Invest Like the Best with Patrick O'Shaughnessy
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.
00:25:33 · Sarah Guo, Patrick O'Shaughnessy
[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. [00:26:19] S0: If I were to build a pie chart of your time now, not when you started the firm, but today, there's meeting new companies. There's helping existing companies. There's talking to researchers. There's time with other people. There's talking to candidates. I have no sense of what it would be. If you had to sum it up, what are the things that you spend your time on and what's the percentage allocation?
00:26:19 · Patrick O'Shaughnessy, Sarah Guo
[00:26:19] S0: If I were to build a pie chart of your time now, not when you started the firm, but today, there's meeting new companies. There's helping existing companies. There's talking to researchers. There's time with other people. There's talking to candidates. I have no sense of what it would be. If you had to sum it up, what are the things that you spend your time on and what's the percentage allocation? [00:26:40] S2: I think I spend two thirds, three fifths of my time working on portfolio company stuff. And that is recruiting, helping people think through things, trying to influence the outside ecosystem in some way. Then raising money. The next largest piece is looking at companies. I don't know if this is right or wrong. Get paranoid about it and, like, move the number, but I probably see four to six new companies a week. It's not a very high volume. My first couple months at my old firm, I saw 500 companies. And so I'm like [00:27:13] S0: Wow.