The Twenty Minute VCThe Best AI Companies Have Unique Data Acquisition Strategies | Simile Co-founder & CEO
EVERY SPOKEN WORD
60 min read · 11,664 words- 0:00 – 1:26
Intro
- JPJoon Sung Park
I think there's a world in which in about two, three years we're running a single simulation session that people will pay $100 million for.
- HSHarry Stebbings
This is Joon Sung Park, founder and CEO at Simile. They predict the future. They're a simulation market that try and predict future human behavior. It is incredible.
- JPJoon Sung Park
My fundamental thesis here is for AI companies of this generation, you need to have an interesting data strategy that's going to be defensible.
- HSHarry Stebbings
This was one of the best AI technical conversations we've had in a long time, and it was incredible to have Joon on the show.
- JPJoon Sung Park
People live through different stages in their life, and they have different careers, different jobs. At each stage of their life, were they the reason why that thing was successful? If you squint, were they the common denominator?
- HSHarry Stebbings
Ready to go? [rock music] Joon, I'm so excited for this, dude. When Shardul told me, uh, that I had to meet you... I'm gonna be honest, Shardul does not tell me often that I have to meet someone, so I was like, "Wow, this is-- I feel honored. Thank you, Shardul." Um, and then we met when I was on holiday with my family, and I remember my, my grandparents were, like, asleep upstairs, and so I was whispering to you. And I remember being, like, so excited by what you were building, but then also having to be incredibly respectful of the sleeping elderly people next door.
- JPJoon Sung Park
[laughs]
- HSHarry Stebbings
But thank you so much for joining me, dude.
- JPJoon Sung Park
Thank you for having me.
- 1:26 – 3:56
The Valentine's Day Simulation That Put Joon on the Map
- JPJoon Sung Park
Excited to be here.
- HSHarry Stebbings
Now, when I spoke to a lot of your investors and friends before, they all said that I had to start on the very unique background you have of specifically kind of became very well known for a particular project, and it centers around Valentine's Day and a simulation that happened as a result. C- can you explain what happened and how that potentially led to the early days of Simile?
- JPJoon Sung Park
For sure. So this was 2023, and we were-- we had this idea that large language models are often used for simple tasks like classification, simple generation, but we thought that these models actually had a lot more potential. One of the early observations that we made was that these models are trained on so much of human behavior data, sentiment data that were expressed on the web. So if you poke at them sort of at the right angle, you could actually extract a lot of realistic human behaviors out of them. I thought that was really interesting, and it was also particularly interesting in that it was domain agnostic. So if you look at the literature in computer science, for many decades, we've always had the vision of creating agents that are meant to be generalizable, that are meant to really be able to act like human in any environment. And my mind went to, well, maybe we have that opportunity here. So what we ended up doing was... Well, if we are to fast-forward many years into doing this, what would be the most ambitious vision that we might have? And that was creating entire lived experience of a town. So the idea here was we would make a game town, and we would populate it with 25 NPCs, so non-player characters, except these characters would actually wake up in the morning, do their routines, go to work, have relationships, and do all that. They would actually remember their interactions. They would actually plan their days. And some of the surprising things you end up seeing was the simulation itself was set the day before Valentine's Day, and you actually see these agents come together, have parties, like self-organize. So they would actually plan parties. They would decorate the café and so forth. We thought that was really interesting. Now, two fundamental contribution from that work. One was it was one of the earliest example of creating agents. So this particular, uh, set of agents were paired with, back in, back in the day, GPT 3.5 Text DaVinci. So we didn't quite have ChatGPT back then. Uh, and then was paired with memory planning and reflection. Really the first times that those concepts came out to be, uh, an explicit part of the architecture in, quote-unquote, "agent tech" workflows.
- 3:56 – 7:07
How Agents Got Memory, Planning & Reflection — The Origin Story
- JPJoon Sung Park
The reason why we actually got that inspiration was if you had more than one agent side by side, you want them to remember each other.
- HSHarry Stebbings
Mm.
- JPJoon Sung Park
Back in the day, language models didn't really have the concept of memory. So I thought, "Okay, you have to give them mem- the memory so that they don't say, 'Hey, nice meeting you,' every time they meet their roommate." So we had them give-- like, have this concept of memory and planning and reflection to make sense of very long-term landscape.
- HSHarry Stebbings
H- how do you solve that memory problem? 'Cause everyone says, "Oh, we have a memory problem today."
- JPJoon Sung Park
Yeah.
- HSHarry Stebbings
How do you solve the memory problem of agents to prevent that from happening?
- JPJoon Sung Park
So back in the day it was actually... The initial idea was fairly simple, whi- which was that these language models are actually quite good at processing natural language, so we'll put everything in markdown text file. That was it. That sort of worked. Now, the, the issue there, however, is because the language models have context window and because-- And even today, even if the context window is getting larger, the kind of experiences that these agents can have in the small game town is immense. And imagine now if we were to bring this to real life in world like the one we live in, the amount of memory that we accumulate is huge. So the, the problem becomes how do you make sense of this large quantity of memory? So imagine you went to get omelet five times in a day. You want to make sense of that aside from, "Oh, I went to get omelet five times," you know, five times throughout the week or something like that. So we had this concept of reflection, which basically was every certain interval, it's like a shower thought, you have... you ask agent explicitly to get bunch of their memory pieces and basically make sense of them. Why did you get omelet so often this week? Were you busy? Do you like omelet? Why are you studying for this test so hard? Like, you are in library every single day. Like, does this matter to you? And they will actually start formulating ideas that are more higher level than what happens on the ground truth. So gradually they start to realize, "Oh, this particular research topic, I'm actually quite invested in it. This might ha- actually have something to do with my childhood or my fundamental memory." This actually shapes who they are as a person. So that ends up becoming a very useful function in creating these agents that have personality, that actually has a point of view on the world, that can actually make sense of a lot of this data. So that's how we did it back in the day.
- HSHarry Stebbings
And so when we think about a simulation model today, for those that don't know, a simulation model is essentially that. It's the creation of agents that then produce a set of activities or actions that then will show us what a simulated future world might look like. Is that correct?
- JPJoon Sung Park
That's right.
- HSHarry Stebbings
Got you. Okay. When we think about then building a simulation model comp-- Would you say Simile is a simulation model company?
- JPJoon Sung Park
We are a company that is creating foundation model of human behavior that can then be used to create simulations of individuals, simulation of subpopulations, and down the line, the simulation of entire ecosystem and even the market.
- HSHarry Stebbings
Do you sit on top of core foundation models? How do you think about the relationship for those listening between an OpenAI Anthropic frontier model provider and you?
- 7:07 – 7:59
Simile vs. Frontier Models
- JPJoon Sung Park
Yeah. So this is a great question. So the way we see it is if you look at large language model companies today, fundamentally, the task they have at hand is to create super rational, intelligent machines that are good at coding, that are good at natural sciences and mathematics. Simile doesn't really care about any of those. What we care about is if we have a person make a mistake in this context, we want our models to make the same kind of mistake. We want our models to be biased in the same way humans are. In a way, we want to be a representation of people's values, preferences, and taste, sort of their subjective half of their brain. That's what we care about.
- HSHarry Stebbings
I love that. A lot of what people say is different to a lot of what people do. How do you think about the chasm of what people say and what people do and how that impacts your models?
- 7:59 – 9:33
Say vs Do: Why Behaviour Data Beats Survey Data
- JPJoon Sung Park
For sure. So say do give is real. And you know, if, if you look at the web data, it is fundamentally data of what people have said, not what they have done.
- HSHarry Stebbings
Yeah.
- JPJoon Sung Park
And obviously, large language models today are trained prelim-- uh, preliminary, um, mainly on this web data. For us, we actually do collect a lot of behavior data. We collect, uh, transaction data. We collect observational data. We also partner with our, um, customers, uh, our vendors to collect some of this data. But my personal hot take here is a lot of observational behavior data and what they're amazing at is actually helping you create a correlation of the observation and what could happen in the future. Good for prediction task. But my take here after interacting with so many of our customers and also being in research, no one really cares about prediction. No one really cares about what's going to happen in the future unless you're trying to predict the stock market. What people actually care about is they want to shape the future. They want to know... Imagine you're a Starbucks. Doesn't really help them to know that your Frappuccino sales is going to tank in two quarters. They'll hear that, and they'll be like, "Well, what do we do about them? That's terrible." What they want to know is how can we prevent it? What do we need to do now to change the future? And there, what you really need is causal mechanism. You need a model that can actually reason about causal mechanisms and counterfactuals. So the kind of data that we care deeply about is a lot of randomized control trials.
- 9:33 – 12:31
Prediction Is Overrated
- JPJoon Sung Park
We actually run a lot of AB testing. We show the models, imagine people have done this versus that, this is how their behaviors will actually change. That becomes a core part of our training asset. So this is actually the data collection that goes beyond observational data that Simile collects.
- HSHarry Stebbings
Is data collection acquisition the hardest element of building simulation models for you? Like, if we think about the kind of core pillars for traditional models, it might be compute algorithms and data. Is, is data the biggest challenge for you?
- JPJoon Sung Park
Data is an important piece of Simile, for sure. Uh, my fundamental thesis here is for AI companies of this generation, you need to have an interesting data strategy that's going to be defensible. And for us, really the data collection challenge comes from two angles. One is actually sourcing people. Sourcing people here is a little bit different than what other language model companies might consider to be their people or their population. We don't go after these expert programmers or expert scientists. We go after people like us, like everyday people, uh, living their everyday life. Um, that's-- But what we care about is are they representative? Do we actually have the same representation of people as we do in the world that we live in? And then actually asking the right questions to these people. What are the experiments? What are the questions that actually get at the fundamental core nature of who they are? Some of the questions we actually ask at the start of our data collection at times is actually saying something like, "Tell us the story of your life. Where did you grow up? What did you experience? What were some of the hardest problems that you had to tackle or decisions you had to make?" Tells you a lot about these people, and that's what we try to do.
- HSHarry Stebbings
In terms of people don't want to predict the future, just so we can drill down on that, I thought they do. Like Starbucks, if they can predict that Frappuccino sales will be down in two quarters, they can amend bluntly their buying cycle. They can change how much they purchase. Isn't that valuable? What-- Um, what am I missing?
- JPJoon Sung Park
But that's the thing. The reason why they want to know is so they can change their strategy. So certainly talking about, oh, how much, uh, resources they actually need to actually serve this market, that is a kind of changing in behavior. But fundamentally, it is about counterfactuals. So while we have this market that we want to serve, we want to maximize our value as a company, what do we need to do to make sure that we react to this dip in the market, whatever it may be? Now, fundamentally, though, the work that we do is about people. We do, uh, we try to simulate people and represent people's perspectives. So the value that we provide is counterfactual in terms of what your consumers, what your population would do.
- HSHarry Stebbings
When you look at what can be done for some of the biggest brands, you mentioned like a CVS there, incredibly valuable for surveys, for customer feedback, for determining what customers really want moving forwards. I don't know how to say this without being rude. How do you... Do you want to just be a next generation Qualtrics, and how do you prevent that being the angle?
- 12:31 – 15:48
Is Simile Just a Fancy Qualtrics? The TAM Question
- JPJoon Sung Park
Yeah. So the way we see it is, again, fundamentally, the core primitive of what we're, what we're trying to build is very straightforward. You tell us what population you're interested in, and we'll go model them. And really so far, the layer of innovation has lived in the more tooling layer.
- HSHarry Stebbings
Mm.
- JPJoon Sung Park
How can we create better survey tool? How can we create a better interview tool? Simulation is fundamentally about something different, which is how can you create the most generalizable model of people so that we can represent people's viewpoints at scale? And that goes beyond simply running surveys or interviews. Down the line, I actually see simulation as a field moving into context where, "Hey, can we actually create simulations of many people interacting with each other so that you can understand all the downstream implications of your, of your decision-making?" Or if imagine you have a new product you're about to launch, can you actually simulate the entire launch and how the audience might actually react, how the market might shift? And this also goes into the scientist part of me also get quite excited by the vision where simulation, I do think, can also be a cure for many of what we call, quote-unquote, "wicked problems." A good example, uh, here might be things like climate change requires collective action across many stakeholders who have different incentives. One of the reasons why such problems are so difficult is actually finding the right equilibrium state where all com- uh, all different parties come together to make decisions for global good is very difficult. Can we actually simulate those decision-making processes? Can we actually simulate even things like in what conditions does a democracy fail? Can we actually predict that? These are the kind of questions that simulation ultimately can answer.
- HSHarry Stebbings
Can I ask you, mm, thinking about, like, democracies failing, elections for a government, this would be an incredibly useful tool. How do you think about who you can and should work with versus who you shouldn't?
- JPJoon Sung Park
Yeah. This is for us where the principles matters so much. The way I see it, simulation as a piece of technology is one of the twin pillars of technology. I'm a fan of science fiction. You read any advanced science fictions, there's always two pillars. One is some form of A- AGI. That always shows up. The other is simulation. And like with any powerful technology, the misuse, the potential for misuse is quite real. And the way we see it, simulation at its best ought to be representation at scale. People have different viewpoints, different perspectives, different tastes. Many of their viewpoints are not considered in rooms where important decisions for them are made. We want to always say we listen to our people, we listen to our customers, we listen to our stakeholders. In practice, very difficult. This is a way for us to ensure that in every decision-making, we actually listen to people at scale. That's the North Star.
- HSHarry Stebbings
How much data do you need to feel confident in an accurate prediction outcome to be displayed?
- JPJoon Sung Park
Yeah.
- HSHarry Stebbings
Is it 100 people? Is it 1,000 people? Is it a million people?
- 15:48 – 16:56
How Many People Do You Need for Accurate Simulations?
- JPJoon Sung Park
You want to have more people represented so that you can segment down to specific subpopulation. If you look at any social scientific literature, if you have a very narrow population of interest, you would usually get statistical significance in the study that you want to run by the time you have 1,000 people. However, oftentimes the kind of ways that people query our system is they want to come in and say, "Hey, filter down to X, XYZ population." Those filters are often created on the fly. For us to then be able to simulate people's responses across all those filters does mean we want to represent the entire population. So that's the journey that we're on.
- HSHarry Stebbings
Is it self-fulfilling? Like, do you get better and better at predicting over time?
- JPJoon Sung Park
You know, I think that certainly is the case because, right, there is the data flywheel. There is the learning that occurs as we get more and more simulated results and see what happens in the ground truth. That absolutely, yes, and this is obviously one of the core value proposition for our early partners because they know in their business context is simile getting better and better and better,
- 16:56 – 19:59
The Data Flywheel: How the World Becomes the Ground Truth
- JPJoon Sung Park
and do they have that compounding advantage?
- HSHarry Stebbings
It's kind of like AlphaGo. You know, they just beat the shit out of the model and played it 1,000 times. Every day of activities and outcomes in the world is another game of AlphaGo where you can correct the model on what was wrong and what you missed and what didn't happen, and 10,000 days in, you should almost be better than the model at the model. Do you know what I mean? [chuckles]
- JPJoon Sung Park
So this is actually quite interesting. Um, you might think of... L- let's actually think about a different example. So how does data flywheel work in simulation, and why would it work? If I were to take a brief detour and talk about coding, the reason why coding agents has had such massive improvement over the years was because their learning, their reward function was extremely clear. If you make a suggestion and your user says accept, fantastic. If they say reject, also very useful. You very quickly know what is good and what is bad. That actually was one of the core learning mechanism for these models. And it might be easy to look at simulation as a field and say, "Well, where are you going to get the reward?" Because fundamentally, all the things that you're trying to predict is happening in the future. It's going to be hard to validate. It is true. At the same time, I actually think simulation has even better mechanism, which is the world is our ground truth. We live in the ground truth world. So what we can do is every single day we can be generating tens of thousands of hypotheses. Each hypotheses is mapped onto an end statement. If this happens, we know whether we can validate the simulation to be right or wrong, and we're basically watching the world every day, seeing which of those hypotheses are answerable at what time. And we can basically say, "A month goes by, we generated a million hypotheses, X percentage of them came true." This is the best way to learn about the world.
- HSHarry Stebbings
Does it take a huge amount of compute to run these simulated environments at scale and well?
- JPJoon Sung Park
Compute is an important piece of simulation. Of course, a lot of the work that we do is to make our simulation be more efficient. So a lot of our compute initially actually goes in to create the initial breakthroughs in technology. So it is actually exploring different ways to train. It is exploring different kind of dataset. Once we have a point of view, we can very quickly make it efficient. So some of the things that I've seen, uh, within Simile as we built this company over the year is right now we have a model that's been in production. This model used to cost about 100 times more to run than it does now. And some of it does happen because we actually found different ways to model, but with the same reward model, with the same philosophy, but just in a way that's much more efficient at inference time. So there are these kind of tricks that we can play and these kind of scientific advancement we can make to make things cheaper. A lot of the investment, however, does go to find that initial point of
- 19:59 – 21:43
Can Simile Simulate Elections, Democracies & Wicked Problems?
- JPJoon Sung Park
view.
- HSHarry Stebbings
Can I ask you, when you look at, like, serviceable market or t-t-total addressable market, TAM in venture speak, you know, you obviously have your CBS's and your huge enterprises who would absolutely want to work with you. It can also be consumers-
- JPJoon Sung Park
Mm-hmm
- HSHarry Stebbings
... like regular consumers wanting to see what happens if, and running their own environments. Is this a play for everyone? Is this a play for the biggest companies in the world? How do you think about the TAM for something like Simile?
- JPJoon Sung Park
So the start of my career really came from research, obviously. And the job of a researcher is to serve the humanity, that we do our research obviously for our own enjoyment as well. We love the process of finding new things in the world. But fundamentally, it is a service. It is a belief that if we are able to make scientific breakthroughs, this is going to down the world, down the s- down the line, serve everyone in our society. That is how I see simulation as a field as well. So right now we do serve enterprise customers for a couple of reasons. One, obviously, uh, I'll be frank, there is the budget, that there is a clear product market fit that we see today, uh, that does excite us. And at the same time, it is an amazing way to validate the technology. It is very important to us that we get the feedback loop to be as tight as possible so we know when our simulation's right, when our simulation's wrong, and we're improving it every single day. And obviously, there's this side, you know, part here that's just as important, which is I have a colleague, uh, when I was at Stanford, uh, my office next, next to mine, uh, was Pat Hanrahan,
- 21:43 – 33:07
Who Should and Shouldn't Have Access to Simulation Technology?
- JPJoon Sung Park
who was one of the founders of Tableau. He's a graphics professor also Tur- uh, he won Turing Award, so very well-known, uh, person in this, in this landscape. [clears throat] An advice he actually gave me and some of my colleagues was, "The best way to get feedback is to actually ask people to pay you." That was their core philosophy at Tableau, and I also see this here. So getting the best kind of feedback matters a lot. So enterprise market, market research right now is a wedge that we found that actually have significant budget, that have immediate product market fit. But down the line, I do want this technology to be used by rest of our society because fundamentally what we are trying to serve is help people make better decisions.
- HSHarry Stebbings
Before we move on to expansions that it could be used for, when did you know you had product market fit? You said you felt that pull. When were you like, "Ah, we, we got product market fit here"?
- JPJoon Sung Park
Many of the Fortune 500, uh, board members and their C-suites reached out. In part, uh, they do come to Stanford to see some of the demos that are happening in the lab, and they all saw the Smallville demo after it got released, and everyone thought, "Oh my God, if we can simulate a market like this, this is going to change the way we operate." So you could immediately sense the product market fit, and really this was the forcing function for us to then say, "Okay, this is actually quite interesting. We're actually going to show and validate that our simulation can not just be an interesting demo, but it's going to be accurate." So we spent about a year actually demonstrating that we can create models of people that are actually amazing and validated at predicting people's behaviors across surveys, behavior experiments, real environment, and we show that we can actually predict people's behaviors and attitudes 85% as accurately as people replicate their own. We put that work out at the end of 2024, and that's really what started the field around synthetic panels, simulations, and that's the market that we're seeing today.
- HSHarry Stebbings
Will synthetic panels be larger than human panels in three years' time?
- JPJoon Sung Park
The way I see it, synthetic panels will be larger than our, what we know to be the current human panel market, in part because this can really raise the ceiling of the kind of questions we can answer. Well, what I see today in the market is actually quite broken. We have so many questions we want to ask about our market if we were to release this product, if we were to have this particular strategy, this particular policy. You're a scientist, then you want to run this study or you want to try, like, this macro scale experiments. You are looking at maybe 5% of those ideas get answered. The rest of the 95%, we never bother experimenting with because we either don't have the ability to do them, especially if it's something at the emergent scale, like we literally don't have a way to run those e-e-like emergent scale experiments. At the same time, we don't have the budget and time for it. So a lot of the decisions that we make as a society, we base on our gut instinct. Sometimes they're good, but sometimes they're very biased based on our own narrow experience. So what simulation will do is unlock that limitation and us to actually test every single hypothesis that we have about the world before we have to launch into the world.
- HSHarry Stebbings
How do you balance the pursuit of the next dollar and serving customers who pay a lot of money, I'm sure, versus research prioritization and maybe focusing dollars there over building out a customer success team [chuckles] and an FD team? How do you balance the profit maximization with the research purity?
- JPJoon Sung Park
So Simile is interesting as a company. So Simile is a company that has a real product and engineering team, but at the same time, we are a research company. The co-founders, the four of the three co-founders are researchers. So we have as co-founders myself, Michael Bernstein, Percy Liang, Lainie Ellen. Myself and Michael and Percy were all researchers at Stanford. So I led research around agents, simulations. Michael was one of the co-authors of the ImageNet that really kickstart the AI revolution, and he's been a leader in human-centered AI. Percy was the person who literally coined the term foundation model. And the vision for this particular area is the vision that we can actually create the next paradigm shift in AI and in the way we view technology and the impact of that technology in the form of simulation. The reason why we are able to, however, operate as a research lab, but also have an amazing product and engineering function and go-to-market function that's led by my counterpart, Lainie, is the alignment between what the technology can do, the promise of technology, is so close to what our market actually requires. The better the model gets in representing people, the better simulation we can create. It immediately means better experience for our users because they'll have much more grounded, much more accurate simulation. It is very difficult to maintain both a lab and a product company if there's not that alignment. But when there is, it can be quite magical, and that's what we are seeing at Simile.
- HSHarry Stebbings
Can I ask you, when we think about the pursuit of some of the largest companies on Earth, we, we mentioned, I'm not sure which customers were able to say versus not say, but you mentioned CVS. People always think it's like multi-year, incredibly long sales cycles. Was that something that you experienced, or was it a different experience for you getting and working with some of the biggest companies on the planet?
- JPJoon Sung Park
What's been fascinating to me coming into the field of simulation, especially in this market, was last year when I started the company and when I-- So I left Stanford, uh, June of 2025, so it's been exactly one year. I actually thought our field will actually-- the market will take about a year or two before they warm up to the idea of simulation. So we'll basically find, build the right foundation for this company and for this market, and we'll go aggressive based maybe towards the end of 2026, was what I had in mind. And that's not what we experienced. What we experienced was our customers were moving extremely fast, also in ways that, like, truly made me change my perspective on corporate America. Our, uh, leaders, uh, that we work with, uh, for instance, CVS, I've been working with this, uh, particular, uh, leader, uh, Shri, uh, who is their VP of insights, extremely forward-looking, extremely ambitious, extremely hardworking, and amazing counterpart to vision like Simile. But what I've also found was the pain they were feeling in their day-to-day work was so real. It was way more acute than I could have imagined, that when they realized that there is or there could be an answer in this market for addressing some of those pains, a very slow experimentation, budget, and so forth, they are ready to drop everything and try us out. So we actually saw some of the largest customers in the world move at a lightning speed for enterprise, where we saw them close deals within three months.
- HSHarry Stebbings
Three months. Wow. Okay. That's very different to what people traditionally think. What matters more to them, speed of output, in other words, being able to get results very quickly on their simulations, or accuracy of simulations?
- JPJoon Sung Park
Yeah. Uh, it is both. There are so many questions that they are truly relying on their gut decision today, that if they can get some form of evidence to at least directionally guide them in the right path, then they're ready to try it. And then they very quickly realize that, oh, this is actually an amazing way to interact with a lot of data. This is amazing way to gain evidence that is actually quite accurate. And they actually-- One of the ways we actually got some of our first customers was in the first call, they actually had a finding from, you know, large consulting companies, and they basically queried our system, "Hey, if we were to rerun this, what would the system say?" And we predicted the outcome of studies that took three to six months, but just within two minutes. That's very powerful.
- HSHarry Stebbings
It must be so compelling in a customer conversation to be able to say like, "You did this campaign. If you had done this campaign, it would have been 12% more effective. Do you wanna buy our product?" [laughs] Like, it is such a good sell. I'm, I'm a seller. To be able to have that data is unbelievable. How do you think about value extraction efficiently? And what I mean by that is, like, if you work with, like, a CVS or you name any of your big companies that you work with, these are massive companies where if you're able to do your job efficiently, you can Move the needle to the tune of hundreds of millions for them, in some cases billions of revenues.
- JPJoon Sung Park
Yes.
- HSHarry Stebbings
Charging like a million bucks feels like a large chasm between value generated and value extracted. How do you think about closing that chasm to be more fair?
- JPJoon Sung Park
Yeah, that's a great question, and I, I see the market moving in this direction. One of the core premise and one of the ways that our customers are actually finding value in Simile is actually avoiding really damaging decisions that could have costed them hundreds of millions of dollars.
- HSHarry Stebbings
Hmm. So it's prevention, not optimization.
- JPJoon Sung Park
It's both, uh, but certainly prevention is a huge... It's an obvious value case, right? That, oh, wow, that could have been a total disaster had we run that. That would have costed us half a billion dollar. We ran simulation and that prevented it. That's a no-brainer. This is a, this is a true painkiller in their case.
- HSHarry Stebbings
If you do your job efficiently, can Kalshian polymarket still exist for a lot of their markets?
- JPJoon Sung Park
So it's an interesting question. Uh, I do certainly think there is an overlap here, in that we are companies that are fundamentally interested in the future and helping people at least get a glimpse of what the future might be. Where I see Simile come in is we are a company that is not just interested in what's going to happen, but more on how it's going to happen and why. So in that way, and this is also the value proposition that our customers are most inspired by, it's one thing to simply predict, but can we actually show here are all the steps that your ecosystem is going to take to get to that particular outcome, and this is the way you can prevent that or you can encourage that. That is ultimately the power.
- HSHarry Stebbings
When it comes to team building, you said about the craft of team building, and I think it's really interesting because it's an ongoing challenge building the best team. What have been your biggest lessons coming out of research in what it takes to build an all-star team with Simile?
- JPJoon Sung Park
So a couple of things. One is the team has to be balanced, that there are certain power that I can bring to the team, but there's also a lot of things that I don't know. I was a researcher. I was not an enterprise seller. I needed Lainie to be my co-founder to lead that part of the game. Um, so balancing the team and being able to see where your team is lacking, especially as we scale, there are new gaps that are emerging.
- 33:07 – 35:59
How to Build a World-Class Research Team That Doesn't Quit
- JPJoon Sung Park
Actually seeing that ahead of time and making sure that we fill those gaps, I do think is a core fundamentals of building a great team. At the same time, I also do think it's important that the team remains consistent in their values and in their rigor. This is more of a painter's analogy. Um, so when I was a painter, I was a figure painter, so I worked a lot with human subjects, portraits, figure studies. There's sort of this untold secret amongst figure artists, which is it doesn't matter who you paint, your subject sort of looks like the painters themselves in some ways, or at least they share the similar vibe. I think building a team is actually a lot like that, in that in the best team, in the team that you care deeply about, you really should see yourself in the team. And for me, couple of things matters the most. One is, and this is the same standard I try to uphold for myself, but one is, are we the common denominator of success? People live through different stages in their life, and they have different careers, different jobs. At each stage of their life, were they the reason why that thing was successful? If you squint, were they the common denominator? If the answer is yes, then what that suggests is a couple of things, that they have extreme degree of ownership, that they are the kind of people who come in and say, "Doesn't matter how everything else goes, I will personally make this successful." It, it also shows the ability to reinvent themselves. So one of my co-founder, Michael Bernstein, he has had a very interesting career as a researcher, where during his PhD 10 years ago, he started his field in crowdsourcing collective intelligence. Then very quickly during his early years as a faculty member at Stanford, he went into, uh, different areas of AI, and then now into generative AI agents and simulations. And at each step of the way, you could sort of see in the work he's done that this is very Michael. You could see that this is the person who led a lot of the success. That's an amazing signal. And another piece, the second piece for me is, this is a little bit more niche to myself, but I found this to be very true, at least, uh, to the way I look at the world. Do my leaders and my team have two superpowers that's not supposed to coexist in one person? Any expert will usually come in with one superpower or even sometimes multiple superpowers, but they're all correlated. You're an amazing programmer who happens to be amazing at mathematics. Very common. Where I found things to be particularly compelling is if people have
- 35:59 – 39:10
The Two Contradictory Superpowers That Make Great Founders
- JPJoon Sung Park
two superpowers that's really contradictory.
- HSHarry Stebbings
The most common one here is actually the greatest CMOs, which there are very few. I count on one single hand, are unbelievably data rigorous oriented, scientific in their approach, and then you blend that with this creative artistry imagination, and they are two relatively opposing kind of mental approaches, I think.
- JPJoon Sung Park
Yes.
- HSHarry Stebbings
And it's very rare to have that in a CMO, but when you have that, that is the world-class CMO.
- JPJoon Sung Park
And that's magical.
- HSHarry Stebbings
Yeah.
- JPJoon Sung Park
Uh, uh, that particular description I actually sometimes have used for my, uh, board members for Dual. Deeply analytical, but he's very intuitive. And I think that's how he makes investment, and that happens to be very successful. So hopefully, Simile will continue on the success. But in my team, the kind of things that I also see as an archetype is, and I also categorize myself as one of these kind of people, on day-to-day basis, for instance, uh, Lainie, uh, one of my co-founders, she's paranoid. She's somebody who will come to the table and say, "Unless we put everything on our table today and do everything possible, we'll lose, we'll fall behind, that everything will fail." But long term, she's religious. This is somebody who fundamentally believes the world is stacked for her, that no matter how this goes, we will make this successful. Actually balancing those two at the same time is quite difficult because if you are short-term paranoid, then you're likely going to be very pessimistic about your future, and you're, you might be amazing at shorting stocks, but not great as a company builder. If you're religious, you have the opposite problem, which is you're complacent, that you sort of feel like, ah, we don't have to put everything on the table today. Things will be okay. Balancing those two needs somebody who is broken in some ways, that somehow they found a way to be deeply paranoid, but at the same time, ignore all the paranoia of today to believe that the world is going to be amazing.
- HSHarry Stebbings
I think it's actually, it's exactly me, and I think it's actually you believe that the paranoia that you hold today helps that future state be amazing. You know, I always talk often to the world's most successful founders, and they all say... I say, "What do you wish you'd known when you started?" And they always say, "Oh, I wish I'd known that it would all work out, and I wish I hadn't been so worried." And I think it's the worst answer you could give me because the fact that you were so worried, and so you did the prep, you put the work in, you stayed up late to do that presentation, that led to the success. Without the paranoia, Lainie didn't hit the quarter. Lainie didn't set the urgency in the sales team. Lainie didn't hire those extra people because you didn't know the access demand would be there. The paranoia drives the success. [chuckles]
- JPJoon Sung Park
Yes.
- HSHarry Stebbings
It's, it's a really interesting one. Can I ask you, Brandon at McCall was on the show recently, and he was like, "Honestly, researchers, they're in the tens of millions of dollars. It is, it is so expensive." Do you find that to be true, and how do you find this intense war for research talent in the Bay?
- 39:10 – 43:28
Competing for Research Talent in the Tens of Millions
- JPJoon Sung Park
Absolutely. So the research talent is very sought-after today, and I have my closest colleagues and friends whose total comp does range in tens of millions. Now, when they join Simile, I, I'm fairly upfront with them. It is not possible, doesn't matter how many hundreds of millions that you raised, um, meeting them at their base salary is tricky. However, the researchers fundamentally care about a couple of things. They care about a vision. If this idea truly come to fruition, like, these are people who have literally seen OpenAI being l- the laughingstock at the, you know, in Silicon Valley to becoming a nearly trillion-dollar business, and these are people who have seen Anthropic go to that same state within the past five years. So these are people who are fundamentally aware that deep, ambitious vision can actually come to fruition. So they care deeply about the vision. They also care deeply about the impact. What are the societal impact of the technology that they'll be working on, and is it actually interesting to them?
- HSHarry Stebbings
Do you worry about the retention problem in the Valley today? You see so many researchers move with such promiscuity, if you can use that word. Do you worry about the retention problem today?
- JPJoon Sung Park
Consistently. And in fact, I actually do view the role of leadership to be, uh, that of obviously hiring amazing people, but also providing a platform where individual members can express their superpower to their maximum degree. And I do think this actually part genuinely does matter. And retention can be challenging, but it can be done. And one of the core sort of, uh, I have a small sense of pride in the way my career has panned out over the past six years or so as a researcher, where PhD students often go from one project to the next, and their entire co-authorship will change, maybe except for your advisor. I've had sort of an interesting career where in the past six years, all my core team members never left, that we all moved from one project to the next to the next together. And now when I said, "Hey, I want to do this thing and build Simile," I was able to somehow convince Mike and Percy, who were-- they were actually my doctoral advisors, to actually come join me. And I think a part of it is, you know, we worked so closely together that there is genuine sense of trust. But at the same time, you know, I am somebody who fundamentally believes that, one, it is my job to communicate the degree of confidence and trust to the team, that they feel like this will work out.
- HSHarry Stebbings
Can I ask you a really important question for me, which is I, I see a lot of amazing people in research and academia who are considering or starting a company-
- JPJoon Sung Park
Yeah
- HSHarry Stebbings
... in the same way that you did, and I always worry that I'm gonna finance a science project, 'cause science projects, although great and although interesting and intellectually satiating, don't always make great companies.
- JPJoon Sung Park
Yeah.
- HSHarry Stebbings
You've been able to do that incredibly well in the last y- year to 18 months. If you were an investor analyzing a group coming out of academia, what would you look for that would give you confidence that they would be able to make the leap from research to starting a company?
- JPJoon Sung Park
The thing that I actually would look for is, are they married to a problem or are they married to impact?
- HSHarry Stebbings
Hmm.
- JPJoon Sung Park
Sometimes researchers are very much, uh, focused on a problem, and something about that problem fascinates them. But oftentimes it's just not a good company or it's not a thesis that can really be formed into a company for various reasons. But there are researchers who are fundamentally driven by impact that they can have in the world, and for them it means finding a problem that can actually reach people, finding problems that can actually generate revenue, and that's what drives them. You want to find researchers who are in that category.
- HSHarry Stebbings
So talk to me. It was Shradul that introduced us, a huge thanks to Shradul for that. Um, but there's a new funding round that's come to be in the last, you know, month or so. Can you talk to me about the funding round, how it came to be and how you think
- 43:28 – 44:33
From Researcher to CEO: What Joon Looks for in Academic Founders
- HSHarry Stebbings
about it?
- JPJoon Sung Park
For sure. So, uh, we raised our $100 million round, uh, about five months ago. Um, and soon after, um, we were preempted, uh, fairly recently, uh, by insiders. So Shradul at Index, uh, led our previous round and including Shradul and some of the other insiders were looking at the market, and Shradul has sort of this comment that he every once in a while makes where he's seen some of the fastest growing market, and his track record does show that he truly, you know, has seen different markets. He has quite never seen this kind of traction, this kind of pull. When that is paired with the technological progress that has been made and also the amount of compute that we can also leverage to even further accelerate our progress, that sort of prompted our insiders to go, "Can we actually put in more money now than later?" Um, so that's how the initial round conversation came to be. And we were not planning on raising at that particular moment, but there are a couple of
- 44:33 – 58:01
Raising $300M in Six Months: How the Round Came Together
- JPJoon Sung Park
teams that I particularly respected in the Valley, um, that if we were to be raising, I wanted to talk to. And I found out that the team that I had in mind as my top of list actually was, uh, Neil Mehta's team at Greenoaks. And turns out, uh, his team actually has been looking deeply into this market and all the players, how the market is going, and were actually prepared to make the investment, and they were looking for sort of the right time to do so. So I reached out and said, "Hey, this is going to be the round. Uh, we're now running a process, so if you'd be interested in joining, uh, you know, we have maybe a few days to make that happen." And they were excited. So the round came together. So we raised $200 million. So it brings our total funding to be $300 million raised over the past six months or so. It gives us a very meaningful, uh, capital to go after this really ambitious modeling challenge and building up this team. It also brings in a lot of really exciting people to that team. Shradul has been a fantastic, uh, partner. We actually have a lot of Index connection, uh, at Simile. Uh, our seed actually was led by Mike Volpi, who runs now his own firm, uh, and Shradul along with, uh, Neil and Greenoaks team, along with Patrick and-- who is their partner.
- HSHarry Stebbings
You didn't need the money, I take it. You raised $100 million six months ago. Was there a consideration of, "We don't need the money. Why would we take $200 million now?"
- JPJoon Sung Park
There was certainly that consideration. Where we netted out was the modeling does take compute, and this is one of those areas where you can actually-- Here's a fundamentally interesting part of our research. With research, you really cannot control the outcome necessarily, but what you can control is the input and the process. And we were sort of at this moment where, yes, we can actually significantly raise the input both in terms of data, compute spend, to actually meaningfully accelerate this progress. That's when we thought it actually makes sense.
- HSHarry Stebbings
Totally get that. What did you not know about fundraising coming from a world of academia research that you now know having been through three rounds?
- JPJoon Sung Park
Well, one actually here was coming in, I was actually fairly skeptical what the roles of VCs actually were. [laughing] What do they actually do?
- HSHarry Stebbings
We, we, we all are. [laughing]
- JPJoon Sung Park
What do they actually do? How do they help? And I will be honest, I can't still quite put my finger on it and say, "This is the way they help." However, if you bring in the right set of people, what I have realized was they can be sort of the greatest partner, and they can also be really strong set of mentors. Because I never ran a company, certainly not one like this. Uh, this is my first real experience building a company, and I have a lot of technical experience of doing research, but so much of what I need to do on a day, day-to-day basis is new. If there's someone I can trust, then that's an amazing boost. Um, initially, um, I started to work, uh, with, uh, Mike Volpi, and we also had other firm, uh, Astar, who also helped, uh, lead our seed. And these funds and Mike's team, and we also work with Ishani very closely there, they really became sort of core mentor as I operated in the field. And they also were the-- Mike actually introduced me to Laney, uh, who ended up becoming instrumental as I thought about the business, and I found a great partner and friend in her, which also has been an amazing part of this experience. So certainly one is the VCs can actually help in some magical ways. Uh, they have seen enough that if you're experienced VC, they can actually provide the advice that the founders might not have coming in. That is one. Another one here is things always happen a little bit sooner than you would expect. Obviously, coming in, I had sort of a, you know, in my mental model, okay, well, if we raise seed now, that means we might raise our A in about a year and maybe B in the year after or something like that. All that happened within a year. Uh, we raised seed, and I think our series A was very soon after, uh, and our next round also came very soon after. So I think the market is always moving perhaps one step ahead of where you are in terms of their interest in investing in you, and it is useful to be prepared for those moments. That's what I've learned.
- HSHarry Stebbings
Do you worry that the market is so frothy that it can get ahead of itself? Like when you announce this fundraise with the people that you have and with the press that you'll get, you'll get more interest for a next round. And like it's an, it's an ongoing cycle and like bluntly, the hubris is very high right now. Do you think about that?
- JPJoon Sung Park
I do think there's parts of market that is actually quite frothy.
- HSHarry Stebbings
Yeah.
- JPJoon Sung Park
For sure. There is a lot of capital going in. There's a lot of excitement. This is where I actually do care a lot about the fundamentals. Well, who are your customers? Who do you actually work with? What's the market pool that you actually see? And what's the technology? One of the most interesting thing about how OpenAI, Anthropic, like these companies grew, was there were very strong fundamentals they could actually map out. They could actually see, oh, the models are getting better at this rate. Oh, and there's this kind of demand. Some of those they could actually foresee, and some of those we can actually see at Simile as well.
- HSHarry Stebbings
D-does the unit economics vary for you on a per simulation basis? [chuckles]
- JPJoon Sung Park
Yeah.
- HSHarry Stebbings
And what I mean by that is like, you know, if you look at, say, for Anthropic and OpenAI and model rooting, some tasks require, you know, frontier models, which are much more expensive, much more token-heavy, versus others which are much easier and can have a degraded or older model and a much cheaper model. Is that the same for simulations? Do different simulations cost different amounts in terms of compute token usage associated?
- JPJoon Sung Park
They do. Um, usually when you have simulation that is trying to answer something that's much more complex, uh, or something that's, let's say, you want to action, there's another downstream implication of your decision, or you want to do market segmentation study across all of the US, much more expensive. What I also have seen, however, is in-- it is in those simulations where we actually get higher ROI for our users because those decisions are some of the most costly decisions if they fail to make the right one. So this is actually interesting for simulation as a field. So you-- we've all seen as a community the inference costs going up and up and up, and we now have these thinking models that are thinking for like half an hour a day and actually start just spending, like token maxing and, you know, spending, you know, a lot of money on just running this process. I actually do think simulation can actually be the next frontier of that, where in my vision, I think there's a world in which in about two, three years, we're running a single simulation session that's going to take ten, $20 million to run. A single session, but it's going to be so valuable that people will pay $100 million for it. That's where I see it go.
- HSHarry Stebbings
And that would be for the world's largest enterprises. That'd be for a government or whatever that may be.
- JPJoon Sung Park
Especially on the sort of the high end of the spectrum, that's what it would be.
- HSHarry Stebbings
What cannot be simulated today that you think will be possible in three years?
- JPJoon Sung Park
So for me, it's actually a little bit less about what cannot be simulated because I actually do think everything that we want to simulate, we can actually create the initial, uh, [lips smack] proof of concept. However, as we all know, one of the core challenges of AI is actually bridging the proof of concept with real value productionizable technology. So that's actually the chasm that I see. So interesting thing here is I see the world of simulation going into this world where we are creating this very complex multi-agent simulation, or we're running a very long study with many different steps of simulations along the way. But we actually started the field from multi-agent simulation when we created the Small Game Town. That was fundamentally that vision. And it sort of also makes sense because we did that because we... My-myself, Michael, and Percy, we sometimes sit together and do this exercise called time machine game. If we were to ride a time machine, go to 10 years into the future, what's going to be the craziest thing we're going to see, and can we do that now? And that was the motivation for running the Smallville experiment. So this can be done, but the question is, can we eval-evaluate, uh, the efficacy of these simulations? Can we actually propose this as a scalable productionizable system that people can actually rely on for making their decision? That's the chasm, and that's the thing that we see getting, uh, bridged every day. A huge part of it also is getting, you know, models to be better, creating bigger simulation, making the system more scalable. All that becomes a part of this.
- HSHarry Stebbings
Can we play a time machine game with me and you?
- JPJoon Sung Park
Let's do it.
- HSHarry Stebbings
In 10 years' time, what is the craziest thing that you can see happening?
- JPJoon Sung Park
A lot of things, but one thing I will actually say is I am someone who is fascinated by history of technology and analogies that we can draw from it. What I see today that's prominent in AI space is what I consider to be the CPU of intelligence unit. You have this one language model that's really large, that's very smart, that can do very complex reasoning tasks. That's like CPU. [lips smack] What I see coming and what I think simulation as a field can offer is the GPU of intelligence unit. As I mentioned before, Simile does not care about creating really smart, super intelligent machines. What we care about is creating models that are as smart as we are. I, I failed at a lot of things. I want to make sure that the model that represents me fails the same way. But the beautiful part about people is individually we have so much diversity, so much different takes in our world that makes individuals so interesting. But also when they c- come together as a large collective, the emergent phenomena that we're able to draw out is some of the most wonderful thing that we can see in our world, creating a society, creating an amazing process that actually allows us to make all these achievement. Can we actually replicate that in simulation? I think it's going to be quite inspiring.
- HSHarry Stebbings
What's the crazy prediction then that every single person will have a replicable twin that acts and behaves like them in a simulated world?
- JPJoon Sung Park
I think that's the vision. The vision here is, again, representation at scale. We as a society have found over the years many different ways to represent our members. Sometimes it's a form of government. Sometimes it's actually companies. Company we are as a society allocating capital to make sure that they serve the society and the needs of people. But if we can actually create an artifact that in a much more scalable and granular way represent all the individuals, what are the new kind of policies, new kind of companies that can be created on the basis of it? I actually do think it's quite interesting.
- HSHarry Stebbings
If I'm a hedge fund, is this not the most obvious buy in the world? If I'm looking for alpha and edge on everyday activity, fuck, sign a million-dollar contract with you and get unbelievable insight. Yeah.
- JPJoon Sung Park
Maybe Simile will actually own a small, uh, hedge fund down the line.
- HSHarry Stebbings
That's a cool idea. Would you be down to do that?
- JPJoon Sung Park
Well, it turns out we actually, uh, do have, uh, quants, uh, in our firm. Um, so, uh, some of the members who have joined actually do have more quant background, and I think right now they're joining not because they actually want to start a quant firm, uh, at Simile. They actually join because they actually see the vision of Simile very much well aligned with their passion and interest, which is to model the world. Uh, but down the line, I think it's actually an interesting idea.
- HSHarry Stebbings
Is there a world... Again, I'm just thinking crazy time machine world. Is there a world where you are so efficient and so good that actually stock markets become uninvestable because the world is skewed to Simile's hedge fund or similar providers, and actually it is not a fair marketplace?
- 58:01 – 1:00:45
Is Simile the Future of Love and Matchmaking?
- JPJoon Sung Park
we need representatives of these people to approximate their perspectives. So far, that has worked in some ways. It has failed in other ways. I actually don't think this is a limitation we have to suffer through in the future. I think there's a world in which we can truly create a layer that becomes a representational layer of our society and of our collective intelligence.
- HSHarry Stebbings
Is the future of love not also Simile? And what I mean by that is, like, if you were able to create effective simulations of yourself, dating itself could be much more efficient. If I could... I- I've got a girlfriend, and she's watching, and bl- if, if you could date 100 people at the same time-
- JPJoon Sung Park
Yeah
- HSHarry Stebbings
... for the first date, of course, not, not onwards. Uh, you'd get in a lot of trouble for that, June. Um [laughing]
- JPJoon Sung Park
[laughing]
- HSHarry Stebbings
But if you could, it would be much more effective at finding the one for you who could pass through to the next stage. Do you know what I mean?
- JPJoon Sung Park
I, I get that. Well, uh, look, I, I think love comes in different forms, and I think just like-
- HSHarry Stebbings
[laughing]
- JPJoon Sung Park
... as we discussed today, people have such degree of diversity. Uh, personally, I, I am a bit of a romantic, I have to be honest. Um, and, and this actually goes to the point that I mentioned about long-term religious. I actually do believe that love will sort of, uh, find, at least in my life, you know, find its way in a more organic way. Um, I, I actually do think the way I meet the person I personally do care a lot about, um, and the fact that we sort of have shared a journey I personally care a lot about. I think that piece of humanity will, I don't think, ever change. Actually, it is experiencing things together, having that shared memory I actually do think is fundamental to the way we form trust. Um, this is also, we talked about process a lot. This is actually a part of it. For your users using your simulation, can you actually bring them along in this process? I think finding love is, it's a little bit like you're co-founding your life with this person. So can you actually find a process that actually would bring them along in this process of living? I actually do think it does matter.
- HSHarry Stebbings
Oh, you're so romantic.
- JPJoon Sung Park
Unfortunately.
- HSHarry Stebbings
Do you, do you know what I... All I'm thinking of, dude, I'm a content person. I'm a content person and investor. Weird mindset, actually, in, in both ways. There's a, a show called Married at First Sight.
- JPJoon Sung Park
Mm-hmm.
- HSHarry Stebbings
Uh, you might not know it. It's where you marry someone on first sight. But I'm just thinking it'd be the most phenomenal advert for Simile if you could do the perfect marriage at first sight because of simulations that have been run before. But, uh, you know, I'm just leaving you with pearls of wisdom that I think would be great. Uh, we're gonna do a quick-fire round, so I say a short statement. [laughing] Well, who's the most underrated AI researcher
- 1:00:45 – 1:04:51
Quick Fire Round
- HSHarry Stebbings
today?
- JPJoon Sung Park
And there are so many, but I actually do really think, um, there are some incredible people who are working at these, uh, larger labs whose names are not known because they work at larger labs and they don't publish, but I think there are some really incredible people in there.
- HSHarry Stebbings
What area of AI do you think is particularly overheated today?
- JPJoon Sung Park
I do think neo-labs without a clear vision for how they're going to impact the world, I do generally think there is some risk that they will turn out to be interesting research project, but not a viable company.
- HSHarry Stebbings
If you were investing in my seat today, what part of the AI landscape would you say is under-invested and most exciting? You can't say simulation.
- JPJoon Sung Park
I fundamentally believe that, um, for AI companies in the future, you have to have interesting data strategy. Do you have access to data that no one else has, uh, has access to? Do you know how to collect data that is very hard to collect? When you see those opportunities, I would invest. Uh, right now, aside from simulation, robotics is sort of an obvious place where this has become the case. Obviously, robotics, there's a lot of money already going in, so I wouldn't say it's under-invested, but I also do think it is a quite interesting area. I also do think, uh, aside from the core sort of, um, robotics or AI space, the inference layer, but also chip layer, the hardware, I do actually think it's quite interesting, and it's a very hard area for people to crack into. But there are a couple of teams that have done, I think, an exceptional job in the recent months or years. I think they're quite interesting.
- HSHarry Stebbings
Who do you think those are?
- JPJoon Sung Park
The recently Etched, uh, came out, uh-
- HSHarry Stebbings
Yeah
- JPJoon Sung Park
... of, uh, their stealth. Uh, quite bullish on their team. I think they're going to be exciting. Uh, so that's one.
- HSHarry Stebbings
Final one for you. What's the kindest thing that anyone's ever done for you? I think it's a nice note.
- JPJoon Sung Park
No, I'm somebody who actually needed a lot of help, uh, throughout my career. I, you know, I didn't come in knowing everything. Well, certainly, I don't know everything now. Uh, but I also didn't come in as, you know, someone who's, who was an obvious candidate. The one person I quickly call out was when I graduated from college, I moved to Palo Alto, living in somebody's garage. I didn't have a job because I was trying to run a startup that didn't really go anywhere. Uh, but that was a moment where I really felt lost. I could sense in the air that AI wave was coming, and I realized that if you want to be a surfer, you need a wave that you can surf. And I want to make sure that, you know, I-- when the AI wave is here, I want to be there to ride it, and I want to help create the wave in the first place to ensure my seat in it. But I had no research background. I'd done no research during my undergrad, which is quite rare. I would like reject... You know, you know, if you're a PhD student applicant and have no research experience during your undergrad, unfortunately, it's very hard. So I actually messaged a bunch of people, and there's this one professor at Stanford, Mary Wooder. She's a theory professor, uh, happened to graduate from the same college as me. She replied, and I still don't know why. I think it was truly out of kindness and the fact that we're from the same school, and she thought, "Well, okay, here, here's a student who is seeking advice. I'll at least spend, you know, half an hour with the student." She very graciously spent a full morning with me and just talking me through, like, how I should think about AI space or how I should think about research. And she actually connected me with an initial set of people that I started to work with and learn from. So that initial set of people who came together to help give me advice and actually let me have a foot into this area of research, it really was not an obvious choice for them. I really didn't think I deserved it, but that was the bet that they took, I think truly for, you know, purely for their own kindness, and I'm very grateful that they did.
- HSHarry Stebbings
Never forget the first believer. [laughs] Uh, Joon, from, uh, quant funds to simulated worlds to love, uh, this has taken many different twists and turns, but thank you so much for joining me.
- JPJoon Sung Park
Thank you for having me.
Episode duration: 1:05:02
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Transcript of episode ya6D6-SizbI
