How I Invest with David Weisburd
00:30:46 · Jerry Ye, David Weisburd
[00:30:46] S1: Yeah. So probably... I think generally, probably around, like, getting their network dump or something. Right? But then this is joining that along with what deals were actually done. Right? And then you could do different cuts of it by geo, stage, sector, you name it. Right? [00:31:07] S0: Yeah. It's... The calendar meetings are a very obvious in retrospect and strong indicator because founders, again, it's reverse selling. The LPs are pitching to get into the top GPs, and the GPs are pitching to get in top founders. What are the odds that a founder will meet with a partner five times, 10 times, and not give them allocation to next round? I would argue it's probably close to zero depending on how how big their check size is. So you could use this calendar and these meetings as this upstream indicator of a GP's access. [00:31:40] S1: Yeah. And it's even even a proxy for, you know, like, decision making before it even gets to an IC or a term sheet. [00:31:47] S0: I wanna go back to what you said about evaluating a firm overall and its strengths and weaknesses, not from an LP perspective, but from a GP's perspective. They wanna get ahead of the ball. They wanna make sure they don't miss the next OpenAI, Anthropic, name your top disruptive company. Talk to me about that. How should GPs use data in order to get a better picture for what's going on inside the firm?
00:31:47 · David Weisburd, Jerry Ye
[00:31:47] S0: I wanna go back to what you said about evaluating a firm overall and its strengths and weaknesses, not from an LP perspective, but from a GP's perspective. They wanna get ahead of the ball. They wanna make sure they don't miss the next OpenAI, Anthropic, name your top disruptive company. Talk to me about that. How should GPs use data in order to get a better picture for what's going on inside the firm? [00:32:10] S1: At least on the data side. Right? There's actually like a parallel to this. Right? So it's not just, you know, did the GP talk to this company, the email. You you can also do the same analogies in terms of given the datasets that we have and the systems that we have, did we identify this deal beforehand as well? Right? So that's kind of the North Star KPI for... [00:32:29] S0: In in other words, let's take instinct. Yeah. There's two mistakes you could have made. One was not seeing it, which you could argue probably is a bigger mistake, and two is seeing it and passing.
00:32:29 · David Weisburd, Jerry Ye
[00:32:29] S0: In in other words, let's take instinct. Yeah. There's two mistakes you could have made. One was not seeing it, which you could argue probably is a bigger mistake, and two is seeing it and passing. [00:32:39] S1: Exactly. Right. So it is tying in the data side of... Did you see it from, like, a inflection point or market mapping or, you know, like, was there a news article on it? Was there a GitHub repo on it or something? And then tying that in with, you know, like, your investor meetings, their emails. Right, like, did they see that? So combined, those two give a pretty good holistic view of that. In terms of more broadly as a firm, I mean, there's probably efficiency things that you can quantify as well. Right? It's like, what is the hit rate? Not just a term sheet or ten years later what the valuation is, but what's the hit rate of this new principal that joined the team in terms of meeting everyone that in in this sector, right, where the deals were done a couple months later. Right? So you can quantify this in terms of hit rate, and what's actually been interesting that other firms have done is just comparing that in terms of, a data hit rate versus, like, a junior associate hit rate versus, a senior partner hit rate. Right? [00:33:41] S0: Talk to me about that.
00:33:41 · David Weisburd, Jerry Ye
[00:33:41] S0: Talk to me about that. [00:33:42] S1: You have data that might source for things outside of the network. Right? And generally, that might actually be what you want is to tune it. You don't wanna see the same companies that you would have otherwise gotten from your network anyway. So you do wanna be able to quantify, like, how many of these companies could we have met with earlier because this flagged it. Right? But that's just a list of companies. Right? That's your numerator. Right? The denominator in this case is how many of these companies were actually done, right, by you or other top tier firms. You could do the same analysis for a principal or, you know, like a GP as well, is of all the meetings, of all the emails that they actually interacted with, that's the same denominator. Right? But how many of are actually done by, you know, the firm or other firms that you respect?
00:34:31 · David Weisburd
[00:34:31] S0: The one thing that I disagree with probably 90% of smart investors is a Peter Thiel question. What do you disagree with that that smart people agree with in your industry? And that's this, to me, absurd idea that you don't really know whether you're a great venture capitalist for ten years. So here's the logic. Well, companies take fourteen years to exit from pre seed to IPO. So when you're ten years in, you haven't even gotten your first exit. And it's this very alluring narrative that people say, there's some usefulness to it, and that you don't wanna be arrogant. You wanna be tracking. You wanna make sure, you know, just because a company has gotten momentum and gotten marked up doesn't mean that's a good company. There's there's some lessons there, but it's absurd because on the path of that pre seed company becoming an IPO company, there's other milestones. Not just funding rounds, seed a b c d e f g, but also revenue, 1,000,000, 10,000,000, 100,000,000, 200,000,000. Also secondaries, company tenders, all these other liquidity events. I think more so today, there's this underrated meme by Marc Andreessen, which is... Today, there's this underrated meme by Marc Andreessen, which is there are no more diamonds in the rough, only diamonds, which explains these $100,000,000 pre seed evaluations, these billion dollar seed rounds is the consensus is coming earlier. So what's second order effect of that? If you know that now who the best companies are, you could more quickly as a firm determine whether you're gonna have a good portfolio and whether or not you're likely to be one of the top firms moving forward.
00:36:10 · Jerry Ye, David Weisburd
[00:36:10] S1: I think, at least in terms of being able to identify companies earlier, that still hasn't changed. Right? A lot of this stuff has, you know, honestly just become table stakes as well. Right? Like, me working on talent data back in 2013 is now something you can get off the shelf from a lot of vendors as well. Right? So the the alpha there in terms of identifying signals is diminished a bit. There's still a lot of interesting data sets that you could, you know, figure out what's happening sooner. But there's just a lot more that you can continuously measure as well. Right? Like, as as you said, funding data, looking at tender offers, right, etcetera. But that also presents, like, other opportunities for exits as well. Right? So it's selling secondary shares. It's selling into a continuation vehicle or or whatnot. So it is kinda more in terms of portfolio management, right, where the bet that you made for ten years generally still applies, but there's just more options in terms of how you can exit that position as well if things turn south. Right? [00:37:16] S0: I wanna go back to this concept of firms learning and firms actually growing. For probably up till about five years ago, venture capital was seen as this cottage industry where it's just the partners, and the venture capital firms didn't even have enterprise value. GP stakes famously, very few have happened in the venture space for that reason. There wasn't seem to have terminal value for venture firms. Do you think in an age of AI that might be changing?