Invest Like the Best with Patrick O'Shaughnessy
00:05:20 · Patrick O'Shaughnessy, Sarah Guo
[00:05:20] S0: Do you actively want to be a great woman in this sense of the theory? [00:05:25] S2: No. I don't mean that from like a personal humility perspective. It's just like it's not on my set of goals. I want to be the best investor in the things that I try to do. So it's not out of a lack of ambition or even confidence. It's... I think some people are driven by like, I want to be the person that made this happen. I thought I was gonna be like a software entrepreneur for the longest time. My decision that from an identity perspective, I was going to be an investor actually had a lot to do with the idea of I'm deeply curious. I like to understand things. I like to be right. I'm very motivated by working with extraordinary people. If I have a set of skills using those to make them more successful, it's like a pretty good fit for early stage investing. If I wanna work with the very best people and I want the companies to have impact, the firm can support a movement and support change we wanna see. But I don't think it has to be me. [00:06:21] S0: If that's the goal, what does it take, do you think, right now to be the best in a very competitive environment? Undeniably, you've done really well so far. I'm curious what it has taken to be that good and what you think it's going to take in the next year plus within Horizon to be that good.
00:06:21 · Patrick O'Shaughnessy, Sarah Guo
[00:06:21] S0: If that's the goal, what does it take, do you think, right now to be the best in a very competitive environment? Undeniably, you've done really well so far. I'm curious what it has taken to be that good and what you think it's going to take in the next year plus within Horizon to be that good. [00:06:40] S2: I think it's been pretty simple so far. My read of the environment was the growth of firms and generational transitions that were happening meant that it wasn't the most competitive landscape in early that it has been. And you had this massive technology transition happening. And if you took the bet on understanding the technology and the community and approach it from first principles, you might have better access and make better decisions than others who are less focused. All you have to do is take the risk and be focused, and then it's an execution play. That's one of those things that I think is, like, pretty simple and it's just hard, this effort. I think today, it has actually been not that complicated. It's more about what your bar is for the people you work with. And my partner, Mike, and I started with a set of preexisting relationships and understanding. And so I think that has been useful. [00:07:30] S0: Surely, must be more than just outworking everyone. Mike said something interesting to me a couple weeks ago, which was there's sort of 250 ish people that he thinks about or you guys think about that are some combination of entrepreneurs and researchers and [00:07:48] S2: Doing the most interesting things on the frontier.
00:07:48 · Sarah Guo, Patrick O'Shaughnessy
[00:07:48] S2: Doing the most interesting things on the frontier. [00:07:50] S0: Yes. The people that are actually showing up in the morning and pushing this whole thing forward. And that one of your goals as a firm is to be as close to those people, know them all and be as close to them and support them in as many ways as possible. I really like that idea. It's a cool idea. And that's more than just doing a bunch of meetings with people that are starting companies From the outside looking in from the cheap seats, it looks like there's more unique stuff going on than just the competition set was low and we focused more and we're executing better. I'm interested in the ingredients that have been so far a part of success. I won't name them, but there's one well known LP that does the survey every year of what all the other fancy LPs who they most wanna invest with. You're either number one or two. There's something going on both with the companies you've invested in with the performance so far with the market perception. There's something more than just, okay. It was a moment in time we worked really hard. I'm I'm trying to get at those ingredients. That's why I'm pushing on it.
00:08:43 · Sarah Guo
[00:08:43] S2: To the point of perhaps making a bet that others wouldn't, we thought very carefully about what markets are going to matter and what might be different about the founders that we look at in this era if we are right about capability growth and the breadth of impact. That would just be non obvious to other people. Where's the biggest difference from the status quo? How does the framework change? And I'll give you two examples. One of the things that we were really looking for in the first year was application areas, workflows and professions, tasks that we thought were a good fit for purpose for the models, which is a very technology forward approach. Lots of people be like, this is nonsense. You gotta think about the customer problem and working from the customer back is the only way. We wanna do both. And if you look at Harvey and the function of the law, rationally, if you think that we can do next token prediction with language and you knew that in late twenty twenty two, then law is structured language. I'm not a lawyer. But from the outside, I'm like, you need to, like, read a lot of documents. And we had retrieval and you need to generate text. And there's a lot of precedent text both inside firms and in common law and history. That feels like a really good match. We also took a very specific view of what is now possible and then what is valuable within what is possible and then who's aligned with us. Winston and Gabe, they believed that AI would transform the practice of the law in a very AI pilled way. We will do enormously complex work with lawyers. I don't know when. It could be next year or five years from now. But from the kernel of I can look at a landlord tenant agreement in California and answer a question, somewhat trivial, to I can project to doing a Activision Blizzard M and A and doing 85% of the work. That's a leap. The thing that appealed to me in that moment was the ambition of what was possible then and the technical logic of, like, why it would work. I think that's probably just different decision making framework from how other people are approaching in that moment.
00:11:06 · Patrick O'Shaughnessy, Sarah Guo
[00:11:06] S0: What is that group talking about today, the two fifty researcher frontiers group? What's the most interesting thing and the most notable difference between today and six months ago or twelve months ago or something? [00:11:15] S2: It is a violently competitive landscape. Think I that was true twelve months ago, but even more true than it was '24 and then '36. I think people are very concerned that it's a globally competitive landscape, and there's insecurity in that because I think it's a bit narrative breaking as well. I'm going to describe a belief and then question the belief. The belief is with recursive self improvement of AI research models that can improve the models themselves, we are a year, two years away from some sort of exponential intelligence. I think that belief is new within the last twelve months for a lot of researchers. Some driver of it that we are two years away, Andrej Karpathy will actually in a very self aware way say, I thought it was two years away for about ten years. And he thinks that again, to be fair. Who can say? I do think that there is some sense of the major labs are so compute intensive and so large from a headcount perspective now that the sense of contribution of I can move the needle of OpenAI as 200 people. There's not that many researchers. The question of like, how do we get there is really up to every single person. Now if the question is, well, I need $750,000,000,000 of compute spend and we have many thousands of people working on this problem. I think people feel less ownership of the outcome.
00:11:15 · Sarah Guo, Patrick O'Shaughnessy
[00:11:15] S2: It is a violently competitive landscape. Think I that was true twelve months ago, but even more true than it was '24 and then '36. I think people are very concerned that it's a globally competitive landscape, and there's insecurity in that because I think it's a bit narrative breaking as well. I'm going to describe a belief and then question the belief. The belief is with recursive self improvement of AI research models that can improve the models themselves, we are a year, two years away from some sort of exponential intelligence. I think that belief is new within the last twelve months for a lot of researchers. Some driver of it that we are two years away, Andrej Karpathy will actually in a very self aware way say, I thought it was two years away for about ten years. And he thinks that again, to be fair. Who can say? I do think that there is some sense of the major labs are so compute intensive and so large from a headcount perspective now that the sense of contribution of I can move the needle of OpenAI as 200 people. There's not that many researchers. The question of like, how do we get there is really up to every single person. Now if the question is, well, I need $750,000,000,000 of compute spend and we have many thousands of people working on this problem. I think people feel less ownership of the outcome. [00:12:46] S0: Oh, that's interesting. So it's just like a function of getting compute. That's the thing that's gonna push this thing over the line, not our own human efforts.