around the fact that moving something from here to there required someone to pick it up and move it. If you wanted to buy a house, you went through a realtor. That’s friction. Sometimes friction is good, actually. But AI is removing a lot of it. The value propositions we used to place on different things is changing before our eyes. People who were extremely good at remembering things, at doing math, at a particular type of skill, those were very valued. I still think there’s value to many of those skills, but the proposition will shift.

Turner Novak:

And you gave a “code red” to your students. What was that?

Karthik Duraisamy:

About a month ago I brought all of my PhD students into a room, a dozen of them, and said: these reasoning models, even before we’re talking about agents, are able to reason through things at the level of my expertise in areas where I’m one of the world’s experts. I’ve had many weekend projects where I have a research idea, describe it loosely to an agent, and it does the research, explores configurations, writes code, tests ideas, comes back with results, writes reports. Things that would have taken me four or five months, getting done in a weekend.

I don’t want to say all of my research can now be compressed into a weekend. But many of these tasks are things I couldn’t give to a second-year PhD student at Michigan, and we accept around 5% of applicants. These are among the best in the world. If AI does some of those tasks as well as a second-year PhD student, it raises a question.

Turner Novak:

So what’s one of these tasks that AI is now doing at the same level or better? Just give me an example.

Karthik Duraisamy:

Before I do, I want to say a PhD student is not just about completing tasks. A PhD is not just task completion. PhDs will still exist. Original ideas exist. There is value in training people, and people come up with ideas in ways that are very different from what AI does. So I don’t want to equate a PhD student to just executing a set of tasks.

But as a professor who wears many hats, researcher, teacher, startup founder, institute director, my time is very splintered. Generally, if I have a research idea, I’d work on it over a weekend or a few weekends. If it seemed reasonable, I’d say to a student, “Hey, why don’t you look at this?” That’s how we did research until two or three years ago.

Turner Novak:

So you’d come across something worth spending a ton of time on and suggest the idea to someone.

Karthik Duraisamy:

Yeah. And sometimes the students themselves go through that process, they work on something for a month, come to me, and their idea is better than mine. Regardless, going from ideation to actually having a research idea that makes sense takes time. Especially in fields like mine, I’m a computational scientist, so most of what I do involves wrangling equations, computing them, looking at physical phenomena, modeling them. Many research ideas in my field can now be tested out very quickly. And as the agent works through ideas, even if you’re guiding it, it can give you connections that steer your own thinking in new directions.

Long story short, I called my students in to say: until around December 2025, I always talked about these tools in terms of future potential. A year ago, absolutely horrible. Six months ago, less horrible. Now, decent. That was my framing. But they’ve reached a threshold where the ideas coming out of these models and the way they’re reasoning is about as good as leading-edge research. If we’re not using these tools right now, we’re missing out.

But there was another part of the code red: you develop a lot of intuition by doing things the slow, rigorous way. There is value in students not using AI and developing their own thinking. I still want students to have original ideas, do things the hard way, make mistakes, learn from them. If you completely remove that friction, you lose intuition for how things work. But if you completely ignore these tools, somebody gets there faster than you. It’s a very hard balance and it’s happening in every field. Code red was about showing them how powerful these tools are, but also not forgetting to do things thoroughly.

Turner Novak:

Yeah. So it’s basically telling them they need to completely master AI and also master not using AI at the same time?

Karthik Duraisamy:

Don’t substitute AI. Maybe we have to separate the learning phase from the creating phase, even though those overlap. The only way you learn physics is by doing problems, by working through them. AI knows the answer, but if you skip that friction, you lose the intuition it builds. At the same time, don’t be oblivious to the tools. They can be used in the right way.

Turner Novak:

It reminds me of the CFA charter. It’s kind of like a CPA but for investments. There are three exams. Level one is basically everything you’d learn in a finance undergrad, one test. Level two is like a master’s in finance. Level three is more theoretical, closer to a PhD in finance.

At the end of it, you need to be able to answer something like: Paul and Linda are in Canada, they have a portfolio in the US with some investments in Bangladesh and France in local currency, they want to hedge in Mexican pesos, they have a kid going to college in 18 years, here’s the portfolio size, here’s the growth target. Now give them a recommendation. And you have to do all these hand calculations, what hedges do they need, inflation, expected returns across different asset classes.

At the end of the day, you’re never actually going to do all of that by hand in real life. You’d Google it or hire an expert. But I had to go through and learn all this stuff and it sucked. And I thought, I’m never going to actually use any of this. But it does give you some intuition for how to think about these things. In investing or finance or business, there’s usually a spreadsheet that someone’s looking at to make a decision, but you need to know what goes into that spreadsheet. Same thing in physics: you need to know what’s influencing the outcome. You don’t have to solve it by hand. But you do need to know how it works.

Karthik Duraisamy:

Yeah. By going through that process, doing it by hand, getting it wrong, having your professor tell you what you missed, that’s how you actually build the intuition to see a problem. Intuition is always useful, but the judgment you need to give can now be informed differently.

Turner Novak:

How have you seen the way students learn change over time? And how do you think it’s going to change going forward?

Karthik Duraisamy:

We’ve had three shocks in the past few years. One is COVID.

Turner Novak:

Oh yeah. I almost forgot about COVID.

Karthik Duraisamy:

I know. People went online, high schools were less rigorous in certain ways, and you saw it show up at the university. Even in 2022, the incoming undergrad class, I could see some mathematical deficiencies. I’d even call it persistence: the willingness to keep going at a problem. I could see a bit of that dropping. And you could say it had been happening over a longer period anyway, probably Google and smartphones started it. I don’t want to say students aren’t great, because they are. But certain levels of mathematical rigor and persistence, especially at the undergrad level, we could see it declining a bit. And then COVID was a real shock on top of that.

Just as we were recovering, we got ChatGPT in November 2022. At that time the models were pretty bad, hallucinating constantly, but they could still do a bunch of things. The bigger moment to me is the last six months, where at the undergraduate level, however hard you make a question, the best AI models can do it in pretty much any field.

Turner Novak:

So any kind of take-home test situation, kids are just acing everything?

Karthik Duraisamy:

I’m actually impressed by the honesty and integrity. We have a tradition in the School of Engineering: for the past 150 years, no exam has ever been proctored. The professor hands out the exam in class and steps outside the classroom. We’ve done it for 150 years and I still see much of that persisting. But without a doubt, students are using AI tools to study and to replace some parts of their thinking. I’m not naive, some students are probably fully relying on those tools. But what we end up missing is that persistence, that struggle.

I teach a class that students find to be one of the hardest in my department, but also one of the most enjoyable, because the subject itself is beautiful.

Turner Novak:

What’s the subject?

Karthik Duraisamy:

Aerodynamics. You learn how wings generate lift, how to design wings for certain properties, how much power you need to move an aircraft. You learn math, physics, and engineering all in one particular way. It’s quite abstract, some students struggle, but everyone enjoys it. It’s a hard class, but students learn a lot.

Six years ago, I’d give a homework problem and half the class wouldn’t even know how to start.

Turner Novak:

That would be me.

Karthik Duraisamy:

I don’t take pleasure in torturing students, but I knew it was excellent for learning.

Turner Novak:

Can you give me an example of something I’d get from you where I just wouldn’t know where to start?

Karthik Duraisamy:

I teach how aircraft fly and how those trailing vortices work, when a plane flies through clouds, you see them roll up behind the wing. And in one homework problem I’d say: “Here’s a flock of geese flying in a V formation. Tell me how efficient flying in this V formation would be”, without having mentioned many of the relevant details ahead of time. Many students wouldn’t know where to start. Even with Googling you could get some help on certain things, but some problems I’ve given, Google has nothing. Students spend a few hours just figuring out how to begin. But you learn an enormous amount in that struggle. The hour and a half where you go nowhere is actually where you learn. And now that’s gone.

Turner Novak:

Because you can just type it into ChatGPT.

Karthik Duraisamy:

Any question, any course, any level, any university, including problems from the most famous mathematician alive, AI will either just solve it or recommend six or seven directions to start. So yeah, something is being lost.

Turner Novak:

The geese question, I’m curious now. What would I even be trying to figure out?

Karthik Duraisamy:

I told you how a single aircraft flies and how the trailing vortices look. Then you treat each goose as a little aircraft with a certain mass it needs to support to fly. You create a bunch of these little airplanes with the corresponding vortices and optimize over the whole configuration. I’d give you the size of the geese, the spacing. Maybe that’s not the hardest example, but for some students even that connection is hard to make.

In general, it is in that friction and struggle that you actually learn. If that’s being replaced, you don’t build as much intuition or judgment. Like that CFA scenario: doing it persistently, rigorously, by hand has a lot of value. And that’s being replaced.

Turner Novak:

And the interesting thing is where software comes into play, all these little mini calculations you have to make. If you make one mistake anywhere in the chain, it breaks the whole thing. A calculator chains those steps instantly. AI just extends that further, it creates all those calculations automatically. So the skill becomes knowing how to use AI and an agent to solve a problem, not how to use a calculator.

Karthik Duraisamy:

The meta, right? But what if one of those agents is doing the wrong thing? You have a sequence of them and one is wrong.

Turner Novak:

So you still need to understand what the agents are doing.

Karthik Duraisamy:

Yeah. And the other way to think about it: if everything can be automated, what value do you bring to the problem? Anyone can do it.

Turner Novak:

Do you think we’re going to have to learn how to create and manage and run agents? Is that what education is turning into?

Karthik Duraisamy:

I actually don’t think so. Did we need a lot of education to use ChatGPT 3.5 when it came out? No. But you needed a lot of common sense to use it effectively.

Two weekends ago, a student came to me with a brilliant idea. He understood it better than I did. Over the weekend I was wrangling with it, so I asked an AI agent, described the problem, gave it all the context, it wrote code, and I wanted it to create a visualization to help me understand what was happening. It did everything well, but the visualization was off. The angle it was showing me was wrong because it had no spatial reasoning. So I said, “Help me help you, describe the view you’re showing me with some numbers, and make the figure interactive so I can rotate it. When I rotate it, those numbers update. I’ll tell you the best angle.” Human and AI working together.

People say, “The great education is how to use AI.” No. In a couple of months, using an agent is going to be clicking a button. The skill is how you synthesize what you know and use your common sense to wrangle with AI. Learning how to use AI technically, that’s never going to be the hard part.

Turner Novak:

So how does the role of the university change, with education becoming either more or less important?

Karthik Duraisamy:

It’s been changing for decades already. We used to be gatekeepers of information that nobody else had. Then slowly the internet and online courses started eroding a bit of that.

Turner Novak:

Like when MIT put their classes online.

Karthik Duraisamy:

That’s one of the most seminal moments. I think people should talk about it more.

Turner Novak:

When was that? About 25 years ago?

Karthik Duraisamy:

Around the early 2000s. MIT took all their class notes, videos, lectures, homeworks and put them on the internet, free, for anyone in any part of the world. Did it change the world enormously? For certain people, yes. But it was passive. It was revealing information, not teaching. Now AI is giving you cognition and knowledge. A lot of what universities used to do will be less valuable than before. As keepers of knowledge, you had to go talk to the expert. That knowledge is now pretty much encoded, in a skills file or agentified in some way. We’ve been keepers of knowledge for thousands of years, and now suddenly this thing you pay $20 a month for knows most of it.

But universities still have a big role. Just having access to content doesn’t mean people learn. Just because knowledge is accessible for $20 doesn’t mean people extract the most from it. Universities still add value by bringing young people of a certain age together in a certain environment. Putting 25 people in a class and introducing friction in a certain way, deadlines, exams, making you think. People learn from people. There’s personal mentoring, especially at the graduate level. And you have access to specific facilities. Michigan has the nation’s most powerful laser, called ZEUS, right here. We use it to study theoretical physics, plasmas, material properties. That doesn’t exist inside ChatGPT.

My work is computational, but many colleagues have physical labs, one of the best battery design facilities in any US university, for instance. These are specialized resources. Many discoveries and innovations come directly from university labs. The president of Arizona State has this great one-minute video where he picks up an iPhone and says there are 800 technologies in it developed at university labs. Apple adds value by putting things together, but the underlying work came from somewhere.

And then maybe now more than ever: credentialing. Universities, for better or worse, filter. Even to get into Michigan, the acceptance rate is around 10%. After you come in, you go through a program where you’re credentialed and graded. I think those credentials become more important now, because there are easy ways to complete work without truly doing it.

Turner Novak:

Because they prove that you know the topic, or at least that you’ve gone through the motions of learning it.

Karthik Duraisamy:

Partly, yes, even just getting in is a signal. And I’m not saying that’s a good thing, but it is what it is. You go through the program at various levels of rigor, the university credentials you. But I would say grade inflation is probably going to slow down now.

Turner Novak:

I’ve seen those charts where the average GPA used to be like a 2.3 and now it’s like 3.8 or something.

Karthik Duraisamy:

Like 3.6. Harvard had big rules about too many people getting As. I think these distinctions will grow and evaluation will be taken more seriously, partly because now there are easy ways to complete work without doing it yourself.

Turner Novak:

Do you know what was driving grade inflation? From your perspective as someone actually giving out grades.

Karthik Duraisamy:

Many things. Students are genuinely better prepared coming into college now than 20 years ago, that’s one factor. And in most universities the expectation has shifted to “if I work hard, I get an A,” which many times is actually correlated with outcomes. But I’m not naive, if you’re paying $60,000 a year, I’m not saying that’s why grades are inflated, but if you dismiss it completely, you’re being naive. All of these things are probably true simultaneously.

Turner Novak:

And you think the inflation is going to stop? Level out or come back down?

Karthik Duraisamy:

I’m seeing a movement where things are either leveling or starting to come back down a bit. Harvard actually pulled it off, showed that 70% of students were getting As, professors pushed back, brought it down. Students complained about mental stress. It’d be stupid to dismiss that claim. It’d also be stupid to completely ignore what the professors are doing. Both are probably true.

Turner Novak:

For a lot of people, the reason you go to university is to get a job, increase your chances of success, get the credential, get the stamp of approval. What do you think is going to happen to the job market over the next decade?

Karthik Duraisamy:

Nobody can predict, but some things seem clear. You’re seeing about 100,000 tech layoffs per year over the last couple of years, and in 2026 we’re already at around 100,000. That’s a large number, but we have about seven or eight million tech workers, it’s roughly 1%. I don’t want to minimize the pain it’s creating, but I don’t think it’s going to be as dramatic as some people are saying.

Turner Novak:

Some people would say AI replaces everything and everyone’s unemployed.

Karthik Duraisamy:

That’s probably going too far, at least in the short term. The economy is built in a way where these impacts take longer to penetrate. I don’t think 20% of the population will be unemployed in the next two or three years. Unemployment is around 4.5% right now, maybe it goes up by one point, which is actually pretty bad. But I find it more concerning for fresh graduates getting into jobs. Many entry-level skills in certain domains can be automated. But if you skip developing those entry-level skills, you never become an expert. It’s a chicken-and-egg situation. And people talk about things like everyone having a billion-dollar startup, but where’s the market? Who’s buying?

Turner Novak:

You’re talking about the one-person billion-dollar startup.

Karthik Duraisamy:

I’m sure there’ll be a few. I think there might already be one. One of my colleagues, Jerry Davis in the business school, has colorful thoughts on what he calls “zero-employee unicorns”, a completely agent-run company. I don’t think it’s out of the question, but those will be exceptions. In summary: people already in good jobs will probably find AI helps them be more productive and do more. But I worry more about entry-level jobs. That’s the bigger concern.

Turner Novak:

It’s just harder to get that first job, because instead of a manager hiring someone new, you just use AI and software.

Karthik Duraisamy:

Not in every profession, and not completely. But you’d have to be naive to think it won’t have an impact.

Turner Novak:

One of the recent guests on the show talked a lot about the US healthcare system. He made the point that throughout American history, we’ve had these job-program-like industries, manufacturing was a built-in employment mechanism. Healthcare is kind of that way right now. A lot of cities and regions have some healthcare system as the largest employer, nurses, admin, people who help move patients around hospitals. A lot of those roles exist partly to employ people. Even if AI makes things more efficient, the government is largely funding it and isn’t going to say, “Let’s eliminate these jobs.” So there are multiple layers of friction protecting those roles.

Karthik Duraisamy:

In the short term, I agree. But I can’t see clearly five years out, and things can get non-linear in ways that are hard to predict. You’re right that many jobs are structured in ways that will protect people for a while. There will still be layoffs, but I don’t think it’ll be as catastrophic as some people suggest, at least in the near term. My bigger concern remains entry-level jobs. That’s the one I keep coming back to.

Turner Novak:

What advice are you giving your students? “Hey, if you want a job, here’s what you have to do.”

Karthik Duraisamy:

It’s hard advice, but it comes down to: whatever you study, get more rigorous about it and add real value. Get really good at what you do. I don’t tell them to use an AI tool. That’s not the priority. The priority is going through the process, building intuition, and then using the tools out of necessity and common sense. I’m at an age where many of my friends have kids in 10th grade thinking about college. People used to think computer science was a sure shot to an amazing career.

Turner Novak:

Yes, becoming a millionaire, coasting forever.

Karthik Duraisamy:

So I tell them: it doesn’t matter what field. Even if you study computer science, there’s theoretical computer science, there are so many things beyond just coding. But if people ask me openly, “What should we study?”, maybe I’m biased, I’d say: physics, chemistry, biology, mathematics.

Turner Novak:

Those are all things AI can do really well right now, right?

Karthik Duraisamy:

AI can do well up to a certain point. But most of the unsolved grand challenge problems in humanity involve those fundamental sciences. And there are deeper questions, what is the nature of life, where did we come from, what is the nature of reality, those aren’t going away. There’s no chance someone produces a unified theory of physics tomorrow and that’s it. So: study things for their intrinsic merit, do it really well, build up your basics. The problems in those fields are never going away.

But also: get really deep, and know how to synthesize things across disciplines. Don’t skip the fundamentals. You need to know something really well to have genuine expertise. Not everything is automatable. AI can do your math, physics, chemistry, and biology homeworks. But the hard skills will matter, as long as you can synthesize information across different fields.

Turner Novak:

Yeah. One piece of advice I always give people: niche down a lot more than you think. If somebody just says, “I want to be a scientist,” that could be anything. But if you say, “I want to be a scientist who works on making paper cups more durable, the strongest paper cups ever”, I know exactly who you are. If I ever come across a problem requiring that, you’re the first person I think of.

It’s like being a soccer coach trying to improve your team. You’re at tryouts and there’s this person who is just insanely good at throw-ins. Maybe they’re not the best at everything else. But you might make the team because of that one thing. Or corner kicks, someone so good at that one piece that they make the squad because of it.

Karthik Duraisamy:

Right. The ideal profile is something like a T. Someone who knows a little about everything is like a flat rectangle. Someone who only knows one thing is like a vertical I. You need enough breadth to see how things connect, but you need to be really deep at some things. That value isn’t going away.

That said, it also won’t be simple. Being smart, being good at something, that alone used to be enough to make it in the world. Those skills are still useful, but not sufficient on their own anymore. The value proposition is shifting toward people who solve real problems. The cost of generating an idea is approaching zero now, because AI can generate ideas. Not all good, but some. What matters is taking that idea and going all the way through, actually solving something people care about. Earlier it was, “He’s smart, he knows math, he can do calculations in his head.” Now it’s, “Okay, so what? What are they actually doing with that?”

Turner Novak:

And you’ve actually seen two different college basketball national championships firsthand as a member of the school. What is that like?

Karthik Duraisamy:

Amazing. I’m a sports junkie. I enjoy college basketball more than most sports, though it’s not my top one.

Turner Novak:

What’s your top sport?

Karthik Duraisamy:

Soccer. I’m incredibly passionate about it.

Turner Novak:

That was actually a lucky guess when I gave the soccer example.

Karthik Duraisamy:

Anyway. When I was a graduate student at the University of Maryland, we won the national championship in men’s and women’s basketball, not the same year, a couple years apart. And I got to experience it again at Michigan a few days ago. A couple of my professor colleagues and I were out on South University with all the celebrating students. I’m sure they had no idea we were faculty. But it was amazing. 64 teams, straight knockout, it’s a real accomplishment. And sports brings out certain emotions that most things don’t. Where else do you see 10,000 people completely happy, forgetting everything else, all celebrating together? To be part of that is something else.

Turner Novak:

Yeah. I’ve seen videos of robots playing sports and people say “soon robots will be better than us at sports.” But I don’t actually want to watch robots play sports. Do you think human sports will ever get fully replaced?

Karthik Duraisamy:

I don’t think so. Computers have been better than humans at chess for 20 or 30 years. Chess is more popular now than it was when computers first beat humans. There’s something in human pleasure that comes from watching people compete. That doesn’t go away.

Turner Novak:

Yeah. There’s this concept called the bionic games. Like the Olympics but you’re allowed to enhance, drugs, robotic limbs, whatever. If you lost your arm and got a replacement that’s stronger than a natural one, you’d be allowed to compete with it. That kind of thing.

Karthik Duraisamy:

There’s a market for everything. One of the most popular sports in the world is motor racing. That’s already human and machine.

Turner Novak:

So we’ll probably just get new sports. That’s what actually happens.

Karthik Duraisamy:

Yeah, that’s very possible.

Turner Novak:

And I think you told me once that you almost died hiking in a national park. What happened?

Karthik Duraisamy:

My wife and I travel a lot, and there was a time we did some serious trekking in different parts of the world, Argentina, here and there. One time in Grand Teton National Park, it was a snowy day and somehow they gave us a permit to climb and pitch camp. An overnight trip. A ranger was coming down as we were heading up and said, “I’m quite shocked they gave you a permit, weather conditions aren’t great.” We said we’d be fine, we’d done plenty of hikes. He said, “Don’t make me come rescue you up top,” and gave us some vague instructions about keeping to the right of the trail if the path disappeared.

We kept going. Light snow at first, we enjoyed it. Then it got heavier, the trail disappeared, and we couldn’t follow whatever instructions he’d given. I fell into a hole, not too deep, and then my wife fell into a separate hole about 20 minutes later. Some of our backup socks got wet. Then it snowed heavily. We found something that looked like a trail, pitched camp pretty high up, and it snowed more. Because of the falls, socks were soaked through, even the backups. My wife’s hands were going numb. A few more hours and she might have lost some fingers. And then coyotes started howling, and she said, “I don’t want to die being eaten by coyotes.”

This was probably 2005 or 2006. Cell phones weren’t reliable out there, but I kept trying and somehow reached a friend in College Park, Maryland. He was able to call the rangers. They came up in the middle of the night with hot drinks and helped us break down camp. And it was the same ranger who had said, “Don’t make me come rescue you.”

Turner Novak:

Oh geez. Was this midnight or the next day?

Karthik Duraisamy:

Around midnight or 2 AM. He was having dinner with his wife when he got the call. So yeah, we’ve had some adventures.

Turner Novak:

Any upcoming trips planned?

Karthik Duraisamy:

Some trips to Japan and Europe. Nothing big on the hiking front.

Turner Novak:

More laid back?

Karthik Duraisamy:

More laid back, yes.

Turner Novak:

Well, this has been awesome. Thanks for taking the time to chat. A lot of fun.

Karthik Duraisamy:

It’s a pleasure. We touched on so many different topics. You were certainly a good host.

Turner Novak:

There’ll be a lot of data out there for the LLMs to train on. Maybe we can teach the language model something.

Karthik Duraisamy:

I don’t know how much real insight I had, but I’m sure I compressed a few things.

Turner Novak:

Yeah. It’s a lot of fun. Thanks for being here.

Karthik Duraisamy:

Thank you for having me.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

We spend 100 minutes talking about how the US healthcare system actually works. Nikhil details how a change to the tax code during World War II changed it forever, all the ways AI is seeing real adoption in healthcare, why physician burnout and independence is one of the biggest problems in the industry, and most common bad startup ideas that come up over and over in healthcare.

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👉 Stream on Spotify and Apple

Timestamps to jump in:

• 1:23 How the US healthcare system works

• 4:26 Why US healthcare is different from the rest of the world

• 12:01 Why healthcare costs keep going up

• 15:58 Core problem: is healthcare a marketplace or not?

• 21:04 How money flows + Two-way price negotiation

• 27:34 Why payments are seeing early AI adoption

• 30:08 How AI could change healthcare delivery

• 35:40 Doctor’s are trapped on a productivity hamster wheel

• 39:28 How incentives shape healthcare delivery

• 43:53 Healthcare is an implicit jobs program in the US

• 48:45 Areas AI is overhyped, worst healthcare startups

• 55:30 Consumerization of healthcare

• 1:01:58 Rise of Peptides, understanding risks and downsides

• 1:09:35 Why all medical software is so bad

• 1:11:58 How to do enterprise sales in healthcare

• 1:14:51 The battle forming between Scribes, Search, and EMRs

• 1:18:33 Why we need more physician independence

• 1:26:51 Starting Out of Pocket in February of 2020

• 1:29:12 Write to meet your audience

• 1:37:54 Using AI as a content creator

• 1:42:13 How to get started writing on the internet

Referenced:

• Out of Pocket

• Doctronic

Find Nikhil on X / Twitter and LinkedIn

👉 Stream on YouTube, Spotify, and Apple

Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Nikhil, welcome to the show.

Nikhil Krishnan:

What’s up? Excited to be here.

Turner Novak:

I’m excited to have you too. We’re going to talk healthcare more broadly, but also tactically, and then some content creation stuff. I guess we’re both content creators and people always want to know about that.

Nikhil Krishnan:

This feels like when Twitch streamers collab on something and the internet freaks out. This is the Avengers of people that are too online, me and you.

Turner Novak:

Yeah. The comments for this, people are going to be absolutely losing their minds about two heavy hitters from the internet just coming together.

Nikhil Krishnan:

From the extremely specific niche of the internet.

Turner Novak:

Yes. But really quick, for people who have never come across you on the internet before, how do you describe yourself and Out of Pocket more generally?

Nikhil Krishnan:

You just described both of us.

I try to make healthcare more entertaining and accessible. That’s the short version. We’re trying to teach people how healthcare works. I write a newsletter, we do a bunch of courses, we have events, etc. The general idea is we want it to be easier for people to understand how healthcare works, and we hope that people will build more interesting things if it makes more sense to them.

Turner Novak:

That makes sense. How would you describe the healthcare industry to someone who has no idea how it works?

Nikhil Krishnan:

I would just step on your foot really hard and be like, “How does that feel?” That’s the entire healthcare system in a nutshell.

Turner Novak:

That’s it. And then you hand me a bill after you step on my foot.

Nikhil Krishnan:

Yeah, exactly. This is for your own good.

Turner Novak:

But you send it three months later, I can’t access it online, and it’s an envelope that gets lost in the mail.

Nikhil Krishnan:

As it should be.

The US healthcare system is messed up. It’s very confusing. The reality is that US healthcare was never a planned system, the way a lot of other countries actually designed theirs. It just appeared and congealed over many, many layers. Because of that, we’ve created bespoke rules for different slices of the system.

What we now have is more like 50, $100 billion micro systems in a trench coat underneath what we call a $5 or $6 trillion healthcare industry. Each one has different regulations, different people who pay for it, different services they’re allowed to offer.

One of the reasons healthcare has so much administrative bloat is that you now have to track all of these different rules. If someone jumps from one system to another, you have to understand what that means and go chase down a bunch of information. It’s a very messed up system. But I really think of the US healthcare system as a bunch of small micro healthcare systems within a larger umbrella.

Turner Novak:

And you said 50. Is that because each state is different, or is it more about different aspects of the system?

Nikhil Krishnan:

Yeah, I shouldn’t have used 50. That’s a little too on the nose for states. The categorization is really more about who pays. It could be a state if it’s Medicaid, or the federal government if it’s Medicare. But now the federal government also contracts with private insurance companies for Medicare Advantage, so that’s a different set of rules. If you get your health insurance through your job, that’s another set. And sometimes an employer will pay medical bills directly rather than go through an insurance company, which is its own set of rules.

It’s actually probably more than 50 if you really think about it, but it’s not state-based. It’s really about whoever’s footing the bill at the end of the day. Compare that to other countries. If you hear “single payer system,” there’s one payer, and it’s the government. In the US, we have a multi-payer system, and each of those payers has different rules.

Turner Novak:

You mentioned this idea of planned versus unplanned. In other countries, was there a moment where the government decided, “We’re taking this over”? And how did it evolve differently here?

Nikhil Krishnan:

For a lot of other countries, the government basically said, “Here’s the role we’re going to play in the healthcare system.” Whether that’s setting prices, running hospitals directly, or creating a marketplace where insurance companies compete, the government made a clear decision about its role.

The US is very different. The weird quirk here is that employers choose health insurance on behalf of their employees. This is one of the original sins of US healthcare, and it wasn’t born out of any planned healthcare decision. It came from a tweak in the tax code.

Everyone was off fighting World War II and the government was worried about wage inflation. The labor force at home had a lot of leverage, so the government said, “We’re going to cap wage growth, but in exchange, you can use tax-exempt dollars for other things.” One of those things was health insurance. So this was essentially a footnote, you know what I mean?

Turner Novak:

It wasn’t really meant to be that big of a deal back in the ‘40s?

Nikhil Krishnan:

Right, because healthcare was also very cheap at the time. It was house calls and people would get cocaine for a migraine and move on with their day.

Turner Novak:

Here’s some hard drugs. Because we didn’t really have a pharmaceutical industry. People weren’t really getting surgeries. You got sick and you died.

Nikhil Krishnan:

Yeah. We didn’t have the level of complex hospital care we have today. A lot of that is a post-World War II development. So because of that, healthcare was a blip in terms of cost and people didn’t think much about it. But that weird tax quirk essentially created, or really poured gas on, what we know as the third-party private insurance industry.

As healthcare got more expensive and more complicated, it had all the factors needed to become runaway costs. Employers had tax-exempt dollars, people weren’t paying out of their own pockets, and healthcare itself kept getting more intensive. Costs started ballooning. Then in 1965, we created Medicare, a whole new set of rules, with the government getting much more involved in payment. You start layering these things on top of each other, putting band-aids on problems, and it was never a fully planned healthcare system.

Turner Novak:

Okay. And when you say tax-exempt, just so everyone knows, when you give someone a salary, the company and the employee both pay taxes on it. But with health benefits, it’s compensation you receive without taxes. The company doesn’t pay taxes on it either?

Nikhil Krishnan:

Yeah. The clearest way to explain it: if you’re self-employed, you’ll probably buy health insurance off the individual exchanges, the marketplace. When you do that, you’re paying with post-tax dollars. When an employer does it, they’re paying with pre-tax dollars. That alone is a huge boost.

If you’ve ever left a job and had to pay COBRA, continuing your old employer’s health insurance, you’ll notice the cost spikes a lot even though it’s the same plan. Two reasons. One, your employer was probably subsidizing a large portion of your premium. Two, you’re now paying it in post-tax dollars. Double whammy.

There are some newer tools getting a lot of traction too, like ICRAs, which are a popular startup idea in VC circles. The concept is employers create a wallet to give you the tax-exempt dollars they’d normally spend on health benefits, but now you can go shop around for health insurance or other healthcare services yourself. Same tax treatment, but more consumer-oriented.

Turner Novak:

And one interesting topic. You mentioned healthcare is five or six trillion dollars, something like 20% of GDP, and it’s always going up. Why do healthcare costs keep rising? Can’t someone just say, “This is ridiculous, let’s cut costs”?

Nikhil Krishnan:

Yeah. A couple things. First, as a percent of GDP, healthcare actually hasn’t gone up as much as people think over the last 10 to 12 years. There’s a lot of debate about why. Some say people are rationing care, there’ve been drug breakthroughs for heart disease that brought costs down, others say the government just pays less now. But it actually hasn’t risen as much as we think.

The caveat is that who pays has been shifting a lot. Within that 17 or 18% of GDP, a lot more cost has shifted onto individuals, through higher deductibles, higher premiums, or wages that haven’t gone up because the money went to healthcare costs instead.

Second, at a macro level, our population is getting older, so people are consuming more healthcare services. And there are new popular categories, like weight loss drugs, that are expensive and in high demand. Which brings you to an ideological question: what should the mechanism be for reducing prices or consumption? It’s not simple.

If you just say “we’re not paying for this anymore,” someone has to ration care, and that gets very political. Healthcare is also the number one employer in most states. If you cut spending, a lot of people lose jobs. Look at the jobs report from the last year. The only sector with consistent positive growth is healthcare. It’s an implicit jobs program.

On the price side, one of the cardinal sins of the US is that we don’t have a centralized price negotiation system the way other countries do. In other countries, the government sets prices or caps how price growth should be. In the US, we don’t have that. So when the government says it won’t pay as much for something, hospitals just raise prices on the private insurance side. It’s like squeezing a balloon. Reduce it in one place and it grows somewhere else.

Turner Novak:

Yeah, that’s fair. You could argue America is a capitalist market, there should be competition that brings costs down naturally.

Nikhil Krishnan:

I think the core problem with US healthcare is that we don’t know which one we want. Do we want a government-intervened system, or a free-market one? There’s no agreement. Each administration goes one way, the next might go the other.

If you ask 50 different people what the ideal healthcare system looks like, you get 50 different answers. And because there’s no unifying theory of what we want, we can’t build toward it. So we’re just regulated enough to prevent competition, and just free-market enough to enable price gouging.

Turner Novak:

Yeah. Depending on who you are, that’s either an incredible opportunity or terrible at the same time.

Nikhil Krishnan:

Totally. The marketplaces are a good example. As part of the Affordable Care Act, the idea was everyone would buy health insurance on an exchange, a real marketplace with competition, where you pick based on customer experience, coverage, deductible, etc.

But for a marketplace to work, everyone has to be in it. If only relatively sick people come to the marketplace, or if most people stay on employer insurance because it’s more attractive due to the tax benefits we talked about, it’s not a real marketplace. It kind of looks like one, but it’s not quite marketplace enough to actually work.

Turner Novak:

Have we ever tried to move off the employer-tied healthcare model, or has it never really been attempted?

Nikhil Krishnan:

The Affordable Care Act was really trying to do that. It had two prongs: make it more attractive to buy your own health insurance on the exchange, and make it less attractive for employers to offer rich benefits. There was something called the “Cadillac tax.” If you offered health coverage that was too good, you’d get extra taxed. The idea was to push people out of the employer pool into the individual exchange pool. But that part of the deal got killed.

Also, employers actually like this system. If you’re a big employer, offering health benefits is an excellent talent recruiting and retention tactic. You probably know people who keep a job purely for the health insurance. A lot of employers are quite pro-status quo.

Turner Novak:

Yeah. The classic startup founder setup. You’ve got your startup, not making money, taking on tons of risk. And then you have a partner who works at a big company with a nice salary and really good benefits. Two-pronged approach.

Nikhil Krishnan:

If anyone’s listening, the startup idea is a dating app between founders and people with Fortune 500 benefits.

Turner Novak:

That’s a big opportunity. So how does money actually flow in US healthcare? How does it all work?

Nikhil Krishnan:

This is a five-hour podcast. There’s not enough time. But at a high level: most people get health insurance through their employer. Your employer picks a handful of plans, you pay a portion of the premiums, the employer subsidizes the rest. When you go see a doctor, the insurance company pays the majority of the bill, plus most of your prescription drugs.

But if I were to draw out the entire flow, it would look like the Charlie conspiracy meme. And it’s very different depending on whether you’re on a government plan like Medicare or Medicaid versus a private plan. Each has a completely different set of rules and payment methodology.

Turner Novak:

You mentioned something about a two-way price negotiation. What is that for someone who’s never heard of it?

Nikhil Krishnan:

This isn’t unique to healthcare. It exists in a lot of industries. The general idea is that a lot of value in healthcare comes from being large enough to negotiate against a counterpart. Hospitals want to get bigger to negotiate with insurance companies. Insurance companies want to get bigger to negotiate with hospitals or pharma. Everyone’s trying to build leverage.

So you end up with a lot of entities that say, “We aggregate a bunch of people together and negotiate on their behalf.” If you’re a newcomer or an individual, you probably want to join one of these aggregate pools. An explicit example: if you’re an insurance company, you probably work with a pharmacy benefits manager who aggregates all the patient lives that different insurers represent to then negotiate with pharma companies on drug prices. Better to negotiate together than separately.

The problem is that a lot of those aggregating entities, who are supposed to be negotiating against someone on your behalf, go to the very companies they’re negotiating against and say, “If we choose you, we want a cut.” They have all the leverage. This shows up everywhere.

Say you’re buying health insurance individually and you use a broker. The broker offers the service for free but gets paid commissions from the insurance carrier. So the person who’s supposed to be helping you pick the best plan is actually getting paid by the carrier. They may only recommend the plans with the best commission structure.

This is true in any industry where a service is “free” for you but paid by the seller. Real estate, financial services, whatever. You think you’re not paying, but you are, in the form of not getting the full range of options.

Turner Novak:

Yeah, the broker thing is classic in finance more broadly. A financial advisor recommending mutual funds where they get paid 8% of the deal value upfront. They’re going to recommend whatever makes them the most money.

Nikhil Krishnan:

Of course. Or getting an apartment or buying a house. Brokers are involved and you think you’re not paying, but it’s baked into the selling fee. You’re paying; it’s just convoluted.

Turner Novak:

My favorite hack for buying residential real estate. I’ve done this twice. The realtor always gets 3%, one from the buyer, one from the seller. I don’t have a realtor. I just find a house I want and say, “I want to buy this. You can be my realtor. Just tell me what I need to pay and I want to win this.” The listing agent gets double commission. So you’ll win, they’ll push your offer harder.

Nikhil Krishnan:

And yeah, there are lots of ways to game the broker stuff. But at the end of the day, it’s just a bad transaction mechanism for everybody.

Especially when you think about AI tools. This is a great use case. A broker exists to reduce information asymmetry between you and the seller. Buying a house, buying health insurance, whatever. “This is complicated, I wish someone would guide me through it.” But now with AI, you can have a copilot that doesn’t have those same incentive problems. Much more straightforward.

Turner Novak:

Maybe that’s an interesting segue into AI and healthcare more broadly. What’s actually getting adopted and actually working right now?

Nikhil Krishnan:

Most of the stuff that’s actually getting adopted touches the payment rails, what’s called revenue cycle management in healthcare. That’s the process by which a provider takes medical documentation, turns it into a bill, sends it to the insurance company to get reimbursed, and then the insurance company pays them, argues with them, or whatever. It’s incredibly convoluted. It’s a lot of transforming text from one format into another, so it’s a good LLM use case.

But simultaneously, it doesn’t solve the core problem, which is that payers and doctors fundamentally don’t want to pay each other. It’s a Tom and Jerry fight. You can add all the tech you want to it, but they’re still fighting. Now you have doctors using AI bots to call payers, and payers creating AI bots to stop the doctor bots. We kind of end up back in the same place.

That said, it’s an area where you can demonstrate ROI very quickly. You go to a hospital and say, “Your accounts receivable are way lower than they should be. There’s uncaptured revenue, there are codes you should be adding, and we’ll get you more money.” Then you go to the payer and say, “You’re overpaying for certain things, and our bots will prevent over-billing.” AI scribing is part of this too, recording a doctor’s visit and transforming the audio into documentation or billing codes.

But I’m not as personally interested in that space. I think there’s a much bigger opportunity in totally rethinking how we deliver care from the ground up. If you were to design a doctor’s office from scratch with AI at the core, what would it look like? Probably very different. The intake form would be dynamic, risk-assessing you, routing you to telemedicine if it’s not serious, or pushing you to an in-person visit only if it is.

There’s a company called Doctronic in Utah that’s testing fully autonomous prescription refills. If you’re on a low-risk medication and just need a refill, the AI does it. Rewrites the script for you. That’s an example of where things are heading: AI autonomously doing tasks, which lets you rethink the whole workflow rather than just speeding up a human-in-the-loop process.

Turner Novak:

With something like Doctronic, you’re talking about providing actual care. Don’t you need licenses? There are probably regulations around that. How does that intersect?

Nikhil Krishnan:

Yeah. One of the open questions is where liability applies if the AI system messes up. One way to work around this, and I don’t have confirmation but I assume this is how Doctronic is operating, is that a doctor essentially grants the AI access under their license. They absorb the liability, but also get the financial upside. If a doctor wants to see 10x more patients and AI enables that, they take on more risk but also capture more revenue.

It then becomes the doctor’s job to evaluate the AI system and make sure it’s doing what they want. The bigger open question: should AI systems be regulated as medical devices, or more like nurse practitioners who work under a physician’s supervision? That’s the debate, and it’s not settled.

Turner Novak:

Interesting. What do you think is the right framework? Because when I think about any doctor’s visit I’ve had, there’s care staff, nurses, who do all the prep work, then the doctor comes in for a couple minutes, makes a decision based on the information collected, and then the nurse comes back to do the rest. Software could technically speed that process up, right?

Nikhil Krishnan:

Not only speed it up. You could also have a more expansive visit. If I can talk to an AI for an hour before seeing a doctor, I’m getting more information into the system. And I can interact with it throughout my life, not just during the visit. You get very different data that way.

I don’t think there’s one right answer. One benefit of having 50 states is that different places are experimenting with different approaches. Some are running sandbox programs, try it, see what happens. Others are saying the downside risk is too high. Areas with more acute healthcare needs will probably be more willing to try AI-driven care delivery.

My sense is there will be shifts in how we think about malpractice and liability insurance for software. And there may be a model where AI has to demonstrate a baseline level of competence through an FDA-like process, and then at the deployment level it lives under the supervision of a doctor or practice.

Turner Novak:

Yeah. And you’ve mentioned before that you think doctors are kind of screwed in the current healthcare system. You’d think it’s one of the highest-status jobs in the world. So what’s going on?

Nikhil Krishnan:

To be clear, “screwed” is relative. In the grand scheme of things, they still have very secure jobs that pay quite well.

Turner Novak:

Yeah, I’m clip farming here. I’m trying to get some clips.

Nikhil Krishnan:

Of course. I think the issue doctors run into is they’re essentially on a productivity hamster wheel. You go work for a hospital, the hospital controls your time and what you’re allowed to do. You’re put on a treadmill of 15 to 30-minute visits, banging them out, hitting productivity metrics to earn your pay.

Turner Novak:

So there are quotas in the backend, like you have to see this many patients?

Nikhil Krishnan:

“Quotas” isn’t quite the right word. There’s a system called RVUs, relative value units, which are scores that determine how you get reimbursed. The score factors in how complex the procedure is, the liability risk, the equipment required, etc. It’s a scoring methodology set by the government for Medicare payments, though it gets adapted for different contexts.

As a doctor, you can get bonuses based on hitting certain productivity levels. You’re expected to do a mix of services that hits a certain threshold. So it’s not as blunt as “you get paid per prescription.” But you are incentivized to do a mix of things that are most productive for whoever employs you. Someone else is making a lot of those decisions on your behalf, while the upside goes to the hospital rather than to you.

That’s combined with the fact that the social status of being a doctor has declined quite a bit. People are generally more skeptical of the healthcare apparatus. Patients come in more bitter, and they’re often more complex medical cases. Doctors are building fewer long-term relationships with patients. Care is more episodic. The social connection that used to exist between doctors and patients has eroded.

Turner Novak:

Yeah. I’ve definitely had doctors walk in, glance at the chart, and be like, “Hey... Turner, right? How’s it going?”

Nikhil Krishnan:

Yeah, that’s my parents. They’re doctors. They come in and it’s like, “Hey, Nikhil, right?”

Turner Novak:

Are you a nephew or a son? I can’t remember.

Nikhil Krishnan:

I’m an only child. I’m the only one.

But yeah, the system doesn’t encourage long-term relationships anymore. It’s a bit sad.

Turner Novak:

So procedures, is that ultimately where the profitability comes from for most health systems?

Nikhil Krishnan:

Yeah, doing procedures. But there are also newer ways hospitals make money through more convoluted schemes.

For example, there are government programs where hospitals serving mostly lower-resource patients can acquire drugs at a much lower cost through a program called 340B, and then get reimbursed at normal insurance rates. So you’re acquiring drugs cheaply and getting paid at full rates. There’s a huge spread in there.

And I don’t think it’s malicious. It’s more that we haven’t figured out how to structure payment for care in lower-resourced areas. We’re still debating who should foot that bill.

Turner Novak:

It makes sense though, right? If you’re choosing between building a hospital in a wealthy area where people spend a lot versus the lowest-income area in the country, you’re going to build where the money is. So you need some incentive to make care exist where people can’t pay full price.

Nikhil Krishnan:

Of course. You need some program saying, “Don’t just cherry-pick healthy, easy patients.” But it’s a tightrope. If you make the subsidies too profitable, people will find ways to game that too. So it’s a bit of a lose-lose situation no matter how you set it up.

Turner Novak:

Is there anything about the healthcare system that surprises most people? Like a dinner party trick, you drop it and it blows people’s minds?

Nikhil Krishnan:

I think a lot of people don’t understand how large healthcare is as an employer. It’s an implicit jobs program in the US. Manufacturing was essentially replaced by healthcare as the backbone jobs program, and healthcare is now the number one employer in this country and in most states.

So if we want to see the productivity gains from tech and AI, we also have to figure out what the next jobs program looks like. Something that people without advanced degrees can earn a meaningful income from. Right now that’s healthcare. What it looks like after is not clear.

Another thing that surprises people: what percent of total US healthcare spending do you think goes to pharma?

Turner Novak:

I feel like it’s either really high or really low. I’ll guess 62%.

Nikhil Krishnan:

It’s somewhere between 9 and 11%. Way smaller than most people assume. I think it feels bigger because expensive drugs get a lot of press, and because a lot of people can’t afford them. Those stories are real and worth taking seriously. But relative to everything else we spend in healthcare, pharma is actually a small slice.

Turner Novak:

I saw a stat once, I’ll probably butcher it, but something like 40% of all cable news ad revenue comes from pharma. Which is a wild number.

Nikhil Krishnan:

The Skyrizi jingle is permanently burned into my brain.

Turner Novak:

It’s a funny juxtaposition. You’re watching a basketball game, peak physical specimens on the court, and then there’s an ad for some drug with a list of terrifying side effects.

Nikhil Krishnan:

It’s kind of interesting. I was debating this with someone recently. Of all the things we want people to consume more of, drug products are actually not that bad on the scale of things.

Turner Novak:

Oh really? How so?

Nikhil Krishnan:

I mean, it’s a debate. But getting more people into sports betting or buying certain other products is probably more net negative than some pharma advertising. There are a lot of people who are underdiagnosed with heart disease who might actually go see a doctor if a condition is put in front of them often enough.

Direct-to-consumer pharma advertising is bad in a lot of ways and it’s easy to make that case. But I think a lot of the frustration with pharma ads is really frustration with consumption culture in general in the US. Relative to other categories that get heavy ad spend, pharma might not be the worst.

Turner Novak:

Yeah. And the effect of one of these drugs might be that it cures your diabetes or meaningfully improves your life. Meanwhile you look at an alcohol ad, all parties and fun, no mention of the downsides, and alcohol can actually be really destructive.

Nikhil Krishnan:

It’s kind of crazy to me that pharma companies have to list all their side effects in an ad, and alcohol companies don’t.

The pharma party sounds way less fun because they’re telling you about all the potential complications. The alcohol ad makes it look like you’ll be cool and having a great time.

Turner Novak:

I might literally die from this drug. But from the alcohol? I’ll be hanging out with all the cool people having a blast.

Nikhil Krishnan:

Exactly.

Turner Novak:

So, talking about AI in healthcare. Are there areas where the attention is a little out of whack with actual effectiveness? Things that are overhyped?

Nikhil Krishnan:

Yeah. A lot of the stuff that’s just speeding up the revenue cycle process. I don’t think making how you submit a bill to insurance faster is going to change that much. The payers on the other side will just build their own systems to slow things back down. The friction is kind of the point of the system in some ways.

I’m not excited about that space. There’s a lot of money going into it, but it’s not durable, and the vendors are pretty interchangeable. You could swap one out and nobody would notice. So I think that gets a lot more attention than it deserves.

Turner Novak:

Are there areas that sound like great startup ideas or healthcare business opportunities but almost never end up working? Classic traps?

Nikhil Krishnan:

So many. Medical tourism marketplaces come up constantly. “It’s all cash pay, people go abroad, what if we just built a marketplace for it?” But the good hospitals abroad already have their own programs and are pretty much full. The rest are often not great. And US health insurance doesn’t cover complications if you get medical tourism treatment and come back. I hear this one all the time and I never think it’s going to work.

Another one is “We’ll analyze your medical bill, find errors, and take a percentage of savings, and it costs you nothing.” That sounds great. And I actually think you can build a small business around it, especially with AI tools today. But the real problem is that it requires fighting with the hospital, which often just doesn’t play ball. It has nothing to do with the tech. And you’re catching a patient at a very specific moment in their journey. Really hard to acquire them, and they’ll only use you once. Very lumpy, non-recurring revenue.

Clinical trial patient recruitment is another one I hear constantly. These can be fine ideas, but they’re often not defensible and aren’t really venture-scale businesses. You can probably build a good services business doing it, but everyone wants to raise $10 million and try to build something huge. That’s just the wrong capital structure for the type of business it is.

Turner Novak:

It keeps coming back to profitable acquisition and retention of customers. Which is why primary care, even though it doesn’t make money on its own, is valuable for health systems. It’s how you acquire people for the higher-margin procedures.

Nikhil Krishnan:

Yeah. And even if a transaction is one-time, you need people to keep coming back for different things. Another problem is insurance-tied churn. You offer a service covered by someone’s plan, they change jobs and switch insurance, and suddenly it’s not covered. Built-in churn.

And just remember, most people aren’t using the healthcare system on a recurring basis. They use it relatively infrequently. So building a business with predictable, recurring revenue in that paradigm is really hard.

Turner Novak:

Yeah. It seems like the answer is always “sell to the employer,” they pay the bills for individuals. Or maybe the insurance company. But I’m not sure how that whole ladder works.

Nikhil Krishnan:

Every level has different trade-offs. If you’re selling to employers, most of them use benefits consultants or brokers to evaluate vendors. So you have to go to the brokers, but the brokers want their cut, have their own preferred vendors, and their own commission structures. Now you’re selling to the broker who sells to the employer. And then you still have to get the actual employees to use your thing. That’s three different sales in one.

Turner Novak:

And then you might think, “There are hundreds of brokers. Should I skip all that and go straight to the insurer?”

Nikhil Krishnan:

Then the insurer says, “Great, where’s your data?” And they’ll probably send you to one of their employer clients to pilot. And you’re basically back at square one.

Turner Novak:

Fair. So what’s happening with the consumerization of healthcare? Is that the movement toward going directly to the individual patient?

Nikhil Krishnan:

Yeah. We’ve swung toward a pretty paternalistic healthcare system, and now there’s a swing back toward something more consumer-oriented. AI tools play a big role. People have much more expertise in their pocket now to make healthcare decisions. There’s also more push to make it easier for patients to access their own health records and get context around their data.

I think people should have more agency in their healthcare decisions, including being able to take on more risk in exchange for cheaper or faster services. Like, “I want this AI to prescribe me this drug. It might get it wrong sometimes, but I’ll get a cheaper, faster service.” That’s generally where things are trending.

But there are downsides too. Dental is a good analog. It’s extremely consumer-oriented, schedulable, price-transparent. But you still get constantly upsold. There are bad actors who tell you that you have cavities you may not actually have. All the pros and cons of consumerization apply.

How far do we want healthcare to move in that direction? Probably further than we are today. But you already saw the GLP-1 situation play out. There’s a whole spectrum of compounded weight-loss drug providers where it’s basically, “Come get your drug here, don’t ask how we make it, just trust us.”

Turner Novak:

Yeah. “It’s super cheap. Don’t ask why.”

Nikhil Krishnan:

Exactly. That’s probably too far on the consumer end. So where’s the middle? That’s the real question.

Turner Novak:

The dentist thing reminds me. Every time you go to a new dentist, they tell you that you have a ton of cavities.

Nikhil Krishnan:

Of course. But now maybe I can take that same X-ray image, run it through an AI tool, and walk into the dentist as a much more informed consumer. That’s probably a net good for everybody.

Turner Novak:

So with people able to just ask Claude or ChatGPT healthcare questions, are the big AI companies just winning consumer healthcare? Or what happens?

Nikhil Krishnan:

A few things. Liability is still an unanswered question. If the AI gives a wrong diagnosis and something goes wrong, who’s responsible? I think it’ll tilt toward doctors or the medical establishment absorbing that liability for now. We don’t have good frameworks for standalone LLM liability, so LLMs will be limited in what they can do autonomously. Prescribing a drug, for example, is a hard next step for them.

The thing is, you’ll eventually hit a point where the AI can get you so far, but the next action requires the healthcare system: getting labs done, getting a drug prescribed, having someone physically examine you. LLMs alone can’t close that loop. So people who can offer those real-world services have an advantage.

Where LLMs are already great is interpretability. People upload their lab results to get explanations of anomalous values, for example. But to generate net new data or trigger net new actions in the world, there’s still no mechanism for them to do that alone.

Turner Novak:

There’s always a break when a real-world action is necessary. Someone touching you, something being sent to you, a physical thing happening. That will probably always require a separate product.

Nikhil Krishnan:

Probably. And it should be under the supervision of a doctor in some capacity. Whether AI is doing the ordering under that supervision is fine, but someone needs to be the orchestrator. And someone needs to be able to escalate when something needs human judgment.

Turner Novak:

One area I keep hearing about is peptides. What’s happening there, and what do you think plays out over the next few years?

Nikhil Krishnan:

Oh, God. At a high level, a peptide is an amino acid chain. But the way people talk about peptides today, they’re basically unregulated pharmaceutical products. Normally when a drug comes to market, it goes through a rigorous clinical trial process to prove safety and efficacy. That takes a long time, costs a lot, and the end product is expensive.

Turner Novak:

But the result is: “This is safe to take. We’ve proven it.”

Nikhil Krishnan:

That’s what the test is supposed to establish. The alternative gaining traction now is: “We should be able to access these compounds faster, take the risk ourselves, and test them on our own terms rather than waiting for a years-long approval process.” There are a lot of products people swear by, claiming to repair muscles, work better than Ozempic, extend lifespan, whatever.

The range is wild. Some claim to treat ADHD. It’s basically impossible to verify because you can say almost anything without clinical data behind it. That’s the fundamental problem with this space.

Turner Novak:

Are they mostly injectables?

Nikhil Krishnan:

No, not all of them. There are ingestibles too. But introducing an unproven product systemically through your body, whether orally or by injection, carries different risk profiles. And since none of this is well-studied, it’s hard to know what those risks actually are.

This goes back to what I said earlier: people want agency over their healthcare decisions. This is one expression of that. Some of these compounds will probably cause serious side effects. There are efforts to standardize how they’re procured and manufactured, but sourcing has been pretty shady.

Personally, I wouldn’t do it. But I’m not judging anyone who does. If you’re in chronic pain and there’s been no good solution for you, and someone says this peptide might help with that knee pain, I understand why you’d explore it. I do think people should think carefully about the risk-reward they’re making, especially if they’re young and relatively healthy. But I understand why this trend is happening.

Turner Novak:

Yeah. I feel similarly. New options are generally good. You can make a choice. But there may always be unknown side effects we don’t understand yet. It took 30 years for cigarette companies to admit the product kills you.

Nikhil Krishnan:

Totally. It’s funny to think about the FDA’s framework here. For food, you can bring anything to market as long as it’s “generally recognized as safe.” For drugs, the bar is much higher, even though a specific drug affects a far smaller slice of the population than food does. Peptides arguably get regulated more like food: “This seems safe-ish, let’s see what happens.”

There’s a real philosophical question about how much evidence we need before allowing something to market. We currently test safety, then efficacy, then validate both at larger population sizes. But maybe after proving safety, you just bring it to market. Let high-risk-tolerance people try it early, and let more risk-averse people wait for efficacy data to build up. We have randomized controlled trials for a reason, to avoid self-selection bias. But some people argue the time it takes to get a drug approved does more harm than good. Everything is trade-offs.

Turner Novak:

Yeah. It’s like McDonald’s. It won’t necessarily kill you, you can eat it every day if you want, probably not the best idea, but that’s your call.

Nikhil Krishnan:

Exactly. You want to try the mystery Doritos flavor? You want a Baja Blast? Salute. That’s all you.

Turner Novak:

Do you know why all medical software is just so bad? You go to a hospital and the screen looks like it was designed in 1994.

Nikhil Krishnan:

You’ve got an MRI machine running cutting-edge physics on MS-DOS. How can these two things be in the same room?

A few reasons. First: all enterprise software sucks. This isn’t just healthcare. Have you ever used SAP Concur?

Turner Novak:

I’ve used SAP. It’s brutal.

Nikhil Krishnan:

It sucks. Any enterprise software sucks. And healthcare is very concentrated in large enterprises, so you see more of it.

Second, healthcare is rife with a principal-agent problem. The users of tools are rarely the people paying for them. The doctors and nurses using the software every day are not the hospital administrators buying it. Usability is probably much lower on the admin’s priority list. So you get software that isn’t designed for the actual user.

My hope is that with new AI tools, the user interface layer changes dramatically. Your front-end experience can look completely different even if the underlying database or enterprise system stays the same. What the user interacts with doesn’t have to look like the legacy system underneath. I’m hopeful, but TBD.

Turner Novak:

What works best when navigating the sales cycle with a health system? For someone thinking about selling into healthcare?

Nikhil Krishnan:

Step one: don’t do it. But if you’re going to anyway, a few things.

ROI timelines have to be much shorter. Within a year, you have to demonstrate clear ROI. A common mistake is assuming hospitals have budget for software. They generally don’t. Software budgets are small and not ROI-producing. So you typically have to target a service line that has headcount budget or active financial transactions happening. Much easier to prove ROI when you can point to labor costs or payment flows.

If you say “we’ll increase productivity per worker,” that’s too roundabout. Hospitals want a direct line of sight to the return. Much easier to say: “If we implement this correctly, you don’t need to add this many new hires this year.” Then you need a champion. Someone who will walk you through the many committees it takes to get approval. Every health system is structured differently in terms of who owns budget and who makes decisions. Find the person who’s going to run with you through all of it.

One newer motion I’ve been seeing: bottoms-up adoption in healthcare, which has historically been rare. OpenEvidence is a good example. Essentially ChatGPT for doctors. Get it in the hands of clinicians who love using it, and that usage creates leverage when talking to hospital leadership. A different playbook than top-down sales.

Turner Novak:

Is OpenEvidence going to hit a wall where the system shuts them out? Bottom-up sounds great until the powers that be say “not allowed.”

Nikhil Krishnan:

It’s a great question. There’s a triangle forming right now: scribe companies that record visits and turn them into documentation, literature review and clinical decision support tools like OpenEvidence, and the electronic medical record systems that hold all the historical patient data.

All three want to do what the other two do, and all three need each other to produce the best outcome. The EMR companies will try to roll out their own scribes and literature review features, but consumer product quality isn’t their muscle. The scribe companies will try to expand, and the literature review tools will try to integrate deeper into workflows. It’s a bit like that meme where everyone has a gun pointed at everyone else. That fight is going to be interesting to watch.

Turner Novak:

Who do you think wins? Does the system of record probably win because it’s so deeply embedded?

Nikhil Krishnan:

It’s a good question, and it’ll probably play out faster in other industries first. Think about Salesforce. Are they going to launch their own agentic tools? Just become MCP servers that charge a toll on every interaction? The healthcare version of that fight is still being figured out.

There are also major lawsuits right now around companies using agents to log in as humans and extract data from EMRs. The EMRs say it’s breaking terms of service. The startups say their users want this and the EMRs are blocking them from it. Not super clear how that lands.

My bigger concern is the consolidation dynamic. A large hospital is not switching its EMR overnight. It’s not going to happen. So the system of record has enormous leverage. The more fundamental question is: why aren’t there more new entrants? Private practices, small pharmacies, smaller insurers. If you had more competition at the small and medium business level, you’d see more experimentation with new operating systems and systems of record.

My hope is that AI-native tools make it more attractive for doctors to go independent. Imagine a product that says: “Come start your own private practice. We’ll build all the agents for you. You can see 10x more patients and capture the financial upside.” That’s a totally different operating system from what exists today.

It doesn’t solve everything. Where do you get patients? If you’re a new insurance company, you need risk capital. But if you make it more attractive for people to start small, you chip away at the enterprise software problem over time.

Turner Novak:

It almost sounds like Shopify for doctors. Just enabling them to start their own business.

Nikhil Krishnan:

Essentially. And a million people have tried to build that company. “Business in a box, start your own practice.” The problem has always been that it doesn’t solve the harder parts, like getting patients.

But if patient demands change, like “I really want a doctor who uses AI to refill my medications faster,” or “I want a doctor who incorporates my wearable data into my care,” then maybe the acquisition problem gets easier too. Physician independence is one of the most important things we have to solve in US healthcare. There are a lot of blockers, but there are a lot of moving pieces that could help unlock it.

Turner Novak:

You’d probably need to build a full stack. Not just the system of record but also customer acquisition. With Shopify, that was solved through Facebook ads and content. I think you actually see this happening in therapy. Therapists building social media followings to bring in local patients, and then a telehealth back office to serve them. Maybe COVID just made it possible sooner in that space.

Nikhil Krishnan:

It’s very cash-pay oriented, and that’s a big part of why it works. In e-commerce, you’re paying out of pocket. With insurance reimbursement, new practices get terrible rates out of the gate. Really hard to make the economics work. But in cash-pay areas like therapy or longevity medicine, that’s not necessarily the case.

Longevity docs are a great example. Strong social media presences, strong opinions on how care should be delivered, cash-pay model. The question is how you solve it for the insurance-covered side. Because not everyone’s going to pay cash.

There’s a movement called direct primary care that’s trying to answer this. Essentially cash-pay primary care where you pay a monthly subscription to a primary care physician and get a defined set of services. 24/7 texting, wholesale-priced medications, whatever the practice offers. The idea is to bring costs down enough that people choose it over or alongside insurance.

Turner Novak:

I feel like that ties back to physician independence. Enabling more people to start their own thing and provide care on their own terms.

Nikhil Krishnan:

Yeah. I really feel for doctors entering practice now. They come out with a lot of debt, feel like they don’t have much agency, and often aren’t practicing the way they’d want to.

What surprises me, and this is anecdotal, is how many residents I meet who don’t want to practice full-time. They’ve gone through all that training, they’re highly skilled, and they’re asking me about health tech product jobs. Something has gone wrong if people who have done all that work are jaded enough to want out. It’s a sad state of things.

Turner Novak:

Is it that the process of becoming a doctor is just so hard and long that people burn out? Or is it something else?

Nikhil Krishnan:

I think for some people it might be that. But more broadly, I think it’s that they look at hospital work and it doesn’t bring the fulfillment they expected, or they want to be doing more interesting things. There’s so much happening in AI and tech, and they want to be part of it.

And that’s great. But maybe there should also be cool, interesting things happening within the traditional medical career that make people excited to stay. Something has to make practicing medicine feel worthwhile again.

Obviously the group coming to me is self-selecting. Probably the most jaded or the most interested in health tech specifically. But I think part of it is just that they don’t want medicine to be the only thing in their life, and it doesn’t always deliver the level of meaning they thought it would.

Turner Novak:

That’s fair. So let’s shift. How did Out of Pocket get started? What’s the origin story?

Nikhil Krishnan:

I was at a company called CB Insights, running the research team focused on healthcare. That’s where I started reading deeply and started writing. Then I joined a clinical trial startup for a bit.

Turner Novak:

Oh, I forgot about that.

Nikhil Krishnan:

Yeah. I was an operator for a hot second and wasn’t very good at it.

Turner Novak:

Failed operator.

Nikhil Krishnan:

Failed operator. I left in February 2020 and started writing again because I wanted to rebuild that muscle. The original idea was actually to make The Daily Show for healthcare. I was really inspired by Jon Stewart. Making boring topics genuinely interesting and entertaining. Could I do that for healthcare?

Turns out, video is really hard. We filmed some sketches and I was like, “I cannot do this consistently as a business.”

Turner Novak:

Didn’t you post some of these? I feel like I’ve seen stuff over the years.

Nikhil Krishnan:

Yeah, I ended up posting a few because the footage was just sitting there. But making it a real business, doing it consistently. I have so much respect for video people now. I was just better at writing anyway, so I kept writing. And the Substack economy was starting to form around that time.

Turner Novak:

You probably got an insane boost from starting at the beginning of COVID. Everyone was home with nothing to do but read and subscribe to things.

Nikhil Krishnan:

Yeah. People were locked in their houses, healthcare was suddenly the only topic anyone could think about, and it worked in my favor. But it’s been six years since then and it’s evolved a lot.

We’re now all-in on in-person stuff. I still do the newsletter and virtual courses, but content on the internet is changing rapidly in ways I don’t love. Standing out in a sea of AI slop feels like an uphill battle. So we’ve leaned hard into events. Hackathons, micro-conferences focused on builders sharing across companies, healthcare coworking spaces. I view content more as a bat signal now: “Here’s a topic I think is interesting. Let’s all coalesce around it and meet somewhere.”

Turner Novak:

So it’s more like top-of-funnel.

Nikhil Krishnan:

Yeah, top of funnel. And also a self-selection mechanism. I want people who can read to come to our things.

Turner Novak:

If you’re optimized for short-form, your brain short-circuits trying to get through a long newsletter.

Nikhil Krishnan:

You know what’s funny? There are a lot of content creators who make content for people they’d never want to meet. You make content for an audience you can monetize well, but you’d hate spending time with them.

I could never do that. For me, the fun is putting content out there to find my people, even if that group is much smaller. Content is not the be-all end-all. It’s more like: let me find the people I actually want to hang out with and do interesting things with.

Turner Novak:

I’ve kind of accidentally figured that out too. My content is either really dumb and jokey, or it’s these long dialed-in conversations where I’m just trying to learn something. And most of the people I meet through it, I end up thinking, “Oh, that person’s actually way cooler than I expected.” So yeah, that’s a good framing.

Nikhil Krishnan:

Yeah. If meeting people is literally part of your job, you naturally gravitate toward making content that attracts people you’d actually want to meet. Some creators view content as the end product. Make it, monetize it, don’t interact with the audience. That’s a totally valid business. It’s just not who I am.

Turner Novak:

So what’s the business model for Out of Pocket? Revenue streams, investing. What does the whole system look like?

Nikhil Krishnan:

The newsletter has banner ads and full-page sponsorships. We co-develop courses with companies where they provide the expertise and we build the content. Those are sponsored courses. Events have sponsors and we charge tickets depending on the format.

I do some investing, but it’s typically angel investing. Honestly, investing is my least favorite hat I wear. I know it’s your whole thing, but I don’t have the delayed gratification you must have. I mostly do it to vouch for people. Putting a little money behind someone is a way of sharing credibility with them in the space.

We have healthcare coworking spaces, a Slack community with some monetization, onsite training and workshops. It’s a hodgepodge of side quests under a bigger umbrella. Purposefully diversified so we’re not too concentrated on any one revenue stream, but we’re definitely all-in on events.

Turner Novak:

On the courses. Are most of them free?

Nikhil Krishnan:

Some are paid. I teach a Healthcare 101 course where people pay directly. Mostly employers signing up their employees to get up to speed. The rest of the courses are usually free for attendees; the sponsor pays us to co-develop and co-market them.

We’ve also been doing onsite workshops with companies. Going in to teach things like claims data analysis, which is a very common data type that healthcare data teams work with. About 60 to 70% of the content is consistent. “Here’s what a claim is, here’s how to do a data analysis.” And we customize the last portion for whoever we’re working with. It’s just easier to keep people’s attention in person than over Zoom for this kind of training.

We’ve started doing some AI workshops too. Showing people the basics of Claude or ChatGPT or whatever their internal tool is, and then introducing more interesting ways to use it. Like connecting to an MCP for a specific vendor and showing them what’s possible. A lot of people just need some handholding. We’ve been testing that model.

Turner Novak:

Yeah. A lot of people don’t think about how large the L&D budget is at companies. Paying a vendor to help your employees get smarter is a real investment with real ROI.

Nikhil Krishnan:

It’s a mixed bag, though. L&D money at larger companies tends to be earmarked for specific things, often tied to existing learning platforms that house all their training modules. If you’re not on that platform, you have to pay a take rate to get distribution. Or you need some recognized certification that gives the company cover to say, “Yes, we used a credible provider.”

It’s frustrating because we can offer training at a fraction of the cost of the big certified names, and I genuinely think our content is better. But if we don’t have the right certification, some enterprises won’t consider us. I get it. They’ve probably been burned before. But it’s still annoying to pay that price.

Turner Novak:

As you’ve swung more toward in-person, how are you thinking about AI in your own work? How do you use it?

Nikhil Krishnan:

At a personal level, I use it constantly. Even just running the business. Plugging AI into our internal systems so I can get a better read on what’s happening. When we’re doing an event, I can quickly look up who in our ecosystem has the most relevant background for a particular talk, or who I last reached out to and why. Context retrieval that used to take forever now takes seconds.

As someone who has to get up to speed fast on a lot of different healthcare topics, it’s incredibly useful. I’ve built enough pattern recognition to know when it’s bullshitting me, which helps.

One specific example: I read a lot of primary source documents. Lawsuits are actually a great way to understand how a specific corner of healthcare works. But lawsuits are extremely long and full of procedural stuff I don’t care about. So I have a Claude workflow that extracts the parts relevant to me. Skip the legal proceedings, just give me the mechanics of what’s interesting about the case. That’s made me dramatically faster at getting up to speed on new topics.

At the same time, I’m worried about people using AI to pollute the internet commons, or to replace their own critical thinking. Every time I see someone just ask an AI, “Tell me why I should care about this,” I think, what are we doing here?

Turner Novak:

Do you think AI-generated content is more widespread than people realize? Or do you think most people can tell and have formed an opinion?

Nikhil Krishnan:

Among people who post on the internet, it’s definitely more common. But we have to remember: most people don’t post on the internet. It’s a really small slice. And among the people who are consuming content? Most of them genuinely don’t care if something was written by AI. A lot of people actually prefer it because it’s more digestible and in a format they like.

That’s probably frustrating to people like you and me who care about craft. You think, “That’s AI slop, everyone can tell.” But not only can they not tell, a lot of people actually prefer it.

It’s not that different from SparkNoting Shakespeare. “I can’t read this. I don’t understand the prose. Give me a simpler version.” Shakespeare would have hated that. But it worked for people. Is this really so different?

Turner Novak:

Imagine Shakespeare reading Gen Z slang. Some kid trying to explain Macbeth.

Nikhil Krishnan:

Yeah. Kind of the same thing.

Turner Novak:

So how should someone think about posting on the internet and building a following today? If I’m starting from zero, followers, brand, reputation, what’s your advice?

Nikhil Krishnan:

I just think people massively underrate reputation. That’s the most important thing.

Smart people care deeply about finding other smart people. It’s not about rage-bait or raw eyeballs. For me, the most meaningful thing is when someone sends me a genuinely thoughtful email. No one else can see it, it’s just between us, and they wrote it because they’re intellectually curious about the topic. I reply to every single one because those interactions matter more to me than vanity metrics.

You don’t even need to post publicly. The question is: how do I find the people I actually want to meet and respect? What’s the best avenue for that? Maybe it’s online posting. Maybe it’s something else entirely. But your reputation compounds. Post as if your reputation is on the line, because it is.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

She shares what it was like failing so publicly, what she’d do differently next time around, lessons on building a brand founders can learn from today, what it was like writing a book and having a Netflix show made about her life, and why she started Trust Fund to back the next generation of founders building consequential companies.

When I surveyed all of you back in January, the #1 request was more stories from founders who had failed. Sophia was extremely candid and authentic about her journey, and I think there’s a lot to takeaway and learn from.

Please let me know what you think after tuning in!

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👉 Stream on Spotify and Apple

Timestamps to jump in:

• 0:59 Selling vintage on Ebay while working at an art school

• 04:31 Lessons in marketing and perceived value

• 12:38 Knowing when to make your first hire

• 18:31 Borrowing from others to build a unique brand

• 25:17 Growing to $120m revenue in seven years

• 27:24 Sharing the pitch deck that raised $50m

• 30:17 Mistakes scaling to 100’s of employees too fast

• 34:48 Downsides of raising at too high of a valuation

• 39:56 Why being a CEO is fun

• 42:57 Declaring bankruptcy

• 50:38 How it feels to fail publicly

• 54:41 Writing a book, Netflix series, starting the Girlboss movement

• 59:13 How to create a new brand in 2026

• 1:05:34 Starting Trust Fund to invest and help founders

• 1:13:45 Raising $5m from a poker game

• 1:18:47 Sophia asks for Turner’s LP pitch

• 1:26:53 Traits of the best founders

Referenced:

• Trust Fund

• Pitch Trust Fund

• Girlboss (Book)

• Girl Boss (Netflix)

Find Sophia on Instagram, LinkedIn, X / Twitter, and her website

👉 Stream on YouTube, Spotify, and Apple

Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Sophia, welcome to the show.

Sophia Amoruso:

Hi, Turner.

Turner Novak:

Excited to have you.

Sophia Amoruso:

Yeah, finally, we made it happen.

Turner Novak:

I know. I was going to say it’s been two years. I don’t know. We’ve been trying to figure this out.

Sophia Amoruso:

I guess you had invited me and then I forgot you invited me and then I was like, “When’s he going to have me on his podcast? Am I not good enough?” And then I was like, “Hey, I want to be at your podcast.” And you’re like, “Dude, I asked you a long time ago.”

Turner Novak:

I feel like I asked you multiple times too. I feel like this was just a broken communication. We just ...

Sophia Amoruso:

It’s probably my memory and ADD and cognitive load and all that-

Turner Novak:

All that stuff.

Sophia Amoruso:

Yeah.

Turner Novak:

So I think we’re going to talk about you started pretty big company, a movement, you built a lot of brands, you sold some companies, started a fund, doing a lot of investing. I think probably one of the most interesting ways to start it, you initially wanted to go to art school way back in the day. So how did you try to do that? How did that get started?

Sophia Amoruso:

I mean, I still kind of want to, but I really wanted to be a photographer. And I shopped this amazing portfolio and applied to California College of the Arts, which is in Oakland and San Francisco Art Institute, North Beach, which is unfortunately no longer there, beautiful campus. And I got into both and I was nominated for a scholarship, but I didn’t get one. And it was 50 grand a year and nobody had 50 grand a year to get me to go to art school. And nobody told me about debt and I’m glad I didn’t know about it. And I decided to ... I was just working a bunch of shitty jobs and was ... Yeah. So I kind of gave up on that, but I wound up working at an art school, which is the last job I had before Nasty Gal. It wasn’t because I wanted to be close to art students.

It was because I had a hernia and I needed health insurance and I had a preexisting condition, which now you can have applying for individual health insurance. But at the time, you had to have an employer sponsored insurance plan to get something fixed. It was crazy that I just didn’t have health insurance at all before that. I don’t know. My parents, they’re like pretty decent parents. I just fell off. I fell off.

Turner Novak:

Somehow.

Sophia Amoruso:

Yeah. I don’t know, but I got back on.

Turner Novak:

So you were working at the art school. I think you were working in the lobby and you had some free time.

Sophia Amoruso:

Had some free time. At that time, my idea of working was, how can I do as little as possible for as much as possible? And as much as possible was like, I don’t know, what, $13 an hour, which seemed like a lot of money at the time. This is 2006. And so I sat in the lobby and my job was to check students in, to go up to the second floor to admissions or whatever. And I had to ask for their student ID and if they didn’t have one, I had to ask for somebody to sign in. And I had some time to kill on the internet. I was on MySpace like everybody else and was getting friend requests from eBay sellers who were selling vintage clothing. And I only wore vintage clothing at the time. I loved it. I knew where to find it.

And I was like, “Oh my gosh.” I clicked through and saw that these sellers were selling stuff for way more than ... I thought Haight Street was expensive, but their auctions were going crazy because there was a girl in Australia and a girl in Copenhagen all bidding on the same thing. And the US dollar was really weak at the time. So it was kind of cheap for them, but it was like a lot of money to someone in the US at the time. And I was like, “Okay.” I got my health insurance. I got my hernia fixed. I quit the job and was like, “I’m going to try selling some stuff on eBay. I’m going to try selling some clothing.”

And I went and found vintage clothing and not all of it worked, but it was my effort to not work for anybody else. It wasn’t because I was ambitious. Technically it was entrepreneurial, but it was not some kind of long-term plan where I thought I was going to build a startup in San Francisco. I was subsisting on mission district burritos and dive bar happy hours at 22 years old. So it wasn’t the beginning of something I thought would ever put me in a podcast like yours, Turner.

Turner Novak:

You are the first vintage seller on the podcast, I think. What’s the appeal of vintage clothing? Why are people so into it?

Sophia Amoruso:

I mean, a lot of the time the quality is higher than what you can get today, especially for the price. I mean, everything’s like mass made, cheap, plastic, garbage. I mean, you find like a vintage wool coat. It’s real. It’s like nice. The cut of the stuff was often really flattering. I liked high-waisted pants and I liked shrunken t-shirts. And it was hard to find things that fit well or look cool. I listened to a lot of old music at the time and I might have well-worn a costume. I mean, it was like high-waisted pants and like platform shoes and like a wool pea coat and sometimes like a big belt or whatever. But I looked like I was a Black Sabbath groupie in 1970 something. So I just really, yeah, I just really loved it. It’s also often cheaper. It’s more sustainable and the hunt is really fun. I think people just love finding unique things and I absolutely did. It was like the biggest thrill to find vintage Chanel in a thrift store for $8.

Turner Novak:

And then you realized, wait a second, I can sell this for hundreds or even maybe more than that online.

Sophia Amoruso:

Yeah. It taught me a lot about perceived value because Chanel jacket could be $8 a thrift store and somebody says that’s what it’s worth or it could be worth a thousand dollars on my eBay store. And I wasn’t the person that determined those prices. It was pure auction. I don’t even think there was a buy it now at that point. I think everything was auctioned on eBay and I started the bidding at 9.99. So anything could have sold for 9.99.

Turner Novak:

This is $10.

Sophia Amoruso:

Yeah, $10. Everything I sold started at $10 and it was just, there was no reserve and some of it did sell for $10, some of it didn’t sell. Some of it I paid a lot more than $10 for, thinking that I could sell it for more than I paid for it. But you take the risk because the psychology is, for the bidder, it’s like, “Oh wow, I might be able to get this for $10.” And then they get attached to it and bid more on it. It’s just an easy entry price, but ultimately the customer set the value of the product and something could sell for 10 or 50 or 100 based on how I styled it or what the model looked like or how I described it and what the title was, what the description was. And like there were all these things that I had to do to make this old thing look cool and something that someone today might want to wear. And so it was just an interesting experiment in turning something that literally had no inherent value to some people into something that other people were fighting over.

Turner Novak:

Yeah. It was almost like you were so close to it that you could like almost perceive that, or you could like discover that perceived value maybe more than somebody who wasn’t as like close to it. That’s why, because you were wearing it all, you understood how people would like up scale, upcycle, upwear. I don’t know if that’s the right word I’m looking for, but like how people would dress things and maybe what they might want. It’s an interesting psychological trick, which I guess I didn’t even realize where you started everything in 9.99. So someone might see something that’s like a $200 piece and they’re like, “Oh my God.” It’s almost like thrifting and treasure hunting for them too on eBay where like, “I think I found this good deal. I got to bid on it.”

Sophia Amoruso:

Yeah. And it just taught me what people wanted and what they were willing to pay for it. So when I, yes, continued selling vintage, but launched the website and started curating from trade shows, I knew what they wanted. It was like the ultimate, I guess, beta test to just see item by item what it is that people wanted and be able to refine my product or thesis or offering or pricing structure, whatever it was when I introduced things that were priced on a website, just like anything else that you buy and weren’t at auction because I really knew what people wanted and I really knew how to present it and I really knew how to describe it. I understood who my customer was really well and yeah, it was just great training ground. eBay was amazing training ground.

Turner Novak:

I remember you were telling me when we talked a couple of weeks ago that you would go into each thumbnail and like tweaking everything about it, like making them catchier, brighter, more contrast. So like literally each item, I mean, you’d have hundreds of listings at some points, right? Did you ...

Sophia Amoruso:

I mean, it was usually like maybe 20 at a time. I would start the auctions on like a Sunday when people were sitting at home and then they were 10-day auctions. So I think they closed on like Wednesdays when people were ... It was kind of like a lull in the week, but the auctions went for 10 days and that was it. But I didn’t put new things up. I just had those 10 days to go find and photograph and kind of list or schedule the next week’s items.

Turner Novak:

Well, you were like in the weeds tweaking ...

Sophia Amoruso:

Yeah. Totally. If there was something I thought was really great and it wasn’t getting any traction, I was like, “Okay, there’s something wrong here.” And before a single bit is placed on eBay, again, I don’t know what it’s like now, this is literally 20 years ago ... It’s crazy. You could still edit the photo and the description, but once a bid was placed, you couldn’t. So if something was sitting there for 30 minutes or an hour or six hours, I would go back in and I would tweak stuff and I’d wait a little bit longer and then I would tweak stuff until I saw it taking off. Yeah. No, it was dynamic, but I was the dynamic engine.

Turner Novak:

And I think I heard you say once that you made your first hire off of Craigslist. I mean, in 2026, that sounds pretty crazy to hear someone say that, but how did you know it was time to make the first hire? How did you hire someone? Because had you ever hired anyone before?

Sophia Amoruso:

I mean, I hired an assistant off of Craigslist like a year ago to help me move.

Turner Novak:

Oh, you’re still using Craigslist. Wow.

Sophia Amoruso:

I mean, I did like a year ago and he helped me for a few months, moved to London and packed my stuff up and whatever, deal with dogs, vet appointments to get them here. But yeah, I mean, where else was I going to find someone? Yeah, I put the job description on Craigslist and it was just like, “I need an assistant for a vintage eBay store,” and listed it. And she came to the job interview, which was just me in a messy warehouse and a shipyard in Benicia, way out in the East Bay in Northern California. And when she came and she was like, “I thought this website was owned by Urban Outfitters or something.” It was just like me with like trash bags and like dirty clothes and like shipping packages and one computer and a little photo set up and a steamer. And she was working at a store on Haight Street that went to trade shows and did the whole thing that I eventually ended up doing.

She kind of introduced me to that and she was like, “Well, I need a full-time job if I leave.” And I was like, “Oh my God, I didn’t even really pay myself at the time.” And she was like, “I want $16 an hour.” And I was like, “That’s more money than I’ve ever made. How am I going to pay somebody more money than I’ve ever made?” And I was like, “That’s not fair.” And I still wasn’t really ... I didn’t want anything. I had like an old Volvo, my rent was $500 a month and I was too busy building this business to want anything. I didn’t even have expensive taste. I did, but it was like in old things. It was still in quality, but it didn’t cost money. It doesn’t cost money to have expensive taste if you’re resourceful.

But I was like, “Okay.” And I took a really big flyer on, well, one, her, but also on having someone full-time because I didn’t know if I needed somebody full-time and then it just kept exploding and her job became very full-time and way more than full-time, not long after that. And I think it’s one of those moments in building a company where you kind of have to believe that the growth will come. You don’t want to obviously overhire, but if you have inclinations and you know that things could really, really break if you don’t bring help on board, sometimes it’s worth taking that risk so that you’ve got someone there to catch the demand when it does come. And that’s exactly what happened.

Turner Novak:

What were things that were breaking at the time where you kind of knew you needed help?

Sophia Amoruso:

Yeah. I mean, I was sourcing everything, shooting everything, writing all the product descriptions, I mean, uploading images to an FTP server, weighing them for shipping, measuring them because there’s a small and a medium and vintage sizing depending on the era and the brand is like all over the place. Then I was shipping them, printing out shipping labels, answering customer questions, editing photos, casting models, feeding them in hamburgers, or trade so that they could have like cool MySpace photos. I mean, you could do that at the time.

Turner Novak:

So you were recruiting models to come in and be like, “Hey, we have these cool clothes. We’ll do this photo shoot. I’ll maybe get you some McDonald’s, but also you can use the pictures on your MySpace.”

Sophia Amoruso:

Yeah. Except it was a place called Burger Road.

Turner Novak:

Burger Road.

Sophia Amoruso:

Burger Road. Yeah.

Turner Novak:

That sounds like the most side of the road, like mom and pop.

Sophia Amoruso:

It almost sounds like road kill. Yeah.

Turner Novak:

Yeah. I mean, I’m sure there were good burgers though.

Sophia Amoruso:

They were good. They were good. I remember one time I went into Burger Road, I threw on a sweater from the racks of clothing and it had like a 499 tag hanging off the front of it and the model was like, “Hey, I’m walking around wearing clothes that are like 499.” I just was like, yeah, I was in full founder mode, I guess, before that was the term. And so she took on all the things that I didn’t have to do that didn’t have to do with taste. She eventually learned my taste and learned what the customer wanted and became the head buyer for the company, but she was answering customer questions. She was shipping stuff. She eventually wrote descriptions, but I trained her how to do that. And even the voice on customer service, it’s like using exclamation point. If you’re describing something, don’t call it sparkly, call it glitter.

I don’t know why I cared about that, but things that I knew made a difference, that made it feel like a human was talking to a customer or someone cool was writing a description or how careful to be when you’re putting a package together. So it looks like, I don’t know, somebody cared. There’s a difference between slapping a label on a package or making it look like somebody kind of lined it up and didn’t just crumple it and sit on it and whatever trash it. And then eventually she hired someone to do customer service and she hired someone to do copywriting and I kept buying and shooting and doing the things that I needed to do, going down to LA and bringing car loads of vintage back. So that’s how it began.

Turner Novak:

And I think when I just think about the space at the time, there was probably a lot of people that were kind of maybe doing similar things. They started eBay store. What do you think you did right in terms of like the branding around it and ... Because it feels like you kind of really had a strong connection with the customers and they really resonated with it. What do you think you did right?

Sophia Amoruso:

I was looking at fashion magazines and following Nylon magazines and these cool Australian fashion magazines like Preen and Russh. I don’t even know if they’re around anymore. I don’t follow fashion.

Turner Novak:

Never heard any of those. I’m not a fashionable guy.

Sophia Amoruso:

Yeah. And I looked at the styling and I looked at the voice and ID magazine with like the wink and there was just kind of like a sense of irreverence and like also joy and levity in fashion at the time that I then kind of borrowed and turned into a retail fashion e-com brand for the first time when people were just dumping a t-shirt on a model, cutting their head off. And it was like, that’s what you got when you were shopping for clothing. Nobody had sunglasses on a model. Why would you put sunglasses on a model when you’re selling a shirt, a model with a handbag that looks like she’s going somewhere? That was like for editorial photography, like advertising photography, but e-com catalog photography did not have that at all. Nasty Gal was pioneering in that. And eBay, that did happen. So I brought that from eBay.

I wasn’t the first eBay seller to like style a model in like a hat and look cute and make it look very like hippie dippy or boho like so many of them were. At the time, the eBay stores were like Spanish moss and Mama Stone and these like very hippie dippy granola-y vintage brands. And I had worked in a record store and I had listened to some more ... like old music, but also kind of like more hard hitting like funk and stuff that was like riskier and edgier. And music has always kind of influenced, I don’t know what I do in the names of things. I always borrow them from disparate and often obscure sources like Girl Boss I’ll talk about. But Nasty Gal’s the name of an album and a song by a woman Betty Davis who’s this incredible funk singer who was married to Miles Davis and who was allegedly like too wild for him and her lyrics are like crazy.

She was just like such a punk and I almost named it iHeartVintage, but I decided to name it Nasty Gal Vintage and it just kind of cut through the noise because I brought this irreverent and edgy style and styling and voice to it. And while I was just an eBay seller, I made sure everything was coherent like a brand is. And the voice was always we, always made it sound much bigger than it was. Not, “Hi, I’m shipping the package to you or I measured it and it was we.” And it always looked much, much bigger than it was because everything was really consistent.

The photography was the consistent, the copywriting was, and I didn’t know I was like a good writer or a copywriter, but everything was imbued with like a sense of humor or it was punny or it was like a funny portmanteau or there was like a way of describing something that was memorable and the name was memorable and everything was very cohesive and I never got like a lesson in branding, but it was kind of like, what would I want and what do I see out there in the zeitgeist and how can I borrow from that without ripping from it?

Turner Novak:

Yeah. So it’s basically just understand what people want and then maybe it’s not like any single thing you did was that unique or like you didn’t really come up with anything specifically that was new, but it was just the ability to borrow from different things and kind of tie them together. I mean, what’s that one phrase that’s like good artists create, great artists steal or something like that? You just took a bunch of stuff and combined it into this new experience for people.

Sophia Amoruso:

Yeah, like an entirely new channel. I mean, it was shopping that hadn’t happened with online shopping in e-commerce. It was like totally pioneering and ultimately it created something that people were obsessed with. It was, “I’m a nasty gal, I am a nasty gal.” And the peak branding is when someone says, “I am a ...” and then puts your brand in the sentence and identifies as the name of the brand. And that’s what Nasty Gal did. And it was much, much bigger than buying clothes. It was an emotional experience and a big part of the customer’s identity at the time and it was really, really, really cool what it became.

Turner Novak:

And it got really big, right? What was the peak of success for? How big were you at that point?

Sophia Amoruso:

Yeah, we did over a hundred million in revenue. It might’ve been 110, 120. I have financials hanging around. I found the original deck that I raised 50 million from Index on. It’s crazy. I mean, I’ve never shared it. I thought the growth went from one to six and a half to 12 to 28, but it went from one to six and a half to 28, something like that, or to 30. I thought there was a stop somewhere along the way and every podcast I’ve ever been on, and everyone’s like, “Wow,” when there’s a 12 in there, but it was like six and a half to 28, and that was just purely bootstrap. That was before I fundraised. But yeah, it was explosive. Year one was 75K, and it was just me in my step aunt’s pool house with a hot plate and like ...

Turner Novak:

What were you cooking on the hot plate? Ramen or something?

Sophia Amoruso:

I had a toaster oven and I would get Trader Joe’s cornmeal pizzas that were so good.

Turner Novak:

Okay. And you just cook them in the toaster oven?

Sophia Amoruso:

I’d just go to Boston Market or it was like in a suburb. I moved out of San Francis, started in San Francisco, but then I was like, “I need to be able to have a car, put stuff in a car, take it to the post office.” I couldn’t even walk all this shit to the post office in San Francisco.

Turner Novak:

Yeah, that’d be rough. Those hills too.

Sophia Amoruso:

Yeah. So I went an hour outside of San Francisco and lived really far from all my friends when I was 22 because I was like, “This is fun. I can flip clothes. I made $2,000 this week. That’s actually insane.” So yeah, first year was 75K, second was 250, and then left eBay about halfway through the second year, brought on my first employee, and then it was 1.1, then it was six and a half, and then 30, something like that. I need to get my story straight, because I know it matters and it’s lore and it’s like, I fucking talk about this, but it’s also like it’s my story.

Turner Novak:

Okay. So you said you found the deck. Do you have it? Can you pull it up? Will you share the screen? Is that possible right now?

Sophia Amoruso:

No one has ever seen this.

Turner Novak:

No one’s ever seen this. This is amazing.

Sophia Amoruso:

I started in ‘06. Yeah. Then it looks like we hit a million in 2010 and by the end of November 2011, five million, and then here we are. I had done a survey of customers. I felt like I had stumbled upon all of the cool girls from NYC and LA’s little online shopping community. I’ve been addicted ever since.

Turner Novak:

This almost feels like you see a lot of people talking about UGC today of people posting content of the product. It kind of seems like you tapped into that just based on just ... Were these customers that were posting pictures about it or was that all the models that you brought in?

Sophia Amoruso:

It was word of mouth. I mean, it wasn’t social media. I mean, Instagram was emerging in 2012, but this is all happening before 2012. I mean, I had like 60,000 friends on MySpace, but you can’t really attribute that either. But I mean, what? 25% of our users came back over 26 times per month.

Turner Novak:

Oh, this is per month. So you had people, half of people came at least once a week and almost 75% came back more than once.

Sophia Amoruso:

Yeah. Yeah.

Turner Novak:

Wow.

Sophia Amoruso:

Competition. So then it was like, this is visits, revenue.

Turner Novak:

This is the Nasty Gal numbers?

Sophia Amoruso:

Seven million, 25 million, and this was Feb through April of 2012 when we raised 50.

Turner Novak:

So you did, you basically did half of last year’s revenue in one quarter?

Sophia Amoruso:

Yeah. Yeah.

Turner Novak:

That’s crazy.

Sophia Amoruso:

Yeah. And then we raised 50 million and rounded up by a hundred million and we were like, “Let’s go from 28 to 128.” And it was just kind of like waving a finger in the air and hiring a hundred people and signing a 40,000 square foot office lease and a hundred thousand square foot fulfillment center lease in Louisville, Kentucky. And yeah, so all this kind of stuff, but that like really happened.

Turner Novak:

And I know there ... I mean, there’s been like a lot that happened after that. How would you describe ... So you closed the round, and you said you’re going to hire hundreds of people open this facility. Did you end up signing a lease, moving in? It looked like I saw 40% international revenue in the next year or something. I saw one of those charts. So it was like, “We’re going to expand to Europe and Asia.”

Sophia Amoruso:

I mean, the business was international by nature of me having shipped worldwide when I was on eBay, and so that’s how the customer base grew. We never really had to open a UK store or ... We just shipped from the US, so there was no like ... I mean, yeah, I guess at a certain point we were running programmatic ads internationally, but it scaled with the original customer base. We hired a hundred people across buying, design, production, planning, project management, a CTO, a whole team of engineers, UX design, a chief people officer, chief financial officer, a controller, a whole finance team, performance marketing, a creative director.

Turner Novak:

A full company, like you had everything.

Sophia Amoruso:

Yeah. And it was like some companies are able to inject that many people into an organization in a year and integrate them and everybody understands what to do when they’re not stepping on each other’s toes or duplicating efforts or ... I wasn’t really very good at that. I don’t think I even knew I was supposed to do that. I just thought all these people with more experience than me would show up and work together and solve the problems and like, I don’t know, have fun or something. And the company would just, I don’t know, just keep growing. The job of CEO is kind of like drive all of that through an organization, drive it through the leadership team and for the leadership team to drive it through the people that report to them all the way to the bottom.

And in a lot of companies that gets lost somewhere at the top or somewhere in the middle. And depending on what team you’re on, there’s an entirely different company culture over in finance than there is on the creative team where those guys are having a blast, but some boss over here is like awful. So, and it became very expensive. It was like, “Okay, who cares?” It’s extremely profitable before I raise money. And that just became as soon as investors came on board, they were like, “Who cares? Whatever. Let’s just plow all this money into growth and build a billion-dollar business.”

And it wasn’t a company that was set up to scale. It was like a mom and pop business that they injected 50 million into with an eBay seller at the helm. So it was kind of naive on my behalf. It was, I think, kind of irresponsible on their behalf and there was no number of executives around me that could, I don’t know, solve the problem of me not knowing how to lead them, but also there’s a lot of other people who could have done a better job along the way with Nasty Gal including my investors.

Turner Novak:

So it sounds like maybe the mistake was you shouldn’t have raised money. Did you not have to? Because it looked like you were making three and a half million a year.

Sophia Amoruso:

We didn’t have to.

Turner Novak:

In EBITDA, it’s just sitting in the bank, right? You were printing money.

Sophia Amoruso:

I can own 80% of a company that’s worth $350 million. I control it. What’s the downside?

Turner Novak:

So in your head it seemed like a great idea.

Sophia Amoruso:

Fundamentally changed the business in ways I never could have anticipated and got really complex, really fast. And financial statements went from a few tabs to 50 tabs. I still can’t build a pivot table.

Turner Novak:

Maybe that’s something you don’t want to say publicly. No, I’m just kidding.

Sophia Amoruso:

It just happened so fast. There’s things that I can do that no one else can’t. And for everything else, you can rent as long as you go.

Turner Novak:

Yeah. So I guess, couldn’t you have just decided like, “Okay, we did raise money, the valuation’s really high, but I don’t have to spend it all.” Or like why was it such a big deal that the valuation of the company was so big from someone who, just kind of hearing this for the first time, was like, “Man, this sounds awesome. It sounds like this sounds like a great outcome.”

Sophia Amoruso:

I didn’t really negotiate the valuation. I met Jeff Jordan from Andreessen who flew down, Jeremy Lou from Lightspeed flew down in 2012. I met with Kleiner. I met with a ton of firms, Alfred Lynn from Sequoia. And some of these guys are LPs. Two of those are Jeremy and Jeff are LPs in the fund now. But I didn’t have like ... I actually found a term sheet from Benchmark, which is crazy because I don’t even remember talking to Benchmark, but I found one recently. But I didn’t know how to play them off of one another. I wasn’t trying to be like, “Oh, I want to ...” I don’t think I cared that much. The valuation was just crazy. And I was like, “Okay.” I mean, they were just like, “Please let us invest.” That was it. It was just like, “Will you let us please?” So yeah, that wasn’t why I did it.

But in terms of how we deployed the capital, I was really deferring to the people I brought on. I had an investor, I had a chief operating officer, these people, she had built startups. He had been alongside founders and taken them public and invested in incredible companies and amazing founders. And so I hired people that were smarter than me and I listened to them. And I don’t think that’s a mistake, but I don’t think I completely grasped what it meant and how fast we were blowing through capital.

Turner Novak:

Do you think if you could go back, would you have maybe sought out more mentors to just spend a little more time talking through things with you?

Sophia Amoruso:

Yeah, I didn’t have any advisors. I didn’t really know any founders. There were no glossiers and away luggages and I didn’t have peers. It was like Neta Porte and Tony Shea and Beachment. So Diego Berdakin in LA who’s now done Cloud Kitchens and is an awesome, awesome guy and a really good poker player. But I did not have ... I had one advisor who had been the CTO at Tumblr and my board was me and my empty seat and Danny from Index. And I kept my empty seat empty. I kept my second board seat empty the whole time.

I didn’t build out a board because I had heard they’re a nightmare. We never voted on anything until we had to vote to send the company into chapter 11, so it never really came down to that. It was mostly me probably not disagreeing enough and trusting the people around me who had so much more experience and were telling me like, “No, no, no, turn down the 400 plus million dollar offer from Urban Outfitters. That’s child’s play. We’re going to build a billion-dollar business.” I would have made, I don’t know, $300 million, $280 million on that sale.

Turner Novak:

So Urban Outfitters said, “We love what you’re doing. We want to acquire the company.” Did they actually give you a term sheet or did you go through any process?

Sophia Amoruso:

I have it. Yes, I have it.

Turner Novak:

You don’t have to bring that one up, because that one might be a little bit confidential.

Sophia Amoruso:

Yeah, yeah, yeah. I think the whole thing is confidential, but no one cares.

Turner Novak:

So you turned down an offer to acquire the company, so why’d you turn it down? It’s just that you thought this could be worth billions, you thought you could build something like 10X bigger?

Sophia Amoruso:

Yeah, because I had signed up to build a billion-dollar business and 400 something million was not much of a markup to what Index had invested in. They’d underwritten a minimum billion dollar exit and I bought into the fact that we were building a billion-dollar business and wouldn’t take that long to get there. And yeah, because I took advice, it wasn’t because I was greedy. It was just more like, “Oh no, we can keep going.” It wasn’t really ever about money. It was about, I was having fun and also trying to do my best on behalf of the company and the team and my investors.

Turner Novak:

Yeah. I feel like there’s that one saying where Zuck turned down a billion-dollar offer for Facebook from Yahoo in the early days and someone asked, “Why’d you turn it down? It’s a lot of money.” And he’s just like, “Well, if I sold it, I would just start a new social network. I’d just start Facebook again. This is what I want to do.” So maybe money went to the equation, but he’s also just like, “Why would I sell if I’m just going to restart it? This is what I want to do. I want to keep working on this.” So it’s like if you’re having fun ...

Sophia Amoruso:

It was so fun. It was like the best ride. It was the most fun.

Turner Novak:

So what was the biggest thrill for you? What was the most exciting about doing this? Because some people might think that it sounds miserable running a company, like being CEO. Some people might think, “Oh, being CEO, building my own company sounds awesome.” What made it so fun for you?

Sophia Amoruso:

I mean, it wasn’t miserable until growth stalled and then I was like, “What? We have to lay people off? What?”

Turner Novak:

Yeah, that’s hard.

Sophia Amoruso:

But it was exploding for a long time. I think it was getting to work with really great people, especially creatives and execute on amazing marketing campaigns, work with great photographers, make fashion that I really loved, just work with some of the most talented creatives and marketers in the world, and to be able to see that out in the world, see it on a billboard or ... We ran a couple magazine ads, but not much. And then see people obsessed with Nasty Gal, run into a girl who was like, “Oh yeah, I’m wearing Nasty Gal.” Oh my God. It’s just like what Nasty Yell did for someone’s confidence, and I’m not making this up and it’s like you can be like, “Oh, clothes shouldn’t make you feel ... You should intrinsically feel good about yourself.”

But it’s like we feel different putting on, I don’t know what, earrings versus not, or it’s human nature to decorate ourselves and how we do that and how we imbue our taste and our hope for our future and how confident we look and feel and what we’re wearing really does make a difference in how we move throughout our days and Nasty Gal did that for women in a really big way. So it was just kind of the thrill of seeing the level of obsession that our customers had with the brand and that it really did make them feel good.

Turner Novak:

It’s like you just made something that people loved and people used and identified with your product.

Sophia Amoruso:

Yeah, it was it. It was just like, “Okay, what do people want?” It was less, much less about what I thought was cool. What do people want? Okay, give them that, show it to them in a different way. Yeah, we were following trends, but it wasn’t like everything Nasty Gal did was it done in its own way until everyone started copying us and Forever 21 was like hiring the same models and Sachs was going into showrooms asking like, “What did Nasty Gal order from a brand’s collection?”

Turner Novak:

Really?

Sophia Amoruso:

Oh yeah, yeah. All that happened.

Turner Novak:

So you mentioned that you said we only made one decision as a board and it was to declare bankruptcy basically. So you mentioned that growth stalled. Did everyone just kind of copy you and it was just harder to stand out or something or like what happened?

Sophia Amoruso:

It’s such a good question. It’s like how in retrospect with all of the things that happened in the business attribute what led to that. I mean, ultimately we ran out of money and couldn’t pay vendors. That’s kind of how it works, but the valuation was really high. I think even at 100 million in revenue, 350 for a private equity to come in and invest or buy the company, they would have valued the company at maybe 2X revenue, which is like not the worst. In the world of venture capital, that’s not the kind of ... You don’t want to lose, Index would have lost money on the deal.

Venture can be fickle. So what someone’s investing in now may not be what VCs think is interesting a year from now. And if you go to fundraise a year from now in this thing that everyone thought was the next big thing, your Chrome extension or your whatever live-streaming, I don’t know what, is no longer seen as like the future, then it can be really hard to raise money. And it was ultimately like a fashion e-commerce business. It wasn’t a technology business. I could have raised capital, but I raised capital at a very high stake. And yeah, so there were opportunities to sell the business. There were opportunities to have someone come in like a strategic and invest and help us get to the next level, but those were things that kind of quietly ... There were opportunities that kind of quietly disappeared, even though I was getting those guys like excited and on board and it seemed like they were like going to like be my partner for the next phase of Nasty Gal, even at like a 200 or $250 million valuation.

There were kind of side conversations happening that I found out about later where, yeah, my investor was like, “If you can’t pay 350, don’t bother showing up.” But like that decision was really up to me. I just never got to make it because they ultimately didn’t show up. I was talking to someone yesterday founder yesterday who was like, “Yeah, I’ve heard that so many times.” He sold his company for like a billion dollars, but he’s like, “Yeah.” But yeah, so I guess I didn’t connect the PE thing and the venture thing, but like when venture is no longer interested, there might be a different kind of allocator who would invest, but it’s not a venture profile that they’re going to ... It’s not a venture valuation.

Turner Novak:

Yeah. So it sounds like what happened was you can either confirm or not confirm, but it sounds like you ... There was still like a pretty good business that was there. It was just the valuation going to be lower than what you raised at, and you were at a point where you were burning capital because you’re trying to grow and you hit a point where a deal was not going to close because the investors that you had disagreed with the decision and so you had to shut the company down, even though it was still a business that was working.

Sophia Amoruso:

Just kick the can over. Then it became Hail Mary after Hail Mary, you know, fire sale. At one point, someone did come in and invest after those much better strategic apparel retail opportunities had dried up and I think they invested at like a hundred posts, but the only way I could get them to invest was if Index invested. I mean, Index must hate this. I mean, I’ve said this on like a bunch of podcasts, but this is my story and this is a story that I want to pass on to other founders and when I invest, I want to make sure that they don’t make the same mistake or raise too high of a valuation or don’t raise enough money or blow through capital too quickly. So the impact of bumming out a firm, the impact of telling the story I think is much bigger than the fallout. I don’t even know.

So the only way I could get those guys to invest was if my investor, my existing investors put in some capital. It just looks really bad if your investors don’t want to keep investing, like even a dollar just like, “Okay, yeah, we’re invested in the company’s future.” And they were like, “We own 20%, we’re good.” And I was like, “You have to put like a dollar in or something. These guys are just not going to because it’s just such a bad look.” And what my investors said was, “We will double what of your own money you put into the company.”

Turner Novak:

Of you personally?

Sophia Amoruso:

Mm-hmm. And so the only way I could get the new investors on board at the much worse valuation than the ones that had come prior to that was to dump my own money back into the business. And this was after I’d hired a CEO, like I was on the board, I wasn’t operating the business, I did not like being a CEO at a certain phase. And by 2015, I had hired a CEO and now I think it’s like early 2016, but so I think the idea was that it was going to incentivize me to like turn things around and like double down on the business, but I wouldn’t have known what to do. I’m not like a turnaround CEO. I’m like an eBay seller. I’m like a great marketer and a great brand person and like a great merchant and like a great amplifier and a writer and like a whole bunch of other things, a great recruiter and a really good investor, but ...

Turner Novak:

But not a turnaround.

Sophia Amoruso:

The idea was that I was like showing conviction, but it’s like, I didn’t know ... So I did, and they did, and it just kicked the can further down the road. And then there was like a series of Hail Mary’s and exclusivity periods where we couldn’t shop the business because someone was in diligence and then time was passing. And then it became like people kind of circle and they’re like, “Okay, we see what’s happening here, we’ll do some diligence, but like we’ll just wait.” And I hate to say that’s what like smart people do because it’s so fucked up, but ...

Turner Novak:

Well, because they’re trying to get leverage and they’re trying to get a good deal.

Sophia Amoruso:

Yeah. I mean, there was ... The company that owns Nasty Gal now had ... We’d spent a bunch of time with them before we filed for Chapter 11 and they were interested in buying the company. And they just waited it out and then they bought it in bankruptcy for 20 million.

Turner Novak:

Man. How do you feel reflecting on it all today?

Sophia Amoruso:

I mean, I’ve just talked about it so many times. I think like I’ll always have a bit of a ... It’s like it sucks. It sucked and the headlines were like awful. I mean, it was like, “Does the failure of Nasty Gal mean millennials aren’t ready to lead?” Like, “What? I’m responsible. I’m the example of a whole generation because I couldn’t pull it off. I built a hundred million dollar business.” Yeah, the level of scrutiny really sucked. And then all the while I had written this book called Girl Boss and I was the Girl Boss and I was supposed to be the example of female millennial leadership. And there was a Netflix series being made about my life that was due to drop four months after we announced that we had filed for bankruptcy and then it came out and then it was like, “This is really bad timing.”

Turner Novak:

Geez.

Sophia Amoruso:

So it almost got worse. It was exciting, like a TV show was made about my life, but it was telling a story of who I was when I was 22, starting an eBay store four months after I, for the first time really in my adult life, was no longer involved with that thing and like trying to like figure out what my next move was or my next identity was and not have everything hinging on this thing called Nasty Gal, which I was incredibly proud of, but also was like ... So then there’s Sophia the CEO, there’s Sophia the girl boss who’s like on the cover of a book like she knows what’s up. And then there’s this relatively abrasive kind of bratty character playing me on a Netflix series that I thought was like really cute, but people were like, “Is she really like that?” It was just this conflation of who I was and chatter and headlines. The worst thing about Netflix’s girl boss is it’s source material.

That’s me. Yeah, it’s the book, but like that’s me. So I think it was like the Netflix series that was the hardest part because it was like this crescendo and then there are people whose jobs are literally titled critic in entertainment. There’s like reporters in business, which I had experienced that, but then there’s like TV critics and that’s a whole nother thing and that’s being amplified into a ... This show’s amplified into it. At the time, it was 130 million homes in 195 countries in like almost every language. That was loud. That was really, really loud. But also like so entertaining to talk about. It’s like, yeah, it was really serious, but it’s also, I don’t take it that seriously. And it’s like so funny to think that that happened in my life. It’s still like very surreal and like, it was all very real, but it was also really fun and just really weird in retrospect.

Turner Novak:

Yeah. Were you like involved in the Netflix series and I’m assuming involved in writing the book?

Sophia Amoruso:

Oh, I mean, I wrote the book. Yeah.

Turner Novak:

Yeah. Okay.

Sophia Amoruso:

What’s this? Is that a hair? Oh, yeah, that’s a hair.

Turner Novak:

That’s a hair. I’ve just noticed it too.

Sophia Amoruso:

What the fuck is that?

Turner Novak:

I noticed that-

Sophia Amoruso:

Ever had a guy do this on your podcast? Please leave that in there so everybody knows that it wasn’t intentional.

Turner Novak:

This is real. This is authentic. I actually do notice sometimes part of my beard, I think this side is just a little longer than this side right now and it just throws me off. I’m like-

Sophia Amoruso:

Do people think my face is shaped like this?

Turner Novak:

Yeah.

Sophia Amoruso:

We know it is.

Turner Novak:

Yeah.

Sophia Amoruso:

I’m kidding. But yeah, the book, I mean, I kind of left out the book part, which is what led to the Netflix series, but the book came out in 2014, so two years, two and a half years before Nasty Gal ended and it published at number two on the New York Times bestseller list. It spent 18 weeks on the New York Times bestseller list, sold half a million copies, and it became like a bigger ... Girl Boss became noisier than Nasty Gal was. Yeah, it was insane.

Turner Novak:

So what was the Girl Boss movement? I sort of remember this, but I don’t really remember, what was it?

Sophia Amoruso:

I mean, it was a year after Lean In and I didn’t intend for it to be a counterpoint to Cheryl Sandberg’s book. It was kind of the first story at scale that women had seen of someone who didn’t go to school, someone who was kind of cool, someone who didn’t have a pedigree, wasn’t like a square who had all the tools that they have at their disposal, eBay, an internet login, a digital camera, access to thrift stores and built something of consequence. And this was a time when the freelance economy was like the gig economy was kind of happening and creative economy and people were leaving companies and freelancing more. And we used to describe our customer as ambitious at Nasty Gal. I knew this generation, I knew them.

And it just really struck a chord because it was kind of a mirror for what could be possible for a generation who had, just like I had thought that people who ran businesses had to have a pedigree and had to carry a briefcase or something. And it was like, no, it can actually be fun and cool and you can learn things about yourself and be creative and be yourself and be weird and look at me. I have choppy bangs, I’ve got like a weird spiky necklace and I still pulled it off and I made it cool and you can too. You can work for yourself.

And that was really different at the time. It struck a chord and a lot of women quit their jobs and started businesses and they still walk up to me in coffee shops at least once a month and they’re like, “Hey, I read your book a really long time ago and I started a business. I quit my job. I got laid off and the next thing I read your book,” and like blah, blah, blah. “I was so lost and read your book,” and blah, blah, blah, 10 years ago or, “I was in high school and blah, blah, blah.” I was like, “Oh my gosh, it’s been so long.”

Turner Novak:

Well, I mean, talking about starting a movement, not even just with Nasty Gal, people buying and identifying with the product and the clothing, but just the idea of even starting a company, you can own your own destiny, you can control it and you can make things happen. You almost spurred the millennial generation to do it.

Sophia Amoruso:

Yeah. I mean, I was definitely part of it. I mean, I think we need examples of people who can show the rest of us what’s possible because it’s when the whole world feels gatekept, why bother? And with social media now, and of course everybody’s a finance expert and everybody’s an expert, but it’s like access to information is a lot more democratized and Robinhood didn’t exist at one point. Investing, retail investing was hard. Logging into Fidelity was really different and ...

Turner Novak:

You had to call your broker, place an order.

Sophia Amoruso:

Yeah. Yeah. We have access to so many tools now and so much information to educate ourselves. And that’s not saying it’s all correct, but we’re able to make decisions and have agency over our lives and careers in ways that we weren’t able to 15 years ago.

Turner Novak:

Yeah. And how would you think about, if you were starting a brand or a movement of some kind today, what do you think is most important to sort of tap into or unlock or are there things you feel like people maybe skip on or don’t put enough emphasis on, like the underrated aspects of it?

Sophia Amoruso:

I mean, I think the whole, every touchpoint of your brand, like from the moment somebody hears about it to the time they open a package, to the time they make a return should be cohesive and should all feel like the same thing. And what people are murmuring about the brand or talking about over breakfast and what you see in a press release and what you see ... or in the news and what you see in an ad on Instagram should all map to exactly what it is that you want it to. And you have to be really clear on what that is.

And then yeah, like visually, materially, everything needs to feel like the same thing. And ultimately, people want to buy into a world. Red Bull’s a world, Liquid Death is a world, Glossier made a world, Nasty Gal’s a world. For someone to have affinity for a brand that’s greater than the product itself, you need to create that feeling. And I think that really comes down to two things, which is a feeling of belonging and I guess for lack of a better word, community and aspiration. So how can I, Red Bull, like, “Oh, it gives me wings,” like Glossier, I can do minimal makeup and still feel good about myself or Nasty Gal, I can put on a leather jacket for the first time in my life and feel like I can take on the world. This motorcycle jacket transforms how I move through my life.”

And that’s not an easy thing to do, but that should be the North Star. Liquid Death is like, “Ugh, murder your thirst.” But I invested in Liquid Death, but they’ve done a really good job. I don’t know if anyone said ... I’m sure there’s Liquid Death tattoos. There’s Nasty Gal tattoos, there’s Girl Boss tattoos. I don’t know if there’s Glossier tattoos. And then, yeah, I mean, it’s noisy. I mean, it feels like every brand has like a Nepo baby celebutant attached to it now.

Turner Novak:

What? What is the celebutant? I’ve never heard that word before.

Sophia Amoruso:

It’s like a debutante who’s like a celebrity. I don’t know. I haven’t heard it in a long time. It’s probably actually a dated word. The creator economy is real. Distribution’s important, right? So there’s all of the brand stuff, and then there’s you talking about it, people talking about you, and then people talking about the product, right? And that goes back to like, and then people want to admire you. They have to see you as an expert, whether you’re an expert in style or an expert in venture capital, or it’s like people are looking for proof or the science behind a skincare brand, like who’s the doctor behind it? Do they have an Instagram following? It’s like there’s all these points of proof that people look for to triangulate influence and trust that now spans so many channels and it’s much more challenging to ... I mean, there were less places to market a brand when I started Nasty Gal, but there’s ... So it’s easier than ever to start, but there’s more noise than ever.

Turner Novak:

Yeah. And I think one thing you’ve done pretty well just over your life is getting press and getting this institutional kind of stamp of approval as well. How do you go about getting press? And maybe press is also like podcasters or creators. How do you actually get them to care?

Sophia Amoruso:

I don’t know. I think I’ll be riding on the coattails of Nasty Gal for a long time, and I have been for a long time and Girl Boss. I did something of consequence and there’s all this lore that was built around me when that happened. I had the best publicist in the world and was on the cover of Forbes and Inc. and Entrepreneur and whatever, features in all those. That stuff built me over time, but it’s been a long time. It’s been a long time since I’ve had frequent press. And I haven’t had a publicist in as long as I can remember, but I’ll get invited. I get invited to guest on stuff because people think of me and they’re like, “Oh wow, that’s a ...” Steven Bartlett, Diary of a CEO, they just reached out. Phoebe Gates and her co-founders, Sophia, who do The Burnouts, which has gotten to be a pretty big podcast, they DM’d me. And I’m an angel investor now, which is cool. Too expensive for the fund.

Turner Novak:

Yeah, that’s fair. One thing I love is that you did have all this press and I just met you at a hackathon. The one hackathon that we went to a couple of years ago when we first met. I didn’t even know-

Sophia Amoruso:

Launch house.

Turner Novak:

Yeah, Launch House. Yep. What a time.

Sophia Amoruso:

That was where we met.

Turner Novak:

Yeah. And I didn’t know that this was all a thing. And then I think when I looked you up at some point, I was like, “Oh yeah, I remember hearing about this. Girlboss. Yeah, I think I remember this.” Yeah. And just you never know how you’re going to meet people. And one thing you mentioned before is that you like being a founder, you thought you were good at that kind of zero to one founding stage. You’re not that good at the turnaround, really mature company type CEO, but you feel like you’re really good at investing. And then you’re doing that full-time now. How did that transition kind of happen?

Sophia Amoruso:

Well, I did a bunch of Angel Investing. First Angel Investment was in 2013. I invested in First Dibs and I invested in Eight Sleep and Liquid Death and Public ... Eight Sleep was I think 200 posts. Liquid Death was 50. The public.com I think was like 100 and I’m an advisor there as well. Invested in Kindbody, invested in Chill House that sold and Passport, which has done extremely well, Passport Shipping. And just really loved working with founders, realized that I had a lot of relationships that could benefit them. I have a platform that I can use to amplify what it is that they’re doing often to an audience who are interested in the kinds of things that I’m investing in would be users of those products that I can get into deals, founders want to work with me. I can make a material impact, both through making introductions that are relevant, through amplifying and evangelizing them.

Even if it’s on a podcast like that, I can list out in my portfolio if you would like me to. And then even though I invest in B2B software with TrustFund, my fund, I bring a consumer lens to the way they present their products and their brand and their marketing, because they’re often highly technical founders that I’m investing in now who are so in the weeds with what they’re doing, that being able to articulate that in a way that even a business user who’s choosing between Squarespace and Wix or Shopify versus an alternative, they’re still looking at the products as consumers. And the psychologies of like a business user is much more similar to a consumer than it was 15 years ago when we were putting out an RFP for an email service provider, right?

Turner Novak:

Oh my God, that sounds brutal.

Sophia Amoruso:

Yeah, you push a button, but it was like someone who would come and do sales and they were like, okay, it was called Bronto, the email service provider that we upgraded from Constant Contact from.

Turner Novak:

So this is the ability to email your email list?

Sophia Amoruso:

Yeah. Yeah. Just MailChimp before MailChimp, but like some guy came in ... Or like Gusto. We had to choose benefits. Some insurance broker came to the office with like Xerox’s ...

Turner Novak:

He’s got like a binder. He’s like opening it up.

Sophia Amoruso:

It was like, they had been Xeroxed so many times they basically had like black and white acne. It was like really bad. And now you have Gusto and Justworks and Deal and you push buttons and you can run a business, but you’re going to choose Justworks or Gusto based on the kind of stuff that made Nasty Gal successful and that’s copywriting, it’s design, it’s brand, it’s how you articulate what it is that your product is, how simple it is to understand for your users and selling the aspiration. If I use Gusto, it’s going to make it so much easier to build a team. If I use QuickBooks over Xero, like, “Oh my gosh, I’m going to have this whole suite of whatever it is,” that like login should be an unlock in the same way that wearing a motorcycle jacket at Nasty Gal, buying a motorcycle jacket made someone feel.

And that’s like, again, and I think software can do that. And I’ll log into Airtable and be like, “Oh my God.” And then I’ll churn and then I’ll be like, “Airtable, hit Airtable again.” And fuck, and I’ll churn. I’m like, “I don’t want to pay all these subscriptions.” But still, software can give a user that kind of exhilarating feeling. And so after Angel Investing and realizing I couldn’t just eternally deploy my personal capital into startups and that I had good taste and access and all this stuff I just talked about that I could do it for a job and knew a lot of GPs, knew a lot of investors at big firms and began to learn about what running fund entailed and listened to a lot of podcasts and read books and studied and talked to a lot of people who helped me along the way.

It’s just like so many ... That’s I think one thing about venture is that, and I think more than ... VCs do this more than founders, which is like, “Okay, how did you structure your blah, blah, blah?” Or, “What’s your minimum check size?” Or it’s like, you and I have talked about the mechanics of fundraising and deploying capital and I don’t think founders get into the weeds as much with that stuff, but it’s such a virtuous ecosystem of people sharing like how they’ve done things. And then also like companies that they’re investing in, not as much LPs, anybody who wants to share LPs with me, like you know where to find me.

And then decided, launched Trust Fund and announced at the beginning of 2023, raised a $5 million fund and have made 15 investments into some amazing companies and just like love working with founders, love the zero to one, one to ish. I know I want to play there over and over and over again. I have ADD, so I get to use like my taste and my access and my platform and my chops and look at a lot of interesting things, talk to interesting people, see where the future’s going, place bets, and make an impact and do it over and over again so that when these companies are ... They get to be successful, they get to do the really hard work and they get to inherit the growth stage that I really don’t like. So I feel like it very much plays to all of my strengths and a stack of things that I didn’t know I had accumulated until I started doing this job.

Turner Novak:

And then the name Trust Fund is like probably talking about like branding and marketing, like one of the greatest venture firm names ever.

Sophia Amoruso:

Thank you.

Turner Novak:

So how’d you come up with the name?

Sophia Amoruso:

I don’t know. I don’t know. I think I was talking to Abe Burns. Do you know Abe?

Turner Novak:

Yes, he was at Sound Ventures.

Sophia Amoruso:

He was at Sound. Yeah, he’s a funny guy, but I think at some point I was like, “Ha ha ha, if I ever started fund, I’m going to call it Trust Fund.” I don’t know. We just used stupid stuff around over text and I think that ... Yeah, it was just like, that’s such a great ... I don’t know. I like naming things.

Turner Novak:

And then you’ve got, I think you have something on your desk right there, the Trust Fund, the bucket hats that you made.

Sophia Amoruso:

Yeah. So rich kids think they’re funny and ironic and people who didn’t grow up with trust funds think they’re funny and ironic, right? So whether you’re an Amagansett or, I don’t know, I’m not going to give an example, but like Silver Lake, whatever, like this is funny.

Turner Novak:

This is going to be the thumbnail for the episode on YouTube, by the way. Just you with a bunch of trust fund bucket hats.

Sophia Amoruso:

Here it is. Yeah. Yeah.

Turner Novak:

And so you mentioned you had to go raise money from LPs. What is that like for someone who’s never done that before?

Sophia Amoruso:

Yeah.

Turner Novak:

Is that easy? Is it hard? What are the things that you feel like you got to nail to get that done?

Sophia Amoruso:

I mean, I talked to a lot of different people. I got a fair amount of introductions to institutional LPs, went to some of the GPLP matching events and conferences that are supposed to like help you fundraise as an emerging manager. And it’s like I’m talking to B of A Ventures and it’s like, “They’re not going to invest in my $5 million fund, but I’m like trying to get meetings with them.” And eventually it was like, “Okay, I’m just going to raise a $5 million fund from people,” because it’s too small of a fund for anybody who’s trying to write checks out of a multi-billion dollar fund because they’re not going to write a million dollar checks. They don’t want that many, they don’t want to invest in that many venture firms. I don’t want to deal with all of the whatever year long diligence that an endowment fund’s going to put me through.

I want to work with stakeholders that I like and enjoy and sometimes know who are like real people who are investing because they believe in me and have conviction and aren’t necessarily running me through like a spreadsheet to decide if I’m a good idea. And so ended up raising from like a lot of GPs at bigger firms and founders. So I mean like Ev Williams from Twitter and Medium and then Anthony Noto who’s the CEO at SoFi, but then it’s like Mark Andreessen and Chris Dixon and Jeff Jordan and Andrew Chen and Jason Calichanis and David Sachs and Rob Hayes and Jeremy Lou are all ... It’s like all these like Midas listers came in, which was really cool and like super validating. Jesse Draper, Paris Helton. So it just ended up people that ... And I met like a lot of these guys, like I said, like in 2012, what was that, 14 years ago. So people were like, “How’d you do that?” And it’s like, “I’ve been around for a long time.”

Turner Novak:

Well, it wasn’t like you showed up and said, “Hey, just give me some money.” It’s like you knew these people for a decade.

Sophia Amoruso:

Yeah. And that’s one thing about venture is like, even if you don’t get it right, if someone’s seen you try to build something and watched you build, and even if it didn’t work out, people still have respect and will often continue possibly funding your startup or invest in a fund. And it’s like Nasty Gal Stories, like an age old venture capital story.

Turner Novak:

You raised a bunch of money playing poker. I think that was like a trick that you used.

Sophia Amoruso:

I mean, it wasn’t a trick, but I mean, I played poker with Jason at CES 10 years prior or something, but definitely I met ... Jason hosted and Brooke Hammerling hosted a poker game after the code conference every year for a very long time. And I would play in that every year. And it would be like Jeffrey Katzenberg and, I don’t know, like big executives and founders and stuff at the big boy table. And I’d play at the baby stakes table, but there were always like interesting people around and that’s where I met Anthony Noto from SoFi who became an LP, but like a friend, really kind of a friend and mentor first. And then yeah, like playing with Jason, Sunny Madra, who founded Grok that sold to Nvidia is an LP, met him playing poker, but it’s like Jason stands up and he’s like, “Who’s going to invest in Sophia’s fund?” And like somebody raises their hand and that was pretty cool.

Turner Novak:

Yeah. Well, because I feel like everyone, you need to figure out some kind of advantage when it comes to raising capital, whether it’s like you’re really good at a certain thing and you lean into it or you find like a channel, like a certain type of investor that you vibe with, whether you’re a fund or you’re a founder, like you need to figure out ... There’s more than just like, “Here’s a pitch deck and here’s a spreadsheet and we’ll give you money.” There’s always some reason, like some kind of emotional connection, a thesis, something like that. There’s always something else that you got to figure out. I don’t know if it’s like a trick or like a hack is the best word for it, but you need to figure out like how to almost like crack something more than just ... People don’t just give you money for no reason.

Sophia Amoruso:

What’s your shtick?

Turner Novak:

I think I’m still trying to figure that out, but it’s usually people that are familiar with my body of work. I mean, maybe it’s same with you, where they followed along for a while. My pitch is usually, I just have a pretty big platform I can use to help founders. It’s pretty similar to yours. I’ve leaned a lot into the podcast where I’ve had multiple portfolio companies that have hired over 10 people from their podcast episodes, which is pretty crazy when you just think about there’s all these funds that they have these big teams that are adding all this value and like, “Oh, we’ll help you recruit. We’ll help you do all this stuff.” And meanwhile, I just put out a podcast episode, I’m sleeping and people are getting jobs. It’s kind of, I don’t know, it’s kind of insane. I’ve had founders just tell me how impactful it is.

So I think I don’t even appreciate it as much at times because I don’t know, for me, it’s just kind of fun. It’s just fun to talk about this stuff and put it out there and like thousands of people listen. I mean, I’ve had some podcast episodes get hundreds of, or if you count the shorter clips, like some of them have cleared a million views on the episode. So it’s like kind of crazy just to think that you helped someone get whatever word out that they were trying to get out and I create all the content just as I would want to see it. I just kind of try to put stuff out there that I’d want anyway. So yeah, it’s kind of this like flywheel that’s just kind of kept building to the point where you need to think about what’s your advantage as an investor.

For me, it’s just founders want to meet, they want me to help them, they think that they can get value from tapping into kind of the distribution. They feel like they can maybe trust you, like they followed you for a long time. And for me, I do a lot of like humorous, funny stuff aside, the podcast is like the most serious thing I do, but a lot of founders are like, “I feel like I just kind of know you, you just kind of feel like it’d be fun to hang out with. Maybe brainstorm some stuff, we’ll work on ... I’m trying to do the launch, like help me plan this out or like I’m trying to get some press coverage for the series A, or we’re thinking about our series B. Who do you think would be worth talking to?” You’re just brainstorming things that you’ve seen that have maybe worked in the past. Yeah, I don’t know. There’s just so many different things to go into it, but similar to you, I have ADD, like there’s no way I could actually build a company. There’s no way I could spend 80 hours a week for 10 years on the same thing.

Sophia Amoruso:

Yeah. Yeah, I did that. And that’s what you tell LPs, that’s what’s in your deck or is it like, “I have special access because of this and I can choose good founders because of this,” because that’s the value prop to founders, but like what do LPs see that differentiates you? Is it like access, obviously impact, like that’s one slide, but you can pick, you get in earlier, like what do you think it is?

Turner Novak:

Yeah, I think there’s a couple different reasons like you want deal flow for later stage stuff. So I’m usually investing in the first or second round, usually at pretty attractive entry prices. I think the average cost basis on my fund two that I finished investing a little over a year ago was 14.8 million. So there’s some companies that are eight million post money, some are 20 million post money, but they’re usually kind of right around that mid teens. And that’s just ... you can go to someone with a straight face and say, “I’m going to deploy this capital into 20, 25 companies and there’s a chance that this fund is going to be a 10X returning fund.” And there’s also like a chance we need to get really lucky, but like this could be a 20, 30, 40, 50X. That’s kind of the point of venture capital.

I don’t think the point of it is like, “Hey, we’re going to get a 3X or a 5X return.” That’s actually not that good in venture, in my opinion. So my pitch is generally like, “You need to get lucky with some of this stuff, but I’ve kind of set up the constraints where the fund could get 10X return on the fund. If you like following onto the breakout portfolio companies, and I will have some, I’ve had some in the past and they’ll be more in the future, and you’re looking for companies that you want to follow onto, either doing SPVs or leading the rounds of, we can do that.” I think a lot of the pitch too, I think a lot about how can I expand the fund over time. So I think a lot of people, the pitch is all get into super hot rounds and like we’ll get the logos on the deck, we’ll get like 100K check into this company, 250K maybe, maybe it’s in a later round too.

My pitch is I’m trying to invest as early as possible and I’m trying to be one of the first investors so that then over time, instead of writing a 250K check, I could have given them a million, I could have given them five. I could buy like 30% of the company over the course of those first couple rounds falling on over time. I honestly don’t know how it’ll all evolve over the next couple of decades, but I just think about it as, am I investing as early as possible, giving them money, could I have invested more? Did the founders trust me? Would they have me invest again in their next company? And that’s usually a stamp of like, “Hey, he did something, he didn’t fuck it up, he was responsive at the very least. Maybe he helped in a couple ways.” So yeah, that’s generally the pitch.

And then generally you think you buy into the media driven strategy, you buy that the internet matters, you buy that that can move the needle for founders. My pitch is usually not, “Hey, AI is the thing I’m trying to invest in AI. I’m an AI investor or deep tech or Web3 is the thing I’m like an expert.” I feel like one of the things I personally do not want to do is, you kind of saw the swing to like American dynamism where everyone’s like, “Oh, we’re experts on defense and like American manufacturing.” And meanwhile, two years ago, you were like talking about how every purchase was going to be on the blockchain and like you’d get an NFT every time you ordered a coffee and then now you’re talking about how like self-defending missile drone things. I just think there’s a little bit of a disconnect there when you think about these, like people try to kind of brand themselves as being these experts in all these different categories.

Sophia Amoruso:

It’s really intimidating. It’s intimidating, honestly, because overnight everyone else became an expert and I still know I can do a very good job at this, but it’s not going to be because I know everything about foundational models and like security. That’s not going to be it, but I have a good feel for what’s defensible and why one of the LLMs would not touch a certain area because it’s like too hairy or too specific or that the general knowledge is not going to benefit, it’s just not going to work pulling from data or just having a customer’s data in a silo. There are a lot of things that I do know enough about to know to a certain extent what’s defensible in AI, durable or whatever D word you want to use, but it’s an intimidating time because it seems like everybody else has like a spiel for it and I don’t have that spiel.

And maybe that’s the kind of honesty that people want or, because I am investing in AI, but it’s not the only thing I’m investing in, but I obviously also don’t want to invest in something that can be vibe coded in five minutes now. And then it goes back to speed and distribution and ability to ... And then it’s like ... Anyway, we can have that conversation offline, but it sounds like we relate.

Turner Novak:

And then what are you looking for then in the founders that you back? It sounds like ... Are there certain traits you feel like you really gravitate more to and do you think lead to building a company of consequence, I guess, if we’re using the same language?

Sophia Amoruso:

I think it’s a founder that I can see using every resource at their disposal to build their product, build their company, and it isn’t just looking at the obvious channels for doing it or the obvious channels for recruiting who are going to ask me for things. I want people who are ... There’s a healthy kind of entitlement that founders have to have. And honestly, I think everybody has to have to say, even if it’s just like they feel entitled enough to ask for advice or an introduction, even if I say, “No, you’re not ready for that,” I’ll introduce you when you’ve got another logo on board because it’s too early to talk to Delta, and I made that introduction.

And then founders I think can build with real product velocity. So the founders that have gone on to raise subsequent rounds and begun to build critical mass and get buy-in from their customers and build sophisticated products have done it quickly. So speed, I think, is important because Nectar Social, for example, it’s like I invested in their pre-seed when they had a deck and a year later, Misbah came over my house, co-founder and showed me their product and I was like, “How did you do this? This is like witchcraft. This is like a very mature product. I have no idea how you ...” Agree.com is another example of that. Founders who are just like, “I’m going to go build this,” and then they just go build it.

Turner Novak:

They’re like the DocuSign with 10 employees instead of 10,000?

Sophia Amoruso:

Yeah. Yeah. It’s free e-signatures. I use it every day.

Turner Novak:

Really?

Sophia Amoruso:

Yeah. Yeah. Yeah. So unless you’re running payments through it, because what they are is the contract is the invoice and so you can connect your Stripe and do one-off payments and recurring payments. So it’s great for SaaS companies. That’s something you pay for, but you get paid immediately because when your customer signs the contract, it takes them through a payment flow and does dunning and payment recovery and all kinds of stuff for you. But if you do free e-signatures, it’s unlimited. DocuSign gives you like one or two a month, I think. It’s insane.

Turner Novak:

I do do some contracts because usually we’re sponsors in the podcast. We just sign a contract because I got burned once, someone didn’t pay me. So I always sign them. Well, this has been a lot of fun. Thanks for taking the time to do it.

Sophia Amoruso:

Yeah. Thanks for having me. It’s like always, I mean, it’s like, cool. I just got to talk to you for an hour and a half. That’s great. If anybody else benefits, that’s just icing on the cake.

Turner Novak:

Yeah. Where can people find you? If they did benefit, if they’re like, “Oh, I love this. Sophia, I want to send you a message. Reach out.”

Sophia Amoruso:

Instagram.com/sophiaamoruso, linkedin.com/in/sophiaamoruso, trustfund.vc, trustfund.vc/pitches. And then if you want to see my website, it just has everything you already know, if you’ve listened to this podcast about me, SophiaAmoruso.com.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

We talk about that deliberate process and the 37 questions they asked each other before committing to build Footwork. They share a few hints of their secret sauce for working with founders, lessons investing in Canva’s Seed round in 2014, scaling Stitch Fix from $0 to $1B revenue in five years with only $17m of capital burned, why AI will enable a new wave of entrepreneurship, and how public company boards are discussing and adopting AI today.

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Timestamps to jump in:

• 0:24 Starting Footwork from a tweet in 2020

• 3:11 Difference between startup and public company boards

• 4:52 20-40% of public board meetings are about AI

• 7:48 How Footwork’s investing in AI today

• 10:37 AI will enable millions of new entrepreneurs

• 15:04 37 questions to ask when starting a VC firm

• 17:40 Importance of investor skillset differences

• 23:08 The pace of VC is faster than operating

• 26:26 Footwork’s secret sauce (free board seat)

• 31:59 Investors should talk to and help employees

• 37:05 Building an equal-carry partnership

• 39:33 How Footwork makes decisions

• 43:21 Navigating politics and short-termism in VC firms

• 51:18 “You’re only as good as your next investment”

• 53:30 Characteristics of great founders

• 58:13 Canva’s Seed pitch in 2014

• 1:02:54 Joining Stitch Fix as 4th employee

• 1:06:40 Scaling Stitch Fix from $0 to $1B revenue in 5 years

• 1:16:48 Raising from Bill Gurley after a failed Series A

• 1:19:40 Footwork’s office near YC

• 1:22:10 Opportunities in consumer health

• 1:25:20 Using flash mobs to win deals

• 1:26:15 Dad life

Referenced:

• Footwork

• Anything

• Table22

• Canva

• Stitch Fix

• Honeydew

Find Mike on X / Twitter and LinkedIn

Find Nikhil on X / Twitter and LinkedIn

Related Episodes

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Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Welcome to the show.

Mike Smith:

Thanks so much, Turner. Thanks for having us.

Nikhil Basu Trivedi:

Thank you, Turner.

Turner Novak:

This will be really fun. We were just talking about how you guys recently started, or actually not that recently, started your firm Footwork. It was 2021. What was the thinking around starting this thing, coming together and building a firm together?

Nikhil Basu Trivedi:

Yeah. Today’s actually the fifth anniversary of us setting up the management company for Footwork, January 20th of 2021. The story of how we came together is, I had been thinking for some time about going off and starting a new firm. I’d been at this firm, Shasta Ventures, for eight years, had a great chapter there, but for a number of reasons was thinking about going off and starting a new firm. I had a list of questions that I wanted to go through with potential partners, a blueprint for what a new firm could look like, and a set of potential partners to go work with, all of whom I’d been on boards with before, who I’d gotten know pretty well over the years.

Mike was basically the wildcard on that list, because he was the only one that wasn’t already in venture investing. He was at a company, Stitch Fix. I think what we thought was that we were pretty aligned on a number of core values, but also very different skill sets. I texted Mike, I shot my shot, and he and I started texting back and forth. This was at the beginning of the pandemic in 2020. Then one thing led to another and we decided to go do this together.

Turner Novak:

Mike, do you remember the text? What was the text?

Mike Smith:

I remember the text. What’s amazing about it is, actually, he texted me about a tweet that a reporter, Jason Del Ray, had tweeted that I should be considered as the next CEO of Patagonia. He asked if I wanted him to amplify it on Twitter. I said, “No, I don’t,” because I was in the middle of having a conversation with Katrina and the board about my plan for when I was going to leave Stitch Fix. I was like, “No.” Then his next text to me was like, “Would you consider chatting about starting a venture firm with me?” Which I thought was a unique bridge to the first question. But because we had known each other for a while and had so much, I think, mutual respect, it was super intriguing to explore that, and so we kick started that process in June, 2020.

Turner Novak:

Nice. It was Imperfect Foods, was that the board you were on together?

Mike Smith:

Yes. We were on that board. I joined as an independent board member, and he had led the investment with Shasta at the seat in the Series A. It was a unique situation because we had a number of venture firms that were involved in that company. There were lots of people in that boardroom. The company did really well, and then had some challenges and we had different transitions of leadership. I think the thing that I really respected most about Nikhil during that time was that he was a truth seeker. He was super high yield in that boardroom amongst sometimes the chaos that was in the boardroom. I just had a tremendous amount of respect for him.

Turner Novak:

I know you’re on a couple different boards, even some public company boards. What is that like? What’s the difference between startup, two people maybe and like the venture investor versus public company?

Mike Smith:

It couldn’t be more different, I would say. There are some things that help me be a better investor as a result of being on these public company boards. I’ll start with the board deck is, typically between 250 and 300 pages of material for a public company board, so that is very different. You want obviously our private companies to be focused on 10 to 15 slides.

Turner Novak:

It’s like actually building the product, not a board deck.

Mike Smith:

Yes. It’s just very, very different. I think there’s also, each board member has one or two specific skill sets that they are bringing to the table on the public company boards. Private company boards, I think both as an independent board member as well as an investor, you have to be way more broad in what you bring because the stage of these early stage companies, there’s needs in go to market and there’s needs in leadership development and there’s needs in branding. I prefer the early stage boards, and the way that we get to work with founders, but there are benefits where being on the Ulta Beauty Board and the Miller Knoll Board as examples, all of those buyers and the leadership team are basically buyers of enterprise software. Being able to actually talk to a buyer of like, what are you doing in AI, helps us make better decisions, helps us support our companies in ways that is, I think, differentiated in the marketplace.

Turner Novak:

Interesting. It begs the question then how our enterprise is buying AI right now? Sure. It’s like we all see it. Everyone’s talking about it, what’s actually being bought.

Mike Smith:

Yeah. I think that the things that are being bought very directly are in customer service and in coding, but the breadth of buying that’s happening in marketing, in supply chain, it is not just experimental ARR, it’s real ARR. Now, I think the bar to get into an Ulta Beauty, and actually become something that actually changes the way that an end worker in an operation works or a marketer works, the bar is really high, so product needs to be great. Of course, many of those buyers are looking at somewhere between 5 and 10 different options that they have, but it’s a very serious wave.

One of the things that I’ve talked about is, I’ve been around long enough that I’ve seen the wave of the internet and wave of mobile and wave of cloud. I think the things that are different in this case with AI, one, the speed obviously in which it’s being adopted, and two, I think the value that it’s creating at that speed that makes it feel very, very different to me than any of the other waves that we’ve experienced before, which is investors, we need to lean into that wave. Obviously, lots of people are, but I think understanding the buyer’s mentality and what the buyer’s actually doing helps us a lot at footwork.

Turner Novak:

It flows up to the board level sometimes on these decisions.

Mike Smith:

Yeah, no. One of the things that’s been really fascinating over the last year is somewhere between 20 and 40% of a board meeting’s content is talking about AI. It’s not just like two members of the board that are leading the conversation or leading the questions. It is every board member is using product, trying product, they’re pushing the leadership team to go faster. It feels very different. I wasn’t on public company boards obviously during these other big shifts in technology, but I think the knowledge, the understanding, the push that’s happening in those public company boardrooms, is very different than what we’ve experienced in the past.

Turner Novak:

That’s fascinating. Yeah, because you’d think of the average board members probably, I don’t know, average age of like in their 50s or 60s, and you just don’t think of them as being like early adopters of new technology.

Mike Smith:

I think these board members typically have made their careers being rebels in some ways, and made their careers being building amazing differentiated category defining companies, or being parts of those companies. As a result, I think they just have a different mentality around technology, and they’re just actively using this stuff in ways that, I think help the companies themselves hear what a broader group of people are, and how they’re experiencing AI.

Turner Novak:

Then, how are you guys investing in it right now at Footwork is probably, begs the next question.

Nikhil Basu Trivedi:

One lane that we’ve been very interested in for the past couple years is vertical specific AI products. We’ve made a number of investments in verticals such as life sciences with a company called Illicit, financial services in agencies, and consulting firms and brands as the customer. I think Mike’s experience has partially informed these investments. We’ve been excited about verticals that we think will not be the earliest adopters of AI, but will have to get there.

Turner Novak:

There’s almost like the... It’s still interesting from an early stage investment standpoint versus like code generations like it’s already here, everyone’s doing that.

Nikhil Basu Trivedi:

There’s so many products, there’s so many businesses that are already far along. Legal ended up being, I think, one of the earliest vertical adopters in AI, but we think that there’s several verticals where huge businesses will be built, such as the ones I just described as CPG is another one, and we’ve tried to go early into companies into several of those. That’s one way that we’ve attacked the AI opportunity in the last couple of years.

Mike Smith:

I’d say that thing I’d add to it too is, and Nicole referenced it, I was a CFO of a publicly traded company, I was a chief operating officer of a company, I was the buyer. What I understand with AI and vertical software and AI today is, I had a finance team that was 100 people and I got to actually see what work they were doing day-to-day in disciplines like accounting and then FP&A and then tax and SEC reporting. Much of that work still is pretty rote and repetitive that are perfect use cases, I think for LLMs and for AI to better the experience of people that are working on that.

I do think though that there will be fewer people in those orgs going forward. I think that is something that the ecosystem’s not talking enough about. It’s just like this huge change management that’s going to happen. I still am in too many conversations where people are talking about sort of, “What’s going to happen is, they’re just going to get jobs in other areas, or they’re going to do the part of the job that they love versus the rote part of the job.” I just think it will be way more disruptive than that in a shorter period of time than we’re ready for.

Turner Novak:

Do you think that it will enable more people to start businesses? If you just think about how technology’s evolved, throughout these different eras, like the internet, so many more people are able to just spin up a Shopify store and sell coffee mugs online. Does it get even more pronounced with AI, or maybe you have to, you don’t have a choice, you have to start a business?

Nikhil Basu Trivedi:

I’ve been a little bit delinquent on publishing my key themes of the year. It’s going to come out soon.

Turner Novak:

Are we getting a sneak peek?

Nikhil Basu Trivedi:

I’m going to give you a preview. One of those is AI enabled entrepreneurship. What I mean by that is, to your point, we think more and more people have to become entrepreneurs as a result of where things ahead in the economy, and how AI will disrupt jobs. AI also just enables people much more easily to be an entrepreneur. I think that’s one of these mega-trends under the surface of what’s unfolding that hasn’t been as publicly discussed. I think it’s an amazing thing. Part of why Mike and I just love this job is that we get to spend time with entrepreneurs every day. We are entrepreneurs ourselves, all of us having started our own firms.

The fact that more and more people are going to be able to pursue something that is an idea for them to make it a reality, I think is pretty amazing. Of course, most of those ideas are not venture back businesses, but that doesn’t matter. You can build an amazing business today just as a single person by typing in a few words and having an app be created. I think we’re going to see a lot more of that.

Turner Novak:

Yeah. You just go open up Lovable, build me a B2B SaaS for water bottle manufacturing companies, make no mistakes, go and spins it up and then you got your software.

Nikhil Basu Trivedi:

Totally. In our case, we love anything, which is a way to build an app that actually you can publish to the app store. Lovable, of course, more for prototyping, but the stories out of anything are incredible. Real estate agent who is suddenly making thousands of dollars a month with an idea that she had for an app. That’s gone from being a side idea, side hustle to now being the main thing, which is pretty incredible.

Turner Novak:

Yeah. It’s just like the barrier to entrepreneurship just like the friction continues to go down.

Nikhil Basu Trivedi:

Yeah.

Mike Smith:

Well, and I think what is happening with AI is that the cost structures of running these businesses is completely changing. The number of engineers that you need to run these businesses, the marketers that you need, it’s just likely, and is already showing significantly fewer people, which means you have a cost structure that allows for people to take more chances to start a business, and actually, have it be profitable and work. Because

Turner Novak:

You can shift a lot of that fixed cost because you have to hire an engineer, hire a marketer. People are actually like, “Sure. I’ll give you the try for a week and if it doesn’t work, I’ll go get another job.” I guess it’s like, I’m probably going to be here for a while. You almost like shift that from being a fixed cost to starting a business to, it’s variable if it works. If it doesn’t work, then there’s no cost.

Mike Smith:

Yes. Totally.

Nikhil Basu Trivedi:

Much lower upfront cost.

Turner Novak:

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One thing you alluded to a little bit earlier that I really want to talk about, you talked about, you had this list, you made a 37 different things you thought about when starting a firm, and Mike was the wildcard on the list or wildcard candidate to talk through. What exactly was the list, and what were some of the biggest things on there that someone should think about when starting a venture firm?

Nikhil Basu Trivedi:

We drafted off of a list that Chris Pack and Jordan Cooper published about when they started Pace Capital together that they had these 33 questions that they went through. Our list became 37. There were a few that we took out, there are a few that we put in, a few that we modified, but credit to those guys for having written the blueprint for this. What we did is we had that list of questions. I think Mike and I actually chatted on a Friday about those questions. We opened up a Google Doc together, and then Saturday morning, Mike texted me being like, “Hey, I’m done with the questions.” I was like, “Whoa, that was really quick.”

I think that having started that exercise with a few people, we could tell from that Saturday that there was something unique about our potential relationship together. It just clicked in a way that didn’t with a lot of other people. I think when you do anything like this, you want to see both sides being super excited about diving in together. That’s what we had from the get go.

Now, what was on that list? Questions such as, how do you make investment decisions? How do you learn who are potential other partners that you would do this with? Who likes you in the market? Who doesn’t like you in the market? What type of brand do you want to build as a firm? What are your economic goals and over what timeframe? How do you think about attribution? How do you think about generational transition? A number of questions about us as individuals and then about what we wanted to build together.

What we found even in that first volley where Mike had answered the questions, and then I pieced it in my answers. It was independently that we answered the questions and we stared at those answers together, we saw that we were aligned on so many of the core principles of what we could do together and just values of who we are as people. But then we also saw that we were very different in a number of skillsets and experiences. It’s that combination that we got really excited about.

Turner Novak:

You got excited with some of the differences.

Mike Smith:

Yeah, I think so. He was an investor for his whole career and I was an operator for my whole career. We felt like for early stage investing and being able to support founders at this stage, we had very different skillsets, but we thought were creative to helping founders in their journey. He grew up in the UK for the first 13 years of his life and then lived in the Bay Area. I grew up in Virginia and then moved to the Bay Area, but there were this underlying, excited and inspired by the tech ecosystem and the innovation and the learning and the risk taking that comes from specifically being in the Bay Area as long as he had been... We both had coded before. There were these continue to find these through lines of consistency in some of the things that we felt were foundational principles in starting the firm, but very different operating experiences.

Nikhil Basu Trivedi:

And just styles in general. I think one of the things we realized is, I am quite micro in decision making. I like to dig into the data and to gauge product market fit. Maybe that’s my initial instinct on every opportunity, whereas Mike is perhaps more macro thinking about the market or the founder, the bigger picture. From the questions to then simulating investment decisions by making angel investments together and treating every conversation with the founders if we had a firm together already before we decided to do this.

Turner Novak:

Were you guys tag teaming calls, joining at the same time?

Mike Smith:

Yeah, absolutely.

Nikhil Basu Trivedi:

Actually, I was thinking about it. I wish we’d made a few more investments now in that window because we saw some amazing companies in that period, even ones that have survived the craziness of 2020 and 2021. But yeah, we made, I think, six angel investments together that year as we were experimenting, and we could just tell that we had different styles, we asked different types of questions, but that the whole was greater than some of the parts.

Turner Novak:

Do you remember an example maybe of one of those times where you really unearthed that? Maybe when you invested in Banana Capital Fund I, and you guys broke it in, you saw how amazing you worked together?

Nikhil Basu Trivedi:

Well, obviously, Mike had known about 200 for a while as being the biggest Stitch Fix ball out there.

Turner Novak:

Yeah. We need to talk about that.

Mike Smith:

Thank you.

Turner Novak:

We’re talking about this.

Mike Smith:

Sounds good.

Nikhil Basu Trivedi:

I think I remember when we... We made our first investment in the fund together during that period. The first company that we decided to put into the fund, a company called Table22. What Table22 does is it enables merchants such as restaurants, bakeries, wine shops to offer memberships to their patrons. It’s a B2B2C platform.

Turner Novak:

Why is a membership important for something like that?

Nikhil Basu Trivedi:

Yeah. That concept of patronage of having a member that comes to your shop, maybe get something every month, but it has some special benefits from being a member. Maybe you remember their name every time they walk in the door, you give them something special every time they show up. That’s been the way a lot of these merchants have worked for many years, but many of them haven’t actually monetized that and formalized the relationship with their patrons. Then it can actually be the best part of the P&L for these businesses.

Turner Novak:

These are like a subscription stream as a restaurant.

Nikhil Basu Trivedi:

Exactly. Table22’s built a really great business in that concept. I’d actually written a few blog posts around this thesis area of business in a box, had invested a number of consumer subscription businesses. Had a very prepared mind for this company. Actually, Cole reached out to the founder. I realized I’d known him in his prior company, and then Mike and I, came together to get excited about it. I think even in that analysis, we got excited about different things. I probably got more excited about the thesis, and Mike got more excited about the founder, Sam. That’s one example that jumps to mind of how we made decisions.

Mike Smith:

I think that the other thing in using Table22 is a continued example, which is I had been in physical businesses with Walmart.com and Stitch Fix for a number of years, and obviously, running a restaurant and then having this new revenue opportunity show up in doing things outside of the day-to-day restaurant hours and having people pick up or having Table22 deliver, is a physical business that I understood the risk with that and also, the upside with that can come if you do that extremely well. To Nikhil’s point, I think we approached the conversation, the diligence, the decision making from different angles, but hopefully, these things make us better and help us make better decisions about whether it’s a great investment for quote work.

Turner Novak:

Are there still things you guys are working through in terms of how you work together? Because it seems like you’re totally different, just like everything you explained, there’s no overlap almost, or maybe there is, but.

Mike Smith:

Well, I think the overlap is the core values and principles and aligned on how we want to individually show up, how we want to show up as a firm. One thing I had to work through, I feel like I’m starting to get some rhythm to this, is just this is a very different pace and very different rhythm than operating a company. It is not as process driven. It is you have to hustle, it’s extremely competitive. I woke up every day for 18 years competing against one of the biggest competitors out there in Amazon, but I think what is interesting about this job is you’re competing against not just one competitor or one style. You’re competing against all sorts of very, very talented people, amazing firms. The people that we want to compete against are the top tier firms. And that is a different rhythm and different way I had to show up every day. And so stylistically, I had to pick up the pace and really understand the rhythm of venture. And I was lucky enough that I had folks that had been doing the job for a long time. They happened to be more famous folks like Jeff Jordan that had spent time with me as I was making the decision, and Bill Gurley and James Slavitt and Alfred Lynn.

And they had been at amazing firms. Many of them had shifted from operating to investing. But they can tell you all that is required to understand the pace and rhythm of venture versus operating. But until you do it, you don’t really understand.

Turner Novak:

Because don’t some people say it’s like retirement, when you become a VC?

Mike Smith:

I knew that wasn’t true. I had one of those four said to me, “You know this isn’t a vacation or retirement job.” I knew that that wasn’t true because I’d worked closely enough with him and the people that I respected the most I knew were busting their tails to do the job. So I wasn’t that naive or even walked into a thinking that. I remember early in our relationship where we met a founder that we both really liked and Nikhil’s like, “Hey, we need to go visit this founder at their place in San Francisco today.” And I was like, “I don’t think we need to go today. Why today? Why don’t we just do it on Monday?” And he was like, “No, today.” And I couldn’t move things around. So he ended up going that day.

And you just realize, when your instinct is, this is a really interesting founder, an amazing business, the answer is now and go today. And that just isn’t something that I had practice with or muscle building for that I do now. Because you just understand, you cannot wait. When you hear some of the best firms and the best investors and the way they operate, and grateful for podcasts like yours where people share how they operate, you’re like, “Wow, if we want to be competing with and against the best, we need to have a now mentality.”

Turner Novak:

So is there a reason you feel like founders usually pick you guys if you’re truly like, “What are the most consequential term sheets being signed this week or next week?” Why do you think people pick you versus probably thousands of choices out there, really?

Nikhil Basu Trivedi:

I think both of us have actually been pleasantly surprised about our ability to win in this first four and a half, five year period. I think we’ve won more than-two thirds of the times that we’ve given a company a term sheet where the founders have decided to work with us. And almost every time we’ve competed against great firms. And so, what are some of the reasons? I think one is, because it is just two of us, we can move really quickly. And so we’re very often the first to have conviction about a company, to give the company a term sheet. And I think that independent conviction does matter to a set of founders and shows up differently than the many firms that are waiting around for something to become competitive before they dive in.

Second, we actually really showcase our thinking on the business as we get to know it. And so we will share our investment thesis with the founders. We’ll tell them, “Hey, here are the questions that we’re debating internally. Here are the things that we’re getting excited about. But here are the things that we see as the risks. What do you think?” We have a version of a mock board meeting that we do with almost every company before we invest so that they can get to see...

Turner Novak:

This is before you even give them a term sheet?

Nikhil Basu Trivedi:

Yes, yes. And sometimes after we’ve given them a term sheet, but usually before, because it’s a great test for us, what will they be like to work with? And we think it’s a great mutual test for them to be able to see what we’d be like to work with. I think another thing that really resonates with a set of founders is that they get this combination of both operating and investing experience when they work with us. And both Mike and I, because we don’t make that many investments every year, we can actually show up for every single one of our portfolio companies. We will both join the board meetings of the companies for the first year after we invest, which is a unique thing that you actually do get both of us.

We never think about, is it Mike’s investment or my investment? We don’t even know how to delineate the portfolio internally. So every investment is our collective investment, and they do get both of us. And, again, that matters for a set of founders. And then I think, finally, fourth point here. So first was speed. Second, thoughtfulness and showcasing our thinking. Third, they get this combo, both of us. And then fourth, I think so much of this business ends up being about the human aspect of it and the relationship between founders and investors. We’ve always met a company in person before we’ve invested, so we’ve tried to really get to know the founders and other people on the team as people.

And so very often just founders opting into the trusted relationship they feel like they’ve already built with us, feel like we’re the people that they want as part of the journey for the next many years, that’s what puts us over the edge. Would you add anything else, Mike, onto that?

Mike Smith:

No, I think those were well covered, but I think all four of those need to show up with intentionality, with thoughtfulness. And I think we do a pretty good job of all those, but nothing I would add.

Nikhil Basu Trivedi:

Yeah. There’s a few other tools in the toolkit maybe we don’t have to disclose publicly, but we try to actually add value to the business before we invest and showcase that.

Turner Novak:

You can’t say there’s other things and then stop. I mean, give us at least one thing.

Nikhil Basu Trivedi:

Well, one thing I’ll just share is, we have a document that we write up about how we’ll work with the company that usually we end up sharing with the company. And so that’s our one-pager, our sheet on why Footwork, and we tailor that to every single new investment.

Turner Novak:

So what’s usually on the list?

Nikhil Basu Trivedi:

Well, it includes some of the things I just tried to discuss. And then, specifically to that company, are there people in our network that we think will be very valuable, potential customers, potential hires, based on the conversations and the back and forth that we’ve had as we’ve gotten into the business, here are the things that we think are going to add it to you and where we can actually be added.

Mike Smith:

Yeah. I mean, I think there’s a level of diligence even in these fast processes of getting to know, as Nikhil referenced, more people on the team. What are their strengths? Where do they want to develop? And then, are there people in our ecosystem that would do anything for us that honestly would also be mentors potentially, not on a monthly basis and generally it’s like a couple times a year, but being able to articulate, “Hey, we care about you both as founders and this duo, but we also care about the development of your team and we care about being able to map people in our ecosystem that we think could be good thought partners and good mentors for them.”

And I think that’s gone over well because, as Nikhil referenced, they end up sharing the doc with the broader team and the broader teams gets to see, oh wow, this firm really cares about us as a company and not just the founders.

Turner Novak:

It’s really interesting how when you talk to maybe just an engineer, they’re like the 10th employee of the company, the investors are these mystical investors.

Mike Smith:

Yeah.

Turner Novak:

“I’ve only met them once,” or something, or they were in the office the other day, but you didn’t actually get to know them. But one thing I found is anytime I find someone like an employee joins a portfolio, I just add them on LinkedIn. I’m like, “Hey, Jacqueline mentioned you’re joining next week. I’m an investor. Let me know if I can help.” You just try to meet them if you can.

Nikhil Basu Trivedi:

Yeah, love that idea.

Mike Smith:

Yeah. I mean, we are very involved in hiring key folks on teams. And I think the feedback that we get consistently is for that candidate, even if they’ve been at an early stage company before, they’re like, “This is the first time I’m ever meeting an investor.” And I appreciate that the investor cares enough about the company-building side and cares enough about me as a person to actually spend time in the interviewing process. There’s bidirectional benefits to that.

We’re evaluating that candidate, but we’re also able to sell the candidate with a broader portfolio of what we’ve seen works and doesn’t work. And we’re intellectually honest about the strengths and the opportunities of any opportunity that they’re joining. And I think they really appreciate that and it’s different than a lot of other firms.

Turner Novak:

Do you guys know why more investors don’t meet the employees of the companies that they’re investing in? It seems like a probably should.

Nikhil Basu Trivedi:

Totally. I don’t know. I’m constantly shocked by how few investors spend time when they’re thinking about the investment getting to know more than just the founder. We see it even in our portfolio companies that go out and raise the next round, that the investors are not asking to meet with more of the team. Whereas we met all three, four co-founders, we met a couple other people on the team in a very short period of time. Maybe it’s just that so many folks have a bunch of different priorities.

And I think each slot for us is so precious that we think really carefully about each decision, the opportunity cost of each decision. And so it does seem that other firms treat those decisions and then treat how they help the portfolio after they invest a little bit different.

Mike Smith:

Yeah. I mean, I think it’s just a different level of diligence, that’s one, but one perspective I was also going to share is, I think on the company side, having been on the company side, there’s many founders that they just want to run their process. They don’t want to involve the broader team. They just want to like, “Hey, I’ll be in charge of fundraising. You be in charge of doing your job and building product or helping run the company.” But in almost every case, when we ask to meet with broader people on the team, we get granted that.

Turner Novak:

They’re usually free because no one else is asking.

Mike Smith:

Yeah, I think because no one else is asking. And, again, I think the founders, the smarter founders also see the benefit of having the team feel like, “Oh, it’s not just me, the founder, that cares about them. It’s this broader group of people that are all going to help me be better in my job and help us succeed.” And so I’m surprised people don’t do it also, but I do think there were reasons on the company’s side why you might look at it as like, “Well, why would I want to waste that engineer’s time? I want them just to code.”

Turner Novak:

Yeah. I’ve had probably a third of the companies I’ve invested in, the CTO will be like, “You’re the only investor that asked to meet me.”

Mike Smith:

Amazing.

Turner Novak:

And I’m always just like, that’s sad, I feel like.

Mike Smith:

Yeah.

Turner Novak:

I don’t know. But yeah, it definitely feels like a level of trust. There’s definitely cases where you go and get dinner with them, you’ve already met the CTO before, and they’re excited that you’re the investor that they’ve met and that they know.

Nikhil Basu Trivedi:

Totally. I think, in general, the better the founder, the more secure the founder, the more excited they are about having an investor meet more of the team.

Turner Novak:

It might be also, maybe I’m hyping us up, maybe we’re just in a privileged position where the founder’s okay introducing us to the co-founders. Because maybe some investors are like, “I don’t know about that.” Maybe, I don’t know.

Mike Smith:

Well, I mean, I do think a superpower or strength of ours, maybe not superpower, is that we do care and we are human. And one of our pillars of the firm is relationships that are human, not transactional. And I do think in cases of meeting people, we hopefully show up that way. And I think that that then earns us the right to get broader access and hopefully make better decisions as a result of that broader access.

Turner Novak:

And one thing I wanted to ask you guys about, so when you first started the firm and you announced it, it seemed pretty clear we’re doing this equal partnership. There’s two of us. There might be more people in the future. It’s still just the two of you that are the GPs. How has this evolved over time? You’re both nodding your heads.

Nikhil Basu Trivedi:

So our entire investment team is still just Mike and me. We have five people in the company, so we have three great folks on the operations side, but investment team is just the two of us. I do think that has also shown up differently in the market and helped us move more quickly on decisions, helped us win over companies in a number of instances, the fact that it is just the two of us. But our original vision was to build a firm that is more than just the two of us. And we continue to think about that every week. Since we started the firm, we’ve had several people who we’ve gone really deep with to see whether or not they should be the third GP here, and we just haven’t quite gotten there with anyone. But that is still our hope.

What we’ve realized is, in the same way that Mike and I got to know each other really well before we did this, we have to get to know that person really well. They also have to be entrepreneurial, excited to go re-found the firm with us. And so the bar is very high for who that person is, but we’re hoping that it does come together with somebody else in the next several years.

Turner Novak:

Is there anything in particular you really want to add or that you haven’t quite gotten there on some of the people you’ve looked at?

Mike Smith:

I mean, I think the thing we want to add is someone that is accretive to the firm, that pushes us in different directions, that has either the way they invest or the way they think about markets increases the surface level and surface area of where we can invest. It’s not particularly helpful to have someone that thinks like us. We want people to push our thinking. And so I think that’s the most important thing to seek, is someone that’s going to take us from what we think is on a track of being a really great firm to actually being a really great firm.

Turner Novak:

I know there’s this whole, the more people are in the partnership, the more you might disagree on something. So it maybe begs another question of, how do you guys actually make decisions? Let’s say I’m like, “Hey, you guys should meet Footwork. They’re an awesome fund. You should talk to them.” I introduce you to some founders. What happens from there?

Nikhil Basu Trivedi:

Practically, one of us has to get really, really excited about every investment that we make. We actually rate companies on a one through four scale, four being strongly supportive, very excited to make the investment, one being strongly not supportive.

Turner Novak:

I will leave the partnership if we do it.

Nikhil Basu Trivedi:

Very, very unexcited. What we’ve said is that, at least one of us has to be a four, obviously, and we are okay with one of us being a four and the other being a two and still making the investment. In reality, in the last four and a half years, in most cases, both of us have been a four. In a number of cases, one of us has been a four, the other has been a three, and we’ve still moved forward with the investment,

But we are very comfortable with disagreeing and committing because we respect the other’s judgment, the person who’s super excited about the spikes in the investment, feeling like we have to make the investment. And I think that that approach fits in nicely with the idea of having three or four GPs one day versus just the two of us. There are things though that would have to change as we grow the partnership. Right now, we can just call each other and make a decision. And what you realize going from two to three is it’s harder to have that happen because suddenly it’s a three person group call.

Turner Novak:

Yeah. That same day, we got to go meet these guys, that continued to get harder.

Nikhil Basu Trivedi:

Exactly. And the node between each person has to be really strong for it to work. And so that’s why the time that we’ve spent with potential people has been a lot of time, it’s been intense time, and will continue to be to hopefully find the person that we want to do this work longer term.

Mike Smith:

One thing I appreciate connecting two dots as you’re talking about this idea that we would do a four-two investment where someone goes a two is, even though there’s only been a few where we’ve been four-threes, after the investment, we do not talk about who was the four and who was the three. We don’t talk about if something’s not working in a company, like, “Hey, why were you a four in this case?” It is a Footwork investment, and whether it’s working or not working, we are all in on helping that company figure it out. And so I do appreciate that about the core values of the firm, of the idea of teamwork and no attribution.

Nikhil Basu Trivedi:

And, in fact, I would say we’re accountable in the other direction and we probably spend more time thinking about ones that we didn’t do and which one of us was less excited about that company and why we therefore didn’t do it.

Mike Smith:

Yes.

Turner Novak:

So thinking about how you not make the mistake in the future?

Nikhil Basu Trivedi:

Exactly. But for sure, every time we’ve done something, we have been all in.

Mike Smith:

Mm-hmm.

Nikhil Basu Trivedi:

And I think you’re right, barely ever talked about.

Mike Smith:

Well, and I think that’s where it’s more challenging for other firms to behave that way.

Nikhil Basu Trivedi:

Yes. For sure.

Mike Smith:

But if something’s not working and I said it was the best investment ever, I think it’s hard for you to trust my judgment, it’s hard for you to support me in helping that company.

Nikhil Basu Trivedi:

I think too many firms have just such a short-term view of “performance” and who gets the credit versus realize this is such a long-term game to ultimately have returns.

Turner Novak:

So why do you think that is?

Nikhil Basu Trivedi:

Well, I do think there’s just a lot of incentives that lead to that. If you gain more power in a firm, you can bring more people aligned with you into the firm. You can have more economics in the firm. You have the ability to do it for a longer period of time. And so a lot of these dynamics show up within partnerships over time. Unless you are very intentional, I think, about trying to be a certain way, it’s very easy to slip into a different motion.

Turner Novak:

So you think that politics, internal politics, you might think of for a large corporation, like a big company, is actually much more prevalent in venture capital firms than most people would appreciate or realize?

Nikhil Basu Trivedi:

100%. I mean, just look at how many changes have happened within venture partnerships in the last couple years, as one proxy for that. And not just bigger firms where people have left, but even small firms, DUOs that are now single managing partners.

Turner Novak:

For somebody who’s hearing this for the first time, what usually plays out in some of those situations? How do these things develop? Maybe if I’m a founder navigating this, there might be some things bubbling below the surface I should keep in mind when I’m choosing who I should work with for the next decade or two. What are maybe some things to just think about and try to observe and keep in mind as I’m making a decision?

Nikhil Basu Trivedi:

Yeah, I think that’s a great question. I mean, I think looking at asking questions as a founder, how do you do attribution? How do you decide who gets credit for something internally? Is that a system that you have? How do you decide?

Turner Novak:

So why is that one a big deal?

Nikhil Basu Trivedi:

Well, because that typically governs someone’s longevity at the firm. And so if you know that someone at the firm is the person who did the Cursor investment and Cursor is working incredibly well, then it’s very likely that person is going to be at the firm for a longer period of time than someone who doesn’t get credit for that investment. And so I think understanding how attribution works, how the firm thinks about whether or not someone’s going to be here for many years, the challenge is that everyone puts their best foot forward in the sales process as they’re trying to win an investment. They’re going to tell you everything that you want to hear as a founder versus the truth.

And so you have to look at their actions over time to really judge them, or you have to talk to someone who’s more unbiased. Maybe it’s one of your existing investors who has the intel. And so I think those are some things that you can do. But I think it’s a great question because not enough founders have probably thought through this, especially for the person that they’re going to have on their board and the relationship they’re going to have with the firm if, in a success scenario, it’s 10, 15 years of working together.

Mike Smith:

Yeah. I mean, using Nikhil’s language from earlier, I think the best founders have other founders that are further along that they ask questions of like, how did you pick that board member? I think the best founders do back channel references on the partner themselves.

Turner Novak:

Not just the firm?

Mike Smith:

Not just the firm. Because, honestly, the truth is, I don’t know if it’s a spicy opinion on this, but I do think firm matters less than individual. To Nikhil’s point, is that person going to stay at the firm?

Nikhil Basu Trivedi:

Well, especially the way most firms work, where you’re working with the individual.

Mike Smith:

Exactly.

Nikhil Basu Trivedi:

Versus we actually do both work every company.

Mike Smith:

Yes.

Nikhil Basu Trivedi:

That’s very rarely how it works at other places.

Mike Smith:

That’s very fair. And so I think I once took a call from a founder who was making a very big decision about who was going to lead their Series A. They were lucky enough that they had term sheets from three amazing investors, and they wanted to know what a particular investor that we had worked with, “What’s that person like over the five years that you’ve worked with them? What are they like in the boardroom when you’re having a bad day or you don’t make numbers? What is their behavior?” And I think those founders that do that bidirectional diligence and are really trying to figure out, “Do I want to be in a marriage or in a relationship with this person for a decade?”

They’re doing that kind of work because they understand the gravity and the importance of the decision of who they’re going to allow on their cap table. And I would encourage all founders to do that because it is a big, big decision.

So I think, not enough founders take that process as seriously as I think they should.

Turner Novak:

Yeah. It’s probably like what sort of, I guess, help you’ll get from the group of individuals or what sort of support or care focus you’ll get.

Because I mean, there might be some cases where we can think of the biggest, most politically bureaucratic firm you could possibly think of that actually you do get a lot from. And they have a big team that they add all this value in all these different ways. And the firm will actually throw the support behind you, but that’s probably if they know they’re going to make money from helping you.

I feel like it’s pretty transactional in that sense of like, “We know that you’re doing really well, we want to invest more, so we’ll help you because you’ll let us put more money in-

Mike Smith:

I mean, very few companies have this up into the right, very linear journey. And so you go through these periods of being in a trough or going sideways. The other thing I was thinking about coming into this is founders get pretty worked up and I understand about valuation at each stage. Very few companies have this perfect valuation. Every round they did was something that they were excited about doing.

Turner Novak:

Yeah, Facebook did a 40% down round in 2008.

Mike Smith:

Yeah. And so what you have to as a founder really think about is like, “Who are the people around the table when I’m going through these troughs? What is their behavior when I’m going through these troughs?” Trying to really understand, “Are these people that really have my back and have the company’s back?”

Versus or thinking about either their next fundraise or thinking about their own position within a firm. That’s not particularly helpful to the founder or the founding team.

And so I think you can’t obviously uncover all of those questions as you’re making decisions about who you’re going to allow to lead your series A, but you can do more work to try to figure out again, what are they like on their most challenging day?

That’s a question that I encourage founders to ask about us and also ask as they’re considering other investors are inviting onto the cap table and treating it like it’s an invitation on a cap table without too much ego. But again, we are lucky for the best founders and the best companies to be a small part of their journey.

And the founder needs to understand, again, the length of time and making sure that they’re inviting people into the party and onto the cap table that they actually want to spend time with and actually can add value in these moments when they’re trying to be great.

Turner Novak:

This maybe begs a question talking about challenging moments. What’s been the most challenging moment or period or thing about either starting your own firm, outfit work? Are there anything that’s just way more challenging than you would’ve expected?

Nikhil Basu Trivedi:

I would say the thing that first comes to mind for me is because it is just two of us, we can’t possibly see every company that we wish we could see and-

Turner Novak:

It’s kind of random and lucky. Just someone thinks of you in a certain moment and you don’t know what’s going to show up sometimes.

Nikhil Basu Trivedi:

For sure. We try to do the input work that leads to it being less random, but you’re totally right. There’s a massive amount of serendipity and luck that goes into just sourcing the next investment and you actually have to maximize the surface area for serendipity for it to happen.

But I think that’s the area that keeps me up the most. Now, we do calendar audits to figure out like, are we spending more than 50% of our time on finding the next investment? And we’ve been pretty consistent for a long time now of actually hitting that input metric.

Turner Novak:

About 50% of time is on sourcing. Okay.

Nikhil Basu Trivedi:

Yeah. So the principle behind that is we’re only as good as our next investment. We need to be spending the majority of our time finding the next one. And we can’t just get bogged down in firm building related stuff or portfolio work. We have to always be thinking about the next one.

So we have little analysis such as that to try to drive the right inputs, but it’s still, I think the hardest thing about starting a new firm, about it only being two of us, about us wanting to be a great firm that many founders put on their list of the 10 firms they want to go to for their CDO Series A is just that top of funnel.

Turner Novak:

Yeah. And I think this might be an interesting time to talk about what is a footwork founder? Does that makes sense. What’s the things you guys think about? And I mean, I know you have a pretty big track record of like greatest hits. Mike, a little less just because you weren’t doing it full-time-

Mike Smith:

I was not.

Turner Novak:

... as much, but maybe there’s certain things you guys think about today throughout the course of your career, what you’ve picked up on of what makes some of them great.

Nikhil Basu Trivedi:

I mean, there’s several characteristics we look for. I think when we started the firm, we had these three characteristics that we publicly talked about, founders that are hungry and humble, founders that know their business inside and out, founders that are magnets for talent.

I think some of the things that have evolved over time are this premium that we place today on a founder’s slope of learning and how quickly they are able to learn and iterate on the business because in this AI era, and obviously the ChatGPT moment happened post our starting footwork. And so the last three years, majority of the investments that we’ve made have been AI first companies.

But we think because of how quickly that world of AI is moving, founders that are able to learn and adjust more quickly, you could say have really strong footwork themselves is just a super important gene for them to have.

And so that’s something we probably face a premium on today.

Turner Novak:

How do you gauge that? Because you’re meeting someone, got to move quick, make a decision in a week. Is it, oh, they got so much more in last week.

Nikhil Basu Trivedi:

Yeah. Honestly, it’s a little bit of that. I mean, you can tell if someone seems like they’re growing and changing, evolving even in the span of a couple meetings that you have with them. There’s questions that we try to ask about what has changed in the business or changed in your thinking about the business even in the last couple of weeks or let’s say in the last quarter.

Actually, one of the benefits of getting to know more than just the founder is understanding the founder better in the lens of the people they’ve recruited to the company and what they’re like and as a proxy for the broader team, as a proxy for the things that the founder cares about.

Turner Novak:

Yeah. I’ve definitely had that before where you maybe meet the co-founder and you get a little bit colder just based on how the conversation went.

Nikhil Basu Trivedi:

We’ve had the opposite, which is we actually get warmer.

Mike Smith:

Yeah, that’s correct.

Nikhil Basu Trivedi:

I can think about several examples in our portfolio today where we were impressed by the founders CEO, but we got super excited to spend time with their co-founder. And then, yeah, Mike, what else would you say are some of the things that we-

Mike Smith:

I mean, I love a couple of the questions that you’ve introduced, which is, this is act one of your business today. What is act two and act three?

The fact that you actually are asking the founders to think about, you might have some product market fit today, but to build a category leading company that actually has a chance to go public, you will have multiple acts.

And I think the best founders have given some thought, even though it’s not super cogent to what those future acts will be. So that’s one that I think is a tactical question we ask.

Another one we ask is, if there were a couple of challenge topics and we’re going to, as Nikhil referenced before, we’re going to simulate a board meeting, what would those challenge topics be?

So what we’re looking for is vulnerability and actually things that aren’t working in the business, but also that they’ve already thought through some of the ways that they are going to fix this thing and they want just some thought partnership along the way. That’s another question or set of questions that I think help us evaluate the slope.

And the third one I’d say, and you have to be careful about this one, but most of these founders have a deck and they’ve presented a lot and they’re very-

Turner Novak:

It’s like a script of-

Mike Smith:

Yeah. And so what you want to do is to test some of the script, not be rude, but ask a question as they’re going through the script to see how they adjust to that question on a slide that you have on go to market or on the financials, just to see, are they able to shift out of the script and into really thoughtful point of view? And so that’s a third thing that I think we try to do to just evaluate against slope.

Turner Novak:

And it’s interesting when you think about the evolution, how you get stronger as a founder. Nikhil, I know Canva, you invested in their seed around. This was 2013.

Nikhil Basu Trivedi:

2014.

Turner Novak:

2014. I mean, LLMs weren’t a thing. It was eight years pre LLMs. But when you use Canva, there’s quite a few AI generation features inside of it. They’ve obviously benefited from it. And it’s you first off don’t even know that AI is a thing. AI wasn’t invented in 20... It didn’t exist in the same context, but then being comfortable of like, oh, they’re appropriately taking advantage of and continuing to evolve over time.

Nikhil Basu Trivedi:

I mean, I think that founders that are just so obsessed by the product about making it better, about continuing to have some sort of a competitive advantage, not just resting on their laurels and resting on things that are working, that’s the type of founder that we just absolutely love working with.

And the hard thing is that I think many of the best founders that we’ve worked with, it was very difficult to tell from their resume that that would be the case.

You look at Mel and Cliff at Canva and there was nothing their resume having grown up in Perth, having started a yearbook company and then a software company around design that they would be that way.

You have to actually spend time with them and understand them and sort of question them to get that out of them.

Turner Novak:

Do you remember what spiked back when you first met them and were making the decision?

Nikhil Basu Trivedi:

I think the thing that was pretty amazing about them in retrospect is the level of ambition that they had from the early days.

Turner Novak:

Even as a yearbook design-

Nikhil Basu Trivedi:

100%.

Turner Novak:

... software?

Nikhil Basu Trivedi:

They were talking about taking on the whole design market, going after more companies like Google and Microsoft than Adobe, even from-

Turner Novak:

Enterprise design tools, in the sense of instead of using Google slides, you’ll use Canva.

Nikhil Basu Trivedi:

Yes. So being the design software product for non-designers and how big of an opportunity that is, much bigger of an opportunity than Adobe. People always put Canva and Figma and Adobe in the same breath.

Turner Novak:

Yeah. I don’t even think Figma and Canva, they have maybe 1% customer-

Nikhil Basu Trivedi:

Exactly.

Turner Novak:

The use case is not the same.

Nikhil Basu Trivedi:

Canva’s a much closer business and total addressable market to Google and Microsoft. It’s the everyday person’s work suite, much like the G Suite or Office.

Turner Novak:

They have video editing tools. I actually, the intro for this podcast episode, my wife made it in Canva.

Nikhil Basu Trivedi:

Perfect, yes.

Turner Novak:

And they have documents also. It’s literally like the productivity suite you would use for doing professional work. They have it all at this point.

Nikhil Basu Trivedi:

And they were thinking that way in 2014.

Turner Novak:

So did people just-

Nikhil Basu Trivedi:

Despite having gotten nos from a lot of investors, worked on the thing for a long time already. Now what they had is they had six months post launch where things were going pretty well and users were growing 30 to 40% organically every month. They were at about 100,000 monthly active users when we invested.

So something was working, but the level of ambition they had for what it could become was pretty amazing. And it was almost... I mean, we talk a lot about founders have to be crazy to go build something really big. What you don’t want is for a founder to be delusional, but they have to be crazy.

And there’s a spectrum between crazy and delusional that Mike likes to talk about that every founder is on. And so what I remember vividly from that first meeting actually is they seem kind of crazy to me, like two founders who are here from Australia who-

Turner Novak:

Surfers.

Nikhil Basu Trivedi:

Yeah. A bunch of people have passed on, kite surfing-

Turner Novak:

Yep.

Nikhil Basu Trivedi:

... but huge ambition.

Turner Novak:

So Mike, that maybe begs the next question is just like, how do you decide? Because all investors see these decks where it’s like the TAM is, it’s crypto, it’s $10 trillion TAM. How do you decide maybe that’s a little too crazy?

But this design software that competes with a Google and Microsoft or this box that shows up with a bunch of clothes, it’s going to compete within inCommerce. How do you just delineate between too crazy and truly ambitious that could actually work?

Mike Smith:

Yeah. I mean, I think on the market and the product, I’m pretty a wide spectrum on what can be crazy and actually delusional because you do want people that are creating these category defining companies.

Turner Novak:

That’s how you build a big business. You have to be in the market.

Mike Smith:

That is exactly how you build a big business. And so I like to hear it all. I mean, there’s certain areas where we don’t have certain expertise or interests or can’t add as much value, but for the vast majority of companies in technology, specifically in AI, we actually think we have a right to be good partners to those businesses.

And I think we’re looking for almost the craziest stuff in terms of what their ambition to Nikhil’s point, what their ambition is for what they want to build and the why they want to build it. I mean, it can’t just be, I was doing some market research and read this McKinsey report on how there’s white space here.

It needs to be some acute challenge that they really wake up every day thinking about why they deserve to be in the world. And you can feel that in a meeting with a founder. It’s like, are they going to wake up every day and say, “This is my mission and this is my reason for being.” And can they attract similar enough people to that mission and that craziness to build a huge company?

And I think most of the best founders, you see that. I mean, one of the things people ask me about is like, “Why did you join Stitch Fix when it was just four people? What was so special about Katrina Lake, the founder?” And that Katrina shows up that way.

She shows up on a good part of the crazy to delusional spectrum. She knew her business throughout. She wanted to have a completely different shopping experience. It felt like data science was an amazing way to do that efficiently and effectively. And she built this amazing company for nine years that she was the CEO and founder of.

And you could just tell in spending, we spent multiple cycles together before I decided to join that she was a very, very special founder.

Turner Novak:

So why did you decide to join? Was there a certain thing that tipped you over the edge?

Mike Smith:

I think it was like 75% her. And what I appreciated about her is that she was intellectually honest about what she was good at and where she wanted help. She was super clear on the vision. If I think back to the first conversation I had with her on the vision for Stitch Fix and what transpired over, again, a decade of working with her, it was pretty aligned.

There weren’t like bumps in that vision road. She was like super clear on what she wanted to build, which then as you get hit by fundraising challenges or just the growth, it went from zero to $2 billion run rate in nine years.

In a physical business that is very hard to do and there’s challenges with growing that fast, but she stayed super consistent on the vision of what she wanted to build. And that consistency was something that showed up early in my conversations with her.

And the last thing I’ll cite is slope. I mean, I didn’t call it slope back then. I just talked about it as raw smarts. But when you talked to her about the business and you talked about what was working and what wasn’t working, and when you talked about what’s the contribution margin today, what’s the gross margin today?

What’s the gross margin going to be five years from now when we’re at $50 million, which is what we thought we would be five years from now. It was a very different and higher number than that. She just had amazing answers for what was going to drive margin expansion and why this was a great natural business to build.

And that was something that I got very attracted to work with her and partner with her on achieving that vision.

Turner Novak:

And so what did you do? Because you had joined walmart.com.

Mike Smith:

I had been at walmart.com. I was chief operating officer at walmart.com, and I picked up my head to see what else was out there for opportunities. And there were some CEO opportunities, but there are a decent amount of COO number two roles with these amazing founders. And I met her and just was blown away by her and was lucky enough to join.

I think she was dating other people like Nikhil was dating other people and building a firm, but fortunately I won and was able to work with her for a number of years.

Turner Novak:

So what was the biggest challenge with Stitch Fix specifically?

Nikhil Basu Trivedi:

Are we going to get to-

Mike Smith:

Yeah, we should be able to. It’s the perfect time to ask him like, why are we used to this?

Nikhil Basu Trivedi:

Did you guys actually talk about Turner in your leadership team and how cool she was?

Mike Smith:

We did because Turner had an amazing following on Twitter and it was very loud and memeish and thoughtful, honestly, about it.

Turner Novak:

I tried to throw out all those sports categories.

Mike Smith:

So we had a good bunch of leaders that knew of Turner and we would talk about Turner in leadership and like how this guy’s so bullish on us. And it was inspiring to have someone that was in your corner. You had boxing memes, I think that you used to use and it did feel like you were physically in our corner.

Turner Novak:

We still use it. I mean, my daughters, their favorite thing is going and getting the Stitch Fix box.

Mike Smith:

Is that right?

Turner Novak:

I think we get it quarterly.

Mike Smith:

Okay. Right on. Yeah.

Turner Novak:

And they like the app where you can pick what you want. It’s their favorite thing to do with grandma.

Mike Smith:

It’s an amazing business. I still am really proud of what we were able to accomplish. I think the biggest challenges in the business, one was just scaling that quickly is hard. As I referenced before, it’s like we had at the peak when I was there, 5,000 stylists, those were the people that would pick the last five things that would go on your fix.

We had 3,000 people in warehouse operations across five different warehouses in the US and one warehouse in the UK. When you’re managing that large a team, it’s definitely not easy. I managed somewhere between 12 and 15,000 people at Walmart. And when you’re managing that many people, things break or it’s hard.

So it’s just managing that level of scale, that many people and just the growth. I mean, the sales numbers, just so you know, before we filed, were a million my first year, eight our second year, then we did 75, then 345, then 760, then 960 and file. So that kind of growth in five years is not normal.

Nikhil Basu Trivedi:

And with a ton of efficiency too, right? How much capital-

Mike Smith:

Yeah. We got to a cash flow positive on $17 million of capital and we only raised in private capital $42 million of capital. The last $25 million we raised we didn’t touch. And so yeah, it was very efficient.

I mean, part of the efficiency gets to the second point of the challenge is people didn’t really want to fund the business. We were-

Turner Novak:

Why not?

Mike Smith:

Well, I think part of it, I get it now being on the other side, some of it. Most of what I still get salty about. On the salty side, I feel like there were times when people dismissed her or dismissed the team.

This is where I do think going back to how we want to show up of engaged and ask good questions, I was a little to a lot disappointed by what I was seeing on the other side. We’d go to these meetings, people would be late to meetings, they would ask not that great a question.

Turner Novak:

What if Amazon does this?

Mike Smith:

I mean, that was one. Another one is like, Trunk Club’s just going to do this. And they wouldn’t listen to the data science machine learning aspect of it. They wouldn’t ask additional questions to try to understand how is that differentiated.

Turner Novak:

So it was real. There actually was some machine learning.

Mike Smith:

Oh yeah. We had at the peak, 152 data scientists and you could see in the data how getting more data and getting more scale and the way that the algorithm teams understood information that we were getting in the style profile and a checkout was driving differentiated performance in the business.

Turner Novak:

So you were like, people were buying more things.

Mike Smith:

Exactly. So you could see if they figured out this algorithm and made it work more effectively, the contribution margin would go up, LTV would go up. So you could drive direct impact from ROI of the investment you’re making in data science and machine learning to actual business results.

And so people didn’t really spend the time to get that in private markets or sometimes in public markets when we would talk to investors. So anyway, we failed to raise our series A as a result of people not liking the business.

We met with 65 firms, 60 of them I didn’t want to be in the same room with after I left for some of the reasons that I cited earlier. And we had to develop, do a bridge and figure things out. But to the efficiency point, we were forced after all of those meetings and being really close to the edge and like a few weeks from not making payroll to figure it out. How do I get this business?

Turner Novak:

Yeah. How did you bridge those weeks of cash?

Mike Smith:

I mean, we bridged it by having plans to reduce the team, but significantly. We were able to get a million and a half from one of our existing investors that allowed us to extend that a little bit longer. But the most important thing is we got really focused on gross margin and contribution margin and what we needed to do to own our own destiny.

Turner Novak:

Were they-

Mike Smith:

It was the same kind of stories that we tell some of our teams that are struggling now is like, “You can do it. You have to make very, very hard decisions, but you can do it.”

Turner Novak:

Were these periods where the top line wasn’t growing, so it wasn’t-

Mike Smith:

No.

Turner Novak:

... or was the contribution growth margins messy? So people were-

Mike Smith:

It was just when you’re buying inventory to fuel the growth.When you see the growth, that is capital that goes out to buy stuff that you wait for customers to buy it from you to get that capital paid back. We had great terms and it was a super working capital efficient model, but you still had to buy the clothes in order to ship clothes to people. And that was the biggest issue is you needed capital to do that.

Turner Novak:

So people were concerned if this is a bad run, you just run out of money.

Mike Smith:

Well, there’s that, but the reason where I did respect certain firms and people in particular’s opinion about the business is on two dimensions. One is we’re a venture capital firm, we’re writing checks between a million and $15 million. If we’re going to write a $15 million check, it isn’t easy to have half of that $15 million go to inventory versus developing product. We had some investors be like, “That’s not a business that I’m interested in. I’m interested in $15 million going to engineers and data scientists and not product.”

So I get that. And the second one was, “This is just not a business that I wake up every day super interested in. Women’s dresses is not a business that I’m super interested in as a business.” And you want investors, having been on the other side, to intellectually be super curious about the business and wake up every day thinking about, “How can I be helpful and where can I ask questions to help advance their thinking?” So those were two reasons that I totally could sleep at night and be fine with why someone passed.

Turner Novak:

Yeah. I probably told you this story at some point, but the way this came about, me tweeting about it, I remember I was just in a group chat of people sharing investing ideas and someone was like, “Turner, what’s your craziest idea of what could be a massive company that no one’s thinking about?” And I don’t know, Stitch Fix maybe, it could be.

Mike Smith:

Wow.

Turner Novak:

I think I was going to share it in this group and I was like, “I’ll just make it like a thread and tweet it out because people might find it interesting.” And it got really popular and it was basically just this thesis of you have the permission to just ship things to people and they just buy it.

And Amazon doesn’t have that and no other e-commerce provider really has that and I think has nailed it. So I kind of was like, “Well, if this does work, it could be as big as Amazon or bigger. You could displace them.” And there’s a ton of questions around you got to get there to do that. That’s a pretty big opportunity. And I just remember my mother-in-law, when she goes clothing shopping, she kind of freezes, doesn’t really know what to get. There’s just so many options. And when she would get Stitch Fix, she would just get most of it.

And it was kind of crazy. I was like, “Wow, you’ve solved that problem for her and she likes most of the stuff.” And then now with my kids, they’re younger, but they just get it all. They just buy all of it. And maybe you’ve solved the problem or maybe you’ve snuck in below the parents and the kids, it’s like a way for the kids to just spend and get new clothes that they maybe wouldn’t have otherwise. But it was just so fascinating. I always thought of it as the recommended bar on Amazon or any retailer, and you convert very highly on this recommended bar that you’re shipping to the people.

Mike Smith:

Yeah. We sat in a very privileged seat of being invited into someone’s home, not really knowing what you’re going to get in a category like apparel that’s very emotional. So you get invited into that home, but you have to deliver. I think there are entertainment brands that get invited in the home, there are food brands that get invited in the home, but there’s not a list of 100 companies that naturally get invited into someone’s home. The home is a very special place and it creates, hopefully, memories for your kids of opening those fixes. So we were very privileged to be able to be invited in there and worked really hard to deliver amazing experiences from that invitation.

Turner Novak:

Yeah. You can expand into other categories. You started with clothes, but I think you guys also do jewelry, I believe, and men’s, kids, and other categories.

Mike Smith:

Yeah, jewelry, shoes, tops, bottoms. It’s pretty-

Turner Novak:

Are you doing makeup yet? Does Stitch Fix says they do makeup?

Mike Smith:

I’m not on the board or involved in the company, so I don’t believe they’re in makeup, but yeah.

Turner Novak:

There’s a lot of stuff you can do.

Mike Smith:

There’s a lot. We would talk about a lot of different aptitude and beyond apparel because we felt this idea of building trust where you could get data from the customer of where their interests were and then delivering an amazing experience on the backend could be applied to a lot of different categories.

Turner Novak:

I think maybe one more question on Stitch Fix. I think Benchmark ended up investing.

Mike Smith:

That’s right.

Turner Novak:

They’re a pretty good firm. Was that in the series A or is it the C?

Mike Smith:

That was in between. When we failed to raise our series A, we changed the model of how we were going to do fundraising after we got the bridge. And it was Katrina’s brilliant idea, which was, let’s identify three partners that we would want to be in a relationship for the next 10 years. Let’s make sure that we felt they had the superpowers that could help us in our journey, and then let’s bring them under the tent and treat them like they are existing investors, treat them like they are board members, do mock board meetings where we would talk about these challenge topics that we were having in the business, and then we would evaluate what their thinking was like.

Not that dissimilar to the way we ask founders to evaluate us or the challenge topics that we asked them to weigh in on. We had these three more famous investors that we had picked, and Bill Gurley at Benchmark got very excited about the model and very excited about Katrina lead the investment.

Turner Novak:

Had he passed in the traditional sense before that?

Mike Smith:

He had not passed. He and I have this funny back and forth on this over the years because I’d interviewed with him for the Uber job with Travis, and I sent him a note when I landed at Stitch Fix and said, “Hey, I think you should take a look at Stitch Fix.” And he said something like, “We’re over 33 on all e-commerce investments, so no thanks.”

And I’m clearly not a good salesperson in that case because then six months after that email, he met Katrina and I ran into him on Market Street and he was like, “Oh my gosh, she is so amazing. Why didn’t you tell me this?” I’m like, “I clearly tried, but it failed. So I’ll take that. I’ll take accountability for that.” And then he got so excited about it as he got brought in under the tent and decided to lead the realm.

Turner Novak:

Wasn’t there some story about their EAs started using it and actually convinced the team or something like that?

Mike Smith:

I never really talked to them about that, but that is the story that’s out there that I think Bill has talked about a little bit, which is he was seeing end customer love. The same reason I joined is I met a friend of mine who was at Facebook at the time and she’s like, “Oh, you’re interviewing at Sitch Fix. There’s six of us that get our fixes on a certain day, and we all try on clothes together and we do trading during that day and we love the brand.” And this was early. There were like 100 customers or something. So to have six at a company that were loving the brand was also good, quote unquote, signal for the company could be pretty interesting, but Bill was getting similar signal from his cohort.

Turner Novak:

So actually Hunter Walk at Homebrew brought this up to me. He said, “Your office is right next to YC.” Any interesting stories with YC companies just being so close? I don’t know if there is one, but...

Nikhil Basu Trivedi:

So we moved to the Dogpatch and got our office, I think two and a half years ago, maybe just after YC also landed in the neighborhood. We have hosted YC companies off to the batch at our office, so we’ve got to know a handful of them pretty well. And then we have just seen in general a number of YC companies walking back and forth on our street and peeking in. And then I think probably each of us has overheard some interesting chatter from these companies over time. I would say the most interesting thing is just getting to know this handful of companies post batch and understanding the pressure cooker environment they’re in during YC and then how that changes post-YC.

Actually, we’ve made an investment in a company, Confido, that was a completely different idea during YC versus what it is today. So our belief is there’ll be a set of companies that during the YC batch take off and work really well, but we’re more interested in the companies that grind it out post-YC and maybe they find something that’s actually different to what they’re working on during YC because a lot of YC companies end up pivoting, and we’re very excited about that characteristic of company.

Turner Novak:

Oh, interesting.

Nikhil Basu Trivedi:

But it’s a fun neighborhood to be in right now.

Mike Smith:

Yeah. I want to give him some credit on this too because, I don’t remember, it must have been three years ago, where he was like, “Hey, we need to stop meeting at Farley’s and actually get our own office space.” And I think you felt really good about this space and how it was going to develop and it is really central to AI and innovation and it’s nice to have physical space as you see the neighborhood developing around us.

Nikhil Basu Trivedi:

Yeah. We didn’t realize that OpenAI was also going to move in pretty close to us, four blocks away from us.

Turner Novak:

Do you guys do any of those, someone’s leaving OpenAI and you just give them a blank term sheet to sneak in? You guys don’t do that?

Nikhil Basu Trivedi:

We haven’t done that yet, but we have gotten to know several people at OpenAI well. So who know what’s in our future.

Turner Novak:

Yeah, that’s fair. One thing actually that maybe is in the future, you talked a lot about consumer health earlier. You mentioned that’s something you’ve been thinking about as maybe a new area Footwork have kind of been exploring.

Nikhil Basu Trivedi:

Yeah.

Turner Novak:

What are you thinking about on consumer health?

Nikhil Basu Trivedi:

I think, rightfully so, the world is obsessed with what’s happening in AI right now, but I think if you were to press me and perhaps us on what’s a non-AI-first area that you’re really excited about, the first thing that comes to mind for me is consumer health. When you look at what’s happened in the last five years post-COVID, many more people are thinking more preventatively, more proactively about their healthcare. We kind of all got educated in COVID by vaccines and masking and I think understanding our health at a different level. Secondly, you’ve had this wave of new drugs that have come out in the last five years that people have started to see a lot of benefit from, such as GLP-1s. Thirdly, you’ve got now a number of companies that have gotten pretty quickly to a level of scale in consumer healthcare.

I’m thinking about stuff in blood testing like function health, rhythm health. I’m thinking about telemedicine plus prescription services like HIMSS and Roe. So there’s a set of ingredients now in consumer healthcare that I think can yield a really interesting crop of new businesses, not to mention the intersection of AI and healthcare. You saw in the last couple of weeks, ChatGPT has now got a specific ChatGPT for health product. So we think there’s a ton of opportunity there and we’ve made several investments in consumer healthcare. A few that we’ve publicly announced, companies like Honeydew, which is in virtual dermatology care, but a few that we also haven’t yet announced that we’re very excited about and that are growing exceptionally quickly.

Turner Novak:

It’s interesting. I think healthcare is 20% of GDP. Some of it’s in insurance mixing around, but it’s basically like, “Can you just go direct to the consumer and can they make decisions?” And maybe it shouldn’t be 20% of GDP, but it should be like 10 or something. It’s still a massive chunk of what people spend money on.

Nikhil Basu Trivedi:

Well, sure. In some ways, you want people to spend less money on it because they are more proactive and competitive. But in that is an absolutely enormous opportunity economically and then just for our society more importantly.

Mike Smith:

This is what’s great about it is we’re all consumers of it too. So when you go to the doctor and you see how much work they’re doing that’s not care work. I don’t feel they’re getting as much joy out of the non-care work. They became doctors to do the care work. And there’s so much administrative things and insurance things, it sort of takes away from their joy in the job. And I think there’s still a ton of opportunity, I think, for technology and AI to have the jobs be more effective and more efficient and also have people get more out of their jobs as a result of where we can make investment there.

Turner Novak:

Is it true you guys once organized a flash mob to win a deal with this?

Nikhil Basu Trivedi:

I think what happened is a flash mob came around us when we were trying to talk through the term sheet.

Mike Smith:

It was amazing.

Turner Novak:

What happened?

Nikhil Basu Trivedi:

It was the only time, I think, that we, for a number of different reasons, were out in Salesforce Park in San Francisco and we were trying to give this company, Hood, a term sheet and suddenly people started dancing around us at the tables that we were at.

Mike Smith:

Yes. Andrew and Vic felt we had sort of engineered this to help our sales process and we did not, but I wouldn’t say it hurt. It helped us lighten the mood and bring joy, but it was maybe an inopportune time of talking about terms, but fun.

Turner Novak:

Yeah. Nikhil, what’s it been like being a dad? Two young kids, how’s it changed your views of the world, how you think about things, getting time for doing things?

Nikhil Basu Trivedi:

Yeah. I think it’s an incredible joy to get to have kids and I feel really grateful for my wife, Chandrika, and our two daughters. I think what is difficult is if you want to be a good spouse and dad and to show up for the family plus do this job at a really high level, it just takes everything out of you.

There’s been barely a day since we started Footwork that I haven’t been working. I still feel I don’t have enough time for the deep, proactive work that I want to have. When we are really excited about something, I can still go very deep, very quickly. In some ways, I feel I can move even more quickly with having a family because I just have to, I don’t have another option, but to drop everything and work on it.

But yeah, there are trade-offs and that’s life. And I think this is a particular season, especially when you have two kids that are both small. We were talking early about you have a nine-year-old and a five-year-old now and are sort of out of the really physically intense times, but I’m also trying to savor this time because you know it only happens once.

Turner Novak:

Yeah, it’s like these weird phases where now, for me, that it’s a little bit easier, you usually sleep through the night or whatever, but it’s more noticeable, like when dad leaves or when dad is not giving you attention. If they’re eight months old, they don’t know in the same way when they’re nine. I’m trying to send an email and my daughter wants to play. Our new thing is making up games. Two days ago, we made up charades, and she just came up with the idea for the game. She would draw a picture-

Nikhil Basu Trivedi:

That’s amazing.

Turner Novak:

... and then we’d write the word together that was on the picture. So I was trying to finish something and she was talking to me. And looking back on it, I just should have closed my laptop because it wasn’t that important. It’s just balancing. There’s so many different demands for your time and you got to prioritize stuff at the right time. So that’s been challenging. I don’t know. Mike, what advice do you have for us on that?

Mike Smith:

It is hard, I would say. I’m on, as we talked about, a little bit on the other side of that, but I always felt lucky that my better half supported my ambition and work and now I have a daughter who’s a young adult who knows I worked really hard to get to where I was and really appreciate that work ethic that’s needed to be great. You don’t realize at these younger ages how much these kids are picking up. And you don’t want them to think that you’re not dad, but they also recognize that you’re dad plus worker plus husband, and they really do pick up a lot of these things. In my case, she’s appreciated how hard I’ve worked and has her own ambition as a result of it. She got to meet Katrina Lake when she was 10 years old. So to meet a founder who-

Turner Novak:

Looks like you.

Mike Smith:

... looks like you, is young like you, ambitious like you want to be, it’s super inspiring. So she wouldn’t have gotten that privilege had she not had a dad that was pretty ambitious for what he wanted out of his career. But you need the right support system and you also have to find time to reflect and you also have to find time to take a little break here and there. Otherwise, it’s very hard to do the pace for 40, 50 years. There are people that do it. I think it’s very unique to be able to do that. And I think you just got to find these recharge moments where you actually end up being better on all dimensions as a dad, as a partner, and as a worker when you find these small recharge moments.

Turner Novak:

That’s fair. Anything else you guys want to talk about that you feel we missed?

Mike Smith:

I don’t think so. This was great. Yeah.

Nikhil Basu Trivedi:

Thanks so much for having us. We appreciate it.

Turner Novak:

Yeah, this was a lot of fun.

Nikhil Basu Trivedi:

And this is the first time I think that we’ve done both of us-

Turner Novak:

On a podcast.

Nikhil Basu Trivedi:

... in person, on video as well, in a podcast. So a special moment for us too.

Mike Smith:

Yeah. Thanks for bringing us together.

Turner Novak:

Hopefully, people that are still listening at this point. Hopefully, they realize how important this moment was in time.

Nikhil Basu Trivedi:

Yeah, totally.

Turner Novak:

Well, cool. Yeah. Thanks again for doing it. Thanks everyone for listening.

Mike Smith:

Thanks.

Nikhil Basu Trivedi:

Thanks.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

AI-enabled services businesses are the recent model to catch the attention of Silicon Valley, and I got a chance to sit down with Co-founder and CEO Chris Hladczuk to talk through everything he’s learned building one from the ground up.

I first met Chris two years ago. We didn’t know what it would be, but I knew he had what it took to build a generational business.

Since then, he recruited his co-founder Nick Puljic, they built a general ledger from scratch, and have grown to over $15 billion in customer assets on the platform.

We go inside their recent $27M Series A, talk about productizing the service layer, helping CFO’s use AI, automating onboarding + manual admin work, becoming a customers most important vendor, and why you should always get on the plane.

Support this Episode’s Sponsors

Numeral: The end-to-end platform for sales tax and compliance.

Flex: Sign-up for Flex Elite with code TURNER, get $1,000. Apply here.

To inquire about sponsoring future episodes, click here.

👉 Stream on Spotify and Apple

Timestamps to jump in:

• 0:37 Financial infrastructure for investment firms

• 1:35 Hanover Park’s $27m Series A

• 5:30 AI-enabled services businesses

• 9:07 Productizing the service layer

• 11:30 Helping CFO’s and investors use AI

• 13:46 Building a general ledger from scratch

• 18:03 Compete against companies with IT departments

• 19:55 Hiring in an unsexy industry

• 21:30 Live in constant paranoia of your customers

• 25:19 Gongs, music in the office, blizzard commutes

• 28:54 Friday night hackathons

• 30:54 Automating onboarding and manual admin work

• 35:05 Real-time visibility on all data

• 38:07 Always get on the plane

• 40:36 Turning customers into raving fans

• 43:45 Using polite persistence in sales

• 47:36 How to master founder-led content

• 51:29 99% of advice is wrong in AI era

• 54:21 Importance of one-way vs two-way doors

• 56:11 Growing from VC into PE and Private Credit

• 1:00:36 When to turn down new customers

• 1:02:22 Becoming a customers most important vendor

• 1:04:00 Chris’ personal AI stack

• 1:07:41 Hanover Park’s MCP

Referenced:

• Hanover Park

• Careers at Hanover Park

• Granola

• Claude Cowork

• HubSpot

• Attio

• Monaco

Find Chris on X / Twitter and LinkedIn

Related Episodes

👉 Stream on YouTube, Spotify, and Apple

Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Chris, welcome to The Peel.

Chris Hladczuk:

We’re back.

Turner Novak:

The second ever second time guest. First one was Dan at Chainguard, which they went zero to 40 in revenue in two years. Those are big shoes to fill.

Chris Hladczuk:

I know. We went zero to $15 billion in under two years, 20 months to be exact. So, we’re going.

Turner Novak:

$15 billion in revenue?

Chris Hladczuk:

That would be a lot of revenue. I’m not cursor. That’s assets on platform.

Turner Novak:

Assets on platform. Okay. So, for someone who isn’t familiar with Hanover Park, they didn’t listen to the first one, which will link in the show notes for people if they want to listen to the year-old conversation. What is Hanover Park for people who don’t know?

Chris Hladczuk:

You can think about us as financial infrastructure for the investment firm. And so today there’s all these insane armies of human duct tape, we call it, that sticks together all the back and middle office to do all of the financial reporting for the limited partners at these firms, for the CFOs and all that kind of stuff. And what we’ve built is unified software plus AI plus services to do all the financial reporting, which would be called fund administration. And then we also have a bunch of portfolio management and monitoring tools. And our thesis is that B2B SaaS is dead, which I said literally on this podcast a year and a half ago that’s come true with Claude.

These thin layers on top of data that you don’t own are things that are being disrupted. And so, our goal is build the system of record for the fund, we did that. And then owning and controlling that data can deliver better product for the CFO.

Turner Novak:

So, I know you just announced some news within the past day or two, three, whenever people listen to this, what’s the thing you just announced?

Chris Hladczuk:

Breaking news heard here on The Peel. We raised a $27 million Series A led by Emergence Capital. Lux, Susa participated as well to build the financial infrastructure for the investment firm.

Turner Novak:

And how did this come about? What was the setup leading into it and the process of raising a Series A? Because it can be hard sometimes.

Chris Hladczuk:

We were growing really fast. We went from one to $7-ish billion of assets on platform late last year. People were super excited and we were like, what I wanted to validate is the size of the opportunity. It’s like you have $100 trillion of global assets, largely these investment firms are paying millions and millions of dollars a year to these outsourced providers. And we said, “Can I probably take this from a human heavy services business from something that’s scalable?” And so, that’s what we validated. And then we went to the market, it was very quick process, it only took a few weeks or so.

And there was this crazy moment where Jake Saper, who joined our board from Emergence, flew in from San Francisco to New York City on Friday night. He’s like, “I’m going to take you out to dinner.” It was like a three-hour-long dinner. And I was like, “When are we actually going to talk about the terms here? Are we just going to keep looking at each other, eating omakase back and forth?” And at the very end, he’s like, “All right, so let’s talk about the terms.” And so, we end up negotiating these terms and he literally nothing, it’s like Friday night at 9:00 PM. I go back to my apartment and at midnight I get a phone call. I’m like, “Why am I still up right now?”

And he’s like, “Term sheet’s in your inbox right now. What are you doing? You have 48 hours to make a decision.” I was like, “Oh, that was fun.” So, I was like, “This is great.” Now, we’d done a bunch of back channeling with Jake and he had been super close to the team at Susa as well and he has been the leader in what I call AI enabled services, which we can talk about. And so, pretty excited to team up with him and the Emergence Team.

Turner Novak:

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That’s probably an interesting thing to get into, this whole concept of AI-enabled services businesses, because you just talked about the industry standard, and maybe we could re-hit on like what does the fund admin market look like even if we’re going back maybe a couple years or if you don’t know Hanover Park exists and you’re talking to a fund admin, what do they look like?

Chris Hladczuk:

Yeah, it’s like army of humans with a set of disconnected tools that people are buying. And so, they might buy, you have a bunch of people in Kentucky, they are fund accountants, they are CPAs, they buy QuickBooks, they buy Excel, they buy bill.com. They don’t build any of their own tech because they call Engineering IT, they don’t have engineers at their companies. And so, a lot of it’s just disconnected SaaS tools with human duct tape on top. And so, the opportunity set is like we’re like, “Okay, if we build this ERP for the fund, which is like, everyone told us from day zero, this is crazy, don’t do this, don’t build this insanely hard thing to build.”

You’re building financial infrastructure for the most complex funds in the world, we did it. And it was like, “If you build this, that gives us the opportunity to have AI agents action on top of that data.” And so, instead of having people clicking buttons, you can have agents actioning. And then the paradigm that we’ve had is AI’s preparing things and then humans are reviewing things. And so, we have well-trained fund accountants on our team that are reviewing outputs to ensure accuracy, but that’s never been done before. Most people, I think in 2018 would be selling software to a fund admin instead of saying like, “I’m actually going to be the full stack fund admin.”

Turner Novak:

Yeah, because a lot of people then would say,” What’s the size of the fund admin software market?” And it’s like-

Chris Hladczuk:

Tiny.

Turner Novak:

Yeah, it’s not existent. They pay for bill.com and QuickBooks maybe. Intuit is a big company, but like-

Chris Hladczuk:

It’s tiny.

Turner Novak:

And so what changed then over the past couple years to just make this possible? And then you talked about how you built your own general ledger. Why did you need to build a general ledger? So, what’s changed and then why did you do what you did?

Chris Hladczuk:

Yeah, I think the big why now about this market is it’s possible now with AI to act as a fund accountant. We call it an AI fund accountant. And so, it’s like, “Okay, we can take this thing that used to be really disconnected toolset with a bunch of humans in a room trying to click buttons and do work to agent can actually click the buttons and do the preparation work for our team to review.” And so, I think it was a lot about what does AI unlock. But what I tell the team all day long is if you don’t have the right bedrock, the right core foundational system of record, it doesn’t matter what AI is doing on top.

AI is not going to figure out bill.com, which randomly isn’t going to give access unless you’re doing some computer use agent. AI is not going to figure out Excel because there’s a lot of limitations. AI’s not going to figure out QuickBooks and other things. And so, we said the 80% is building the general ledger from scratch, and then on top of that, having agents’ action on. So, I think the big change here was it’s now possible to have agents doing work that would require an army of people, and now our fund accounting team can just review work.

Turner Novak:

And it sounds like really what a fund administrator is basically an accounting firm for an investment fund in the most simple terms.

Chris Hladczuk:

Simple terms, financial reporting, they also do investor services.

Turner Novak:

And you talked about how we’re entering this era of you can build an AI native services company. So traditionally, if I’m thinking from a technology business perspective, I’d look at a fund administrator and say, “They’re not building any technology, it’s just mostly people. It doesn’t scale in the most efficient way.” So, what’s happening in this broader AI native services? Are there certain things that needs to be true in order for that to unlock this massive opportunity?

Chris Hladczuk:

Yeah, I think we think a lot, and this is something we’ve spoken a lot at the board level in the past quarter or so, which is how do we productize the services layer, which is we are tracking key metrics on things like what percent of cash is auto categorized? What percent of times when a customer sends us an email with a request that AI agent can action on it with no edits by the human team?

Turner Novak:

This is like a customer service type of automation as well. And so, you’re automating the accounting, you’re automating the customer service, you’re automating other software that you’re starting to build on top of things.

Chris Hladczuk:

Yeah. A lot of it is productizing these core services delivery metrics, we call it, which are things like cash categorization, email agents that are action on customer requests and other stuff. And so that’s been the key part. Without that, and I think the reckoning will happen in 2026 for AI native services businesses that are growing revenue, but not actually productizing the services part.

Turner Novak:

Oh, Wow. Is that a common thing?

Chris Hladczuk:

Very common. I think a lot of people are doing that.

Turner Novak:

Are there certain categories you think it works best in? Fund admin? I’m assuming that’s one of them.

Chris Hladczuk:

Well, I’m talking my own book now. This is talking my own book. I think there’s a lot around what is the data layer that we have access to that can enable us to deliver a better service. And so, part of what we’ve done is said, how do we build wedge products around the core fund admin? Because if I go to someone and be like, “I’m going to give you slightly better fund accounting,” or “I’m going to give you 10 times better fund.” That’s still interesting, but not the most compelling. And so, we built a lot around AI native waterfall modeling, which is adjacent or portfolio management, which is adjacent or portfolio monitoring, which is adjacent.

So, it’s like, what does the full bundle look like that’s not just the core fund admin?

Turner Novak:

And so, the core customer or core user, buyer decision maker of this is a CFO of an investment firm, like the finance leader and an investment firm?

Chris Hladczuk:

The CFOs are my heroes.

Turner Novak:

Yeah, I know. I know you love CFOs. So, what can they use AI for? Because you just mentioned so many things like a waterfall model. Maybe you can explain why that is, maybe it’s not worth explaining, but you can just make that in Excel, right? So, what’s the importance of all this stuff?

Chris Hladczuk:

So, I think our goal is to become the default for how fund CFOs adopt AI. I think there’s a lot of noise in the market. There’s a bunch of providers, I call it the army of overpriced SaaS tools that are offering like, “Here’s what AI is.” And the CFO’s like, “This doesn’t actually help me.” And so, part of our goal was we’re launching an MCP server, which you can think about that as an API for AI. Which is like, I can have all of this data that Hanover Park has inside, which is your LP data, your portfolio data, your fund performance data, all the data that might be in disconnected systems is now in one place.

And now you can give that and feed that directly into Claude. You can feed it into ChatGPT or Gemini. And now you can instantly access and say, “Generate me a report with Yale endowments commitments across our five funds with a pretty graph, with my logo on it, with our top five performing portfolio companies by ownership percentage, MOIC and Gross IRR.” That is literally hundred hours of work to do across a bunch of random tools. Today, and now you can instantly do that through Hanover Park. And so, I think that’s some of the power is being able to feed this into the tool you’re already using as well.

Turner Novak:

And that’s something the IR team might make. So, it sounds like it’s not even just CFOs, it’s like other people at the firm can maybe start using the tool too?

Chris Hladczuk:

What we realize is CFOs and finance teams are drowning because they get hundreds of data requests that are like, “Hey, can you pull this random initial versus follow-on thing that of my venture fund portfolio companies that we have no idea what the stat is?” And the CFO is like, “Oh my God, what do I do?” They’re like, “Do I just run around and try to go into 17 spreadsheets that haven’t been updated in three months to figure out where this is?” This enables anyone to instantly access your data as well as the managing partner, the founder of the firm.

So, they don’t have to be begging their CFO to get back to them in three days. They can instantly access it, which gives a lot of credibility to the finance team.

Turner Novak:

There’s a lot of these, I don’t even know what you call it, banker AI tools or junior credit analyst type AI modeling tools. Does it start to flow into that? Not really? Where’s the delineation between all that stuff?

Chris Hladczuk:

Yeah, I think part of our, we call ourselves the anti GPT wrapper. There’s a lot of companies that are fine that are basically like, “I am going to take GPT, I’m going to verticalize it for something specific, I’m going to enable it to be better for a lawyer, a banker, or whatever it is.” Part of our goal was actually, we’re the opposite of that because we did the really hard thing first. Building a general ledger from scratch is one of the hardest technical challenges you have so hard that our early investors were like, “What is going on? This is crazy.”

And so, we’re like, “Once you do that though, you unlock all the ability for AI to action on data that you couldn’t before.” And so, part of this was building the GL sets the stage to build the magical MCP server on top versus the opposite, which is I’m going to get data that everyone else has access to, and then I’m going to make it slightly better on the AI front. So, that’s how we thought about the paradigm, which was different.

Turner Novak:

And it’s basically people you might describe, if you’re trying to hype up this in the most simple terms possible, you’d say it’s like an AI native fund administrator.

Chris Hladczuk:

Yeah. I think AI native fund admin, strike that. Fund admin, most unsexy term in the world. I think about it as financial infrastructure for the investment firm. So, Stripe did this for payments, Ramp did this for expenses, Hanover Park’s doing it for investments. That’s how we think about it.

Turner Novak:

And you didn’t even start with like, “Hey, we’re building an AI model for your fund.” You started with we’re building a general ledger that, general ledgers have existed since the invention of commerce, basically like ancient Italy or whatever.

Chris Hladczuk:

Yeah, I think the vertical integration story is around, okay, what is the core job to be done? Every single fund in the entire world needs to generate quarterly financials for their limited partners. Great, you’re going to have to pay someone to do that. May as well be us and think about that as the core layer. But on top of that, now it’s like, “Okay, I need to help a CFO make better decisions.” Great, all that data is now sitting in Hanover Park to help you make better decisions. On top of that, maybe in the long term, it’s like, “How can I unlock Alpha for the investment firm? How can we have this investment firm make better decisions on top of all the source of true data that Hanover Park has?” And so, that’s how I think about the layering from most unsexy to most sexy.

Turner Novak:

And so, what makes it so hard to make a general ledger because it’s just like some accounting, it’s like debits and credits. I don’t know, it doesn’t seem that hard.

Chris Hladczuk:

Yeah, I know. So, I think partnership accounting, and this is tuned out if this is the most boring thing you’ve ever heard ever. So, there’s a lot of complexity with fund accounting. And so, we’re like, “Okay, build debits and credits for a simple small businesses like table stakes honestly.” But saying, “Okay, I have all of these different limited partners in a fund. They have different allocation percentages, they have different side letters waiving economic terms, they have blockers and splitters and aggregators and they have 50 legal entities for one fund.”

All of that complexity of the interaction between all these legal entities and all of the partners in those entities gets crazy.

Turner Novak:

And then there’s multiple funds sometimes too, right?

Chris Hladczuk:

Dozens of funds, and then you have a management company that needs to talk to the funds. And so, I think the complexity lies in, think about these investment firms as the most complex financial institutions in the world and organizing that data is crazy. And so, we had to build a system that can handle the most complex investment firms in the world.

Turner Novak:

And is this why it was just like a, let’s just throw some bodies at this and people would just manually do it?

Chris Hladczuk:

Correct.

Turner Novak:

And that’s how it falls?

Chris Hladczuk:

And I think a lot of the underlying tools that were built. There’s some specific fund accounting tools that people had tried to build, but it’s like the problem is there was a disconnect between the people doing the work, the fund accountants, and the people building the product, random engineers at a different company. That disconnect created tons of problems because no one’s actually talking to their “customer all day.” And so, what we’ve done is said, “Hey, we have this 60-person room in Flatiron where we all go to every day called an office. Let’s all put the best fund,” I call it the Navy SEALs of fund accounting, “alongside lead engineers to work in harmony.”

Turner Novak:

And the last time you came on the podcast, we had an interesting conversation where the traditional fund admin sees an engineer, they call them the IT department. Your job is to get people access to the HR system or something or sign up for QuickBooks and generate someone an account on the different software using now.

Chris Hladczuk:

I love you talking about accounting. This is hilarious.

Turner Novak:

Well, and so just basically who you’re competing against, you’re like, you are an engineering product-led company that is competing against people who think about it as an IT department basically. It’s like a backup house versus front of host thing.

Chris Hladczuk:

Yeah. I think I think about if you’re one of the greatest engineers in the world and we’ve built this hacker culture, it’s only dropped out of Harvard, he was a sophomore in college. We have a team USA drone racer, we have people that have multiple ex founders, former and future founders, I call it. So, we built this really talent dense engine product team. Those people would never in their wildest, wildest considerations or nightmares join a legacy fund admin who doesn’t care about them. And so, our goal is how do we build this financial infrastructure for the most complex firms of the world?

If Ramp can make expense management sexy, we can make investments sexy. And so, I think about this as the most elite engineers on the planet will never join a legacy fund admin because no one cares about them there. What we’ve done is say, “How do we build this world-class team of engineers?” People that drop out of Harvard, Team USA drone racers, shout out JT, former founders, future founders. People that are top 1% that might join an AI lab say, “Hey, I have this deep obsession with building for FinTech and building for the most complex investment firms in the world.”

This is a series of insanely hard technical challenges when you think about the data complexity that we have, let me go do this here. That’s very different, obviously value prop than a legacy fund admin.

Turner Novak:

Yeah. Okay. So, you’ve built this environment where the top 0.1% of engineers want to go. How do you convince people to join? And then maybe another interesting thing to hit on is how has that changed over time? If somebody listened to this first conversation, they can hear your original philosophy, what’s changed over the past year?

Chris Hladczuk:

Yeah, I think I say growth solves a lot of problems. We went zero to $15 billion in 21 months since our first line of code. That level of growth proves to the most skeptical engineer on the planet like, “Hey, this is working. And if I have a deep interest in FinTech, this could be compelling and interesting.” And the second piece is you are what you prioritize. There’s a lot of companies who have 100 people on the team and 80 salespeople. That is not the company that I’m building. I have still scaled us to $15 billion of assets with no other salespeople on the team.

Primarily because I’m like, “I’m just a founder with a product and a plan. We don’t need an army of salespeople.” That resonates with the best engineering teams and the best engineers on the planet because they’re thinking like, “Okay, what is prioritized, what is hired for?” If this is an eng-first hacker culture, that matters deeply when I think about how I’m going to be prioritized first-class citizen, et cetera, in the type of company I’m at versus an army of salespeople that are pushing on engineering when it comes to building.

Turner Novak:

So then, how do you guys figure out what to build when you’re talking about the engineering being first class? And it sounds like it’s literally just you. Are you talking to customers? Are engineers joining calls? Are engineers using, demoing the products? I think I’ve seen something around when I was hanging out in the office one day, everyone uploads stuff or something. I may be remembering the story wrong, but.

Chris Hladczuk:

So, we have demo night every Friday night where literally people are showing what they’re building, both to the fund accounting team as well as on the engineering side, which is really fun. But how we think about what to build is we have a Slack channel with every customer. I literally, I said I live in constant paranoia that my customer is finding a better solution, just like this Jeff Bezos quote of like, “Oh my God, live in fear that not that your customer’s unhappy that they’ll find something better.” And so, we constantly want to be pushing the envelope to invent on our customer’s behalf.

And so, we take that super seriously when it comes to feedback. And being in a Slack chat with every customer, our engineering team is blown away by how much this matters for the CFO. If we are their most important vendor, the things that we deliver for them can really change everything. And so, the level of importance creates a sense of urgency, especially on the team. And then on what we’re building and what we’re working on, it’s like we really said, “Start with the unsexy, automate core financial reporting, automate core fund admin.”

And then now we’ve built a series of portfolio management and portfolio monitoring and all the adjacencies that should live in one place that haven’t, but stay tuned for more stuff.

Turner Novak:

So, can you give me some examples if I’m not super familiar of what some of those tools might be and what I’m using them for?

Chris Hladczuk:

Yeah. So, say you invest in Hanover Park at the Series A, you get a set of legal documents or cap table or something like that. Typically, these legacy providers, all they do is save those docs randomly-

Turner Novak:

It’s in a folder. It’s a random folder.

Chris Hladczuk:

It’s a randombox.com or Google Drive or something super random and be like, “Oh yeah, I guess I’ll look at that if I need to look at it for audit at the end of the year.” And they’ll book some super boring accounting stuff that the CFO doesn’t care about.

Turner Novak:

Sometimes there will be a shared drive though. You can access it from your browser and you can download it.

Chris Hladczuk:

You can download the file. And so, what we said is we were like, amazing, we have this leveraged position because we’re doing your fund accounting that we have all those docs in real time. And so, you just forward that over email to us and say, “Hey, I don’t need to click 50 buttons in a UI that was built in 2018 to create more work for me as the CFO. I can just delegate to a Hanover Park AI email agent.” That email agent reads it, it uploads it, AI extracts and process 150 key terms. And so, now I’ve enriched and been like, “Hey, not just cost and fair value. I have how much do I own in this company? What’s the ownership percentage from the cap table?

What’s the latest valuation, post money valuation? Who are my co-investors in the deal?” All these other important details that should live in one place that don’t. And so, we bundled this “AI portfolio management tool” in for free alongside the core fund admin services.

Turner Novak:

And I think you mentioned it took you about nine months to kind of make this. Am I remembering that number right?

Chris Hladczuk:

Boy, yeah. So, this is a very complex technical, if you’re an engineer listening to this, it’s like you want the hardest technical challenges in the most complex industry, like this is the place for you. We’ve processed 200,000 documents at this point. I’m talking random Indian language docs where AI translating into English and all this crazy complexity in the pursuit of how does the customer lift zero fingers just forward it over to us and let us take care of the rest.

Turner Novak:

And so, what was the hardest part about doing all this? Is it natural language processing extraction? Is it organizing it in a sortable, efficient way?

Chris Hladczuk:

The extraction is really complex because it’s really simple if you’re doing a Series A in the United States like we did, but if you’re doing a deal in Europe, if you’re having a random mistake made by the lawyers on the cap table. Okay, if we have a random Excel file that doesn’t make it in that we don’t get access. And so, there’s just like, I call it the land of infinite edge cases, which is just highly complex documents that you know what the answer should be at the end potentially, but it’s really, really hard to figure out with all the different legalese that’s in there. And so, that was part of the challenge.

Turner Novak:

So, you walk into the office at Hanover Park in the morning, what’s it like?

Chris Hladczuk:

So, at 9:00 AM exactly on the dot, I literally walk out of my office and we just have a speaker blasting music. My team will hate this, but it’s literally one of two songs every single day for the past 21 months since we founded the company. It’s One More Time by Daft Punk or it’s Levels by VG. There was a period where we shifted a little bit, given literally my entire team now says to me, these are the songs that I never want to hear outside of work ever again, but we do that. And so, it’s a fun environment where we’re blasting music. We go right into engineering stand-up, we then go right into the accounting team stand-up and we get right into our day.

But my goal is how do we create this overwhelming sense of energy and how much we’re building and how fast things are moving?

Turner Novak:

Is there a gong in the office, I hear-

Chris Hladczuk:

How did you hear about this, Tyler? You heard about the gong? So, we have this gong in the office. So, we’ve moved offices three times in under a year. It’s been very entertaining for everyone involved.

Turner Novak:

I actually haven’t seen the new one yet.

Chris Hladczuk:

You haven’t seen the new one.

Turner Novak:

How close is it?

Chris Hladczuk:

Yeah, it’s right near here.

Turner Novak:

Can I stop by right after this?

Chris Hladczuk:

Come stop by after.

Turner Novak:

All right, I want to see it right after this, on my way to the airport.

Chris Hladczuk:

So, in our first office, which was this tiny little office in FiDi, we had this gong. Anytime we would get a sale, anytime something great would happen, we did this. Now we’ve moved offices to much bigger offices and we still have a small gong. We’ve gotten people to say, “Why do you have this tiny gong? You need to have a bigger gong.” Adam Newman, we crash style, we have not upgraded yet, but the gong is very ... The only people allowed to ring the gong are one, customers.

And so, we literally had a customer in the office the other day and I told Johnny, I was like, “Johnny, I know this sounds really weird. I’m going to now interrupt the entire team of what they’re doing and you have to ring the gong.” So, he comes over, he rings the gong and the entire team goes wild. Literally people are cheering their belt, I think people are going to jump on top of their desks at this point and people are getting fired up. Or we ring the gong when it comes to we ship some massive product update or close the sale.

Turner Novak:

And so, those are the only people that are allowed to ring it, right? No outsiders?

Chris Hladczuk:

No outsiders.

Turner Novak:

So, technically I cannot ring the gong?

Chris Hladczuk:

No investors.

Turner Novak:

I’d have to be a customer.

Chris Hladczuk:

You’d have to be a customer.

Turner Novak:

Which I need to hit that institutional sky. I don’t have a CFO, it’s just me.

Chris Hladczuk:

We’re just CFOs.

Turner Novak:

Soon. Soon. We’ll be on that level soon. And so, you had this one guy who I think he biked to work during a snowstorm one time or something. What’s the story with that?

Chris Hladczuk:

So, I got to give a shout-out to Philip. We literally, it was like a Sunday at 2:00 PM, there’s a crazy blizzard happening in New York City. And of course, I don’t expect people to be in the office. This is like hunker down and survive. Somehow Philip decided to go and rent a city bike and bike in a 10 inches snowstorm to the office. And he shows up and he’s like, “I just biked here.” I’m like, “How are you alive right now?” And he just goes right into coding and he’s like, “I’m good. I’m just biking.” So, I’m like, “Okay, this is what we screen for though. We have forces of nature that are figuring stuff out.”

Turner Novak:

Was the subway down because there was so much snow or what was going on?

Chris Hladczuk:

I just think he’s anti-subway. That’s what it is. He’s not just a subway guy. He’s like a bike guy.

Turner Novak:

Interesting. The streets would probably be open, very clear. You could probably just keep cruising, you probably wouldn’t have to stop.

Chris Hladczuk:

Yeah, maybe. Yeah, it’s crazy. It’s like if it even got plowed that day.

Turner Novak:

So, then what do you guys do on Friday nights? I know you have a Friday night thing that you do.

Chris Hladczuk:

So, we have this thing where we love to celebrate our product velocity is everything. We’re literally doing multiple releases a day. I would say compared to many legacy players who call Engineering IT and don’t actually ship net new product, our goal is how do we have the highest product velocity out there? And so, we have multiple product releases a day. And what’s really fun is we have engineers demo what they built every few weeks on Friday at 6:00 PM where they’ll literally like everyone will huddle around what we call the podcasting couch. It’s not actually for podcasting-

Turner Novak:

Podcasting coach. We should have done this there on the podcasting couch.

Chris Hladczuk:

Yeah, we should have, podcasting couch. We have this massive couch, which people have gotten excited about that literally everyone goes onto that couch and we demo the latest and greatest and people go crazy. We’re playing music, people are getting fired up about what’s going on. And so, it’s been a fun little ritual to inform the broader team as we’ve scaled a lot in terms of team in the past six months to tell everyone what we’re working on.

Turner Novak:

So, this is not like a hackathon random side project type of thing. It is you’re showing what you built?

Chris Hladczuk:

Yeah, exactly.

Turner Novak:

Okay. And you said you’re shipping multiple things per day. So, is it Friday, you spend an hour and just everything that was shipped, people just go through and show what they shipped?

Chris Hladczuk:

Yeah. And there’s a lot of times when people get feedback, so we’ll ship something and it’ll be an iteration and the fund accounting team will be like, “Oh my God, this could be amazing with these two changes.” Or, “Hey, we get CFO feedback and people are excited.” I actually want to start bringing customers to the office for these Friday demos to see the latest and greatest. Because what we’ve had is I had a customer call me and be like, “Hey, you’re our most important vendor. I love Hanover Park.” I don’t think anyone’s ever said that about a fund admin before.

“I love Hanover Park. Can we host a customer conference at the office and can everyone just jam and huddle about what the future of finance can look like in this space?” And so, we’ve not done that yet. Stay tuned, but it’d be great to get more live feedback at these demo nights.

Turner Novak:

So, when somebody comes across Hanover Park, what is the reason that they choose Hanover Park versus, there’s a couple other options that are out there. What is usually the discovery process of finding it and then the decision-making process usually?

Chris Hladczuk:

So, all the problems come from, if you’re just a $50 million venture fund and it’s super simple and it’s one person in a room, things are super simplistic, there’s not a lot of complexity. But as you scale, all of these manual processes break. And so, you’re like, it went from, “Oh, I did this one manual process for two hours a month to, I’m doing five hours of manual processes a day just to tread water.” And so, therein lies the problem with all the legacy players where things are broken and held together by legacy, human duct tape and Excel. And so, that’s problem one. It’s like, “Oh my god, I can’t scale.”

Problem two is I’ve heard of all these good things about AI, ChatGPT is out, Claude’s out, but what has it even done for CFOs? Nothing, and people are frustrated by that. And so, it’s a combination of things are breaking what we scale and oh my god, how can I adopt AI? And so, we’re at the intersection where it’s like, “Hey, instead of buying a random army of overpriced SaaS tools on one side to compliment a broken system of record,” which is your core fund admin today. “Why don’t we marry that into a single source of truth?”

Where you can expose all of your data to an MCP via LM to Claude or ChatGPT or Gemini and be able to action on your data. And so, it’s this like, “Oh my god, my data’s super stale. Everything’s broken as we’re scaling. I have no idea what’s going on. I have no visibility.” To, “Oh my god, the only way to duct tape this together is buy 20 other SaaS tools.” And we solve both those problems.

Turner Novak:

Isn’t it really hard to switch a fund admin though? It’s got to be one of the most difficult problems in the history of humanity.

Chris Hladczuk:

So, I joke, I literally, I’ll be on a first call with a CFO and I’ll be like, “Look, I know I’m asking you to marry me on the first date, but here’s what we’re doing.” And I take that insanely seriously of the level of trust that our customers put in us, that’s what keeps me up at night of how much I care about their trust and what that matters. And so, one, it’s a massive decision. Two is, oh my God, Hanover Park’s amazing, but isn’t it such a pain in the to switch? That’s literally what everyone says.

Turner Novak:

That seems like that would be the disqualifier like, it seems cool, but I don’t want to do this.

Chris Hladczuk:

I don’t want to deal with this.

Turner Novak:

I’m not going to put up with this.

Chris Hladczuk:

And so, I literally tell our team, we’re building a one click migration future. Today it’s not one click, obviously. We did migrate someone in only a handful of days, which was pretty crazy. Shout out Mackenzie at Asylum Ventures, she’s amazing. And so, we did that. But our goal is now what’s possible with AI is there’s a lot of long horizon agents that can run for hours and hours and days and days, and we’re experimenting right now on how that can supercharge migration. And so, I see a future where we get to one click migration.

Turner Novak:

Really? What needs to happen you think to get there? Just do the models have to get better? Does the context windows have to get longer?

Chris Hladczuk:

I think it’s just if you’re a world-class engineer that’s looking at this, this is a example of a really fun technical challenge to work on, which is super challenging technical problem, wrangling the most complex financial data in the world. It’s really just end bandwidth plus some pushes on the frontier of how do we think about these long-running agents that need to operate over days and weeks, not minutes.

Turner Novak:

So, that is probably the big just overarching problem that people are trying to solve right now is cracking how to speed this up.

Chris Hladczuk:

Yeah, I think it’s a massive problem that traditionally people have hired an army of humans to solve it. And it’s like, well, if that doesn’t deliver a 10X better experience, what would? And I think there’s an opportunity to do that.

Turner Novak:

And I know there was a time where you, I think when you first started the company or getting customers, and this even harder ask back when you started it. I think you promised that you’d literally sleep in people’s office until the migration was done. Is that still a thing that you promised or?

Chris Hladczuk:

So, people have been like, “Don’t sleep in my office.” “What are you talking about?” “I don’t want you here, Chris. I don’t want to spend more time with you.” It did, I’ll never forget though, one of our earliest customers, and by the way, I have a crazy place in my heart for our earliest customers that trusted us with their most important financial data. And I was one of our earlier customers and I’m on a call with him on a Tuesday and I’m like, “Hey, where do you live? Where are you in?” And he’s like, “Why are you asking me where I live?”

And he’s like, “I’m in this random town in Florida.” And I’m like, “Okay, I’m booking a flight right now. I’m coming down to Flo-, this was the first call. He’s like, “Who is this guy?”

Turner Novak:

So, he wasn’t a customer yet?

Chris Hladczuk:

No, a prospective customer. And I literally booked a flight and I go and literally me and him spend five hours at his house until 2:00 in the morning basically talking about how the future of finance is possible. They became one of our earlier key critical customers and has been a massive advocate for us. So, it’s credit to them and what happened. But in the early days, do whatever it takes to win.

Turner Novak:

What did you talk about in that five hours?

Chris Hladczuk:

Oh my god.

Turner Novak:

What was the biggest takeaways?

Chris Hladczuk:

Everything that was broken about the current system. He had been using a legacy fund admin, he had been duct taping additional tools on top. And because a lot of the problems is like, oh my god, this might be surprising for the audience, but CFOs don’t have visibility in real time of anything that’s going on. That’s crazy. Think about you’re making a $100 million investment decisions, but you don’t know what’s going on today with your data. And so, he was constantly struggling with, “I have no idea what’s going on. I’m asking these humans to book cash and do super basic things. It gets delayed by weeks and weeks and weeks and months.

And I tried to buy another SaaS tool, but it had the same problem because it didn’t talk to my underlying data.” So, it was just a frustration layer. And with what’s possible with AI now, I think now is the time where we should solve that problem.

Turner Novak:

Speaking of real-time data, I have another portfolio company, she was just on the podcast a couple weeks ago, Artie. They basically enable real-time data, basically syncing between all your databases, the data warehouse. I don’t know if you guys have tried it yet, but I’m giving a plug.

Chris Hladczuk:

Oh, this is the plug.

Turner Novak:

I’m giving plug.

Chris Hladczuk:

On pod plug. I have not tried it.

Turner Novak:

You’ve not tried it. It might be interesting. We can maybe talk about it after. I’ll throw a link in the show notes if people want to check out that episode. But basically, their thesis is if you have AI agents that are doing things automatically, you just need real-time data, everything needs to be synced. Because if something’s off by, let’s say you have to wait for a sync or the data’s off and the agent starts to make a decision, you’re making decisions and it’s just doing things with incorrect information.

Chris Hladczuk:

100%.

Turner Novak:

So, you mentioned getting on the plane. You jumped on this plane, went down and talked to the customer in Florida, spent five hours at his house. The first episode, we spent a lot of time talking about what you had learned about sales before you started Hanover. You spent some time at Meow. I don’t know to what extent we should re-hit on that stuff and to what extent we should talk about how you’re thinking about understanding how to sell things, understanding customers has evolved over time. But it may be interesting for the first people hearing this for the first time, what was Meow and what was your journey like there?

Chris Hladczuk:

Yeah, so I ended up joining Series A FinTech that was doing business banking. Helped us scale from call it 10 to 1,000-ish customers, became chief revenue officer of the business. Brandon and Bryce, the founders of Meow, were the first checks almost pre-idea into Hanover Park today. And so, a ton of respect and credit for them. But it taught me a lot. Literally SVB collapsed, we scaled exponentially, closed hundreds of customers myself personally. And I wrote this almost manifesto, which I call always get on the plane that I think has become popular now. I think people are just stealing my tweet and retweeting it, but it’s like-

Turner Novak:

Did you came up with this?

Chris Hladczuk:

I came up with this. So, I wrote this thing of always get on the plane. And the key principles of following, if you are asking someone to trust you with something as important as what we build and what we sell, the fact that if I won’t get on the plane to spend four hours in a room with them, this is a decade-long relationship.

Turner Novak:

Yeah. And they really trust you if you’re not going to do that.

Chris Hladczuk:

And It’s like, I can’t build trust over a Zoom call. And so, I did this, I popularized it, and now it is one of the key philosophies, especially for us. And so, there was a 10-person finance team and a massive private equity firm that we’re talking to that I literally told them, I was like, “I’ll just go to your office wherever in random place in California, and I’m going to be there for five hours with their whole finance team.” After five hours, you really get a chance to know someone and you build that level of, let’s just be honest with each other about what the trust and the blunt feedback is.

And so, I’ve obsessively been focused on this, always getting on the plane thing, and I’m glad to see people are taking it.

Turner Novak:

Yeah. So, how do you manage that though? You’re trying to run a company, you’re trying to hire and recruit people, and then suddenly in two hours you got a flight, you got to jump on and you’re spending two days in a random town in California.

Chris Hladczuk:

So, look, time management, prioritization, there has to be a level of like, “Okay, how do we think about what is the highest ROI thing?” I think a lot about, I’ve taken a lot of inspo from Elon on this of what is the limiting factor in the company. And then how do I parachute myself into the biggest problem, and how do I take that and obsess over that problem until it’s solved? And so, limiting factor is migration. We need to go parachute in, work with our head of ops, Emily, who joined, and obsess over what does AI native migration look like. So, at different times, whatever the limiting factors in the company, my job is jump into battle and figure it out.

Turner Novak:

One of the things that you’ve done really well, people call it founder-led sales, where I think you said you’re the only person that’s on the sales team.

Chris Hladczuk:

Still, I don’t know how. I’m just a founder of the plan, I’m not even a sales guy.

Turner Novak:

Yeah. And maybe it’s not sales, it’s just like trust building. You’re trying to understand customers, trying to solve their problems. So, what’s been the biggest learnings you’ve had over the past 21, I was going to say 21 years, 21 months since you wrote the first line of code and started onboarding customers.

Chris Hladczuk:

Yeah, I think for ... And we’re definitely in founder-led sales mode too long. We’re at $15 billion of assets on platform that’s probably a little too long. And it’s been something I’ve held onto just given how critical and customer trust is and how that interacts with product and eng on that side. But for me, I’ve really tried to transition from how do I get our first five, 10, 15 customers into a position of how do I make sure that these people can become raving fans? Which I think is very different philosophy.

And all the worst parts about sales in general of like, I’m just trying to get them some sales guy with commission. Obviously, I’m a founder with no commission. And so, what I think about is if our number one goal in 2026 is every customer’s a raving fan, that is very, very hard in our industry. That’s like the craziest thing ever. And so, with how unhappy most customers are at their current providers. And so, my goal is that, how do we set and manage expectations prior to signing any contract to ensure that they can be successful in their first hundred days plus?

And so, it’s a lot of been like, “Hey, we’ve proven we can do it with your peers, with your counterparts. Here’s five other customers that are exactly like you guys that we can service. How do we make sure that you’re a raving fan in 12 months?”

Turner Novak:

You mentioned it was different getting the first five to 10 customers and making people raving fans. How did you get the first couple of people on board? Because you had built this general ledger from scratch, you had no customers, and then suddenly you’re like, “Hey, trust us to manage your fund?”

Chris Hladczuk:

So, I got to give a lot of credit. I’m going to give a shout-out to Chad at Susa, founder of Susa and Pratyush. Kenny, who’s their CFO. I love you, Kenny. They were our first customer. And so, I literally was like, “Hey, we’re doing this, you’re our first investor.” They didn’t want to move over everything to start. That’s a crazy thing to be like, “Hey, move over all of your funds.” to an unknown provider called Hanover Park that you invested in a seed around for the most important thing for your firm, which is all of your LP relationships.

And they were literally like, took some convincing. It took some trust building, but they ended up doing it. And that was obviously a critical first customer. But the people after that, it was like, “Hey, I will literally do whatever it takes to ensure that this is successful. I don’t care if I need to sleep at your office. I don’t care if I need to book the entries myself.” It’s just all about, “Hey, we’re taking this journey together, here’s why this can be a super compelling future, but look me in the eyes and say, I will deliver for you.” And that’s how we got the first five or 10.

Turner Novak:

And you had this concept of, I think it’s called polite persistence. Is that the phrasing?

Chris Hladczuk:

Yeah, yeah.

Turner Novak:

So, was there a lot of that? You had to continue to be getting in front of people, continuing to ... What was that process like?

Chris Hladczuk:

Text, call, carrier pigeon, email, LinkedIn, DM, Twitter. It was really anything. And I see in the earliest days, and even now it’s like whatever it takes to win, do. And I think people don’t take that seriously enough of I think any company, it’s like you should have a level of obsession and desperation where it’s like, I will literally do whatever it physically is possible to make this happen. And so, I took that very literally, like 10, texting, calling, voicemails, whatever. And because the reason why I did that is like, “I know that this is the right future that you should be moving to. I know this is scary, but we got you.

We’re going to execute well for you and we’re going to make this happen.” And so, that was a lot of the early trust building plus just obsessive, play persistence.

Turner Novak:

How do you avoid being annoying in that process though? Because when I have somebody that’s constantly like, “Hey, did you see this message? Hey, I’m just following up.” I usually start to tune that out. So, how do you not do that?

Chris Hladczuk:

A lot of jokes. Yeah, yeah. If someone’s calling you being, like me calling you. “Hey, it’s me randomly calling you Turner. I’m sure you’re really excited to hear from me right now.” There’s a little bit of humor and fun to it. It comes off as very authentic if you’re actually believe in what you’re building and selling versus some random sales guy who’s sending a friendly follow-up 58 times, which no one cares about.” And so, that’s been a lot of the focus, which is like, how do I build trust while also being persistent?

Turner Novak:

And so now you’re making this transition and making people raving fans. How do you be a raving fan? As a CFO, what are you happy about? What are you telling your friends and the CFO group chat of Hanover Park is so great?

Chris Hladczuk:

So, there’s the three elements to a raving fan that I think about. One is amazing onboarding deployment migration. That’s the one click migration thing we talked about earlier, which is how do we make this experience where literally you sign a contract, you give us data access and you put your feet up on the table and never think about this again. That is incredibly challenging. And we’ve done this now for customers and people have been like, “You told me migration wasn’t going to be bad. It’s amazing.” So that’s the first piece.

The second piece is taking the fund admin services from reactive to proactive. And so how do we use AI to supercharge services delivery by saying, “No, I don’t need to wait days or weeks or months to see this action happening. I can see things in almost real time.” All right, so that’s the second piece. And then the third piece is jaw dropping AI native product experiences. And so, it’s not good enough to just deliver fund admin or the core service delivery. It’s how do we unlock and build something magical on top of that data that no one else could offer?

And so, that’s been the three elements and hitting those are things that I care deeply about tracking and monitoring and focusing on.

Turner Novak:

Was there a time when you knew things were working, like you were just getting the scale tipping towards more positive feedback in momentum versus not?

Chris Hladczuk:

Yeah, I think after $5 billion of assets, now we’re almost $15 billion, after five, things started moving downhill. Ecosystems started to pick up of people being like, “Have you heard of this Hanover Park thing?” Despite us doing not a ton of marketing aside from me tweeting and posting on LinkedIn? And so, we started to get this positive momentum and ecosystem of like, “Oh my god, this could be the future.” The idea of vertically integrating the core accounting system and building all these magical moments on top could be possible.

And so, that’s when things started to kind of snowball and then now it’s just been like, “Let’s make sure we can catch the snowball rolling down the hill.”

Turner Novak:

And hitting on the content that you post online, you are probably one of the ... If somebody were to say, “Who’s a founder that’s doing founder-led content?” Well, I’d probably send them your profiles and just like, “Check out what Chris is doing, follow his stuff.” What’s been your process for thinking about things? And then maybe even you started making content online before you even started to Hanover Park. So, what’s that whole journey for you been like?

Chris Hladczuk:

Yeah, so the quick primer and background on it is was a college kid at Yale, literally was like, “Okay, I was reading it.” And before Sam Altman was cool, in 2020, he wrote this blog post-

Turner Novak:

This is pre-ChatGPT.

Chris Hladczuk:

Pre-ChatGPT, pre-he was cool. He wrote this blog post, How To Be Successful. And I obsessively read that and was like, “This is insanely cool.” And I was like, “Okay, this start-up thing might be interesting, but my dad sells commercial insurance, had no background, no ecosystem, nothing.” And I was like, “Okay, there’s really two ways you add value in early stage.” Writing code or selling code at the earliest stages wasn’t a good end. So, I wanted to figure out distribution and sales and said, “How do I write compelling content on the internet that people would actually click on, watch, listen to?”

And so, I did this whole interview series where I interviewed Emmett Shear at Twitch and Michael Seibel at YC and Kevin Ryan who found a MongoDB and I turned all of that into Twitter content that went super viral. And so built the following to a few 100,000 people over the past few years. But when I started Hanover Park, I was like, “I actually don’t care about virality,” which is a little bit contrarian. It doesn’t matter if Elon retweets me, which he did one time, which got me 50 million views or something, that doesn’t matter.

It’s actually like if I’m trying to use this as a way in which CFOs find us, I need to write for them, which is obviously a much more niche audience than some random hustle tweet about whatever.

Turner Novak:

You’re tweeting about the accrual-based accounting method closing faster. The average person reasons they block you.

Chris Hladczuk:

Please block me. Yeah, they’re like, “Block Chris.” And it’s really the problems that a CFO faces of you’re up at 2:00 in the morning, your LP calls you and says, “This number’s wrong. Do you ever want that problem?” No, you don’t. So, it’s a lot about that in terms of how I think about the positioning and how I think about what we’re doing. And so, one of our largest customers came in bound off a LinkedIn post that had 10 likes.

Turner Novak:

So, it’s really not about going viral. Going viral is not how you do founder-led sales. It’s resonating with the problem that your customer is facing.

Chris Hladczuk:

It’s really just obsessive. All I do all day is talk to CFOs. They tell me their problems, they tell me some of the things that they’re having issues with, and then we try to invent the future for them.

Turner Novak:

Do you then take that feedback? So, you take that feedback, put in the product, then do you also ... I feel like a decent amount of your content is like, I just talked to a CFO at a billion-dollar private equity firm, they said this and that’s the content.

Chris Hladczuk:

Yeah, I think about it as any conversation that I’m having could turn into content. So, the frame I have on this is with all the AI slop out there, you should only be writing and creating content of things that no one else could create and a lot of that is personal conversations. And so, I talked to the CFO about X, Y, Z, here’s what I learned. No one else had that conversation that day. So, it’s a lot about how do I, in a world of abundance of content, how do we have something scarce?

Turner Novak:

Do you keep some sort of document or note where you’ll dump ideas in and you come back and visit it? Do you plan it out Sunday night, write some content for the week? What’s your process?

Chris Hladczuk:

Yeah. In real time, I’ll jot down things as I’m having phone calls, conversations, et cetera, and that will be the fodder for the week’s content.

Turner Novak:

And so, I think we maybe hit on this a little bit earlier. I actually don’t even remember if it was before we started recording or not, but you talked about there’s a lot of the advice you got or read about and heard about starting a company and building a company, I think you said 99% of it just ended up being wrong. What’s the advice that you found is most wrong?

Chris Hladczuk:

So, I think my take on this is I think almost all advice, 99% of advice on scaling a company is incorrect right now. And so, the traditional advice is like, “Oh my God, delegate everything, hire this massive team, hire VPs to do different actions and whatever.” And I’m like, “A lot of that is noise and basically creates this hierarchical political system by which people do well by figuring out ways to take credit.” My goal is how do we do the anti that? And I took a lot of inspiration from Elon on this. Be at the bare metal as much as possible.

Literally don’t talk to three levels of whoever. It’s like talk to the engineer writing the code on this AI email agent and how this works and figure that out because the problem that creates ... If you don’t do that, you create frustration for your best performers. The best performers want a hard problem, they want the room to run and go cook on that problem. And so, we’ve really been anti this hierarchical system by which information is filtered in different ways and say, “Let’s just cut to the source and go direct.”

Turner Novak:

Isn’t there an argument though that you, “need a grownup,” or “need someone who’s done it before and knows the playbook?” If you don’t have the VP that’s done this before and can share the knowledge with the team or knows what it looks like, do you miss out on that or?

Chris Hladczuk:

So, I think what’s changing a lot now with what does it mean to build a company in the AI era, which is very different than I think the SaaS era of the 2010s, which is like the playbooks are being rewritten, what’s possible is different. It used to be you have a massive sales and marketing team, now you have inference cost and that’s your sales and marketing team. And so, a lot of this is, I think, changing of instead of being an army of GTM, you have the product does the talking, I think has changed a lot. And so, you look at companies like Cursor that have scaled crazy with a very small go to-market sales team.

And so, I think a lot of the playbooks are changing, which means someone who’s reusing a playbook from the last era is probably going to be not effective. However, that doesn’t mean we have multiple people that have been at companies that have exited before, they’ve been senior engineers there. There is some level of how do we counterbalance the hacker culture of the 20-year-old who drops out of Harvard to what I call the experienced. I’ve seen all of these infrastructure problems at scale on the engineering side.

And then obviously on the fund accounting team, we are obsessively focused on bringing in expertise for the Navy SEALs of fund accounting with ... That obviously requires a different level of experience in terms of how situations have been adopted over time.

Turner Novak:

Are there any things that you try to do based on someone said, “This is how I’ve done this in the past,” and it went horribly wrong that you feel like hopefully people can maybe learn from. And specifically with the way that building companies have changed over the past couple of years with these AI native businesses?

Chris Hladczuk:

Yeah. I think honestly, one of the things that we preach every single day is this idea of one-way doors versus two-way doors. Jeff Bezos invented this where it’s like a one-way door is like, if you make a decision in this way, you literally can’t go back. It’s impossible. So, an example would be the investors you have on your cap table, you can’t get rid of them, they’re one-way door, they’re here for the life of the company. The two-way door is, “Hey, we can make a decision and it’s easy to go back at any time.” And those are almost all decisions in a start-up.

And so, what we’ve done is drop your ego to the ground, be intellectually honest about truth seeking of what’s the right answer to the company and make a fast decision because you can always revert back really easily at any point in time. And so, empowering the team to say, “Make really fast two-way doors without bogging it down in process to create a committee to figure out if that makes sense to make a decision.” It’s better to have iterated five times before the committee even gets out of bed.

Turner Novak:

How do you know if something’s like a one or two-way door? Is there a gut check on like, “Hey, we know this is reversible or won’t blow up the product or customer trust or something like that?”

Chris Hladczuk:

Examples would be shipping an internal accounting update for our fund accounting team is very two-way door. If it doesn’t work in five seconds, we can update it and nothing changes. Versus one-way door is a massive change to our LP portal, which is what the massive endowments and banks in the world log into every day across our 10,000 different limited partners. And so, I think it’s more like that’s on the product decisioning side of like, okay, great. That is still a two-way door because you can revert quickly, but it’s something that will take a little bit more seriously.

Turner Novak:

And so, you started initially, you mentioned Susa Ventures was the first customer-

Chris Hladczuk:

Customer one shout out.

Turner Novak:

Shout out to Chad and Pratyush and Kenny.

Chris Hladczuk:

And Kenny. Kenny’s the legend. He’s the one with all the credit.

Turner Novak:

Nice. I’ve never met Kenny before. Next time I’m near the office, I should try to-

Chris Hladczuk:

Do it.

Turner Novak:

... try to meet all of them or try to meet Kenny. And so, how did you expand customer base over time? Because first couple customers were all VC firms. You seemed like you have been laddering up almost. I don’t know how to describe this, but what’s the process been like of expanding?

Chris Hladczuk:

Yeah. Part of our goal is we want to build for complexity, which is the most complex firms in the world have blockers and splitters and aggregators and triple parallel funds and UK subsidiaries and different currencies. There’s all this noise. If you were just built for a $10 million venture fund, obviously you are not the right partner and customer for them or customer fit. And so, what we’ve done is we said, “Okay, we need to solve this cold start problem. We need to get our first 10 customers, we’re going to do that in venture capital.”

But we’ve quickly expanded into other asset classes like private equity and soon private credit as well, where we knew the needs are much more sophisticated and we built for that future and now’s been a great opportunity to expand in these different types of asset classes. And so, we launched a product recently, breaking news on AI native waterfall modeling. And so, in natural language, I can run my deal-by-deal waterfall if I’m a CFO, which would take hours and hours and days and wrong and it’s not correct. And it’s like seven different system exports that we’ve made instantaneous in natural language to run these scenarios. And so, that would be a product that a private equity firm would fall in love with.

Turner Novak:

So how do you know that something like that is accurate?

Chris Hladczuk:

Yeah, great question.

Turner Novak:

Because I fuck it up even when I make it manually in Excel. I’ll forget to drag a formula and I’ll notice it later, error checking everything.

Chris Hladczuk:

Yeah. One of the key product principles of the company is what I call trust but verify. And so, it’s like if you just give someone a number, assume every single CFO, they’re the most skeptical people on the planet, they’re going to assume it’s wrong. You need to be able to have them click into it, export, run their own calculations to verify the accuracy of a number. And so, the trust but verify approach on the waterfall modeling plus the explainability. The beautiful thing is AI can tell you where the work’s coming from. It can say like, “Hey, we went through these three different tiers of our waterfall and here’s exactly what happened.” And so, that plus the auditability and explainability has been a critical pillar.

Turner Novak:

And when you export to Excel, it comes with linked formulas and everything. Am I remembering this right?

Chris Hladczuk:

Yeah, this is actually really fun. I think there’s been two different approaches in the market when it comes to this. One is, “Hey, it’s a bunch of manual humans running around with really big Excel files.” That’s one. The problem with that approach is its very error-prone and manual and slow, but it gives you some auditability because it is formulas. The other approach, which is a very 2018 SaaS approach was like, “Hey, I’m going to give you just a UI.” Great, it’s at least something, but it’s just hard coded export randomly with no connectivity.

We don’t think either of those are the right solution. So, we actually built an Excel replacement in the browser similar to Ramp sheets or TryShortcut. There’s these tools that do this generally. We built this specific for our industry for fund CFOs, which enables us to instantly generate all the financial reporting without having to go into this crazy offline process. And so, we think we’ve married best of both worlds when we think about the reporting aspect with the auditability of formulas across the different Excel replacement that we built.

Turner Novak:

Can you still export it to Excel?

Chris Hladczuk:

Always.

Turner Novak:

Yeah.

Chris Hladczuk:

Everything’s exportable.

Turner Novak:

I was going to say, I just don’t think ... If you told a CFO everything we’ve just described about Hanover Park, but you can’t use Excel anymore.

Chris Hladczuk:

It’s over.

Turner Novak:

Yeah, that’s even harder than the migration of a whole provider is getting them off Excel. The industry runs on Excel. And I think you have a nickname with your friends. They call you Chip?

Chris Hladczuk:

Oh, boy-

Turner Novak:

What’s going on with you?

Chris Hladczuk:

... this is coming up. So, my friends will shout out Raj and my friends. But when I started the company, everyone was like, “You’re crazy. What are you doing? You just left this job and previously left Goldman Sachs and this is insane. What are you working on? What are you doing?” And I basically had this thing where I had this massive chip on my shoulder. I love this quote from Josh Wolfe at Lux Capital, which is, “Chips on shoulders put chips in pockets.” And now that Peter and Lux have invested, it’s entertaining for it to be full circle.

Which is everyone started calling me Chip because they were like, “Wow, he has this massive chip on his shoulder because everyone doubted that this would be successful.” We still have a lot of work to prove it though.

Turner Novak:

Yeah. I think one thing that’s been interesting is despite you have this chip on your shoulder, you really want to be successful, there’s been times that you’ve turned down customers, somebody you don’t think that you can take them on and give them the right kind of service. How do you know when to do that?

Chris Hladczuk:

Yeah. I think ideal customer profile, which is a common term, has become insanely important for us. I am deathly afraid of disappointment, which is disappointing a customer that is really excited about Hanover Park, that we can’t deliver what we need to deliver for them. And a lot of the problems in that is you take on too many customers that are not the right profile, that have different needs from who you’re focusing on, and so you don’t end up building for them. You end up with a bunch of people that are asking for things that don’t match and that’s a massive problem.

So, I’m turning away customers left and right to ensure the people we do partner with have the A+ raving fan experience that we want to deliver. And so, that’s been the focus of, it sounds crazy, but we’ll turn away customers all the time to ensure that the customer base we have can become raving fans in 2026.

Turner Novak:

But don’t you want to grow super fast?That’s like the point of hyper-growth start-up is grow as fast as possible. Isn’t that what they say?

Chris Hladczuk:

I think grow as fast as possible with the right constraints of who you’re building for. And so, obviously there’s a massive market of opportunity. And we’ve just focused, I would say, on mid-market enterprise, which are larger firms with finance teams, more sophisticated CFOs than some of the smaller firms. That doesn’t mean in the future we won’t go there, I just think it is not a near term. It might be a long term as we are able to democratize the services delivery that we have and make it scalable.

Turner Novak:

Talking about longer term, what is the longer term 10 years from now? I don’t know if we’re going to do this every single year-

Chris Hladczuk:

Yeah, every year for the next decade?

Turner Novak:

Yeah. But talking about 10 years from now, whatever the long term is, I’m reading the S1 for Hanover Park, you’re spilling it all out there. What am I going to be reading about?

Chris Hladczuk:

Yeah. Well, I think what’s pretty awesome is that when Jake and Emergence led our Series A, they wrote this memo and they said, “This, if executed incredibly well,” we have a lot of work to do, “is a Veeva-like opportunity.” Veeva is the largest vertical software business in the world right now. It’s around $50 billion market cap. But what’s funny about Veeva is they started as a CRM for life sciences. That is the most niche boring thing of all time. Now, they’re the board level vendor for Moderna and Pfizer and all of these different things, right?

Turner Novak:

Board level of vendor?

Chris Hladczuk:

Board level, meaning they’re the most important vendor for Moderna and Pfizer. Moderna and Pfizer pay them tens of millions of dollars a year. And so, our goal is to do that in finance. And so, it’s like, “Okay, we want to become the most important vendor for Blackstone and KKR and Vista Private Equity.” And pick your name of really important investment firms and not just do one siloed specific thing that they’re used to. We want to own the core back office, own the middle office, and own all the software tool spend for the investment firm.

And along the way, have the opportunity to bundle all these additional products and service offerings around it. And then in the super long term, if now all of this data that used to be in 50 different random tools is now in one centralized system of record, why can’t we help people make better decisions and unlock Alpha and do that for the investment firm? And so, that’s the super long term.

Turner Novak:

So, I have one more thing I want to ask you. Your personal AI stack, what you use personally as Chris. So, I think a year ago you told me you were using Claude Artifacts and you were also using something called Hemingway for writing.

Chris Hladczuk:

Yup.

Turner Novak:

Still using those?

Chris Hladczuk:

Number one tool right now, Granola. It’s just like AI call. So, what we do is a lot of our internal calls is we’ll basically record any meeting and then we have this transcript repository that you can query into a bunch of analysis on. And so, that really helps with context sharing across teams, especially as we’ve scaled the team really quickly. And so, that’s been one. The other is I am obsessed with Claude Cowork. Claude Cowork has changed everything. Claude Cowork is my ChatGPT moment, actually.

Turner Novak:

So, not even ChatGPT. Claude Cowork was yours?

Chris Hladczuk:

Cloud Cowork is 10 times better than for ChatGPT than me. It’s crazy. What it’s basically done is I used to be an investment banker, I used to have to put PowerPoints together, I used to put financial models together. I am now, I did this massively important presentation for a trillion dollars of assets and legitimately Claude Cowork took call transcripts, notes, all this stuff. And I was like, “Here’s the format of a different deck, build me this 10-slide thing from scratch with all of these graphs.” It one shotted it.

I made an hour of modifications of something that would’ve taken me 10 hours and I was done. And so, it’s been transformational. When I think about all the data analysis stuff as well as building presentations.

Turner Novak:

You said you built this central to do list thing. I remember you were texting about like, “What is it exactly?” Did you use it or it’s just a fun thing you tried to do?

Chris Hladczuk:

So, I’ve literally, as the non-technical CEO that wants to be closer to the bare metal, I was like, “AI coding tools are so much better.” So, I literally whipped up Claude Code this past weekend. I was like, “I’m going to vibe code a CEO command center basically to make decisions.” And it was pulling in all Slack, all of email, all of Google Calendar, all of my Granola notes, all of these different sources to build a dashboard to help me be like, “What are the decisions that I need to action on right now?” And then a self-updating to do list that’s sync with my email and other things. And so, it’s been pretty fun. It’s still super experimental. I did it for two hours on a Saturday, but it’s been pretty fun to play around with.

Turner Novak:

Is it like you get a feed that’s updating every minute synced with Slack and you’re typing in and hitting enter to make it do things? How does it actually work?

Chris Hladczuk:

Yeah, it’s pretty crazy. So, it’s updating, I think it’s every hour or something pulled together from any activity over Slack, over email, over calendar, over anything. Linear is another one. We pull in our product issues and stuff like that. And so, all of that activity feeds in. And then if I want to update the dashboard, it’s like I’m just connecting to a bunch of MCP servers and then I’m literally prompting Claude Code to do things. And so, it’ll just auto like, “Hey, you need to respond to this email. It’s super critical about this deal.” Once I respond to the email, just auto updates.

Turner Novak:

In the dashboard.

Chris Hladczuk:

Yeah.

Turner Novak:

Yeah. Because it’s the thing that I’ve always had, the issue I’ve had with these to do lists is you make your to-do list and then you go and do the thing and you go and change the to do list. It’s like-

Chris Hladczuk:

What’s going on? Yeah.

Turner Novak:

So, I’ve always had super simple to do lists that are basically high level banging off this hour thing and then maybe it’s worth updating. But some people make these super elaborate, very minute to do list. It’s like you’re spending half your time on your to-do list.

Chris Hladczuk:

On the to do list. I think it’s this productivity optimization sometimes is procrastination, I think is the piece of it of if you’re doing too much, the simplest execution should be like, “I’m doing this thing today and here’s the most important problem to solve and everything else is noise.”

Turner Novak:

And you just announced, I think you mentioned earlier the Hanover MCP. So, what is that and what can you do with it if you’re a customer?

Chris Hladczuk:

So, breaking news, Hanover Park MCP is live now. What this enables us to do is MCP, think about it as API for AI. And so, instead of having many different disconnected data sources, the really cool thing about Hanover Park is because we are doing your core accounting execution, we have all of your system of record data. That’s what gets audited, that’s what gets sent to your limited partners, that’s what you get your financials. On top of that data, we built some magical things like now we have all your LP data organized, we have all your portfolio data extracted, we have all your KPIs collected, all this stuff.

Now with our MCP, it can be like, I don’t even need my GP to log into Hanover Park. I can just tag it right into Claude alongside our CRM from Attio and I can just query the full context of my data and generate PDFs on the fly. And so, it’s been a pretty magical opportunity for customers and excited to see where it goes.

Turner Novak:

You mentioned Attio. Did you switch over to Attio?

Chris Hladczuk:

I didn’t. I’m a HubSpot Stan. It’d be old school. Use HubSpot for CM right now, but I’m thinking about new things like Monaco and others.

Turner Novak:

Oh, nice.

Chris Hladczuk:

Shout out Sam Blonde.

Turner Novak:

Yeah, he actually respond to my DM and was like, “Hey, why not come on the podcast? Talk about Monaco. Everything you’ve learned about sales and growth.” And he’s like, “Yeah, let’s do it.” I think we’re going to do it in a couple weeks. So, if you’re listening to this, look out for that episode. I think we have a couple, let’s see. I think it’s either right before this or right after. We have one with my friend Chetan at Benchmark. We’re talking all about software. We have a episode with Scott Stevenson at Spellbook. It’s the fastest growing AI company in Canada. We think that that’s a true statement based on-

Chris Hladczuk:

Is that true?

Turner Novak:

We think based on-

Chris Hladczuk:

Legal, right?

Turner Novak:

... what he’s heard from, yeah. It’s like cursor for contracts. So, if you’re a lawyer, a lot of the work you’re doing is with contracts. It’s essentially a Microsoft Word plugin where you can edit contracts like you would code essentially. And if you’re a lawyer, a lot of your job is just writing legal documents and editing legal documents. It just helps you do it faster. And I have a couple other, it’s almost like Claude Cowork for doing legal things. So, you can say, “Hey, write me this document based on these things,” and just go and do it. It’s pretty interesting. I’m trying to think of who else we have. We have Mike and Nikhil at Footwork.

I don’t know if you’ve ever met them throughout the course of fundraising, but Nikhil was a seed investor in Canva, Farmer’s Dog, probably missing a couple multi-billion-dollar companies. And then Mike was the COO of Stitch Fix, zero to $1 billion in revenue, ran walmart.com, was in charge of all that, thousands of employees. Walmart.com is pretty big now. So anyways, a lot of good conversation coming up on the podcast people should continue listening to.

Chris Hladczuk:

Love it.

Turner Novak:

Subscribe to the show, subscribe to the newsletter. I might not even have to do an outro now. We can just do the outro right now. Also, thanks to Numeral and Flex for sponsoring the show. Check out numeral.com for sales tax. Do you guys have sales tax you have to collect? Are you using Numeral?

Chris Hladczuk:

We’re not using Numeral yet. What are you talking about?

Turner Novak:

You should use Numeral.

Chris Hladczuk:

Oh, whoa, whoa, that’s good.

Turner Novak:

And then other sponsor is Flex, which I use for the fund. If you check out the link and description, sign up for the wait list, you’ll get the Flex Elite personal card. It’s like business and personal banking all in one.

Chris Hladczuk:

Let’s go.

Turner Novak:

And they give you credit. So, there’s a lot of guys out there that’ll say, “Oh, we’ll give you a credit card” just based on your bank account and what kind of cash will give you a percentage of it. Flex actually gives you a credit card, underwrites it like Amex. So, worth checking all those out.

Chris Hladczuk:

Let’s go.

Turner Novak:

Thanks for listening. See you guys next time.

Chris Hladczuk:

This was fun. See you soon.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

It also might be the largest company in the world built on a Microsoft Word plugin.

Scott has been building in legal AI longer than almost anyone (since 2018). We talk about how legal software was untouched before LLM’s, why legal AI is so hot right now, if the hype is sustainable, how vertical AI tools should navigate product differentiation vs ChatGPT and Claude, and why Spellbook uses a bottoms up go-to-market motion when most AI legal software has gone top down.

We talk about why fine-tuning your own models was the biggest early mistake AI companies made, building a network effect as a vertical AI product, how $30 trillion per year flows through contracts, and Spellbook’s philosophy of “Don’t sharpen your axe when the chainsaw is coming out tomorrow”.

Spellbook spent a few years finding PMF before really taking off in 2022. Scott shares their playbook for launching over 100 product experiments in 3 years, how they knew when to lean in, scaling Spellbook post-PMF, and what he’s learned working with Keith Rabois after raising a $50m Series B from Khosla Ventures in 2025.

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👉 Stream on Spotify and Apple

Timestamps to jump in:

• 0:30 Spellbook: “Cursor for Contracts”

• 3:08 Building the world’s largest Microsoft Word plugin

• 14:06 Why legal software was untouched before LLMs

• 18:32 $30 trillion moves through contracts annually

• 20:51 Why ChatGPT won’t replace vertical tools

• 25:15 Fine-tuning was the biggest mistake in AI

• 30:00 Differences between pro and amateur gamers

• 37:38 Top-down vs. bottoms-up in legal AI

• 42:27 The long-tail of legal AI software

• 47:24 Building for models that don’t exist yet

• 51:20 Skating where the puck is going

• 1:01:35 The legal bill that cost 50% of his bank account

• 1:09:33 Testing 100 landing pages in 3 years

• 1:14:06 The moment Spellbook hit PMF

• 1:19:17 Building new brands for each product experiment

• 1:23:10 Raising a Series B with a tweet

• 1:27:41 What Scott learned from Keith Rabois

• 1:31:16 Scott's favorite new AI tool

Referenced:

• Check out Spellbook

• Careers at Spellbook

• Playing to Win by David Sirlin

• Find the Fast Moving Water by James Currier at NFX

• Spellbook’s Case Study with Replit

• Twin (Scott’s favorite new AI tool)

Find Scott:

• X / Twitter

• LinkedIn

• Blog

Related Episode

👉 Stream on YouTube, Spotify, and Apple

Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Scott, welcome to the show.

Scott Stevenson:

Thanks for having me, Turner. Great to be here.

Turner Novak:

Yeah, I’m excited. So I heard that you are the fastest growing AI company in Canada. Is this true?

Scott Stevenson:

We have been told this by a couple investors who have a very good, I would say, visibility of the Canadian market.

Turner Novak:

Interesting. Okay, so for people who don’t know Spellbook, because I feel like not a lot of people even heard of you before, so what do you guys do?

Scott Stevenson:

Yes, we’re basically Cursor for contracts, so an AI copilot for contract review and drafting. Yeah, we have 4,000 customers in 80 countries and we go very deep on this problem of commercial legal work. So if you are building a company or hiring employees, launching a coffee shop, anything you do in the world economically often is tied to a contract if it’s any substantial kind of transactions.

Turner Novak:

So this could be like signing a lease, hiring someone, doing a business deal of like, “We’ll pay you this and you’ll give me this much back for you to deliver this value to me in these products or services”?

Scott Stevenson:

Exactly, yeah. So we laser focus on that part of, I guess, the legal market. And we sell both to law firms and to in-house legal teams and in-house contract management teams and so on as well.

So our software will do things like catch mistakes or risks in contracts, help you standardize your contracts to, say, your company’s standards, help you draft more easily or doing something like a venture capital financing transaction. You could take a term sheet and then use our agent kind of like Claude Code to draft the 10 agreements you would need to do that transaction.

Turner Novak:

And you mentioned you have 4,000 customers. There’s a couple other big players, Harvey, Lagora. I think Harvey has like 1,000. Lagora has almost 1,000. So you have like double both of them combined.

Scott Stevenson:

Yeah. Yeah, we have quite a few customers. Yeah.

Turner Novak:

So then why has nobody really talked about Spellbook? What’s going on?

Scott Stevenson:

Well, people do talk about us. We’ve definitely taken a different approach to the market, and actually we were the first company in the world to bring a generative AI product to lawyers back in the summer of 2022, so it was a little before ChatGPT. I think we’ve had a little bit more of a heads down approach and we’ve had a bit more of a bottoms up approach in building our products. So rather than doing these big top down sales to like Am Law 100 law firms, we really sell bottom up to the lawyers and the contract managers who are using the software and kind of organically expand upwards from there. So we’re really focused on sort of like the end user versus just trying to get these very large top down deals pushed down to super large firms, yeah. So it’s a slower, I think, build of our customer base, but definitely compounding and snowballing.

Turner Novak:

Yeah. And the product is literally a Word plugin, like a Microsoft Word plugin. That’s essentially the product. I may be distilling this down, I’ll make it a little simpler. So how does it work exactly?

Scott Stevenson:

Yeah, so it’s a lot like Cursor or GitHub Copilot was our original inspiration. And the core of the product sits on top of Microsoft Word, which is where most lawyers are doing their drafting and reviewing work. The vast majority of contracts all go through Microsoft Word and we sit on top as sort of this intelligence layer. Now, we do have another separate desktop app as well that’s a little bit more something like Claude Code where it can do these kind of like complex multi-document projects, but the original core of the app is kind of based on top of where lawyers work.

And yeah, our idea of a great product for lawyers is that it should be like an electric bicycle. So lawyers know how to ride a bike already, they’re already drafting by hand. We want it to be an electric bike. So they’re still steering, they’re still pedaling. They’re in the same environment that they were before. It’s not like they got a cyber truck and now it’s like auto self-driving them around town or a plane. They’re still just driving their bike, but now they can get up over the hills a lot easier. And I think that’s like, I come from an engineering background and that’s what I liked about a lot of the coding tools is that I’m still very much in the driver’s seat, still in control, not completely doing something completely different than what I was before.

Turner Novak:

This episode is brought to you by Flex. It’s the AI native private bank for business owners. I use Flex personally and I love it because I use AI to underwrite the cashflow of your business, giving you a real credit line. The best part is 60 days of float, double the industry standard. Flex has all the features you’d expect from a modern financial platform, like unlimited cards, expense management, bill pay that syncs with your credit line, and their new consumer card, Flex Elite.

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So for somebody who is not a lawyer, and maybe this actually might be helpful for any lawyers listening, but what is an example of something you can do with AI here beyond ... I guess I’m thinking when you say Cursor or GitHub Copilot, I’m thinking I’m typing something and then it just fills out the line for me and it starts to write for me. Can you just kind of explain just how the product kind of works?

And then I think the Word plugin’s kind of an interesting dynamic where there’s not very many products that are Word plugins that have gotten ... You probably are the biggest Microsoft Word plugin ever.

Scott Stevenson:

That should be our claim to fame, like biggest Microsoft Word plugin ever. Yeah.

Turner Novak:

Oh, man. So then instead of the VC saying, “I don’t invest in ChatGPT wrappers.” You’re like, “A Word plugin.”

So I’m interested just like what are the things you do with it? And then how does a Word plugin work as a product?

Scott Stevenson:

Yeah, the first thing we had is what you mentioned, so like this sort of auto complete functionality where you start typing and it continues. And that was like what GitHub Copilot was. We do a lot more than that today.

The biggest thing that we do and the most popular thing is contract review. So you can take any contracts, say like a lease, a sales agreement, and you can instantly kind of review it for risks and issues and it will learn over time what you tend to flag and what you don’t so it gets better and better.

And I think a misconception people have about legal AI and contract reviews that there’s some right answer, but it’s actually contract review is completely subjective. It’s almost more like a YouTube recommendation algorithm of like, what do I think this lawyer is going to care about in this contract? So they can run it against a contract, get sort of a sorted list of things we think ... Changes to the contract that we think they’ll care about. Maybe they really care about payment terms, maybe they really care about data security and data privacy. We bubble those to the top and then we make suggested edits to the contract.

So a lawyer using the product can kind of go through all of these suggestions and accept or reject them. And it will automatically apply that to the contract with track changes on and everything. So it makes it really easy to do these.

Turner Novak:

This is like the red line thing. If you ever got in a legal doc, there’s always a redline version of it where you-

Scott Stevenson:

Yes. Yep.

Turner Novak:

People who don’t know, it’s basically the same document, but there is a second version that has a red line where everything got deleted and then like bolded things that were added. And it makes it really easy if you get something back, you can look at like the three changes or whatever, the red line version.

Scott Stevenson:

Exactly. Yeah. So yeah, that’s how lawyers operate, always with red lines or track changes turned on. So we do that. And then we have another version of that. So we’ve been growing very quickly in the enterprise in-house segments. So that’s been a huge, huge focus of us for the past year.

At the beginning of the last year, we had about almost no revenue from enterprise legal teams like eBay and Dropbox use us. And now it’s almost 60% of our revenue, so it’s growing very quickly. And what they love is our Playbooks feature. So Playbooks is like a review, except the legal team can set up a set of rules. Maybe they have 30, 40, 20 rules that dictate how they negotiate contracts, what they allow, what they don’t, what they’ll bend on. So if you’re a company reviewing thousands of contracts a year, thousands of NDAs, thousands of sales agreements, you can run them all through kind of your set of standards and your negotiation playbook and it will kind of automatically do that negotiation.

Turner Novak:

So these are almost like skills in Claude or something or like an artifact where you make your playbook basically.

Scott Stevenson:

Yeah. Yeah, yeah.

Turner Novak:

So what are some things a lawyer might do? What might be in a pretty standard playbook that someone might have?

Scott Stevenson:

It may be data residency. So if you’re a large company that really cares about your data security, maybe you mandate data residency needs to be in the US or Canada or something like that. So you could flag that on every sales agreement, make sure no one signs an agreement with data residency in another place or something like that. So that’s just one example.

There’s payment terms are really popular, making auto-renewals and all of these sorts of like commercial terms. Limitation of liability is definitely another big negotiated term that, depending on your negotiating power, you’re going to have different stances on what you will allow there and what you want. Yeah.

Turner Novak:

And how do you build a Word plugin? I’m super curious just like how that even works?

Scott Stevenson:

You got to go to Microsoft University. Yeah.

Turner Novak:

Yeah, so how do you actually build a Word plugin?

Scott Stevenson:

It’s pretty simple. It’s actually just a webpage. What actually is shown in the plugin is basically a web app that connects to the Word API to be able to do certain things. So it’s pretty straightforward.

Turner Novak:

Is it pretty simple to build? Would I have to go back and learn something or if I know like Java, TypeScript-

Scott Stevenson:

Yeah. Yeah, it’s based on JavaScript, so yeah, yeah.

Turner Novak:

Okay.

Scott Stevenson:

Yeah, a JavaScript, Typescript. So yeah, you can use that. Yeah, it’s pretty somewhat straightforward. The hardest part though is dealing with the actual manipulation of the Word document. And this is a file format that’s been around since the ‘90s at least. And there’s so many nuances, when you actually look under the hood, how these documents are represented. It’s incredibly complex. You can have like embedded software inside a contract. You can have embedded spreadsheets. There’s all sorts of weird hidden features, so-

Turner Novak:

People do that a lot?

Scott Stevenson:

No. But like every now and then there’s a lawyer or a law firm who just has this really weird file that they’ve kind of been adding onto since like 1997 and they throw it into Spellbook and some error will come up because there’s something in it that we’ve never seen before, but we’ve hammered ... We’ve been around since 2022, so we’ve hammered all those issues out.

And then the formatting is really nuanced. Lawyers really care about, is the formatting pristine? Are the sections labeled correctly? And it’s anyone who’s done complex formatting in Word knows it can be pretty challenging to deal with. So we’ve spent a lot of time on those. That’s the hardest part of integrating with Word.

Turner Novak:

Okay. Actually, my first job out of college, I worked in a bank as a credit analyst, we’re like lending money to businesses and we had to write a memo on each company like, “Here’s what they do, here’s their cashflow profile. Can they pay back a loan?” We did a collateral analysis. And all these things are pretty simple, but we did have actually embedded spreadsheets. Our template memo that you’re supposed to use was literally like a spreadsheet, like a cashflow model that was embedded into Word. And then we did the same thing with the collateral and it was just basically to make sure everyone just used the same standard. We’re all on the same page, which I always thought was like, it was super frustrating because if I ever had to do anything that was not standard, which is pretty much every time you have a separate spreadsheet and then you’re like figuring out how to get this thing into the Word memo and it was weird but-

Scott Stevenson:

There’s deep, deep features hidden in that format. Yeah. It’s almost like a programming language of its own, but yeah.

And then we do have what we call Spellbook Associate too. So as I mentioned, we have like a separate surface area that’s a little bit more of like a ChatGPT or Claude Code kind of shape, but really geared towards working on legal documents in Word. So it’s like using like Cursor’s agent or using Claude Code. And yeah, you can take like a term sheet, ask it to draft 10 other docs, or you could even throw in 1,000 docs for something like a data room review and have it build a table for you, extracting all the data, surfacing anything that’s concerning and so on.

Turner Novak:

Interesting. And I guess this kind of leads into some other stuff I want to talk about. Legal AI is probably one of the hotter areas of AI. There’s just a lot of momentum around it. It seems like it’s useful and like the adoption is there, but I’m just interested, as somebody who’s in it, so what is kind of going on right now?

Scott Stevenson:

Yeah, yeah. Yeah. Yeah, I think if you’re outside it, you might wonder like, yeah, is the hype real? It is probably one of the hottest verticals. Besides AI for coding, it’s maybe the hottest and most talked about vertical for AI right now. And I think there’s a reason why it has taken off so quickly.

One analogy I use is that large language models launching in like 2022 or with GPT-2 were kind of like the spreadsheet moment for lawyers. Accountants back in the ‘80s when spreadsheets were introduced, they started to be able to automate a lot of the work of like running a financial model. Before spreadsheets, it used to take like basically a building full of people to run a complex financial model. After spreadsheets and databases, we were able, using computers, to be able to automate a lot of the basic rudimentary math and formulas of kind of financial models.

And finance, maybe back in the ‘70s and ‘80s was run by like an army of people, humans. And now today, it’s maybe like actually 95% automated. If you think about the volume of transactions, how much bookkeeping is like semi-automated, software like Stripe and all of the tools we have to automate finance. We’ve automated a lot over many decades.

That has not happened in law or has not really started to happen until large language models in 2022. So that’s like, most verticals have seen decades of software adoption and automation, whereas law, basically up until 2022, still just ran 100% on an army of humans. The biggest advancements we had were like the word processor and email, and that made things go a little bit faster. But still, the core problem was software could not deal with unstructured text, so it couldn’t read unstructured text and it couldn’t write unstructured text. And that’s all lawyers do is they deal with 60-page documents of unstructured text and we just had no way to ingest it to understand it.

Turner Novak:

So you would just kind of have to just read it and/or you had 10 years of experience and just know these are the things I care about and I kind of know where to find them and it might only take me 20 minutes or something or five minutes, but it’s still-

Scott Stevenson:

Yeah. But you still have to read every word. If you’re reviewing a contract for a client, 60-page contract, you have to read every word basically. There could be something in there that you’re missing. So I think there’s just been radical inefficiency in the practice of law, and now it’s like the dam is breaking. There’s all this pent-up demand for legal efficiency, just like in every other vertical that has been able to adopt software. And now it’s finally able to be met because large language models are finally allowing us to actually help with the actual work that lawyers do.

Turner Novak:

So what was the software stack of a lawyer, I don’t know, five years ago, like pre-LLMs?

Scott Stevenson:

Word, Outlook. That’s pretty much it.

Turner Novak:

Did they have like a phone, like maybe Zoom or something?

Scott Stevenson:

Yeah, Zoom, maybe. I’d say five years ago, they’re still doing a lot of calls by phone and it’s like the phone bridges.

Turner Novak:

So basically, then they were doing all these, they were producing documents basically and they were reviewing and writing written documents with basically Word and email and then talking on the phone to communicate of what would be changed in the document essentially.

Scott Stevenson:

Yeah. Yeah.

Turner Novak:

Okay.

Scott Stevenson:

That was essentially the lawyer stack.

Turner Novak:

Okay.

Scott Stevenson:

It’s some deeply specialized software for things like entity management. If you have a complex entity structure with parent and children orgs, you might have like a chart of that.

Turner Novak:

Yeah, to like a design the org structure or something. Yeah.

Scott Stevenson:

But beyond that, especially for like commercial lawyers, which is where we’re focused, there really hasn’t ... E-signing or DocuSign. I forgot about that. DocuSign.

Turner Novak:

Oh, that’s fair.

Scott Stevenson:

Yeah.

Turner Novak:

Okay.

Scott Stevenson:

Yeah, yeah.

Turner Novak:

And I think, I don’t know if you mentioned it, you might have talked about it earlier when we got lunch, but there’s about 30 trillion in contracts that are signed per year.

Scott Stevenson:

Yeah, about $30 trillion moves through contracts every year.

Turner Novak:

So this is like economic value that is under a contractual agreement of some kind?

Scott Stevenson:

Exactly. Yeah, that’s right. Yeah. So there’s just this massive money flow moving through these contracts. And what inspired us to start the company is if you think about how inefficiently it’s happening, these contracts are probably taking 10 times longer than they should to be drafted and reviewed. And then things are still being missed because just imagine just being a human reviewing a 100-page contract and it’s like 8:00 PM, you have a deadline. It’s an almost hilariously impossible task to actually review 100 pages with a fine tooth comb on a tight timeline.

Turner Novak:

So did they not do it or did they do ... Well, how did they do this back before AI existed?

Scott Stevenson:

They would try their best. Yeah, yeah. Lawyers would try their best and contract managers would try their best.

One approach is standardization. Before AI came around, I think the hope was things would standardize more and more so you could kind of see, okay, here’s the standard SaaS agreement. YC has like SaaS agreement that’s pretty common that most startups use. And you can kind of do a diff between like, “Oh, what is different about this agreement compared to the YC standard agreement?” So that was one method, like shortcut you could use if there was a standard template. And with venture capital financing transactions, there’s a standard set of templates. You can easily see what’s changed. So that was one approach that would make these reviews easier, especially with complex transactions. But I think most of law has been surprisingly resistant to standardization because the deals people want to make are all kind of unique and bespoke, yeah. Yeah.

That actually connects to like where we started with Spellbook. So Spellbook was the second product we launched in, or one of the, or kind of the last product we launched in 2022. But we initially thought we were going to drive standardization with templates and that was kind of where we had started.

Turner Novak:

Yeah, I definitely wanted to ask you about that, but I think maybe while we’re still talking about just general kind of like AI, non-Spellbook specific stuff, when I think of a couple months ago, I made a contract, I just go to ChatGPT and just say like, “Make me a contract, make no mistakes,” et cetera. Couldn’t lawyers kind of do that? Is there some sort of thing where sort of the generic AI products trip up on legal stuff?

Scott Stevenson:

Yeah. Good question. So the first thing I would say is lawyers don’t actually draft anything from scratch for the most part, especially contracts because they want to start with a trusted precedent that they understand inside out. Because if they go to ChatGPT and ChatGPT outputs the whole contract, then they have to review every single word of that and make sure they understand it completely. That’s very difficult.

So ChatGPT is not great at modifying existing work or building on kind of your existing library. So we have a few features in Spellbook, like one, you can start with the precedents that you’re familiar with and we’ll kind of modify those. You can start with a sales agreement and be like, “Make this GDPR-compliant,” and we’ll kind of surgically make those edits for you. We also have a feature called Library where we can kind of have your whole history of all the deals you’ve ever worked on and use that to kind of influence the output of Spellbook as well. So these are some of the ...

One of the things I would say is working off of your existing corpus of docs as a lawyer is really, really important and ChatGPT doesn’t do that super well. But two, I think lawyers want things built into their existing workflow. I think like the chat interface is great, but it’s still like the terminal UI of AI. I don’t think chat is the be all and end all. And we just have a lot of unique user experiences that would just never fit inside the shape of ChatGPT.

For instance, one thing you can do in Spellbook is compare it to the market. So if you’re, say, signing a commercial lease in Manhattan, you can say, “Compare this to the average commercial lease in Manhattan and tell me what’s not normal.” And then you can actually dig into the data, into the charts and actually look at the data that we’ve collected in real time from millions of contracts and explore that through this visual interface that has nothing to do with chat. It’s very, very distanced from that. So I think there’s a huge number of experiences that people want that don’t fit in a chat box.

Turner Novak:

Yeah. So this is all within the Word interface?

Scott Stevenson:

A lot of this is in the Word interface. Some of it is in our Spellbook Associate product as well. Yeah.

Turner Novak:

Okay. And this market data, it’s basically you take everything that’s run on Spellbook of every customer and it’s anonymized and you can see what dates of comps or something like that? Or it’s like some kind of database of data where you can compare it to the contract or-

Scott Stevenson:

Yeah, so it’s like an opt-in model and most of our customers have opted in. And the way it works is, we take anonymous aggregate statistics. We only capture things like what’s the average, I don’t know, price per square foot in a commercial lease in Manhattan, what’s the average late payment interest rate for SaaS agreements? The only thing we end up capturing is these very high level statistical pieces of data, and that’s what gets exposed so it allows it to be really privacy-friendly, and yet it’s an alternative to the approach of fine-tuning. This idea of fine-tuning was really hyped for a while. I think every founder wanted to sell VCs on fine-tuning because it sounds very complex and defensible and you’re going to have this great moat, but it actually works pretty terribly for a whole bunch of reasons, and I can talk about that if you want.

Turner Novak:

Yeah. I think it’d be interesting just because that was the meme or the meta was if you are an AI company, you must build your own model because there’s no defensibility and you probably need to buy a bunch of GPUs and you need to train them all. And it’s like if you’re just a ChatGPT wrapper, it was like this derogatory slur basically to call someone a ChatGPT wrapper.

Scott Stevenson:

Yeah, so I think that was very wrong. I think this is an idea where there are narratives that founders learn investors are hungry for, and then they pitch them because it’s very legible and easy to understand for an investor. This idea of training models are like, “Oh, it’s going to be like OpenAI.” OpenAI trained their model, and it was expensive and cash was like a moat for, basically became a moat, but that did not really pan out in really many other areas for a bunch of reasons. You saw Bloomberg made Bloomberg GPT, that was one of the early ones and they spent I think millions training it, and then GPT-4 came out and just completely beat it at finance tasks so it was a waste.

Similar things have happened in legal AI where a number of companies have tried to train their own legal specific models and fine tune them. I do not know of a single one that is still in use today at any of the major application providers so it ended up being this big waste of time. I think the much better approach is to build value around the models. And I think there’s a lot of really great ways to do that. I think RAG is actually really, really good and actually superior to fine-tuning. Are you familiar with RAG, like retrieval augmented generation?

Turner Novak:

Yeah, it’s basically when you take the model plus just the internet or external sources essentially.

Scott Stevenson:

Yeah, exactly. The way I think of it is fine-tuning and training is like injecting things into the long-term memory of a model or almost putting into the evolutionary fiber of the model, giving evolutionary instincts as well. But if you’re asking a model to cite case law for a litigation case, you don’t want it looking at its long-term memory or its evolutionary instincts, you want it to actually look up the information and make it hard citation that you can actually cite. And it’s a much actually less hallucination prone method of getting legal specific data to work in these systems.

One, relying on RAG, which we did from the early days, you actually get citations that you can trust and inspect. Whereas if you’re fine-tuning models, you’re still going to hallucinate and you have no way to inspect the data. Two, when you use RAG, you can filter. We can filter. If you’re a lawyer in London, UK, and yeah, you work for a healthcare company, you can actually filter down the data to say, “I want to compare my contract to only other healthcare related contracts in the UK.” You can filter the sources down, whereas when you train a model, you end up with this one size fits all model. And a lot of lawyers will complain, “Well, ChatGPT is too biased towards the US,” or it’s too biased towards public company contracts because those are the only ones that are available to train on.

Which gets to my third point is no one wants to ingest private legal data into these proprietary models because there’s always a chance it could be spit out again. So by using our approach with the statistics, people are actually comfortable allowing us to ingest private data because it’s fully anonymized whereas people are not comfortable with training models on their private legal data because there’s the chance that it could be spit back out again, and that’s not something they can accept.

For those three reasons, I think RAG and what we’re doing with this real time data with market comparison in Spellbook is just much, much more superior or very superior to fine-tuning for the most part. I think it was this case where the herd just ran in completely the wrong direction. And I tweet about this all the time, things that are hyperlegible, the story just sounded right like data is the new oil and fine-tuning these models, cash as a moat, it sounded right. But the reality I think is just much more nuanced and complex.

And now we see a lot of these companies from 2022, 2023 who went did that fine-tuning approach, a lot of them are shutting down and they’re probably every two to three weeks, I’ll hear from one of these companies who’s now, they’re now looking to get acquired because the market’s matured so fast. They spent a lot of time doing this deeper R&D that they actually wasn’t that effective. And that’s very core to our culture at Spellbook is... I wrote a blog post about this back in 2022 when we launched and it’s called like, is GPT-3 too easy? We used the foundation models, and back when we launched, people would say, “Well, is this too easy? Where’s your team of machine learning engineers? Shouldn’t you be training your own models?”

And I cited this book, have you ever read or seen this, called Playing to Win? It’s by David Sirlin, he’s a professional Street Fighter player. Have you seen this before?

Turner Novak:

I have read your posts, but I’ve not seen the book.

Scott Stevenson:

Okay, it’s an amazing book where this guy professionally played Street Fighter at the highest level, and he talks about what is different about the mindset of a professional player versus what he calls a “scrub” or an intermediate or a bad player of the game. He said the scrub basically loses the game before it even starts because they have this totally wrong mindset, and I’m paraphrasing, but they basically have this romantic vision of the game that if they do play the game this super proper way, this romantic version of the game that they’ll come out on top in the long run, whereas the pros basically relentlessly exploit whatever they can to win, even if it looks cheap, even if it’s easy, they don’t do things because they look hard. If you’ve ever played Street Fighter, have you ever called someone cheap that you were playing against in one of these games?

Turner Novak:

I haven’t really played Street Fighter competitively, but it reminds me if you ever played Halo 2, you could do this thing called double shotting where you could basically take a shot, but you would shoot two bullets and if you-

Scott Stevenson:

Okay, I never learned that trick.

Turner Novak:

So all the best pro players in Halo 2, it’s like you could literally do twice the damage of one shot so everyone got good at double shotting. And if you were a purist and you’re like, “I’m not doing that,” you can’t beat people who do it.

Scott Stevenson:

Exactly, yeah. I think there’s this thing in AI where it’s like, people almost, I think engineers in particular who love complexity almost can’t accept how simple these systems can be to add value to customers. And there’s this attraction to complexity, fine-tuning, R&D. I think it’s starting to die out now finally and people are realizing, okay, building on top of foundation models is probably the best approach a lot of the time. But yeah, I think a lot of the herd ran in the wrong direction and it’s pretty fascinating.

Turner Novak:

Do you think part of it is this dynamic of what if OpenAI builds this or what if Anthropic builds this? Because if you’re not building your own model that has any kind of differentiation, they could just tweak ChatGPT to work better for lawyers, or something like that, is that a part of this? And how do you then navigate that as a founder of building a product that’s not in the strike zone?

Scott Stevenson:

Sure, yeah. I mean, I do think that that is a reason why this narrative took off, but I think the pendulum swung too far in the direction of differentiation rather than customer value, so you had so many companies and investors focused so much on how do we differentiate and doing really complex and hard things that weren’t very useful. And seriously, so many of these companies are shutting down and selling off now, but that’s the reason it happened, but how do you pragmatically deal with it? Yeah, I mean, that threat is there. I think the vertical AI providers have to work very hard every day to continue to add unique value to these customers.

And I tell our team, we have to be two years ahead at all times in terms of delivering state-of-the-art experiences to lawyers, two years ahead. We should be shipping things today that other competitors or other companies will be shipping in two years. I think you have to have this ruthlessly fast culture continuously adding unique value. I think the way you add the value is one through the data. We have real time data from millions of contracts that we can use to deliver better results to our customers. We also have preference data, so we learn from each of our customers what they care about, and ChatGPT and Claude are not really doing that for contracts specifically.

The data is super important, and the features are important, but then it’s like, how do you fit into the workflow of your customers? Lawyers are so busy, they have so much going on. If you don’t fit super neatly into the workflow, they’re just not going to use it. And the reality is Claude’s not in Word, it’s not designed out of the gate to give a lawyer value, and there’s a million little friction points because of that and I think if you... We always say our goal is to build a toaster, a toaster product. We are really good at doing one thing, toasting contracts I guess.

Turner Novak:

With a $30 trillion size, market size or whatever.

Scott Stevenson:

Exactly, yeah. If we can just do that one thing well, and if you optimize your product for that purpose, you just make so, so, so many decisions differently that would never make sense for ChatGPT to make. There’s so many little nuanced decisions that make that toasting experience very easy, simple, and effective for our customers.

Turner Novak:

And then there’s, because I feel like there’s the seven powers of just how a business has competitive advantage. I feel like we’ve maybe almost forgot about them, but when you were describing some of the different features, when I think of network effects as a pretty powerful business and it’s just the more customers you have, the more market data you have, the more useful that feature becomes. Then there’s maybe a point where you have so much data that one additional point doesn’t matter, but to get to that point, there is some strength to that, some positioning strength and then-

Scott Stevenson:

I mean, it scales more than you would think because it’s like, oh, what does it matter whether you have one million data points or two million data points? Well, it’s do you have data in Manhattan? Do you have data in London? Do you have data in SF? Do you have data in the healthcare industry? Do you have data in the aviation industry? Do you have data in manufacturing? Do you have data in energy? When you think of it that way, these are all industries that you have to conquer to deliver the best product to all of the lawyers in those industries.

It might sound like, well, again, what’s the difference between having a million data points and two million data points? Well, it’s less about that. It’s like, how many industries are you in? How much geography are you in? And do you have a statistically significant sample where you can provide useful insights? And yeah, I think it is legitimately a really great data network effect that we have.

Turner Novak:

Yeah. I feel like we almost forgot some of these rules for a while and just, I feel like almost economies of scale took over in the sense of the ability to train the models and having the capital on the balance sheet that you could utilize, which all this stuff is important, but the other stuff still matters too, I guess.

Scott Stevenson:

Yes. Yeah, I think so.

Turner Novak:

One thing you mentioned that we jumped past it, but I want to talk about, you mentioned this difference between top down and bottoms up sales cycles in legal AI. Can you just talk a little bit more about that and how that’s played out in the industry?

Scott Stevenson:

Yeah. I think there’s been a divergence of products built in legal AI. There’s products like Harvey and Legora which, great companies, we don’t actually encounter them that much because we’re so specialized and we service a different customer base, but they’ve had to optimize to sell to the innovation teams at the AmLaw 100 firms. Back with a previous product, we’ve done that kind of sales cycle before, and it’s very different because these innovation teams are going to push top down across a very large firm and mandate usage, and they’re usually going to bring you this long list of 50 things that they need in order to move ahead and it’s a decision by committee sort of thing.

We had an early experience before we hit PMF with Spellbook of working with these committees, and they send you in very strange directions and ones that I think are maybe not best for the product. For example, at a lot of these large law firms, they operate on an hourly billing model and just decreasing all their billable hours is not a positive incentive. The incentive structure is really misaligned with AI.

Turner Novak:

So you don’t want them to get more work done almost?

Scott Stevenson:

Yeah. I mean, lawyers make a lot of money, especially in these large firms from billable hours. And so what you found was a lot of the time what the committee’s most concerned about is how do we advertise this to our clients? How do we do a press release to show that we’re innovative? How do we just constantly shove into our client’s face that we’re an innovative law firm so that we don’t look bad compared to the firm across the street? And we noticed that that was happening and the committees would ask for things like client portals. Well, we want our clients to be able to log in and see the innovation firsthand and-

Turner Novak:

What is that? What is a client portal?

Scott Stevenson:

It’s a place where a client of a law firm can log in and interact with the lawyer or do their work there. We actually built one of these in an earlier product we had called Rally because we also had this request and clients hated it. They’re like, “Why can’t I talk to my lawyer in email? I don’t want another login to another website.”

Turner Novak:

So is this thing tracking what each piece of work that’s done and it automatically puts it in? I can log in and see what you did or something like that?

Scott Stevenson:

Yeah. It’s like you can log in and see the documents together or collaborate on the documents together. And then the lawyers didn’t really like it when we launched it because they didn’t want to the client to see all their messy, how the sausage was made kind of stuff. I’ve seen this feature request a ton, a lot from the really large firms with the AmLaw 100 firms that want to show the innovation to clients, but I’ve mostly just seen it end up failing.

And so with that experience, we decided to take a really different approach where we’re going to sell bottom up to the actual end users, the lawyers. And the vast majority of our customers, we don’t even do a sales call or a demo call. It’s like, “Welcome aboard, Turner. You are going to use Spellbook today, and in five minutes, you’re going to be set up and actually using Spellbook for some real work or demo work and clicking around and getting value from it.”

And so that evolutionary pressure has enabled us to, I think, build a very different product that is much more Cursor-like in how it’s baked into the user’s existing workflow. It’s not this grand design thing that you’re rolling out across a massive firm, it’s like a really practical tool that is always within arm’s reach, that’s a win at the back of the lawyer. And because of that, we have amazing retention metrics like our net revenue retention of 130% plus. We’re doing more of a land and expand motion rather than top down, but we have a lot of customers and they’re growing their usage, expanding their seats. And yeah, we really like that way of building a product because it subjects you to different influences, the influences of the actual end user who’s going to be using this thing to get work done.

Turner Novak:

There is quite a few different legal AI software products that have gotten a lot of revenue. People are using it, whatever. What is the market leaders in some of these different legal AI categories look like? Because I think we talked about too, Harvey and Legora, but I think there’s like a lot more. I don’t know. Is there an easy way to educate people for a couple minutes on what’s working in all these different subcategories of legal?

Scott Stevenson:

So yeah, Harvey and Legora, fairly similar, started with the law firms and the AmLaw 100 types of customers, what I’ve been talking about.

Turner Novak:

What is their product? What do you use when you’re using-

Scott Stevenson:

I would say it’s a very broad platform that’s broadly like ChatGPT for law. They have a number of different things they do, but it’s quite broad because they’re rolling it out to a whole legal team that might include litigation teams and transactional teams and so on so it’s like if you imagine tuning ChatGPT or Claude for a legal use case.

Turner Novak:

Is it like a whole operating system to run your law firm on?

Scott Stevenson:

The work, yeah. I think that’s more what they’re building is this all encompassing operating system kind of thing for a firm. But very tuned towards the law firms, whereas we’ve had really amazing product market fit with the in-house legal teams who don’t care about the billable hour who... This is the other type of customers is the in-house legal team. They’re starting to sell to that customer base too, but it’s very different because they don’t care about the billable hour, they don’t care about showing the clients, their clients the legal innovation they’re doing. They really just want tools that they can switch on and deal with this hair on fire problem of, “I have too many contracts to deal with. I need to clear my queue. I need some way out.” And so that’s where we’ve really been shining is in that segment. In terms of other companies-

Turner Novak:

It’s probably personal litigation type of stuff?

Scott Stevenson:

Oh yeah, there’s litigation, there’s like EvenUp’s done really well for instance.

Turner Novak:

That’s personal litigation?

Scott Stevenson:

Personal injury.

Turner Novak:

Oh, personal injury. Okay. There’s that one, EvenUp, people that know EvenUp listening to this will, though they can write in the comments what EvenUp does. I’ve definitely heard of that one before.

Scott Stevenson:

Yeah. EvenUp is AI for personal injury cases.

Turner Novak:

Okay. And then they’re corporate advisory type stuff. Isn’t there a company called, it’s called Hebbia?

Scott Stevenson:

Oh, yeah. Hebbia was pretty broad at first, but from what I see, they’re really trying to take the position of being for finance now.

Turner Novak:

Oh, interesting. Yeah.

Scott Stevenson:

They’ve gone very deep down that angle. I don’t know if they’re still running their legal arm anymore.

Turner Novak:

And is there a couple others or there’s maybe a longer tail?

Scott Stevenson:

There is a very, very long tail. I would say other smaller companies that have launched. Sandstone is one that does in-house enterprise legal that they’ve launched pretty recently. And then there’s a really long tail of other startups doing similar things that I think a lot... This vertical has matured. This AI vertical has matured so, so fast that it’s been, I think, really, really hard for this long tail of companies to catch up to the point where it’s almost like every three weeks now, one of these small companies comes to us looking for maybe an acquisition or something like that. They’ve built decent customer bases, but it’s shocking how fast this vertical has moved.

Turner Novak:

Oh, so have you guys done any acquisitions or exploring some or?

Scott Stevenson:

We are actively looking at two now, and yeah, part of our strategy this year is definitely to roll up some of these smaller companies that couldn’t quite get a foothold, the market moved a little bit too fast. They’ve built maybe something similar or more lightweight. They have a little bit of a customer base. We look at the math and it’s like, we could spend money on Google Ads or we could just acquire a bunch of these small companies so I think there is consolidation happening for sure. Legal AI has been very hyped.

Turner Novak:

That’s fascinating because you think there would probably not be as much consolidation this early into a hyper growth market, but it’s probably that there’s... It’s just these massive fluctuations in product capabilities, adoption.

Scott Stevenson:

Yeah. I mean, it’s so fast. I mean, the speed you have to move to keep up with the market is really, really fast.

Turner Novak:

Have you guys found, were there certain times where the models would maybe like OpenAI or Anthropic would release a new model and just suddenly Spellbook worked so much better? I know a lot of people have had those.

Scott Stevenson:

Yeah, I mean, that definitely has happened. We started building Spellbook on GPT-2, so that was tough. GPT-3 was a little bit better. And yeah, it was funny when ChatGPT came out, everyone was like, “Oh my God, what’s going to happen to Spellbook?”

Turner Novak:

Oh, really?

Scott Stevenson:

This was 2022, everyone was worried about it. And when ChatGPT came out, our growth just exploded because the models started getting better that we were using, and lawyers were getting their feet wet in these generalized AI experiences and then searching on Google, “I want ChatGPT for lawyers.” That’s literally what they search and then they would find Spellbooks.

So every time the generalized models and platforms have launched new things or gotten better, it’s generally been very good for us, both from a capabilities’ perspective. And in terms of just getting lawyers interested in AI enough to look a step deeper. Yeah. One mantra we have at Spellbook is that I think a lot of other companies have maybe gotten wrong, and I think it’s important for everyone to think about when they’re adopting AI is we say. “It’s time to chop down trees with a blunt ax,” There’s the Abe Lincoln quote, “If I had six hours to chop down a tree, I’d spend five hours sharpening the ax.”

And I think a lot of engineers and a lot of knowledge workers, we’re used to the idea of mastering a tool and then getting dividends from that mastery. But the reality in AI now is there’s no time to master anything. Every six months a new tool or a new model comes out and we’ve had to teach our engineers like, “Look, we can’t sit around optimizing around GPT-3 because GPT-4 is going to come out in six months. And we can’t try to master this thing. No one is going to have time to master this thing.” So a really important part of our culture is there’s no point in sharpening the ax when the chainsaw is coming out tomorrow. And so what we teach our team to do is drop the ax, pick up the chainsaw, stop and keep moving on, marching forward, implementing new models, new techniques, delete old code very quickly when it’s not needed.

And as an engineer, I think it’s like an un-intuitive culture. I think one of our advantages is also just the culture that we’ve been building these products since 2022. And our team has kind of learned how to do this, which I think the natural instinct of a lot of experienced engineers is kind of in the opposite direction. I’m going to build a really complex, robust system around this model, but then the next model comes out in six months and then you just have to delete all that code. So yeah, it’s an interesting time to build software.

Turner Novak:

Yeah. Because I mean, I guess isn’t there this risk though that the models don’t get better? The chainsaw doesn’t come out, right?

Scott Stevenson:

Yeah.

Turner Novak:

And then it’s like your ax isn’t sharp and you’re screwed. I mean, I guess they can kind of go both ways.

Scott Stevenson:

Yeah. I mean, there is that risk, but yeah, I mean, I don’t think we’re there yet. I don’t think we’re seeing this sort of plateau yet.

Turner Novak:

That’s fair. And is there a reason that you have that specific viewpoint? What are you seeing to make you so confident? And then maybe how does that relate into how the AI software is going to change? When you look five years in the future, is there still just so much more room to use current capabilities to make the products in the future so much better or?

Scott Stevenson:

Yeah. I think you just look at the trajectory. It’s like, I’m Canadian, you’re skating where the puck is going. And the thing is, the puck, if you just draw a trendline, the puck is moving way faster than it ever has before. We’ve never seen technology advance at this pace in our lifetimes. So a lot of people, and just trying to do the math of skating where the puck is going, they’re thinking about the puck speed that you might’ve had in 2015, but it’s actually this really accelerated speed. And I think it’s just like the math of what angle do you want to go at, how ambitious do you want to be? If you are looking at the trend of where things are going and you point your angle to meet the puck at the right place, you’re going to be a lot more ambitious. And we’ve done that again and again.

What we do is, when we start building a feature or product, we aim to build things that are not doable today. And that’s like a really, really important feature that is a hard thing to tell your engineers. It’s like, “Your goal is to start building something now that will not work today. It will work in six months when the models get better.” That’s how you actually time the building of these features. Because if you build something that’s achievable today, it’s not going to be that impressive in six months.

Turner Novak:

How do you know what’s like an okay degree of like not quite yet possible, but will be possible soon?

Scott Stevenson:

Yeah. I mean, it’s intuition. It’s like shooting a basketball or something. You get a feel for just watching the technology. I think being plugged into X is really good for just sensing the velocity. I think X is generally like two years ahead of like LinkedIn on this stuff. So if you’re plugged in there, you’re going to be seeing what researchers are talking about. You’re going to be seeing what engineers are hacking on. And if you understand how the tech works, you’ll see that there’s a sequence of advancements that will inevitably be made that are going to make things easier.

And I think a lot of the advancements now are not even at the model level necessarily, but it’s just in terms of figuring out the right techniques for like, how do you schedule an agent to operate in the background rather than needing to be prompted? What techniques do you use for planning and how do you implement planning for long range tasks? These are things that are rapidly being iterated on and you can pull those into your software very easily.

Turner Novak:

Are those things you guys have thought about at Spellbook?

Scott Stevenson:

Yeah. A lot. Yeah, quite a lot.

Turner Novak:

Is it there yet? Is it in the product right now?

Scott Stevenson:

Like planning for long range tasks or like the scheduling of?

Turner Novak:

Yeah, like agents doing stuff.

Scott Stevenson:

Agents doing stuff. Yeah. I mean, so we definitely have agents that can do very long-running like drafting tasks today.

Turner Novak:

So what’s an example of that?

Scott Stevenson:

Yeah, one example would be like the financing transaction, like VC financing transaction, example I gave. So you have a term sheet and you need to draft a full set of NVCA docs. That could be like 1,000 edits. It’s actually quite detailed. I don’t know if you’ve seen like the full template set, but like before a lawyer has edited it, but it’s like there’s tons of optional language, there’s math you have to calculate. It’s a very deep problem. And so that’s something that we can do quite accurately and as like kind of a long range task. And it’s not just like filling in the blanks. It’s like you’re cross referencing these documents, making sure they’re consistent, doing math, making sure that the math adds up, like you’re calculating share prices and things like that. So that’s kind of I think where the state of the art is.

But that product, so Spellbook Associate is our agent product, we started working on that, like I said, before it was possible. We launched the first version of that product in Alpha almost two years ago now. So it was the first long-running agent for multi-document legal work ever launched and it didn’t really work when we launched it, but then the models got better and better and we got feedback and then today it works really, really well. And then the next thing that we’re really excited about is like agents working in the background. I think when people think about like, is AI overhyped or not, the biggest thing on my mind is the way most people use AI today is you put in a prompt and it works for like five minutes and then you get an answer back.

Turner Novak:

It’s just kind of like better Google.

Scott Stevenson:

Yeah, better Google. But the thing I think about is imagine having an employee like that. You go to the employee, you ask them a question, they work for five minutes and they give you an answer and then they do nothing. That’s like the worst employee ever. That’s what we have today. It’s like the worst employee ever, who if you go over their shoulder and ask them a question, they’ll work hard and give you something, but after that they do nothing. They just kind of sit there and there’s such an easy gap for us to jump to say, “Well, how do we make these agents work in the background all the time, like an actual employee pushing the boulder forward without us?” I think that is going to be like a 10X for AI and agents.

So I don’t think people understand how impactful this technology is going to be because when you have like a AI coworker in your Slack who’s like, I know phishing through your emails, finding work to do, looking at what your clients are asking for, say if you’re a lawyer, that’s going to be just this massive leap forward in productivity. So that we’re working on that now for like basically getting to the point where your Spellbook agent can be in Slack as like this artificial legally competent coworker that you can delegate stuff to.

Turner Novak:

Yeah. Because I can think of from the VC perspective, it’s like you almost create this thing where you figure out the first engineer at Spellbook leaves and their LinkedIn says they’re working on a self-startup and my tool automatically messages them and gets on a call and even it’s like an AI agent that’s doing the call and then I get all this information and I just get an email, it’s like, “Would you like to invest or not?” Or whatever. That’d be pretty incredible if it does that.

Scott Stevenson:

I don’t think we’re far. I don’t think we’re that far from that. Yeah. I don’t know how we’re going to deal with the noise of AI agents calling and emailing everyone all the time.

Turner Novak:

And I get that a lot. You get all these like spam.

Scott Stevenson:

I do. Yeah. Yeah. They’re not very good.

Turner Novak:

And I don’t think, if I’m that founder, am I going to do a call with a VCAI associate? I probably wouldn’t though, but I might do a call with the guy who is making the investment decision or whatever. But in a sense, you do maybe skip through some pieces of the process and just more efficiently get to like, it’s like humans making decisions ultimately based on information from the AI. So maybe you do skip some, save some time. I’m not sure. I do go back and forth of like personally trying to wait out what are the ways that I just completely lean into AI and what are the ways that I just completely just choose to not do it at all and just lean more into like, podcasts is interesting, meeting in person, hanging out for two hours.

There’s like no technology really, aside from like cameras and mics and we’re just talking and communicating about this thing and that’s probably a good way to just get to know you better and build a relationship versus like, I don’t know, we could have like had our AIs exchanging information or something, but like...

Scott Stevenson:

That’s not interesting at all. Yeah.

Turner Novak:

Yeah. This is like you almost transcend and go above the technology in a way. I don’t know.

Scott Stevenson:

That’s true. That’s true. Yeah. I think about that so much with writing and tweeting. To me, AI writing, like creative or informational writing is so obvious still today, like the GPT-isms that everyone makes fun of. It’s not this, it’s that thing. You know what I’m talking about?

Turner Novak:

Kind of. Yeah. I honestly don’t even catch it because I don’t do enough AI writing. I don’t use it enough for writing.

Scott Stevenson:

Yeah. There’s like all these tropes that you just pick up on and the minute I see them, I feel like, “Oh, if it wasn’t worth the time for this person to write this, then it’s not worth the time for me to read it.” Because in a way, I think the fact that we’re taking the time to sit here and have this conversation or the fact that someone’s willing to actually sit down and write something themselves indicates that they thought it was important enough to invest that time. And that means for the reader or for the viewer, while that’s an indication that this might be worth my time as well. And it’s kind of like a proof of work with Bitcoin and stuff like that. I think if it’s like writing is sort of like this proof of work and the minute someone like at Spellbook sends me a recommendation of something we should do and the whole proposal is obviously written with AI.

I can’t trust that you actually thought about this enough. So to your point, we’re to not use AI. I think writing is an area, I’m like creative writing, writing recommendations, something I’m careful with. Luckily, contracts are very like, they’re not meant to be creative whatsoever. They’re very formulaic. Lawyers are not trying to be original. And so that’s one reason I think also why legal AI has taken off so much, especially in transactional work, is like there is no desire for really originality in contracts. People want to use standard language.

Turner Novak:

Yeah, because if you think about it, a business contract is almost the, it’s like the programming language of business, I guess, or something like that. If you want to really get philosophical about this stuff.

Scott Stevenson:

Oh, exactly. Yeah. It’s just like code. And so AI for code and AI for legal, I think in both of these areas, you’re not trying to write creative original code or creative original contracts. You’re trying to make functional documents. And that’s why I think AI works really well in those areas.

Turner Novak:

Yeah. And I want to talk a little bit about maybe early Spellbook stuff because you have some interesting history of the company, but going back even further than that, your first company that you started, you made an instrument, like invented an instrument. So what was that and how did it go?

Scott Stevenson:

Yeah. So that was my first kind of like ill-advised startup. I was super into electronic music and electronic... I studied computer engineering, but I grew up making electronic music, DJing, things like that. And I met this composer who was composing these awesome pieces of classical music, but incorporating these electronic elements. And he was like, “It’s really frustrating that there’s no electronic instrument that fits into that atmosphere that the audience will really appreciate.” If you go with a DJ turntable to a classical concert, people are like, “I don’t really understand, is this person just hitting buttons or whatever?”

Turner Novak:

They press play, but then they’re tweaking things.

Scott Stevenson:

Yeah. It’s like, “Are they actually doing anything? Are they not?” And so we conspired to build this instrument that would allow an electronic performer to really show the cause and effect of what they’re doing for these sorts of electronic performances and shape kind of like guitar or something. It has this beautiful wooden frame, kind of like an acoustic instrument.

Turner Novak:

And you almost hold it like you would an accordion in front of you like this.

Scott Stevenson:

You hold it on your lap or whatever. So you can face towards the audience. You can use it in a desktop mode as well, but it has all these lights and things, so it makes it obvious.

Turner Novak:

And there’s buttons and sliders kind of or something.

Scott Stevenson:

Yeah, button, sliders has like a synthesis engine. It has like a drum machine. You can use it in all these sorts of ways. And that was like my first very naive startup when I was fresh out of college, very little money.

Turner Novak:

So you like created this thing, made it, and were like kind of mass-producing them, but not...

Scott Stevenson:

We started producing them, selling some of them. We ran a Kickstarter. A couple of things happened, three things happened that set it off, of course. One was I got a really big legal bill. So one of my first bosses, this guy, Wally Haas, who ran this company, Avalon Microelectronics that I worked for was one of my first internships. He invested 20K in the company and that was a lot of money for me as like a broke student. And he was like, “This will get you to your next milestone.” And then one day we got like a 10K legal bill by surprise that took half that cash out of the bank account. And for me, that was an enormous amount of money. It was half the angel check we had and the amount of value we had gotten from that seemed like very, very little.

So at that point I started thinking about, “Okay, I think there’s like a way bigger problem to solve than electronic music instruments.” So that is where the idea for Spellbook came from was that like, frustration. But some other things that happened through that experience, like a hero of mine is this guy, Roger Linn, he built one of the first digital drum machines in the world. So if you listen to like ‘80s music and hear that like snare drum with like the big echo, that might be like a LinnDrum. And I got to go to NAMM, which is this big trade show for musical instruments. And I got to meet Roger Linn, this hero of mine and awesome guy, super friendly, but his company was still only two people, and he had dedicated his life to building these instruments. And I was like, I’m really glad he did that and I really appreciate it, but I don’t know if electronic instruments are like what I’m going to dedicate my life to. I don’t know if it’s a great market. And the TAM for niche electronic instruments is pretty small.

Turner Novak:

Maybe a million dollars, maybe a little more or less.

Scott Stevenson:

Yeah. These are like, they’re expensive to build. And I learned a ton about producing hardware and it was really fun, but maybe we’ll get back to it as a hobby someday. But like the legal market, having that pain myself and just seeing the size of the TAM of how many people touch contracts every day, it’s gigantic. Companies like Harvey and Legora really focus on the lawyers and the lawyer market. And I’ve talked a lot about lawyers, maybe 20 million lawyers in the world, but if you think about how many people touch contracts, it’s way, way, way beyond that 20 million number. So I was really inspired through that experience to start Spellbook.

Turner Novak:

Because like every salesperson, when you close a deal, there’s a contract related to it that you probably touch.

Scott Stevenson:

Yeah. Exactly.

Turner Novak:

I mean, it’s really any kind of business transaction that happens. There’s some handshake deals. Maybe you don’t sign a contract, but like most people do. Even the handshake deals that I do will do like a one-page contract, which is just like, we won’t screw each other, but we’re just making sure that we can’t screw each other basically with our very rough contract.

Scott Stevenson:

And even like an email can be considered a contract legally as well.

Turner Novak:

And so you were like, “Holy cow, I paid half of my bank account for this legal bill, I’m going to try to fix the legal market.” What happened from there?

Scott Stevenson:

Yeah. So I actually, I stewed on the idea for a while. I worked at a network monitoring company and was the director of engineering there, building out that product for a while. And then I was working on this in the background, trying to figure it out. And we went through a ton of different iterations. First in my mind, smart contracts were big and I was like, “Oh, maybe Ethereum smart contracts will be this automated type of contract we can all use.” That really obviously wasn’t going to work for a bunch of reasons. We showed blockchain smart contracts to a lawyer and they’re like, “I will never use this.” So we threw that away pretty quickly. But where we landed was, we had this product called Rally. It was a template-based product. So there was no AI at the time.

We actually launched in 2018 originally and we sold that to about 100 law firms. And basically what it let them do is build these really advanced legal templates. So if you’re doing a bunch of NDAs or sales agreements, you can build a template on our platform, which does a lot of things that you wouldn’t be able to do in a normal templateing engine. It’s built on Word. It can ingest legal data and then you could kind of spit out contracts much more efficiently.

But for a bunch of reasons, there wasn’t real PMF there for a long time. We were able to do 100 sales. We had raised some money. Our board was kind of like, “Let’s get on with it and scale this thing.” And we’re like, “No, we don’t think we have PMF,” a product market fit. Our view of product market fit is basically the customer is pulling the product out of your hands faster than you can keep up with. And until we hit that, we’re not scaling the company. So we kept the company super lean for a really long time as we built that out and we actually launched like over 100 landing pages.

Turner Novak:

This was like a three-year period, right?

Scott Stevenson:

Yeah.

Turner Novak:

I think you posted this one chart where it was just like the revenue I think of the company and there’s a point, it was maybe 2020 where you’re like, “We lost half our revenue or something.” What happened there?

Scott Stevenson:

Yeah. So we built out the platform. We did sell to the big law innovation committees at first and we had some very lucrative kind of early customers. And one of those customers churn was half our revenue and like we literally lost half our revenue overnight, but we felt that that wasn’t bringing our product in the right direction. Again, what I talked about earlier of like working with these innovation committees, they’re not the actual users. And so we’re like, “You know what? We’re going to start selling to these small firms solo lawyers to start and kind of snowball our way from there.”

Turner Novak:

So this is around when you started to test like a landing page. So you launched 100 landing pages in three years. What does that mean on practical? You were basically doing one every two weeks roughly.

Scott Stevenson:

Yeah, exactly. So we launched one every two weeks. Sometimes we would actually launch a product variation with the landing page. So at one point, our view was like, if we roll the dice enough times, eventually we’ll figure this out and find product market fit. And we’re optimizing for the number of at bats we could do. At one point we launched Shopify for law firms. So we took our templating engine and we put a store on top of it. So like a law firm could stand up like a Shopify store, like need an employment agreement, like click here and then it’ll use the template to like spit one out the backend. We tried an absurd number. We launched the client portal and we built landing pages for a ton of these different angles and things that we were trying.

Turner Novak:

So what’s the importance of launching a landing page for someone who’s like, “What is this even? What is the landing page?”

Scott Stevenson:

Yeah, a landing page is a single webpage that usually has no links to anything else that has a single message, an image that is trying to get you to sign up for a product or sign up for a wait list or something like that. And often you drive traffic to these through advertising or through social media through campaigns. So it’s not like people are landing on your homepage and finding it, you’re finding a way to drive traffic to it. So what we would do is launch these like little ad campaigns with maybe 1,000 bucks or something like that. And then we would drive traffic to the landing page and we would see what’s the cost per conversion, like how many dollars do we have to spend in ads to get a lawyer to sign up for the product from this landing page? And we literally tracked that over 100 landing pages where we could see, “Okay, we ran this experiment with client portal and that cost us $100 per lawyer and then we ran this experiment and that costs us $500 per lawyer.”

And you really get a sense of like what’s resonating and what’s not. And you really learn like how do you get a message straight into someone’s, past their like blood-brain barrier into their brain really, really fast. And yeah, then eventually we launched, the AI product was just another landing page. We were like, “Okay, GPT2 was around,” I had used GitHub Copilot for coding and we were like, “Oh, this is cool. We’re going to try launching GitHub Copilot for lawyers. We’re going to basically just launch another landing page with this.”

Turner Novak:

What did you call it? What was the buzzword because ChatGPT had not launched yet, right?

Scott Stevenson:

No, it had not. Yeah.

Turner Novak:

So how did you describe it in a couple words?

Scott Stevenson:

Well, we did call it Spellbook. The big thing we had was an image. The thing that we would do on a landing page is there’s a headline and there’s an image, and we would design the image or GIF or video. Our thesis is like, your image plus headline has to deliver this visceral sense of value in five seconds flat. And I think our headline was just like, “Draft and review contracts 10 times faster. Spellbook uses GPT-3.” People kind of knew what GPT-3 was on LinkedIn and stuff. It was a little bit of a buzzword. Even before ChatGPT, people were curious about what is this GPT-3 thing, and, “Spellbook uses GPT-3 to surgically redline your documents,” or something like that. But the important part was the image. We had an image of the word window and someone just clicking draft and it just drafts a clause instantly. And that was the sort of magic moment that once a lawyer saw hitting a button and drafting a clause from a headline.

Turner Novak:

Versus having to hand type or copy paste from somewhere.

Scott Stevenson:

It was just cuts through. And there’s this... Have you ever read this blog post that’s Find the Fast Moving Water on NFX? Have you ever seen it?

Turner Novak:

I don’t think so. What is it?

Scott Stevenson:

It’s a really good blog post. I forget who wrote it at NFX, but the author talks about their first experience of seeing Cabulus, which was like a precursor to Uber. And seeing this app for the first time, I think someone else had it, like his friend had it or something, and he was looking over their shoulder. And he was seeing this app for the first time and was like, “I had a neurochemical response to it. My pupils dilated. Blood rushed to my head and I just saw that the future ahead of me was going to look very different than what it has been when it comes to transportation.”

And I think that was the magic moment we were trying to hit with Spellbook, and we hit it. And within three months, we had 30,000 wait list signups. Within three months, we had more revenue from that product than the other three years we had selling everything else. When you hit that kind of resonance, you feel it. You see it in the numbers. It’s also unmistakable. I don’t know if you need to actually measure it as much as we did because when you really hit it, it’s like PMF resonance. Yeah, it’s unmistakable. You will know it. Yeah.

Turner Novak:

It’s like something that you can’t quantify because it’s just vastly order of magnitude or more of just way more resonance and usage of the product.

Scott Stevenson:

Yeah, signups. I mean, yeah. I mean, we couldn’t measure it too. The other things we were selling, the landing page might have cost us 100 or $200 in ads just for a signup. Now, when we first launched this, it was like $5, $10 signups, just almost like an order of magnitude cheaper in advertising to get someone to sign up.

Turner Novak:

So you basically pay $5 to get someone to sign up, and then if they convert and use it, they may pay 10 bucks a month or something and you might get like a 20% conversion rate from free signups to paid, and then you could do the math of saying like, “Okay, we need to acquire a fully converted user costs us about $25 and they pay us 10 bucks a month, so within three months, we are making money off of that customer.”

Scott Stevenson:

Yeah, yeah. That would be like your CAC payback. Yeah. And I think our original price was like 49 bucks a month. Today it’s more like 500 bucks a month. So VCs also talk about like, “Oh, our price is going to go down.” Actually, our price has only gone up as we’ve added more and more value to our customers. But yeah, that’s how you can do the math. And it is measurable. And when we hit that moment, our salespeople’s calendars were just completely blocked. Every day of the week, they would have eight sales meetings or onboardings actually. We didn’t do sales meetings. It’s just onboardings.

Turner Novak:

What were some of the other biggest things you think then you learned over that launch or landing page period and then just this thing worked and you just had to start going? Any lessons?

Scott Stevenson:

Yeah. I think the biggest lesson for us, or what I would tell other founders is yeah, if you keep beating... Resilience is incredibly important, obviously. If you believe in something enough and keep beating your head against the wall long enough, you will probably figure out something eventually. And if you keep your burn rate low, you can make money last a very long time if you’re scrappy. But the secret is you have to really believe in the problem. If you don’t believe in the problem, if we didn’t believe in the problem that we were solving, we never would’ve gone through a hundred landing pages for years of grinding with this super small team with very low salaries.

The reason we were able to do that is because we believe that legal efficiency was unsolved, that software still had not really changed how law was practiced and that a lot of people needed legal services, less expensive, a lot of in house teams needed more efficiency on their contracting, and we just really believed both, one, this problem is huge when you think about the scale of it, and two, that it is unsolved. And if you believe those things, you can be very resilient.

Turner Novak:

So then when this thing really started to work, you did not have a product yet, and were you just like, “Oh, we got to actually build this”?

Scott Stevenson:

Oh no, we did. I built the product, a really crappy version of it that our eng team tore apart. First we thought this was going to be a lead magnet. We didn’t even think this would be a product. We thought it’d be a cool marketing splash like, “Oh, first company to bring GPT-3 to lawyers. Wouldn’t that be a cool press headline? And then people will convert to our other product.” That’s what we thought would happen.

Turner Novak:

Really? Okay. And then eventually this became the product, right?

Scott Stevenson:

It is the product. Yeah. Yeah. It is 99.9% of our revenue is from Spellbook today, a very small amount from our previous products. But yeah, we actually built the prototype on Replit. This was before Replit had AI features. I built it in a couple weeks of evenings and weekends. It wasn’t a board goal. No one really knew it was being worked on. It was just a fun little side project.

And I built a really crappy version of it on Replit. And Replit ended up using us as a case study after, because the thing I said is it was such an amazing platform because I had this bolt of inspiration, and it would have perished. If it weren’t for platform like Replit, I would have just not found time to work on it and it would have just died in the shower where I had the idea. But because ideas are perishable and you have to rapidly chase them down because I was able to deploy it really fast, yeah, we actually did have a product on day one. It wasn’t great. The engineering team ended up having to basically rebuild it from the ground up.

Turner Novak:

Well, and I think the other interesting thing that I’ve heard you say before is that you were never attaching these experiments to kind of a legacy product. This was called Spellbook. It was a different sort of product.

Scott Stevenson:

Exactly. Yeah. I think that’s one of the things I would have done even more differently. We learned this towards the later phase. I think there’s this instinct for founders pre-PMF to just keep stacking on features and the list and the website gets longer and longer and more complex. That does not make it easier to pitch your product, especially in the earlier days. What you want is a pointed, easy to understand thing. So with Spellbook, our goal was like, “We’re not going to talk about any of the other things we built for the last three years. We’re only going to talk about this in a really pointed way to make it super simple for people to comprehend.” And that was part of the success. Yeah. So yeah, my advice to founders launching these landing pages would be just focus it on one thing. Don’t list off everything you’ve built because chances are if you’re pre-PMF, like 80% of what you’ve built doesn’t matter and you can delete it and there will be one or two things that matter.

Turner Novak:

Yeah. I think the other thing too is you think there’s all this historical context and history of the product, but literally 99% of the people, just they’re seeing you for the first time and they don’t give a shit about the history.

Scott Stevenson:

People are so busy, they are not paying attention.

Turner Novak:

Yeah. Yeah. And one thing I think I need to do more is just generally tweeting like, “Hey, by the way, I’m also invested in startups, whatever,” because I don’t do that enough. And there’s so many people that they’ve literally, they’ve followed me for years and they’re like, “Oh, I thought you were just kind of a meme account or something. I didn’t know you actually were an investor.” They’re like, “Oh man, I got to... “ So sometimes it’s just saying the message over and over again, reminding people the thing that you do almost.

Scott Stevenson:

Yeah. This is one of the things I’ve been learning from Keith since KV’s come on board. I think that’s something we were going to chat about.

Turner Novak:

Well, I wanted to ask you, so you guys really took off. You started growing really quickly. So what happened? Was it instantly you’re like, “Okay, we have PMF, we’re leaning into this”? Was there a debate of like, “How do we need to figure out this legacy old thing?” How did you manage that?

Scott Stevenson:

It was funny. No one knew we were working on this thing. The board had no idea, but by the time we got to our board meeting, it was almost like a mic drop drop moment. It was like, “Surprise, we launched a new product. Here’s the growth chart.” And it was just immediate consensus. It was like, “Wow.” Everyone’s like, “You need to go chase this thing. Don’t worry about the other stuff.”

Turner Novak:

And you’d been holding off, right? Weren’t your investors kind of like, “We have PMF. We need to start scaling”?

Scott Stevenson:

Yeah, yeah.

Turner Novak:

But at this point it was like, “We truly do now.”

Scott Stevenson:

Yeah, yeah, yeah. I mean, yeah, it was a pretty amazing moment for the team because we were so... We went through the 2020 era of the Zurp era, pure Zurp era where people were scaling way too fast pre-PMF and we really, really resisted it and sometimes we felt insane for doing it.

Turner Novak:

Yeah. Are you missing out because you didn’t do a remote video calling tool or something?

Scott Stevenson:

Yeah, yeah, yeah, and for not scaling the company. All the companies around us were just scaling, scaling, scaling, whether they had PMF or not. There was a lot of capital sloshing around and our investors were feeling the pressure also, I think to some extent to like, “Okay, let’s get the show on the road.” And it was a very validating moment for the whole team to be like, “This is what we’ve been talking about. This is real product market fit.” And the board was like, “Yeah, you’re right. I’m glad we waited for this moment.” And then it was just very fast consensus. It was like, “We have to scale this now.”

Turner Novak:

I think when I first came across you, you tweeted, it was a graph of your ARR growth or something and you were going to San Francisco to fundraise or something, and you just posted the growth. And I remember seeing it, I was like, “Oh, nice. That’s pretty cool.” And I just retweeted it because I was like-

Scott Stevenson:

Thank you.

Turner Novak:

... “I hope that you have a great fundraising round,” or whatever. And so how did that process kind of go of raising this... I think that one specifically was the series B.

Scott Stevenson:

That was our recent series B. Yep.

Turner Novak:

Okay. So just take us beginning to end, just how that process went.

Scott Stevenson:

So I mean, I will say it was the easiest raise we’ve ever had because it’s just numbers and metrics at this stage.

Turner Novak:

Yeah. Were you just showing the spreadsheet?

Scott Stevenson:

Basically showing the numbers, showing the growth, and it’s been really good. And it’s much easier than the pre-PMF phase where you’re convincing of just on pure vision. At least for me, it’s easier when you can kind of point to the numbers. But yeah, we started the raise. I made a tweet and I was like, “My goal of raising is usually to compress it into as tight a time as possible because I want to be working on the product. I want to be working with our customers.” No offense to... I like hanging out with VCs, but to move the company forward, I have to be constantly working with our team and our customers. And so you want to compress it, and you also compress it in order to create deal tension. If you dilute the deal tension across six months or whatever, no one moves, everything is sluggish.

And so I made this tweet. I tweeted our growth chart and I was like, “Yeah, raising our series B, I’m going to be in New York this week and SF the week after.” And that was it. And so it went viral maybe because of the chart, and we got bombarded my email. I still have emails I’ve not responded to from that moment from funds who reached out. And yeah, I set up in a hotel in New York for like a week and most of the investors all came to us. So one of the strategies I learned from another founder is try to, if you can sequence your meetings, we rented a boardroom in the hotel and we just said, “Hey, come here. We’re taking meetings this week, can dictate the schedule.”

So we had a lot of investors come by there, visited a couple offices and then did the same thing in SF basically. And then we got in touch with Keith and he was like, “I only take in person pitches.” And he was in New York and I was in SF and I had to fly all the way back and cut the SF time short.

Turner Novak:

And wasn’t he the only one who didn’t come to you basically? Yeah.

Scott Stevenson:

Yeah, basically. Yeah. I also went to the KV office as well, so I met Kanu there as well.

Turner Novak:

And what was kind of the difference? I know you said there was a pretty big mindset difference almost between East and West Coast investors, but what did you kind of experience there?

Scott Stevenson:

Yeah. For me, it was very night and day. For the most part, besides Keith, who I think is a very... He’s smart on the numbers, but he’s unique in how qualitatively he assesses the world. He’s very willing to bet on qualitative things. But the New York investors are extraordinarily quantitative and it seems like they almost have all converged on the exact same spreadsheet that they use, the exact same metrics, exact same benchmarks, exact same spreadsheets, to the point where it’s kind of a little bit absurd because if everyone’s looking at the same spreadsheet, then where’s the alpha? If everyone’s looking at the same thing, they’re going to pay the same price, everyone’s going to bet on the same companies.

I found there’s very little emphasis on the qualitative side with a lot of the New York investors we pitched, whereas in SF, there was a much deeper focus on the qualitative vision of the company and how is the future going to play out and why, kind of like the idea maze, kind of exploring the idea maze and understanding where things are going to go. Very different. Yeah, very different.

Turner Novak:

And then you end up, Keith and KV, Khosla Ventures led the round. What has it been like working with Keith just over the past couple months?

Scott Stevenson:

Yeah, incredible. So yeah, we’ve had a few board meetings and Keith’s an incredibly sharp investor. He’s been in the weeds. He really understands how things work on a deep, deep CEO level. And yeah, we just learned a ton. He obsesses about performance and what best in class people look like, and you just learn so much from someone like that.

One of the biggest things I’ve learned from Keith and that I’m learning is how to communicate really well. This guy is like a nuclear grade communicator, how incredibly concise he is, how he’s able to... I think he’s really good at counterpositioning companies and opportunities to cut through the noise in a way that other people really struggle with. And I mean, and he thinks about it deeply. It’s not by chance or he just happens to have this talent. It’s like he’s consciously very good at thinking about how to get the message to listeners and how to create a movement with a message in a way that... I think it’s one of the hardest skills for anyone to learn.

It’s a marketing skill. How do you get your message out into the world spreading? To do it, you have to have a really simple message and you have to repeat it a lot, like you were saying, people not knowing you’re an investor. You have to find a way to repeat that message or get that message in front of people because people are so busy, so distracted, they have no time. Getting someone to read more than five words is just an extremely hard challenge for the most part. Getting someone to watch more than a five-second video clip is pretty difficult a lot of the time.

So I mean, he’s just so aware of that and so good at dealing with it. An example I’ll give you, we did a series B announcement video and I think we booked 45 minutes for Keith to come down and talk about his view of the company and the opportunity. And we’re sitting down in front of cameras kind of like this and Keith sits down next to me and he just hits his lines. He’s just like, boom, boom, boom. He has five bullet points about why this opportunity is incredible, why the contract opportunity in particular is really special and how the market data that we’re collecting is going to change how contracting is done. I mean, he communicated everything in like five minutes and then he was like, “Okay, I think we’re done.” He couldn’t think of anything else to say. That was it.

And that’s the pattern that you notice. In the board meetings too, it’s like you’ll ask for feedback and he’ll say it in one sentence and then he’ll be quiet. And it’s like he’s so good at getting to the heart of the matter and then letting the core message breathe and be received. Yeah.

Turner Novak:

Are there things you’ve changed about marketing or messaging over the past couple months then?

Scott Stevenson:

Yeah, definitely. I think focusing more on delivering our core message, what we’re all about and repeating it to the point where it can become kind of boring for the speaker, I think just doing that more and doing it better.

Turner Novak:

One last thing I wanted to ask you about. So I feel like you’re maybe like bleeding edge of AI, quote unquote. Personally, what does your personal AI stack look like? What are you using? What kind of products and things are you taking advantage of?

Scott Stevenson:

I’ve tried a ton of stuff. Obviously I use Cursor and Claude Code on the engineering side for mainly building prototypes and things like that. The product I’m loving right now is Twin. Have you tried twin.so? Have you ever seen it?

Turner Novak:

No, I’ve never.

Scott Stevenson:

I mean, it’s on the surface very simple, but I think they just got like this generalized agent formula really right. So how it works is you can go in there, you can say, “I want to build an agent.” You build an agent by prompting. You don’t have to write code or connect together boxes or anything like that.

Turner Novak:

That’s always the most frustrating... There’s one called like N8N, I think.

Scott Stevenson:

Yeah, N8N. Yeah.

Turner Novak:

I never ended up getting it to work because I was like, “I don’t care enough to figure this out.”

Scott Stevenson:

Yeah. The way this works is you’re almost vibe coding these agents with a couple prompts and it’s really good at scheduling the work in the background, like what I was talking about earlier is you don’t want an agent that you have to prompt to do work. You want it to work on its own. And so I built probably about five agents with Twin now that I use daily.

Yeah. One of them is my Canada recruiting scanner, and what it does is every morning at 8:00 AM, it scans Twitter. Generally, we hire in the US, but most of our team is in Canada. We basically scan all of tech Twitter and find Canadians who are tweeting about AI and looking for engineers, designers and interesting people saying interesting things. And that fills our queue for recruiting, and that’s been a huge help.

Turner Novak:

Do you reach out to them or is it the team? What’s the process then for that?

Scott Stevenson:

Yeah. I mean, we manually kind of evaluate. Myself and our hiring managers will then look at the candidates and then we will do the reach outs ourselves. We’re not at the point where the agent goes and reaches out yet. We’re kind of tuning the quality and the filtering and stuff like that. So yeah, that’s been really useful.

We have another agent that does a similar thing where it just digests all the feedback from every channel, from Slack, from email, from HubSpot, and will basically summarize all of our product feedback every day in Slack. Why are people churning? Why are people expanding? Things like that.

Turner Novak:

So that’s probably the biggest one, twin.so?

Scott Stevenson:

For me, that’s the thing I’m loving the most right now. It’s just so easy. It’s so fast. I think you also, you want something that’s so easy that you want to make it so that if you’re dealing with a problem like, “Oh, I need to find my next podcast guest,” that it’s so fast to set up an agent to do that, that it almost takes you no extra time. So it’s like, “Oh, I’m going to go look for podcast guests or search Twitter,” or something.

Turner Novak:

I was going to say, you almost want the process of creating the agent is actually faster than just going on and doing the thing.

Scott Stevenson:

Actually, yeah, that’s right. Yeah. It’s actually faster. And Twin is the first thing that’s actually hit that level for me where it’s like, “I might as well create an agent,” and it actually works.

Turner Novak:

Yeah.

Scott Stevenson:

And the other thing I love, it has so many integrations, so it can suck in from so many things and then it can pump into Slack. I think the thing I always think about with products, the hardest thing is getting people in the habit of actually using them and getting the products in people’s faces and getting our team to go to a new agent product and changing their habits is really tough. But if we can pump the agent output into Slack, into channels that people are in, the usage is much better. Yeah.

Turner Novak:

That’s pretty cool. Well, I’ll throw a link in the show notes, people can check it out. I’m going to try it. I’ll see. I’ll let you know what I do with it. Yeah. But this is a lot of fun. Thanks for coming on the show.

Scott Stevenson:

Thanks for having me, Tuner.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

We get into his recent investment in Manus and its $2.5B acquisition by Meta, how cloud companies beat on-prem incumbents, how the same thing is playing out again in AI, why he thinks the cloud incumbents should be making big AI acquisitions today, how public market investors are begging for AI companies to go public, and go inside Benchmark’s current investing strategy and what they look for in founders.

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👉 Stream on Spotify and Apple

Timestamps to jump in:

• 0:08 Inside the $2.5B Manus acquisition

• 6:24 Manus' three main use cases

• 11:08 Taking heat on Twitter

• 15:10 Starting to tweet about software in 2018

• 22:50 The history of application software

• 29:15 Benchmark’s 25x Fund 7

• 31:33 SaaS incumbents got too dominant by 2020

• 31:48 Going all-in on AI software in 2022

• 39:31 Benchmark didn’t invest in the big AI labs

• 40:48 How cloud companies beat on-prem competitors

• 44:33 Why AI companies will beat legacy cloud competitors

• 50:04 Software incumbents should make big AI acquisitions

• 57:35 Why incumbents have not bought more AI companies

• 1:04:43 Public markets are starving for AI companies

• 1:10:14 Inside Benchmark’s fund strategy

• 1:14:14 Benchmark’s history of non-traditional VC rounds

• 1:17:56 Is the 20% ownership model outdated?

• 1:19:20 Chetan’s rebirth as a consumer investor

• 1:22:39 What Benchmark looks for in founders

• 1:25:01 AI coding and AI software gross margins

Referenced:

• Benchmark

• Workday’s S-1

• The Innovator’s Dilemma

• Try FOMO

Find Chetan on X / Twitter and LinkedIn

Related Episodes

👉 Stream on YouTube, Spotify, and Apple

Transcript

Find transcripts of all prior episodes here.

Turner Novak:

Chetan, welcome to the show.

Chetan Puttagunta:

Thanks for having me.

Turner Novak:

I want to jump right in. First thing on the docket, so you’re coming off, you invest in a company called Manus.

Chetan Puttagunta:

Yes.

Turner Novak:

And I saw your partner, Eric, who I had on the podcast this summer. He tweeted something about, it was 1,000% IRR, something, I forget exactly what he said, but it was like, “Oh, that’s a pretty big number.” What’s the story behind Manus?

Chetan Puttagunta:

Absolutely. And you had a great episode with Eric too. Manus was just one of the unique consumer AI agent products that really spiked. And obviously, the Meta acquisition has been announced, and it was an incredible journey to be on that product journey with that team. They’re just, the six founders, which is a large group of founders. But I think that seems to be a theme in AI, which is to have a lot of founders.

Turner Novak:

The more founders, the better.

Chetan Puttagunta:

Yeah.

Turner Novak:

So if I meet someone 10 with co-founders... instant check.

Chetan Puttagunta:

That’s right. Are just some of the most resilient, brilliant and kind people that you’ll meet. And the story really starts with, these dates are directionally accurate, if not precise. But first week of March, they posted a YouTube video with a demo of Manus. And I saw it in the first couple of hours of them posting it. Somebody I follow on Twitter posted it on Twitter saying, “I saw this cool demo of an AI agent.”

I clicked into the YouTube link, saw the video, and then went to the website and signed up for the beta. And I think a couple hours later, I got beta access to Manus, and I used it and I was thoroughly wowed by the experience. And the reason I was thoroughly wowed, so this is March of 2025. Obviously, we’re well past the ChatGPT moment. I was a user of ChatGPT, user of Claude, user of Gemini, user of Cursor, et cetera. But what they were presenting was an agent product that could actually get further on tasks than any other AI product had at that point.

It really felt magical when I first tried it, and immediately, I texted all my partners and I said, “Sign up for the beta of this thing. It’s really magical.”

And then I just reached out to the founders because I wanted to know who they were and just wanted to know about what they were working on and how Manus worked. How did Manus get so much further on task than a regular AI chatbot? What were they doing? What was their key breakthrough? What did they learn to get there?

Turner Novak:

Yeah. What was it?

Chetan Puttagunta:

And so what they had figured out was, I think at that point, people at the application layer had learned that you could use multiple models at once to do more with a task. What I don’t think people had quite tried was breaking up a task into a thousand little tasks. And then for each sub-task, using multiple models in parallel, trying to get past that substep. And so they had just taken the idea of breaking a task into subcomponents and using lots of models to solve subcomponents to such an extreme degree. I don’t think anybody had tried that yet, and so we reached out and they got on a Zoom with us on a Monday and explained to us what they were doing and it was super compelling. And at the time, for some reason, the beta had gone really viral in Japan, and so the team was in Japan trying to figure out why it was going viral in Japan.

Turner Novak:

So you had to go to Japan to figure that out, apparently?

Chetan Puttagunta:

Yeah. So they wanted to just talk to users because they wanted to be like, “Why is this breaking out in Japan?”

Interestingly, I don’t know if you know this, but when ChatGPT first launched, Reddit Japan actually was one of the places it went viral early. And so there was something about consumer chatbots and consumer AI and the Japanese market that seems to be an early signal of things that can go viral in consumer AI.

Turner Novak:

Oh, sure.

Chetan Puttagunta:

And so the team was in Japan talking to users, running a bunch of user meetups because they wanted to know why it was going so viral in Japan. And so we did a Zoom on a Monday and I wanted to meet them in person. And so they were like, “Well, we’re in Tokyo, so you’re welcome to come hang out.”

And so I think that Friday, I flew out. And before that, a couple of the founders were in San Jose at the time, and so I met them in San Jose. And then on Friday, I got on a flight, Friday night I think, I got on a flight to go to Tokyo. And then Saturday night, Pacific Time, they presented to the partnership and then-

Turner Novak:

Over Zoom?

Chetan Puttagunta:

Over Zoom. And I was there in person with them in Tokyo. And then after their presentation, Red, who’s one of the founders and CEO, Red and I went for pizza and beer and got to a handshake. And then that was the start of the relationship. They ended up launching the product in general availability first week of April. They basically ran a one-month beta with a closed beta where you had to sign up and then they would let you in. And then the product exploded, and in December they announced that they had gone zero to a 100 million ARR in eight months, and 100 million ARR, and then if you counted the consumption revenue they were generating, it was 125 million run rate. I think that’s the fastest company to have ever gone zero to a hundred. I mean, eight months is just an outstanding speed record to go zero to 100 million.

Turner Novak:

Yeah, that’s pretty good.

Chetan Puttagunta:

The interesting thing about this product was where it was being used and how it was being used. The three primary use cases that emerged were deep research; coding, which was like a fascinating use case.

Turner Novak:

Interesting.

Chetan Puttagunta:

And three was slides. The three primary things that consumers were using Manus for was those three things. So if you dig into each of those components, Manus was getting further on deep research and writing further, more detailed reports than other AI chatbots. On coding, it was interesting that Manus was being used by non-technical people to code websites, applications, prototypes, mobile apps, whatever, and they were basically using it as a technical companion, and largely by people that were not technical. And something about the user experience, something about the UI, something about how far Manus would get on a prompt with a website or developing a mobile app really attracted a lot of consumers, prosumers. And then finally, the third one was slides, and that of course makes a lot of sense. If you’re really good at deep research and people are doing a lot of deep research, they want to turn the deep research into a bunch of slides that they could use for work or whatever. And so those were the three primary use cases that emerged.

And yeah, they just kept building features based on consumer pull. And then obviously it caught the eye of Meta and they acquired the company and couldn’t be happier for the founders. They’re incredible group, incredible technologists. I think that they had built a product that really blended multiple AI models, the three AI models. They exclusively used Anthropic, OpenAI, and Gemini models, and they had just created a way to blend the APIs of these three models to just get further on tasks. And I think it’s just... From my perspective, Meta is acquiring a team that’s very deeply knowledgeable about how these APIs work, and how to get further on a task, depending on the kind of task with a certain set of APIs. And I think if you just project out the consumer market for the next couple of years, I think you’re going to see more and more consumers and consumers want to just get things done and this Manus team certainly has figured out a way to do that.

Turner Novak:

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So you got some heat on Twitter when you invested. What was going on there?

Chetan Puttagunta:

People are welcome to say whatever they want on Twitter, and we had a great deal of conviction that this company was a great company founded by a great set of entrepreneurs. And ultimately, the company at acquisition was about 105 people roughly, maybe a little bit bigger. And the team was 95 people in Singapore, a couple people in Tokyo, and a couple people in the Bay Area. And the company was pure technologists, pure product engineering people, building exclusively on American models like Anthropic, Gemini, OpenAI models. They weren’t fine-tuning or post-training or building any of their own models. They were using APIs from American models, and their product was hosted fully on American clouds, so they were using largely Google Cloud, AWS, and Azure to host the product.

And so if you just looked at what they were doing, they were delivering incredible consumer value for a great price and had built an incredible business. The founders happened to be of Chinese origin, and for a company headquartered in Singapore. And for us, we want to invest in great people. And I think that if you just looked at, for example, this has been published on Twitter or X a lot, which is rosters of great AI scientists and AI research scientists.

Turner Novak:

They’re all Canadian.

Chetan Puttagunta:

Yeah, sure, a lot of them are Canadian, a lot of them are American, and a lot of them are of Chinese origin. And Jensen Wong of NVIDIA recently said something like, “Half of the world’s AI scientists are Chinese and half are American.” And so I think there’s a large population of AI researchers, AI engineers, and AI product people that are of Chinese origin.

And I think that to me, I want to back the smartest people building great consumer products, and this was a great consumer product targeted for the world market. The primary users of this company were in the US, Japan, Europe, Brazil, India, et cetera. The product wasn’t available in China. Manus couldn’t be accessed in China, they didn’t have a business in China. It was a worldwide business. And to me, those are the kind of businesses you have to back as a generalist investor, especially as somebody that was looking for consumer AI.

For me, going into 2025, it was pretty clear to me that somebody was going to build a consumer AI agent or a consumer AI product that was going to allow people to get tasks done. I mean, this was pretty much the thing that everybody was talking about, if you remember towards the end of 2024, they were saying 2025 was going to be the year of the agent.

Turner Novak:

Yeah, it was starting to piss me off a little bit, people couldn’t shut up about it.

Chetan Puttagunta:

And so if you just were paying attention to what everybody involved in AI was saying was that 2025 was going to be the year that we were going to start to see agent products really come alive. As soon as I saw the Manus demo on YouTube, it was pretty clear to me this was the instantiation of exactly what people were talking about. And you just had to do diligence on the company and the founders to realize this was a great company, a great product, and was going to really do a lot for consumers and provide a lot of value. We had all the confidence that this was a great company, this was a great, great set of founders. And if people want to say a bunch of stuff on Twitter, that’s their problem, not mine.

Turner Novak:

I feel like you’ve gone through these waves of being active on Twitter.

Chetan Puttagunta:

Sure.

Turner Novak:

I feel like when I first met you, it was maybe one of your earlier waves and you didn’t, because I actually realized I had notifications turned on for your tweets.

Chetan Puttagunta:

Oh. Do you still have them?

Turner Novak:

I do. Yeah, I do.

Chetan Puttagunta:

Oh, wow. Thank you.

Turner Novak:

I’ll notice, sometimes I’ll like or reply very quickly, but there’s definitely a period where you didn’t, I feel like.

Chetan Puttagunta:

That’s right.

Turner Novak:

So how do you think about your being more active on social or pulling back a little bit?

Chetan Puttagunta:

I ramped up my activity on Twitter a lot starting in 2018, 2019, primarily because at that time there were a lot of companies in software going public, a lot of public activity around software, and then we entered the pandemic where I was in front of a computer basically all day, like most people, so I was on Twitter and X a lot. And then software, as you know at that time, experienced an extraordinary moment in time of exponential growth. And in that moment, you realized that software valuations that were in the public markets and private markets really did feel unsustainable, but you had no choice but to accept the unsustainable nature of those valuations because you had to play the game on the field.

I think that other venture capitalists, including Bill, have talked about this, which is that in venture, you only have one strategy, which is you can only go long. In our asset class, there’s no such thing as like shorting something. That’s just not a thing that we-

Turner Novak:

It’s just like selling maybe.

Chetan Puttagunta:

Maybe.

Turner Novak:

If you own the shares, you sell?

Chetan Puttagunta:

Maybe.

Turner Novak:

But that’s-

Chetan Puttagunta:

But even that, if you’re an early stage investor buying 15%, 20%, 25% of a company.

Turner Novak:

Just, I’m going 25%.

Chetan Puttagunta:

Right, it’s not a highly liquid market for that, and so you’re going long only, and so you’re reacting to the market constantly. And so in that moment, it just really felt like we were in a really wild time, and the kind of growth that software companies were experiencing, the kind of earnings acceleration software companies were experiencing at that moment in time, the amount of spend that was going into clouds, cloud infrastructure, it was just a really fascinating time, and I decided to just talk about that because it was something that was super fascinating to me. And I just started, at that time, just starting to listen to every earnings call.

Turner Novak:

As one does in their free time.

Chetan Puttagunta:

Yeah, that’s what I was doing. And yeah, I was just summarizing and highlighting the things that I was learning off these earnings calls. And for some reason, it found a niche, large niche audience on Twitter. And yeah, I mean, it ended up building a pretty sizable audience, which was frankly surprising. I was quite surprised that that many people were that into software companies and software trends.

Turner Novak:

And it’s such a simple thing too, and it’s something where you arguably would’ve done that anyways, and maybe you would’ve also summarized it, just instead of sending it to a friend or a couple of coworkers, you just put it on the internet.

Chetan Puttagunta:

That’s right.

Turner Novak:

And there’s a big audience for that.

Chetan Puttagunta:

That’s right.

Turner Novak:

People want to get smart.

Chetan Puttagunta:

That’s right.

Turner Novak:

People want to learn things.

Chetan Puttagunta:

So that happened for a bit. And then I think May of 2021, so these are... Again, I think this is when we reopened our office, the Benchmark office in San Francisco, and that’s when you can clearly see my Twitter activities start to fall off. And so once we reopened our office in San Francisco, the number of people that wanted to just meet in person went through the roof. I think there was just, we had basically been meeting everybody on Zoom for over a year, and I think there was a moment where as people started to move back into San Francisco, the early stage entrepreneurs just wanted to do everything in person. And so we saw probably around the summer of 2021, a lot of activity moved to in person, and a lot of people just wanting to meet in person and reconnect in person, much like we’re doing now. And I think that’s when we may have also met for the first time around that time, or maybe we met in 2019.

Turner Novak:

I think we met before COVID.

Chetan Puttagunta:

Okay.

Turner Novak:

I’m trying to remember.

Chetan Puttagunta:

And then we met again because I think you made a trip out after.

Turner Novak:

Yeah, there was one, we got breakfast. Was it at the Rosewood?

Chetan Puttagunta:

Yeah, in Menlo Park. That’s right.

Turner Novak:

Yeah. It was maybe the second time we-

Chetan Puttagunta:

That’s right.

Turner Novak:

... met? Maybe that was the first time actually that we met in person. I can’t remember.

Chetan Puttagunta:

I can’t either, yeah.

Turner Novak:

Everything is hazy.

Chetan Puttagunta:

Because we’d met a bunch of times on the Zoom while we were in the pandemic. Anyway, so I think as a result of my schedule just going back to mostly in person, I just stopped tweeting, and I just thoroughly enjoyed meeting people in person far more than listening to earnings calls and stuff. And so I just stopped listening to earnings calls. And then if you stop listening to earnings calls, there’s not a lot to summarize. And then I just stopped paying attention to public software companies as a result and stop... You always still pay attention to public software companies in your own personal time and to understand the industry and stuff like that, but I wasn’t paying as close of attention as other people were, and I wasn’t looking at it first and offering first impressions. So it started to go back into the standard practice of I’d read earnings releases maybe two weeks after they came out or I’d read analyst report two weeks after it came out. And whereas in the pandemic, I would read it the day of.

Turner Novak:

It was like your entertainment, your event was like, “Oh man, Salesforce earnings at 1:30.”

Chetan Puttagunta:

That’s right, so that’s what I was doing. And so once we came back into the office, it was just delayed again. And then I think if you didn’t tweet about it right at the moment, I think there was a moment in time interesting too, which I thought was interesting that revealed itself, which is like, I tried a couple of tweets that was on earnings that were a month old or two months old and it was basically like people were like, “I already knew this.”

Turner Novak:

Yeah, you usually need a chart. You need a good chart that showed how AI adoption maybe was changing in Salesforce or something, but you run the rest of the day of earnings, somebody probably grabbed that and tweeted it.

Chetan Puttagunta:

That’s right. And so there was a timely nature to it. So I didn’t do that, so I stopped doing that. And then I think I’m back more on Twitter now with all this AI stuff happening. And I think the reason that I’ve started to reengage is-

Turner Novak:

Watching launch videos?

Chetan Puttagunta:

Yeah, sure. Launch videos are really impressive now.

Turner Novak:

They are very good.

Chetan Puttagunta:

Very high investment. I think the thing that I’m starting to tweet more about is just that the stuff that’s happening at the application layer with AI is a fundamental shift. And I think that it is a fundamental shift on the same scale of on-prem to cloud, and on prem to cloud took a long time.

Turner Novak:

Can you maybe give us... So Everett on your team was like, “Oh, you got to ask Chetan, give us a history lesson on the eras of software.”

Chetan Puttagunta:

Sure. Absolutely.

Turner Novak:

Can you maybe walk us through as early as you can, it’ll recite and talk us through all the way to today?

Chetan Puttagunta:

We’ll go all the way back to the abacus. Start there.

Turner Novak:

So King Nebuchadnezzar, back in the Babylonian empire.

Chetan Puttagunta:

That’s right. We came up with the number system, no, and then look, I think it started with mainframes. Mainframes is when we started to get real application software level stuff and we went from.

Turner Novak:

So this was massive machine that weighed 50,000 pounds.

Chetan Puttagunta:

That’s right, and that’s when people started to create customer applications to automate stuff. And whether it was for the defense sector, whether it was for the public sector, whatever, for private enterprise, that’s when you started to automate things using mainframes. That then transitioned to client server, and then you had the internet show up, and then the internet obviously changed everything with regard to application software so all of a sudden you had consumer applications and then you had B2B applications. Internet also helped centralize servers and how clients were served, it created edge networks.

Turner Novak:

Why is that all important? How did that change the business model?

Chetan Puttagunta:

It allows... There’s two, so every new wave, two things happened. The number of applications created exploded each time.

Turner Novak:

Every time?

Chetan Puttagunta:

Every time. The number of applications created to serve either businesses or enterprises exploded each time.

Turner Novak:

Is this it increases an order of magnitude, like 10X?

Chetan Puttagunta:

Yeah.

Turner Novak:

Okay.

Chetan Puttagunta:

But the internet was 100X, so it was two orders in magnitude. Each time there’s a big jump in the number of applications, and then there’s also a big jump in the number of companies creating applications for either consumers or businesses. And so these are enabling technologies. And then you had internet, internet showed up, and then it went from client server, and then you went to basically the cloud model, which was you centralize where all the servers are hosted, and then you serve people by the internet, the browser, and that ended up being the genesis of cloud. And then the cloud thing ended up being extraordinarily transformational.

Turner Novak:

What was the biggest kind of transformation?

Chetan Puttagunta:

I think number one of all was Amazon. And so Amazon, when they released EC2 and S3, which was cloud computing and cloud storage, in ‘09, ‘10, and ‘11 is when you started to really see that become very, very significant. And if you were around the Bay Area around that time, what you noticed was a dramatic shift in net new companies, they were all using Amazon. So if you were an app developer when the App Store first came out in ‘09 and your app was really working, you still had to go get server storage in downtown San Francisco.

Turner Novak:

You had to buy computers?

Chetan Puttagunta:

You had to buy a rack, and then you could go to Equinix and they’d sell you space and then you would have to rent servers from them, or you’d have to go to Dell or HP or you could go to Quanta if you really wanted commodity boxes and they would sell you these servers. And if you bought your own, you’d have to come put them in and then install them and wire them up. Or you could get the person that ran the facility to.

Turner Novak:

Hook it up for you.

Chetan Puttagunta:

You could rent the servers from them, or you could buy the servers from them, but this is what it required. And the crazy thing is that not that long ago, this is 2009, 2010. It’s just not that long ago.

Turner Novak:

Yeah, that’s like relatively speaking, I mean, I guess I’m almost 35 now. So I was graduating high school. To me, that’s not that long ago.

Chetan Puttagunta:

Not that long ago. And that was really the cloud. And it just so happened that it coincided with the launch of the App Store, which was in 2009. And so you had this cloud thing happening and you had mobile devices about to explode worldwide. And so the total demand for applications from both consumers and businesses was about to go... It was going to have two factors that were going to increase orders of magnitude so it was 10X multiplied by 10X. It was like you had cloud happening, which basically meant more application developers could develop applications.

Turner Novak:

It was just easier to get off that, off the launchpad.

Chetan Puttagunta:

That’s right. You didn’t need to think about servers. You didn’t need to think about renting space. If you were running demo apps or prototypes, the standard way to have done it was to bought a server and plug it into your apartment, and that’s where you’d host it. And then if you were a solo entrepreneur, this is how you do it, and then you’d go ship it. Once you were ready to launch it, you’d go get some servers, plug it in, and then hopefully it would work. And this thing was just, it was, one, capital intensive because you had to go invest the capital on compute and storage.

Turner Novak:

You also had to figure out how do I run this server thing?

Chetan Puttagunta:

100%, and it was expensive. The barrier to entry was high. And again, this is the other thing about each wave of technology. Each wave, the barrier to entry for developing an application went down. And so with cloud and mobile, it was, one, the cost of deploying a prototype or an initial version of application went way down because you could go put it in the cloud. And then two, the cost of getting to an end user also went down because you had this amazing distribution mechanism through the App Store, and so-

Turner Novak:

Just like a kid in the class would be like, “Check out the Snapchat thing.”

Chetan Puttagunta:

100%.

Turner Novak:

And just all these kids just downloading it and using it.

Chetan Puttagunta:

100%, that’s right. And then you could pay Amazon on a credit card and it would be consumption based.

Turner Novak:

So you technically didn’t even actually really pay upfront.

Chetan Puttagunta:

That’s right.

Turner Novak:

You could not pay your credit card for 30 days or a year really if you didn’t want to.

Chetan Puttagunta:

That’s right, you got a loan. Yeah, you got a 30-day loan, essentially. And so it was a remarkable unlock and you saw an absolute explosion in app layer. Obviously mobile apps, anything from like Instagram, Twitter, Snap, that all was unlocked there, Uber, Airbnb.

Turner Novak:

You’re just listing off Benchmark portfolio companies.

Chetan Puttagunta:

That’s right. Maybe not Airbnb. Yeah, Airbnb wasn’t, but.

Turner Novak:

Did you guys have a bet in the space? I’m trying to remember.

Chetan Puttagunta:

Of rentals?

Turner Novak:

Home travel type of-

Chetan Puttagunta:

This was before my time.

Turner Novak:

Got it, okay.

Chetan Puttagunta:

I don’t remember.

Turner Novak:

You’re not an expert on the portfolio?

Chetan Puttagunta:

No. I should be, but I’m not, unfortunately.

Turner Novak:

Bill’s going to be listening to this like, “Come on.”

Chetan Puttagunta:

We may have had some, but I think they did great. I think the 2011 fund is legendary for having just gotten mobile and cloud perfectly right.

Turner Novak:

Even WeWork. WeWork wasn’t even mobile or cloud, how did that even get in there?

Chetan Puttagunta:

That was Bruce, and so you’re going to have to go to him for the history on that one. I don’t know.

Turner Novak:

Yeah. What was that fund? What’s the performance on that one?

Chetan Puttagunta:

Benchmark VII, and we don’t publicly talk about our performance. You can find it on the internet, there’s plenty of-

Turner Novak:

Okay, what’s the number on the internet? What does the internet say?

Chetan Puttagunta:

That’s a good question. I don’t know. I think the last thing it said is... Yeah, you should Google it.

Turner Novak:

I’m Googling it right now.

Chetan Puttagunta:

Benchmark VII, and I’ll tell you if it’s high or low.

Turner Novak:

Said it was around $550 million fund in 2011. It says it was roughly 25X before fees.

Chetan Puttagunta:

There you go. Sounds pretty good.

Turner Novak:

Directionally correct?

Chetan Puttagunta:

Correct. Directionally correct. It’s a good fund.

Turner Novak:

And then you added in your Manus 1000% IRR.

Chetan Puttagunta:

Yeah.

Turner Novak:

Pretty good.

Chetan Puttagunta:

So that really unlocked the number of applications, ease of deployment, lowered the capital required. So that was mobile, that was cloud. We’ve been on that wave basically until, call it 2018, 2019. I think what you started-

Turner Novak:

And then COVID was an incredible-

Chetan Puttagunta:

Accelerant.

Turner Novak:

... period also, right? It kept going.

Chetan Puttagunta:

It was huge accelerant.

Turner Novak:

And almost foot on the gas even.

Chetan Puttagunta:

That’s right. Huge accelerant through 2020. And then the other thing that was happening though was that the SaaS companies, incumbents were starting to get really dominant. They controlled distribution, and they would start to eat up not only their core category, but would start to eat up adjacent categories. And so this is how Salesforce went from being just CRM. They did CRM, Service Cloud, integrations, et cetera. It became huge. Same thing with ServiceNow, they started with ITSM and then it expanded into HR, sales and all this kind of stuff.

These SaaS companies started to get really, really big. And if you just looked at it from a startup perspective, it was actually harder to get distribution in 2020, 2021 because the incumbents had gotten so big and so powerful and basically penetrated every enterprise account. And if you were a brand new startup, you would show up and be like, “Well, I could do this nichier thing 15 to 20% better.”

And then your buyer would be like, “Well, I could just go to the Salesforce thing and ask them for a discount and they’d probably just give me 5% off and that’s probably easier.” And so it was frictionful.

Having a net new company to find its niche and to be able to really go after a big horizontal category was really hard. And so what you ended up seeing 2020, 2021 was a lot of hyper vertical application software companies get created.

Turner Novak:

Give me an example for someone listening.

Chetan Puttagunta:

There were a whole bunch of companies that were created for the construction vertical as an example. There were a number of companies created for the compliance vertical. Some of them are doing really well now, of course.

Turner Novak:

So it’s basically like a what are things that Salesforce isn’t doing or won’t do?

Chetan Puttagunta:

Yeah. Salesforce, Workday, ServiceNow, what are they not doing? There’s still opportunities, and then people would go attack them. And some of them obviously became successful, but you didn’t see the same Cambrian explosion of applications that you saw in ‘09, ‘10, ‘11, ‘12. It just wasn’t this extremely fertile ground to create new startups, especially at the application layer.

Turner Novak:

It was mostly just a big, it got most of the value or-

Chetan Puttagunta:

That’s right.

Turner Novak:

... can store most of the value.

Chetan Puttagunta:

And obviously, things dramatically shifted starting in 2022.

Turner Novak:

Yeah. So how do you dissect what was going on in ‘22, ‘23?

Chetan Puttagunta:

GPT APIs were available in 2022, and this was the earliest signal we had gotten at Benchmark that people were thinking about developing new sets of applications. We would meet entrepreneurs that would be playing with these APIs and saying, “Hey, have you guys seen this thing? You can make this API call, you could do this and that, it’s generating all this interesting stuff.”

Turner Novak:

Maybe how like Jasper was maybe a breakout at that time.

Chetan Puttagunta:

Yeah. There were a bunch of people doing copywriting as the first use case because you could only interact with these things through APIs. And in 2022, November of 2022, obviously ChatGPT comes out. I think everybody that used the product that moment in time thought it was the most magical thing. And I think feeling like it was magical wasn’t a particularly unique insight. I think everybody thought it was-

Turner Novak:

Most obvious insight.

Chetan Puttagunta:

Yeah. It was like, “Wow, this is incredible.”

Turner Novak:

Yeah.

Chetan Puttagunta:

I think what we as a firm did at that moment in time, which I look back on was particularly insightful, was to then say, “This is obviously the way of the future. This is clearly what the experience of the future is going to look like, which is there is going to be some automated system at the back giving you the answer or finishing the task.”

It became pretty obvious to us around the table that that was the way of the future, and so what we decided to do at that moment was focus heavily on AI applications. I think this is one where if you’ve just been investing in software like I had for at that time, probably over a decade, this is the best thing that could happen to you as a software investor because now, as an early stage software investor, you’re a late stage software investor or a private equity software investor, you now have a lot of portfolio companies that might be threatened. But if you’re an early stage software investor, if you recognize the catalyst of a shift change and you think this is the next-

Turner Novak:

Shift change.

Chetan Puttagunta:

Yeah. As next catalyst as big as a cloud, it’s incredibly exciting to go all in on software applications again at that moment. So then, it became pretty clear to us that every large horizontal category was up for grabs again, and it was a good time to go back into it and.

Turner Novak:

So the Salesforce, the Workday, the ServiceNow.

Chetan Puttagunta:

All of those. All horizontal categories were up for grabs, and all we needed was entrepreneurs that saw the future by playing with ChatGPT and the OpenAI APIs, and then there were also net new categories to be created. You don’t only have to go after incumbents, you could create brand new categories and sell software into areas that hadn’t bought a lot of software before.

Turner Novak:

And that’s because the software could solve new problems for you that it couldn’t?

Chetan Puttagunta:

That’s right. And so this is where coding assistance and legal software, very specifically, those are not categories of software that are pre-established, legal as.

Turner Novak:

That was a dead zone.

Chetan Puttagunta:

It wasn’t great.

Turner Novak:

That was like a do not touch.

Chetan Puttagunta:

It wasn’t great. I mean, to be fair, I had one successful company there, a company called Logikcull, which was an eDiscovery, which I invested in and had a very successful outcome. It was acquired by a PE firm, of course, because it’s like once you get eDiscovery customers, there’s a lot of cash flow that shows up as a result.

Turner Novak:

What is eDiscovery? This is the process of learning more about the case and collecting evidence or something like that?

Chetan Puttagunta:

Yeah, so basically whenever you have to go through litigation, you have to go discover a bunch of facts against that case that shows up in emails and documents.

Turner Novak:

This is like when someone says, when we see those posts about the Steve Jobs and Apple.

Chetan Puttagunta:

That’s right, yeah.

Turner Novak:

It was through the discovery process.

Chetan Puttagunta:

Yeah. That was an eDiscovery software that found that email. And so those companies ended up being pretty interesting. There was a company in Chicago called Relativity that ended up becoming big. There were some companies in Europe.

Turner Novak:

This is basically just a SaaS document storage that was verticalized for legal.

Chetan Puttagunta:

100%, with search. Yeah.

Turner Novak:

Okay.

Chetan Puttagunta:

And so that was the only category in legal software that worked. But when AI shows up, you now can start to meet entrepreneurs that are dreaming big again in horizontal categories, and that also allows you to invest in AI app enablement. And so it’s like all of the frameworks, all of the new cloud infrastructure to host all these AI things, these all open up as opportunities. And so if you look at our investments on 2022 onward in AI, there’s a one central theme to it all. It’s all AI applications and application enablement, and that was very much on purpose because we just found that there was tons of opportunity there. In our view, it was also opportunity that a lot of people weren’t paying attention to in our industry because I think people at that time saw the magic of ChatGPT, rightfully, and then a lot of AI labs ended up getting funded at that moment in time in 2022.

Turner Novak:

How many did you guys invest in at Benchmark?

Chetan Puttagunta:

We invested in one open source one.

Turner Novak:

That feels like a classic Benchmark approach is the open source version of something.

Chetan Puttagunta:

Oh, yeah. I mean, as you know from our history, we deeply believe in open source, and in this case it would be open weights. And there were a lot of open weights models, including Llama models, but then more recently, if you look at models that are coming out of China that are open weights, these are really unlocking a lot of additional models in the US that people are borrowing techniques from these open weight models. They’re borrowing these open models themselves and then customizing them, crafting them, fine-tuning them, applying RL to it, all that kind of stuff. And so open weight models in general are a category that I fully believe in. And I hope that Llama continues, Meta with Llama models continue to keep them open. But I think that we spent a lot of time meeting with entrepreneurs building AI applications and entrepreneurs that wanted to enable the next generation of AI applications.

If you look at that fund and you look at the investments that we made, we made a number of seed and Series A bets at that time in 2022 in companies like Sierra, Legora, Fireworks, Levelpath, LangChain, that just are entrepreneurs building very horizontal applications that are attacking gigantic markets with truly innovative tech. And when you open this application and you experience it for the first time, the SaaS comparable looks really bad. It just looks like-

Turner Novak:

So give me an example of one of those contrasts that I might come across in the wild.

Chetan Puttagunta:

I think if you just ever interact with Sierra as an enterprise customer, if you were to buy, for this podcast, if you ever wanted a customer agent, you have two options. You could go to a traditional SaaS provider, legacy vendor.

Turner Novak:

So who would that be?

Chetan Puttagunta:

You could get a ticketing system, plus a CRM system, plus some kind of auto responder.

Turner Novak:

So what is this? Salesforce or Workday?

Chetan Puttagunta:

You could get Salesforce, you could get HubSpot, you could get Zendesk.

Turner Novak:

Zendesk, okay.

Chetan Puttagunta:

You could piece together lots of these softwares. And by the way, we invested a lot in these SaaS companies and I invested a lot in these SaaS companies, so they’re all great companies, have a great deal of respect for all the entrepreneurs.

Turner Novak:

They have good distribution that they built.

Chetan Puttagunta:

Incredible. But it’s the same thing that happened with these cloud vendors versus their on prem rivals. So Salesforce versus Siebel and Oracle, Zendesk versus Remedy.

Turner Novak:

We forget the name.

Chetan Puttagunta:

Remedy was-

Turner Novak:

Remedy, okay.

Chetan Puttagunta:

... I think the ticketing software before that, Workday versus PeopleSoft. The on-prem incumbents when the cloud software showed up just felt like a terrible piece of software because they were slow, they had all the setup, they were expensive, they were not interactive, they couldn’t be accessed anywhere.

Turner Novak:

Why didn’t they just go to the cloud? Why didn’t they just go to cloud app and fix it?

Chetan Puttagunta:

Because it’s really hard. I think you’ve read Innovator’s Dilemma and it applies to technology companies.

Turner Novak:

I actually haven’t read it.

Chetan Puttagunta:

Really?

Turner Novak:

I mean, I know the premise of that, haven’t actually read the book.

Chetan Puttagunta:

You should read it.

Turner Novak:

Maybe I should actually read the whole book.

Chetan Puttagunta:

You should.

Turner Novak:

Page to page.

Chetan Puttagunta:

You should read that. You should really read it, and you should read also Competitive Analysis. And so it’s like these classic business books and it’s... If you look at, if you have a great business and then you make a technology architecture and you build a large application, you now have built an application that’s serving thousands of customers and then you’ve built a huge distribution force to take that out and sell.

Turner Novak:

Yeah.

Chetan Puttagunta:

And so the business machine that you’ve built runs on this thing sustaining. So if you need to re-architect your platform, you need to pause everything, pause distribution, break your architecture, move it to the cloud, by the way, and then actually get it to work, have it scale and all that kind of stuff, and then teach your distribution networks to distribute this brand new product and then basically start from scratch, it’s really hard.

Turner Novak:

That’s hard.

Chetan Puttagunta:

It’s really, really, really hard. The thing that I think people really forget is that when Salesforce was really growing really fast, Siebel created a product called Siebel On Demand, which was their-

Turner Novak:

That just sounds like a not good product.

Chetan Puttagunta:

Siebel On Demand was the Siebel answer to Salesforce, and there were people that were covering Siebel at that time that said Siebel On Demand would crush Salesforce. That would be the end of Salesforce, would be Siebel On Demand.

Turner Novak:

Were they both publicly traded at the time?

Chetan Puttagunta:

Yes.

Turner Novak:

Okay. So I’m sure Salesforce stock dropped like 10% the day of launch or something.

Chetan Puttagunta:

I’m sure, whatever. But Siebel On Demand didn’t work because the Siebel on-prem product was just so profitable that, and Siebel On Demand may have worked as a product, but it had deficiencies against the Salesforce product. And then Salesforce was, because they had built that company from scratch, was able to distribute it far more efficiently than Siebel could. Just the company structure was built for selling licenses for 5,000 bucks or 10,000 bucks to a company where Siebel was built to distribute licenses that were million dollars or greater. And so Siebel On Demand would come and quote you $100,000 and the Salesforce rep would be like, “You can have mine for 10,000.” And yeah, Siebel On Demand is 10% worse.

Turner Novak:

Why would you even sign up for that if you’re doing any research at all?

Chetan Puttagunta:

That competition becomes so uneven.

Turner Novak:

So that the distribution wins, that assumption assumes that they’re not going to look at comparable product when it’s so easy to just-

Chetan Puttagunta:

That’s right.

Turner Novak:

... salesforce.com, click a button, we’re in the product-

Chetan Puttagunta:

That’s right.

Turner Novak:

... and you can put in your credit card-

Chetan Puttagunta:

That’s right.

Turner Novak:

... and just use it.

Chetan Puttagunta:

That’s right. Now, fast-forward to today with AI applications, we’re going through the same thing again. For an AI application to become, for a SaaS company to beat a native AI application, I would argue that they would have to break their fundamental architecture and rebuild the application and redo the business model and reteach their distribution on how to distribute the thing. The advantage of having an AI application company start from scratch is you build this thing AI first from point zero, you don’t create any code that’s not AI friendly.

The first set of salespeople you hire and the first set of marketing hires you make are all intended to distribute this AI product, priced as however you want to price it, consumption, seats, whatever, but it’s priced like an AI product. You go against the SaaS vendor in these things, it is, to me, the early signs are astounding how fast these AI applications grab traction and grow and how quickly they’re able to displace traditional SaaS vendors. And I think on Twitter, or X, we’re seeing a lot of-

Turner Novak:

You can call it Twitter, I refuse to call it X.

Chetan Puttagunta:

I think you see a lot of people saying the SaaS companies can be really replaced by Claude Code. I’m not sure I buy that. I think that-

Turner Novak:

Of the, “Hey, make me Salesforce.”

Chetan Puttagunta:

That’s right.

Turner Novak:

“Make no mistakes.”

Chetan Puttagunta:

That’s right. Yeah, or build me a billion dollar software company, you make no mistake.

Turner Novak:

Yeah, 10 billion.

Chetan Puttagunta:

Right. I don’t think that is what’s going to happen, but I do think that if you look at legacy SaaS, their business models are seriously going to be challenged by AI applications that deliver 10X the value for a third of the price. And if you’re an enterprise and you can buy an AI native version, you will buy it. I can’t see why you wouldn’t. You see the experience and it’s that stark.

I’ll give you a couple examples. If you look at Sierra and you deploy it as a customer service agent, it is absolutely a mind-blowing experience. You don’t need a ticketing software, you don’t need call routing software. You don’t need all this stuff to make tickets that customer issues that are coming into your company go away. It just goes into Sierra, and there’s a resolution. And guess what? Your customers are happier and you’re basically replicating your single best customer agent to infinity. That is what Sierra is.

So all of a sudden, your customer satisfaction goes through the roof, your business metrics get a lot better. Your renewals get better, your expansions get better because customers are actually happy with their experience. And so comparing that with legacy, which is chaining together four or five software solutions, it’s just hard to really compare them. And then the question then becomes why does an existing software person just like do it?

Turner Novak:

Yeah, because I was going to say, if I’m Marc Benioff at Salesforce, I went through this, I see why I won and I see the advantage I had over Siebel. Do I look at saying it’s like, “Oh man, this could happen to me.” I should know that this is coming, shouldn’t I?

Chetan Puttagunta:

That’s right. I think that if... And of course, look, I think Marc Benioff is one of the greatest entrepreneurs of all time. He’s also been an extraordinary friend of startups, and how open he’s been with Salesforce APIs and the ecosystem and all that. I think I only have great things to say. I think that Salesforce should buy a lot of companies now. I think that one of the things that Salesforce has always done really well historically is buy the right companies at the right time.

Turner Novak:

I was going to say Slack is an example. Is that a bad example?

Chetan Puttagunta:

No, it’s not a bad example.

Turner Novak:

I feel like that one’s been ridiculed of too high a price maybe.

Chetan Puttagunta:

Too high a price. Yeah.

Turner Novak:

I have a friend at Slack, she’s like, “They fucking ruined it.”

Chetan Puttagunta:

They ruined it?

Turner Novak:

Yeah. She’s not bullish on Salesforce, or Slack as a part of Salesforce.

Chetan Puttagunta:

Yeah. If you just look at Salesforce history, I think people forget that it was founded in the late ‘90s, and just in different waves. For example, when Marketing Cloud took off, they went and bought a great marketing cloud company. When commerce took off, they went and bought Demandware. They were making key acquisitions at the right times throughout their growth trajectory, and they were actually very good at M&A, and Salesforce Ventures is an incredible investor. They’ve invested in great companies and they continue to invest in great AI companies. I think one of the things that they should do, and of course I’m telling a public company what to do so I have great level of humility to assume that they would listen, but I do think that if you’re-

Turner Novak:

I’m going to cut that part out, by the way. I’m not going to let you say that, I’m going to make you look like, no, just kidding.

Chetan Puttagunta:

I think if you’re a SaaS company right now, you should really think about spending 10 to 25% of your market cap to buy AI applications. I really think that’s a good idea. And I think if you’re an AI application company like Salesforce or ServiceNow or Datadog or whatever, name your favorite SaaS company that’s public or private, I think they really should go buy AI applications that they then could feed into their distribution networks or these AI application companies come in and build them a new distribution network. I think it’s one of those things, if you study the history of software, the history of application software, there are moments in time when the on-prem vendor should have just bought the cloud thing. The best example of this is BMC, which was ServiceNow before ServiceNow, should have bought ServiceNow way before it got as big as it got.

Turner Novak:

Aren’t they the seventh or either biggest software company now or something?

Chetan Puttagunta:

Yeah.

Turner Novak:

Yeah.

Chetan Puttagunta:

Of course. But BMC should have bought it, and there were moments in time where I think BMC could have bought it. And I think if they showed up, they had the market cap to buy it, they had the wherewithal to buy it, but they didn’t because it was like, “Well, we’re trading at X revenue multiple and I don’t want to give ServiceNow 20X revenue multiple,” whatever. But in retrospect, it completely changed the game.

The other one is obviously Salesforce. I mean, there were times when it was rumored that Microsoft was going to buy it. There were rumors that could Oracle ever buy it? Salesforce just completely cleaned out CRM from all the on-prem vendors, and all those businesses just ended up going to zero. And so it could have taken Salesforce out much earlier in its journey. So if you just look at every big category winner in SaaS, there was an opportunity for the on-prem company to make a big acquisition and make something of it, and they didn’t.

And I think that if you’re watching the AI application thing happen, you’re starting to see M&A sort of pick up, but it’s not really at the pace that I would encourage these companies to think about it, which is just, you really should jump into this game and buy some of these things because these companies are about to get gigantic. They’re getting to 100 million. I mean, Manus went zero to 100 in eight months. These companies are getting really big, really fast. They’re going to go 100 million really fast. Once they’re getting to 100 million, they continue to scale beyond that.

There’s lots of questions about margin profile and all this kind of stuff. And I’m telling you these AI application company P&Ls, maybe they don’t look as pristine and as predictable as super mature SaaS companies, but the early days of SaaS companies, those P&Ls don’t look that pristine either. People just should go look at the Workday S-1 and look at the gross margin of Workday in the early days.

Turner Novak:

What was it?

Chetan Puttagunta:

So interestingly, the software had around 75%, 80% gross margin, but they were selling services to implement the software at negative gross margin. So blended, they had gross margin some years in the 50s or even below 50. And that was fine. And I think there was one year, if you go to pull up the S1, either it was the first or second year they had negative gross margin, which is fine because.

Turner Novak:

So in the S-1, you’re going public as a software company.

Chetan Puttagunta:

Like one of their original years.

Turner Novak:

Two years out was like, “We have negative gross margin.”

Chetan Puttagunta:

Yeah. And then it got better, obviously. And then the year right before they went public, they had great gross margin.

Turner Novak:

Really good, yeah.

Chetan Puttagunta:

But you could see that evolution in that S-1. And so to me, it’s like if you’ve been around software long enough, you’ve seen some of these patterns before. It’s like, do not complain about a negative gross margin software company if you’re seeing the patterns that you saw in the last phase, which is like people are implementing these things, treating these things as systems of record, whatever. There’s some kind of gravity around the workflow or the data or whatever, and these things end up becoming a core part of a business. They’re not getting ripped out. The only way that business account goes to zero is if the business customer goes bankrupt or goes out of business, otherwise they’re paying for this piece of software, it becomes that essential to the business.

And if you look at that kind of trend, it’s like, “Yo, this is happening.” And I think you’re going to start seeing the first set of S-1s for these AI applications 2027, 2028. And I think people are just going to be really surprised at how much these companies look like software companies. It’s like, yeah, they look like software companies. And then instead of paying a ton of gross margin to the cloud vendors, we’re just paying a ton of gross margin to inference providers.

We’re either paying OpenAI, Anthropic, Google, or paying Coreweave or Fireworks for inference tokens. That’s where the cost of goods is going, and that’s okay. And then companies get better at optimizing that and getting more efficient, and so it’s a real wave. I think the private markets have fully realized its opportunity. And I think this is why you’re seeing application companies are, it’s a very attractive category for venture today, but I’m not sure that the public markets have quite embraced this, and I’m not sure public companies have quite embraced this.

Turner Novak:

So why not? Because if I’m public market CEO, I look at my stock and I trade three times revenue or whatever, and I’m like, “These fucking kids are getting 200 times revenue and they’re so small,” maybe they’re growing fast, whatever. Why do you not think they pulled the trigger on some of these acquisitions?

Chetan Puttagunta:

Well, I think it’s human nature. I mean, imagine you’re a software company that’s run the company for a long time and you’re trading at three times revenue as a SaaS company and you’re like, “Okay, I should go buy the AI version of this thing,” and you have to now pay 20 times revenue, 50 times revenue. You’re at 90% gross margin. Your AI alternative is at, I don’t know, let’s say 20% gross margin. That’s a pretty-

Turner Novak:

Bad deal.

Chetan Puttagunta:

Yeah.

Turner Novak:

It seems pretty stupid, the way you just described those numbers-

Chetan Puttagunta:

That’s right.

Turner Novak:

... sounds not good.

Chetan Puttagunta:

Imagine being in that room where you’re trying to pitch that deal to your management team, or to your board, or to yourself.

Turner Novak:

And it’s probably 20% of your market cap or something like that where it’s like almost to bet the farm.

Chetan Puttagunta:

That’s right.

Turner Novak:

It’s probably close to that threshold.

Chetan Puttagunta:

This is, again, I would just ask people to look at the public markets of 2012 and look at where SaaS companies were trading then and compare them to their on-prem rivals. SaaS companies were trading at 20, 25 times revenue. If you just look at where ServiceNow and Workday went public, they were trading at the time and you could just look at their coverage of these valuations. People were calling them absolutely insane. That’s what they were calling them. Salesforce was always considered an ultra expensive stock in the beginning. So was ServiceNow and so was Workday. They were all considered wildly overpriced.

Turner Novak:

Well, a lot of it too is just they don’t have any cashflow profitability. If you’re trying to pull up the financial statement and just, oh, according to the statements, free cash flows, they’re trading at 800 times free cash flow, it’s overvalued.

Chetan Puttagunta:

Sure, all of that is fair. But the interesting fact on ServiceNow actually is that they were cashflow positive from year two or something, something outright. It was an ultra efficient business.

Turner Novak:

But most good businesses are, they’re quickly profitable.

Chetan Puttagunta:

Yeah, they’ve become very capital efficient. Salesforce was very capital efficient too. So if you just look at 2012 as a case study, look at where SaaS companies were trading, and look at where the on-prem vendors were trading. I just talked about how those on-prem vendors should have bought the SaaS companies. They should have just paid 20 or 30 times and that would’ve been the right business answer.

Turner Novak:

Yeah. I’ve heard actually from Benioff is he would’ve sold. He needed a 40% premium and people would only offer him 30%.

Chetan Puttagunta:

There you go.

Turner Novak:

He just never sold.

Chetan Puttagunta:

There you go.

Turner Novak:

Because he never got the price he wanted.

Chetan Puttagunta:

There you go. And so there were good deals to be had, but at the same time, they look like bad deals to the on-prem companies because it was like, “I’m trading at 20 times free cashflow or two times revenue and you want me to pay 25 times revenue and 800 times free cashflow to buy this SaaS thing?” It seems like.

Turner Novak:

It could be a fad. SaaS could be a fad.

Chetan Puttagunta:

100%. SaaS could be a fad. AI could be a fad. This is where you get into the circular thinking of not doing the deal and you just get stuck. And I think if you just play this out, these AI application companies were much cheaper to acquire in 2023, and they were in 2024, they were in 2025, then they’re going to be in 2026, they’re going to be in 2027. And it’s happening. It’s happening right in front of us. We’re just seeing this happen.

And I have to tell you, from a venture investor perspective, it’s a really fascinating cycle to live through, because I was an investor in the first cloud cycle. I would always think, “Why aren’t these people making the move? They should buy these SaaS companies. This is obviously the logical thing to do.”

And here we are, again, for me, sitting in a second cycle and I’m saying the same thing. And it’s really interesting that the SaaS companies have forgotten their own state that they were in. They have forgotten their own position in 2012 and ‘13 and ‘14, and what it would’ve taken for an incumbent to buy them. They are now the incumbent and not embracing what it takes to buy the upstart.

Turner Novak:

So if I’m an upstart, if I’m the founder of an AI company, and I just heard everything you just said, why would I sell? Because I’m going to beat the SaaS companies in three years.

Chetan Puttagunta:

Yeah.

Turner Novak:

I’m going to be bigger than that, why would I sell to that?

Chetan Puttagunta:

That’s right. I mean if you, like you mentioned, some founders just know there’s the number and there’s all these famous stories about Google named a price to Yahoo and Yahoo said no.

Turner Novak:

Yahoo could have been the largest company in the world.

Chetan Puttagunta:

Yeah.

Turner Novak:

Google, Facebook. I bet they talked to Amazon at some point.

Chetan Puttagunta:

I’m sure. There’s a story of Facebook and Yahoo, something like-

Turner Novak:

Billion dollars or something.

Chetan Puttagunta:

Ordered a billion, and then there’s some kind of counter, something, whatever. There are these famous stories of-

Turner Novak:

And Zuck was like, “Well, what would I do if I sold? I would just start another social network.”

Chetan Puttagunta:

That’s right.

Turner Novak:

“So why would I sell it?”

Chetan Puttagunta:

That’s right. There are these notes in history where companies, they named a number to the incumbent and said, “Okay, you just have to get here.”

And of course, in some cases, the incumbent did get there and that’s how you have giant SaaS acquisition, or the incumbent didn’t and then those companies went on to be independent and got gigantic. And so I think that some founders may just have a number in mind that if the incumbent hits 40% premium to their current stock price or whatever, that might be attractive.

But I think what I’m surprised by is how few people are trying. I would’ve expected many of these doors to be M&A people all over those companies being like, “What would it take?”

Turner Novak:

Do you think part of it is that there’s so much late stage capital that if I’m a founder, it’s not as hard as it maybe could be or should be to fundraise? And yeah, I could take a deal I could sell to Salesforce, but also there’s 18 people that are giving me $100 million and to keep going, it actually makes that easier-

Chetan Puttagunta:

It does.

Turner Novak:

... to stick on the path.

Chetan Puttagunta:

Absolutely. The private markets have gotten way bigger today than they were in 2012 and 2013, 2014, so that makes it much better. I think the other part of it is going public has gotten harder for companies, and I think there’s just-

Turner Novak:

Why is it harder? It’s the same thing. I mean, what’s so hard about it now?

Chetan Puttagunta:

The difference between today and even 2007 and ‘08 is the number of things you have to do to be a public company, there’s just more to do. Now, is it really difficult? No.

Turner Novak:

You should just hire a couple more people. It’s not that hard.

Chetan Puttagunta:

Hire a couple more people, pay a couple more consultants and they’ll do it for you. And so what I think is going to happen, and I think you already see it from the bankers, is there is a growing demand from public software investors, software PMs.

Turner Novak:

Because they’re looking at their universe and they’re like-

Chetan Puttagunta:

That’s right.

Turner Novak:

“This thing’s shrinking.”

Chetan Puttagunta:

That’s right.

Turner Novak:

“What is up with this company?”

Chetan Puttagunta:

You can already tell from our conversations with investment bankers, they’re already telling us, the software investors are telling us to bring them the AI application companies. And so for the first time in a long time, you’re starting to see investment bankers talk to companies under $100 million of run rate saying, “Do you guys want to start doing non-deal roadshows where you start meeting PMs of public software investors?”

This is quite a shift. A couple years ago, people would say, “Oh, you need 500 million of ARR before you can talk to any public investors because if you don’t get there, nobody wants you to go public.” Very different.

Whereas we just had a conversation with a banker who wants to organize a non-deal roadshow for one of our companies, and the company’s not yet at 100 million because it’s fundamentally really interesting technology, and there is a great deal of demand from public software investors to meet these companies. And for the first time in probably a decade, I’m hearing bankers say things like, “Yeah, 100 million ARR, we could probably take that public.” Haven’t heard that in a while.

Turner Novak:

Really? And it’s probably just because, I mean, it’s really the companies are growing fast and that’s all investors care about. They just want you to grow-

Chetan Puttagunta:

100%.

Turner Novak:

... as fast as possible-

Chetan Puttagunta:

That’s right.

Turner Novak:

... in a semi-healthy/will be healthy at the end state.

Chetan Puttagunta:

That’s right. I think if you just look into the public markets, how many companies in software are growing greater than 30%?

Turner Novak:

It’s zero.

Chetan Puttagunta:

That’s right.

Turner Novak:

It is right now, right?

Chetan Puttagunta:

Yes. And so-

Turner Novak:

You can’t be growing less than 3X to raise a Series A in venture land.

Chetan Puttagunta:

There you go.

Turner Novak:

If you’re below 3X year over your growth, I would say it’s probably better just to get the growth rate up, how to grow faster versus spending time.

Chetan Puttagunta:

Actually, I think the 3X thing is probably overdone. I think if you’ growing 2.5X still private.

Turner Novak:

Okay. Well, fair, yeah. But there’s a lot of private companies double from 50 to 100.

Chetan Puttagunta:

100%.

Turner Novak:

That would be really attractive.

Chetan Puttagunta:

And there are a whole bunch of private companies that want 50 to 150.

Turner Novak:

Yeah.

Chetan Puttagunta:

And it’s just-

Turner Novak:

And there’s public market PMs are like, “Give me that. I want that.”

Chetan Puttagunta:

That’s right. That’s exactly what they want. And they know because public market PMs also are stepping into private markets and meeting these private markets companies on their own and saying, “Wow, there’s a ton of growth in these companies.” If you’re sitting here as a software PM, you’re looking at your universe of software companies that are available to you as a public market investor. If I was sitting in that seat, I would be demanding the bankers bring me-

Turner Novak:

Give me this.

Chetan Puttagunta:

... AI applications, because again, if you’re a software PM that invested through SaaS, and I was talking to a hedge fund manager who was, at one point through SaaS, he was telling me he was long on a hundred software names.

Turner Novak:

Okay. And it would probably all grow in 50% a year.

Chetan Puttagunta:

He went long software starting in 2010, and he just decided this is clearly the future. And every time a software company came public, he figured out a way to enter that company, and they were extremely successful, et cetera. And his comment to me is, “When are you bringing your AI application companies to the public market? We need those in the public market because we’re completely starved for growth and all the growth is being just taken by these AI native companies. They’re all taking all the growth.” If you just look at net new ARR added, where’s it going? It’s really just going to all these AI companies.

Turner Novak:

I think maybe you saw the stat, you might know what I’m talking about, since ChatGPT launched, I believe this is about a quarter ago that I saw the stat that OpenAI and Anthropic added as much revenue as every single publicly traded software company. Did you see the stat?

Chetan Puttagunta:

I buy that.

Turner Novak:

And I mean, this was three months ago, so it’s probably even bigger now.

Chetan Puttagunta:

Right. And so I think there’s press about OpenAI revenue that had gone from six to 20 billion this year. I mean, that’s a lot. Net new 14 is a lot.

Turner Novak:

Yeah. Well, and part of the argument though for some of these AI companies is, oh, the valuations are so high.

Chetan Puttagunta:

Yeah.

Turner Novak:

How do you square that up then if you’re thinking about, what am I investing into? Maybe you’re doing a Series A, you’re doing a seed round, or you’re doing, it’s a public company but.

Chetan Puttagunta:

That’s right.

Turner Novak:

How do you justify the higher valuations on some of these companies?

Chetan Puttagunta:

I think it just depends on your fund size and your strategy. We have a very specific fund strategy. We’re a 500 million dollar fund with four equal partners. You know Eric, you know Ev, you know Peter, you know me.

Turner Novak:

I actually don’t know Peter, I’ve never met Peter before.

Chetan Puttagunta:

Oh, well.

Turner Novak:

I want him on a podcast. It’d be cool to meet him and have him on sometime.

Chetan Puttagunta:

Yeah. So it’s four of us and we’re investing in seed and Series A companies. And the primary goal of each of our investments is that we want to be the primary board member for the company. That’s the goal of every investment. And if a company’s not looking for that, then we don’t have a role to play. And so the valuation frankly is not the governor in our investment decisions. If you were to be a fly on the wall in one of our partner meetings, the discussion really isn’t about the valuation or the deal structure. We don’t spend very much time on that at all. The conversation is really about the company and does the partner that’s advocating for the company want to work with that entrepreneur, and do the rest of us want to work with that entrepreneur too and help them support and build something really meaningful?

And oftentimes, what you’ll find in our conversations is that when one of our partners is excited about a company, you’ll quickly find that the other three partners encourage you to lean in. I think this is where our incentive structure really helps because we all share economics equally famously, and so if my partner is excited to work with an entrepreneur to help them build something big, I want them to go do that. It’s like, “Yes, please, go invest.”

Turner Novak:

Go make me money.

Chetan Puttagunta:

Yes.

Turner Novak:

That’s, yeah.

Chetan Puttagunta:

100%, and so our incentives are fully aligned on that. And so when somebody gets excited, it’s very clear the firm helps rally and helps them gain elevation, helps them finish that investment. And so to us, that’s the governor. And then at the other side of that is also by having four partners, each of us probably has capacity to do two investments a year. And so as a fund, we’re doing, call it eight, nine, maybe 10 investments a year?

Turner Novak:

Someone gets really excited in one year.

Chetan Puttagunta:

That’s right. And so that means that you have on any given day, your time as the governor of, where do you want to spend time with? Who do you want to spend time with? And so that’s ultimately it. And that’s our strategy. And so that means that we have decided that that means that there’s a typical investment size and a typical ownership that we’d like to go for. And of course we’re very flexible on that, it’s like there’s no rules. We don’t have written rules that say we’re only going to do it if we get this much or that much.

Turner Novak:

The classic venture model is 20% Series A, $15 million check or something like that.

Chetan Puttagunta:

Well.

Turner Novak:

Or maybe, I don’t know.

Chetan Puttagunta:

When I started in the business, it was a little less than that.

Turner Novak:

Yeah, I don’t know. It’s all over now.

Chetan Puttagunta:

We’re doing something.

Turner Novak:

I mean I saw a, $4 billion seed round the other day.

Chetan Puttagunta:

There you go.

Turner Novak:

I don’t know, what is this anymore?

Chetan Puttagunta:

But I think for us, to be clear, we still have those rounds where you can write small checks and get meaningful ownership. This is part of the incubation effort that we have. We have EIRs, we’re helping incubate companies, and I think those opportunities still exist when you’re building relationships that early. And then there’s certainly companies that are much further along that have a little bit of traction or whatever, and they’re commanding a different market price, and that’s okay. And for us, as long as we have a relationship with the entrepreneur and can serve on the board, that’s cool. We’re flexible on that.

Turner Novak:

What’s the most untraditional kind of venture round when I’m a venture purist and I would scoff? What’s the one you think they would be the least characteristic of a-

Chetan Puttagunta:

You know what’s really interesting is that if you look through the history of Benchmark, there are times when Benchmark did these non-traditional investments. So if you look at the internet era, I don’t know if you know this, but Nordstrom spun out nordstrom.com and Benchmark invested in that corporate spin out as an example.

Turner Novak:

I didn’t know this. I know you guys invested like Jamba Juice. That was probably the craziest one I saw, it’s a movie shot.

Chetan Puttagunta:

If you want to scoff at traditional venture, there’s examples like this throughout our history. And perhaps the most famous, and I remember because I was just entering the ecosystem then, was when Benchmark did the growth round at Twitter, that was a very unusual move for Benchmark at the time.

Turner Novak:

Was it a Series C or something?

Chetan Puttagunta:

Yeah, that’s right. It may have been a Series B or a Series C, and that was considered a late stage round at that time, and it was very unusual to see a very early stage firm like Benchmark do that round. And so I think the thing that people like this narrative of somehow there was only one set of ideal deals that Benchmark has ever done since the founding for 25 years and all of a sudden the model has to change. It’s like, no, the model has always been you have a small set of partners working with companies that they really want to work with. A set of partners that really want to back a certain of fundamentally game changing ideas that end up becoming really large standalone companies. And that idea then has resulted in lots of flexibility on the other side of what does that structure look like? And the one thing that we haven’t done is created a family of funds and we haven’t gotten big as a partnership. We’ve continued to be small.

Turner Novak:

There was an era, early Benchmark, you went to Europe.

Chetan Puttagunta:

That’s right.

Turner Novak:

I remember an Israel fund maybe too.

Chetan Puttagunta:

That’s right. There was Benchmark Europe, Benchmark Israel, Benchmark US. I was in the venture business then. I wasn’t at Benchmark, but yeah, Benchmark had and then decided to get small again, and we’ve been small since.

Turner Novak:

And you think that was the right move?

Chetan Puttagunta:

Absolutely.

Turner Novak:

To get smaller?

Chetan Puttagunta:

Absolutely. I think now other people have built incredible franchises by getting big.

Turner Novak:

Yeah. There’s people that do more in management fees every year than your fund size.

Chetan Puttagunta:

That’s right. Multiples of our fund size in management fees. I think that’s a great business. And I think you’ll eventually see venture firms that are public too. And I think that’s okay, I think that’s great. And I think ultimately, you have to come back to the partners themselves and what kind of organization do they want to be a part of.

And for us, we want to be a part of an organization where you could do deals like Manus and Sierra and Legora and Fireworks and LangChain all in a span of 12 months. All of those deals are completely consistent with how we want to practice the venture business. And specifically, in all of those, you have a Benchmark partner partnering with a founder, joining the board, and working with the entrepreneur to create a really big business.

Turner Novak:

Do you think that is maybe an outdated model of that we must own 20% in your Series A? That approach of I have this rigid portfolio construction based on the rules that have become memes over the years, whatever, is that not a good approach to venture anymore?

Chetan Puttagunta:

I think everybody should approach venture however they want to approach it. Everybody’s on their own journey, everybody has their own strategy.

Turner Novak:

This is the most complicated, but I mean, I’ve heard Eric told me, he’s just like, “We’re trying to just find people building the best companies and just be there and own a part of it and that’s how you make money in venture.”

Chetan Puttagunta:

That’s right. And I think, look, there is a model that works really well, which is the YC model. They have a very specific structure, specific amount of money, there’s an incubator program, and there’s a specific ownership on the other side of that. And I think that’s a fantastic model. And I think YC is great value for founders.

Whenever founders ask me the question of, “Should we do YC?”

I say, “Absolutely, yes.”

Turner Novak:

A good chunk of your personal portfolio companies have done YC, right?

Chetan Puttagunta:

That’s right, yeah. I am a huge fan of YC. I think it’s a great program, I’m a big fan of the partners there. And so I think YC has a very specific structure.

There are other incubators and other early stage funds that have very specific things that are like, “We only want to do deals that are this specific tech size, this kind of ownership.”

And I think that’s a winning strategy as long as you don’t have FOMO and you very specifically focus on, “I only want to do these kinds of investments.” Okay, great.

But the way we’re structured is we’re generalists, we’re a group of generalists, and we want to invest in really exciting companies. And look, the last investment I did was a crypto company.

Turner Novak:

Oh, this was Fomo.

Chetan Puttagunta:

That’s right.

Turner Novak:

I was like, “What the hell is this?” I was like, “What?” You’re like, enterprise... I always thought of you as enterprise software.

Chetan Puttagunta:

That’s right.

Turner Novak:

Manus is consumer AI, and crypto.

Chetan Puttagunta:

Yeah, consumer crypto. So if you look at the two investments that I made in 2025 were both consumer apps.

Turner Novak:

You’re rebranding, you’re going through a phoenix moment of enterprise SaaS has been burned to the ground by the public markets.

Chetan Puttagunta:

But I think fundamentally, if you go back to, again, what is that motivated by? I just thought the entrepreneurs in both cases were extraordinary. And as a generalist, you understand what they’re working on. You have a deep appreciation for the product they’re building, and how they’re approaching the problems. And honestly, I just wanted to work with both of them.

I just really think at Fomo, it’s Paul and Se, they’re amazing. They’re just extraordinary entrepreneurs that want to bring a totally brand new experience of crypto to consumers. You’re well versed in this, versed in crypto, but.

Turner Novak:

I mean, I don’t know. I’m just like, “It’s all a scam.” It’s just people scamming you. So what’s the different thing? Are they scamming you in a more polite way?

Chetan Puttagunta:

Absolutely not. Crypto is, to me, the way I think about crypto is that it has a huge on-ramp and there’s a big barrier to entry. I think that’s part of why there is fraud and scams is because it’s very hard to onboard onto crypto, and very hard to manage crypto.

Turner Novak:

Yeah. I remember buying my first NFT. Everyone’s like, “Oh, this is the future. It’s so easy.” It took me 30 minutes to-

Chetan Puttagunta:

100%.

Turner Novak:

... get MetaMask up and I bought this NFT.

Chetan Puttagunta:

That’s right.

Turner Novak:

And I was like, “There’s no way.”

Chetan Puttagunta:

That’s right.

Turner Novak:

“There’s no way this is like, your plane ticket is an NFT? Come on.”

Chetan Puttagunta:

That’s right.

Turner Novak:

This is not.

Chetan Puttagunta:

The experience is so frictionful that... And then it’s very easy to analogize back to my own personal experience, which is like crypto to me has been hard for me to experience as a software investor because it was such a frictionful experience to get crypto or to get an NFT or to experience anything on chain. And I met Paul and Se, and they told me to download the Fomo app and try something, and I did and it was consumer grade, and I was like, “Whoa.” And there’s a built-in social graph and all this kind of stuff with UGC. All these kinds of elements that are very classically software just applied to a new industry.

Turner Novak:

If I search Fomo in the app store, will it come up?

Chetan Puttagunta:

Yeah, absolutely. And I think that these are the kinds of things that I look for.

Turner Novak:

It begs another question of what do you, or what do you feel like Benchmark looks for in founders that you’re trying to back? We’ve talked a lot about a bunch of different stuff, but if I were to soundbite this, what is it that you guys are looking for?

Chetan Puttagunta:

The through line for the entrepreneurs that I’ve worked with is that they have some deep insight on the problem that they’re really passionate about. And it could be just serially going backwards from the investments I’ve done, it’s like could be consumer crypto, could be consumer AI, it could be legal AI, it could be document processing, could be sales tax. It could be stablecoins, it could be payment rails, it could be integration software, all of these things. The common line in all of them is I’m typically investing seed, Series A, usually there’s no product, usually there’s no revenue, usually there’s no metrics.

For example, Manus, I did pre-launch. This was a beta product, and there’s some deep insight that the founder has, some deep perspective that the founder has. And intuitively, as soon as I hear that, you’re like, “Yes, that’s absolutely obvious and that’s how the world should function.” And when I hear that, I want to work with those founders. And for me, that’s like the number one thing above all else.

The great thing about being an early stage investor is you get to go with these founders on these journeys and then it’s okay if it doesn’t work. The thing that is a mistake as an early stage investor is missing out on the companies that work, not investing in companies that don’t work. If a company doesn’t work, it’s okay. It’s a 1X error. If a company works, it can generate a lot of returns.

Turner Novak:

1000% IRR.

Chetan Puttagunta:

Yes, that’s right. And so you just want to be in companies that work, and so you don’t worry about the downside. And so when you have this view, as I do, that you just want to work with founders that have a deep passion for a sector or a problem and have some unique insight as to why that opportunity is now available and why they’re uniquely positioned to address this problem, I want to back them.

Turner Novak:

Maybe this is an interesting, probably last thing we can talk about. You told me that one of those interesting insights was with code gen, some things that you saw, some stats. It’s also a company where they publicly, there’s negative gross margins. There’s people like, “Oh, these companies are...” You read some of the consensus maybe a year or two ago, it’s like six months and these things are going bankrupt.

Chetan Puttagunta:

Right.

Turner Novak:

So what was the thing you got excited about there? And then the margins, how did you get comfortable with that?

Chetan Puttagunta:

Look, we’re investors in Cursor. I think code generation, and look, one of the Manus primary use cases was also code. I think that we’re very early in how much code can be generated for the world. And two, the thing that surprised me is how much demand there is for code generation across consumers, B2B, prosumer, there’s just massive demand for code generation products. I do think that the margin question at the moment is a little too early to make a final verdict on, we don’t know. And the thing that is happening that you may have seen, for example, is where Eric is on the board of a company called Cerebras, which is a very specific AI chip that speeds up inference. Once that chip starts to propagate and you start to see AI technology run on that chip as an example.

Turner Novak:

It’s like an AI native chip, right?

Chetan Puttagunta:

That’s right.

Turner Novak:

Where it’s built for running AI native workflows on top of that.

Chetan Puttagunta:

Inference goes way faster on a Cerebras chip as an example. So if you speed up inference dramatically, so the thing we don’t have yet is we don’t have AI specific chips beyond NVIDIA, we don’t have AI specific clouds. We’re starting to get that. We’re starting to get AI chips, we’re starting to get AI clouds like Fireworks. We’re starting to get AI infrastructure built. Once all of that gets built, then we’re going to start to see a stabilization of the infrastructure parts, and then only then are we going to actually understand what the gross margin characteristics of these things are going to be.

But right now, I think it’s too early to judge the P&Ls of these things. All you can actually just get a sense of is the consumer, prosumer, and B2B demand. And right now, we haven’t hit the ceiling of that demand. The more we produce coding models, the more we generate code, the more we make code generation faster or more efficient or more accurate, there just seems to be more and more pull of it. And I think if you just look at the amount of revenue generated by code generation, it’s gone zero to a couple billion really fast.

And you can count that at the inference layer, you could count that at the application layer, whatever you want. It’s probably the fastest growing software market in the world right now, so you can judge this demand side of it. And I think it’s way too early to understand what the long-term margin characteristics of this sector is going to be.

Turner Novak:

Is there anything else you want to talk about at all?

Chetan Puttagunta:

No. I think it’s perfect.

Turner Novak:

I had a bunch of other stuff. I know we got to get going.

Chetan Puttagunta:

Oh, I’m sorry.

Turner Novak:

I probably need to eat this banana before we cut the film. Actually, do you know how to break a banana in half?

Chetan Puttagunta:

No.

Turner Novak:

Have you ever seen this?

Chetan Puttagunta:

Wow.

Turner Novak:

Have you ever seen that before?

Chetan Puttagunta:

No. That’s amazing.

Turner Novak:

Yeah. One of my skills in life is opening there.

Chetan Puttagunta:

Amazing.

Turner Novak:

Well, this was a lot of fun. Thanks for doing this.

Chetan Puttagunta:

Thanks for having me.

Turner Novak:

Do you want the other banana?

Chetan Puttagunta:

I’m good.

Turner Novak:

That’s a good place to cut it right there.

Chetan Puttagunta:

Yeah.

Turner Novak:

You’re refusing my banana offering.

Chetan Puttagunta:

Yeah.

Stream the full episode on YouTube, Spotify, or Apple.

Find transcripts of all other episodes here.

I get into the weeds with Jake Stauch at Serval on building an AI-native product and what it’s like going up against publicly traded incumbents.

There’s 4x more people working in IT support than software engineering, and we talk about why IT departments haven’t had much automation historically, how Serval built what can only be described as “vibe coding for the IT help desk”, and the single question they asked customers that led them to the idea.

Jake also opens up about navigating the first 12+ months with no customers, why they never pivoted from building a full platform from the start, building a mirror instead of a system of record, how they structured early design partnerships that actually converted, raising a Series B in one day after announcing the Series A, and the importance of increasing talent density as you scale a company.

I’m also trying something new. You asked for clips of the episodes in these emails, and I’m including a few below. Let me know what you think (and follow + like + reply on Twitter and LinkedIn for the algorithms bless your feed with more!)

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👉 Stream on Spotify and Apple

Timestamps to jump in:

• 0:14 AI-native employee support

• 5:15 How an early work trial almost ended the entire company

• 9:05 Why IT hasn’t had much automation

• 13:09 Vibe coding for IT professionals

• 15:31 Competing against publicly traded incumbents

• 23:32 Having less than three months of runway for seven years building his first consumer health hardware startup

• 33:15 Lessons from five years at Verkada

• 39:11 The single question that birthed the idea for Serval

• 44:19 Navigating 12+ months of zero revenue

• 52:05 Knowing when not to pivot

• 55:15 Finally landing the first three customers

• 58:07 Getting pre-empted for a Series A

• 1:01:04 Getting a Series B term sheet the next day

• 1:05:54 How to structure design partnerships that convert

• 1:08:48 Building a mirror instead of system of record

• 1:13:49 Make the implementation part of the product

• 1:15:24 How to increase talent density as you scale

• 1:21:32 Why every new hire should help you recruit

Referenced:

• Try Serval

• Careers at Serval