00:37:20 · Joon Sung Park
[00:37:20] S1: We're running this company, and we're delivering so much value, but there's a small part of me that still is the researcher part. And as a researcher, I am inspired by questions that are truly societal and generational. Every generation have a few of those questions. One of my research heroes is Thomas Schelling, who was behavior economist back in the 70s and 80s who introduced what we called the model of segregation. And racial segregation was a big topic. That was the generational topic. So he decided to apply simulation to solve that, to at least have an understanding of its causal mechanisms. So I do think about, well, what are those questions that we have today? Well, one of them is climate change. Climate change is hard not just because of the physics of it, but because it's about coordination. There are so many actors with competing incentives who have their own things they're trying to optimize for. When they come together and clash, the equilibrium state they generate is nearly impossible to navigate. So if we can simulate that over and over again and see what kind of outcome we might get depending on what policy, that could be potentially quite powerful. Another one is, well, if we believe AGI is here or if it is imminent, then obviously we as a community, at least in some pockets of this community, have presented a few different ideas. One of them might be UBI. There might be some other ideas. Can we simulate these kind of findings? Instead of running five year study on UBI, could we actually have simulated that and shown that this is where it worked, but this is where it didn't? Those are good simulations to run.
00:39:06 · Molly O'Shea, Joon Sung Park
[00:39:06] S0: Have you simulated who you should be taking money from? I know you just raised a huge round. Well... You must have, right? [00:39:15] S1: In fact, what I often do is I do simulate these kinds of conversations. So like obviously, know, Molly, you have a very big online presence, so of course you bet I simulated the conversations before walking in. [00:39:30] S0: Is this one of them? [00:39:31] S1: Okay, here's It the funny did warn me that you might ask whether we live in a simulation. [00:39:39] S0: Of course. [00:39:41] S1: So I should have taken that advice more seriously. [00:39:44] S0: Pull up your results. Let's go through this right now. What else did it simulate? [00:39:48] S1: That was a big one. And thought it was... It's not something... And this is interesting, right? Because that's not something that every interviewer's asked. But for some reason, this is actually a good question that the simulator actually thought, given everything that we know about you that you might ask. [00:40:06] S0: Well, I do tweet Elon a lot, so that is part of it. I am curious, though, you have raised 300,000,000 in total at a valuation of 2,000,000,000 reported, and you have really great names like I mentioned Green Oaks, Index, you also have some really cool individuals like Fei Fei Li and Andre Karpathy. How did you collect such an interesting base of investors and angels? Right.
00:40:36 · Joon Sung Park
[00:40:36] S1: So we were, in a way, fortunate to have worked with many of these people in the past. So many of them were actually our colleagues. So Fei Fei, of course, a close colleague at Stanford. One of my co founders, Michael Bernstein, worked closely with her for many years. And even today, are colleagues. Andre, one of my other co founders, Percy Liang, was actually on his doctoral dissertation committee. So we've known Andrei for a long time, and in fact, when I first published the Generalizations paper back in 2023, Andrei actually was one of the first people to really amplify that particular work. So in that way, this sort of felt like we're coming full circle. But I think in many ways, we were also fortunate because the idea, the core research, and the core need for this technology is so real. Now I truly do believe that simulation in many ways is inevitable, that there's this deep, innate desire and need for people to better understand who they are. And just like how we write autographs, autobiographs, or we paint portraits, simulation is a lot like that. We are, in many ways, painting a portrait of who we are as a society. And I think many of our angels, see that, that this is an inspiring idea. This is an idea that does need to exist, and that's I think what got them excited.
00:42:05 · Molly O'Shea
[00:42:05] S0: Who was it? It was Alexander Wang. He was talking about how when he was first exposed to Open Claw, think from Nat Friedman, the prompt that Nat gave him was to like do a deep dive psychological analysis based on all of the information and Open Cloud just like ingested everything off of their computers, photos, like emails, everything like that and gave like a detailed psychological analysis. And Alexander Wang was shocked by it. Is that an angle as well? Like, how do you see how much of the data you need?
00:42:42 · Joon Sung Park
[00:42:42] S1: So it is both how much, but it's more about what data you need. So it turns out asking the right questions actually matters quite a bit. So we do interviews with people just like what we're doing right now. In fact, where I actually got the idea of doing interviews with people to learn about them was back in 2023 and 2024, I actually was on a lot of podcasts for the academic work that we led at Stanford. And I realized that after talking to these interviewers for about an hour or two, they know a lot about you, especially the good ones. If they ask the right questions, they know a tremendous amount about both your field and who you are. So that's where actually we got the inspiration from. So this is where we ask questions that are both topical but also quite general. We found that asking people to just tell us the story of their life actually matters a lot. It reveals a lot of interesting pockets of knowledge about those people. But we also collect a lot of behavior data. Behavior data might be observational in part. It's like transaction data, mobility data. It makes a lot of sense. But what really matters is actually the causal mechanism. It's the randomized controlled trials. Imagine you have a same experimental setup and you're varying a particular variable, whether it's a product feature, whether it's a messaging, and actually observing people's behavior change. That data actually matters a tremendous amount because what we've learned over time is people are actually not that interested in predicting the future, which is, to me, that was a little bit surprising. But unless you're trying to win against the stock market, what are you going to do if I told you that your market is going to crash in two quarters? They're all going to then ask, wow, that's shitty. What do we need to do now to change that? But it's always the second part that matters more. So what do we need to do now to shape the future? And this is where for us, it's not just about finding correlation. It's about understanding the causal mechanism, the causation, because that's ultimately the lever that we have to shift the direction of the future.
00:44:54 · Molly O'Shea, Joon Sung Park
[00:44:54] S0: So who makes up the team? What kind of talent are you looking for? Is it researchers? Is it scientists? Is it engineers? How is it composed? [00:45:03] S1: Right. So we are now a team of about 100 people. 20% of the team actually is our lab mates and our academic relatives. So these were people who were my lab mates in Percy's group, in Mike's group, in the computer science department at Stanford, who had then gone on to frontier labs, then coming back to work with us to build these foundation models of human behavior. But we also have an amazing group of people in that category, and what is our relative circles who are much more of a methodologist as well. It's not just about creating models for us, it's actually about studying people, understanding people. So we actually do have a small but growing set of methodologists who have backgrounds more from social sciences as well. So that's the science part of simile. So about 20 to 25% of the company, but modeling eval and the science part of conducting these simulations, That's a very important part of this company. And then we have an amazing group of engineers and people in the product side, because this technology is getting handed to many of the Fortune hundreds and other users today. So we are creating this technology with this amazing group of people. We actually, recently invited our head of eng, Bhattan, who came initially actually from DoorDash, And there he was an amazing leader. Recently By [00:46:36] S0: the way, I heard DoorDash has amazing AI talent, like very underrated.