How I Invest with David Weisburd
00:50:45 · Brijesh Jeevarathnam, David Weisburd
These funds, they come back to market, they raise capital quickly. We have to really broaden our coverage. We're looking at many more funds and sectors and so on than before. In that sense, to a certain extent, it's become like direct investing because you're investing in a much more noisy space with arguably even less data than before. And that's the challenge. And that's where we kind of leverage our, not just the data, but the investment judgment of the people we have. The cliche about it's a people business is true in investing. And we're fortunate to have a team that is now approaching 40 people strong that spends all their time selecting managers. And some of the folks on the team have been doing this for ten, fifteen, twenty years like myself. And it takes that long to kind of get the pattern recognition engine working. It's still not a static engine. It's got to be dynamic as we keep adapting to the market. [00:51:56] S1: I'm doing the math, 4,000 managers, 40 people, still a lot of managers for every professional to handle. How are you leveraging AI to help?
00:52:05 · Brijesh Jeevarathnam
[00:52:05] S0: I'll start with the premise, the true premise that venture investing is a people business. And some of that lends itself to using AI, a lot of it does not. So let me start with the top of the funnel. Part of our job is to know attribution, not just at the venture firm level, but at the individual partner level. And that we can use AI for. Let's call it individual track record. But to know which person with a great track record is going to leave their franchise in potentially three months, six months, who are they thinking about pairing up with? And we have a few live examples of this as we speak. I don't think AI can do this anytime soon. And this comes from us being in the ecosystem, talking to people and being in the Bay Area, especially, you'll appreciate this. You're fully surrounded by other folks. Go to my... Whether it's my kid's high school or a soccer game or a social event, you see somebody, you pick up some new information. That is very much a human process. And we got to go to where the puck is going to be because often when these great people start a firm, the best ones is an invite only list. And you got to get there before they really are kind of in official fundraising process. That's an example of where AI is not helpful. It's very much a human process. But where AI can be helpful is, I mentioned the data we have in house and we're always ingesting more data. This data is sitting in very, I would say disparate parts, unstructured data sometimes. We're using AI to help us connect those dots. Something could be in a meeting note somebody took versus a quarterly report versus an advisory board deck. Everything is confidential, but we got to learn about this particular GP or the particular company. We have to have at our fingertips the collective information base that Adams Street has within its four walls. And AI is really helping us do that. In addition to the more, I'll call it process elements of due diligence, writing the memo, for example, or doing some of the quantitative data cuts, for example. And we're still scratching the surface here. We can do a lot more with AI. But I would posit that on both the funder picking side and the early stage founder picking side, it remains very much a human centric business for now.
00:54:43 · David Weisburd
[00:54:43] S1: I want to double click a little bit on Adams Street, the company, and your platform. So you don't only invest in funds, you also invest in companies. Maybe talk about the general strategy.
00:54:56 · Brijesh Jeevarathnam
[00:54:56] S0: We have one investment strategy, I. To invest in companies that are seeing rapid growth or will benefit from dislocation and change, and therefore to capture that alpha, that one investment strategy is executed through five different teams. And the five teams quickly, and I'll go to the specific question you asked. We invest in funds, we call it the primary business. That's the team I'm part of. We invest in venture companies directly, usually at kind of the early growth stage to later. We invest in buyout companies directly, usually as co investments alongside our GPs. We invest in private credit, which is the leverage for leverage buyouts. And the fifth is we invest in secondaries, both LP and GP secondaries. So specifically on these two, I talked about the information asymmetry before, and that is still an edge. That edge is, I would say on the margin declining compared to five, ten years ago, because there's just so much more data out there, even about private companies. But we have confidential information within our four walls that we only share with ourselves, the investment team, that we can leverage to get to the best companies one or two steps earlier. We always work with our venture investors in that manager. We never try to go around or bypass the existing venture syndicate. We leverage our relationships with these great managers. And this ties back to a prior question you asked, which is what does a great relationship with the GP get you? It gets us kind of, I'll call it privilege information in some cases and access in many cases as well. That is our secret sauce combined with our investment judgment to pick great venture companies to invest into directly. If I had to summarize the thesis for that direct investment program, I mentioned before 7% of venture backed companies now, and the sample set is like thousands of companies, 10,000 plus companies now in our database. 7% of those companies drive all the returns. Our job with the direct venture investments we're making into companies is to double down our exposure as much as possible to what'll end up potentially in that 7%. Because in the stage we're going into, there's a few more data points to say, well, this will be in the winner's circle or not.
00:57:32 · Brijesh Jeevarathnam, David Weisburd
And of course, nobody bats a thousand, we don't bat a thousand either, but we leverage the access and information we have to get more exposure to the outliers that are emerging. [00:57:46] S1: I talked to a USV benchmark, they're raising their growth funds for the first time. They believe that power loss still exists at the growth stage. You're investing directly into these companies. Do you think power loss could still really exist at that late stage of a company?
00:58:00 · Brijesh Jeevarathnam
[00:58:00] S0: Yes. It depends on how late stage we're talking about and depends on the category in the company. And I say that because the world and the venture industry didn't foresee the scale of exits now. If you take acquisitions, Wiz at $32,000,000,000 was a big number and the record is broken by Cursor at $60,000,000,000 as you know. And I have no doubt there'll be other bigger acquisitions in the coming years, right? So the bar keeps getting raised on what that exit value is. Not to mention IPOs. What's stunning to me is that we're even talking about a trillion dollar plus valuation for an IPO because it's easy to forget until what, five, six years ago, there were no companies in the world. I think Apple became the first one to cross a trillion dollars in value. And even today, are maybe 10 companies, 11 companies in the world with a trillion plus in market cap. So for a startup to come along, and it could be talking Anthropic or OpenAI or pick any company, and to be in that very short list of trillion dollar plus companies potentially right off the bat is just unthinkable. I say all that to say that we can still find venture, can find outliers at that scale of addressable market and therefore value creation, the power law will still apply and be true in the growth stage as well. There's a lot of ifs I put in there in terms of both the company TAM, the execution, the public markets, the buyer universe, etcetera. But I don't think they're wrong. I do think if one picks correctly, the outliers are so extreme now, one can still earn venture multiples at the growth stage.