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.
00:46:36 · Molly O'Shea, Joon Sung Park
[00:46:36] S0: the way, I heard DoorDash has amazing AI talent, like very underrated. [00:46:39] S1: They are killers. Yeah. The thing I always appreciate about DoorDash and that particular team they're soft spoken in some ways, but they fought in an extremely brutally competitive market and they won. That speaks volume. So we have amazing group of engineers, and product and talent, and this is now matched with amazing group of go to market and people on the deployment side. We are very forward deployed as a company. So that part is led by one of my co founders, Lainie Yellen. But our fundamental thesis of this company is one, the data, the model is extremely important. Simulation is basically in its GPT-four, GPT-4.5 era where the technology is here to make impact, but this technology is changing and evolving every single month. And a lot of this battle is also going to be in the market in our for deployed motion. And this is something that I look to the previous sort of cycles in the recent AI cycle. For instance, I look at coding, agent decoding era. I actually have found that companies that were forward deployed actually found a lot of success. Their relationship became stickier, became much more defensible over time, and I think that's also going to repeat in simulation because I really do see the simulation space as one that's going to reach its maturity to a point where in about a year and a half to two years, we'll be where a coding agent as an industry is today. So I think there's a lot of learning that we're also taking from that era.
00:48:19 · Molly O'Shea, Joon Sung Park
[00:48:19] S0: We're also in an era of spies and people leaking information and flight risk employees. What just happened with they had a spy or someone bugged them? Yeah. Do you, do you help HR teams try to determine who to hire, who not to hire? [00:48:39] S1: So these are some of the use cases that we get and we do in some cases collaborate. And of course, we do this with informed consent and autonomy, again, from the members of our customers' companies. But some of the use cases that we hear is many of the companies, especially as they get large, they're deeply interested in retention. How can we make sure that our members are excited and especially our most most performant, members are committed to the company? And that is something that we can help simulate. Another one is they also want to recruit. So imagine right now you are a company that is trying to recruit the most talented students from Stanford, MIT. What is the right way to communicate to that population, that is also something that we can help simulate. So it's not just about understanding the consumers or the customers. It's really about understanding everyone that's in our ecosystem, and many of those important stakeholders are actually internal.
00:49:43 · Molly O'Shea
[00:49:43] S0: I have to say the more we talk and the more you answer questions, the more nervous I'm getting, and I feel like you can read my mind a little I don't know if that is a common occurrence that you have, but I genuinely am like, shit, you did your research. What does the simulation say? Okay, so as we close out, this is one of my favorite questions. You kind of already answered it, but I'm worried, like, I'm curious, maybe you can... Maybe you have a different answer. But one of my favorite questions is our partner question with Brex, which might have showed up in your simulation, but it's all about performance. So Brex is about performance, spending smarter moving faster. I take this in a personal bend. So I think that's a matter of who you surround yourself with. Some people say it's your five closest friends. You might have a more scientific answer to that, but it could be a mentor, it could be someone who's inspired you along the way. I know you were also a painter, so maybe it's an artist, I don't know. But I am curious, who is that person for you or those people?