Invest Like the Best with Patrick O'Shaughnessy
00:12:46 · Patrick O'Shaughnessy, Sarah Guo
[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. [00:12:53] S2: I definitely think there's a large contingent of researchers who would feel that one of two things is now true. What I do doesn't matter anyway because the model is gonna do it. Or two, the only thing that matters is compute scale. And both of those are somewhat disempowering. [00:13:07] S0: So then what are they doing? If I believe both of those and I'm a top five researcher or something. [00:13:12] S2: That says something about your psychology because you want to do something that matters. I think there's also a set of scientists who want to work on the thing whether or not it matters that they're working on it. An entrepreneur asked me yesterday, I would not work on the companies if I felt like we couldn't change the outcomes a little bit for them. He asked me yesterday, would any of the companies that you backed not been backed without you at that round? Would people just said no? 5%, 10% max. They were resourceful, really talented people. They find other investors. There are lots of smart investors in the world who wanna take that risk. Maybe they wouldn't have gone the next $100,000,000 of compute. I don't think every researcher doing frontier work at these top couple labs right now feels like they're essential to the machine.
00:13:12 · Sarah Guo, Patrick O'Shaughnessy
[00:13:12] S2: That says something about your psychology because you want to do something that matters. I think there's also a set of scientists who want to work on the thing whether or not it matters that they're working on it. An entrepreneur asked me yesterday, I would not work on the companies if I felt like we couldn't change the outcomes a little bit for them. He asked me yesterday, would any of the companies that you backed not been backed without you at that round? Would people just said no? 5%, 10% max. They were resourceful, really talented people. They find other investors. There are lots of smart investors in the world who wanna take that risk. Maybe they wouldn't have gone the next $100,000,000 of compute. I don't think every researcher doing frontier work at these top couple labs right now feels like they're essential to the machine. [00:13:56] S0: What do you think is unhealthiest part of everything going on right now? Like, what worries you about what people are trying to accomplish and what are the things that you're most worried will inhibit the future that you wanna see?
00:13:56 · Patrick O'Shaughnessy, Sarah Guo
[00:13:56] S0: What do you think is unhealthiest part of everything going on right now? Like, what worries you about what people are trying to accomplish and what are the things that you're most worried will inhibit the future that you wanna see? [00:14:09] S2: I think compute is one. People, I think, that very well. The version of us having enough compute over the next five to ten years. And people are like very much thinking about like 2032 at this point at scale. I was talking to the leader for infrastructure at one of the hyperscalers earlier in the week. And he's like, there was nothing that is gonna move the needle for us at sufficient scale before 2030. That's depressing. Where can we get sufficient natural gas? Americans and entrepreneurs have been able to build new technologies and new capabilities very, very quickly in the past. I don't think it is a technology or capability or capitalism problem. I think it is a regulatory problem and an alignment problem. And I don't mean like AI alignment. I mean, if you want to build data centers in New York, you need to convince the people of New York they should want data centers there or America should want data centers there. If you want to make the price of nuclear competitive as baseload power, then you need to convince people it's safe and you need to allow enough construction [00:15:21] S0: of AP1000s. Yeah. Rest of cars.
00:15:24 · Sarah Guo
[00:15:24] S2: Yeah. In order for the price to come down because we understand how that cost curve will change in theory. I don't think we lack the technical and entrepreneurial capability to build abundant cheap energy for the data centers. That's one. The physical supply chain is just a tough reality. That makes me worried that the learning to build things and having the tacit knowledge and the labor and the raw materials that can't go as fast as software or even decision making. The only way through that is through. We just have to invest in it. On the investing side of what worries me, I was talking to one of our founders, several of our founders who are researchers and explaining that the quality of their storytelling and their ability to make the companies that's legible to investors is like obviously very important to their success. That's going to be a CapEx intensive play upfront. The reality of the financial landscape for all of these people is I'm not a research scientist. You're not a research scientist. And all of the capital is not research scientists. It's on entrepreneurs to go explain their story. But one of the challenges is there are a lot of people attempting to invest as they should in technology bets. It varies how much fundamental understanding there is. There's a lot of proxying of judgment to pedigree or to other legible signals. Who's invested? References are always good. Who are the referral sources? And so the decision making is less fundamental. I asked a extraordinarily good investor friend. We had a debate like you do. And I like, I don't understand. What is this company gonna be that would be big? Explain it to me. His explanation was essentially, do you know the quality of this person? Yes. I've known the quality of the person for eight years. The technical theory in the business don't make sense to me. And people are making large scale research bets without any intuition for them or without any opinion on them. And I'm like, it's not all gonna work. And I may not be any better at deciding, but we want to get to that intuition. And I think not having a point of view on the business besides the pedigree of the person is dangerous.
00:17:48 · Patrick O'Shaughnessy
[00:17:48] S0: Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up to date proof of trust without all the manual work so you can close deals faster and with less friction. Customers report a 526% return on investment, and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake. Get started with $1,000 off at vanta.com/invest. Ridgeline is the first end to end system of record with embedded AI for investment management firms running portfolio accounting, reconciliation, reporting, trading, and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. I've been hearing from a lot of investment managers about AI and they fall roughly into two camps, with some unsure where to even start and others convinced they can build their own order management system over a weekend. The reality is that running an investment firm will always require governance, controls, and a single source of truth for your data, and no amount of AI enthusiasm changes that requirement. Ridgeline is built on exactly that foundation, which is why I believe that the firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation, and you can request a demo at ridgeline.ai. Who is a researcher that has most blown you away, and how did they do so? What's something that they did that to you felt extraordinary back to the great person theory? There are a number of these people who I think the history books will write about a contribution of theirs as a truly extraordinary thing that created this kink. What's an example of a person like that and something that you've seen them do?
00:19:39 · Sarah Guo
[00:19:39] S2: My partner, Pranav, and I were introduced to Tony Zhao and Cheng Chi at Sunday Robotics when they were PhD students at Stanford. They worked at Toyota Research and DeepMind and Tesla, and so they weren't just academics by any means. They were young PhD students, I think they were, like, 25. I think one of them didn't finish. They just, like, started the company. What I thought was impressive about them then and then now, I remember looking at their body of work, took me a while to get oriented. But I think that they have contribute dual handedly most of the interesting ideas in robotics AI over the last four years. That's a pretty weird thing for two very young people to do. If I try to characterize the type of ideas, it is how can I use modern AI to solve the robotics generalization robustness problem in a very practical way? It is believed in robotics that if we just had the Internet of robotics data, yeah, we'd have fully general robots everywhere. This is clearly gonna work. One of the big research and practical problems is like, where do you get that data? Some of the ideas that Tony and Chang have worked on is how can you be clever about collecting the data in the cheapest way possible in a way that supports the distribution of real world environments and tasks. And so I think the creativity of thinking about the actual constraints, we don't have the data, we don't have incident dollars to spend on the data and we're gonna treat as a technical problem to solve of the shape of the data and the collection and how does it interact with the model and how much of these cheap data collection can we transfer to model learning. That's super interesting. That's very outcomes driven. I was blown away in the first meeting. We said yes immediately there, thankfully. It's just under two years that the company has been around. Nothing is true until it is shipped. This entire team believes that we are going to have general semi humanoid robots doing things in people's homes first in beta end of this year. That is not something that any of us believed. It blows me away that you can move that quickly from a bunch of cardboard in a Stanford basement to the full stack thing that is manufactured here.