🎧🍌 How To Generate Alpha in Venture Capital | Albert Azout, Level Ventures
Why you want to be first to the new consensus, capital flows drive all returns, and networks are more persistent than performance
Venture investing is hard. This conversation covers years of research unpacking exactly how to generate alpha in venture capital.
Spoiler, there are three main sources of alpha: your network, your knowledge, and fund size. There’s also sub-sources around analytical edge, informational advantage, brand, and speed. We talk through how each of source of alpha works, how they intersect, and how they change over time.
We also talk about consensus vs non-consensus investing, and why the highest form of alpha is actually being first to the new consensus, and benefitting from follow-on flows, a concept Albert calls Criticality Investing.
His firm Level Ventures is one of the most interesting Fund of Funds investing in emerging venture capital managers, and we talk about how Level picks and backs emerging venture managers to invest in, and Albert gives a demo of the custom internal software they’ve built.
Thank you to Jake Kupperman, Sasha Kaletsky, Nathan Benaich, Amanda Robson, and Dave Fontenot for helping brainstorm topics for the conversation.
Presented by Ramp
If you're running a finance team, you know how much time gets wasted on expense management. Chasing receipts, categorizing transactions, waiting for expense reports. It adds up quickly.
Ramp handles all of this automatically. Its a corporate card and expense management platform that over 40,000 companies, like Shopify, CBRE and Stripe use to streamline their financial operations.
But here's what makes their corporate card different: every transaction gets automatically categorized and matched to receipts. No more wondering what that $47 charge was for three weeks later. You can set spending controls, get real-time alerts, and even block certain merchant categories. It's like having a finance team member embedded in every purchase.
The platform integrates with your accounting system and ERP, so everything flows through without manual data entry. Whether you're issuing cards to a few employees or managing spend across departments, Ramp gives you visibility and control without the paperwork.
Stop chasing receipts: check out ramp.com/ThePeel to get $250 and see what a corporate card can actually do for you.
Time is money. Save both with Ramp.
To inquire about sponsoring future episodes, click here.
Timestamps to jump in:
5:01 Top 3 forms of alpha in VC
10:11 Other ways to generate alpha
12:47 Avoiding false positives
17:11 Optimal fund size and portfolio construction
22:25 The role of Luck
23:55 Spin-out vs outsider funds
25:43 Level’s backchannel reference process
29:29 Finding alpha in Criticality Investing
34:45 Why capital flows drive all returns
43:53 Early, consensus investing has the most alpha
48:46 Networks are more persistent than performance
52:03 The strongest and weakest networks
58:41 Demo of Level’s internal software
1:04:48 Building a Fund of Funds around their data
1:10:01 Ideal LP GP relationship
1:12:39 Benchmarks are relative
1:15:39 VC funds using AI
1:17:43 How venture will change in the next 10 years
Referenced:
Level’s Research Papers
Find Albert on LinkedIn his Newsletter.
👉 Stream on YouTube, Spotify, and Apple
Transcript
Find transcripts of all prior episodes here.
Turner Novak:
Albert, welcome to the show.
Albert Azout:
Thank you for having me.
Turner Novak:
I feel like you've probably written the most on this topic of anyone that I've ever come across, talking about generating alpha in venture. I feel like that's the general theme of this conversation.
Albert Azout:
Yeah. Yes, well, we write about it a lot for sure. We're always trying to figure out what it is and how to get it.
Turner Novak:
Yeah, hopefully you can generate it.
Albert Azout:
Exactly. That's the idea. If you write about it, at least you're getting closer to generating it hopefully.
Turner Novak:
Yeah, or people think that you're generating it.
Albert Azout:
Exactly, which is maybe just as good.
Turner Novak:
Yeah. How do you generate alpha in the venture capital asset class?
Albert Azout:
I think one of the things that we think about all the time is how is the market changing? So I think answering that question, you first need to have an understanding of the context of the market and the market participants, because the markets change around you and alpha requires that you adapt your behaviors, adapt your investing strategies, etc, based on what's happening in the marketplace. I think that question is always temporally dependent on where we are in a particular market. And so I think it's important to ask that question because I think it then inspires you to think about what does the market look like today, and what are other players doing so you can counteract against them? Because alpha is always a relative thing, at least the way we think about it.
I guess the way that the VC ecosystem has become more institutionalized, it's just grown, there's been a lot of capital flows, etc. And I think it's separating, as you know, into two parts, which are, one hand you have these large multi-stage firms that are essentially aggregators of capital. And of course, they serve a very important purpose and they serve a purpose for large LPs as well. But they have a multi-product strategy. The expectation is you'll have a lower cost of capital there. And then you have this large market, highly fragmented set of emerging VC funds, and you can say that the middle is sort of hollowing out. You're either super scale or you're subscale, and very few will survive in the middle. I think that happens in a lot of industries. It's not unusual.
And then you have these highly fragmented emerging funds, and just by the sheer size and the structure of the market, they're mostly driven by carry and economics. In venture, I think there's a few areas where you can develop alpha. Some of it is structural and some of it is more strategic. But one of the things that we know about of course, is access, access to networks, monetizing your networks, having networks that are both redundant and economically important at some point in time.
Turner Novak:
Wait, you want a redundant network?
Albert Azout:
Non-redundant. Non-redundant.
Turner Novak:
Oh, non-redundant. Okay.
Albert Azout:
Yeah, non-redundant.
Turner Novak:
I was going to say, that doesn't sound like you can monetize it.
Albert Azout:
No, you don't want redundancy. You could have redundancy, but you want to have non-redundancy because you want to be sort a broker of the network in some ways, and be able to monetize it independent of others. So, some aspect of it has to be non-redundant, unique access. And then it also has to be economically important, because there are networks that you can have a lot of access to but are not important at some point in time. Because it's where the opportunities are, are where it's important. And so that's one area, is what we think about. The other area we think about is more knowledge advantages, which are related because a lot of the knowledge that we have is embedded in our networks. But you typically have individuals at some point in time that might have an advantage in some area of specialization or set of knowledge, horizontal or vertical, that is non-redundant and also economically important. And so I think that's another area where we think about alpha.
The third area, which is more structural is size, which is because of the nature of small funds, their ability to essentially write checks that are less competitive, be able to get into financings, be able to move around more flexibly, and also to have certain outcomes that are at the tail of the outcomes, but nonetheless can deliver a lot of performance and convexity. Which is why you see historically the 90th percentile of VC funds are small. Performance are usually small funds, because there's just a structural advantage. You can just do things that others cannot do. And so, those are some of the areas in which we think about alpha. And there are more, and we have some of the articles on our website, but that's how we think about it when we're underwriting a GP specifically.
Turner Novak:
Okay. Yeah, we'll throw a link into the description if people want to check out. You guys have a lot of good posts. I think I read almost all of them over the past.
Albert Azout:
So, you're our only reader. Yeah, you set the record.
Turner Novak:
Yeah. I actually saw this really interesting stat, I think it was from the World Bank or something, and they showed on... You know how all these think tanks will publish these papers and they're hundreds of pages? They put out some data on how many clicks or how many reads those things get, and it is a significantly lower number than you would think, and an embarrassingly low number. I don't think people realize how few people actually read some of this stuff, but some people do read, if it's good and you get a lot of views. If it's super highly valuable stuff, that's a very valuable eyeball that you got.
Albert Azout:
No, for sure. For sure. I guess the reader really matters, and the right area and all that.
Turner Novak:
Yeah. Well, so you mentioned I think there's other forms of alpha. We got to get as much alpha out of this podcast episode as we can. So what are some others? These are emerging forms? Are they less prevalent forms of alpha or something?
Albert Azout:
There are a few areas. Access and sourcing is one of them. We spoke about structural advantages. There's also analytical edge and speed, which is another way that we think about it. In some environments, especially in venture, and we think about this all the time, is the ability to have a speed of analysis and have conviction and the ability to execute really, really quickly in certain situations is an advantage because you have a temporal advantage that others might not have. And these are all kind of related areas.
Turner Novak:
So there might be a somewhat unique opportunity or founder and you're familiar with what they're doing, you know people that know them and you're like, "Oh yeah, I know this guy Albert. Oh, this is a good idea. Sure, yeah, here's 1 million bucks. I'll invest right now."
Albert Azout:
Exactly. There's an index strategy, which is, by definition it's speed because it's very light on diligence. But if you're a more concentrated strategy, the ability to move quickly and be able to make decisions, whether it's through pattern recognition or whether diligence or some sort of structural process that you've put together, information, etc, that allows you to move quickly, I think is definitely an advantage in environments where others might be moving more slowly. That matters, especially if you're trying to lock in price before... In these markets, the minute you have an option, price will get away from you.
Turner Novak:
Yeah, so speed can almost be a way to, I don't know, prevent an auction from happening, which is maybe something that founders don't want to hear right now, but-
Albert Azout:
Exactly, exactly. And all these things work together. It's not like there's one source, it's like, oh, you have access, but also you can move quickly and also you can do before an auction happens and whatnot. And the other thing we spoke about is knowledge. There's also just informational advantages just generally, where you have some asymmetry. And venture, especially, in many markets information is not equally distributed across individuals. And so having an informational advantage about something or about a set of things is another way to have an advantage. And then there's also other things like timing and cyclicality, those things. But those are some of the sources. Some are more important than others. They work together. And also the market's evolving underneath you, so you always have to be adjusting how you play it.
Turner Novak:
Are there certain forms of alpha or venture firms or types of investors that generally have the highest persistent alpha, if that makes sense? Or maybe another question would be, Level, you're out there trying to invest in some managers today, what does it look like? What does the highest alpha-generating firms or people typically look like?
Albert Azout:
Yeah, I think it's a mixture of all those things. Brand is very important, development of brand. Brand is also, of course. There's alpha and brand and there's flywheels. But I think generally speaking, you want to develop alpha in a way that has increasing returns and feedback loops. So it's not like a single one time use of alpha, it's something where you develop it. And the next situation that you encounter benefits from the prior alpha, so it's like, you want to develop this flywheel within the firm or within the investing practice such that you have escape velocity and the next deal is just more likely to choose you, etc. And so what we've seen is that early investments, early successes, these networks sort of compound. And what we try to look for is that early signal that would create a founder magnet over long periods of time, and it's not just a false positive in the portfolio or in a set of investments. There's actually something very durable underneath that is sustainable and that will have compounding. And that's a lot of the things that we try to think about when we're investing.
Turner Novak:
When you say founder magnet, is it just founders are drawn to that person and want to be associated with them or spend time with them, get advice from them, work with them?
Albert Azout:
Exactly. At the end, that's what it is. It's like you're attracting talent, and the more talent you attract, it should attract more talent. There should be a feedback loop that's pretty strong over a long period of time if it's set up correctly. As opposed to one hits and things like that where you could have really good performance in one fund, but that we know in venture generally, persistence is relatively low, especially for emerging funds. So you really need to unpack the portfolio and really try to understand where are these durable sources of alpha.
Turner Novak:
Interesting. I want to ask you about persistence of returns, but you did say something about a false positive. Is there specific examples of maybe what a false positive might look like?
Albert Azout:
Yeah, it comes in different ways. It could be like a hit. You can never judge a process purely by its outcomes. Meaning, you can look at a portfolio and say, well, there's one hit or something, and that doesn't necessarily mean of course it's repeatable. Because most of his business, or a lot of it can be just luck, just position and luck. And so, at least the way we think about it, is you need to unpack. You can also have statistics, like if you have enough hits. You have enough shots at that, you're going to have some hits. But what we try to really understand is underneath the surface what's happening. That's what I meant by a durable source that could compound. And so a false positive, it could just be for the company that just had, it was a breakout. But you can't judge based on that.
Turner Novak:
Would it be like that individual portfolio company didn't have any kind of relation to the core competency? Like, "We're an AI fund, we know 18 people that worked in OpenAI and they were in my wedding," but you invested in some CPG company or something?
Albert Azout:
Yeah. It could be very happenstance timing, just luck and timing. And also it could just be also structural, like you just had a small fund back then and you were able to do things you couldn't do now because you're trying to build a 10x bigger fund, which is more competitive. It's just timing, circumstance unrelated to the underlying process or sources of alpha. And there's nothing wrong with that. It's good to get lucky. But if you're going to make a decision on a new vehicle of the same manager, you got to make sure that there's really something there. And so, yeah, that's what I mean by false positives.
Turner Novak:
And one thing, you've kind of hit on this a couple times, smaller funds tend to outperform. You just said a mistake or a change in strategy, raising a massive fund. Is there a way to think about just a fund size that is appropriate or makes sense? When do you say, oh, that's a big fund versus small fund? Do you have a framework for that?
Albert Azout:
We have a framework. It's always, it depends. Because I don't think there's a true size, but I think the way we think about it is there's a relationship between the statistics of the ecosystem and the hit rates, and the underlying ownership of the companies that you expect at exit relative to the fund size.
Turner Novak:
What are those numbers? Can you run through a high-level for people?
Albert Azout:
Yeah, again, this is all depends on dilutions and different markets, but whatever. If you think about it, if most of the exits are below $1 billion, but if you're able to get, let's say $1 billion to $10 billion exits, if you're going to benefit from something like that, which is very low chance, then you want to make sure that the returns of the fund of course are multiples. You want to have several turns of the fund if you're going to hit that, because it's hard to hit and you might only hit one. So the way you think about that is you say, well, what's my portfolio size and my expectation around that hit rate? What is the entry ownership discounted over dilution at exit? And at what point do I hit that? Because that's the idea. It has to be in the context of the total market value that's created on an annual basis in venture, because you can say, yeah, I'm going to have $80 billion in enterprise value, but that's very unlikely on an annual basis.
And so when you do that math, you end up realizing that one of the best mechanisms to be able to do that is, the smaller the fund size, the more upfront entry ownership. And with enough shots like that you're going to have high convexity funds. And so that's how we think about it. You could have a larger portfolio, you have more bets, but then again there's an expectation on hit rates as well. You have to balance all these things together to see, is this a reasonable strategy relative to how much value is created in venture on a percentage basis?
Turner Novak:
Where do you guys sit on that whole concentration versus diversification debate? Because I know some people will say, especially early stage, you need to have at least 50 shots on goal.
Albert Azout:
I don't know. From our perspective, in the best case, if you can scale portfolio size infinitely, if you think I'm going to just scale it infinitely, but be able to maintain ownership upfront. So if I had a platform like HF0 or something like that where you're getting a deal, where you have let's say 5% upfront, you could have 90 companies in your portfolio and there's an expectation around quality and hit rate, that's a really good platform.
Turner Novak:
You're saying with each new portfolio company that you add, the hit rate doesn't go down so you're still getting the same quality. So as the fund size increases, you're still getting the same multiple of return because it's still the same quality?
Albert Azout:
Yeah. If you tell me I'm going to do 100 investments, but I'm going to have 10% ownership in the beginning of it and my fund size will be relatively constrained, that's an amazing portfolio construction. It's not doable. It's not always doable because there has to be a mechanism for being able to lock in that kind of price. Something has to be special to be able to do that, whether it's programming, like it's YC, or if it's the HF0 or if it's some of these other like ZFellows or something like that. If you can't do that then and your strategy is more like it's not a numbers game, but it's more like it's based on picking and selection and all that sort of stuff, then you don't want to be overly concentrated, I don't think, at seed, because I think it's very difficult. It's just inherently uncertain. But you want to manage that size. What we see is somewhere in the area of 20 to 40, is kind of like that's the strategy where you end up.
Turner Novak:
You think if I came to you and said I'm doing 10 investments for my first check, first round fund, you'd probably say just the data shows that that hit rate is probably going to... You're more likely than not to lose all the money.
Albert Azout:
It's just incredibly higher volatility. What you have to believe is that, I'll take the risk, but I have to believe that you can execute on it. And from our perspective, we're building a portfolio of hits, a portfolio of potential high convexity outcomes. It's just higher volatility. You have to believe that you can execute on that strategy in a market that's completely uncertain. You got to be very good to execute on that, I think.
Turner Novak:
Actually, one thing you mentioned earlier was that luck plays a pretty good role or pretty big role. I feel like a lot of investors would not like to admit that, or maybe people can admit that in certain senses. But what role does luck play?
Albert Azout:
Yeah, again, we don't know by definition what it plays because there's no counterfactual, there's no way to re-simulate what occurred.
Turner Novak:
Can you measure it somehow? How do you underwrite it or incorporate it into your process?
Albert Azout:
Yeah, it's more like we think of it as randomness. And so that's why portfolio construction really matters, I think, because there's always an inherent uncertainty on things. And luck is really when there's an opportunity and you're prepared and you're able... So even luck itself is like, there's a context to it. If you sit at home and you don't do anything, it's harder to have luck. There's some action that needs to happen.
Turner Novak:
That could be a measure of a pretty defensible persistent return network though, is you sit at home, you do nothing, and you still are getting the grand slams.
Albert Azout:
Different things to come your way?
Turner Novak:
Yeah.
Albert Azout:
Yeah, no, I think you just got to just be comfortable with randomness and just the randomness of things. We cannot predict the future. It's inherently unpredictable. What you can do is try to position capital that will benefit from future states of the world, I think, is the way we think about it. And some of those could be just luck or just being at the right place at the time. Those things, you just never know.
Turner Novak:
This is a question from a little bit different topic from my friend Sasha at Creator Ventures. Is there anything you're seeing on data versus spin-out versus new newly created or outsider type funds? Have you guys done any research on that?
Albert Azout:
We haven't done specifically research on that topic. I think there's a set of personas that we like, just we gravitate to. We do like the outsider either operator-turned-investor. We like that a lot. We think it's a really good persona. I think it attracts founders. I think there's just a different way of looking at the world often. We also like investors that have worked their way up, not necessarily partner GP level, but hungry early, have really deep networks but they want to go on their own. We think that's a really good persona and we think that that strategy has done well. Versus a large GP leaving and trying to build a big firm. It's just not the persona we tend to like as much.
Turner Novak:
How do you suss that out? Because there's one argument to say they're unshackled from the partnership and the investment committee and they can maybe put some of their own deals through. There's another argument that says, oh, they don't have the brand of the mega platform, and the way they get their deal flow is probably going to change whether or not attached to that. How do you guys underwrite or get comfortable with how that'll change?
Albert Azout:
Yeah, I think it's a lot of chemistry also with the GP and just getting a sense for them. There's a lot you can do. We do look at a lot of data. Depending on where they've come from, if there's attribution, not attribution, or usually we're looking at a portfolio that's already... They're already developing a portfolio and we can look at it that way. Even with a few data points, we can start to get an assessment of it. And then we do a lot of back channel on that particular thing with founders and other GPs and other people in market, to get a sense for whether the GP is a signal for them in the market. And that's how we think about it, we ask the inverse question.
Turner Novak:
You ask the inverse of what?
Albert Azout:
Meaning, we ask the market, if this GP were to show you a deal, would it be a high single for you? Just like, is that the key thing? Because we want to see how the GP actually is a signal for the market. And you can't fake that. You really can't fake that.
Turner Novak:
Don't people kind of do like, "Hey, man, I'm raising my fund. When people reach out, tell them this. Here's your talking points." People kind of do that, don't they?
Albert Azout:
No, but we don't ask them for references. We go out and we talk to people that will give us an unbiased opinion.
Turner Novak:
Oh yeah, fair.
Albert Azout:
Everybody communicates differently. You have to read between the lines of a lot of what people say and just try to understand the picture and try to figure out what you think you need to de-risk about this particular GP. But yeah, we try to just get as many counter data points as possible and try to form an opinion. And then a lot of it's also just, in life I think you get the investors you deserve. And a lot of it is just the chemistry and the interactions over a period of time that will make us comfortable just generally. At the end of the day, we're investing in people.
Turner Novak:
Actually Amanda Robson at MTF mentioned that you had the most intense reference process or most references out of anyone else. I don't know if that's a good or bad thing.
Albert Azout:
I don't know.
Turner Novak:
But it seems like you're doing that at least.
Albert Azout:
Yeah, I think we're trying to be very objective. If you look at our data approach and understanding the embeddedness of a GP in the network, in a set of talent flow, and then being able to back that up with in-market checks, I think is important. And so that just creates a more objective approach. Versus just, how else would you do it? You have to look at their presentations and their advisors and all these things that are maybe more intangible. We try to get to the bottom line.
Turner Novak:
Yeah, when you're meeting a new manager or doing diligence on someone, what does the general process look like? Is this a one Zoom call, one day type of thing, or does this take you five years? I'm assuming it's somewhere in the middle of that.
Albert Azout:
It really depends on how fast things are moving, to be honest. We try to pace ourselves with how fast an opportunity is evolving. And if we have more time, then we'll take more time. I don't think most LPs will tell you that, but it's the truth. If we're not in a rush, we'll try to take our time.
Turner Novak:
Yeah, I feel like a lot of people mess that up on the fundraising side, is you don't give it a time and a box.
Albert Azout:
For sure. But also maybe the other way around is they're also somewhat short-termists and they try to squeeze people to an answer, but they're not right yet ready and it takes time. So you have to figure out each LPs are different and each GP is very different. But yeah, we try to obviously just synchronize with the timeline that's set forth that we think is fair. Because we see a lot of opportunities, so we have to manage that, and then manage then with a team with only two of us on the investing side right now.
Turner Novak:
I really want to do a demo of the software. Maybe we'll do that in, I don't know how long this will take us, 10, 15 minutes later. But just thinking about, you mentioned something earlier that I know you've written a lot about criticality investing. Did I say that right?
Albert Azout:
Yeah, criticality.
Turner Novak:
Yeah. So what is that for people who don't know?
Albert Azout:
Yeah, the team and us, we've been thinking for a long time, how do markets work? How does the world work?
Turner Novak:
Good question.
Albert Azout:
That's the question we always ask ourselves. And there's certainly higher-level phenomenon about the way the world works that you can ascertain and use as a guiding principle or a frame of reference when you're investing, a mental model. It's not to say that it fits every situation, but generally speaking, I think there are things that are generally true, like true phenomenon about the world.
Turner Novak:
What would be an example about something like this?
Albert Azout:
Yeah, I guess with criticality I'll give you an example. We know that, for example, in the world there's things organized into networks, generally speaking, which is the relationships between things.
Turner Novak:
This is not even investing-related, this is even-
Albert Azout:
Generally, yeah. If you think about, for example, social networks and, I don't know, protein networks, chemical signaling networks, the neurons in the brain, even human made networks like the grid, etc, etc, airports and travel paths, etc. All those are essentially networks. There's entities in the relationships between them. And networks that form in real life, they have a set of properties about them that make them really interesting and difficult. For example, the degree of distribution in networks, meaning, how many friends or how many outlets you might have for a specific edge, is parallel distributed. There's going to be ones that have hundreds and hundreds of relationships, and there's going to be many that just still have very few.
Turner Novak:
You're saying people, people that have many relationships?
Albert Azout:
It could be people, but it could be anything. It's not just people. It could be even in proteins and all kinds of networks. That's how they merge and then form, generally speaking. That's just one property of them, and there's many other kinds of properties about them. But these networks are very complex and they evolve in complex ways and they're unpredictable, because if you think about, for example, a tweet on Twitter, we don't know exactly which tweet will cascade and suddenly all Twitter knows about it, and which ones won't. And a lot of it depends on the structure of the network where it started, who tweeted it, when, and the context, etc. So a lot of the behavior of networks tend to be very unpredictable.
But one of the things networks do is they end up organizing... Networks, and I also talk about markets, the same things, they end up organizing themselves in a way that they're both in a state of order but also can change very rapidly. We have a whole article about how that works. But whenever you have a situation where there's a rapid change and the networks reorganize, for example, if you think about the AI wave, and we'll talk about that in the context of criticality, but before this LLM moment, the network was organized as such. And it was maybe focused on vertical software and FinTech and all these different things. The network of investors was organized a specific way. Then you have this moment and now the whole network needs to reorganize and change, because now you have different talent clusters, you have different companies that evolve. You have talent that goes in different ways. You have new investors that have come in because they have relationships with OpenAI, etc. So the whole network evolved.
You could say the networks really was stable before, and then suddenly there was a new event and then now it all shifted. And the only way for that to happen is that the network itself had to have in it the ability to change really rapidly, because all these behaviors are decentralized. It's not like people are coordinating at large scale, they're just making local decisions and then suddenly there's a structure that forms. So that's the context. The way we think about criticality is that, a couple things. One is that the way we believe that technology evolves, it's similar to the LLM moment, is that there's very, very slow, or is apparently slow, under-the-radar progress. And then there's a moment where there's a punctuation event, and then there's a big shift. And the reason why that happens is because a lot of these changes were under the radar, per se, or under the attention of most, and then there's something that happens that changes the way people perceive things. And then there's a really big shift.
And so the way we think things evolve and why we call it criticality, is that networks are in a state of criticality. There's a market that maybe has a lot of incumbents in it and hasn't changed, hasn't iterated, but something that necessarily happens, then the whole market reforms and re-changes. It's due to the technology innovation, but it's also due to other factors. That's what we call criticality investing. What we're basically doing at any point in time is we're looking for where in the world are there latent stresses that maybe the market hasn't perceived and trying to figure out if those may change at some point in the future. And that's how we think about things.
Turner Novak:
So it's like your favorite networks to invest in. You think about yourself, your investing networks, you're trying to find networks that are near some sort of a massive changing moment of some kind?
Albert Azout:
Networks and markets that are under a lot of stress and duress that's unobserved. It could be like there's a big disequilibrium in the market, there's a lot of friction that maybe the market hasn't yet perceived. Internal people perceive it, but the market doesn't perceive it. When I say market, I mean capital hasn't perceived it yet. And then something we think will change, whether it could be even a company that comes online. For example, like Palantir or even Anduril in DefenseTech. Before Anduril there really wasn't even a focus. There wasn't a lot of capital going into DefenseTech at all. In fact, VCs never invested in DefenseTech as a rule.
Turner Novak:
I remember Google was going to do big deal with the Pentagon in ‘17, '18.
Albert Azout:
They just shut it down.
Turner Novak:
And people protested, like, you can't work with the government. Or work with the Department of Defense, I guess. It was like anti-signal. I don't-
Albert Azout:
You could say the military industrial complex was under a lot of this equilibrium and, let's say stress to some degree, because they had this cost-plus kind of approach and they were sort of behind on innovation. There was a lot of dependencies and frictions in manufacturing and scalability and legacy systems. You can say all these different things. And that existed. And the reason why it stayed that way is because besides technology, there's a lot of social technology that's in place that creates friction. And all these things were already there and accumulating, accumulating, accumulating, and it wasn't maybe until there was geopolitical crisis, unbundling with China, and then maybe then Anduril. All of that together and suddenly everybody's rotating capital over. So if you had positioned capital in the right companies before or if you had your ducks in a row right before that change, that's probably where the most symmetry is, because if you think about it, that's where the most money would be made.
Turner Novak:
It's almost like in a way, this going to sound really bad, but front-running capital flows almost. That's really what it comes down to is, to your point, it's like allocate a bunch of money to defense, a bunch of capital flows into defense, and you benefit from being in front of all the other capital. Maybe that's not the only thing that you do, but-
Albert Azout:
Well, it's the truth. If you think about the markets, generally speaking, it's very rarely that markets are in a state of efficiency. They're usually in a dislocation of value and price. Almost always. They're always in this equilibrium of price and value.
Turner Novak:
So they're either undervalued or overvalued.
Albert Azout:
Yeah, exactly. There's either fear or greed or whatever, and they're almost never in this state of equilibrium. And so if you think about the returns even over time in the markets and the S&P, etc, most of that has come from multiple expansion. 90% I think has come from multiple expansion versus actually earnings growth. A lot of money's made by capital flows. And in fact, if you knew where capital flows were going, that's even more important than investing. If you were a value investor in the last 15 years, you've lost money anyway. Anyways, if you think about it that way, capital flows is pretty much everything. And I think even more so these days because people's attention, the complexity of information processing and attention, etc, it's just much more simple to invest in an ETF and passive investing in factors as opposed to thinking about individual companies or these sort of big scale changes.
And so capital flows really is what drives price, and price being completely dislocated from value, and it can be dislocated for very, very, very long periods of time. And so I think capital flows really drives almost everything.
Turner Novak:
And capital flow is almost a downstream of attention or interest or-
Albert Azout:
Yeah, exposure. Eventually, asymptotically, of course you need to invest in a company that has higher earnings potential, high pricing power, can have economies of scale, etc. And eventually they merge at some point.
Turner Novak:
There's a come to Jesus moment, it's like, do you make money or not?
Albert Azout:
Exactly. And for sure it will happen, but I think it's more important to predict not so much if something's overvalued, but where are the valuations going, which is hard to do. I'm just saying that that's what we've noticed. And so criticality, that amplifies. That's why criticality works, because it's amplified by flows and narratives and attention because the markets are not rational. Yeah, we know that.
Turner Novak:
Yeah. Well, I feel like then there's a big storytelling component of it. I've always thought Elon did a good job with this. I know people say like, oh, you could criticize a lot of things he's done, how by the books he did things, etc, but he did a good job of getting attention, and specifically in the capital markets, reducing the cost of capital, the cost of equity, whatever form you choose to do that. I feel like it's more of played out and well-known now and a lot of other people are using this strategy, especially in 2025, second half of 2025. But I feel like he did a really good job of that probably over the past 15 years using that to his advantage, when maybe he didn't have enough there yet. You think of Tesla being overvalued for such a long period of time, but now they have higher operating margins like every other car company. You could argue it's a better business even if it's always been overvalued.
Albert Azout:
Yeah, hopefully you can use a narrative as a way to bootstrap a business. But a lot of things move with narratives. It's very common, I think. Most narratives are overly coherent. They sound good and they make sense, but if you think about in practice and what that means and what does it say about the process it's talking about... For example, maybe humanoids, just not to bring into it, if you think about that, okay, what does that actually imply? Yeah, we're going to a place where there's labor shortage and there's going to be more automation and these robotic foundation models. But what is the underlying thing that needs to happen? There's a lot of regulation. So the time scales might be very different than the markets are responding to narratives, but it gives a lot of these humanoid companies the ability to get more capital and to build a future that they want. So, it works both ways.
Turner Novak:
Yeah. When you think of the narrative of that, it's like, labor. How much your GDP is just people doing work. We just played out digital labor is OpenAI and Anthropic. Now the humanoids are physical labor. The entire economy basically, AI is going to transform. I actually saw this, it was probably the worst argued point I've ever seen, but someone said the AI market was like a $370 trillion opportunity. It was kind of one of those things where you're like, this has to be the top.
Albert Azout:
Yeah, for sure. There's always truth in a lie. Yeah, for sure it's completely disruptive and it's going to be large scale changes in society for sure. And at the same time, the time scales and what it means is going to be different than what people expect, I think.
Turner Novak:
Is that something you think people get wrong a lot with this, is just the timing? It's that whole adage of less happens in one year than you think, but more happens in 10 years.
Albert Azout:
I think criticality has a lot to do with that and these cycles of narratives, and then the overly coherent narratives and then the breaking down of the narratives to realism, and then back around and back. So that whole process has a lot to do with time scales, because the reason why there's convexity in some of these things is because time scales are off. That's the whole point. Essentially people, when they're investing in something that's overvalued, they're saying that they're bringing the returns from the future deeply into today with heavy, heavy, I guess what they call hyperbolic discounting.
Turner Novak:
I've never heard it described as hyperbolic discounting, but okay.
Albert Azout:
Yeah, that's the behavioral mechanism. It's a behavioral finance term. But you're discounting deeply, the future. And that is just a function of distorting time. You can think about it that way. As opposed to thinking about time being over the years, you can think about it as a distortion of time. And so yeah, that's something that investors do. It's something I do, and sometimes it works.
Turner Novak:
There's a really interesting... Probably related to some of this in a way, from Martin Casado at a16z. I'll read the tweet. We'll throw it up on the screen for people too. It says, "The idea that non-consensus investing is where the alpha is, is actually quite dangerous in the early stage. Follow-on capital tends to be more and more consensus aligned."
Albert Azout:
I agree. I think that's a very, very solid statement.
Turner Novak:
But wouldn't you say looking for alpha, you want to be like non-consensus? That's-
Albert Azout:
No, what I'm saying, and that's why I never used the word non-consensus, I never used it in that definition, because at the end of the day, you need to go where capital is going. We just spoke a little while about capital flows. You can't ignore capital flows. You want to position yourself to where capital flows are going. There's many ways to do that with alpha, or there's several ways to do that with alpha, and you want to do it in a way also that you get paid for the risk. Because especially with fund sizes, if you're paying $50 million caps on your seeds, it's going to be very hard to return a fund. So there is something about price.
Turner Novak:
You almost have to be early consensus. You want to be the first of the consensus and then you benefit from the follow-on flows. And that's actually where the highest form of alpha is.
Albert Azout:
Yeah, that's what we call criticality. That's what we call it in our business, what we call criticality investing, which is the way to do that, you can't ignore the fact that technology, it needs to solve problems for sure. There are areas where there are pronounced problems that are not yet in the capital rotation, but they're going to be. And so that's one way to think about it. Another way to think about it is, there's the first order stuff, like the AI platforms, and then there's the second order, which is you have to of predict, because of this, what do I need? Well, we need to think about MCP servers. And then there's a third order, and you want to figure out where the consensus will be in second and third orders before others do. And so that's the other way to think about it. But yeah, you can't say we're going to be non-consensus, because that doesn't make much sense, I don't think.
Turner Novak:
Yeah. No, I mean, you have to be consensus.
Albert Azout:
Capital is more and more concentrating. It's like saying, oh everybody's after Nvidia. Why do we invest in Nvidia, because everybody's doing Nvidia? That makes no sense because capital is concentrating more and more and more in the winners. So that wouldn't make sense. But what matters is positioning your capital to where capital will be rotating to in a correct way.
Turner Novak:
Is there a time to think about getting out of a trade? For example, the Nvidia one. Nvidia has been the biggest winner in AI. It probably has more than 100% of all free cash flow generated in AI. They've obviously been the big beneficiary. How do you figure out capital flows changing? Is there a way?
Albert Azout:
I think it's impossible. I really, truly think it's impossible to predict the markets, and anybody who predicts it, you should just tell them to go away. It's impossible. Economic forecast we know over time, or it's better to just pick things randomly. No one knows the future. What I think you can say about Nvidia and what you can say about the market structure today, is that it's mostly driven by passive capital flows, and passive capital flows are inelastic to price. So as things, for example go up, they continue to go up because these ETFs need to rebalance and you have a situation where there's inelasticity. And if there's any negative news in that sort of environment, there's going to be a big sell off.
Turner Novak:
A big shock. Because the people who are active immediately get out.
Albert Azout:
Yeah, everybody will get out. It will just roll and it will just roll. It just continues to roll. I wouldn't be surprised with you have a 30% or 40%, 50% drawdown, I don't know, in the market. We just don't know when it happens or if it happens, etc. But that's a structure of the market today, and so if you have enough gains, I guess you just want to manage risk. You always want to manage risk.
Turner Novak:
I actually saw a chart, there are now more ETFs than there are individual publicly-traded companies. I don't know if you saw this too.
Albert Azout:
I didn't see that, but a lot of the data that we track, we also track a lot of the public markets, and we look at a lot of the ETF flow. Not the flows. We're looking at the ETF holders in different companies and trying to understand how that works. It's interesting to us to understand that, especially because we're looking at the intersection of the private markets. But yeah, there's an overexpression of both active and passive indexers and mutual funds, and it's a very noisy, very noisy market.
Turner Novak:
Talking about noisy, and we talk about randomness a little bit, we were talking before about persistence of returns and venture. Do you think, are returns persistent? I think you mentioned you don't think they are.
Albert Azout:
There are for very few, but they're mostly not, I guess. Our data shows it's really not persistent. Top core, top persistence from year one, I think is very, very low. I forget the exact numbers. It's less than random. It's random. And also, by year three it starts to improve a little bit, so if you're top decile in year three, I think you're 40% likely to be in a top quartile, and then it gets better and better over time. But there's very little persistence. In fact, when the GP is raising their fund, their new fund, they're usually banking on a time when persistence is the lowest in returns, persistence in returns. And we look at it by cohorts. We look at it like yearly cohorts as well. So we don't just look at the funds, we look at how have they persisted over time based on cohorts of investments. We should publish more about this.
But yeah, persistence is very low. And the question is why? You're going to ask probably why. We don't know. We can probably say a couple of things. One is a strategy drift, which I think is as they get larger, it's harder to maintain quality, adverse selection, things like that. I think that's one major thing. I think the other thing is that markets change. The nature of complexity is what we spoke about, is the actual structure of the market and then opportunities change. Networks gets stale, opportunities go elsewhere and capital rotates elsewhere, etc, etc. It just means that your next set of investments may not be as important as the first set, for example. So, that could be it. Yeah, and there's other kind of dynamics that happen I think in the markets that just change how different cores of investments perform.
Turner Novak:
And you mentioned there talking about networks and the importance. So what is more persistent, returns or networks, because it sounds like they can both change.
Albert Azout:
We always say networks are more persistent than performance. So that's your answer, I guess. Yeah, networks are persistent. Well, the quality and the importance and economic viability of networks might change, but the networks are persistent. But yeah, so we actually believe networks are more persistent.
Turner Novak:
So you may have an incredible network of early WeWork executives and employees, and that was maybe a good network in 2019 or something. And maybe today it's less of an exciting network.
Albert Azout:
Yeah, exactly. And our models, which we try to predict top decile performance based on these network features, we know that our models are much more persistent. Which we can publish as well. Our models are much more persistent, so for example, I think in year three if they're top decile with our models, I think they're like 75% likely to stay in the top quartile. So there's definitely something to be said about networks as a feature for understanding persistence and performance.
Turner Novak:
What are some of the most interesting networks right now that you're seeing? When you talk about top decile networks, are there things that stand out when you look at it today, just based on your data?
Albert Azout:
Like the actual qualities of those networks?
Turner Novak:
Yeah. What are you specifically looking at and what shows up in the data as being a high-quality network?
Albert Azout:
Yeah, the problem with our models is since they're deep learning, it's hard to explain them completely. I think there's motifs of things that work. I think there's some networks that are really tight and they do well. Some networks are just people that are just investing in many things. So different kinds of networks that we've seen. From a thematic perspective, in our first fund we thought a lot about physical worlds, for example. We thought a lot about where would the next wave of innovation be. And we felt that the application of AI, as well as hardware and hardware/software-enabled services to the value chain in the physical world and defense would be important. We did a lot there, which I think was prudent. I guess it did play out that way. So a lot of capital did rotate there. For example, now we're looking at networks within computational life sciences, which has been depressed a bit in the public markets.
Turner Novak:
Yeah, some of them trade for less than cash, right?
Albert Azout:
Yeah.
Turner Novak:
They're just completely... Yeah.
Albert Azout:
Which is interesting. It's like there's so much innovation taking place, and hopefully there's going to be a better regulatory environment and so much more data upstream, especially around discovery. And there's all kinds of new kind of modalities, therapeutic modalities, and there's also gene editing and gene synthesis and all these areas. The expectation is that there's going to be some unique things that happen in that space. And so it's kind of non-contrarian, I guess, just to use that word. We're looking at those networks. And then everything around data infrastructure we've always liked. The way you develop and deploy software, especially with AI and security as a primitive is always evolving, and I think that's an evergreen kind of area for us. And then these days we're looking at some consumer networks as well. We're getting to a point where we can maybe have 100x better experiences and new modalities in consumer/prosumer as well, and application level, which we didn't do in the last one. But we don't know. No one knows anything.
Turner Novak:
Yeah. Yeah, it's kind of interesting on the consumer side. I go back and forth on it having done some consumer. I try to actively avoid investing in consumer because it's just a tough category. I feel like with a B2B company, you can just make a spreadsheet and you can be like, "Yeah, this will probably work," or whatever. With consumer, it's just binary. It can seem like it's working and just people change their behavior.
Albert Azout:
Yeah, you have to have good investors. You mentioned Sasha. I think Sasha is very good in instincts. Really good. Sasha and Casper, really good instincts on consumers and what they will like and what's durable, and they have a good sense for... Yeah, so you have to have someone with really good instincts.
Turner Novak:
And taste, as the kids call it nowadays.
Albert Azout:
Yeah, 100% taste. Yeah, it's hard to find really good. I think there's a few funds that are good in that space, but not that many.
Turner Novak:
What would you say maybe looks like a good network on paper, but probably turns out it isn't based on the data or structure of a network or something.
Albert Azout:
Yeah, some of the ones are on YC could be very false positive. Because there's a lot of demo day kind of networks that you have all these investments that you made at random, kind of sparse networks, things like that. I think unpacking YC is very hard. It's kind of a hard problem.
Turner Novak:
You'd probably rather just own the YC. You'd rather just invest in the YC fund probably versus like a-
Albert Azout:
Yeah, there are funds that are indexing against YC. Some have grown. There's Soma and there's others. And there's others that are around it that have maybe made a couple bets demo day and been right and whatnot. But those are not sustainable, I think networks, and they're usually paying high caps and things like that. So I think in a way that's a hard network to underwrite generally.
Turner Novak:
Do you have a bar when somebody says high or low cap? Is there a point in your mind where you're like, that's low, that's high? And if I'm telling you, "Oh, I do pre-seed or seed," and I say this number, you're like, "Eh, that's probably not pre-seed or seed"?
Albert Azout:
We like to see high look-through ownership relative to the fund size. We think that drives the most volatility. Other strategies can work. People can argue that the outcomes are getting larger. So what was once a $1 billion outcome is now 10, 20, $30 billion. But those are still 0.01% of the outcomes. And so I think the expectations, I forget who did this, I think it was Josh Kopelman, but there's the venture arrogance score or whatever.
Turner Novak:
Yeah. It's like the exits you need as the percentage of total-
Albert Azout:
Of the market, right? Yeah, we have seen that, people with very high venture arrogance scores. It's a rare event. It's a rare event. There's something about pricing power that's unique as long as it's not adverse. Of course, it can't be adverse selection. But there's something unique about being able to get in early and have a good price. And I think it says a lot about the sources. It says a lot about the channels of alpha, one of the ways in which you can really sort of manifest alpha.
Turner Novak:
I could, in theory, invest in all of the hottest seed rounds, but under the hood it's like an uncapped safe after the round. Or it's like 2x over price of the last round's price, and that's not really pricing power necessarily because-
Albert Azout:
No, it's not. You could have nice logos, but it won't do much for your fund, unless it's a tiny fund, like a $1 million fund or $2 million fund. So it just won't do much. It won't do much at all for you.
Turner Novak:
On your software side, we were talking about doing a demo. Yeah, it'd be fun to just walk through it. I don't know. I don't know, we can think about it as you're demoing it for a smart person who maybe has never seen it before. Maybe that can be the assumed audience for this.
Albert Azout:
Yeah, sounds good. I can show you a few things. And just to maybe highlight a couple things, we have a few dashboards that we use. We also do a lot of tracking of our own companies in our portfolios and whatnot. And just maybe to level set our data, where it comes from, we have a lot of data that comes from a lot of different sources. It includes not only private market data, which is transactional data, but also we track a few hundred million people profiles.
Turner Novak:
100 million people?
Albert Azout:
400 million. It's around 20, 25 million that interact with Venture in some way, shape, or form.
Turner Novak:
Oh, wow. That's a lot of people.
Albert Azout:
It's a lot of people. I can tell you more about that. I can't tell you where we get the sources from, but-
Turner Novak:
Is this like an employee at Apple is interacting in some way?
Albert Azout:
Yeah. For example, looking at LinkedIn profiles and things like that, understanding work experiences, flows of people, where they're coming, where they're going from. We do a lot of that. We also track other, what we call communities. We look at, for example, all of GitHub and PyPy, also scientific journals. A lot of crypto market data, public market data, and things like that. We've also been doing more Discord. We're trying to do some Slack now, trying to understand the community. So where people hang out and what they're saying, I think is important to us. But generally speaking, this is one of our investor dashboards and what we use to diligence. Managers, there's a lot here. Maybe I'll just show you a couple things.
Turner Novak:
Yeah, show me the most interesting stuff that you use to make a decision.
Albert Azout:
Yeah, some of the most interesting stuff is, for every single transaction we have a lot of different data points that we've curated over time. Everything from how early. We have an earliness factor, how early they were in the company. And we can estimate returns. The quality of the actual syndicate, whether the company's maybe... Is it likely to graduate or not graduate based on various population statistics. We do a lot on just understanding different updates. This is all private data for us, but different updates that we're getting from the market on the company, just to have multiple data points. And then we have a lot on talent. This is the basis of understanding the SOI of the manager. We classify every single company into different categorizations, like high-level ones, what we call verticals, but also very, very low-level categorizations across the market to see what's changing and evolving, to get a sense for managers exposed and where they have created value. We do that sort of stuff. We do a lot within trying to understand topics, topic areas and evolving topics.
Turner Novak:
It looks like that one in particular is a lot of data center-related and machine learning.
Albert Azout:
Yeah, it's one of our funds. It's a data infrastructure fund, so yeah, you're going to see a lot of that for sure. And then network is really more around focused on the actual co-investor network of the manager, and how that's evolved over time and what it looks like.
Turner Novak:
And is this some off-the-shelf software you can buy on a Google marketplace or something or AWS marketplace? Or is this built it all in-house, custom data source-
Albert Azout:
Yeah, everything's in-house. Yeah, we built everything from scratch.
Turner Novak:
And it's a lot of unstructured data too, I think. You literally take LP updates and just forward it in.
Albert Azout:
Well, some of it's structured. We obviously have licenses with a lot of data providers. Some of it's structured and some of it's unstructured. But this is just showing you what it looks like underneath the surface.
Turner Novak:
Okay. So what am I looking at here?
Albert Azout:
You're looking at a relationship between one of our funds and Andreessen, and the flow of information between them. And in this particular view, the arrows represent the strength of the relationship and the flow of it as well, and the recency, frequency recency. There's a lot of different features that go into this, but this is just giving you a sense for the scale of how we do network reconstruction and how big these networks are. If you were to open up Sequoia, you would see how big the network could be of the interrelationships between different entities. And this just forms the basis of some of our algorithmic work that we do on trying to understand the position of a manager in the network.
Turner Novak:
So there's a lot of arrows pointing into Sequoia. What would that tell me about Sequoia specifically?
Albert Azout:
Yeah, it could be arrows both ways, which means there's a lot of co-investing taking place between the entities, or there could be some after investing and pre-investing in different ways in which the investing takes place. But they're obviously very central.
Turner Novak:
And do you want to see that?
Albert Azout:
You want to see certain configurations that are persistent is, I guess the way I would answer that.
Turner Novak:
And then when you're looking at adding a new manager, are you thinking about exposure to a network that doesn't exist specifically or a network that you don't have in the portfolio yet?
Albert Azout:
No, we don't do that. We actually don't do that.
Turner Novak:
Oh, you don't? Okay.
Albert Azout:
No, we've thought a lot about it. We like to spread out for sure. There's going to be overlap in the networks. I don't know, there's a few thousand deals done a year, seed deals or whatever, and there's going to be overlap in the networks. And we're okay with overlap. In some cases we have a distributed network, but it's unlikely there's going to be a lot of overlap between networks.
Turner Novak:
So you mostly just like good, high-quality network?
Albert Azout:
Yeah, the models are really focused on good, high-quality networks close to the core, good single for the market and good patterns of behavior. So, good persistence and recency and things like that. And that's how we get a signal, and then from there we do our work. Yeah.
Turner Novak:
Makes sense. And then I think this maybe begs the question, why launch a fund of fund around the software? You have all this data, you see who's the best investor, you just follow onto them versus have a fund of fund? What was the general thinking? Maybe this gets back in the history of stuff a little bit.
Albert Azout:
Yeah, yeah, certainly. We sort of felt into this in some ways, but in reality we felt that... Obviously, building a VC is hard, and for us to try to compete in series AB deals with no brand when we first started, would be impossible and it'd be adverse selection. We felt that the highest convexity part of the market was the emerging VC funds. And there are other fund of funds that are good. We felt like there was a way to do it very, very systematically and where you can add value to the GPs in unique ways, especially through technology, be very tech-enabled, have really interesting tech primitives. But yeah, so the idea was if you select really good managers and those managers select really good companies, then coming out of your portfolio should be really good companies.
Turner Novak:
True. Yeah, in theory, yeah.
Albert Azout:
If that's the case and the managers want to continue to follow-on on their winners and we're a capital source for them, then we should have access to a very asymmetric, high alpha portfolio. And that was the idea. In order to do that well, you have to have a good data lens on being able to track your look-through portfolio, track the market generally. And that was the original idea. And being a fund of fund puts you in an interesting structural position because you see so much. I thought there was a paucity of good fund of funds or good LPs, smart LPs in that market. There's a lot of LPs, but there's just very few that are really intently focused on being extremely tech-enabled.
Turner Novak:
The return profile of something like Level, do you think of it as you're getting diversified seed exposure, but then there's also... Because you do some co-investing, follow-on, secondary type stuff later. Is it like Series B exposure blended with some diversified seed or pre-seed exposure? Is that generally how you might pitch it to an LP generally?
Albert Azout:
Yeah, we've evolved, but we think of it as pitching it as holistic. It's a holistic way to get exposure to this part of the market, to the frontier of technology innovation. And the buckets are essentially primary LP commitments, opportunistic LP secondary commitments, and that's in a similar vehicle, and then doubling down into co-investments. With co-investments, our approach is we try to empower the GP to put more money through their best companies and that we can be a partner for them there. And the way we like to do it is if we can get capital in before round happens. It's kind of like the best opportunity for us. But in that particular bucket, we're trying to manage risk as well, so we don't want to be too early. So usually the entry point is as early as A, but typically B. We just did a C. We like to manage risk there and the combined platform should deliver the results.
Turner Novak:
When you talk about getting in before rounds, how do you generally do that? Is it some kind of uncapped or discounted safe, or are you buying secondary, primary from employees?
Albert Azout:
You want to know my secret.
Turner Novak:
I don't know, I'm just curious. This is going on the internet. Millions of people hear this, so I guess you're-
Albert Azout:
No, I don't know, it's not that hard. It depends. I think if we know that the round's coming together and we like the company, we can definitely do it uncapped safe. We're happy to do that. We've done that before. If there's a period of time between, because we also don't want to anchor price... Just because we're not a lead investor and we also want to be careful about the relationship and about their own financing process, so that's what we'll do.
Turner Novak:
So it's kind of a way to just signal we'll participate in the round, and we're in. We don't care who leads it. Consider this as part of the round when it comes together, but we're securing our ticket.
Albert Azout:
And of course our data comes from our GPs, and we have a lot of asymmetric access and information about the companies themselves, and we've developed a mindset. We've developed information and a history with the company ourselves internally. So that's how we think.
Turner Novak:
And you probably knew for a year or two or three or four, you're like, "Oh, this is pretty interesting. We want to follow this. We like it more and more over time." And so it's not like a shotgun, like someone sends you an email and you're making the decision-
Albert Azout:
No, no, no.
Turner Novak:
Yeah.
Albert Azout:
No, no, we had not. It's much more. But that's what we try to do. It's never perfect, but again, everything's a moving target in this business. But that's what we try to do.
Turner Novak:
Yeah. What do you think an ideal LP-GP relationship looks like then, in this case specifically, but then just more broadly. You mentioned not enough people are taking a data-driven approach. What does the ideal look like?
Albert Azout:
What do you want from an LP, I guess is where you'd want to start. I think you'd want to have strategic guidance with an understanding of the market and how it's evolving. Because I think the benefit of having an LP that is investing in the market is that they understand the market and they maybe have perspectives that you don't have as a single instance in a big market. So I think that's one thing, and that could be informed by lots of data. That's one thing. The second thing is essentially helping with capital formation. If you have conviction on a GP, helping them with being a signal for the market so that if there's a fundraising, it takes place pretty quickly.
The third is, to the extent that you could enable them to have more information than they would've had on their own. So whether it's having some of our tools that we use for network search, maybe for sourcing or for sourcing support or for portfolio support, hiring, things like that, if we can enable them with our data so that they can do things that they couldn't do before, that I think is a really amazing relationship to have with a GP over a long period of time. So those are some of the ways we think about it.
Turner Novak:
Do you think there's things that maybe other fund of funds maybe get wrong with their approach?
Albert Azout:
I do think a data approach, however you want to do it, I think it's really important to have a data-centric view on things so that you're not making decisions based on presentations and maybe some ad hoc calls to your network or whatever. So I do think you need to have a data-centric view.
Turner Novak:
And you just think people just don't do that enough on the LP side?
Albert Azout:
Yeah, a lot of people don't do that. They do a lot of work on the actual GPs, but they don't look at the market. It's like they don't look at holistically the market that the GP is operating in, and how that's changing and evolving and what those networks look like and etc, etc. I think there's a blind spot, I think for many there. And it doesn't mean that you don't make good decisions, it just means that you're missing information that may be important and relevant. I think that's one thing.
Turner Novak:
Yeah. Well, speaking of using data, what do you think about benchmarks? It's a big thing. People say, oh, we're a top decile or quartile and whatever, certain benchmark or category. Do you think, are they important? Are there things to be aware of or poke around with with benchmarks?
Albert Azout:
Yeah, benchmarks, by definition it's relative and there's a lot of ways to game it. I'm not sure how correlated DVPI is with DPI. Yeah, we don't know, to be honest with you. They're somewhat correlated, I imagine. And so, yeah, it's a rough measure. It's a coarse measure for something that you're trying to understand better, and it is what it is. At the end of the day, what matters of course is cash on cash returns over a period of time that makes sense from a cash flow perspective. Which you can try to ascertain as a proxy by benchmarks, but it's just one of several potential coarse measures.
Turner Novak:
I think it's kind of interesting, you might outperform a private market benchmark, whatever, but then pretty much every venture manager probably underperformed Nvidia, the biggest, most liquid asset in the world too. I don't know, it's all interesting how much people obsess over this stuff.
Albert Azout:
Yeah. Yeah. Although, if you're going to do a public markets equivalent, you have to definitely look at the cash flows. You can't just say cash on cash, 10 years. It doesn't work that way. You have to look at the cash flows and see what does it look like over time. And yeah, I'm sure you're going to find that most funds are going to be worse than Nvidia for sure, almost all of them. But you're going to have funds that it could look like 20%, 25%, 30% IRR, etc.
Turner Novak:
But would you have predicted that it was Nvidia that was going to be the one that did this? I think at the time we were coming off the crypto Web3 bubble where they were just being used to mind Bitcoin and there's no other use case. And LLMs basically weren't invented yet, for lack of a better way of describing it.
Albert Azout:
The information was there. I didn't act on this of course, but I remember meeting people really early on when I had my last startup and we were doing machine learning, and meeting some researchers that were starting to deploy models on GPUs. And we're like, "What's a GPU?" I remember the information was there, it was starting to gather, but the market could never have foreseen the impact of that and that Nvidia would win that, and etc, etc. Yeah, or the similarity between matrix computation in video games and large scale matrix computations in deep learning models, etc. I don't know, that's how you... But that's what investing is about, I guess.
Turner Novak:
Yeah. Well, so speaking about AI, have you seen anyone interesting, maybe outside of Level, but anyone using AI and data internally in an interesting way? Any funds? This is maybe more like a selfish thing because I'm trying to, besides just ChatGPT to summarize things and make some things faster.
Albert Azout:
Yeah, there's the QuantumLight guys, the founder of Revolut, he has a fund in Europe that they're just building.
Turner Novak:
Oh, yeah.
Albert Azout:
Yeah, yeah, I think it's called QuantumLight. Yeah, we saw some of their work there. It's good stuff. They basically track the market trying to figure out where to invest from a Series B perspective, similar to our infrastructure. I thought that was very good. Very good work. To be honest with you, we haven't seen so much out there. On the LP side, almost nothing. There's almost nothing. There's people that have reporting interfaces where they report on their portfolio, but there's very little general market work. Yeah, it's surprising that for such a tech-centric ecosystem that there's very little tech out there.
It's like a lot of them just don't need it. Some them have automation tools and things like that, but they're just using their networks and sourcing. It's not something that's front and center. Maybe more and more with LLMs and the ability to do more analytical work and research work, I'm sure a lot of people are using those tools. We use it ourselves. We have a lot of tools that we use and we integrate with our data sets for getting up to speed quickly on a market, those kinds of things. I'm sure there's a lot of that happening.
Turner Novak:
Yeah. I don't know if you know Samir Kaji at Allocate, they kind of do some, I think I'd describe it as it's like software for private market investors, family offices, giving them access to certain stuff and tools for their portfolio. I know that they made AI investment memo underwriting tool. I put in my deck and it spit out a bunch of information and gave me a grading and a score. It was kind of interesting. But again, it's just kind of like the investment memo summarizing use case. What have you seen just generally over the next decade, how do you think venture is going to change as an asset class? More data? Maybe that's a big one, hopefully.
Albert Azout:
And I think there'll be more data out there for sure.
Turner Novak:
Meaning, people using data? Hopefully.
Albert Azout:
Yeah, definitely more exhaustive data, more and more pockets of data that you can leverage, if you're able to combine it together to get a better view of things. Yeah, the later stage market I think is becoming much more institutionalized. For sure I think that'll continue. Maybe we'll see one of these firms go public and it become established as sort of a public asset class.
Turner Novak:
You're saying like a management firm?
Albert Azout:
Yeah, I can see that happening. Like a Blackstone kind of. Yeah, we can see that. And the secondary markets will evolve. I feel like there's not really a good market making approach there. Matching buyers and sellers, both on the LP side as well as direct side, etc, I feel like. And people are going to want to be able to be flexible on when they exit oppositions and those kinds of things. I think that that market will evolve, especially as venture just becomes... The timelines are so much longer and companies will stay more private longer because there's a lot of benefits to staying private. Especially if you can get liquidity, you don't even need to go public.
Turner Novak:
Yeah, Databricks just did their Series J or K. Yeah, Series K.
Albert Azout:
You're going to see a Series Z one day.
Turner Novak:
Yeah.
Albert Azout:
Yeah, exactly. If you can sell, there's a way to exit positions and sell, and investors can sell, there's really no reason to. And so I think that will happen.
Turner Novak:
Well, plus too, if you're an investor and you have a public market fund, you probably charge 1% management fees roughly and 10% to 15% carry, versus private markets you can charge two and 20%, and sometimes more than that honestly, especially if you're an OpenAI. If you look at the look-through fully-blended costs of some of those triple layered SBDs, it's like, I don't even know what the management fees are charging and the carry their charges.
Albert Azout:
Yeah, it's crazy. It's crazy.
Turner Novak:
It's just so much more profitable as an asset management firm to be doing private markets. It's like the incentives are there to just continue doing it. Not even respect of the company side, but on the investor side the capital's there.
Albert Azout:
For sure. Those are some of the things that we think will happen.
Turner Novak:
Cool. Well, this is a lot of fun. Thanks for coming on the show.
Albert Azout:
Yeah, it was a lot of fun.
Stream the full episode on YouTube, Spotify, or Apple.
Find transcripts of all other episodes here.