B (2:28)

It's probably useful to just talk about, you know, the really unbelievable data that we have access to from Angellist, which is tens of thousands of very, very early stage financings and then the resulting share price trajectories of those companies over time. What's really unique about that data set is, is really twofold. So one is you don't have the bias, the kind of survivorship bias that comes from looking at the behavior of seed stage investments when you look them up on PitchBook or other external data sources.

A (3:01)

Right.

B (3:02)

A huge fraction of seed stage companies are founded, make very, very little noise in the world and then die without kind of telling anybody. And those companies don't end up in external data sources in a way that you need to adequately assess the asset class. So having actual data on what is happening to the breadth of CCH companies, both winners and losers, is very interesting. The second real data strength that Angellist has is the price per share. So you do not need to guess at dilution, you don't need to work based on headline valuations. And that tends to result in actually very radical, like very different interpretations of the quality of the asset class strictly as a function of the assumptions that are made.

A (3:48)

Just to put a little bit color to what you're saying is that the breakout companies, the one that look on paper like they're 100x block loss sometimes are actually undervalued versus the, the middling companies are actually overvalued. What does that mean? One example, I was in Anthropic, they just raised 13 billion at a $183 billion post, which is of course is first of all a crazy valuation, a crazy amount of money to raise and also the minimal dilution from a percentage basis, I don't know what, 13 by 183, but somewhere around 5% dilution. So you have these winners that are outperforming everybody else. And also on a per share basis, they're also being diluted less.

B (4:30)

Correct. And so that's the real significance of actually having access to the underlying data, is that you can, you don't need to make assumptions about how much, oh, I'm going to invest in this company, it's going to go through six rounds. Each round is going to have, you know, between 10 and 20% dilution. You get very, very different perspectives because so much the returns are driven by your highest performers. And yeah, having an accurate measure on, like how well those high performers actually do is really crucial. It's very easy to build a model in an Excel spreadsheet that makes seed investing look pretty terrible because you assume, okay, this company's, you know, how many, how many rounds, how many rounds does anthropic onto, you know, we're going to go through eight fundraising rounds, it's going to do 20% dilution every time. And you end up with this like very skewed advice from your spreadsheet where it says to, you know, the most important thing is to defend ownership. You should always follow on. You know, that's the only way to make any money in the asset class by following on. And the asset class honestly doesn't look very good because you have all this dilution that's, that's pouring in. That's just incorrect. And it is a, it is a lack of. People don't really publish price per share. It's a pretty closely held piece of information. I mean, it's hard enough to get even if you're like an investor that's directly on the cap table of a business. It's often a little bit tricky to get to like, hey, what was the last, you know, email a founder, like, hey, what was the last price per share? When you just raised. It could be tricky. And so the challenge is like you're, you're using external data sources, you're looking at, you know, releases that get published on TechCrunch or whatever, and you're just trying to guesstimate, like, that's not right. And so the ability to use our data and have the visibility into what's actually happening on an investment level is incredibly, incredibly interesting. And I do think, you know, touched on a bit. I would say there's probably like three big insights that I've kind of taken away over the past six and a half years. And one is that I think there are actually, you know, venture capital is not an asset class. It is actually three different asset classes. Seed investing is its own thing. Series A investing is its own thing, and series B and later investing is its own thing. And where this comes from fundamentally is some research we did very early on when I started angellist on power law returns. What's interesting about a power law is it's defined by the single quantitative parameter called alpha, and the quantity qualitative behavior of the distribution changes as the alpha parameter changes. So you actually have this really kind of Interesting split based on the rounds and this alpha parameter. So for seed investing, we think there's an alpha less than two power law, which means that essentially you get this weird dynamic which is largely borne out in practice, which is that if you make more investments, you get higher average return. Essentially there's unbelievable, there's unbounded opportunity cost of missing, you know, the next outstanding investment. And so you know, the optimal strategy if you don't kind of crystal ball about which investment is going to be the next Uber, is to invest in everything that could become the next Uber. That's seed. Series A, I think, is actually the most interesting of these asset classes. I think it's the hardest stage to be investing in because you have to, you know, you have to do work, you have to take a board seat. It's very challenging because I think it has aspects of the opportunity cost for missing a great investment is still extraordinarily high. But at the same time the right strategy is not to invest in everything. And so I think it's quite tricky and I have a great deal of respect for people to do series A investing. And then the later stage stuff, series B and beyond corresponds to the alpha parameter being greater than three. And the power laws that have that level of alpha parameter, they start looking very, very close to the behavior of public markets.

C (8:02)

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A (9:29)

We chatted first episode four, we talked about, I think something like eight investments ever within Angellist didn't have like a positive expected return from this power law framework that the person shouldn't have invested at the time. Could you unpack what exactly that means and unpack that into practical wisdom? So if somebody had a million dollars to invest, like how would they change their strategy?

B (9:53)

Knowing this, it's super interesting. It gets to really the heart of the matter here is that because of the dynamics of the power law, these two contradictory notions are at the same time true. It is true that you actually have a rank order list of investment quality where you, you know, you can. It is possible to say, hey, these are the best seed investments. This one's not as good as that one, this one's not as good as that one. You can actually have a rank order list of investments. At the same time, it is also true that you should at least have exposure to everything that's sort of up the bar. And the way we think about that is roughly it's actually kind of a loose proxy for that rank order list. In terms of investment quality is probably price. So when you start at the top of the list with a deal that sort of has everything, every positive feature and I've brought up the example of a company that I think has, you know, every positive feature would be the earliest round of Sensara Repeat Technical Founder MIT PhD Doing it again already had an enormous exit. Every positive quality you'd want about an investment's there. And it was also super expensive when it got done. I think the first round was like 50 million. And this was years ago, it was like extremely expensive, but has every positive quality our results kind of indicate. So I think one of the unique aspects here is that the market is relatively efficient. It's very, very close to the investment that's raising at a 50 million pre money is probably about 10 times better than the investment that's raising at 5 million pre money. And so you can kind of go down this rank order list maybe roughly, you're thinking about price as you go down. What you see is that the prices get cheaper but the distribution of exits starts dropping off even faster than the than the implied price. And at a certain point, this alpha less than 2 threshold, the distribution of exits has fallen so much that you're no longer drawing from this magic alpha less than 2 power law distribution. And that's kind of the credible deal threshold. So this weird, weird mathematical phenomenon which where you can say it's not from a position of ignorance, it's not from saying like, well, because of The Alpha lesson 2 power law, nobody really knows anything. You're just drawing for these extreme distributions. Samsara is no better or worse than no. Like. You can say like Samsara is a fantastic early stage investment, right? It is by the best early stage investment. But that does not mean that you should put all of your money into Samsara. It means that maybe you should wait more exposure to it. But really anything that's above the threshold for investment quality you should have some exposure into. It is really very, very, very contradictory to have those two things right. And it is actually a huge argument that people give you about why you shouldn't index broadly, right? Well, the issue when you index is you get the bad companies and you get the good companies. But like we do all this research so we know what the good companies are. I think what's interesting about seed is that you can do the research and figure out what the good companies are and you should still invest in virtually, you know, in everything that is credible seed deal. And that is a strictly a consequence of this very, very, very extreme, odd distribution of returns that these investments draw from. But I think the big takeaway there is the market efficiency. You know, that that investment that's raising it at 50 versus 5, you know, it may only be 11 or 12 times better than, you know, the 10x price. There's not just dollars lying on the ground for people to build quant models to like pick up. And that I think was very intriguing is I think when I started Angellist, I had this idea of okay, can we go and use quantitative approaches to disrupt early stage investing? And now I'm of the opinion that actually I don't think that early stage investing will be disrupted by quantitative approaches. There are some pretty narrow exceptions to that statement. But in general I just, I don't think the opportunity is there, right? If you do all the research and you're like, wow, you know what's a great thing to invest in? Repeat founders who've been successful and went to mit. It's like, dude, everyone knows that, that that deal is priced, you know, four times higher than the deal that doesn't have those characteristics. So like you, you may be correct, those are better companies, but you are also paying for the privilege of investing in that company. It's not like there's just, oh, I found this signal that nobody else knows about. It's like if it's in a deck, it's 80, 90% priced in. You're not uncovering the hidden mysteries of the most effective way to like, carefully tailor one or two seed investments like that is absolutely the wrong. There's no support for that approach in the data whatsoever.

A (14:08)

And using that example of Samsara, when you say it's, it might be 10 times better distribution of outcomes, that's, that's on an expected value, meaning that includes the chances of it being a ten or a hundred billion company. That's kind of the mean return. That's 10 times. Is there some convexity there where it might be in most, most cases, you know, 10 times better, but in some cases a hundred times better. How do you account for these kind of like a fat tail?

B (14:32)

The correct answer is you've already made an error asking the question because you use the concept of expected value. And if you actually believe what I'm saying, you can't talk about expected value. There is no expectation. It doesn't exist. I would describe it as the wildness of the distribution is higher than what would otherwise be anticipated. But you can't even refer to the concept of expected value. It literally doesn't exist.

A (14:58)

I'm very intrigued. So you can't say 1% chance of a hundred billion.1% of a trillion. Can't put it into. And, and why what The Alpha less.

B (15:08)

Than 2 power law means, which is that this is the correct way of thinking about it, we don't deal with the, these, these distributions are in some sense very inhuman. They're not, they're not something we get to experience a lot in, in life. So the notion is, and this is, you know, you have to really kind of think about this, but if I told you that my tallest friend is at least 6 foot 9 inches tall, the correct way to think about that is, you know, how tall is Abe's tallest friend? You would say 6 9. Right. The probability that my tallest friend is 610 or 611 is actually dramatically, dramatically lower than 6 9. It has this, you know, normal typical distributions, by which I mean both normal distributions and virtually every distribution that's not an alpha less than 2 power law have this quality that if you chop off the right tail and you ask for, you know, kind of what, what happens beyond that right tail? A huge fraction of probability mass is clustered right at that threshold. No matter where you draw the threshold. Like the probability of getting further out on the tails is like exponentially smaller than where you drew that threshold. What's weird about alpha less than two power laws is that actually you have escape type behavior where, you know, if you say my best investment return is more than 100x, you're you. It is not correct to me to say, oh, Abe's best Investment return is 100x, might be 200x, might be 400x. You know, there is that kind of escape behavior. The other way of contextualizing that escape behavior I have is, is in Black Swan, Nicholas and seemed Taleb has, has a line about the, the refugee probability distribution, which is that sort of like for every day that a refugee spends outside of their homeland, the expected number of days before they will return increases by more than one. You get this kind of escape behavior which where if someone's, you know, you know, years and years and years and years, you know, the anticipated return is many, many, many, many, many years down the line as well. And so that's what happens to the seed stage investing where you, you don't have this behavior where like, hey, I drew this threshold that's kind of, you know, it's really unlikely that you'd have anywhere beyond 100x. Like it's not thinking like, oh, it's unlikely to get 100x, therefore, whatever. It's like just telling you that I got an investment return beyond a hundred x. It doesn't tell you that I have 100x return. It might be a 200x, it might be a 400x, it might be a thousand x. And that's the really interesting behavior around these distributions. And if you're thinking like, wow, that's weird, that totally doesn't go with my intuition. That doesn't make a lot of sense. That's true. Like these, what we think it's like a pretty radical, like, distribution of returns. It is in some sense, like fundamentally unnatural what these return distributions look like.

A (17:37)

So why is the takeaway then not to invest in the local maximum quality meaning go to Samsara? Because in 99% of the cases, it'll be 10 times better. In some percentages of cases, it'll be infinitely better. So you want to price in that, that asymmetry.

B (17:55)

The number of opportunities is so large. That kind of what we believe is that the number of opportunities that meets this threshold is very large in the thousands each year. And so much of the returns of the asset class is driven by the top handful of those thousand. The actual chances that Samsara will be in that top handful is actually quite low. Even if it is the best company, the chance that it will be one of those five enormous returners is actually quite small, is not that much better than just random picking. And so that's kind of the tension here is that it's not like, oh, Samsara has an infinite expected return, all the other seed investments don't. It's like they all kind of have an infinite expected return. Sansar is maybe a little bit better than the other ones. The core argument is that like if so much of returns are going to be driven by that, those five, you know, investments each year that just absolutely go crazy and there's so many of these companies that the chance that even your a priori best evaluated opportunity is going to be one of the five is very small.

A (18:55)

So let's translate that to practice. Let's say there are a thousand credible, just to use a round number, a thousand credible investments in a year and you have a million dollars. Wouldn't the practical thing be, would be to invest a thousand dollars into all thousand companies? If not, why not?

B (19:12)

You know, you could think about maybe doing a slight. It sort of depends how much you think stuff is priced in. My, it's very difficult to sort of back this out but anecdotally I would put the number at 80 or 90% is, is priced in. And so you know, you're thinking there maybe one over that. So maybe you want to have like 25% or 50% above average of your money into something like Samsara and like a little bit less of your money in the really, really marginal ones. But like yeah, that's the practical implication. And here's the challenge, right, is that nobody sees a thousand, Nobody sees all 1,000 of those high quality opportunities. And I think one of the way of thinking about the world and I actually think the, the work that seed stage investors do is very interesting and very valuable. Right? The ability to kind of from this latent pool of interesting investment ideas, bring some of them or help bring some of them to life and go on these journeys is I think really fascinating. The thing that I think a lot of seed investors learn is that if you're good at it, you probably have one or maybe two areas of expertise and the other investments that are outside of your area of expertise are not very like, you do a bad job making seed investments. And so I think it is this, this almost this contradictory thing where you have again, you know, speaking about how weird this asset class is, how fast Danny it is, is that you have a You know, the job, the early stage VC is really to be very narrowly terror tailored to their area of expertise and find the 10, 15, 20 companies in their area of expertise that are, that meet this threshold. And then I think it's, it's the perspective from, the perspective LPs from asset allocators, they should be broadly exposed to a whole bunch of very, very specific gps because I don't think that anyone really has the capability of doing super broad investing themselves. The best VC that we have in our data for doing that kind of investing in terms of like they do a ton of early stage investments and they seem pretty good at it is Founders Fund. But like it's, I would not say that just, you know, just because Founders Fund is very, very, very good at that does not mean that, that, that if you just show up and you're like, well, you know, Abe said the right way to do this is just invest everything. So everyone who approaches me for money, I'm just going to give money to that is unlikely to work well. And it's because the deals you're seeing are actually probably below that credibility threshold. And I think, you know, understanding and defining that credibility threshold I think is, is the job that seed stage gps do. And I think it's a really interesting and fascinating one. The other thing I want to mention about this is in terms of like the, the radicalization of the way that I've approached this asset class is so there's this, this idea from quantum mechanics, sort of the underlying nature of the universe and John von Neumann, who, you know, possibly has the quality of being the smartest human ever live and did, you know, foundational work in virtually everything including, you know, finance, economics, game theory, the, the bomb, but did a lot of the foundational work in, in quantum, quantum mechanics as well, had this idea that the underlying sort of fundamental nature of particles and of the statistical distributions of particles was a question of ontic and not epistemic uncertainty. So what I mean by that is there is a sense of like, oh, when I'm trying to study a certain particle and I'm going to measure it like, you know, this idea is that the particle sort of already existed. And I'm just, I just didn't know which, which kind of particle or which direction or whatever it was going because I didn't have enough information about it. And so I can learn that information and then I understand it. And John von Neumann's perspective was actually like weird. And I think it's kind of been pushed out of physics because it has been so weird. Was that actually the act of observing that particle, like the act of measuring that particle actually brings it to existence. It did not exist before you measured it. And he had this whole thing about how like we're in this chain of observation. And he was like, he kind of like defined consciousness as the layer in which like he had this theory where consciousness couldn't be modeled in terms of quantum mechanics because of this ontic phenomenon. It's like almost gets crazy spiritual in terms of like what consciousness is, what are, what the universe is. But what I think is interesting is that actually, and I bring this up because I think that this model of ontic versus epistemic uncertainty is the correct model to have when you think about seed investing. So literally what I think seed investors do is actually sort of make the decision by funding ideas with capital to actually go and create these particles by particles that here are the startups they actually, there is not a sense of like, oh, they're going to learn a lot of information about these companies then pick the right ones based on the information they learn. It's literally by deciding to go on this journey, they're actually creating the universe. Which I think is like absolutely mind blowing and fascinating. It's also fundamentally the reason I think that quant approaches cannot succeed in this space is because it's not like, oh, I wouldn't have made that startup investment if I had just known, you know, X, Y and Z about the, about the company. Oh, I made a mistake, whatever. I wish I had that information. Like that is like the wrong perspective. It's not an epistemic question. It's not like, oh, I just didn't know the information. It's like you are actually deciding to create the startup to create the part of the observe it on its journey. And that I think is like a really fascinating perspective. The other maybe like simpler soundbite is I frequently Kevin Lawless, who's one of ageless founders and is really big on the research side and he's just put it this way, which is like the cheapest and most effective way to diligence a pre seed investment is to write a check. Which is like I think puts it in a nutshell, right? There is actually there is no way to know. The way to know is to write a check to see that company come to life and actually see if what was in that deck was true or not or if there was a business somewhere in what was described in the absolutely and totally inaccurate pre seed deck. That I think is the way this role works. It's not something where like, oh, I found out, like I have all this private data and I like managed to like create this model and this is like the thing that I want to invest in. It's like, that's not correct. That's not a correct model of what's actually happening.

A (25:26)

Said another way, in the pre seed round, the true traction is so sparse that it pales in comparison with what that founding team will do. So whatever you're investing in, whatever thesis, whatever is in the deck is such a small percentage of what will become the company that you might as well have just invested in the founders without an idea that it would essentially be the same exercise.

B (25:47)

I would say your perspective is a little bit closer to the less radical epistemic version of like, oh, if I just knew what would. But I actually, I think, I think I've gone full von Neumann. I think I've gone full ontic. I think there's actually like, you know, the world in which you write a check or in the world you don't write a check, those are actually different universes.

A (26:06)

But doesn't that assume that it's the lead investor? In other words, if you were the lead investor, you sure?

B (26:11)

Although I think yes, but I think the investment traction is something where like, oh, we got a commitment for this person, whatever. Like I do. And, and then I think you actually see, I mean, you observe this behavior in terms of seed investments as well. I mean, I, I've seen it for companies that I've started where, but before you have kind of more than half the round committed. And then at that point VCs start looking at this as not a question of like, should this company exist or not, but rather like this company exists, do we, do we want to kind of go on this ride or not? Like the, and, and that to me is like a really, like. I think that sort of illustrates the kind of the power that the earliest stage investors really have. That, that's not to say are there pieces of data that would be incredibly valuable for seed stage investors. There are, but they're not, they're not attraction metrics for a company. Right? They're, they're. If you had access to Alfred Lynn's email and you could know every single startup that's coming up for partners meeting at Sequoia, that would be a very, very, very valuable thing to know. But that's, but like, that's very different than, I think, what I've never, I've never seen the pitch of a data Driven VC where they say that they've hacked into, you know, top partners, emails of various vcs and are and are going to front run them. That, that would, you know, like I said, invest in that because it's probably legal. But like that's, that's the kind of data driven strategy that you'd want. Not like oh we scraped LinkedIn.

A (27:35)

It's like going back to this hypothetical thousand portfolio. Let's just make it easy and say that you invest $1,000 into a thousand credible startups so they pass a certain threshold and you invest into each one of them. How would that rank versus a concentrated seed portfolio? Would that be top quartile? Would that be mean? Would that be bottom quartile? And how do you think about kind of that versus doing say a fund of fund or investing to gp?

B (28:07)

Yeah, so it would be the mean almost by definition. And what you tend to see with venture capital is that the mean is around the 75th percentile of funds. And I think one of the challenges with this asset class and investing in this asset class is there's a lot of navel gazing, ego stroking around like what do the best investors do? And when you look at the strategies of the best investors, they don't invest in the way that I talk about what they do is they'll make extremely concentrated bets. They'll, they'll follow on huge. And what you see there, I think what you're observing are just folks who took on risk and got lucky. Risk cuts both ways. It's not always like oh that was a really risky investment, so it was bad. Risk can be beneficial. And there's enough investors who do that kind of investing that if you look at the top decile of early stage investors, they will all have that profile. They'll all have the profile of making concentrated bets, of doubling down on their winners. And the challenge is that that's not, it's, it's not a replicable piece of advice to tell someone of like oh yeah, the best way to do seed investing is just get really lucky. Like comes out in the data.

A (29:14)

How do fees play into that? So if you're investing in a 2 and 20 fund versus let's say you make those thousand investments direct. Are you now above that top quartile? How should one think about that?

B (29:24)

I, I don't, I don't think fees play a significant part of the, they don't play a really significant part of the story. And that's because the, well, interestingly the fees that play the most part of the story are, are actually the management fees and not the carry. And the, the reason the management fees play such a large part of the story is that management fees cut into the amount of money that actually gets invested. It's, it's so funny. This is all consequence of the power law, right? The venture capital as a just historical is so driven by the vintage years that have been super successful and those are, you know, and so you have this dynamic where you know, the real returns of the asset class are being driven by the, the, the years in which you know, the, the cap, you know, the venture capital average will be, you know, 5, 8, 13x or whatever over, over the lifespan of these funds. The manager fees actually are super expensive in those cases because you're not getting, you know, oh, that vintage year every dol in 10x. You know, it actually becomes really expensive if you have a 20% management fee because that's, that's, you know, you, you're literally like losing several multiples of the fund just from the management fee. And I think this is actually something where, where my perspective has changed since running Angelus is like when I joined, you know, I have this, my friends and I were doing this investing out of this fund called Indicator. It's just our money, we don't have LPs and you know, it's like, ah, I don't need to invest in someone. Like, I don't need to pay carry to someone else. Like I know how to do seed investing.

C (30:53)

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B (31:46)

Great. Like, and I think one of the things that really changed my perspective was, you know, kind of seeing fund performance and research into fund performance and, and frankly, I'll give him credit here just directly Meeting Ryan Hoover, product hub founder of this one. I started angel as t. Yeah, he's an awesome guy and I just, you know, he, he's like really good at consumer product investing. Right. He founded Product Hunt. He's so plugged into that world and like he's just, you know, he's, he's got the, like he's just the consumer facing investor. Like he's just really good at it. And ask you know, if he like, you know, we tried an indicator just consumer stuff and they were always terrible investments. Like by far like the very difficult space, very, very hard. Especially if you're like where indicator was successful or has been successful, hopefully has been in kind of deeper hard tech investments. And so you know, when we were seeing like I thought we, we, we tend to see like pretty good like companies that want to do something in space. Like we tend to see a lot of those opportunities and that was like pretty good. By the time a consumer company like gets to us, like man, everyone's passed on those guys and like it those are, that they were just bad investments. And I had this, you know, I can't do this kind of investing. Ryan can. And like if he's making me money, why shouldn't I pay him carry? Like. And so that has been a real change in perspective of like look, I don't adequate coverage for my own portfolio to this space. And so you know, I did the investment in Wicked fund. Another one that I did was N49P run by Alex Norman who's kind of the angelist Canada guy in Toronto is like super plugged into everybody. Every Canadian doing any kind of startup or star of investing goes Alex Norman. You know, I don't, I'm not plugged into Canada. I was like, okay, this guy will give me differentiated deal flow into a narrow area of expertise that I'm not already exposed to. And if he makes me money, I don't mind paying in carry. And so that has been my, my real kind of perspective shift on this is that I don't really, I don't you know, obviously fees are a zero sum game. $1 paid in fees is $1 loss of returns. But it, it really seems like given the dynamics of the space and just like frankly the inability to replicate the quality of the portfolios, you know, I'm not saying like, hey, if you haven't, if you're a seed investor and you, you have a certain slice of deal flow, whether it's like, oh, I used to work at Uber, so everyone ex Uber, I'm not saying you should invest like funds that overlap a lot with your existing investments might not make sense to do a fund investment off. But like if you have, if you have access to funds that are seeing, that are high quality and seeing differentiated slices of space, you don't see, I have a really, really hard time. Oh yeah, like, but I see, I see some consumer companies. It's like, dude, like Ryan Hoover sees you're not on his level. Like there's very few people who are. And if he, if he's giving you the opportunity to invest in his fund, like, then you don't have to do any more consumer. But you don't have to do any more shitty consumer investments because it's like, oh, I got Ryan Hoover.

A (34:45)

So per your model, you believe that the market is efficient for the deals that get done, but there is still adverse selection.

B (34:52)

Oh yeah. So I think adverse selection is, is really maybe, maybe the entire story about venture capital. I think, I think that's the most important concept in, in all, in all venture capital. So we have some research that's going to be published pretty soon that actually tries to unpack adverse selection from the perspective seed stage check sizing. So think about it this way. So again, you're a seed stage gp, you're writing checks. And you know your check size that you typically write in C company is you have your typical check size some of the time. Actually it's generally about two thirds of years. You'll kind of observe this behavior where you'll either have a big check which is like more than twice as large as your typical check size, or you'll write a small check which is less than half your typical check size. And what's cool about the Angellist data is we are looking at a Data set that's 15,000 seed investments. And so it's very unlikely that you will pull out any other conclusions other than the ones we found because we have access to the, you know, such a huge slice of seed investing universe that like this, this is what's happening in the space. So it's not like, oh, we have this anecdote about this one small check we wrote. It's like there's thousands of investments that are going behind this. So what we found was actually twofold. I'll put it this way, in a normal asset class, if you approach this from a, like this is where what quantitative finance was defined around, I'm going to hire a bunch of PhDs, I'm going to come up with these signals and then what I'm going to do is I'm going to more or less, you know, depending on you. Have you actually precisely articulate this? In the case of like some pretty decent, like reasonable assumptions, you, you pretty much allocate proportional to the quality of the signal of the company. So if a company is great, you want to put a bunch of your money in. If your company is less good, but you know, you should, you put less of your money behind that and that gives you the, if you want to interpret it as like the highest mean with the lowest variance, the, the best risk return profile for your for your portfolio will be from investing more money behind better signals and less money behind weaker signals. And in general, if you have people who have skill, which like we think, yeah, we think our GPS as a whole do. But you should see this phenomenon where big checks outperform small checks just because big checks are made with conviction, all with signal small checks are not. What you actually see is, is kind of the opposite. So what we see is that consistently small checks are the highest performers. Now it's, it's not consistent in like every single small check is the best investment. But like it definitely shows in the data, small checks are the best investments, small checks relative to a typical check size for that gp. So that again, a small check is less than half of the typical seed investment check that you'd write that year. Those are the best performing investments. As an investor, I'm sure you, you know, right.

A (37:33)

Similar thing where the return of an asset class is inversely proportional to how close the investment is. So the VCs that are investing in Austin from San Francisco, those, those returns tend to be better than San Francisco. San Francisco. And the thesis is that if somebody's willing to jump on a plane and go to board meetings, they must love it that much more. Is this the case here where they love the investment so much they're willing to take a smaller percentage of the company because they see the upside? Is that kind of narrative or.

B (38:00)

Yes, but really it's because they're being squeezed down by better investors.

A (38:04)

Right.

B (38:05)

That's what's happening. These become the, the very hot rounds where there's a lot of interest in participating and you know, we know what people's typical check size would be and, and they're willing to write a, you know, Instead of a 250k check, they're willing to write 100k check to participate. Now so that's one side of the market. The other side of the market is the big checks. Right. Someone has a typical check size of 250k, they're going to write a 750k check in a seed round of a startup. So we did also check. Oh, is this like an ownership percentage thing? It's not an ownership percentage thing. We have a lot of. It's, it's something different. So you would only write if your typical check size 250k. You're writing a 750k check with this company. You got high conviction, right? This thing's going to be a winner. What we see from the data is that big checks are like maybe a little bit better than typical size checks. And so what is, what is happening here, what's happening here in for the big checks is that you have this adverse selection problem where you're, you know, you, you are writing a big check, you know, presumably in an area of your expertise, you know how to pick companies. Like you can always just opt for writing your typical size check, but you have opted to write a very, you know, more than 2 size, 2x your typical check into this company. What's happening? Like why are you doing that? You have so much conviction that this thing's a winner, but your conviction is almost totally balanced out by the fact that that capacity exists in the first place, that a better investor than you is not taking your capacity. And so that I think is the most, one of the most fascinating parts is that what we observe in the seed and the seed asset class is that the individual idiosyncratic signal is worth almost nothing and the common signal is worth everything. And so this is why I'm saying like, oh, you know, it's not, it's not a question of data about finding out weird facts about a company or whatever that other people don't know. Your idiosyncratic signal doesn't really matter. What matters is the common signal of every of everybody's kind of collective ability to evaluate the quality of a company. And so that I think is, you know, when you think about what the seed asset class looks like, that's kind of a beauty contest dynamic, right? That makes the asset class look a lot more like vintage Rolexes or you know, sneakerhead Air Force ones where everyone kind of collectively agrees like, oh yeah, this kind of Rolex is worth more than that kind of Rolex. Even though you're like, for someone who's outside that space, you're like those, I don't know, their watches, they both work, they're both, you know, 50 years old. I don't. They seem the same. You're like, oh, no, that one's worth three times as much. Because everyone sort of agrees that it's worth three times as much. That that's something I think is really fascinating about this asset class is like, you don't have the dynamic of having a bunch of like, dedicated researchers finding all this super interesting information to come up with this idiosyncratic edge that you would in bond investing or in hedge funds. You have something that looks a lot more like, like trying to buy, you know, Air Force ones and trying to get like a cool colorway for Air Force Ones. And that I think is like, what. What is one of the aspects. So the adverse selection side of that is really, really huge. Right. The fact that like capacity is available for you to take is in and of itself a negative signal. Where you means anyone who's not his name again, anyone who's not Alfred limited. The fact that capacity exists for you to take is a negative signal about the company.

A (41:24)

So unless you're a top 10% investor in that domain, in that space, you having capacity, you should have the reflection to sit around and say, why am I getting this capacity if I'm not one of the top 10% investors? It's probably a negative signal. And can my conviction overcome that negative signal of me having capacity?

B (41:44)

And you should, if it's kind of outside your area or something like, you probably, your conviction is probably wrong, the correct thing to do, even if it looks, I mean, this is something we ran into an indicator with our consumer investments. Like we didn't know what we were doing and we wrote a bunch of bad checks. Like, but they all look great, they all look better to us than the checks we wrote into, into deep tech companies. But like the fact that this company was being offered to us and that, you know, we didn't know, we, we should not have made those investments from the perspective like we don't have the ability.

A (42:11)

I think it's actually a contra signal because what it's saying is that the valuation is being driven by popular or generally acceptable narratives that are accepted outside of the industry and that it's essentially almost the definition of dumb money, none that's dumb, but that it's not specifically smart in that domain. So it's like a contra signal. It's not only should you not be investing, but you're probably investing at a higher valuation.

B (42:35)

Yeah, I think that's correct.

A (42:37)

If you've done hard tech for 10 years and let's say you've done space technology for 10 years and you know everything, something that is Obviously not good for you may still have a narrative that might work for everybody else. But if you're passing on that, and if people like you are passing on that either the entire industry is wrong. Which happens once every, you know, several decades. Although that happens much less within venture. It usually happens. You know, NASA might be wrong, but a bunch of space VCs are unlikely to be wrong. All things being equal, I think it's a contra signal. It's worse valuation and worse signal.

B (43:10)

There's as well and as when we think about adverse selection, I think where this can manifest itself most principally in terms of investing in GPS is the concept of style drift where like you know, LPs don't like it when GPS are doing something different or whatever. And, and I think a chunk of that that hasn't been articulated is you know, if you, if you're pivoting to a new space, if you have a new thesis, ah, now I'm going to like, now I'm all about clean tech and I'm going to do, you know, carbon is the number one issue of our time or whatever. If you're not doing that pivot in like, you know, you were a SaaS investor, now you're doing carbon investing. If you're not doing that pivot in such a way that you're going to see the same sort of rank order list quality of companies. It's a, it's not good, right? You don't, you, Your LP's mate may be less upset about like I disagree with your thesis about where you're going to find returns than it is like you're just going to see worse clean tech companies and you saw SaaS companies and so like that, that will be the, the issue with your style drift not oh, you're changing your perspective on where venture returns are going to come from for the next decade. Adverse selection explains so much actually in terms of the, of the behavior and actually you know we had an experience like this when we first raised money for our first startup which we had a VC do like very deep diligence actually like correct diligence on us and like was like oh, I think we're, you know, we're going to pass for these reasons or whatever. And then like the round came together and a bunch of other you know, VC's good names were participating and the VC who did this diligence came back and was like, oh can we like write a check? You know, just for the, for the, the optionality of participating. And at the time it was like Completely baffling to me because I'm like, well, you did all this. They did the best diligence they could have done. And it was really, they were accurate actually in terms of the success of the company, but they still wanted to participate. And I think it actually, as insane as it sounds, this phenomenon actually kind of makes sense from a data driven perspective, which is that like the company has a pretty good common signal. Maybe you're, maybe you trust your idiosyncratics. The less maybe you still kind of participate just in case the common, your idiosyncratic signal actually turned out to be wrong. And that I think is actually like, it's weird to me because that was an experience where I was like, boy, this doesn't make any sense. Why you'd ever do that much work and come to the correct conclusion and still like go against it. And now, you know, years and years and years later, 10 more than 10 years later, I have this perspective of like actually what they did kind of makes sense. You know, if you, if you, if it is about adverse selection, if it is about the, the common signal that, that VC's share, you should trust yourself a little bit, a little bit less. It's fascinating to see how, how my perspective has, has shifted on this. A lot of the practices that I thought were completely incoherent and irrational, honestly sort of makes sense. And I think it, this goes back to the original point. Right. I think VCs are actually doing a pretty good job. Almost pains me to say that, but yeah.

A (45:55)

So the good news is that VCs are good investing. The bad news is Alpha is very difficult. I know you're a purist, so I know whatever you believe you are productizing and you do. So you're CIO of Strawberry Tree Management Company which is an independent affiliate of AngelList. What strategy are you using with your own money and in your business?

B (46:15)

So Strawberry Tree is. We're independently operated in the sense that Angel's people don't dictate how we do investing. So we're, we're an ria. And so our kind of mandate is to do the investing on the Angels platform that Angels itself can't do as a ERA or, or as a non ria. And so one of these is a fund of funds. And so we started a, last year we started a fund of funds to invest in the best GPS on the platform by, and you know, anticipated future performance performance. And I think we had to develop like quite a few. You know, I'm not, I go back and forth on, on defining whether one of these metrics are novel or not. Right. What we try to do is mechanize insights about the way you pick GPS so that we just put them into Python and every month we get a dump of here's the funds that we should invest in. And we go out and invest in these funds. You know, where we're from is now we've kind of, we've done more than 60 fund commits, so we've done more than one fund commit a week since our initial close. And I believe we are. I can't prove this, I don't know, but it is my belief that we are the most prolific venture fund investor over the past 15 months anywhere of anybody from a team of two people and a Python script. And so, you know, we're trying to invest fairly broadly, but that's kind of the strategy that we have been pursuing. Because I do think to a certain extent and okay, how did I get to this point? Right When I joined Angellist, I think I had the idea of like, hey, I'm going to use all this quant stuff. You know, I'm a quantity guy, I use all data, let's use it to pick startups. And like, I don't think that works anymore. I just don't, I just don't. For all the reasons we've talked about, I don't think it exists. But the most prominent reason that it doesn't exist is pricing. The issue is, you know, the company that's raising at 50 might be 11 or 12 times better than the company that's raising at 5. But like, there's a lot of variability in the space. Like, you're not going to harvest sustainable alpha by doing that kind of investing. And then their access and adverse selection becomes a huge problem as well. So if it's not startups, then I was like, hey, well, what else do we have? Funds. The interesting thing about funds is that funds more or less charge the same thing, right? You know, 1% admin or management or 2% kind of fees and 20% carry and they all kind of charge the same thing. So if you have, you know, positive quantitative signals, you can kind of directly convert those to alpha and a way that you can't with startups, right? I can do all this fancy modeling and say like, oh, this is a great startup, but like you're, Everyone else sees those signals too and you're paying for it with a gp. If you have some metrics, oh, this person's really great. They're, they're Charging the same. And that's actually even different than, than I know, you know, top hedge funds will charge, you know, 0 and 50 or 3 and 30 or whatever. You don't see that kind of price discrimination a lot of the time in, in, in ventures. And so it's what we, we sort of, you know, in general, I think fund of funds are, are terrible financial products. I, I do think, you know, for a sword that we've built, something is, is not. I wouldn't be doing it if I thought it was a terrible financial product. I didn't start with the idea of making a fund of funds. It's where the data has guided me in terms of where, where the data driven alpha in the space is. And it's not from picking startups, it's from picking the GPS who pick startups.

A (49:15)

Because those GPS will see opportunities without being adversely selected and in theory have some kind of alpha.

B (49:22)

The core idea is that like okay, can we find signal that has some positive relation to the performance the fund we're going to be investing? Okay, we have those signals. The magic is you don't have to pay for those signals, right? The, the, the GP who has those signals is charging you the exactly the same as a GPU who doesn't have those signals. It's not priced out in the same way that the like, yeah, this refeet founder or a successful company, you went to MIT, like that's priced in bro, but like the GPUs doing their fund two or fund three and they had some decent success. They're raising a little bit of a bigger fund. They're still charging you know, one or two and 20.

A (49:55)

And the way to rephrase that is there's some signal but either it's not generally accepted or it's some access alpha. In other words, there's something different about their picking, but it's not priced into a general market such that the price is higher because of this insight.

B (50:11)

Correct. I think it's really for maybe three reasons. So, so I think we have three reasons. Maybe start with one of them which is that in general performance it's pretty random. So when we do an investment from sda, we have written these letters for people for their data rooms about why we're making the investment and GPs generally like that because like you know, they get this, they get a very quantitative letter that show other LPs like oh, these guys think we're good from the data. But what we say in the letters is that every fund we invest in has an above Average chance of being an above average venture capital fund. That's the level at which we're willing to say, right? We're not writing checks to like, you know, oh, this person's the best person on the platform. They're so amazing. Like they're, I'm giving them all our money. Like, it's like we think they have an above average chance of being an above average fund. And so one of the reasons you don't see pricing discrimination is like, and, and like we've invested a serious amount of time and money and research time and ingenuity into coming up with our signals, which I'm happy to talk about and find like, I'm, I'm delighted to share those if you want to go to those. But like, even with all that sort of data, understanding the edge is pretty small. And so it's like, what that means practically is like, people like someone's like, it's very hard to have the, a level of consistent outperformance. You see this for big funds too, that publish it like that. Like you actually can, can get 3 and 30 from the market that people are willing to pay that because like, you know, your next one might not like, there's a decent shot that it won't be above average. And then, and then you just burned everything. Right? So I think there's that GPS themselves because the inherent vault variability in space don't have a lot of pricing power in the same way that like a top quant hedge fund might.

A (51:46)

Right?

B (51:47)

Hey, this is the kind of trade we're doing. These are realized returns. We've been crushing this for 11 years. We've never had a down quarter. We're going to charge you 0 and 50.

A (51:55)

There's actually data to suggest that the larger funds charge a higher management fee, all being equal. So as funds get larger, they have more, they become more de risked. They're later vintages. They become also a different asset class. I would say first three vintages versus different vintages are different asset classes. Well, neither here nor there, but they're certainly not priced at. In the earlier stage. You kind of have like the Lake Wabagon effect where all your funds are above average. Put it to dollars and cents. Like what kind of alpha are you looking to get from these? Above average.

B (52:25)

Maybe, maybe kind of our rank order list might have a 0.4 correlation with future kind of performance. It's not super strong. It's there and how that manifests itself. If someone says like, hey, I have this signal. It has a 0.4 correlation to future performance. The right way to behave around that is to take a lot of coin flips, right? You have this somewhat. It's, it's not to say like oh point four correlation, give me the top three names and I'm going to invest in all of those. It's to say like give me the top 70 names and I'm going to invest in all those. Because each of those becomes a weighted coin flip. And if you want to come out better on average, you want to take a bunch of those coin flips, not just flip three coins with a point four crop.

A (53:10)

How do you think that translates into returns? So let's say you got, you had a fund of fund, you had hundreds of these funds. How does that move the mean?

B (53:18)

We have told our RLP's for the first fund that our target was to get the 75th. Just based on kind of average is, is the 75th percentile sort of blended vintage year IRR compounded for seven years. That's our target return which, which I believe was, was if you look at the different vintage years we looked at was somewhere between a 3.6x and a 6x net return to LPs. And so we've invested, investing for 15 months. I didn't have any connection with these GPS whatsoever. They're, they're on Angelist. The Python script said to invest in them so we invested in them.

A (53:48)

Outcome of the process is the good thing. So some companies die, some companies do really well, but it's the process that's actually working.

B (53:55)

We've gotten lucky, but I think we've also put ourselves in a situation to get lucky. And then the, the thinking on it is this kind of weighted coin flip idea is that actually it happens to be the case you maybe this is related to the underlying volatility asset class which is why the signal is so noisy is that like investing in a whole bunch of funds and getting exposure to you know, thousand plus thousands of startups is, is what I was saying at the start is the right way to invest in seed stage venture. Right? So we have this like really nice alignment where like the right way to invest with our signals is to write a lot is to write you know, relatively small checks in a whole bunch of funds which will then go and write relatively small checks in a whole bunch of startups which actually is the correct way I think to invest in, in startups. And so we're, we're fortunate to be in an asset class where the fact that we don't have super strong Signals, you know, kind of implies this chain of strategic behavior that is the right way to behave. Now the reason that could be self referential is like, you could say, like, hey, if the space weren't so insanely volatile, maybe we could build signals that had higher fidelity. Yeah, I mean, I think that's, that's, it kind of fits together. Right. We don't have a super, we don't have a super clean signal. But the behavior that's implied by not having a super clean signal is, is also like in my opinion the correct way to, to invest in the asset class. So it's a, it's really, it dovetails really nicely, I think. And, and that kind of for me was a huge motivator to even go and pursue this idea was that like, hey, you know, the implications lead down a logical path that I have that I had already kind of accepted. But I do think so. So for the fund of funds, it's just really so. So a chunk of it is the, the, the lack of price, there's access that we get because we are, you know, an Angelist affiliate or we can like every GP on the platform has made the affirmative decision to work with Angellist. So they all have a person that they deal with at Angelist almost every single day. So like we have generally been very successful. And this is something I have real eyes on when we're investing this fund is like how many of the funds we want to invest in do we actually get money into? And our rate has been over 90%. Know we've missed out on a couple where people are just like, you know, when we had our first close, some people were already like fully committed or whatever and we've gotten to their next funds instead. But our rate, our rate of success is placement is very, very high. Which is if you do believe this is an inverse selection thing like that's, that's the feature you want, you want to maintain that. And then I think that the data side is, is so interesting as well. So one of the big signals that we use that actually the most prominent signal that we use when we do, when we make this invest is, is called Markov S over baseline. So we look at all the investments that GP has made over the past. It's like smart graduation rate. This is not, there's not like a crazy like, oh wow, we threw AI all this advanced machine learning. It's just like, it's straightforward, right. We look at all the investments someone's made over the past three years and we know from our data the baseline rate of the chance of those investments being marked up or marked up means priced equity round at least 10% higher. So if you made a seed investment 18 months ago, that rate is probably 16%, right? So we go through, we see, okay, you made 20 investments, you've had three markups. The expected number of markups you should have is 5.8.

A (57:03)

One of the most absurd, repeated things in all of venture capital is that you can't know whether somebody is skillful until there's DPI. In other words, taken to the extreme, you made 10 investments, eight of them are on their series, folks, none of them have done DPI. And this person's off 30x on paper. You can't say that that person's a good investor. I think it's absurd. It's just one of these things that sound smart that people repeat.

B (57:27)

I would take that a step further, which is I think I would say that DPI is not even relevant. Not because it's not relevant to investors, but it's not relevant as a future facing signal because it takes so long to assemble enormous dpi. You know, you're talking about someone who is, who is maybe great at investing or maybe they got lucky 11 years ago and like, are they retired? Are they still plugged in? Like, are they still like hustling and scrapping for like those same seed stage deals, whatever that was, you know, that was more than a decade ago. Their lives are, it's probably completely different.

A (57:55)

Life said another way, the DPI as a signal, even if it has some positive correlation, is outpaced negatively by what's happened to that person over the last 10 years, whether they've had style drift, whether the funds have gotten bigger, it's.

B (58:11)

Compressed or washed out. But I would actually go so far as to even be critical about, which is that you might say if you tell someone that like, hey, you know, it's 10 years before these things get really real DPI. So like, it's probably not a great future signal because like, who knows where they're at now versus 10 years ago. Someone will be like, well, yeah, but this guy had a super unicorn exit three or four years into investing. So they know how to pick the really, the really good winners, which are the big winners that happen super fast and they know what to do. And I actually would go so far as to argue that that signal is in fact negative, provided with fall caveat. The caveat is you need to be good enough at investing to have a giant early success, right? If you're terrible at investing. And there are some GPS on the Angels platform who we assess at being terrible at investing that simply would not have the chance of having a giant, you know, humongous exit three years in after they made a seed investment. Provided that you are of the skill level to put yourself into that situation of having a giant exit. I believe it is actually a negative signal for future performance to have that giant exit versus not have that giant exit. And the reason why is not anything you do but rather the behavior of other LPs. All it takes is a bunch of, of several LPs to be like, wow, and you had that giant high profile exit, so you know what you're doing. Here's a bunch of money. Immediate effect of that is for you to suddenly be, to never have to like fight for money ever again. At least for the next decade, right? At least though people forget about the huge exit, right?

A (59:41)

It's upstream of style drift of going into other domains. So it hurts your alpha as an investor.

B (59:47)

And then you start investing in, you know, now you have all this money to invest. You invest in whatever you want, you start, you, you just lose the discipline that, that may have helped you. And so actually it is if you can disentangle the like skill. There is a skill part of having a giant exit, right? You have to be good enough to at least put yourself in a position if you can disentangle the luck from the skill. I would every single day of the week invest in the GP who is skillful but didn't get lucky. Then the GPU is both skillful and lucky. And that's not because, you know, all else being equal, their, their predicted future performance, you know, based on the skill signal might be the same, but all else is not going to be equal. The GPU ad that giant early exit is getting a bunch of money from a bunch of other LPs who think that they've, you know, cracked the code about how to do investing and their future performance is just not going to look as good as the gpu. Didn't get lucky.

A (60:36)

There's a word for these conscious signals, these negative driving forces. It's actually called negative alpha. It's not, it's not a term that many people use. It's literally you have alpha signals and you have negative alpha signal. Abe, it's, it's been too long. Where should people go if they'd like to follow your research? They'd like to learn more about what you're working on and, and just keep, keep up to your work.

B (60:57)

So the Angellist blog angellist.com blog and if anyone wants to, you know, my email is abe@angelist.com to talk about research. And yeah, I'm, I'm always, always open to that. I get a lot of good ideas from people just emailing me about, you know, what's some interesting stuff that we could look at with our data set. And yeah, I'm, I'm open to hearing back from, from folks on, you know, kind of what, what this asset class looks like because I think it is. I was reflecting the other day that that kind of, I've been at in the Angelus universe now for six, six and a half years which is you know, longer, longer than I've done really anything longer than I've worked any job, longer than it took me to get a PhD and I think there is, there's just some really fundamentally, just like intellectually interesting, weird, fascinating stuff about this asset class that like I maybe didn't expect when I started. I think when I started I was like, I'm gonna run this stuff, crunch it, you know, we're going to crush it with machine learning model and then it'll be solved. And that's the journey that I've been on, has been on its own kind of fascinating trajectory.

A (61:56)

Through the podcast I get to meet some of the best investors, some of the best asset managers in the world. And one recurring theme that was first brought to my attention by Mike Maples I think is one of one of the greatest early stage investors and he really focuses on find your early believers, find the people that see the world like you see it and don't try to convert the non believers, focus on your believers and focus all your energy on that. And I think certainly it makes sense why there's some people that believe Venture, you know, their cousins, uncles, sisters, startup that they met at a bar is going to do as well as a well vetted diversified portfolio. Those people should go invest in that. Some people should invest in fund of funds and everything in between. But appreciate what you're doing and appreciate you taking the time.

B (62:42)

Yeah, thank you so much for having me on again. It's been, it's been a pleasure and yeah, maybe, maybe in another few hundred shows I can come back on for round three.

A (62:50)

I would love that.

C (62:51)

That's it for today's episode of How Invest. If you're a GP with over 1 billion in AUM and thinking about long term strategic partners to support your growth, we'd love to connect. Please email me@davidcapital.com.