Invest Like the Best with Patrick O'Shaughnessy
00:48:13 · Patrick O'Shaughnessy, Gabe Stengel
[00:48:13] S3: Why do you think they didn't believe? [00:48:15] S1: For a lot of reasons. I think people underappreciated the TAM in finance which was silly but it's partly because I think SF has less intuition for finance because they didn't fund ION Group or Bloomberg or SMP or FactSet or PitchBook Morningstar and so on. And so there actually haven't been great venture backed businesses in this market and so there's no intuition for the kind of contours of the market and how large it is. Two is the product was terrible. And so every investor said, oh, I work in finance. Let me use it. Is it gonna transform what I do? And they tried it and it was wrong half the time. They go, this is never gonna work. And it's like guys, you know we're on the exponential. It's gonna work in six months or twelve months or eighteen months or whatever it is. I'm going to figure it out. And the final reason was people didn't think I could figure it out. People didn't have enough data points of watching me issue glass and watching me figure out what the next iteration would be. [00:49:08] S3: What do you think changed after that? Because now it's a who's who of CapDibble.
00:49:08 · Patrick O'Shaughnessy, Gabe Stengel
[00:49:08] S3: What do you think changed after that? Because now it's a who's who of CapDibble. [00:49:12] S1: Well, because I met all these guys very early on and I met them at every single round. And so every time I said, hey, we're gonna do this. And they said, yeah, there's no way. And then every time we would do it. And by the way, a lot of the times it would manifest in a different way, You would actually lose the key employee you needed or that actually excellent customer that you thought you were in land totally dissipated. But we kept figuring out what to do and kept navigating the market. And I think when you're investing in a market like today, especially at Applied AI where it's so turbulent, so ambiguous, you need to underwrite the founders being able to be extremely dynamic. [00:49:48] S3: Why do you think there aren't more credible financial services competitors?
00:49:48 · Patrick O'Shaughnessy, Gabe Stengel
[00:49:48] S3: Why do you think there aren't more credible financial services competitors? [00:49:52] S1: Distribution is really hard to crack. I think it's far more a people problem on building trust and delivering value in working with these institutions than just an engineering and product problem. But then there's an enormously high engineering and product burden too. The standard to execute to really crack this market I think is pretty high. And I was just so lucky that a number of the early people I hired were ex finance and just killers. And we just hired folks from inner network who worked at Goldman or Citi or Jefferies or Apollo or Ares or Blackstone and so we had a team of people who were die hard, ambitious, curious, smart and then just good humans. Low ego, humble, young enough to be open minded and to wanna e class too. [00:50:31] S3: I'm also really curious how you've dealt with technical talent and what matters in a technical person on your team and how that has evolved. One intuition might be the value of domain expertise from the technical person has gone up as the ease of execution has changed a lot. You don't need to be super technical to write good code or less so than in the past. So what does the shape of the engineering or technical team look like over time? I'm asking this again because I'm curious about thinking about other companies that wanna tackle this and whatever their industry might be.
00:51:02 · Gabe Stengel, Patrick O'Shaughnessy
[00:51:02] S1: So I would say there's definitely problems where domain expertise is increasing in value and product intuition and having more of a GM like mindset versus an engineer like mindset is super super important. There are also parts of our product surface area where it's like you just need raw gritty engineering talent because you're scaling things up so aggressively. But if I look at the folks on our team who have been so excellent, a lot of them are former founders. They're folks who started businesses, persevered, ate glass, had a lot of product intuition, figured out how to channel it, and then the business maybe petered out. And to the point you made, there's been a lot of companies trying to tackle financial AI. There's been a lot of really smart product minded, engineering minded domain experts who have tried to tackle finance AI, and we've acquired six different fledgling financial AI startups. And so those former founders who can be galaxy brained about the future of the product and navigate a course on what the UX should be, but also have engineering chops so they can constantly make the right decisions is super important. [00:52:02] S3: You said earlier to me before we were recording that this is the first time that we have like a major innovator's dilemma in this category. Can you describe what you mean by that?
00:52:02 · Patrick O'Shaughnessy, Gabe Stengel
[00:52:02] S3: You said earlier to me before we were recording that this is the first time that we have like a major innovator's dilemma in this category. Can you describe what you mean by that? [00:52:12] S1: Yeah. Well, I I think for a lot of investment firms and a lot of high finance financial services, the last ten, twenty years have been pretty good. You can make a lot of money being a capital allocator, being an investor. It's a very hard industry to enter. And then especially if you're in private markets, your businesses have some natural momentum because when you raise a fund and another fund and another fund, it's hard to mess up the business after that. And I'm sure there's a 100 people who have started funds who know it's a 100 times harder than I just described. But to be fair, there hasn't been a moment and a shock to the market where every investment firm, every bank is saying, oh wow. I need to completely rethink what I'm doing. And now there's gonna be an opportunity for hundreds of disruptors, AI native investment firms, AI native investment banks to attack my business model and I need to figure out what I'm gonna do now. And there has not been an innovator's dilemma for private equity firms, for hedge funds, for investment banks in a long time. AI native. [00:53:09] S3: What does that term mean to you? Like what is the definition of that?
00:53:09 · Patrick O'Shaughnessy, Gabe Stengel
[00:53:09] S3: What does that term mean to you? Like what is the definition of that? [00:53:13] S1: In some ways, think it just means willing to constantly reinvent everything you're doing and being so AI pilled that you don't worry about what's possible or what might seem completely far fetched, but you were just charting a trajectory towards integrating this alien fundamental technology into everything that you do. And it's showing up in every part of the business and there's no part of the business that you hold sacred that is immune to being revolutionized. [00:53:37] S3: How do you do that? Is it like so you're a leader in a company that's very AI pilled that wanna make sure to... Yeah. Do you wanna do everything you guys said in rogo? And yet I'm sure there are areas that you're unhappy with the status of how much AI is used to do x y or z. How do you do it as a leader?