Investing Billions - E4: Dr. Abe Othman | Why Large Portfolios Outperform Small Portfolios | IRR Games VCs Play | Quantitative Investing in Venture Capital

Starting point is 00:00:00 We did a study on founders' alma maters with the performance of their startups. What we found is that those founders, actually, their markup rates were higher than baseline. It is a positive signal to invest in someone who went to GSB, but that it's not as positive as you think. Because what's happening is that the startup founders are aware that they possess this positive signal, and it's translated into higher valuations for their deals, which means which lowers the chance of future market. Welcome to the Limited Partner Podcast, where we talk about venture capital through the lens of limited partners.

Starting point is 00:00:39 I'm your host, David Weisberg, co-founder and head of venture capital at 10X Capital. The world of LPs can be notoriously private and discreet, but on this podcast, we speak candidly with limited partners about their true feelings on venture, the ecosystem, and 2023 and beyond. Good afternoon, Abe. Abe Offman from AngelList, head of research. Pleasure to meet you and welcome to the podcast. Thank you so much. It's great to be here.

Starting point is 00:01:09 So let's get started on something fun. So one of your most famous slash infamous blog posts is what AngelList data says about power law returns and venture capital. And in this article, you claim or you claim that the data says that only top quartile or top 25% of VCs beat the entire basket of venture capital assets. And explain what you mean by this and how this could possibly be true. Sure. So this was one of the earliest things that I published at AngelList. I would encourage folks that are interested in seeing a little more detail on the research of power laws within venture capital to take a look at something that we published at the end of 2019 called Startup Growth and Venture Returns, which is a little bit more, I would say, rigorously grounded. But the conclusions are relatively the same, which is that

Starting point is 00:02:01 early stage venture, we're talking pre-series B and ideally seed round is governed by a very extreme power law in return multiples, meaning there are many ways of characterizing what a power law means. One is, I think maybe one of the easiest ones is the divergence between mean and median or the divergence between average and typical outcome. And I think for really wild power laws like what you observe in seed investing, it's completely sort of unintuitive what happens there. So let me just give you sort of an illustration of that. The AngelList platform as a whole has returned something around 26% net LPs on a yearly basis. What that doesn't mean is it doesn't mean that a typical AngelList SPV investment will earn somewhere between 20% and

Starting point is 00:02:53 30% a year. That's absolutely 100% not what that means. What that means is that the typical AngelList SPV investment will more or less be flat and that there's an incredibly small minority of investments that are responsible for producing virtually all of those returns. Consequently, if you are a venture capital firm and you are picking 10 companies to seed investments in, your returns are really going to be driven by how many of those companies end up returning in the top decile. And, you know, the market return is to have, you know, one-tenth of the companies trying to top decile, including all of the most outstanding companies are going to be in the market basket portfolio. And so you as a venture firm, like in order to beat that market basket, really need to have two of your 10 picks be in the top decile and hopefully like have one of those picks be like relatively far into that top decile in order to beat the market.

Starting point is 00:03:50 So that's kind of the suggestion there is that the typical venture firm will not be diversified, will not have enough bets to achieve anything other than maybe a small lift off of the typical investment return. And given the wild power lot play here, the divergence between average and typical outcome, the average return, which is the market basket, will tend to be much, much higher than the median venture firm. You know, there are some shenanigans that happen in terms of venture capital funds. You know, we had always, Angelos had always closely looked at SVB's balance sheets. And, you know, a huge source of SVB's profitability were capital call lines of credit, which, of course, have a completely innocuous explanation of, you know, time shifting and making sure

Starting point is 00:04:40 that funds can pay for a quarter's investments or whatever, but also have a very not innocuous explanation, which is that they are used to juice funds, IRRs for reporting. Particularly if you're a fund manager and your goal is to have as high as possible of IRRs two and a half years in when you're raising your next fund, capital call lines of credit are a great way to do that. So I think actually some of the skepticism around this result is more a function of the steps that GPs take to get their fund to have an artificially high IRR, at least in terms of reporting metrics where they want to talk about it. Obviously, using those capital call lines of credit, the end result is a lower TVPI for your LPs. But in the short term, it can be used to juice IRRs. And I've heard a lot of people criticize that capital line of credit. But at the same time, that capital is not coming from LPs. Why is that an artificial metric? Why is that a vanity metric

Starting point is 00:05:40 when LPs are not actually funding that capital? It depends on what you think the point of, what the implication is when you report an IRR and a prospective LP. If you are reporting those IRRs with the intent of making a suggestion, even if there's a bullet point on your slide that past results are not indicative of future returns, I'm saying like, we're going to return this much

Starting point is 00:06:03 in the future on an IRR basis. It's very misleading to have a juiced IRR from capital and credit. Obviously, by a financial perspective, you know, that presumably you're not committing fraud, like that actually is the IRR refund. But it is misleading for LPs to think, oh, wow, that's like a, you know, 70 plus percent IRR. And, you know, it does lead to, I think, in general skepticism about those numbers, discounting. I think, you know, I think the market is fairly efficient from that perspective, but it also tends to hurt funds that don't sort of play this game, that essentially you sort of have to, because if you go out there and sort of you've

Starting point is 00:06:41 done well, you know, first few years and you're honestly reporting a 25% IRR or something, you're going to look worse than the person reporting a 45% IRR. That may have made the same or even worse bets, but just been much more clever about how they've handled their first set of capital calls. And you mentioned the median and the mean. You, of course, reported on the whole market, the 26% across all of AngelList. You have other niche funds like the Access Fund, which invests alongside the top VCs. What has produced better? And what are the learnings from that comparison? I think one of the learnings, and we still don't really have a great sense of this, is that there is a minimum size or a minimum number of startups

Starting point is 00:07:23 that you need to be exposed to to of the benefits of being broadly indexed. And that if you're not there, if you're only making 10 investments, it's sort of a different game than if you're making, say, more than 100. The Axis Fund has in general, in terms of the slice of investments that it has had the opportunity to invest in, Parker and the Axis Fund has shown alpha in selecting those investments. The actual returns of the Axis Fund, because of some investment sizing issues, are not as high as they should be, but in terms of the perspective of just like, Parker got a vote yes or no, the substantive things that end up getting Axis Fund investment, do those end up performing better than the universe as a whole? The answer is yes. That said, the access fund is investing in a lot

Starting point is 00:08:08 of stuff, something like 20% or 30% of deals. So it is pretty broad as opposed to a narrow selection of a small group of startups. There's a question, does Alpha exist in venture capital? And I think actually this is one of the lines of research that we have. I believe the answer is yes. So in a couple ways. So first, you have the existence of the Axis Fund through whatever means that the investment committee is making their decisions. We have a very clean historical, it's a really nice experiment, right? Because we know the investments the Axis Fund had the opportunity to invest in. We can see their performance relative to a random similar size subset or just a blanket kind of yes to everything. And the Axis Fund does appear to have alpha, consistently across years. So it does appear that who knows how they do it. Maybe they do

Starting point is 00:09:03 a deep dive in the slide decks. Maybe they invest in companies that, you know, their logo is the color blue. I don't know. And I don't think it matters from the perspective of the question of like, is there alpha? Does it exist? Yes. I think the access fund is certainly a positive example. We also looked at this, and I don't think we've published this research. I think it's somewhere in our queue. We also turned the question around. Is there a subset of syndicated deals on the AngelList platform that were sort of a priori identifiable as being worse quality than other deals? And what we did for that identification,

Starting point is 00:09:37 based on discussion with the investment committee, was look at a list of co-investors. So if you have a deal, an early stage syndicated deal, where the only co-investors that have listed co-investors, so the GP has entered this information, and the only co-investors they've listed only ever appear in that deal, which is about 10% of the deals of the early stage syndicates, that subset is noticeably and consistently worse performing than the market as a whole. So these characteristics suggest to me that there is, in fact, despite some appearances

Starting point is 00:10:15 the contrary, there is, in fact, a rank order list of seed investment opportunities. We can dive in on what our shape of that universe looks like. You know, the question of like, I don't think it's accurate. I don't think it's supported by our data to have the view of early stage investment as being throwing darts at a dartboard, or being, you know, completely random or some undifferentiated mess. I don't think that's supported in the data. And just to double click on that, you're saying that only 10% of all deals on AngelList would not fit the quality bar. Can you explain that?

Starting point is 00:10:52 I think that they are a priori identifiable as worse than deals that do have a recognizable. By recognizable, I mean they do multiple deals, co-investors. The interesting thing is these investments take time of season. If you go back to the startup growth and venture returns paper, you see that what happens is that as companies compound returns, the return multiple alpha parameter gets lower, i.e. the return multiples get wilder over time. And so right now, the return multiple that we fit to these like bad or maybe it's better described as like marginal subset of deals is above two.

Starting point is 00:11:30 But there is a reasonable likelihood it will fall to below two. Even this marginal subset of deals on the Angelus platform will fall to below two. I think realistically what that means is probably the sort of the double gate of the Angelus platform, which is like first, your deal has to be approved to be on the platform. And then there have to be enough LP. You have to get enough LP interest to actually close the deal. It probably puts the bar higher than what we identified in the startup growth and venture returns paper, which is like the credible deal threshold, which is worth diving into, I think. Let's step back for a minute and define power laws. How would you explain power laws

Starting point is 00:12:07 and venture capital to a layperson or to finance professionals and other asset classes? The easiest way to describe power laws is that they have the opposite intuition of everything you've been told about in terms of the typical world. So I'll just give you an illustration. I've been working with this stuff for years, and still, this surprised me. We had an inquiry from an AngelList GP whose startup had just made an early stage investment. They'd just raised Series A.

Starting point is 00:12:35 Their investment after two and a half years was now marked at 2x. And they asked, hey, would it be possible to find out how long does it take for the typical AngelList investment? Is this fast? Is this slow? How long does it take for the typical angelist? Like, is this fast? Is this slow? Like, how long does it take for the typical angelist investment to like get to 2x and then get to 3x in terms of return multiple? I think there's a vision of this world, again, where it's like, well, you know, well, the typical investment is going to return, you know, about, you know, 15 or 20% a year. And so, you know, you're going to get like a noisy

Starting point is 00:13:02 distribution around that. And so it'll be four years for that investment to double. And in reality, when we looked at the actual returns of these deals, the median was flat and even the 75th percentile took years to get to a 2x return. And that really it was even the question itself begged a structure of returns that don't exist for venture capital. It begged this idea that I think is common in the public markets of like, well, the market's returning about 10% a year. So what I'm going to get is some noisy distribution around this 10% a year. And that is not what I'm saying for venture is it doesn't look like that at all. In a normal distribution, the average outcome and the typical outcome are the

Starting point is 00:13:45 same. And we have become very, very used to thinking about things in that context. Like so much of our mental kind of framework for the world is based around thinking about things like that. And in power laws, that's just wildly false. Especially for the most extreme power laws, they have a well-defined median and the mean doesn't exist. It's, you know, in some sense it is unbounded, right? I'm not going to say incident, but so they're like a direct challenge to, I think, a lot of our implicit assumptions about how things work. Hey, we'll continue our interview in a moment after a word from our sponsors. The Limited Partner Podcast is proudly sponsored by AngelList. If you're a founder or investor, you'll know AngelList builds software that powers the startup economy.

Starting point is 00:14:28 AngelList has recently rolled out a suite of new software products for venture capital and private equity that are truly game-changing. They digitize and automate all the manual processes that you struggle with in traditional fundraising and operating workflows, while providing real-time insights for funds at any stage, connecting seamlessly with any back office provider. If you're in private markets, you'll love AngelList's new suite of software products. And for private companies, thousands of startups from $4 million to $4 billion in valuation have switched to AngelList for cap table management. It's a modern, intelligent equity management platform that offers equity issuance, employee stock management, 409A valuations, and more. I've been a happy

Starting point is 00:15:05 investor in AngelList for many years, and I'm so excited to have them as a presenting sponsor. So if you're ready to level up your startup or fund with AngelList, visit www.angellist.com slash TLP. That's AngelList slash TLP to get started. Back to the show. I think from the perspective of finance and perspective asset classes, I think what I like to say about early stage venture capital is that it is probably best contextualized as the opposite of a bond market. So in bond, like traditional bonds, not like weird high yield bonds or anything. Typically for bonds, like there are bond index funds or whatever. And they measure thousands of thousands of bonds. And you have bond index funds that you can invest in that have like a few hundred of

Starting point is 00:15:50 those bonds in it. And you know what? They track almost perfectly. Whereas if you have a few hundred startups versus the 15,000 startup universe, there's a very high risk you won't get anything close to tracking. In bond funds, it's actually really easy to beat the market because the whole index is exposed to the random couple bonds that end up defaulting. If you just have a bond portfolio that just doesn't have any of that rare default event

Starting point is 00:16:14 bonds, congratulations, you've outperformed the market. Bonds, their price movements over time are just insanely correlated and have virtually no idiosyncratic... There can be ratings changes perhaps, but there's virtually no idiosyncratic component to the movement of bond prices over time. Whereas for startups, it's all idiosyncratic. There's virtually no market effect at all. It's all, did this startup hit the milestones that it needed to to get to the next fundraising market? And you said something there. You said a 200 startup portfolio would not get close to a 1,500 or 15,000 startup portfolio.

Starting point is 00:16:56 Can you explain that? It would not reliably track the market. It might outperform. It is more likely that it will underperform, but it will definitely not track the performance of the market as a whole. And I think that's one of the most challenging things about, especially one of the foundational things that my research has tried to show is that early stage investing is its own asset class. And that when we take ideas that are

Starting point is 00:17:20 borrowed or otherwise co-opted from other asset classes, like public markets investing, it just doesn't work. So again, to give you an illustration, there's like, oh, I invest in a certain kind of company, kind of like I'm a sector-specific investor, and so I should only be compared to like, sure. If you look on AngelList, B or one of the highest performing sectors on AngelList is called productivity tools. And it has an IRR of, I don't want to quote this, but it's very, very high IRR for productivity tools. And you might think, wow, that's like a really growing market with work from home or whatever. That's like super exciting, like got to make some product too. Really, that's Notion. It's because Notion has just blown up. I love Notion, by the way, use it all the time. Great, great product. It's because Notion has just blown up. I love Notion, by the way.

Starting point is 00:18:05 I use it all the time. Great, great product. It's not the productivity tool sector that's done well. It's the investment node as Notion that's done really well. That is kind of a challenge to people who are used to thinking about like, oh, I want like sector overweight or sector underweight. Like that's not at all what's happening here. It's these

Starting point is 00:18:25 individual idiosyncratic companies. So again, this is an example of a model where we go from public markets investing, and it just maps very badly onto what's actually happening in the early stage universe. So let's assume that idiosyncraticity and the power laws, I think that's something that's widely at least quoted or stated in venture capital, whether it's implicitly understood or not. In terms of signals, what percentage of those power law, what percentage of those notions or Facebooks and Googles are identifiable as top 10, top five, top 1% opportunities at the time of investment at the early stage? Has your research done any

Starting point is 00:19:05 on that? And also, what signals, more importantly, do you believe and does your research show are predictors of this? I think this has been one of the most surprising results by research. Because I tend to not have the highest view of venture capitalists. My research has suggested is that certainly not on the individual investment level, but on one level zoomed up, the venture market is actually relatively efficient. And I think that's very, so like by that, I mean, price tends to have a meaning. And like a deal that's priced at 2X, another deal will tend to have a structure where it will return 2X the amount of money. That's what pricing efficiency

Starting point is 00:19:45 kind of means in this context. That's kind of surprising. One reason that's surprising is that what you're buying when you're an early stage investor, in eight years, when you look back on that investment, it will almost absolutely be like, there's virtually no early stage investment where you're like, man, I got a solid 10% of your return out of that investment. And it was pretty good. It's either going to zero or it's going to make at least 3x, 10x, 20x, 2000x, right? You are either very much overpaying or very much underpaying for every single one of those investments. And yet, when you zoom up a level, it does look fairly efficient.

Starting point is 00:20:22 What that means is that it actually, and as much as I would like this to not be the case, it actually minimizes the values of signals significantly. So again, to give you an example, we did a study on founders, all the modders with the performance of their startups. And what we found was that actually, okay, I'm going to invest in like, someone went to GSB or someone went to Harvard or someone went to MIT. What we found is that those founders actually, their markup rates were higher than baseline. So it is a positive signal to invest in someone who went to GSB, but that it's not as positive as you think, because what's happening is that the startup founders are aware that they possess this positive signal and it's translated into

Starting point is 00:21:04 higher valuations for their deals, which means, which lowers the chance of future market. And we actually think that's probably more than half of the like boon of this signal is actually captured by the founders. And so as a result, if there is alpha in deal selection and venture cap, like there's a sort of naive thing, which is like, you know, the signal is still positive. It's not like GSB founder deals are so overpriced that it's actually a negative signal and you should never do a GSB deal. It's more that it's like a lot less positive than you might think. And this is also the case for, say, you know, repeat founders of successful companies, a positive signal, but you're paying for that signal. And that payment is what really reduces, like that kind of substantially reduces

Starting point is 00:21:43 investor returns. Like it makes it less of like a clever arb and more of, like, an appropriate value that you're paying for a deal. We think that if there is alpha from, like, signals, it comes from, like, it comes from unconventional places. So, like, I believe the top three schools for markups by founders were good schools that are a little bit unconventional. They're University of Washington, Waterloo, and Brown, I believe were the top three. So it's like very good schools, but not, you know, Harvard, Stanford, MIT. So, you know, that's like a second order is where you'd find any investment opportunities. And even then, you know, opportunities are still relatively small small and you are still paying

Starting point is 00:22:27 a little bit for that signal. But like the benefit of having a Waterloo founder is like a little bit better than the markup that they might charge for that signal. The way this fits into the rank order list is that we do think this rank order list of deals exists. And, you know, from like best seed deal all the way down to worst possible deal. As you go down that list, what happens is that the signals become worse and the price gets lower, but that we think the exit possibilities actually fall faster than the price falls. And so at a certain point, because what

Starting point is 00:23:02 we're looking at is this alpha less than two power law as a threshold, at a certain point, because what we're looking at is this alpha less than two power law as a threshold, at a certain point, the exits no longer justify the price and you actually get sort of a different asset class. And so somewhere on that list, there's a bar that says like everywhere above this is going to pull from an alpha two or lower power law. And those are credible deals. And they are early stage startups that can grow into huge markets and produce huge returns if they do well. And you should have exposure to all of those. Now, you could have differential exposure. You could wait more towards the highest signal ones. That's fine. But in some sense, you need to have exposure to all of those deals.

Starting point is 00:23:41 Below that threshold, the rules are very different. There's a lot more benefit of being more selective. But that's the universe as we see it. And I think the most surprising thing is that actually the startup pricing is actually at least relatively within deals, is actually quite efficient. And I think, just given my dim view of VCs, I think a lot of that pricing efficiency probably comes from founders who are probably very able to say like, well, that guy's company got a 30 million pre-money valuation, but like my company is doing better.

Starting point is 00:24:15 So I should have a higher value. Like, I think a lot of that pricing efficiency probably comes from founders and not from VCs. But, you know, I'm also willing to say like, I came into my research with a very, very dim view of VCs. And this is something where I've actually been like, hmm, VCs are not leaving $100 bills on a sidewalk. They might be leaving like $10 bills on the sidewalk, but there's no like one weird trick that will like make you an obvious investor that all great startups have. Instead, what you get are a bunch of signals that are sort of mutual.

Starting point is 00:24:44 Everyone kind of sees them. The founders know what they mean. They're reasonably efficiently priced. They're still positive. The reason that I think this model is correct, it has kind of one implication, which is that there is one weird trick, which is there's one positive signal that you don't have to pay for. And that's the participation of a top tier co-investor in the deal. And so if my model of the world is correct, what you would see is you would see a lot of venture firms whose only kind of job or only thing they do is try to sneak into deals that are being led by or priced by a top tier venture firm. And that is, I think, the exact world we live in. I'm not going to name names, but there are, I would say, dozens, possibly even hundreds of venture firms whose

Starting point is 00:25:29 first question in diligence is who else is investing. I think that's the world we live in. And I think what this research suggests is at least a mathematical justification for why. And that's because it's the positive signal you don't have to pay for, as opposed to every other positive signal about a company. Let's say Sequoia is investing in a Series A, a very high signal. I think you would agree. What are some ways that that signal is dimmed? What are other criterias that makes it less strong of a signal when a strong signal from the lead is present? I think every startup is so wildly idiosyncratic that, I mean, how big is that data set?

Starting point is 00:26:05 Every startup in it is unique. So I actually think that's probably a fool's game to try to think about that. One famous fund, and there's even an article comparing Peter Thiel and Founders Fund to AngelList. In many ways, Peter Thiel takes the opposite philosophical approach and has done quite well. Are there cases when a concentrated portfolio will beat this market approach? A couple of things with that. So the first is that Founders Fund, we have them as one of the most prevalent co-investors in AngelList data. So they're not backing a handful of founders. They're backing a lot of folks and well into the three figures

Starting point is 00:26:41 annually of startup investors. We see a lot of founder funds deal. I think they're either number one or number two, depending on how we have Y Combinator classified in terms of like co-investors. They're a very, very frequent co-investor. Now that said, there is a very interesting study, I think, that I want to talk about from Ben Evans, who I think used to be Andreessen, but is not anymore. But he had this study where he looked at the performance of venture funds and found that in the top decile of performing funds, there were actually more goes to zero than in kind of the next decile. And the idea being that a higher fraction of goes to zero. And the idea being that like the real best venture funds really swing for the fences. And I've actually heard that as justification for GPs making certain investments because it's like, oh, you know, this is like a big bet, but we're swinging for the fences.

Starting point is 00:27:30 And I actually, I disagree with that. What I think you're seeing are that top 10% of funds are successful early stage investors where you have a higher risk of failure, but because they are successful, their successes look like giant home runs. I don't think they were intended to be swinging for the fences. I just think what you're observing in that top-down style are the most successful real estate investors. So it is the case that if you look at the best performing funds, they will have relatively concentrated portfolios of several companies that have done exceptionally well. But that doesn't necessarily mean the best

Starting point is 00:28:04 way to produce good returns for your LPs is to take a concentrated portfolio. We joke that the best portfolio is to put all your money into the next Uber. And the second best portfolio is to invest in everything, or at least have exposure to everything. So yeah, I would push back on that in a few ways. It is absolutely the best thing in the world to have a concentrated portfolio and to get lucky. You're going to do that. Congratulations. You've done exceptionally well. But for those of us who are not necessarily banking on luck, a concentrated portfolio will tend to just increase volatility and lower returns as opposed to being a magic ticket to doing exceptionally well.

Starting point is 00:28:44 You mentioned something there that's really interesting in terms of top decile and loss ratio. Have you seen some kind of almost power law distribution in terms of loss ratio and performance? Have you seen any meaningful correlation there? Yes, but it's lower than you think. So we don't do, I mean, it's kind of loss, especially given the timeline here, it's kind of loss ratio, which is more just markup rates. So like, did a deal you made three years ago get marked up? You can assume that if the answer is no, like there's a very,

Starting point is 00:29:15 very good chance that investment's going to certainly be a, maybe not a total write-off, but at least on the negative end of the spectrum. If your investment hasn't been marked up in three years, it's probably not the next Uber. So that said, we think there is actually a surprisingly light correlation, put it maybe around 0.4, 0.5, between market rate over what would be expected and actual kind of IRR and TB guy of that fund. The differences are like GPs who are very lucky or very unlucky. Like they've gotten a lot of markups. So for whatever reason, those markups seem fairly small. And I actually, when we were doing this research originally, I actually called up like on the phone, I actually knew the guy, like the, the, the GP that we identified on the Angelus platform as being the most unlucky GP. So having the, uh, the most, you know, excess markups, like a lot of deals that were, that

Starting point is 00:30:10 were getting, you know, uh, getting to series A or series B, but pretty bad returns, like not still positive because like, it's, it's hard to have a lot of markups and not have positive returns, but like IRR of, I think it was like 10% a year, uh, net tailpiece. And I actually called the guy up and I'm like, hey, so you were identifying your data. Are you trying to hit singles instead of swinging for the fences? And he was like, I'm incredibly surprised to hear that. Like all the companies I'm investing in, I think could be, you know, a thousand extra returns.

Starting point is 00:30:37 You know, I haven't been super happy with the returns we've been getting. I guess the markups have been nice. And, you know, obviously this is an anecdote, but I think it was telling that I do think the structure exists. There is a correlation. It's hard to have a lot of markups and not make money. And it's hard to have a lot of write-offs and at the same time make money. So there is a correlation, but it's weaker than you'd think. And I actually think both of these are informative for future returns, both current returns as well as how frequently your deals are marked up or written off. So what is the correlation between TVPI and DPI?

Starting point is 00:31:12 In bear markets, DPI has become popular again. How would you characterize that? I can't talk about it with Angelus. We have such a small DPI, I think. For what it's worth, the platform is huge. So we have, I mean, I feel like I should give a plug, you know, AngelList has distributed more than a billion dollars to its LPs, but that's a small fraction of the money that's been invested on the platform. Like we don't have a long enough data set. We don't have enough exits. One of the challenges

Starting point is 00:31:40 with AngelList data, like this, I'm just telling you, it's a weakness of our data. AngelList really started humming, you know, maybe 2018, 2019, in terms of just like being able to say like, oh, this is actually the venture market. And so when you talk about exits, it's like, you know, you're looking at companies and from like the 2015 era of AngelList, where, you know, AngelList was capturing like a slight, maybe 10% of the markets of 90% of those exits are not reflected in AngelList data, my answer to that is like check back in a decade and we should have really good data on distributions and stuff. It's funny, actually, for what it's worth, this is not the first question I've gotten about DPI. And I think in one of the access fund faces, I kind of joked like, hey, if you're looking for cash on cash returns, like early stage venture is not the place for you. Like you should go buy a multifamily apartment building outside of Houston. But I don't know if you've

Starting point is 00:32:29 seen the news lately. Actually, it seems like multifamily apartment buildings outside of Houston are not doing particularly well right now. So I do think there's no sure thing. And what the world looks like has changed a lot over the past few years. So tell me, a lot of people asked me to ask you about the Quant Fund. What could you say about that? Yeah, I think this is a good illustration of kind of the shape of the way that we're doing investing. So the Quant Fund is using most of the money is going towards investing in the companies that are hiring the best on what is well-found, what used to be AngelList talent, in terms of tracking top applicants. And when we were backtesting this, one of the cool things is that we knew backtests, which is very rare for venture.

Starting point is 00:33:15 So who are the companies that we would have wanted to invest in in the past? And we identified like Bolt, Calm, Chime, Divi Homes, DoorDash were all companies that were at or near the top of this list Years and years ago when they were only stage companies and so we're like, hey, this seems like a pretty auspicious signal I also think you know, how does this fit in general with the model? Well a few things the model the world is that as an LP you should want exposure to every credible seed stage deal Where credibility I can tie it back to that alpha lesson to power law thing on the rank order list. The challenge that you could

Starting point is 00:33:50 give to that credibility threshold is like that word credible is doing a lot of work. What it essentially means, does that mean that GPs should just broadly spray and pray? No, they should invest within their area of expertise to be able to know what a credible deal is. And that, I think, is what distinguishes Seed as an asset class, is that it's very easy to know what a credible Series A is, right? Is there a professional venture capitalist taking a board seat as part of an equity financing? That's a credible Series A. For Seed, it's like two dudes, a slide deck, they've kind of been working at it for six months, sort of. Company might not even exist yet. And it's like, is that deal, is that credible? Is

Starting point is 00:34:31 what they're talking about credible? Is this credibly going to be a billion-dollar company? That requires specific expertise. And I think what the results suggest is that like the best, so LPs should broadly invest broadly among a number of GPs, each of which have their own kind of specialized area of expertise to be able to evaluate this credibility threshold. And that's the model of this world. So how does this play in the quant fund? Well, we think that we have this interesting credibility filter, which is looking at this kind of inbound hiring signal. And we are investing broadly within it. So we write really small checks, 100, 150K. And we're trying to invest in as many companies as possible.

Starting point is 00:35:14 So 100 plus companies with these small checks. And that is the game plan of the quant fund. So we think we have this little edge from our proprietary data, and we are investing in such a way that is compatible with the way that I think that the math speaks about what the world looks like. And you mentioned credibility. Would you go with a partner versus fund? Would you go with a top decile firm or a top decile partner that has left to found his own firm? I can answer this from a data perspective and then kind of from a startup founder perspective. So from a data perspective, I think the preponderance of evidence leans towards partners. Just to give a good illustration, I want to give a shout out to Jonathan Hsu of Tribe Capital,

Starting point is 00:36:03 who's very helpful, actually, when we were talking about the early steps of the quant fund. It was very encouraging. Jonathan's a great guy. He left social capital to start Tribe. We have Tribe as one of our best co-investors, like in terms of our own internal rankings of co-investors. We have Tribe as one of the best ones.

Starting point is 00:36:20 I think there's some other examples that I might be able to less closely correct that I don't want to mention. I think the preponderance of evidence is towards partners and not firms on the data side. That said, as you know, wearing a startup founder hat, one of the, for, especially for an early stage company, one of the best values that a VC brings to a financing is that you can put their logo on your webpage and prospective employees will view your company as much more credible because you're backed by a venture firm that they may have heard of. And so from that perspective, maybe it's firms. I think it's a little bit of a balance, but I would encourage startup founders to not be afraid of going for founders. I do think it's the sense with going with partners and that there is a sense in

Starting point is 00:37:07 general that I think, in general, AngelList, of course, is a little bit taught in our book here, but we think solo GPs or small partnerships tend to outperform larger partnership megafunds. You said something very interesting there about solo GPs versus traditional incumbent funds. Is that going back to the signal? Is it that solo GPs have less capital to deploy so they invest at a lower valuation? What is some potential rationale for that phenomenon? Part of it might be that solo GPs tend to hustle harder. They may also have, again, I think it goes back to this idea of the ideas that an early stage GP needs to be able to evaluate credibility within their specific domain of expertise.

Starting point is 00:37:50 I think it's probably also the case that for a solo GP versus a larger partnership, especially a more junior partner, they might know that a company is a good company, but not be able to get it through the partnership. And similarly, they may, you know, assent to deals where they don't really, you know, this is not my space, but if, you know, this other partner says it's good, I guess I have to go along with it. Or there could be horse trading within the partnership for various investments. I think the solo GP structure tends to just clarify it. It's like, do you have an area of expertise where you can adequately assess credibility? And I think one of the reasons you see the solo GPs tend to overperform is that the answer to that is yes. Because chances are, if you don't know what you're doing,

Starting point is 00:38:37 but also think you know what you're doing, you're not going to go out on your own and raise your own fund and do that one thing that you think you're doing when you can have a safer partnership structure in place. I also think there's probably something with early stage investing where, I mean, yeah, it is smaller checks, but it could just be because it's earlier stage. I think there's something with earlier stage where, in general, a partnership structure is hard to make work well because you are looking for fairly disruptive companies. And so anything that maybe tends towards consensus or consensus view could be really limiting to that. So I tend to really like solo GPs or small partnerships.

Starting point is 00:39:22 I think their performance is generally quite good. And I think it's a good fit for the model of, can you assess credibility of a deal that might not even be a company yet? Zooming out, you've been publishing research for a long time. How have you seen the LP community or broader ecosystem evolve on these topics? At the time, it was a very contrarian view. Have you seen them kind of accepted a bit more? Or how have you seen it evolve? I'd like to hope that the idea of going broad for early investing has been more accepted

Starting point is 00:39:59 for LPs. That said, for the quant fund, it was hard to raise that fund. And actually, we struck out with all... We have some, but not many conventional venture LPs. And actually, our largest investors and frankly, the easiest checks to get were from quant hedge funds or principals at quant hedge funds, where the idea of like, you know, you pitch them and we're like, we invest based on this proprietary signal. And they're like, that's what investing is. You know, like that, of course, of course you do. That's the nature of what an investment means. And they're not, you know, they're not traditional venture LPs.

Starting point is 00:40:37 So I don't know if my research work has resonated enough to make that fundraise easy. And, you know, certainly I do think there's also a sense of, even with saying like spray and pray, right? The idea, and I brought this up in the startup growth and venture returns paper, it sounds a lot better, right? To say, hey, I'm going to use my network to find, you know, five to 10 startup founders

Starting point is 00:40:59 who are like really, you know, they're going to change the world with their narrow set of businesses that I'm super excited about. And I'm going to work closely with them. And like, you know, it just, it sounds like you're being a better steward of LP money than if you're just like, we're going to blast off so many investments, I'm not going to know what these companies do. It just, you know, one of these things sounds like a bespoke, artisanal, like really kind of thoughtful way of managing somebody's money that frankly justifies carry. And the other way sounds flippant and unserious in some sense.

Starting point is 00:41:33 And I think what's still provocative is that I actually think the latter way is a better way to do real estate investing. And so that is, you know, I think the biggest mistake that LPs can make is not being exposed to enough companies. I know one of the big know, I think the biggest mistake that LPs can make is not being exposed to enough companies. I know one of the big changes I've had as an LP putting, you know, a third or fourth hat on for this interview since I started AngelList is I am now an LP in several venture funds. Whereas before I was like, well, I do my own early stage investing. Like I don't need to pay someone carry for deals. Like I don't want to do that. I was now just like, if there's differentiated credible deal flow that someone will give me, like I will definitely be an LPN fund. So for instance, like one of the funds I'm an LPN is, is Ryan Hoover's weekend fund.

Starting point is 00:42:16 Ryan Hoover is the product founder and he is like an exceptional high end brand within this like space of kind of like fun consumery companies companies. And that's something I don't know anything about those companies. I don't see opportunities to invest in them. And then the ones that I do, I can't assess at all. And so I have no problem paying Ryan Carey to do a good job of finding and investing in those deals because it was literally not something I could do myself. And at the same time, I think that those deals, that's playing in the deep end of the pool. That's playing in the credible seed deal threshold area. So yeah, I think it's certainly changed my behavior, this idea of trying to be broadly exposed. I think it will be a matter of time before it changes behavior more broadly,

Starting point is 00:43:03 but I do think it's the way It's the way things are going There's there's also just there is like one hack with the broad investing as well Which is that typically when someone discusses their their prowess as an investor the way you see that prowess is by listing their like number one or top three investments that they've made and if you've invested a ton of companies your top three will tend to look quite. And if you've invested in a ton of companies, your top three will tend to look quite better than someone who's invested in 15. And so, you know, there's like, if you want kind of a life hack way that this kind of broad investing is likely to, you know, possibly take over as a modality, it's that it's that it's a it's the hack around this like, thing where you don't see the 97 failures, you just see the three huge unicorns.

Starting point is 00:43:47 And therefore, you're an awesome investor and therefore people want you on their cap table. Another variation on that hack that's well known is investing right before the IPO and saying, I was a private investor in this company. That's a common one in VC. You mentioned Ryan Hoover and his consumer expertise. How much of a signal and what has the data showed you in terms of somebody's domain of expertise with Ryan or the hypothetical GP that's really good in one sector, be really bad in another sector? That's a great question for future research. I think that's actually a blog post that we should do on AngelList by the end of the year,

Starting point is 00:44:27 is to see if there's sector effects. I can tell you that the early stage investing that my friends and I do is through a partnership called Indicator. At our portfolio reviews, after years of investing together, we now joke that we make three kinds of investments. We make deep tech investments, we make fintech investments, and we make bad investments. And the funny thing is that we continue to make bad investments. They look really good.

Starting point is 00:44:53 And we're like, this one's going to be different. And then almost inevitably, they end up in the loser column. And so it's kind of been like a honing of our area of expertise. So I mean, just based on my personal lived experience suggests that sector effects are 100% real. Whether or not we have good enough, fine-grained enough data to tease those out on AngelList is another question. Because I think it's like the sector labels can be hard. It can be fuzzy. Startups can change what they do.

Starting point is 00:45:21 Are you enterprise? Are you B2B? Are you SaaS? I think we all have those different tag labels. That can be tough. But I do think it's a super interesting question. And my guess is that, yeah, I will say that I think that is something from the public market that does carry a relation to hedge fund investing that carries over to GPs, which is style drift is a known real negative thing. And I would imagine in hedge fund investing, and I imagine that's a negative thing. Venture, and I mean, you see those effects

Starting point is 00:45:53 as well, right? Like our most hedge fund managers style drift because they do really well in their narrow area of expertise. And suddenly they have to manage all this huge money. And they're like, they become macro investors making bets on the price of oil. Whereas like before they were like investing in, you know, stage two biotech companies or something, right? For GPs, I think you see the same thing. Like, you know, you have a fund that's a small fund that's only investing in this narrow area and maybe they scale up and raise a ton more money and then suddenly they're doing all sorts of wacky stuff. I would not be surprised if that analog to the public markets did hold up in venture investing, the data. Speaking of style drift, AUM drift is a big issue in venture capital.

Starting point is 00:46:32 How do you look at that and how do you expect that to change the signal and the nature of returns? In terms of power laws also, in terms of valuation and stage, maybe you could talk a little bit about that, the growing AUM and the growing valuations. It has been, just to maybe like slip it around a bit, it's been one of the, and it's hard because I mean, there's real people with real dreams of real money being invested. But like, it's been frustrating to see in the data how I would describe it as stubbornly,

Starting point is 00:47:01 persistently high early stage valuations have been even over the past kind of crash. And I think we have a piece on the blog that's going to be coming out soon. But up until second quarter of last year, the median price equity seed round was down to $20 million in pre-money. And now it's at like $17 million in pre-money. And we think that, and now it's at like 17 million pre-money. And we think that needs to go down to like 12, 12 and a half for it to be like a sustainable recovery and to return. Like, I think that's where we're going to end up. And I, my sense is that the glut of money that is present is what is support. Like, I think there's a couple of things, whether it's structural or not. A glut of money that's going earlier stages might be supporting these valuations and keeping

Starting point is 00:47:51 them artificially high. But that said, we also think that there's going to be over the next year a ton of pricing pressure, at least seed, now that Series A investments have been kind of moved in price a decent amount. If you do a lagged, what were seed prices a year ago versus Series A prices now, that ratio peaked a year ago at 6x. And I think that encouraged a lot of price inflation at seed because investors could just be like, well, yeah, we can double the price of this and we'll still make 3x, so it's not a problem. Now that ratio is at 2x and still falling. I think that will encourage pricing discipline

Starting point is 00:48:30 or may encourage pricing discipline on the part of earlier stage investors. What happened with SoftBank? Maybe that era of mega fund is over. And a scientific experiment was run and it came up with a failure. Um, and that to me is like, yeah, I mean, I, I can't be completely sure what the future looks like, but my sense is that the prices still need to come down, that the presence of folks who are, who are keeping these prices up as, as just serving the delay, kind of the inevitable pricing recognition. Cause I do think it's kind of a, like prices just need to be a certain amount to be,

Starting point is 00:49:07 to accommodate risk appropriately. And I think that, you know, folks that are doing, I think the idea that of SoftBank, the SoftBank self, like vision fund style things is probably done for an investment generation.

Starting point is 00:49:23 I'm sure in 10 years, muscle will be back with future vision fund that will be even bigger. But I think that's done for the near term. I think a lot of people misunderstand the SoftBank thesis, which was capital as a moat, which is an interesting thesis. In retrospect, every VC is a genius and could predict every potential outcome. But I thought it was bold and interesting, although obviously, ultimately not successful. As a concluding question, what do you wish people knew about AngelList? And what are some misconceptions? What would you like

Starting point is 00:49:57 to resolve and for people to know about your platform? What's out there tends to be pretty bad. I happened to look at the AngelList Wikipedia page recently, and I was like, this does not at all look like what AngelList is. I've gotten that most people are familiar with it because it's a jobs platform, obviously not anymore. And then if they're familiar with it beyond that, they're familiar with it as an LP, where they had to click through a bunch of screens and maybe it pulled an ACH to their banking account. I think AngelList's actual clientele are GPs and founders. I think that's probably the best analog to what AngelList actually is, is like a financial services company that happens to have this startup thing

Starting point is 00:50:40 attached because for whatever reason, like real companies, when they need to raise debt or equity, they talk like real companies, you know, when they need to raise debt or equity, they talk to bankers, but startups, the founders do their own banking. And so it is like a financial services company that has a very vigorous founders and startups arm. And I think that's probably the best analog to what AngelList is right now. And yeah, I think it's really hard to see that. I think certainly from the outside, because, you know, those products are like, most people don't know what a prime brokerage for a hedge fund does. Like, that's just, you know, the people who need to know that know exactly, you know, what a prime brokerage is and who they use for a prime broker.

Starting point is 00:51:20 But it doesn't affect people's day-to-day lives on the street to know what that is. And so I think what's weird about AngelList, I think one of the gaps is that for a long time, most people only saw AngelList as like the thing that touched the most people was the jobs website. And the thing that touched the second most people was the investment kind of LP closing flow slash investment dashboard. And then what AngelList actually is, is something kind of different that virtually nobody actually sees. And that I think is the cause

Starting point is 00:51:49 of kind of the maybe misperception of AngelList in the market. Great. I'd also add you guys have a great GP fund admin platform in many ways, streamlining a lot of the pains of some of the most famous and biggest services platforms out there.

Starting point is 00:52:06 So Abe, it's been an absolute pleasure. It's great to catch up and always chat with another quant nerd such as myself. Thank you for elucidating a lot of interesting things, a lot of things that keep me up at night and that I've always wanted to know and look forward to catching up live soon. Thank you so much, David. Great questions. Enjoyed being here a lot. Thanks for listening to Limited Partner Podcast.. Thank you so much, David. Great questions. Enjoyed the interview a lot.

Investing Billions - E4: Dr. Abe Othman | Why Large Portfolios Outperform Small Portfolios | IRR Games VCs Play | Quantitative Investing in Venture Capital

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