Invest Like the Best with Patrick O'Shaughnessy
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.
00:21:55 · Sarah Guo, Patrick O'Shaughnessy
We have done hundreds of iterations of hardware on model data collection and translated it into tasks and test it in all these real world environments, it's gonna work. [00:22:05] S0: Right. Computers are very cool. [00:22:06] S2: I think the speed of that is mind boggling. I still think the broad view, not everyone, but like lots of people in robotics are like, now it's a question of when, not if. It surprised me that the team was like, if not this year, next year. [00:22:21] S0: I'm very curious about the moments of your investment decisions. Like you just said in the first meeting, were sort of like, we're in. Is it always like that? Or are there examples of things where you like hem and haw? You end up doing it and it works. I would love to hear more about how you and your team make investment decisions. The actual in the room process for, okay, this thing is interesting. We're gonna do it or we're not. We're debating it. What is that process? What is it like?
00:22:44 · Sarah Guo
[00:22:44] S2: It varies based on company. I'm very instinctive on people. I often know I want to do something immediately. If I know what somebody has worked on and then I interact with them and I hear the idea and I have some basis, some background in the idea, We have a rating scale, a one to 10 scale. I'm like immediately an eight or a nine. What I'm then doing between that and a real decision is often figuring out where the holes in my understanding, where my judgment of their premise or them is incomplete or wrong. What do I not know? If you're an investor looking for the most ambitious, impactful companies, you can't know every domain. We have biology and defense and robotics and law. I'm like spending the next day to few weeks desperately trying to ground myself and being like, okay, what does everybody else believe about this space? Are they the people I think they are? That's what my process looks like. And then I wanna get feedback, I wanna get like second reads on people. I wanna understand what the core questions are. I'm a memo person. Even at the very beginning of the firm when it was just me, I would like write the full memo, perhaps Greylock style, ship it off to a friend that was an investor who I trusted for their perspective outside of the funds. You and I have talked about what do you value about a partnership? And I'm like, I'm comfortable making investment decisions, but I think other people can make me better. I want people to push at the logic and have me reflect. So I used to take the memo and send it to John Lilly or Dylan Field or something. We ran a memo. I go see what Bella or Pranav or Mike wants to know about the person, try to complete the picture. Other people I think are like more even in their decision making. They're like thinking about it and thinking about it. They climb to conviction versus I start there and then I like work backwards. But I think a similarity between both Mike and I is we'll start at a point and explain what will move us. I met a really interesting company earlier this week with my partner, Bella. And I'm like, okay. Instinctively, I, like, am positive on it, but I don't know enough about the science here. And I have to, like, go make sure this makes sense. Can you really be an eight or a nine if you don't get it? No. I couldn't get there.
00:25:01 · Sarah Guo, Patrick O'Shaughnessy
Then as we were talking about is one of my concerns for this period of time where I'm like, if you don't feel like you have any grounded intuition on the bet itself, what are we doing here? I'm trying to think about if that's fair. If Bret Taylor wanted to like Yeah. [00:25:17] S0: That's like what the in [00:25:18] S2: volcanoes or do dog streaming or something, I'd be like, yeah. Of course, man. But he wouldn't do that. [00:25:24] S0: There's some version of I don't even care if it doesn't compile in my brain. The person is so undeniably good that I would just back them. Brett, as an example. [00:25:33] S2: Yes. I feel like these things are so inextricably intertwined in my mind because part of what makes people great... Like, was trying to help one of my companies, the candidate, yesterday. And they're like, oh, what did you see in these people? And I'm like, they're just so right. He's right all the time. Their industrial logic is impeccable. They love these great character traits too. They're amazing at recruiting, but they have a point of view that I think is going to be right in the world. I've seen them just make repeatedly correct decisions, including when I am wrong. And I'm like, I have a lot of respect for that. When you say like these people are amazing and part of what I think makes them amazing is I believe in their judgment. If I don't understand what they're doing, I can't have an opinion on their judgment. Brett's doing enterprise AI. I understand what he's doing.