How I Invest with David Weisburd - E286: How LPs Can Actually Find Alpha in Venture

Episode Date: January 20, 2026

How well do venture capital returns really reflect skill versus structure? In this episode, David Weisburd speaks with Abe about what large-scale AngelList data reveals about seed investing, power-law returns, and why traditional assumptions around expected value, conviction, and diversification often break down. Abe explains how adverse selection shapes outcomes, why access matters more than insight, and where data-driven strategies may — and may not — apply in venture capital.

Abe, before today's podcast, I looked up when our first podcast was. It was actually episode four. There's a fourth episode. Now, this is going to be roughly 250. So it's good to have you back on. Unbelievable. Like, just congratulations on the success. Like, that's absolutely amazing.

I think since the last time we talked, I've also had two kids since the last time we chatted. So it's all even two years. A lot of stuff has been going on since the last time we spoke. So congratulations. I guess to all of us for, yeah, all for you. The accomplishments. It's amazing seeing the amount of traction you've gotten. Like, yeah, it's super cool. And it's such a privilege to be invited back. Yeah, that's really neat. You've been at Angelist for six and a half years. You start out as head of data science. Today, you're a consulting researcher. You've had

access to some of the most interesting data in, I think, in the entire venture capital ecosystem. what's one thing that you've changed your thinking on in the last year? The big perspective that I had, and I think this is pretty common for people who get into the venture capital ecosystem from starting a startup. A sense, you know, you start a company, you go out to raise money, you're introduced to a bunch of VCs, and it kind of hits you. You're like, what is this? Like, who are these people? What are their jobs? Why do they behave the way that they behave?

Why do they never say no? why do they constantly talk about circling back? You know, cultural awakening. And I think one of the reasons that I was so keen to join Angelus when I had the opportunity was, you know, given the size of the data that I'm able to work with, was a sense of, look, can I try to rationalize the behavior of venture capitalists? Can I try to like say, hey, you know,

existing culture is weird and broken and is wrong. And here's what the data says about the correct way to behave. Just things that would be shocking to me to hear, you know, six and a half years ago is that, you know, actually venture capitalists are doing a pretty good job from the data, actually, a considerable amount of respect for them and for the work that they do. And, you know, when I took the job with the idea of rationalizing the asset class, I think what's actually happening is that the data has sort of radicalized me. What do you mean that you sought to rationalize and you were radicalized and unpack some of the

insights that you've been able to glean over six and a half years and tens of thousands of startup data sets. It's probably used to just talk about, you know, the really unbelievable data that we have access to from Angelus, 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 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, right? A huge fraction of CED 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 CED stage companies, both winners and losers is very interesting. The second real data strength that Angelist 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. 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, sometimes I actually undervalued versus 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. 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 percent dilution. You get very, very different perspectives because so much the returns are driven by your highest performers. Yeah, having an accurate measure unlike 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 companies, you know, how many rounds is Anthropic Hunter? You know, we're going to go through eight fundraising rounds. It's going to 20% pollution 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 classes by following on. And the asset class honestly doesn't look very good because you have all this dilution that's pouring in. That's just incorrect. And 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 praise? It could be tricky. And so the challenge is like 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 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 have 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 age lists on power law returns. What's interesting about a power law is it's defined by the single quantitative parameter called alpha and the 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 unbelievably, there's essentially unbounded opportunity cost of missing, you know, the next outstanding investment. And so, you know, at the optimal strategy if you don't cat a 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 at 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 to people to do series A investing. And then the later stage stuff series being 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. One of the hardest things of investing is seeing what's shifting before everyone else does. For decades, only the largest hedge funds could afford extensive channel research programs to spot inflection points before earnings and to stay ahead of consensus.

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Check it out for yourself at alpha-sense.com slash how I invest. Start with C. So when we chatted first, episode four, we talked about, I think, something like eight investments ever within Angelus. 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 knowing this?

It's super interesting. It gets to really the art 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 and 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 investment is there. And it was also super expensive when it got done. I think the first round was like 50 million of this was years ago. It was like extremely expensive, but has every positive call. 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 in a 50 million pre money is probably about 10 times better than the investment that's raising a 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 implied price. And at a certain point, this alpha less than two threshold, the distribution of exits has fallen so much that you're no longer drawing from this magic alpha less than two power law distribution. And that's kind of the credible deal threshold. So this weird, weird mathematical phenomenon where you can say it's not from a position of ignorance. It's not from saying like, well, because of the alpha less than two power law, nobody really knows anything. You're just drawing for these extreme distributions. Samsara is no better or worse than it. 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 you should put all of your money in the Sansara. 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, 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 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 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 as I think when I started Angelist, 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 deal is priced, you know, four times higher than the deal that doesn't have those characteristics. So like, 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 percent 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. 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 chance of it being a 10 or 100 billion dollar company. That's kind of the mean return that's 10 times. Is there some convexity there where it might be in most cases, you know, 10 times better, but in some cases, 100 times better? How do you account for these kind of like a fat tail? 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. I'm very intrigued. So you can't say 1% chance of 100 billion. 1.1% of a trillion, can't put it into, and why? What the awful less than two power law means, which is that this is the correct way of thinking about it. We don't deal with these. These distributors are at some sense very inhuman. They're not, they're not something we get to experience a lot 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 six foot nine inches tall. The correct way to think about that is,

how tall is Aves' tallest friend? You would say six nine, 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 mouthful into power law have this quality that if you chop off the right tail and you As per, you know, kind of 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 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, it is not correct to me to say, oh, Abe's best investment return is 100x. Might be 200x, might be 200x, might be 400. You know, there is that kind of escape behavior. The other way of contextualizing that escape behavior I have is in Black Swan. Nicholas Assim Talib has a line about the refugee probably 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 where if someone's, you know, years and years and years and years,

you know, the anticipated return is many, many, many, many, many, many years down the line as well. And so that's what happens to seed stage investing, where you don't have this behavior. We're like, hey, I drew this threshold. That's kind of, you know, it's really unlikely that you'd have anywhere beyond 100. Like, it's not thinking like, oh, it's unlikely to get 100x therefore or whatever. It's like just telling you that I got an investment return beyond 100x doesn't tell you that I have 100x return. It might be a 200x. It might be a 400x.

It might be a 1,000x. And that's the really interesting behavior around these distributions. And if you're thinking like, wow, that's weird. 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 distribution look like. So why is the takeaway then not to invest in the local maximum quality, meaning go to some Sarah 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 asymmetry. 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 classes driven by the top handful of those thousand, the actual chances that Samara 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 ram and picking. And so that's kind of the tension here is that it's not like, oh, Samara has an infinite expected return. All the other seed investments don't. It's like they all kind of have an infinite expected return. Sansara's maybe a little bit

better than the other ones. The core argument is that like if so much of your returns are going to be driven by those five, you know, investments each year that just absolutely go crazy. And there's so many of these companies, the chance that even your a priori best evaluated opportunity is going to be one of the five is very small. 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. 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? You know, you could think about maybe doing a slight, it sort of depends how much you think stuff is priced in. It's very difficult to sort of back this out,

but anecdotally, I would put the number at 80 or 90 percent is priced in. And so, you know, you're thinking there maybe one over that. So maybe you want to have like 25 percent or 50 above average of your money into something like Smsara 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. Nobody sees all all 1,000 those high quality opportunities. And I think one of the way of thinking about the world, and I actually think 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 fascinated. The thing that I think a lot of seed investors learn is that like, if you know, 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 taking 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 standing it is, is that you have, you know, the job of the early stage VC is really to be very narrowly 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 perceptive 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, very good at that does not mean 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 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 radicalization of the way that I've approached this asset class is. So there's 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, gain theory, the bomb, but did a lot of the foundational work in quantum mechanics as well, had this idea that the underlying sort of fundamental nature of particles and other 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 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, then I understand it. And John von Neumann's perspective was actually 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,

where 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 Antic 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 company. Oh, I made a mistake, whatever. I wish I had that information. Like, that is like the broader 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. who observe it on its journey. And that I think is like a really fascinating perspective. The other maybe like simpler soundbite is, is I frequently Kevin Lawless, who's one of Angelous founders and is really big on the research side. And he's just, he's just put it this way, which is like the cheapest and most effective way to diligence of precede 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 inaccurates pre-seed deck. That, I think, is the way this rule 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 wanted to invest in. It's like, that's not correct. That's not a correct model of what's actually happening here.

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 a company that you might as well have just invested in the founders without an idea. But it would essentially be the same exercise. 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 have. But I actually, I think I've gone full von Neumann. I think I've gone full on tick. 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.

different universes. But doesn't I assume that it's the lead investor? In other words, if you were the lead investor, you, sure, 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 then I think you actually see, I mean, you observe this behavior in terms of seed investments as well. I mean, I've seen it for companies that I've started where before you have kind of more than half the round committed. And then at that point, VCs start looking at this is not a question of like, should this company exist or not, but rather like this company exists, do we want to kind of go on this ride or not? Like the, 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'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? 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 going to front-run them. That, that would, you know, like I said, I'd 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 screen. grape LinkedIn. It's like, going back to this hypothetical thousand portfolio, let's just make it easy and say that you invest a thousand dollars into a thousand credible startup. 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 fortile? And how do you think about kind of that versus doing, say, a fund of fun or investing to GP?

Yeah. So it's, 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 cost, investing this asset class is there's a lot of naval gazing, ego stroking around like, what do the best investors do? And when you look at the strategies and 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 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 desile of early-stead investors, they will all have that profile. They'll all have the profile of making. concentrated bets of doubling down on their winners. The challenge is that that's not, 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, it comes out in the data. How do fees play into that? So if you're investing in a two and 20 fund versus, let's say, you make those thousand investments direct, are you now above that top quartown? How should you want to think about that? I don't, I don't think fees play a significant part of the, uh,

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 actually the management fees and not the carry. And the reason the management fees play such a large part of the story is that manager fees cut into the amount of money that actually gets invested. It's so funny. This is all a 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 were being driven by the years in which, you know, the cap, you know, the venture capital average will be, you know, 5,8, 13 X or whatever over the lifespan of these funds. The manager fees actually are super extensive in those cases because you're not getting, you know,

oh, that vintage year, every dollar I put in 10x, you know, it actually becomes really expensive if you have a 20% manager fee because that's, that's, you know, you're literally like losing several multiples of the fund just from manager fee. And I think this is actually something where my perspective has changed in Trini Angelus is like when I join, 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, I don't need to invest in someone else. Like, I don't need to pay carry to someone else. Like, I know how to do C investing. I'm doing Support for today's episode comes from Square,

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slash go slash how I invest. Great. And I think one of the things that really changed by perspective was, you know, kind of seeing fun performance and research in the fun performance. And frankly, I'll give him credit here, just directly. Meeting Ryan Hoover, product kind of founder of this one, I started an angelist. He worked. Yeah, he's an awesome guy. And I just, you know, he's like really good at consumer product investing, right?

He found a product hunt. He's so plugged into that world. And like he's just, you know, he's got the, like, he's just the consumer facing investor. Like, he's just really good at it. And how's pretty much like, you know, if he, like, you know, we tried an indicator. You just like we're super stuff. And they were always terrible investments. Like by far like the very difficult space.

Very, very hard. especially if you're like, where an 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 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 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, those are that, there were just bad investments.

And I had this perspective, like, 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, Kerry? Like, and, and so that has been a real change in perspective of like, look, I don't adequate coverage my own portfolio to the space. And so, you know, I, I did the investment in We could fund. Another one that I did was N49P run by Alex Norman, who's kind of the Angelus Canada guy in Toronto. It's like super plugged into everybody, every Canadian doing any kind of startup or start of investing goes Alex Norman. You know, I don't, I'm not. 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 and carry. And so that has been 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. One dollar paid in fees is because $1 less of returns. But 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 the space you don't see, I have a really, really hard time. Oh, yeah, like, but 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's giving you an opportunity investor in his fund, like, then you don't have to do any more consumer, but you have to do any more shitty consumer investments because I got Ryan Hoover's. So per your model, you believe that the market isn't efficient for the deals that get done, but there is still adverse selection.

Oh, yeah. So I think adverse selection is is really maybe, maybe, maybe. the entire story about venture capital. I didn't actually the most important concept 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 you're a seed stage GP, you're writing checks, and, you know, your check size that you typically are in a seizure 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 absurd 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 rent a small check, which is less than half your typical check size. And what's cool about the Angelus data is we are looking in a data set that's 15,000 seed investments. And so like there's no, 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 us. So what we found was actually twofold. I'll put it this way. In a normal asset class, if you approach this from like, this is 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 actually precisely articulate this in the case of like some pretty decent, like, reasonable assumptions, you pretty much allocate proportional to the quality of the signal in the company. So if a company is great, you want to put a bunch of your money in. If your company is less good, 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 best risk return profile 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 and all the signal, small checks are not. What you actually see is kind of the opposite. So what we see is consistently small checks are the highest performers. Now, 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 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'd be a thing there, right? 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 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 the narrative? Yes, but really it's because they're being squeezed down by better investors, right? That's what's happening. These become the very hot rounds where there's a lot of interest in participating. And we know what people's typical check size would be. And they're willing to write a, instead of a 250K check or we'll only write a 100K check to participate. Now, that's one side in the market. The other side of the market is the big checks, right? Someone has a typical check side of the 250K. They're going to write a 750K check into Ced around 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 something different. So you would only write, if you're a typical check size of 250K,000, you're writing a 750K check in 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 happening here? What's happening here for the big checks is that you have this adverse selection problem where you're, you know, you are writing a big check that, you know, presumably in an area of your expertise, you know how to pick companies. Like, you could always just opt for writing your typical size check, but you have opted to write a very, you know, more than two 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 that capacity exists in the first place, that a better investor than you is not

taking your capacity. And so that I think is one of the most fascinating parts is that what we observe in the seed asset class is that the individual idiot. 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 everybody's kind of collective ability to evaluate the quality of a company. And so that I think is, is, you know, when you think about what the seed asset class looks like, that 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 Ons, 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 they're their watches they both work they're both you know 50 years old I don't they seem the same you're oh no that one's worth three times as much because everyone sort of agrees that it's worth three times and 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 You have something that looks a lot more like trying to buy, you know, Air Force One's

and trying to get like a cool color way for Air Force One's. And that I think is like 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 Lin. The fact that capacity exists for you to take is a negative. signal about the company. 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? And you should, you know, if it's kind outside your area or something, like, you probably, your conviction is probably wrong. Like the correct, the correct thing to do, even if it looks, I mean, this is something we're going to do. an indicator with our consumer investments. Like, we didn't know what we were doing. We wrote a bunch of bad checks.

But they all look great. They all look better to us than the checks we wrote 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 should not have made those investments from the perspective. Like, we don't have the. I think it's actually a conscious 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.

Not that's dumb, but that it's not specifically smart in that domain. So it's like a contrast signal. It's not only should you not be investing, but you're probably investing at a higher valuation. Yeah, I think that's right. 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, it 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, 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. There is as well an aspect. 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 drug, where like, you know, LPs don't. like it when GPs are doing something different or wherever. And I think a chunk of that that hasn't

been articulated is, you know, if you're pivoting to a new space, if you have a new thesis, oh, 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, 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, your LPs may be less upset about like, I disagree with your thesis about where you're going to find returns. And it is like, you're just going to see worse clean tech companies than you saw SaaS companies.

And so like that, that will be 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 know we're in a past for these reasons or whatever and then like the round came together and a bunch of other you know vCs good names were participating and the VC who did this diligence in back and was like oh can we like write a check you know just for the for the the the optionality of participating and at the time

it's like completely baffling to me because of like well you did all this they did the best diligence they could have done and it was really and 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 idiosyncratic single 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 make sense. You know, if you, if it is about adverse selection, if it is about the, the common signal that that BC share, you should trust yourself a little bit, a little bit less fascinating to see how my perspective 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 this goes back to the original point, right? I think VCs are actually in a pretty good job. Almost pains me to say that. Yeah. So the good news is that VCs are, again,

investing in the bad news as 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 a CIO of Strawberry Tree Management Company, which is an independent affiliate of Angelist. What strategy are you using with your own money and in your business? So Strawberry Tree is, we're independent. operate in the sense that Angelous people don't dictate how we do investing. So 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 as a non-R-IA. 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 an anticipated future

performance. And I think we had to develop like quite a few. You know, I'm not, I go back and forth on defining whether any 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 can just put them into Python and every month we get a dump of, here's the funds that we should invest. And we go out and invest these funds. You know, we're following this 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 Nish close. And I believe we are, I can't prove this. I don't know. But it is my belief that we're 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 Angelus, I think I had the idea of like, hey, I'm going to use all this quant stuff. 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 reason we've talked about, I don't think 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 does we have? Funds. The interesting thing about funds is that funds more or less charge the same thing, right? you know, one percent admin or management or two percent kind of fees and 20 percent 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 in 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 everyone else sees those signals too and you're paying for it. With a GP, if you have some metrics, oh, this person is really great, they're charging the same. And that's actually even different than I know, you know, top hedge funds will charge, you know, zero in 50 or three in 30 or whatever. You don't see that kind of price discrimination a lot of the time in venture. And so it's what we sort of, you know, in general, I think fund of funds are

terrible financial products. I do think, you know, Fort and Sour, I think we've built some of it is not, I wouldn't be doing it by that 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 al-a and the space is. And it's not from picking startups. It's from picking the GPs who pick start. That's because those GPs will see opportunities without being adversely selected. And in theory, have some kind of alpha. The core idea is that like, okay, can we find

signal that has some positive relation to the performance of 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, the GP who has those signals is charging you the exactly the same as a GP who doesn't have those signals. It's not priced at. In the magic, same way that the like, yeah, this repeat founder or a successful company, you went to MIT. Like, that's priced in, bro. But like the GPU is 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. 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. Correct. I think it's really for maybe three reasons. 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 for us, we have written these letters for people for their data works about why we're making the investment. And GP's generally like that because like, you know, 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 really to say, right? We're not writing checks to like, you know, oh, this person is the best person on the platform. They're so amazing. Like they're, I'm putting 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 price and discrimination is like, 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 it. Fine. 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. Like it's very hard to have a level of consistent outperformance. You see this for big funds too that publish it like that like you actually can can get three 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 the GPs themselves, because the inherent vault variability of space, don't have a lot of price and power in the same way that like a top quant hedge fund might, right? Hey, this is the kind of trade we're doing. These are a realized returns. We've been crushing this for 11 years. We've never had a down quarter.

We're going to charge you zero and 50. There's actually data to suggest that the larger funds charge a higher management fee of being equal. So as funds get larger, they have more, they become more de-risk. They're later vintages. They become also a different asset class. I would say first three vintages versus different vintages are different asset class as well, neither here nor there. But they're certainly not priced 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 sounds like what kind of alpha are you looking to get from these above average? Maybe, maybe kind of R-R-R-N quarter list might have a point four 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 and has a point four correlation to future performance, the right way to behave around that is to take a lot of coin flips, right? You have this somewhat pro- 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 of 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. 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? We have told our LPs for the first fund that our target was to get the 75th, which is based on kind of what happens is is the 75th percentile sort of blended vintage year IRA compounded for seven years. That's our target return, which I believe was, was if you look at the different vintage years, we looked at was somewhere between a 3.6, X, and the 6x net return to LP's. And so we've invested in investing in 15 months. I didn't have any connection with these GPs whatsoever.

And they're an angelist. The Python strip said to invest in them. So we invests in it. 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. We've gotten lucky, but I think we've also put ourselves in a situation to get lucky.

And then the thinking on it is this kind of weighted coin flip idea is that actually it happens to be the case. maybe this is related to the underlying volatility of the 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 startups. And so 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 invest in the asset class. So it's a, it's really, it dovetails really nicely, I think. 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 had, 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 that the, the, the, the, the, the, the, the, the, the, the, the, the

Acactively the price. There's access that we get because we are, you know, an Angel's affiliate where we keep like every GP on the platform has made the affirmative decision to work with Angelist. So they all have a person that they deal with at Angelus almost every single day. So like, we have generally been very successful. And this is something I have real eyes on when our rest is 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%. You know, we've missed out on a couple where people are just like, 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 of success placement is very, very high, which is if you do believe this is an inverse selection thing, like that's the feature you want, you want to maintain that. And then I think that the data side 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 into invest, is called markups over baseline. So we look at all the investments the GP has made over the past. It's like smart graduation rate. 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 around 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 markets you should have is 5.8. 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, take it to the extreme. You made 10 investments. Eight of them are on their series F. 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 sounds smart that people repeat. 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 single because it takes so long to assemble enormous DPI. You know, you're talking about someone who is maybe great at investing or maybe they got lucky 11 years ago. And like, are they retired or are they still plugged in? Like, are they still like hustling and scrapping for like those

stage stage deals or whatever? That was, you know, that was more than a decade ago. Their lives are, it's probably completely different lines. So in another way, the DPI as a signal, even if it has, has some positive correlation is outpaced negatively by what's happens that person over the last 10 years, whether they've had Staldrift, whether the funds have gotten bigger. It's 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 single because like, who knows where they're at now versus 10 years ago.

Someone will be like, well, yeah, but this guy had her super unicorn. an 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 you do. And I actually would go so far as to argue that that single 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 Angel's 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 LPs of several LPs to You're 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 don't people forget about the huge exit, right? It's upstream of style drift of going into other domains. So it hurts your alpha as investor. And then you start investing in, you know, now you have all this money to invest you. And that's whenever you want. You start, you just lose the discipline that may have helped you. And so actually it is. 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 up 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 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 at that giant early exit is going to get a bunch of money for 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. There's a word for these conscious signals, these negative driving forces. It's actually called negative alpha.

It's not a term that many people use. It's literally alpha signals. You have negative alpha signal. Abe, it's been too long. Where should people go if they'd like to follow your research, if they'd like to, learn more about what you're working on and, and just keep, keep up to your work. So the angelus blog, angelus.com slash blog. And if anyone wants to, you know, my email is abe at angelus.com to talk about research. And yeah, 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 this asset class looks like. Because I think it is, I was reflecting the other day that that kind of I've been in the Angelous universe now for 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 that 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 going to run this stuff, crunch it, you know, we're going to crush it with machine or remodel and then it'll be solved. And that's the journey that I've been on has been on some kind of fascinating trajectory. 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 first brought to my attention by Mike Maples, I think is 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 nonbelievers. 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. Yeah. Thank you so much driving me on again. It's been a pleasure. And yeah, maybe in another few hundred shows, I can come back on for round three. I would love that. That's it for today's episode of how I 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 at David at Weissburg Capital.com.