🎧🍌 Untold Startup Lessons from Dozens of Academic Research Papers with Dan Gray at Equidam
Why a single pivot increases success, the reason 70% of startups fail, what VC's get wrong about pattern matching, why all startups look the same, and the role of mega funds in the ecosystem
If you’re a tech and investing nerd, you’ll love this conversation with Dan.
We talk through everything he’s learned reading dozens of academic research papers on startups and venture capital, debunking many popular industry narratives.
I guarantee you’ll learn something new.
We talk about the dangers of pre-mature startup scaling, the importance of origination stage investing, the concept of startup catering and why so many startups look the same, plus why he thinks the VC mega funds are still an experiment, yet play a crucial role in the ecosystem.
We also discuss what the data says about concentration vs diversification in a venture fund, specialists vs generalists, what VC’s get wrong about pattern matching, and why pivoting is more valuable than you think.
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Timestamps to jump in:
5:18 Equidam: helping investors value startups
6:43 What’s the required rate of return in VC?
9:29 Venture capital needs new definitions
18:23 Are we in an AI bubble?
24:07 Re-branding early and late stage venture
28:25 We need more origination stage capital
40:05 Survivorship bias in emerging manager outperformance
42:57 Incentives driving larger fund sizes
48:10 Raising overvalued rounds re-risks a startup
52:08 Startup catering: why all startups look alike
58:42 Are VC mega funds still an experiment?
1:08:06 Late stage VC is competing with PE
1:13:42 a16z’s Fund 1 strategy
1:18:18 How diversified should VC funds be?
1:25:06 Performance of Generalist vs Specialist firms
1:30:35 How to value a startup
1:40:58 Why VC firm location correlates to returns, but startup location does not
1:44:05 Founder background doesn’t predict success
1:48:27 Startups with one pivot are most successful
1:50:24 Premature scaling kills 70% of startups
1:54:47 Does mega fund model work for origination investing?
1:56:15 Value of Twitter and writing online
Referenced Research Papers:
Referenced Books:
Other Referenced Items:
Find Dan on X / Twitter and his Blog
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Transcript
Find transcripts of all prior episodes here.
Turner Novak:
Dan, how’s it going? Welcome to the show.
Dan Gray:
Thank you very much for having me. It’s great to be here.
Turner Novak:
Yeah, I think this’ll be fun. I’ve been following you on Twitter for couple years. I don’t know. I don’t know when you’ve really ramped up on there, but you share a lot of really interesting stuff and that’s what I wanted to talk to you about, a lot of research around venture. I feel like there’s not enough people talking about that kind of stuff, so I’m glad that you’re here.
Dan Gray:
It does feel like I found a niche, an underexplored niche. I really enjoy shedding light on some great research around VC that has really important implications for investing, but doesn’t get the attention it deserves.
Turner Novak:
Okay, and is it related to what you do? I know I just wanted to ask you super quick, so your title is head of insights. You work at a company called Equidam. What is it exactly?
Dan Gray:
Yeah, Equidam, our mission in a nutshell is to make really unique, innovative, idiosyncratic companies more legible for investors to better explore... There’s this two-sided equation of pricing and venture. One side is fundamental value. What is the future cash flow going to be of a company, and how does that discount back to today? Financial math side of valuation, and then there’s the auction house mechanic, where you compete on pricing and bid stuff up.
Our perspective is the first part is poorly understood, partly because it’s complicated and it’s a chore to get all the data-
Turner Novak:
Pretty hard.
Dan Gray:
Yeah, for sure.
Turner Novak:
Yeah, I mean, how do you predict the cashflow of something that’s not even a business yet? That’s hard.
Dan Gray:
Yeah, exactly. Our perspective is if we can make that easier and better understood, then it’s good for everybody.
Turner Novak:
And it seems like you probably just spend a lot of time reading and doing research around all this stuff.
Dan Gray:
Yeah, I would say so. I mean, it’s extracurricular for me in most of the VC research side, I look at it, I share, or I write about is personal interest. Probably about a quarter of it relates to Equidam. I did a lot of work quite recently on understanding the typical required rate of return for VC, so then we build that into our methodology for the VC method.
Turner Novak:
What is the required rate of return in venture?
Dan Gray:
It basically boils down to a pre-seed investor should be looking to hit at least a 200x in value. And then as you go up through the stages, it obviously goes down in the multiple that you need. But there’s a ton of maths behind it: looking at the dilution you expect to occur over time, the success rate at each kind of milestone that a company goes through, a few other pieces in there that basically tell you what you need to achieve for an investment to be good.
Turner Novak:
Interesting. I swear I’m not lying to you, that is my general kind of window I think about where you probably want 100x return on capital, you want 100x just generally return, increase in share price or increase in value or position.
But you usually get diluted. I usually just say you probably get to lose. Might be more, it might be less. I mean it depends, and so I just say usually you probably need 200x return in valuation in order to make the math work.
Dan Gray:
There we go.
Turner Novak:
Yeah, I mean, it’s super lazy math, but I feel like people overcomplicate a lot of this stuff, too. It is pretty simple. It’s like you buy a piece of a company that’s not worth very much that could be worth a lot in the future. That’s all it is, and getting that right once is very difficult. The more times you do it in a little basket of investments, the more likely one of them will happen, and I think there’s a trade-off of, do you need to do it 1,000 times? You need to do it two times?
I think the data says 40 is somewhat optimal, highest chances of optimizing returns. Is that right? Or I guess it depends on the stage.
Dan Gray:
It depends. Yeah, I mean, there’s obviously very much diminishing returns after a certain point. I think you can go up to about 80 and it’s still reasonable. Yeah, certainly 40 to 80 is a good space to aim for.
Turner Novak:
Yeah, I think public markets, they generally say past 20 to 25 you start to get diminishing returns to diversification, which is interesting. I guess it’s the level of liquidity that you have that could be it, or maybe it’s maturity stage of the businesses.
Dan Gray:
Yeah, predictability is a big factor.
Turner Novak:
Yeah.
How do you think about just venture today? What are your general thoughts on the market when you’re thinking about it from everything that’s going on and what you know should be happening, shouldn’t be happening? I don’t know.
When I ask you about this, what’s your response?
Dan Gray:
The hot take is what is venture, really? So much of the activity that we’re all looking at at the moment is technically venture because it’s like venture allocation. It’s in the same bucket for LPs as venture capital.
But is it really? I would say maybe not. If you’d asked a lay person what venture capital was 15 years ago, their imagination would’ve been like a small firm managing a ton of money going out, looking for people, building crazy stuff in their garage and making outsize returns of that kind of investing. Whereas over the last 15 years it’s increasingly become financial engineering and investing through a spreadsheet. Engineering markups with lazy processes and more oriented towards the fee collection part rather than the carry part. All these incentive problems that have been pretty well-hammered, but nevertheless it seems to be the case.
Turner Novak:
Yeah, it’s interesting when you just say you pull up a pitch book or any of these data or at least a report on the amount of capital that’s invested in venture capital as an asset class over time. We always see the big takeaways and the top five numbers, and it’s like the past probably year it’s been like OpenAI, Anthropics, SpaceX, maybe Ramp. Maybe I’m biased because they sponsored the podcast. There’s probably one other one that’s raised hundreds of millions, billions of dollars. There’s going to be a deep tech one that I’ve never heard of that’s raised a billion dollars.
Those are probably pretty mature businesses, all those companies. OpenAI is probably one of the top five most used consumer products in the developed world. It’s not really a startup anymore. You can argue margins. How profitable is it, really? They do generate a lot of revenue. People pay for it, but it’s not a startup anymore. There’s not uncertainty on will people use the product, so it’s almost like is it a different type of financing? Is it fair to classify the OpenAI $40 billion round that they’re putting together, whatever the number is... They’re building data centers, whatever with the amount. Versus a pre-seed, they’re raising a million dollars to whatever, something completely different. Not AI, it’s like biotech or something. We’re inventing cancer drugs.
They’re just different things, I feel like.
Dan Gray:
You’ve also got to think about the definition of a startup is surprisingly not simple, I think. Is OpenAI a startup? For all the reasons you mentioned, probably not. But also they’re thinking about branching into wearables. Probably they have a ton of stuff on their product roadmap that we can’t even imagine right now. Is the chat interface going to remain the primary way that we interact with their products? Maybe not.
There’s still a ton of risk left in the company or a ton of uncertainty left in the company, which maybe means it is a startup. It’s just an unprecedentedly large startup.
Turner Novak:
Yeah. Well, I think one of the recent episodes that came out right before this was with my friend Dan Feder, the University of Michigan, and he did make the point. I think he buckets into two categories, where it’s capital for ventures and ed to venture capital. It’s basically ed to venture capital is like we’re going on an adventure. We’re trying to figure this thing out. And then capital for ventures is like, you have a venture. You have a business. We’re going to put some money in because we know it works and you scale up this thing that works.
Yeah, and it’s an interesting point of there’s probably some companies that are only raising a couple million dollars where it’s just a capital for their venture. It’s like it’s not a lot of risk. It’s like we made a SaaS tool that works. It’s this AI agent thing. We have good margins. Everything looks good. We want to put the pedal down and go really fast, and we know this works. Versus to OpenAI, we don’t know if people are going to use their wearable products. Who knows? They might not.
Dan Gray:
But unfortunately the fundraising for that SaaS company I think is unnecessarily difficult today because they don’t fall into the camp of explosively fast-growing AI companies I think, which are absorbing obviously a huge amount of investor attention.
There was more experimentation with different models of venture, a few firms that tried to do more moderate risk investing, looking at maybe smaller companies with more stable growth with better margins, but not traditional venture levels of risk. But most of that seemed to die out in 2022. That reset took away whatever little innovation there’s been in the venture market in the last couple of decades. It really started to emerge in 2020, 2021, but sadly was washed out the next year.
Turner Novak:
I’ve definitely seen people, I don’t know who is actually doing it, I bet there’s people that are doing it, but really it’s a dividends. You’re actually paying out dividends because the companies have cash flows.
I think the debate there is always like, why are you giving people an 8% return with a dividend, or whatever the number is? They should be reinvesting in the business to grow faster. I think it’s the trade-off with a startup, which I think to keep it simple, you probably shouldn’t do that. It is easier, I think, and more simple. It’s easier to bucket it in. If you’re an institutional investor, you’re like, “Oh, Dan, you have this fund. You just do early stage 50 companies.” They’re all going to fail except for one. This is a good strategy, we get this. But if you’re like, “Oh, but some of them... We might be giving you cash flow back.”
Some of them are tokens because it’s crypto, and some of them are equity, but half of them are safes and even some of the safes are going to start paying dividends. We don’t actually have the corporate documents formed. There’s income coming through and there’s all these K1s we have to solve for. It’s complicated. I think it almost becomes not worth it to dig in for the average LP.
Dan Gray:
For sure. Yeah, I think it’s a specific product maybe for a specific kind of LP that needs returns on a certain horizon.
I think an overlooked fact in a lot of VC commentaries, like the different categories of LP and the different preferences they have, it does shape what they look for a lot.
Turner Novak:
Yeah, because the thing that I think a lot of people forget about is QSBS, which is basically... I mean, I think they actually made changes to this rule recently, but you can get a certain number, I think it’s like 10 million in gains or maybe 50 million in your gains, in things that meet a certain requirement tax-free.
If you’re investing in smaller-ish funds where you’re, let’s say you’re 10% of the fund, you invest $5 million, you get 50 million back or 55 million back, that entire 50 million of gains is all tax-free. I’ll throw a link in the show notes QSBS. I forget how they changed it, but I think you need to hold it for five years. It needs to be a certain type of company. It can’t be real estate related or something like that, and it can’t be a few other things. Maybe can’t be biotech or must be healthcare related or something like that.
There’s different classifications for it, but that can change how you make decisions, too.
Dan Gray:
For sure, definitely.
I think there’s also a second less well understood QSBS provision that allows you to write down some of your losses, but it’s for some reason not generally applied in venture.
Turner Novak:
Yeah, we don’t write things down in venture. It’s not good. It doesn’t make us look good.
I actually have a friend who has a pretty interesting strategy, where he donates to charity his overvalued startup stock. You get the benefit of the tax write-off of saying, “Hey, I’m donating this equity, I’m donating this valuable thing to a charity,” and then it is not worth that much, obviously, because an overvalued startup stock. It’s an interesting way to do tax optimization. I think that’s one of those secrets of the “wealthy” type strategies you might see a clickbait YouTube video for. It’s an interesting way.
I mean, that’s part of it too is, how do you optimize on taxes? Not a lot of people talk about, which is less fun. It’s way more fun to just say we’re backing the next Elon Musk and this is the next SpaceX. Get in or else you’re going to miss out. Way easier to say.
Do you think are we in an AI bubble right now?
Dan Gray:
That is something I’ve spent a lot of time thinking about. If you’d asked me as little as a month ago, I would’ve said absolutely, obviously, but actually maybe not.
Turner Novak:
This is a classic top of bubble type thing to say.
Dan Gray:
I know, I know. Well, yeah, it depends, right? Because I think when you think about a bubble, you’re thinking about investing in a speculative asset that is massively detached from the intrinsic value.
The problem with that definition as it relates to AI and VC today is I don’t think VCs are generally acting like investors. I think they’re acting like traders. If they can get out of their positions before the thing crashes, then it’s a good trade. I think they in a cynical way recognize that. They all see there’s a crash going to happen, but they are just hoping they can get out before then. And if they do, it’s a good trade. If they don’t, it’s a bad trade, but it’s not necessarily a bubble.
I think a bubble, if we get to a point where OpenAI has gone public, Anthropics gone public, maybe Cognition, a couple of others.
Turner Novak:
I think Mistral was supposed to or something, but I think they recently did a round with ASML, I think.
Dan Gray:
But once they’re in public markets, then I think we could see a bubble because obviously more liquid market, it’s more responsive to sentiment. It’ll climb much faster and crash much harder.
But for the time being in venture, I think it’s like you have to recognize it as a trade on the wave of AI. If you invested in an OpenAI in 2017, that was an investment. If you started investing in AI companies in 2022 as a strategy, probably that’s better characterized as a trade on the momentum of AI. You are hoping those companies will ride that trend, generate good markups, maybe get a good exit, but primarily you’re betting on the category.
Turner Novak:
Yeah.
Are you sure 2022? I feel like that’s a little early.
Dan Gray:
Yeah, that could be a bit early. Let’s 2023.
Turner Novak:
‘23, yeah. I mean, ‘23, it was like the entire market was just scorched. That was the only thing that was moving.
I think there’s just so many incentives, I talked about with this Dan too and that other episode talking about relevance, where you have to remain relevant as a VC, as an investor. It’s something I struggled with a little bit, especially during the Web3 mania. When I did my very first fund for Banana Capital, I invested in a little bit more consumer stuff than I do now. I’d been going into 2019, 2020 thinking that like e-commerce just generally is a trend, and it got stretched pretty far during COVID as I deployed the fund and as I first invested the fund.
I think I messed up on not just rationalizing that. I think it’s so hard when you’re in those moments to really realize what’s sustained. I personally still don’t really like going to stores. I personally do a lot of things online. I’d rather order ahead and pick something up. But I had a lot in my LPs that are like, “Turner, why are you not a Web3 investor now? We thought you’d be doing Web3. Why aren’t you in Web3?” It’s pretty easy today to look back and...
Dan Gray:
I bet they’re happy about that now.
Turner Novak:
Yeah, I think that fund will do okay, my 2021 fund that was over-allocated to consumer. I don’t know how these things are going to benchmark. I’m going to make money. I’ll give people their money back. I’ll get a little bit of carry from it.
Dan Gray:
At least on a relative basis to the market, you probably do very well because the market as a whole I think is going to suffer that for that year.
Turner Novak:
Yeah, but that’s another thing I think you need to remember in venture is there’s relative and absolute.
In 2021 I was on a relative basis. I was like, “Oh, I’m not investing in Web3. I’m not investing in these like pre-line of code things at 100 million plus money, I’m doing it at 50.” That’s half off. That seems so reasonable. But then when you back out and look at it over a full economic cycle or a 20, 30-year period, that’s still a pretty high entry point that’s still... You might be 50% lower than everyone else, but you’re still two, three, 4x probably higher than you should be. Yeah, they’re all 10x higher than they should be, but you’re still fucked up at the end of the day.
The point of this is make money. I think the point of venture, you have to basically outperform every asset class. That is the job. That is the role of venture. People aren’t in venture to give me some buffer when bonds are down, I need my venture portfolio to do X thing. It’s uncorrelated, but it’s uncorrelated because it’s like just find people starting things that will be worth a ton in 10 to 20 years.
Dan Gray:
If your definition of venture is what we think of as traditional-
Turner Novak:
True venture capital.
Dan Gray:
... venture capital. Yeah, exactly.
Turner Novak:
Yeah, traditional early stage. Yeah. It is a good point of, I think we just need to think we need a new name for later stage venture, late stage venture. I don’t know. I like capital for ventures is a good one.
Dan Gray:
Capital for ventures is good. Leslie Feinzaig calls it consensus capital. Kyle Harrison calls them like the agglomerators, I call them the venture banks. It does need to be recognized as a different strategy. Maybe still within the same bucket, but definitely a different strategy because it changes so much about the expectations.
Turner Novak:
I think it’s hard too because if you’re any of these data providers, you don’t always know what the nature of a round...
I mean, this is a classic thing in 2021. Someone would say, “We raised $100 million for our FinTech company,” and it was five million in equity and 95 million in debt. And so my general, when I see a number of like, “Oh, you raised X amount of money,” if it was around a seed series A, I usually assume seed you saw about 20% of the company if it’s a series A. I assumed you probably sold 25. You maybe got diluted about 30 because there’s some options and stuff like that that went in there.
If I see that number, “Oh, we raised $100 million and it was a series A,” I’d be like, “Oh wow, 400 million plus money. What’s an appropriate evaluation? They must be pretty far along,” but it’s like, no, they only raised five million. They have one customer. Or actually I guess that’s the other side is, if you got a debt vehicle that you raised, you probably have a pretty robust business, too. Hopefully you probably passed certain levels of sophistication and majority and able to prove that.
Yeah, I don’t know. It’d be hard to really actually figure out how to do that. It’s almost like it’s just easier to just not worry about it and just call it all venture.
Dan Gray:
It’s an interesting point though about what that does to the perceptions of valuation because you have people making calculations, like you said, where...
Turner Novak:
Because it’s all comps. I just say, “Dan did this. This is public, this is what he did. I’m better than him or I’m at least as good, so I want to get the exact same amount.” If it’s not, then it’s not fair.
Dan Gray:
You have the debt part of that which complicates it. You also have obviously the terms. Was it raised with a 2x liquidation preference? What are the more investor friendly terms were attached to make that valuation work? You never really know any of those things. It has a very cyclical effect on pricing.
Turner Novak:
And then I have some friends who they strategically release and drip certain information. They’ll say, “We just crossed 40 million in ARR,” but they’re actually at 80 or something like that. They strategically will put things behind.
I don’t know if you know that company bolt.new, the text to website creation. I kind of figured it out from talking to him and we talked about it on the podcast is he’s like, “Yeah, when we announced where we were at, we were actually a little bit ahead of that.” Because he was in a case where they were just exploding and he was every day waiting for it to taper off and he’s like, “It just never died. I was just afraid that if I set a number and the number was no longer there... So I was like, ‘Okay, let’s hedge a little bit.’”
I think that goes into it too is... And then there’s the other side of we’re like, oh, we’re at 100 million ARR. It’s like a creative definition of the word ARR. Again, a lot of that stuff is so tricky.
Dan Gray:
Yeah, that’s a huge problem. It’s interesting to me as well the extent to which a company hoping for an exit for an IPO knows that eventually one day they’re going to have to get to gap compliant financials. They’re going to be audited, so for how long can you keep that mirage growing early on of the creative bookkeeping? It’s dangerous.
Again, there are incentives on both sides. Some VCs like it too because it makes it very easy to generate markups on multiples.
Turner Novak:
I don’t want to miss this question because we talked about it a little bit earlier, but you mentioned that you think basically all of the returns in venture ultimately come from the first check. True venture capital, the traditional, how we defined it earlier is like you’re starting a company, extreme amounts of risk and uncertainty. What’s the importance of that kind of origination round that first round, whatever we want to call it?
Dan Gray:
Okay, it’s axiomatic a little bit because obviously if you don’t have a first check, you have no checks. You don’t exist.
Turner Novak:
You need that risk capital, the inception stage, origination stage.
Dan Gray:
Yeah, exactly. Investors that focus on that level, the real genuine first check investors, and I’m not talking about the ones that need to follow someone else into the round and need traction or whatever, they determine what venture capital is in a way. They determine what are the exits going to be in 10 years.
Turner Novak:
How do they determine that?
Dan Gray:
If it never enters the funnel.
Turner Novak:
Got it, okay. They’re determining what is allowed to exist, what startups are able to have a shot at trying to do something.
Dan Gray:
Exactly.
Turner Novak:
If they’re only funding certain categories...
Dan Gray:
Yeah, the quality of what of is in the whole venture fund or venture funding pipeline from origination to all the way to exit at any point is downstream of those investors. Their ability to provide good coverage, to be able to explore every corner of the economy, every category, every industry is super important.
Turner Novak:
Should there be more people doing that, more creative... What does the data show?
Dan Gray:
I mean, there’s been a very good study done looking at VC in Europe. It showed essentially looking at programs like big programs by the EIF, the European Investment Fund, where they put a ton of capital into VC. You get the same uplift in productivity from that investment if you just move the current allocation earlier than if you double the amount of capital.
Expanding the early stage origination area of the market has just as good effect as doubling the total pool of capital.
Turner Novak:
If you’re investing 100 million in startups, tech, innovation, whatever Europe’s doing, and then they wanted to double their impact, probably the most consensus thing. Well, let’s just give them twice as much money. They’ll have twice as much of an impact. But it actually have double the impact if they just went half as earlier or how earlier? Do you know how early you have to go?
Dan Gray:
It wasn’t too specific, but I think essentially it’s ensuring there’s enough money at the pre-seed C series A maybe kind of stages. Make sure those companies have a path to really prove the business model and become success stories.
Turner Novak:
Why do you think more focus is around this origination stage?
It’s interesting, I feel like a lot of people say we’re investors, or we back founders from the earliest stages, or whatever they say.
Dan Gray:
Yeah, the biggest question there is not necessarily who classifies themselves as an origination investor or a first check investor. It is what is the strategy that they apply to origination? Where are they looking? How do they source companies? Is it through a network of peer analysts or firms or other GPs that they know, in which case it’s already kind of coming through a filter, or are they genuinely doing essentially the equivalent to a startup of go to market? Are they doing that? Do they have a unique defined strategy to go out and find their own deals? There’s a few examples of firms that I think do that famously well. My favorite is always 1517 fund because they invest $2,000 in a college kid with a dream who has nothing, but that kid is like, okay, now I have the money to test this idea a little bit, and if I then start a company, guess who I’m going to call? It’s going to be Danielle.
Turner Novak:
The one who gave me the 2,000 bucks And it’s a grant, right? I guess free.
Dan Gray:
Yeah, exactly. It’s literally just like at that point it’s a Venmo.
Turner Novak:
Yeah, the corporate Venmo account of 1517.
Dan Gray:
Yeah.
Turner Novak:
I wonder if that’s actually there. I should ask. And so what do you think about maybe a YC? Is that the example of a good one?
Dan Gray:
That’s a very good question and I’m kind of torn on that because written at length about YC and YC’s track record and their performance, I’ve always kind of defended against accusations that YC companies are overpriced because if you look at the historical success rate, they should be priced higher. They have a higher success rate, higher rate of exits. The question is that involves looking at data from companies that are obviously at least 10 years old. So has YC changed in the last decade, I think is an interesting one to consider. If you look at the breakdown of the cohorts 10 years ago, 15 years ago, how much thematic consistency was there? How many companies were in a very specific category of technology? Essentially how diversified was it across different ideas compared to today, where do you see people saying there’s 20 different windsurfs for X in YC currently?
Turner Novak:
I think the interesting thing that YC has too is they kind of have this built in price making mechanism. If you think of it, an auction market, there’s price makers and price takers and YC basically gets... As valuations keep going up, they have been able to keep this lower price that they come in at. I guess to your point, the way the market’s changed, they’ve changed where they now do put a bucket, a check in at that higher price, that kind of uncapped safe that they go alongside the first check. So I bet there’s sort of a weird step... There might be a step function change. Maybe some of the YC funds was an 8X fund, one was a 12X whatever, but I bet you get a slight bump down by a full multiple that might be a 7X or an 11X.
But it’s probably a bigger pool of capital that you get to work with. So it’s probably okay. If I’m an LP, it’s like, okay, instead of my LP exposure to YC being 5 million bucks, I had to put in 20 and I’d actually rather get a 7X on 20 million than an 8X on five. I don’t know, I have no idea if those are YC’s numbers. Those are pretty good numbers. I bet they’re actually higher than that, honestly is my guess. But to your point, I think an interesting kind of topic that brings up though is looking at historical data, some of these studies are in completely different market structures, completely different market regimes, so there’s one end of the spectrum where you could say things are different. You can’t rely on this data. All the maybe non-data supported things that the venture industry does is actually correct because the old... All the data’s wrong.
The other end of the spectrum, that is just a classic thing that you say when everything is about to be over. History doesn’t matter. The data we don’t follow. It’s like the whole, do you want to make spreadsheets or do you want to make money argument?
Dan Gray:
Yeah, I share a lot of old papers. I get a few different forms of pushback to this. I shared one a couple of weeks ago and a GP replied to me and he was like, this is from 2017. There’s no way this is relevant today. Venture capital in the traditional sense that we’ve been talking about has not really changed that much. It’s not changed much at all for 30 years I would say. There’s the bigger end of the market which has been added, but the traditional part, the risk factors, the decision making process, the portfolio strategies, all of that is the same.
Turner Novak:
So it’s sort of like venture capital started as this pretty small pool, very beginning stages of the market, and it stayed there, but it’s also expanded to become further and further along and all that expansion is like 10X, 100X bigger than what the first initial inklings of it was. It’s still there, but this whole expansion just takes up most of what we think of it today. So it’s just like it’s still the same, but it’s evolved I guess. There’s new extensions of it
Dan Gray:
And I think it’s evolved in a very expected way in that the larger it’s gotten, returns of commensurately fallen and you almost think of dilution of the strategy, it’s literally more capital which allows for the scale of returns to be increased but not the efficiency of returns to be increased.
Turner Novak:
What does that mean? Can you explain that?
Dan Gray:
It’s like if you’re a big sovereign wealth fund and you have to write checks of a minimum a hundred million into funds that you’re investing in, you can put that into a venture bank, a 16Z or general catalyst type firm and they can put that to work and maybe you’ll get a two to 3X on that money. You can’t really invest it in the early stage of the market because it just doesn’t fit. You can’t squeeze the check that large into a smaller manager.
Turner Novak:
You’d have to do 10 or 20 different funds to get that $100 million deployed.
Dan Gray:
Exactly. But if you could break it down like that, maybe you could get a higher total multiple, but it’s more work, more risk potentially.
Turner Novak:
That’s sort of the pitch for a fund of funds then where it’s like, hey, give us a hundred million. If we have a $500 million fund in the top whatever, 20, 30, 40, 50 best managers and all their breakouts. It’s kind of like hiring a consultant to manage your venture portfolio for you, but they just kind of do it. It’s one line item on your portfolio and one headache. You have to manage one update call, one relationship versus 30 or 40 or 50, which that’s a lot of time. You need a whole staff to do that.
Dan Gray:
If there’s one startup idea that I could be tempted by in future, it would be trying to design an algorithmic fund to funds, so it could be very low fee, very efficient, but identify... Basically index emerging managers to try and manage a lot of the uncertainty involved in that, but to build a process around managing the biases involved in selection, all the uncertainty involved in selecting managers with no track record. It would just make for that section of the market, which is like the origination layer too, which I obviously care quite a lot about. It would make fundraising so much easier if they had access to that kind of capital.
Turner Novak:
I don’t know if it’s true, but I’ve kind of had this maybe hypothesis. Is there some sort of survivorship bias in... I think it’s fairly well known that people say emerging managers outperform, fund ones outperform and you should just... If you want true venture, if you want the best venture returns invested in fund ones or whatever, emerging managers. But I’ve always been curious, is there a survivorship bias of let’s say you and me both start funds, you crush it, you’re doing really well. I fucking suck and I just drop off and does my data leave? And so all that they see is you crushed it. I sucked. We offset each other to equal or maybe we’re worse, but I dropped off. So only your data shows up. Have you seen anything related to this before?
Dan Gray:
That’s interesting. Nothing specifically to that. I mean, I will say some of the perception about how emerging managers perform or outperform is based on a lot of charts that the pitch book have released where they show who have been the top 10 funds in each vintage by size.
Turner Novak:
Is this pitch book or Cambridge maybe?
Dan Gray:
Could be Cambridge, you might be right about that.
Turner Novak:
They’re biased. I mean, they’re trying to sell institutions on new funds.
Dan Gray:
That’s true, but they consistently have shown small sub-fifty million funds, for example, dominating the top 10 each year. But actually if you look at the underlying fund data, those small funds are massively overrepresented in the data set, so it’s obvious that they’re going to take more of those top 10 positions. It doesn’t necessarily mean that there are a better bet because there are a lot more of those funds that also obviously don’t do well.
Turner Novak:
That’s true. I mean, it’s extremely difficult to raise capital for a fund and especially when you have not done it before or you’re first getting it started, so there’s probably not very many large early funds. And also if you’re good, you just keep getting bigger. So it’s kind of hard to be like a... It’s really hard to just stay small.
Dan Gray:
Which is one of the problems with the origination part because if small firms focus on origination but success naturally inclines them to grow, then they slowly move out of being able to invest in that such an early stage and they start to invest larger checks later rounds so that whatever talent they had at origination is kind of washed out, which is a shame because there’s already very high churn in the industry. There’s already relatively weak persistence, so it’s a difficult part to make talent stick.
Turner Novak:
Can you explain for somebody who may not understand this of why do you want to have a bigger fund, why does small funds that do well ultimately end up getting bigger?
Dan Gray:
I think there’s probably two main forces. The first one is the obvious one, which is the financial incentive because you have as a part of your compensation as a GP, you have your management fees and you have your carry. And the carry is, you could consider it like a performance bonus if your investments do really well past a certain hurdle, you get to share in that profit, which is if you’re a great investor, it’s incredible and can be a huge part of your compensation.
Turner Novak:
You need to liquidate the asset though. It takes a while. In venture, it’s probably like, I don’t know, eight to 10 years until you get any of it. Probably longer, honestly.
Dan Gray:
You have to first return all the capital to LPs. It’s a headache. It’s difficult. Whereas much easier is the other part, which is the management fees, which are guaranteed you get them pretty much no matter what and that’s like 2% a year or probably these days more like 2.5 I think a lot of the time, probably over 10 years. But then there’s extension periods and caveats around that as well. But if you consider it like 2% 10 years, it’s 20% of the fund.
Turner Novak:
Yeah, you raise $100 million, you get $20 million guaranteed. You think of that like an ACV as a SaaS business, that’s an incredible business. That’s you raise a billion dollars, you get 200 million in revenue, it’s guaranteed over 10 years. That’s an incredible business and you just keep doing it again and you raise another fund. So you raise one fund, that’s a billion, you get your 200 million in revenue, you raise a 2 billion fund, because you did well now you got 400 million stacked on top of that 200 million and just like let’s say you started, your first fund was a $10 million fund and you got a 10X return on that fund, you took that 10 million and you turned it into a hundred million. So you got 90 million in profits and you got 20% carry, you got 20% of that, so you get 18 million bucks in profit, you probably get it 10 years, 18 million in profit. That is like if you go back to that billion dollar fund that you raised, you’re getting 20 million a year.
It’s like, why am I around waiting for all my carry? Just raise a big fund. It does make sense, but I think the issue then though you might trade off, and this is something I’ve thought a lot about, is you have to build that infrastructure, pay for it, which is usually a team, a lot of people. You have to manage a lot of things. You spend a lot of time on that operational institutional design. So yeah, you get a lot of management fees maybe, but depending on the circumstances, it’s not quite as lucrative as you might think, but you do get leverage depending on who you bring in. You may be investing in things that are defensible network builders or brand builders or something like that. So it gives you more capital and power to work with.
Dan Gray:
I think it’s kind of useful to look at the extremes. Consider a small 50 million fund, just a couple of GPs and very little else in the firm. They’re spending their management fees on living, travel expenses, events, whatever powers their origination strategy that they want to do there, but it’s basically covering the cost of operations and then they cross their fingers that the carry is great, whereas the far other end of the spectrum, like an A16Z, the range of services that they provide, startups now they have their own compute that they provide to AI companies that they invest in.
Turner Novak:
Oh, so that is a thing. I thought that was a meme.
Dan Gray:
No, no, that’s a thing.
Turner Novak:
I thought it was a joke, it’s real? Okay.
Dan Gray:
That’s a thing. And they have other financial products that they offer. They have the whole platform team.
Turner Novak:
They have anything you might want. Any department that your company will have, whether it’s recruiting, marketing, capital raising, go to market, yeah, growth, brand, engineering. They have consultants that can help you come in. It’s everything. It’s like any role you might hire for, they have it. They can help you with it. It’s an interesting value prop to some companies. And there’s also just the brand halo of A16Z. All your friends know A16Z. They’ve all heard of them, they’ve seen the podcasts. Your parents probably use the Mosaic browser back in the nineties. Why would you not take Mark Andreessen’s money versus some random dude with a podcast that no one’s heard of? It’s an interesting value prop.
Dan Gray:
It is. That’s a whole fascinating rabbit hole itself. What is the cost of taking money from these big firms?
Turner Novak:
What do you think it is?
Dan Gray:
There’s a great thread by a VC called Rob Go, and he reflects on this as re-risking because essentially their value prop, part of it at least is that they will be less price sensitive when they invest. So they will give you much more money on much more friendly terms potentially.
Turner Novak:
This is a larger agglomerator of capital type fund?
Dan Gray:
Correct. Yeah, yeah. Like an A16Z. Exactly.
Turner Novak:
Not Rob Go, next view. Is that next?
Dan Gray:
Yeah, that’s right. That’s right.
Turner Novak:
Okay.
Dan Gray:
So the downside of that is the growth that you kind of obligated to deliver as a part of getting that huge check is way higher. The stakes of failure are higher. The risk you’re taking on as a company by accepting that much money is much greater. The fragility of your future vision, how you can adapt if things don’t go to planned, that’s much more difficult. So essentially what should happen through a startup’s life is as they develop, the risk slowly goes down. You end up raising these huge rounds and your risk shoots back up to the top again, and then you need to work it down again. For some companies in some categories, it’s the right move. Yeah. But for many it’s probably not.
Turner Novak:
How do you decide that? Is it how much capital do you need? Is it a competition? Is it a certain product type, like capital efficiency or something? Do you know if there’s a way to gauge that?
Dan Gray:
To some extent, it’s a bit of a competitive force. I think if all of your peers are in the same category are raising similar amounts of money. To some extent, maybe you kind of feel like you have to as well to compete with them, and that ends up with an ugly situation where everyone’s spending huge amounts of money bidding their own margins into the ground against each other.
Turner Novak:
Yeah. Some kind of a paradox. I’m sure there’s a scientific word for this, but some sort of losers prisoners dilemma type paradox or something.
Dan Gray:
Yeah, it’s basically venture predation. There’s a good paper on that.
Turner Novak:
I’m going to throw Rob’s thread and I’ll throw this one in the description too.
Dan Gray:
Yeah, that one’s looking at, I think it uses specifically the example of Uber versus Lyft. So they both absorbed a ton of VC money and a lot of that money was put to work basically trying to suffocate the other company. Who can undercut the other company’s pricing enough that you steal all their business and can you survive long enough doing that before you die yourself?
Turner Novak:
Interesting. And it’s crazy how I think Uber is... Uber’s this fascinating example where it’s super profitable now or something, right? They just basically 3X prices over the past couple of years and it’s all free cash flow. I haven’t looked, I don’t know the number, but it’s pretty much they just had a ton of pricing power that was kind of hidden and we’re all paying it and they’re making a ton of money now.
Dan Gray:
They also nailed their timeline. I think the period of time from inception to exit that they had luckily was kind of perfect. They could have exited maybe a year or two later. I think it was 2018, their IPO, something like that.
Turner Novak:
Yeah, 18, 19 probably.
Dan Gray:
But if they’d been intending to exit in like 2023 and then suddenly they would’ve hit that speed bump when all the capital dried up, that could have been fatal.
Turner Novak:
And they kind of did hit some speed bumps right before the IPO also kind of with the whole management team turnover.
Dan Gray:
Yep. Yeah, that’s another interesting rabbit hole.
Turner Novak:
And you turned me onto this interesting concept, I think it’s called startup catering. I think there’s a research paper on this. What is startup catering for people who don’t know? I think of food, I think of getting lunch for the company, but what is startup catering?
Dan Gray:
It’s probably my favorite paper on VC, which is an incredibly nerdy thing to say, but whatever, I’ve said it.
Turner Novak:
Hey, we’re in an audience of nerds here. We’re all on the edge of our seats.
Dan Gray:
It’s a safe space. It kind of crosses over the valuation part, which is obviously what I spend a lot of time doing with the what gets funding question, which I think is really important. So essentially catering, it’s a consequence of the fact that investment decisions in venture capital, mostly relative judgments. So you look at if you’re going to invest in a company and you’re trying to understand is it a good investment and if so, what price should I come in at? You probably look at peer companies, you look at comps. So if you were investing in a B2B SaaS company in 2021, pricing was super easy for you. There’s thousands of them. You probably know a ton. They’re at every stage in every region, in every vertical. So you can find a ton of companies that are very similar to the one you’re looking at.
Turner Novak:
And it’s from all stages, from inception stage, all the way to mature public markets. I have a ton of comps and there’s a history of how these things go.
Dan Gray:
So in terms of information friction, to understand the company you are looking at through the lens of all these comps, it’s super easy, makes investing very simple. It also means downstream funding is going to be very easy for that company. When they get to the next round, they’ll be able to raise very easily as well, probably at pretty good terms. It’s just like life is so much easier if you’re in one of those categories. The downside obviously is that it’s like a blood-red ocean, super competitive, maybe overvalued if investors are all piling in there. There’s real downsides too from the investor’s perspective, but the problem from the founder’s perspective is if you are trying to do something that is really unique and innovative, you are like Valoratomics or Rainmaker or someone, you go talk to VCs and try and explain to them your company and why they should invest.
They have no comps. There’s no relative judgment to make. There’s no other set of peers they can look at to understand your company. They have to if they want to invest and probably they should because those companies are often incredible. They really have to understand first principles foundational value or fundamental value, and that’s much more difficult. So the upside of all of this basically is that founders basically have such a hard time raising money for these really interesting, unique ideas that they tend to cater to VC’s preferences. They tend to cater to where there’s less information friction, so they’re more likely to go and build a SaaS, which I think we both agree is pretty sad.
Turner Novak:
Yeah, I mean, you can solve problems, but the easiest problems to solve are probably the most over catered to I would think. So then why are the VCs investing in it? Because you just said, I mean, based on the last, I don’t know, 53 minutes of our conversation, I would lead to be like, yeah, those returns are probably going to be pretty low. Why do people do it then?
Dan Gray:
Gets into the question of what is the primary motive of VCs today?
Turner Novak:
What do you think it is?
Dan Gray:
Depends. I think there’s examples of both. I think by far the most capital is moved by investors seeking markups, seeking proxy metrics that show their fund is doing well, so they can raise another fund and get those management fees. I think that moves way more money, but ultimately I think the most returns over time are generated by true investments, which is like you invest because you see this company as a generational outlier and you think the exit is going to be huge. You’re focused on the end game DPI rather than the proxy metrics along the way.
Turner Novak:
And is there a good way to do that? If I’m listening to this and I’m trying to change my ways, I’m like a markup addict and I’m trying to get clean of the markups and purify myself with DPI. Is there anything you’ve kind of seen that maybe you can kind of think of for cling to get through that?
Dan Gray:
I think there’s something said by Michael Dempsey in your podcast with him, Michael Dempsey from Compound, he talked about, if I remember correctly, it was their 2020 fund and they intended to deploy it over four years, but they ended up taking five because they just didn’t find the opportunities that they wanted to invest in. That’s such a good attitude, and I think you would find that is relatively rare in VC, whereas most people are just trying to put capital to work, get it out the door, get the next fund, but they said, we’re only going to invest in companies that pass a certain bar, and if it means we have to wait longer, which means we essentially have less income to our firm, then we’ll wait. And I think that’s a great attitude. They definitely suffered for it in terms of versus the income they maybe could have had if they deployed faster, but long-term, the right choice to make and those decisions I think are how you build a generational firm and end up being able to charge 3% management fees if you want to in the future.
Turner Novak:
Well, because sort of related to this whole raising the mega fund model, big pools of capital, from my perspective, I kind of see it as it kind of works, it kind of solves this problem in the market. I don’t think it’s going away, but you mentioned before that you still think it’s actually an experiment, so why is it an experiment still? It kind of seems like it works. I don’t know, and maybe that’s not true, but...
Dan Gray:
Yeah, that’s a good question. I mean, I agree with you that I think it definitely has a place in the market, and I went personally from two, three years ago, I was very critical about it because I thought it’s just basically grifting, it’s just collecting fees. But actually, if you look objectively at what the big firms have done, it’s pull a ton of capital into tech, which has got to be a good thing. Ultimately, the question is how does that strategy work out for their LPs?
And I think we kind of have to see to an extent just how that shakes out over time. For example, I think AI is kind of the backbone of their strategy at the moment. It’s where they dominate. They have enough money to back the big foundation language model companies. They have the clout, the media presence, the marketing pull to not just promote the portfolio companies like normal, but to promote the whole category, to build momentum for it, to push it in Washington even, and certainly to pull in other investors to herd in more capital. So to an extent, are they manifesting a wave and then trying to ride that wave by indexing a ton of companies that kind of join onto it? Is that going to work? It could be incredible. It could be six, 7X on a huge pool of capital, which would just be amazing results. It could be a failure ultimately, or it could be something they can’t repeat in future. That’s why it seems like an experiment to me. Is it going to persist beyond the current AI wave?
Turner Novak:
Yeah, that is a question. It’s like it feels like we’ve... If you just look at the history of venture over the past 15 years, it’s like we keep finding new bigger opportunities. Some people might call them new bubbles, horizon bubbles. Look at NVIDIA stock price. NVIDIA at the top of crypto, I was like, this is crazy. Insanely overpriced. There’s no fucking way NVIDIA goes any higher than this, and it’s like 20X higher than the top of the crypto bubble. People are using the NVIDIA chips to mine crypto, and with VC, it was ZIRP. It was low interest rates that were just fueling higher asset prices. We had the COVID to work from home. All the SaaS companies were overvalued. We had kind of the Web3 stuff. It’s like everything is going to be on the blockchain, the economy is like $100 trillion.
Fintech is a massive market, it’s all gonna be on chain. And then with AI, it’s like labor. Literally the entire economy now is software. So we figured out, even though the Web3 thing popped and is gone, it’s like we somehow figured out how to find an even bigger thing that venture could eat up and it’s like, what else is there? It’s like we’re interplanetary now. The number of earths and the number of economies is 10X bigger and they’re all software, so we’re going to be colonizing Mars and the moon or something. The animals start using software. So your dog needs a like a CRM or whatever. I don’t know. Obviously, I think one way is biology or something, new things that have not been impacted by software yet, like healthcare. It’s probably an interesting example where they have not fully utilized the internet or something like that. So I don’t know, it’s again, you could say, will AI, the impacts of it continue to be felt in 20 years? Probably. I mean, there’s still markets now that are changing from cloud, changing from the internet.
Dan Gray:
It’s also kind of a question about LLMs particularly. You could look at it a couple of ways. You could say using the example of NVIDIA, this doesn’t resemble historical bubbles because the video’s revenue has actually gone up so much. Whereas in the past, look at Broadcom and the dot com boom, the separation of the two is much greater, but then there’s other signals which do feel very bubbly. The narrative around what an LLM can do has gone from in 2023 it was like, we’re on the verge of AGI. It’s coming, everyone’s going to be out of a job. And now it’s like Sam Altman saying the biggest risk is that maybe we use too many M dashes in our writing. It’s calmed down so much and why? Has the steam gone out of it that much? How much of the capital that’s gone into it was predicated on AGI and is that now not happening?
Turner Novak:
Yeah, I think it’s interesting from a technological perspective of the cost is coming down and also the effectiveness is going up. So when you just say, like the internet, I don’t know what you used to pay to access the internet, like a AOL dial-up. It was maybe like 40 bucks a month, 60 bucks a month or whatever. It kind of sucked, but it was good at the time. It’s gotten better, but it’s not like we pay less. I need to actually go and call Comcast. I paid $230 for my Comcast internet last month for my office, which is way higher than what I initially signed up for. Totally different tangent and I’m like, “Fuck.” Well, I hate how they always increase the price on you. Like, “Oh, it’s 80 bucks for an intro deal,” and they increase the price, but they keep increasing the prices.
Or on the other hand, you think of something like the cloud or whatever, it’s like you connect to the cloud, the internet is on and you didn’t get better cloud, maybe the price came down, they allowed new services where you keep paying higher prices but you don’t get better cloud. It’s like, “Oh, it’s like a better optimized cloud.” Maybe marginally, but I think the interesting thing with AI is over the past couple of years it’s gotten way better and the cost to service it has come down magnitudes every, I don’t know, nine months or I don’t know, every couple months. So it’s one of those things that’s getting way better and also the cost is coming way down. I think you can argue that if you want to keep using the bleeding edge models, the pricing is more expensive, but they’re still getting way better. And there are cases where you don’t have to use a newest model.
So it’s kind of one of those technologies where it’s getting better and also cheaper, which I don’t know if it’s happened to that extent ever. Maybe if you go back to the Industrial Revolution or something like that with... I think some people made that argument of if you think that LLMs are automating manual software labor, like computer labor, the Industrial Revolution was automating physical labor and it was making it cheaper and faster. So that had a pretty profound impact on the world. And so I think that’s maybe an interesting parallel to think about it with AI. I don’t know, it’s probably not totally correct, but that’s one framing I would use.
Dan Gray:
There’s the incremental improvements as costs come down and it becomes more accessible and more useful, which I think is definitely true. There’s also the productivity shocks, so when AI passes a certain threshold to really do something very well for the first time. So I think the recent MIT paper that looked at adoption in the enterprise was kind of showed that the pilots generally weren’t working out, like 5% of pilots were successful. And I think that’s essentially a consequence of it’s not quite reliable enough yet, hallucinations are still a problem.
On the other hand, if you look at Google’s recent image model, Nano-Banana, fitting for this podcast, it’s like, it’s so good and so reliable that if you’re a fashion company you can actually use it to do AI photoshoots. If you’re an little independent fashion company, you take a picture of the outfit that you’ve made, you describe a setting and a model and it gives you a picture that is so close to real, it’s actually usable. And that’s a fairly small but meaningful productivity shock, like a real jump in value that it creates. The more of those accumulate, obviously the more important AI becomes. Some of them are going to be huge probably.
Turner Novak:
Yeah, that’s a good point. Thinking about how do you quantify that productivity.
Dan Gray:
Yeah, I guess it’s cost replaced essentially.
Turner Novak:
Yeah. One other thing, when talking about the mega funds and the way that they fit, it’s not like an LP who’s putting capital in these things is saying, “I’m going to take my origination dollars, stick them in this 5 billion vehicle.” It’s usually like, you’re probably taking some public market exposure and saying, “I actually think I’m getting higher returns.” So I think a common kind of dunk on these funds is like, “Oh, you’re only going to get a three X, only going to get a four X.” Could actually be higher, could actually be shifting.
That shift into that three or four X might actually be higher returns than they were getting than what they shifted it from. Like shifting that allocation might actually improve their overall expected returns. Because you could say that, let’s just say A16z for example, we’re going to invest in automation and software in the internet and AI in factories and physical things and that might actually be a better investment than an industrial bio private equity firm or something. I don’t know if that’s true, but you could probably make that argument. I’m sure there’s a ton of data to support that of you might get a two x on that bio fund, but you get a three x on the mega fund software, industrial bio thing. I don’t know, it’s probably a bad argument, but I’m sure... That is the way, I’m sure if there’s more data you could support that if I knew what I was actually talking about. I’m sure. Yeah.
Dan Gray:
Not only are you right about that, but I think you kind of make a deeper point, which is that if for all that those dollars are currently allocated in the VC bucket for these LPs, if it’s not actually competing with actual capital should go to VCs traditionally, and actually it’s probably displacing PD money. Maybe that’s because the strategy is really PE and it be grouped in with PE. It’s kind of a, not something that anyone at a big fund would want to hear that they’re basically private equity, but really maybe they are.
Turner Novak:
Yeah. Well and it’s interesting, one of the prior guests of the show, actually two guests it I think one of my only, I might actually be my only episode with two guests. It was the founders of a company called Solugen. Have you ever come across that one?
Dan Gray:
I don’t think so.
Turner Novak:
They’re basically just making chemicals in a different way. They’re making chemicals. They’re all the same compounds that go into petroleum waste and petroleum type products, they’re creating it from plants essentially. And the chemicals industry, I think it’s 4 trillion of just headline kind of GDP economic activity in the world that is chemicals. So you use chemicals on clothing, in healthcare products, in your computer, in cars. Everything is coated with chemicals in some way and the entire industry is very old and it’s very asset heavy, high bearish entry, basically everything... The whole industry is revolving around getting extremely high volumes on really low margins. It’s been competed away and super outdated and there’s no real room to innovate and anything and change anything. And it’s kind of caused, you could probably argue we’re literally taking oil that’s on my clothing, it’s touching my skin. That’s not good.
I’m not dead because of it, but if you think about pouring gasoline on yourself or being exposed to nuclear radiation on the extreme end, that’s not good. And it’s kind of interesting. They’re basically a venture backed company that’s kind of competing in the chemicals industry against these people that have 10% gross margins and 2% profit margins that has bad customer service. It’s actually interesting, when I was talking to them, I didn’t believe them. They’re like, “When you order chemicals, they give you a four-week delivery window.” Imagine if I sent you a calendar invite for this and it’s like a four-week window of like, “Hey, show up at some time in these four weeks and record this podcast with me.”
You’re going to be like, “How the fuck do I get anything done?” That’s so inefficient. But that’s kind of how that industry works where you don’t know when this thing can show up. And so you could say that using that kind of operational rigor, that kind of technology to say, “We’ll deliver this thing at this time and you’ll get your product and it’ll be made differently. Our margins are way higher. We make things in a way that’s way faster and more reliable. The customer service is better.” You can actually compete with those industrial bio type private equity type model, that form of capital. So I don’t know, I think it’s net good to your point, more forms of capital for entrepreneurs is good. Generally speaking.
Dan Gray:
For sure, there’s a ton of big industries with high idiot index scores still, but are ripe for the disruption by that kind of capital.
Turner Novak:
So to your point about bringing capital forward, it’s like you take... That is a very late stage capitalism type of capital, late stage economic activity that you’re shifting the capital into an earlier section of the stack. It’s not origination necessarily, but it’s still earlier than what it was before. So you’re in theory creating more economic profit of some kind, which I hope is good for the world. Generally speaking, I think that’s generally a good thing. So you commented one time about A16z is kind of like a common punching bag of the mega fund model. You actually said their very first fund had a smart good strategy. What was the strategy in that first fund? I’m kind of familiar with it, but I think people would probably be kind of interested because it’s probably different than what you’d think.
Dan Gray:
Yeah, it’s interesting. This is kind of the moment when I came across this when I started to realize maybe I’m judging them too harshly because I had this sole perception of them as the mega fund, that archetype. But actually if you go back to the fund one, they were a traditional and very well architected VC firm. They were targeting a level of diversification in that fund that was uncommon, is still uncommon but I think is sensible. And they had a follow on strategy for subsequent rounds that was more sophisticated than many VCs would consider today. So on the whole it was like, “Oh, these guys actually really do understand VC, so whatever they’re doing today is an evolution.”
Turner Novak:
So what was the strategy?
Dan Gray:
If I remember correctly, I think they were targeting something like 60 initial investments in their first fund, whereas the typical today is probably like 20, 25.
Turner Novak:
Maybe 30. I guess it depends on the stage you’re at. Yeah.
Dan Gray:
Yeah, for sure. For sure. Could be a size as 30 definitely.
Turner Novak:
So they were more diversified on average.
Dan Gray:
Which means they were able to take a bit more risk. They were able to be a bit more idiosyncratic, choose companies that are more outliers, less consensus, which I think is good. This is all good stuff. And then they had the strategy for investing a little bit in the A and then the B maybe, being able to double down on winners which has been described... There’s a very good paper by Joe Millam called Process Alpha and he describes generating systematic alpha through how you allocate capital. And this is one of the things he talks about a lot, like, how you do reserves is actually quite important and they had a very good grasp of this in 2009 or oh eight I think maybe.
Turner Novak:
What is the general best practice in around reserves? Because I feel like there’s a lot of people that say every end of the spectrum. What have you seen that you’ve picked up on?
Dan Gray:
Most recently I’ve seen a few GPs make comments along the lines of they don’t do reserves because the subsequent round in that company is so overvalued that they wouldn’t want to, it’s not worth it for them for the risk, which is a crazy thing to say because is that increase in value not reflecting an increase in potential?
Turner Novak:
Yeah, you’re saying my performance numbers are fake and they’re not real.
Dan Gray:
Exactly. I’ll take the markups, thank you very much. But I don’t want to be involved.
Turner Novak:
Yeah, I’m not selling any secondary either, which is maybe then also a question is like, “What?”
Dan Gray:
For sure. But I think the biggest thing, and this seems very obvious to say that in any subsequent round of a portfolio company, you need to be able to separate yourself emotionally, completely, look at it objectively. “Is this a good investment? If I had never met the founders before, if I had no track record with the company, if I’d never posted about them, never written a Substack about them, is this a good investment?” You have to be able to do that to make good judgments. I’m going to keep doing this to you, I can send you links afterwards. So it was a very good paper that came out last year.
Turner Novak:
Do you remember what it was called? I’m taking notes right now. So we’re going to throw them all in the description.
Dan Gray:
It is called the Sunk Cost Fallacy in Venture Capital Staging. And it basically describes how VCs grow attached to portfolio companies and probably they grow more attached to the ones that struggle a bit, because they’re the ones that need the most help and they don’t want to admit they’re wrong in the investment. So ultimately they pile in capital when they probably shouldn’t and it goes badly. So for all that it seems obvious to say you should evaluate each investment independently as an objective judgment. It doesn’t seem to be what is typically the case.
Turner Novak:
Interesting. Okay. And one comment, I think we had sort of a public... I think it was a friendly disagreement hopefully on portfolio construction. I think there’s been some studies that show in theory optimal portfolio construction and venture is way more diversified probably than most people typically do. I think my stance on it is the point of venture is getting as high returns as possible. That’s the only point of being in this asset class. So that probably means you need a more concentrated portfolio than not. And I think your view on it’s a little bit different. What’s kind of your view? And I think this is what the papers show in theory too.
Dan Gray:
Yeah, the question is essentially, what do your LPs want? Do they want that you get an eight X fund and then your next is a three X and then your next is a 1.5 and then your next is a 10 X? Or do they want four X, four X, four X, four X? Which is preferential? So that is going to depend a bit on the VC. Sorry, on the LP of course. But I would say generally LPs would be happier if the GPs they invested in had good public market beating stable returns. I think that’s pretty fair. If you accept that’s the case, then more diversification is almost a given because it’s how you achieve that slightly narrow band of returns, that slightly narrower band of returns, but still above benchmarks, still good solid VC returns.
Turner Novak:
Does that beat the public markets, you think? A four X? Like guess it depends on the time to liquidity on that, but...
Dan Gray:
Yeah, it depends a bit on the era, but even then it’s like it gets very complicated with the maths because... And the theory. It’s not that it makes a four X more likely, it’s that it makes four X being your minimum more likely. You can still outperform it if you are well diversified and you pick well, you can still do a 10 X fund in theory or it starts to get difficult, but you can still get great returns. It’s more how it limits the chance of complete failure. And if you look at the number of VC firms that completely fail, that should seem to be a priority to me.
Turner Novak:
Yeah, I think it’s a little bit of too high of a number. The numbers that completely fail. I think the thing that I honestly, I hate about that chart from that study. It literally, you cannot get above a six X. I don’t know where the cutoff is, but there’s zero samples, zero of N that hit anywhere above six X. I’m like, six X is pretty good for venture, but you want a shot at a 10 X, 20 X, 30 X, and if that chart should be... I think the chart that encapsulates that study should look differently. I think that chart biases my head to be like, “If there’s no chance of getting a 10 X, I don’t think that’s a good venture strategy.” But to your point of, yeah, if you’re an LP, you want a relationship with a manager who will give you a very consistent strategy over a long period of time. So yeah, there’s so much nuance on all this stuff.
Dan Gray:
There’s a lot. Yeah, there’s a ton. So the chart itself, just to touch on that, that’s based on data that looks at what is the typical distribution of returns in VC. So it’s kind of looking at an average profile of returns and how do you use strategy to maximize that. So for sure, like I said, you can definitely outperform that. You can do way better than that. It’s just trying to understand based on strategy, where is the average return likely to be? How does it influence the rate of complete failure versus the rate of success? So it’s almost a distraction from the point because it leads people to make a lot of incorrect conclusions. The other two little things I think are worth adding onto that is research shows generally speaking, a more diversified VC is more likely to take or is more comfortable taking more risk on a investment basis.
So they’ll go out and back things that might seem crazy to other people more often. And a lot of the time I think that can help accommodate more real outliers in the portfolio. The other thing is research on how... This idea of you should have super conviction, you should be concentrated in your winners and that’s how you deliver outlier returns is true in theory, but research shows that overconfidence is more of a negative than a positive more or less. Managers tend to have a better perception of their own ability than is realistic. And when they therefore concentrate, they’re more likely to do themselves harm than benefit essentially.
Turner Novak:
What else do people tend to get wrong about portfolio construction? Is there anything else? Because portfolio construction is basically risk management. It’s like how do you control... It’s like the thing you can’t control. You can’t control how well a company is going to do ultimately at the end of the day as an investor. It’s on the team, it’s on the founders. You can have a 0.1% impact, but you can control your own portfolio construction and risk management. So what is another thing people tend to get wrong?
Dan Gray:
Probably the two we’ve covered I think are the biggest ones. One is diversification and the other is reserves and follow-ons. Outside of that, I mean probably there’s something to how VCs think about diversification, not just in number of portfolio companies, but in exposure to particular themes. Because if you’re entirely in crypto and then the bottom falls out of crypto, your whole fund is toast. But if you’re a generalist and you’re a little bit of crypto, a little bit of consumer, a little bit of enterprise SaaS...
Turner Novak:
Got some biotech, whatever, deep tech space.
Dan Gray:
For sure. You’re more robust basically.
Turner Novak:
Have you seen those studies that specialist versus generalist? Which outperform? What’s the data show on those? I feel like I’ve seen some data, but I’m wondering what your opinion is.
Dan Gray:
There’s been a few studies, some of them... This is an interesting question that gets into good data versus bad data, which is I know something we were going to talk about. This question of specialist versus generalist is one of those questions that has been in VC for decades and decades, similar to the question of persistence, does VC performance last over time? So both of these questions have a ton of data, a ton of studies. I tend to bias towards looking at the more recent data because to the extent that VC has changed a little bit, it’s probably worth looking at the most recent findings and maybe they’re the most indicative.
So on the question of specialist versus generalist, the most recent research that I know of was PitchBook, I think a year or two ago. And they found basically broadly generalists outperform for basically the reason we just described. They can cover a ton of different themes. They’re not tied to a particular theme through that LP agreement. So if something really interesting happens in year two of deployment or year three, they can get into it. The exception is fields where there’s real hard science, like life sciences.
Turner Novak:
Some kind of expertise that’s needed.
Dan Gray:
If you need real deep scientific expertise to understand a company in a field at all, then yeah-
Turner Novak:
It’s like a blood testing startup, it’s like, “Does this blood testing actually work? Yes or no?”
Dan Gray:
Well, you say that, but that was diligent out by Google Ventures in a simple little test they did. But yeah, it’s a good point all the same.
Turner Novak:
I think another lens I’ve seen on that with, I’m piling onto your, because you just supported my narrative of being more of a generalist investor, but it seems like specialist funds will outperform early in a sector or an area of specialization, and then that kind of gets competed out. So an example might be Crypto Web3, let’s say it was a crypto fund in 2019, 2020, you’re deploying, it’s less consensus. But then if you’re still doing crypto only in 2022 and all your entry prices are when that is a consensus thing, you kind of match the generalist returns, you no longer get that sort of out-of-favor return. So I think specialist funds make more sense in a sector or in an area of specialization that are not within the realm of a generalist necessarily.
Dan Gray:
Yeah, it kind of depends on, you have to be specialized in an area that has amazing and pretty immediate potential that few other firms have realized. And if you can make those judgments, you’re in the right professions.
Turner Novak:
But then it’s like, “Okay, that gets competed out and you don’t have an advantage anymore as a specialist.” So you need to have some kind of truly defensible institutional advantage within that area of specialization that continues to give you an attractive entry price and reason to win against maybe the more generalists. So I don’t know. I think it can work both ways, but...
Dan Gray:
I think to be honest, almost with the exception of the life sciences stuff, just be a generalist. And if you have a thesis on the side, “We’re a generalist fund, but at this point in history, we think there’s an outsized opportunity in biotech,” then you can just start investing a little bit more in biotech. You don’t need to build a whole fund around it. And I think that is from a basic perspective of understanding outliers and venture capital and recognizing that the greatest companies are generally not from well-understood sectors, it just kind of makes sense to me that that’s how you should be.
Turner Novak:
Yeah, I think my friend Rex Woodbury had an interesting post about this, and it was kind of a chart. You’ve maybe seen this table, it was like years and then it was most valuable company founded in that year, and then it was hottest sector at the time during that year, or maybe some of those rows and columns are switched, but it is basically just showing in every year the most valuable company that was started was not in whatever the current thing was. And so I don’t think you should just say like, “Oh, AI is super hot. I am not investing in AI just because of that.” But it’s like data that shows, it kind of goes back to that point of if you want to actually generate returns, you need to make sure entry prices are attractive, but then it’s like velocity of dollars deployed. How much capital could you move? You probably should just invest in the stuff that’s moving the fastest, even if it’s overvalued.
Dan Gray:
It depends. Yeah. Are you an investor or a trader?
Turner Novak:
Yeah.
Dan Gray:
That’s the question. Are you chasing ultimate outcomes or the proxy metrics that raise another fund? That’s the core of all of this really.
Turner Novak:
Okay. So maybe moving to a slightly different topic, but what do you think is the correct way to value a start-up? And you do a lot of it through Equidam. How should I think about valuing a startup?
Dan Gray:
That’s a deep question. So going back to what we were talking about towards the beginning, a lot of VC users relative value as a way to understand how to price a company or how promising an investment is. And it has all those issues we discussed around catering and it channels capital towards areas of consensus, let’s say. So the flip side to that obviously is if you are in the camp of what I would describe as real VCs during origination, you have to have a lens on what is the fundamental value of a company. And that means you have to understand essentially what is the future cash generating potential of the company, which is obviously a ridiculously difficult question to ask it pre-seed.
Turner Novak:
I mean, it’s one of the things, you can’t answer it. You can’t say, “Hey, there’s these two people. What’s the free cash flow going to look like in year 12?”
Dan Gray:
Yeah, it’s true, but you also can answer it because... This gets really deep into the theory. But essentially you have to understand a few different things as the foundation, one of which is valuation is always based on the future. It’s like a hurdle for future performance. It’s not a reward for past performance. The other one is it’s always an opinion. There is no calculation in the world that tells you the real value of a company. It’s always an opinion.
Turner Novak:
When you look at public markets, it changes every day. Every stock price changes pretty much every day. The price changes of every stock.
Dan Gray:
Based on what do people believe basically.
Turner Novak:
Yeah.
Dan Gray:
So then the question becomes, you look at a company at pre-seed, you can see the market they’re going after. You can see the product. You can maybe see what existing solution are they displacing, what budgets does that allow this company to tap into? You can think about how much competition is there in the market, how much better is this solution than incumbents? And that tells you a little bit about what their moats might be, which allowed them to preserve the margins over time. And basically, you look at all of this and you can make a more or less a gut judgment of does this company... The way I always put it is, can it unlock a ton of economic energy? Can it tap into something that has a huge amount of cash waiting to be deployed into a better solution or a different way to do things? And is that going to sustain over time? Are they going to be able to keep an edge over the market to let them to create a monopoly more or less? And I think you can make those judgments a pre-seed. And not only can you, but that’s kind of the job. If you care about the ultimate exit and not the incremental metrics to go back to that point again, that’s kind of the job I’d say.
Turner Novak:
Yeah, that’s almost like the role of the founders is to figure out the execution of getting there. And you can help maybe. Most good investors probably help in some capacity, but really just gauging, is this even possible? Is this an opportunity that I want to allocate my capital to, I guess with my crystal ball in a way? I like that framing of market potential. I think a lot about just customers. Do I think that you can generate a lot of cash flow from these customers? Do I like a company servicing this customer base?
I have one them looking at right now, really interesting idea, but I’m just like, “I just don’t think you can make enough money on this. I just don’t think this is a customer that I want to go after.” And it’s just tricky because they might be able to actually expand the scope of what it is, but I just don’t think they will be able to. I don’t know. But I think a lot of it, like customer base, do I like going after this customer and do I think you can generate a lot of economic profit serving this customer or serving this market to your point?
Dan Gray:
And every VC, or let’s say maybe like 99% of VCs, probably 99.9 would say that like a DCF, a discounted cash flow model evaluation where you model out the future revenue, the future costs, you get to the future free cash flow and you use that to determine the value today, they would all say that has no place in venture capital. There’s way too much uncertainty. It’s not practical. You can’t do that for a pre-revenue company. It’s ridiculous. But actually what you do when you think about whether or not to invest in the company is basically like a simple mental DCF. The only benefit of putting it out in a spreadsheet if you need to is you can pick apart the assumptions. Are you really going to grow at this rate? Is this really going to be your margin? And it’s not to get to a precise calculation, but to explore what are the assumptions and the opinion.
Turner Novak:
My general super lazy just gut check initially is just you just assume something’s 10X revenue is your exit multiple or whatever. Sometimes it’s lower than that. Sometimes it’s higher, but it’s like, okay, if it’s a tech company growing at a relatively fast-paced, outsized margins like 10X revenue at exit is probably semi-reasonable. Hopefully it’s higher. I think you want that uncertainty of, ooh, 22X revenue multiple, whatever. But I feel you start to get in trouble though, where you go in and you say... Let’s say you invested at 100X revenue multiple and you’re going to exit at 10, you need to grow 10X to then keep your valuation flat at a 10X multiple. So I like to think, okay, if I’m only investing at a 50X, that cut in half the return expectations. You only have to grow 5X, or if you come in at a 20X, you only have to double to hit that same exit valuation.
And if you 10X, then you’ve got a 5X increase in valuation at the exit if all things hold true. To your point about almost re-risking the business, I think about that a little bit when I’m just generally thinking about this is how high is that hurdle that I’m coming in at that the founders are giving themselves? And then in the context of the full portfolio, a basket of all of these, how is it going to all culminate when it all gets said and done in five to 15 years when some of the stuff really starts to play out?
Dan Gray:
And a big part of that is the multiple drops over time as the growth of the company slows, as it matures for sure. But also, as you go from pre-seed to a public market company, the quality of your revenue matters a lot more. And that, if you’re investing on an ARR multiple basis, it’s the point I made earlier about GAAP accounting and adjusting to that life. For how long in a company’s life can it be valued on ARR and invested on that basis and still be able to do a hard handbrake turn at the end have become a reasonably healthy company in a public market or in an acquisition scenario because those buyers do care. It obviously created a ton of problems with SaaS companies in ‘22 because they could not turn around that quick.
Turner Novak:
Yeah. One thing I think too, when you look at just generally public market, I feel like you need to use public markets for this because there’s not private market data on what drives returns, but I think generally in public markets you’ll see them, I forget the numbers, but the majority of returns are basically driven from multiple expansion and earnings growth. If you say, in venture you’re typically fighting against multiple expansion, you’re probably going to get multiple compression.
But the holy grail is can you actually get multiple expansion on top of earnings growth? That’s maybe the, when you look at a hundred baggers in the public markets, that typically happens. It’s like you bought a company for 4X earnings and now it trades at 20X earnings and it grew a 100X, and now it’s become this compounding durable sustainable growth. And the market knows, and that’s why it trades at 20X earnings, but it was trading at 4X before and you bought it at 20 million in earnings, so you bought it at $100 million market cap and now it’s worth 6 billion because you expand your multiple X amount and you also grew earnings by X amount.
I talked to a lot of my public market friends and be like, “Oh, this venture is, it’s micro cap penny stock investing almost.” Where yeah, if you go look at a public company that’s worth $12 million, it’s probably worth zero. But you’re trying to find that diamond in the rough of this is a 300 banker, or this is an 8 billion company that you invested in it, 12 million market cap or something like that, and it’s like treasure hunting. You’re just going through, you’re trying to find undercover gems really at the end of the day, finding founders. But it’s cool because in venture, they come to you. If you’re a public market investor, you’re probably not getting these companies emailing you, and if they are, it’s probably pretty bad, right? But in adventure it’s like they’re coming to you to seek out capital. So it’s like this weird treasure hunting.
Dan Gray:
Well, that again, it depends on your strategy, the whole origination question. Are you relying on inbound? How good is your process for screening inbound? Are you relying on referred deals? Are you going out hitting college campuses and hackathons? It’s a pretty deep question.
Turner Novak:
There’s this one study that you brought up that I think you posted about this on Twitter recently, which sounds non-intuitive, but generally speaking, investment firms that are located in popular startup hubs typically outperform the investment firms that are outside of popular startup hubs, but not necessarily on a company by company basis, just generally company based outside of San Francisco might actually outperform in San Francisco. How is that even possible? How does that play out and why is that in the data?
Dan Gray:
Yeah, it is a fascinating bit of research, and the bottom line that I try and that I hope VCs take away from that is that they should be looking at opportunities in non-traditional hubs because they’re great and they’re out there, but essentially I think it’s driven by the friction for a Silicon Valley VC to go and invest in a company in Ohio is very high so they’re only going to make the effort if they think it’s really an outstanding opportunity. And also, at the same time, because they’re a Silicon Valley VC, maybe with a big name, they can win that deal over any local investor. And also probably, they have an easier time raising from LPs than the Ohio VC does so they have basically a lower cost of capital, they can invest on more favorable terms. They basically have all the advantages and the ability to pick opportunistically, whereas the local VC is a bit more limited
Turner Novak:
Because I feel like that’s generally what I see. If you’re the best company in Cincinnati, Ohio, you’re going to want to raise money from the best investors. You’re going to be seeking out though you want a16z involved, you want their great brand. They will talk about you on podcasts, you’ll get the news mentions, you’ll get Twitter threads about you. You’ll show up in all the reports. People will know that you exist and you’re not going to get that from, I don’t know, Midwest Ohio venture capital fund, but I don’t even know where, I think Cincinnati’s in the South. Have you ever been to Ohio? Because you live in Wales, right?
Dan Gray:
Yes, I do, and I’ve never been to Ohio.
Turner Novak:
Okay. Have you ever been to the US?
Dan Gray:
Yeah, I spent about six months living in Miami and I visited other parts, mostly West Coast.
Turner Novak:
Oh, interesting. Okay. Because I think in that one paper specifically, it talks about how proximity to portfolio company has no correlation to return to. Maybe there’s a negative correlation, I forgot which one it was, but it’s not intuitive because you think, oh, if you invest in the thing down the street, you can monitor it and check in and add value way better than if it’s 420 hour plane right away.
Dan Gray:
Yeah, I guess whatever that adds in terms of friction is priced in so that you therefore would only do a deal if it was really seen as worth it.
Turner Novak:
Interesting, okay. And there’s another study that shows actually that it’s related about founding team and the quality of the founders is not actually a predictor of investment success. It’s much more about the actual initial quality of the business, but a lot of times there’s no business, I guess were they’re investing in a team. So what is that study that shows the actual business quality predicts, and then how does that actually play out in this origination stage venture when there’s not really a business yet?
Dan Gray:
There’s three papers I can think of that have all found basically the same thing, I think that the best of which is called Predictably Bad Investments in Venture Capital. They basically all look at what are the founder attributes that VCs pattern match for? And it’s stuff like where they went to school, where they worked before, maybe socioeconomic background or they go to the same squash club or something. A lot of very weird, slightly tangential attributes that end up influencing VC behavior and investment decisions, but probably shouldn’t in theory. And when you look at the data, clearly shouldn’t, have quite a negative influence. Not only do they lead to bad investments, but they lead to missed good investments, which is a huge problem. There was a second part to your question, I can’t remember what it was now.
Turner Novak:
Well, that actually the quality of the business is what actually predicts the outcome, not the founders.
Dan Gray:
Yeah, and how you understand that at a very early stage.
Turner Novak:
But yeah, if you’re investing in, there’s no customers yet, how do you get business quality? Is it potential business quality or something or?
Dan Gray:
There’s a quote from Peter Thiel, he was asked a very similar question, do you focus on founders entirely? What else can you look at early stage? And his answer was it’s a complicated package. You have to look at the whole thing. So in order, obviously pre-seed, pre-product, all you can really look at is the founders. But you can understand the quality of the founders and the competence through the lens of how they articulate the idea, how they think about the markets. Do they understand the financial side of things? Do they understand the growth, technical aspects? It’s very easy for a lot of early stage VCs to say, “All we care about is the best founders. We don’t care about ideas or we don’t care about markets.” But actually, that just means you’re ignoring information that could be useful, even if it’s the wrong market, even if they end up pivoting the idea, the way they talk about it and understand it and articulate it is still really useful information.
Turner Novak:
And it’s like how did you come to your idea? What kind of research did you do? What was your process like? I have one portfolio company that when I invested, they had one idea that I thought was really good. They ended up pivoting. And the reason I invested was I thought, I bet if they change concepts, they have a good framework for doing this. I trust them with the capital, I trust that they’ll be able to do something. And they just pinpointed this almost perfect storm, perfect category. I mean, it’s turning out to be a really interesting business, still pretty early, but it’s one of those things I’m like, “Damn, that was incredible pick. How the fuck did they find this?” It’s also right in front of you kind of a thing.
But yeah, I feel like that’s definitely a good way to think about, it’s just the founder ideation, quality discovery, quality understanding of what it takes to go into almost like Porter’s five forces or the seven powers type equation of when you’re starting a company, what are the ideas to pursue related to margin structure and dynamics in an industry and how those could change over time based on new technology.
Dan Gray:
Yeah, and you have to reconcile that with the fact that other research I’ve shared shows that high-tech startups are actually more likely to succeed after they’ve had at least one pivot, or no, after they’ve had one pivot. It goes down a bit after that, but one pivot is actually a positive, which is crazy because it means everything you think about the business at the first pitch meeting is probably going to be wrong if it’s a good company.
Turner Novak:
What is considered a pivot in that time? Is it like we are a consumer social media app and now we’re a B2B AI, vertical SaaS thing? How do you define a pivot in that one good pivot thing?
Dan Gray:
It’s a good question. I can’t remember how the paper described it off the top of my head, but it was a pretty fundamental shift in the direction of the business so it was significant.
Turner Novak:
Yeah, because when I think about all, every single, probably half of my investments I made are just pre-customer investments sometimes. Sometimes they’re working on getting customers or in the discovery phase or whatever, but pretty much every single one of those, the thing that was in the pitch deck is the main product that was going to be used to generate the business revenue is not true anymore. There’s a lot of cases where generally the customer space or the general idea, but the way they actually solved it just completely different than the pitch deck. The pitch deck was just wrong, but it’s this discovery process of meeting the team and you’re almost sussing out their ability to go execute on this and figure it out really, because it’s an experiment, it’s an adventure. You’re figuring this all out.
Dan Gray:
And that ties into a few things we’ve talked about. Also, maybe another paper I would recommend, which is Premature Scaling by Startup Genome. So this ties together a few of the topics quite nicely. They looked at, basically they were trying to study what are the main causes of failure in startups, and they were surprised by their own finding, which was that I think in 70% of the failures they looked at, a significant problem of the company was that they’d raised too much money. And essentially what that meant was before the startup had properly validated what they were working on, that they had product market fit and it was the right direction for the company, they raised a ton of money and invested a ton of money in growth and in hiring and technology and development, and then when it was wrong, the company just explodes.
Whereas if you go piecemeal step-by-step, get the very small checks from a nice, small friendly originating VC fund, you can work that stuff out as you go and you have a partner to help you through that. The parallel there on the other side is maybe the mega fund VC who would give you the huge bag of cash in the beginning and then just stand back and maybe you take off and it’s amazing, but quite possibly you explode. And maybe they’re okay with that trade-off. Maybe that’s built into the maths of their model.
Turner Novak:
Yeah, I would think so. Did the explosion or the failure happen because you set a super high bar of what the company needs to hit in terms of the valuation of metrics to reach that valuation and you’re just unable to raise more capital and you run out of money, is that generally why the explosion or the implosion happens?
Dan Gray:
Yeah, basically you have a very fragile future. The thing that you pitch them on that they invested a ton of money in, it has to work and it has to scale quickly, and if it doesn’t, you’re done because the hurdle for that next round is now so high, you just can’t clear it.
Turner Novak:
Interesting. Yeah, and if you just think about a $10 million fund that writes a 100K check, that’s 1% of their fund. A $100 million fund that writes a million dollar check, 1% of their fund. A billion dollar fund that writes a $10 million check is also 1% of their fund. So in all those cases, if I invest, $10 million fund, 100K, if it doesn’t work, it doesn’t work, that can drive my return. But I was like, “Hey, that’s okay. You guys tried,” doesn’t screw, there’s a reason I have a fund. Let’s say I wrote 100 checks of 100K each, it’s fine.
For a fund that’s a billion dollars, wrote $10 million check, didn’t work. You’re like, “Eh, that’s fine. We tried to get to the Series A that we wanted to put 3% of the fund into, didn’t work, whatever. It’s not a big deal.”
So I mean, as a founder, if you’re trying to play that game, I would say you’ve got to find more funds that you can find that very small percentage of the fund option check. They’re there. I have portfolio companies that have done that, that have been $10 million option check from a big pool of capital to keep it going. Sometimes it works, sometimes it doesn’t work, but it’s an option there for founders.
Dan Gray:
The question that it leaves me with is the mega funds, when they start doing origination themselves, how well set up are they for that? Or do they even have slightly different incentives? If they’re doing this strategy of trying to ride the waves and trying to produce essentially venture beta rather than alpha, do they necessarily care about finding the crazy little outliers? Perhaps that’s not how they’re oriented, and that shows in the data where they actually seem to be increasingly concentrated in San Francisco software companies rather than branching out as they get bigger. So yeah, I have lingering questions about that and their involvement at the very early stages.
Turner Novak:
I mean, ultimately their business model is put a bunch of capital into only a couple companies, and you can’t put a bunch of capital at the origination stage, you need them to get further along. You’re probably on a cost weighted basis. Let’s say one of these mega funds fully plays out and you just look at, we’re talking in 2040, we’re looking at what they were doing in 2025. I mean, their average entry point on dollars is probably a Series B or maybe even a Series C because it’s 5 million at the seed, 10 million at the A, 2050. They might put 100 million in right before an IPO. So your average cost basis was almost at the IPO, really at the end of the day. But you basically use that origination check as a right to get there, to get into that company.
Dan Gray:
And actually, you want it to whittle down. You want to start with whatever they’re doing, 100 seed checks a year, and you want that to be two companies by the end to concentrate all the rest of the money into.
Turner Novak:
Yeah, because you want to make as much money as possible while doing as little work as possible. As an investor, you want to give someone want to give them cash a year later, they give you 100X of it back and you did nothing. Probably for an investor that is the most ideal. No one will admit that publicly, but I mean that’s ultimately what every investor’s going for. We first connected on Twitter a couple years ago, I don’t know, I just feel like I’ve been reading your stuff for a while. We’ve talked back and forth. How did you first get started with writing?
Dan Gray:
My blog, which has the very unusual name of credistick.com, which is a Shadowrun reference, unfortunately a misspelled Shadowrun reference for anyone else-
Turner Novak:
Shadowrun, is that a game?
Dan Gray:
Yeah. It’s originally a tabletop RPG that became video games later on.
Turner Novak:
Yeah, did you play the video game?
Dan Gray:
Yes.
Turner Novak:
Oh, so this is the one where it was a gun game, it was shooting, but there was magic?
Dan Gray:
Kind of, yeah. It was an isometric turn-based setup, but basically it’s a connection to science fiction, which is what I originally intended to start writing about, the intersection of science fiction and technology today. That led into writing about Web3 Metaverse because I was super interested in that. I’m a big fan of books like Otherland by Tad Williams, Permutation City by Greg Egan, Neuromancer of course, they all have connections to this. And then Web3 turned up and I was like, “Oh, this is amazing,” so I started writing about it and I quickly realized the people investing in Web3 didn’t have any familiarity with the science fiction, the literature about it with existing virtual worlds. They didn’t know about Second Life, they didn’t understand World of Warcraft. I need the research on these topics.
So much interesting stuff was out there to be a very useful resource that wasn’t being applied to this wave of Metaverse and Web3 companies. And that’s when my writing took a pretty hard turn into being like, “Hey, venture capital is kind of broken.”
Turner Novak:
Okay. What were the name of those three books? I’m going to throw a link into the show notes for people.
Dan Gray:
The first is a series of four books by Tad Williams called the Otherland Books, the Otherland Tetralogy. The second was Permutation City by Greg Egan, and the third was Neuromancer by William Gibson. And there’s also, there’s a ton of others you could mention in that same kind of genre, but those are the three I think about the most when I’m near future sci-fi that is still relatable. So if you’re a VC and you’re thinking, what’s the world going to be in 10, 15 years? I think those are good books to read and think about.
Turner Novak:
What are the general takeaways in terms of what’s the world going to look like? Is it not Web3, everything’s tokenized? Is it more of virtual economy? What’s a way to think about it?
Dan Gray:
I’d say Neuromancer is interesting because it’s cyberpunk, so it’s dystopian. It’s like what would happen if the FTC didn’t exist and corporations are allowed to do whatever they want?
Turner Novak:
Oh, that’s basically today, right?
Dan Gray:
Kind of.
Turner Novak:
Almost.
Dan Gray:
You can see the direction travel a little bit.
Turner Novak:
Did you see, well, I’ll throw the picture up on the screen. I think Trump posted a picture, it was an AI generated picture of him looking at a computer that just said, “Intel $30, buy Intel stock,” or something. Did you see this?
Dan Gray:
I didn’t, I missed that.
Turner Novak:
Oh man. I’ll send it to you right after this but it’s so funny. I was like, “What are we doing? Why are we allowed to do this?” This should not be allowed.
Dan Gray:
It’s crazy. It’s crazy, Neuromancer gives you a lens of what the worst end state of that would be. Also with some fun stuff like human augmentation, biohacking sprinkled in. Otherland is really quite literally what the metaverse should or could be like, and this is with brain interfaces, AR, VR, all this future visions of current technologies, which is really interesting and it is not set too far into the future so it’s quite attainable relatively. And then Permutation City I think is more what science could look like in the near future, biology particularly as well.
Turner Novak:
Interesting. Yeah, I’ll throw links to all those in the show notes if people want to check them out.
Dan Gray:
I should start a VC sci-fi book club because I could talk about that endlessly.
Turner Novak:
Yeah, you should. I feel like people appreciate that kind of stuff. Well, any advice for someone who is starting to think about writing online? I’m assuming you probably did a lot of not online writing before this. Is there a transition that you went through or made or a change in mindset?
Dan Gray:
It’s maybe tricky. There’s something, there’s a leap you have to make when you go from just posting on X and being in the timeline with hot takes on topics to when you start being like, “Okay, I’m going to have a blog or a Substack,” and the transition is what I write is no longer really ephemeral. I’m carving it somewhere.
So you have to be comfortable being like, “This piece of writing is a part of me or a representation of me, my view on the world in a certain way.”
You have to be willing to be wrong or to look dumb, to have people send you comments or DMs and be like, “Here’s 500 words about why you couldn’t have a more idiotic take on this topic,” and maybe they’re right, maybe they’re wrong. Maybe it changes your opinion or you end up rewriting something. But that has to be part of the process because the alternative is you do what most VCs do, unfortunately, which is rewrite the same perspective on the market that everybody else is saying that adds zero insight.
Turner Novak:
Valuations are too high, mega funds are bad, whatever, AI is going to change the world.
Dan Gray:
Yeah, why whatever my portfolio is mostly invested in is the best opportunity right now.
Turner Novak:
Yeah, exactly. That’s good. You should write about that if you’re a VC, go for it, I’m not going to stop you. What was the most bold cold email you’ve ever written?
Dan Gray:
That would be by quite a wide margin. A few years ago I was working at a media company in Berlin. We were focused on data science, machine learning, more or less what you would call AI today, but people didn’t back then, and it’s a media company. It’s a grind, it’s horrible. You’re trying to do anything you can to get page views, and I saw Mark Cuban had this new app, so I just sent Mark Cuban an email, and I was like, “Hey Mark, can I interview you about this app launch?”
And he replied 30 minutes later and said, “Sure.” I had a 0.1% chance in my head that he would even see the email. But it was pretty amazing. It taught me from then on, you got to take those shots because there’s no cost, there’s no downside. You might as well, and it can have big payoff.
Turner Novak:
Actually, dude, I’ve heard Mark Cuban responds to a lot of stuff. I’ve heard so many people say, they’ve emailed and he respond. He actually, he once retweeted. A couple months ago, he retweeted a clip from the podcast on the Twitter account, and I was like, “Oh, was not expecting Mark Cuban to see this and retweet it.” So I DMed, I’m just like, “Hey, want to come on the podcast sometime?”
We’re trying to figure it out. I probably got to have to fly to Dallas and deal with him in person, or maybe I can get him to do it virtually. He actually has a surprising amount of podcasts and it’s hilarious because if you look at his background, it’s the messiest office with just shit everywhere. Just YouTube Mark Cuban and just scroll and look at his YouTube Thumbnails on YouTube. You can tell he does not care.
Dan Gray:
Quite often with NBA trophies, if I remember correctly.
Turner Novak:
There’s just random stuff, you can tell that it’s just a guy’s office. It’s a 55-year-old dude’s or 60-year-old dude’s office who’s, he’s just got stuff going on. There’s stacks of papers, there’s snowboards or football, basketballs and trophies, massive stacks of papers and binders and yeah. But anyways. Well, this was a lot of fun. Thanks for coming on the show.
Dan Gray:
I really enjoyed it. I would do this anytime, all the time. You’d have a hard time getting me to shut up, but I really enjoyed it.
Turner Novak:
Actually, before we sign off, what’s a good way for people to follow you? Twitter seems to be probably your main channel. You got a blog too, where you write sometimes. What are those?
Dan Gray:
Yeah, pretty much everything on Twitter, to be honest. My goal with all my writing is to put it in the most accessible place so that for the time being seems like Twitter.
Turner Novak:
Yeah. Okay, cool. Yeah, we’ll throw links to those in the show notes and people can find them. Well, cool. This is a lot of fun. Thanks again for doing it.
Dan Gray:
Thank you for having me. It was great.
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