Simulation: the new Scaling Law — Joon Sung Park, Simile AI

Today we have Joon in the podcast, excited to kick this one off. Very exciting company. I want to kick off and ask you the question, you know, talk us through the story of your life. How have you gotten here?

Yeah, for sure. So really excited to be here. A story of my life. So I was born in Korea and I lived there for a good 11 years or so of my life. And then my family moved to Boston, so we moved when I was 11, and my parents were doctors, so they were basically going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years actually at the Boston Children's Hospital. So I grew up there, not too close to tech actually. I was very much like music, artsy—

Painting.

Painting, like that kind of guy. I actually got into painting a little bit later in high school, but that's what I used to do, and then I grew up Mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania, and I got into more of this tech scene in college. So I was originally trained to be an artist. I actually thought that would be my actually professional career. So it wasn't a hobby. It was actually like, hey, let's make a living out of this. And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium. And the best medium that we had available today was actually in computation. So I decided to go deeper into that, and one thing led to another. And obviously, we can go deeper into this, but I decided that research was something that gradually that I got interested in, and here I am.

So there's obviously a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.

Yeah.

Feel free to call back to anything else that you mentioned, but most people would have heard of you from this, obviously. Do you have any statistics of how many people have read it? Archive gives you something, right? Some stats?

Yeah, it's a good question. How many people have read it, I'm actually not sure. I know that we do keep track of the number of citations, which I know is going up quite fast, but the Google Scholar has 72,000.

I feel like it made a bigger impact. And it was actually a pretty instrumental paper. It was one that got cited so many times.

It is frequently, when people ask what is the best paper of the year, best paper you've read recently, it's this one.

I thought the memory component was pretty underrated, very good early memory system, but yeah, one of the biggest papers.

Yeah.

So maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford. And that was the year when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there's this new class of models that was just becoming available in the market. And the team got very intrigued, and the general consensus was, well, is this model actually going to be useful for anything? It's really strange that these models are not trained to do any particular task, but we decided to take a bet. So a large group of scholars at Stanford, and it was actually led by one of my co-founders, Percy Liang, came together.

Who coined foundation models.

Who coined the term foundation model. We wrote this paper where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular. But it was— its premise was it could do anything and everything. It was like a stem cell if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are actually trained on this very brought data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these kind of data. So if you poke at the right angle, then you could see human behavior that would just pop out that's actually quite realistic, and we've never seen that before. So that got us really interested. The exercise that we decided to do— and this is something that we— this particular group of colleagues that I have, myself, Michael Bernstein, Percy Liang, who ended up becoming my co-founder at Simile— we sat down and we played this game that we call the time machine game. Imagine we were to get on a time machine and fast forward 10 years and look back. What would have been the single application that will have mattered, that would be the most interesting and inspiring? And what we thought, well, what if we can just recreate the world that we live in? I mean, it's really hard to get more ambitious than that. Let's just create a world. And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.

Before you go further, were there other candidates for the most ambitious thing in the time machine exercise?

Exercise?

Yeah.

What could have been?

What were the next—

What was number 2 and number 3?

Okay. If you remember. So there is a close second that we were considering, which basically ended up becoming more of these automation tools, but especially the vision around really personalized agents. that would actually do things for you.

That's also happening.

It's also happening. But it was sort of interesting for us in that the reason why we decided to go with the idea of simulation, one, I mean, I was a huge science fiction nerd. And this idea of creating simulation, I was personally really just fascinated. I love the idea. It's really cool to see a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you actually need first is an amazing model of your users. So for instance, I tell the model, hey, can you go buy late dinner for me? And it orders Hawaiian pizza, and I do not like pineapples on my pizza, then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, for instance, if we have our family, closest friend, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people, ought to precede— the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take actually here, though, is I don't think we've actually seen a true personal assistant that's actually useful in ways that actually meets the ambition of that particular line of work. I think there are early applications that are Obviously interesting. And if you talk to even ChatGPT nowadays or Claude, they obviously know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored. But I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.

So like Open Claude, all these kinds of personal agents, what do you want to see from them that they don't currently have?

I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models, I mean, OpenCLAW, and what it's basically leveraging is basically Markdown file. And I think it's quite clever, right? So if you look at the generative agents paper, this actually was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and this is like back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model? All these kind of things, and what we decided to do was, no, no, no, just forget about all this. These language models are actually quite good at modeling text and understanding and reasoning about text, so just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that, but also there is limitation. It's the way you retrieve and make sense of data that's extremely large. It takes a lot of work. So I think that technology is getting better. I also do, however, think there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there's these kind of work that I do think does need to happen, and obviously it is happening. The question is, how far can we take it? How do we source data? And how do you also create an ecosystem where the people are continuously feeding data to this model so it's learning about you?

What's the intuition between why you need to do it in the model?

My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train is it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the model has yet, at least the models that are out in the open, has yet learned the complete mapping of social physics of humanity. Right. This actually is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like whatever was available on the web. And these are really interesting datasets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn really deep behavioral nature of people, not just what people say they do online, but what they actually do in real life. And this is actually one of the sort of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these kind of data that would also need to get factored into the model creation.

You call it Behavior Foundation Model.

Yeah.

There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about actually modeling, doing a behavior foundation model?

We think about data in 3 buckets. So one bucket is actually interview data, for instance. It's quite interesting. Qualitative, rich qualitative data is interesting. It's not behavioral, but we would literally ask people, hey, tell me the story of your life. Yeah, which is what we're doing here.

Exactly.

The question that you all asked at the beginning of this interview literally is the question we also ask. And obviously, we ask our participants to go a little bit deeper than how far I went. Maybe I can actually give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you actually get a lot of texture around this model, like this person as a model. So even understanding their childhood memory or even their trauma, their first love, these kind of things, quite informative in ways that's really hard to predict. So that's one. Then there are sort of 2 tranches of what I would consider to be the behavioral data. So one behavioral data actually is observational. So these might actually be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data would be— these datasets would be interesting, right? Because they give you the base statistics of people's behavior. But then there's the last category of data that I personally think is perhaps the most important, which is the data that basically describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really where you get to see the most behavioral aspect of this actually is in randomized controlled trials like RCTs. Imagine you basically have the same setup, But you have a few different variables that you are trying to tweak. Can you actually get realistic human behavior out of it in ways where, oh, imagine you had to make— imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drank coffee versus the day you didn't drink coffee, does your behavior change? That's a dataset that describes a causal mechanism. This actually is quite important. in actually modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation actually isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.

Mm-hmm.

But most people, most decision-makers, what they want to know is how can we shape the future? It doesn't really help you to hear that your sales is going to tank in 2 quarters. They're just going to say, wow, that sucks. What they want to know is, well, what do we need to do now to avoid that future? That's causal mechanism. And this is also very hard data to come by, right? Because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of dataset almost rarely happens. So this is the reason why this dataset Dataset is both hard to come by, but also quite important if you're trying to model human behavior.

So the behavior, I think, is the hardest dataset to acquire. What is out there? What is possible even? Because you're not going to know a lot of details about my life. I don't even have data for myself. I want to analyze my own health or habits, and I just don't log everything. So how can you have that data?

So we actually run a lot of randomized controlled Yeah.

But you put people in the lab, they watch them sleep or what?

So we do actually care a lot about the consent process. So people know that we are— we invite them to be a member of this community, to both share data and also have themselves represented in different forms. But we bring a lot of people to the lab or virtual lab where we design experiments that would actually pose them real behavioral decisions. And often in these kind of experimental setup, what makes the difference between what is attitudinal versus behavioral is if the stake in your decision is real. That's ultimately what makes it behavioral. So in these kind of setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, what the kind of techniques they utilize is, imagine there is an online store that you're inviting people to come buy. Then whatever they purchase in this experiment, they actually get that item delivered, for instance. These are the kind of things that makes the stakes real. So we run a lot of these experiments, and we also do partner with firms. And also right now, we also have customers who are quite excited to at least give us a glimpse of the kind of behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.

I think on the customer side, they have a lot of data about their users, who has bought. They have the action data. Can you kind of walk us through an example of what does someone come to you for? What questions would they want solved in the process of— do you customize a model for them? Do you have something off the shelf? What does that look like?

Today, when people leverage our models, it's often to better understand the population of their interest. So usually the start of the relationship We basically come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then it might be fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about these populations and we go recruit these people with consent and with incentives, and we basically collect some of their data and create a model of these people. And then what our product allows you to do is basically query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. Environment can literally be survey questions. It can be behavioral experiments. It can be A/B testing. Oftentimes, the core use cases are things like concept testing. to start with, but also people sometimes want to do focus group, or one of the sort of fun use cases that we also serve is actually even modeling things like the earnings call for public companies. So these are the use cases that we often start with.

Concept testing, is that an established term? I've never heard of concept testing.

Yeah, so it basically has to do with they have, let's say, different messaging, different products, different ideas.

This is like a marketing exercise?

Yeah.

Okay, got it, got it. Politics?

We do have a strategic partnership with Gallup. And of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area, just yet, however.

I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing users or people.

I think there's certainly demand, but we are very much mindful of how this technology gets adopted and the societal impact that we end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.

I'll give people an example. One of my favorite shows is The West Wing. I don't know if people have watched. One of the key storylines is the president has but they need to figure out how to disclose it. So, they run a poll with a fake governor and ask people to respond on the poll, and they try to make decisions based on the results of that poll on how well they'll be received, like, how should we play this? And I'm like, well, I think those kind of counterfactual things, I would actually use a simulation for this if I could trust it.

I'm sure.

Yeah.

In that show, how'd it go?

In that show, it basically was kind of like a foregone conclusion. They were like, we know it's bad, we just don't know how bad. And then the poll came back, it was like, it's really bad. And then they just did it anyway.

Part of it is it's a show, right? So you're looking at the—

Maximizing drama.

How bad could it be?

Oh, it's horrible.

And to some extent, I think that is part of the trick of the challenge or with being a customer of yours, which is that if I know it's bad, If I roughly know and can intuit what the effect is going to be, do I need you? What sensitivity of effect do I need in order to make a decision? So for example, if my approval rating is 50% and they have this negative piece, news item comes out and it drops to 30%.

Yeah.

If it drops to 20, if it drops to 40, do I care? No, I know it drops. It's negative. When do I care about simulations?

You do something that's clearly bad, that's not popular, and people don't like you. Like, yeah, I mean, it's like—

You don't need a simulation. Yeah.

Well, so there are a couple of things. One actually obviously is there are use cases where like every day, for instance, developers, designers, policymakers, marketers, every single day they create assets. They create new products. And turns out, it's actually many of the decisions in hindsight is sort of obvious. Yes, of course, this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is is actually quite difficult, even if we feel like, of course, this makes sense. I mean, this is the reason why we make so many mistakes. Every time somebody goes online and says something that has huge backlash, You look at that and like, what an idiot. However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is actually different from prediction. In simulation, in the ideal case scenario, so what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, Sometimes the next step that we're suggesting might actually be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but as I mentioned, I'm a huge fan of science fiction, and I don't know how many of the audience members have read things like the Foundation series by Asimov.

We've mentioned Psychohistory a number of times.

Okay, fantastic. So I might actually be talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, oh, our galactic empire is going to collapse and we're going to have 30,000 years of unrest. And they basically run Psychohistory, the simulator that tries to teach them, okay, how can we keep this unrest to 1,000 years? And they plan this out, and the first step of that plan is to get the scientists who say, okay, this is coming, exiled into this random place in this galaxy.

Terminus.

Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of Galactic Empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that actually was the move. It's these kind of things, right? And the reason why this kind of reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, here is a goal that we have. In the context of Foundation, we want to keep the unrest to 1,000 years. What is the path that we need to take now to get to that particular future? And that's what simulation allows you to do. Now, translating that into real market, imagine you're an automobile company and you're about to release an EV, and you're trying to understand, well, how do we market EV to make sure that our stock price goes up? But what if the answer comes out that, well, you can market your EV in XYZ way, But that might change people's perception around the cars that's not EV and actually make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV sale, and that's the only thing that you're tracking. Then that might actually result in a completely wrong solution, or at least different solution than what you would've expected, whether it's right or wrong.

Yeah.

That's the power of simulation.

For listeners, we covered a similar topic with Mikhail Parkin from Shopify, where they're working on SimGym. I don't know if he ever talked to you about it. It's very similar. The goal is increased conversion, but then the journey is very unusual.

Journey is unusual.

Yeah. He's actually trying to look for interventions on a shopping trajectory, which is similar to what you're saying. It's not about the attitudinal, is your word for it? It's about behavior.

It's about behavior.

And that's exactly the difference, right? It's not about the near-term direction, but it's more about how do you affect multiple turns of interactions.

You had a good quote at the start about this as well. It's not about people wanting to know the outcome, it's about how they can change it, change the way to get there, something like that. But I want to take it back to how do we know this is grounded?

Yes.

How do you run evals? How do you test that simulations come through? Basically, if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these things, how different are the answers we would get if I give it the same goal, the same objective? make a decent system. You're saying that you need to change the model weights. You have your own solution to this, but how far off are we, and how do you check if it's grounded? You have some interesting stuff on your site that actually points to how you run ReLievALS, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, LLMs hallucinate. You're just hallucinating layer after layer, right?

The way we do this, and this is actually the paper that we worked on after the Generative Agents paper that really became the at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. The paper is called Generative Agent Simulations of 1,000 People. Here's what we've done. For this paper, we actually brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we basically have done was we spent 2 hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data. whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavioral data and so forth, whatever we can collect within 2 hours. And then we would actually send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we actually have the list here. Which basically included things like the behavior economic games. We would run literally like Big Five personality tests, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS, and we would have their digital twins predict how the source individuals would've acted in these studies and surveys, and this is where we basically could replicate people's behaviors and attitudes 85% as accurately as people would replicate their own.

Wow.

So that actually was the first really paper that gave this validated results that we can actually model individuals in an accurate way. And what we ended up finding now, of course, in AI space— so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.

For listeners who are not seeing the YouTube, I just want to say the headline figure is 85%. accuracy, which is a big improvement over all the other methods that you showed.

But the part that was actually particularly striking to us, especially as we improved this technology even further, was the generative agents model— generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really, really good at today is they're trying to basically become these super rational objective machines, right? So you go get their data from places like Mercure, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile actually doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make such mistakes, the model has to make the same kind of mistake.

Oh, that's very hard.

That's very hard.

You're solving Moravec's paradox.

That's exactly right. And this is actually a completely different kind of data and training objective. This is also where we actually see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models and Simile's model and the models that are getting created in the space. Where in some cases the model performance of frontier models go all the way down to 20%, 30%, especially if you go into that more niche population on topics that our customers would actually care about. On more gen pop, it might be around 50% to 60%. So it's not very robust. Like, you wouldn't want to make your decision off of these kind of findings. If you can bring that up to 85%, that is ultimately what people end up getting very excited about.

Yeah. Do we want to keep going on the paper route?

Yeah, for sure. So the last one was sort of an interesting one. So this paper was the follow-up paper that we had to the 1,000 agents paper, where basically the idea was, now can we augment the models even further and actually post-train the model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this platform called Open Science Foundation. So the audience might be familiar with this. There has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. So it was a bit of a crisis that scientists acknowledged where we rerun the study and we don't actually see the same finding.

Oof.

It's tough. And the reason why that was often the case was there's basically this survival bias where the papers that get published often need to maintain what we call the p-value of less than 0.05 in the experiments that we ran. That basically suggests that only— there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, there's still a 5% chance that whatever we publish is actually totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to preregister their studies. So before running an experiment, they would go to this platform and say, here is the data, here is the population that we're collecting, and here's the hypotheses. And they would just say, here is our hypothesis. Like, this is what we believe. And you cannot retroactively change those hypotheses. This is what actually gives us more scientific statistical confidence that whatever effect that you ended up seeing is actually true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are actually really high-quality, professionally designed behavior studies and randomized control trials. So we actually got the data and the studies from this platform and basically used that to make a point. And obviously, this particular model is not something that we're serving commercially because this obviously was a part of the open science. But this particular dataset helped us make a point that by collecting a lot of these randomized controlled trials that are really well designed, we can make significant improvement in models' capability to predict human behaviors. So that's what this paper was about.

Is this stuff done on an individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?

So this particular model actually was trained— the data we actually had at the level of individuals, but this particular model actually was trained— we experimented with both. And this is actually what we end up doing at Simile2. We always train 2 distinct models. One is what we call the population-level model. The other is what we call the individual-level model, and both actually take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do actually think that is a harder task in many ways, but that's what we have done.

Have you seen anything on the questions that humans can solve that models can't solve? So currently it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive? The model will say, oh, walk to the car wash. And you don't have your car.

Yeah.

Is anything like this a problem in simulation? You would assume very simple for human to think about, but if the model is saying you should walk to the car wash, Anything here?

It's less what can we solve, but I think it's more about what biases or mistakes do people make that models miss. For instance, imagine that you are— when I was studying at Stanford, I lived in Palo Alto, so it's about, I would say, a 40-minute walk from the campus. You ask the model, okay, let's go home. What can I do? It would likely call an Uber or Give me the bus time. But for the longest time, I actually really liked walking back. And the reason why I wanted to do that was not for efficiency. It actually really helped me think. And I like to walk for half an hour, 40 minutes or so a day, where I just get to just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and actually understands the importance of that activity, it would actually miss these kinds of features. So that actually, I think, is fundamentally what we're trying to model. What is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.

I'm curious if there are some datasets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a dataset? All of LinkedIn, all of Twitter, all of Facebook.

To be honest, it's a little bit hard to rank, in part because there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what kind of feedback. I think it's a little bit like that.

So just whatever is bigger.

What about a different domain? Say it was, what about all of Amazon data? Shopping data, right?

Shopping data. So Amazon data is interesting. in that it's very much behavioral. Although, like, what people do on social media, you could sort of squint and say that is also behavioral, but the transaction data is always interesting. It is also most commonly available, however.

Yeah.

If we were to look at purely social media, like, if you really, you know, if I were, you know, if I had to really pick, Facebook likely is interesting because I actually do think it is most sort of a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. You go to Twitter, and Twitter, people have their own crazy personas, or depending on who you are, right? My Twitter profile and persona is very much, initially was, I was very much an academic. Hey, I'm here to share my studies. Now I share things that's related to Simile. But Facebook is one of those more private spaces where people just connect with their friends. In that way, I actually do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick Facebook.

Yeah. And you're interested in the whole person and their background in philosophy. I guess, is it too clinical or too machine learning oriented to just say this is just ways to inject variance and biases? The broad question, I guess, is, is this any better than a randomized combinatorial explosion version? So we have a link to the Tencent Billion Persona paper where they basically did not do any of the groundwork that you are doing. They just sort of did a cross matrix of here's all the professions in the world, here's all the possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.

Yeah.

This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.

So this actually was an interesting paper. What I admired about this paper when it came out was the scale. And obviously, you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. It is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is actually not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, if this works, then we actually have solved simulation.

Right? Because I survey, okay, 5% of the US population is in construction. The other 5% is in medicine, whatever, right? And then you just keep going down the list and then you do the other side. 5% has the Big 5 personality of neurotic or whatever. That's it.

That's it. So if you believe that the underlying dataset and the platform that we're leveraging has all the right statistics, then this actually will have solved it. You're at that point merely retrieving the knowledge that is already embedded in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people, That if you just take one example, it might feel very mundane, but it's actually quite rich when you put together that you actually do need to do a lot of bespoke data collection to better understand people. And this is also, I think, what makes this particular job fun, which is you want to deeply understand people, and the process of deeply understanding them actually requires a lot of attention to the details, and you do need to pay attention to and pay respect to I want to talk about scaling simulation.

So what can't we simulate? What can we simulate? And how does scaling affect this? So how big are the models? What if we go from 8B, like a couple hundred million, like 100 billion parameters, trillion? Do we get scaling? Any interesting emergence, like at a certain scale, at a certain amount of training, you uncover anything unusual? And any learnings from that?

What we are seeing is at Simulai— so we do post-train our own model. The thing that we're actually seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you actually start to get predictive and predictable gains of the model performance in simulating and predicting people.

We need a scaling law curve.

Scaling law, whenever you find it, it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So basically creating this multi-agent simulation. Down the line, you want these multi-agent simulations to also live in a very rich environment. What we are really trying to get to at that point is, hey, can we actually create— let's do a time machine game again. And 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting, and that really is the vision. And once you get to that kind of state, the kind of questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people. So for instance, the kind of questions that I get excited by, and maybe this is a bit— I have my academic side in me.

Okay.

And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists, would often call the wicked problems, problem where you have many actors with competing incentives, Who are trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we actually understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the kinds of problems that we can solve. So that's really the ambition of this field. And I also think, yes, I mean, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's an amazing societal impact that we can have to help people make better decisions.

Nobel Prize in economics?

In economics.

I see.

I see.

Rooting for you to write that paper.

One of these days. But one of the scholars that I was deeply inspired by when I was coming into the space of simulation actually is this scholar named Thomas Schelling.

Schelling point.

So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was like in the 1970s and '80s. It's very early days, but this was truly one of the first examples of simulations. And one of the canonical models from that time, and of course, many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic that we cared about. And what they've done was they actually created this grid world where they had red dots and blue dots. And these dots were—

Right.

back in the day, like they were the agents and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color, and if that goes above certain threshold, then you move to a new location at random. One of the striking findings of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism. But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute, but the very small difference actually causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this actually, this particular work ended up informing housing policies. Mixed-income housing, for instance, got really inspired by this kind of work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms is agent-based models for the longest had impact in the 1980s, '90s, to some extent early 2000s, but it has now sort of gotten forgotten by the community a little bit because As you can imagine, red dots and blue dots is not really a rich description of people. But with the emergence of things like generative AI and in particular generative agents, we do have an opportunity to create these kind of agent-based models that are high fidelity, enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the kind of work that will result in a Nobel Prize.

Yeah. For what it's worth, I grew up in Singapore. 80% of Singapore is in public housing, and the public housing has enforced racial quotas for exactly that reason, which is really interesting. Okay, so we talk about scaling, we talk about all these agent possible applications. I'm scared about the cost. Let's just keep it to the US, not 8 billion people.

Yeah.

How much does it cost to model so many hundreds of millions of people?

Oftentimes today, obviously, we don't start at that scale, this stage of industry and simulation as technology, but we can actually get to our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands of people's data. And we actually have panel partnerships that get us to tens of millions of people globally. So that's what we do today.

And just as a side note, once you've collected one person for one study, can you reuse that same person for all the subsequent studies?

That's exactly right. The beauty of this model and these agents is the fact that they are domain agnostic, that what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And obviously there are a lot about people that does change over time. Even things like how many times have you been to CVS the past week? Obviously that will change. But there's so many traits about people that are also known to never change. Your risk tolerance doesn't really change over time. It's very consistent. So it's these kind of things that we're trying to learn. But the scale we are operating is right now hundreds or tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, this is more than enough population to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they actually want stronger statistical guarantees. It's more that can they actually filter down to any population of their interest? However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly. I mean, there's definitely a reason for us to create an entire data center worth of simulations. Or my hunch here is, I do think in the next some number of years, we will start creating simulations that will actually cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. I mean, right now, even today, we are training a bunch of new foundation models just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society, that would actually solve climate change, I would run that today. I would raise the money right now just to run that.

Amazing. I guess the follow-up question is, does it also compound if you let the simulations talk to each other, or do they already do that today? They don't, right? As far as I understand.

It depends on what kind of simulation you're trying to run. In the multi-agent simulation setup, the agents do talk to each other.

Which is exactly Smallville, right?

That's right.

But a lot of times, for example, in e-commerce, you're just by yourself, so there's no point talking.

But there's always levels, right? Like you decide what you will buy based on what other people around you buy and talk about, right?

It depends. Again, I'm coming at this from a cost point of view. I'm like, oh my God, if there is some combinatorial thing of thousands of people talking to thousands of people, then that 1 million Xs might cost—

I have a very different view as the cost point aside, running these studies, In reality is actually a lot more expensive, right? Running any study like this is, you gotta have people do it, you gotta sign people up. It's very expensive and sometimes like not feasible to actually run the study, but the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, you know, the overall process costs $100 million, might as well, right? There's a lot of value to be had there. It's a small cost, but I'm excited on the cost side actually.

Yeah. To some extent, you know, and obviously when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can basically make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology actually is making an argument that no, it's actually the upside, that by making this better decision using simulation, you have saved yourself Or major sell hundreds of millions or even billions of dollars. And that's a case to be made.

Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to train a model that's very sparse? You're expecting to do multimillion-dollar runs. Are you thinking about this in a model architecture standpoint or inference efficiency or... You're still at the research phase of it works, it works, we're not super there yet?

Efficiency, we actually do think quite a bit about. I mean, this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process a significant number of queries that are trying to simulate the populations in the world. So efficiency is a consistent thing. Obviously, we don't want to over-optimize too early. So I wouldn't say like this is the higher bid right now, but this is definitely something that we think pretty carefully about.

Yeah.

Are there other case studies? So you talked about CVS, talked about Gallup, Deloitte, Wealthfront.

Wealthfront is an interesting one because one of the things they're trying to do, they were one of the first customers that wanted to actually do product testing. That goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is they can be given a domain or a website URL and actually go use it for a while. It's these kind of things. And Wealthfront was one of the first customers that was very excited about this possibility.

Well, have people been asking, like, is there any demand that we have not covered? Like UI testing, right? I want to try a new— I want to ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?

Today, a lot of the demand does come from Basically, like the places where people have historically used human panels, we can basically now replace with agents and these synthetic populations. And this is obviously not replacing human panel. In many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me. Turns out, there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, we listen to people, we have consulted our users, but in reality, that is rarely the case because getting to people and actually asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available.

Right.

If I had to answer 1,000 survey questions for this one particular vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people is always represented in rooms where the decisions for them is made, right?

Right.

All the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.

In my mind, that means it skews towards more consumer focus, right? Like anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics just for people who are not familiar with this market in general? What's the market size that— I'm sure you have some rough numbers. Obviously, market size is like a vague question, but how much do people spend?

So market research is a $100 billion industry.

Yeah.

But the thing about simulation is simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is the TAM here is actually quite tricky, right? Because it's easy to say, well, market research TAM is roughly $100 million or $100 billion. So is that a TAM? And not really, right? Because in many ways, You're trying to inform all human decision-making. You're trying to basically inform every decisions that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest, I have a scientific background, I have a research background, so I didn't come into the field actually calculating, oh, what is a TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about humans for humans, that has to be big.

Something valuable.

Exactly.

I mean, to some extent, you are unicorn founder now, and you have to care as a CEO. But I do think, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, you have to say, well, here's what you spend on humans, and here's what we save you. And it's 85% similar.

And certainly the value case is something that we care deeply about. What is the value that we actually provide to the users and the decision makers? But this is also where As a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation in general because that's not what also motivates a team, or certainly doesn't. Again, the interesting thing about researchers is we are happy living in academia, getting paid next to— I mean, we get paid okay. I mean, We don't get paid that much. I mean, as a researcher, if you're in academia, but it's the impact and it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes, I mean, that has to be great business and we see that in numbers and we do care deeply about that upside story. But that's the higher bid.

Do you have any timeline predictions? So we talked about scaling laws of simulations. You brought up, okay, maybe one day we can simulate how to solve climate change. Where are we now? If that's not the end state, what is an end state and what does progress look like?

So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was. For the AGI saga, which basically is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data and obviously in algorithm. And there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly sort of where we are.

I think that was about the rough set of topics. Anything else that we should have asked you or you wish people asked you more about, about Simili?

I think for me, what's actually quite fascinating about simulation It is very impactful technology, but it actually is also very interesting technology, both in terms of what it means for human society, our philosophy. And the way I sometimes interpret simulation is— so going back to my background, I actually, as I mentioned earlier, I started my career as a painter. It was a professional pursuit, and I actually did oil painting for figures. So I got my training originally in sort of the realism studios, and that's what I spent a lot of my years doing. Simulation is a lot like painting, right? The best paintings teach you something deep about the subject that you're trying to represent. And it is always not a perfect representation. No painting is perfect. There's always some small differences and discrepancy. But what it does is it tries to highlight the thing that matters the most about the subject.

The essential essence.

The essential essence.

He's brought up some of your work.

Just nice to put it up.

Yeah, so these are some of the works. So this is actually from my personal website that I maintained when I was still a researcher.

I think a lot of people will say, like, you know, like a Picasso, like anything postmodern is Like very much focused on the essence.

Yes.

All right. Yeah, but I don't know if any one of these evokes something that you'd like to tell the story of.

No, it's one of those things where each of these paintings, drawings, whatever it may be, it is trying to surface something about the subject that you feel deeply about onto the surface. When I was a painter and artist, the topic that I cared really deeply about actually was the more mundane aspect of human lives. This actually shows up in some of the work that I've done where like I did this entire sort of study of a rural town where I basically went around and took photos of people for not really doing anything special, but just living their everyday lives. I thought that was the most interesting thing. I'm somebody who has this perspective where the world is oriented around this fractal shape, and you have 2 choices to understand the fractal shape. You either go outward and try to explore as much as you can to understand the broader shape of the fractal, or you go inward because the outward resembles the inward shapes, and understanding the mundane aspect of it was very much that. Simulation has a lot of this, right? You're trying to understand even the most mundane aspect of people when put together, teaches you something really deep about that individual and the society. So I think that's what's interesting about simulation, sort of the way— the same way that AGI helped us better understand or really think critically about humanity and human intelligence, simulation is really an exercise of understanding more about human society and our collective lives. So that I find to be particularly interesting. Yeah.

Now you're reminding me that some of the best biographers, documentarians, and even photographers, they're taking a photo of you. But before I take a photo of you, I must spend— I must follow you for a week just to understand you, which some artists, some do. Part of your work, there's a very famous book called Working. I don't know if you've been referred to it before.

Yeah.

It's very, very famous, to the point of having a Wikipedia page about this kind of really in-depth understanding and interview of people about their lives, which seems mundane, but it's told in a very Yeah, 1970s as well.

Okay. It was an amazing decade.

Actually, before closing question, you said that you started Simile with your 10-year question, right? If we do that now, 10 years down, what can we simulate? What would you simulate if you've made significant process? Are there any questions outside of the ones that we brought up, anything that you think is Most impactful, anything that you would go vision 10 years out?

In many ways, as I mentioned, I am somebody who's very much impact-driven. So what would actually inspire me is I would want to ask 10 years later, what would actually be the most important societal question that we as a society have to ask? I would love to tackle that. For instance, do we need UBI? That could be an interesting one.

Ooh, has anyone done that?

Well, I mean, we're thinking about it.

Can we get access?

Can we just— OpenAI, this is just trivia now, OpenAI, or I think Sam Altman actually funded a study on this in Africa, and the answer was no.

The answer was no. But was it something about the implementation?

Yeah, I know. Or was it something about the—

Skill issue. But this is the thing. See, when Sam Altman funded this particular—

He spent $14 million. Oh my God.

Quite a bit. But this is the thing. This is the reason why you want to run simulation. You spend 5 years, $40 million on this one study and have one finding. But if you can run simulation many, many times instantly, then that's the value.

I feel like that one you could have done in a simulation. If you can do the housing study, you can do the UBI one. I mean, come on.

I think sometimes people will spend the money because they want to verify what you think, right? Sometimes you just want to— Is it actually right? You got to test it.

Okay, closing question. What are the chances we are in a simulation right now?

It's a fun question. And I started at some point, I just answered, yeah, we're definitely in a simulation. But what I do feel, however, is whether we are in a simulation or not, I don't think that makes our experience any less real. And I think that's fundamentally what I believe in. Maybe we live in a simulation, maybe not.

It's real to us.

Yeah, for me, I don't really care.

Yeah, unless you die and you wake up in the level higher or below.

That would be interesting.

I feel like you wouldn't care. Once you die, then you find out you're in a level higher.

I'm worried about that when I die.

I think the other thing that— okay, so I like the mathematical answer to this, which is the sheer number of possibilities that you are in a simulation far outweigh the sheer number of possibilities that you're not, except for the— Simplest answer, which is it is computationally very expensive to have you be a simulation. Okay, great. You've been very generous with your time. Congrats on all your success. I met you just after your Smallville paper and had no idea that you could build such an enormous company. And then now you're like, well, it's a $100 billion market, but that's just where we're starting. So this is very exciting.

Actually, the $100 billion market was not the TAM. That was only part of it.

Yeah, exactly. It's like if you're thinking too small.

Well, I do believe that maybe my final note here might be, again, I love science fiction. You look at any advanced civilization in science fiction, there's 2 twin pillars of technology. One's AGI in some form, and the other is simulation. So I think the market's pretty big here.

Yeah. Tell us about the company. You guys just raised a lot. You're half a research lab, half a company. I guess you're hiring, or are you based?

Yeah, so we're based in Mission Rock, so not too far away from where we are right now. So we're in SF, but we are also bicoastal. So we have our team— I would say our headquarters is in SF, and we have a lot of our technical talent in SF, and we do have a smaller office that just opened up actually in New York. We are, as a company, an interesting one in that today, obviously, there are AI NeoLabs, and then there are AI product companies. Similarly, it truly is both. So this is a company that was founded by 4 co-founders, myself, Michael Bernstein, Percy Liang, Lainey Yanlin. Michael, Percy, and I are all researchers. So of course, Michael was one of the co-authors of ImageNet, kickstarted the AI revolution back in 2013, has been instrumental in human-centered AI. Percy coined the term foundation model, and obviously is one of the greats of the AI researchers today. And Lainey is my business counterpart, where she led some of the fastest-growing AI-native companies from their seed to A and B. But we have this DNA at the company where the vision of the technology that we're creating is continuously developing, that we are getting people who were basically my labmates. We are right now about 60 or so people. 15%, almost 20% of the company population actually are just my labmates from my compersist lab. And we are— it's actually quite fun because many of them then had gone on to OpenAI. Google Gemini and these places. And so it's been a few years since we really got together and had a chance to work together, but now they're coming back and really building out this vision that I find to be quite exciting, and that excitement is shared. So there is that motion at Simile where we are a group of researchers trying to do something that no one is working on that we find to be the most impactful potentially. But at the same time, this is, again, technology that can make impact today. So we have an amazing group of engineers, product people, and designers who are sitting here with us basically trying to imagine what does it look like to help people understand what simulation can do and make real-world decisions with this. Having both and then deploying it to some of the largest customers in the world today, it feels quite unique.

Yeah, it's very compelling. One part of it was— this is the call to action— like, who are you hiring? You've done part of it, which is you've got a very talented group. Who are you hiring? Like, what roles?

So honestly, at this point, we're hiring across all sections. We are always excited to bring on amazing research talent. So if you're interested in working with our labmates, we're always welcoming of amazing researchers. But also, we hire amazing engineers that Some of whom I respect the most. Many of them actually come from places where we have personal connections with. So many of the members are from Figma, Notion, Arvy, and so forth, but also more broadly from the companies that we as a team have really admired. So engineers both on the product side, infra side, we're all looking for those hires.

Well, lots of people. I think you made a really good case. So thanks, and we'll see you in the simulation.

Amazing. See you all there.