The AI Company Simulating the Entire Economy | Simile Co-founder & CEO, Joon 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 it.

>> This is Jun Song Park, founder and CEO

at Simily. They predict the future.

They're a simulation market that try and

predict future human behavior. It is

incredible.

>> 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.

>> This was one of the best AI technical

conversations we've had in a long time

and it was incredible to have Jun on the

show.

>> 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?

>> Ready to go?

Jun, I'm so excited for this, dude. When

Shardul told me

that I had to meet you, I'm going to 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."

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. But

thank you so much for joining me, dude.

>> Thank you for having me. Excited to be

here.

>> 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. I

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.

Can you explain what happened and how

that potentially led to the early days

of Simily?

>> For sure. So this was 2023. 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 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 practically 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'd

actually see these agents come together,

have parties, like self-organize. So,

they would actually plan parties, they

would decorate the cafe, 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 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 it was paired with memory,

planning, and reflection. Really the

first times that those concepts came out

to be an explicit part of the

architecture in and {unquote} agentic

workflows. 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. Back in the

day, language models didn't really have

the concept of memory. So I thought,

"Okay, you have to give them the memory

so that they don't say, 'Hey, nice

meeting you' every time they meet their

roommate." So we had them give have this

concept of memory and planning and

reflection to make sense of very

long-term landscape.

>> How do you solve that memory problem?

Cuz everyone says, "Oh, we have a memory

problem today." How do you solve the

memory problem of agents to prevent that

from happening?

>> So back in the day, it was actually the

initial idea was fairly simple, well,

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 that 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 in a 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 were 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 have 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 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.

>> And so, when we think about a simulation

models 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?

>> That's right.

>> Got you. Okay, when we think about then

building a simulation model company,

would you say Similarly is a simulation

model company?

>> Yeah. We are a company that is creating

a foundation model of human behavior

that can then be used to create

simulations of individuals, simulation

of sub-populations, and then the line

the simulation of the entire ecosystem

and even the market.

>> 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?

>> 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. Similarly 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.

>> 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?

>> For sure.

So, say to give us real and you know, if

you look at the web data, it is

fundamentally data of what people have

said, not what they have done. And

obviously, things models today are

trained uh preliminary 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 uh 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, "What 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. 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 Simility

collects.

>> Is data collection acquisition the

hardest element of building simulation

models for you? Like if you 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?

>> Data is an important piece of Simility,

for sure.

Um 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 and 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?" Tell us a lot about these

people.

And that's what we try to do.

>> 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 blindly their buying cycle. They

can change how much they purchase.

Isn't that valuable?

And what am I missing?

>> But that's the thing. The reason why

they want know is so they can change

their strategy. So certainly talking

about how much

resources they actually need to actually

serve this market, that is a kind of

changing in behavior. But fundamentally

it is about counterfactuals. So well 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 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.

>> 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 didn'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?

>> Yeah.

So the way we see it is again

fundamentally the core primitive 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. 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? 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 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. I 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 here might be things like

climate change requires collective

action across many stakeholders who have

different incentives.

One of the reasons why such are so

difficult is actually finding the right

equilibrium state where all

all different parties come together to

make decision for global good is very

difficult. Can we actually simulate

those decision-making processes? Can we

actually simulate

even in 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.

>> Can I ask you

thing about like democracies failing and

elections for a government has been an

incredibly useful tool.

How do you think about

who you can and should work with versus

who you shouldn't?

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 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 taste.

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.

>> How much data do you need to feel

confident in an accurate prediction

outcome

to be displayed? Is it 100 people? Is it

1,000 people? Is it a million people?

>> 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,

often times the kind of ways that people

query our system is they want to come in

and say, "Hey, filter down to X with 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, that mean we want to represent

the entire population.

So, that's the journey that we're on.

>> Is it self-fulfilling? Like, do you get

better and better at predicting over

time?

>> 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 similar getting better and

better and better. And do they have that

compounding advantage?

>> It's kind of like AlphaGo. You know,

they just beat the out of the model

and played it a thousand 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? So, this is actually quite

interesting. Um

You might think uh

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 you're

trying to predict is it 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 hypothesis 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 a best way to learn about the

world. Does it take a huge amount of

compute to run these simulation

environments at scale and well? 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, is exploring different kind of

data set. Once we have a point of view,

we can very quickly make it efficient.

So, some of the things that I've seen,

wouldn't similarly as we build 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 view.

>> Can I ask you, when you look at like

serviceable market, or like total

addressable market, TAM, in venture

speak, you know, you obviously have your

CVS's and your huge enterprises who

would absolutely want to work with you.

It can also be consumers. 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 Similarly?

>> So, the start of my career really came

from research, obviously. And the job of

a researcher is to serve the humanity.

Then 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

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, I'll be frank, there is the

budget. That there is a clear product

market fit that we see today.

And 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

when I was at Stanford. Uh my office

next next to mine

was Pat Hanrahan. He was one of the

founders of Tableau. He's a graphics

professor, also

he won Turing Awards, a very well-known

person in this in this landscape.

>> [clears throat]

>> One 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 want to see this here. So, getting

the best kind of feedback matters a lot.

So, enterprise market market research

right now is an 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.

>> 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 got product market fit here."

>> Many of the Fortune 500 board members

and their C-suites reached out. In part,

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 showed 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.

>> Will synthetic panels be larger than

human panels in 3 years' time?

>> 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.

What 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

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 the limitation and us to

actually test every single hypothesis

that we have about the world before we

have to launch into the world.

>> 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 and an FDE team?

How do you balance the profit

maximization with the research purity?

>> 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, and

Liane

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 kickstarted 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 impact

of the technology in the form of

simulation.

The reason why we're 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 Liane,

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're seeing at Simulate.

>> 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 we're able to save us

and 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?

>> 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 1 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 the build the

right foundation for this company and

for this market, and we'll go aggressive

base maybe towards the end of 2026.

What's 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 that we work with at for

instance CVS, I've been working with

this uh particular uh leader uh Shree,

uh who's their VP of insights, extremely

forward-looking,

extremely ambitious, extremely

hard-working,

and amazing counterpart to a vision like

Simulate.

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 of 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 3 months.

>> 3 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? 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 an 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 3 to 6

months,

but just within 2 minutes.

That's very powerful.

>> 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 want to buy our product?"

>> [laughter]

>> 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 that 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, and in some cases

billions of revenues.

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?

>> 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 Simily is actually

avoiding really damaging decisions that

could have cost them hundreds of

millions of dollars.

>> So it's prevention not optimization.

>> It's both. But certainly prevention is a

huge It's an obvious value case, right?

That oh well, that could have been a

total disaster had we run that that

would have cost us half a billion

dollar. We ran simulation and that

prevented it. That's no-brainer. This is

a This is a true painkiller in their

case.

>> If you do your job efficiently,

can Cal Poly Markets still exist for a

lot of their markets?

>> So, it's an interesting question.

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 Simily 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.

>> 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

similarly.

>> So, couple of things.

One is the team has to be balanced.

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 Laney 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, the or new gaps

that are emerging.

Actually seeing that ahead of time and

making sure that we fill those gaps, I

do think it's 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 artist, 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 the 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 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 a 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 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. Fairly

common.

Where I found things to be particularly

compelling is if people have two

superpowers that's really contradictory.

>> The most common one here is actually the

greatest CMOs, which there are very few

I can 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.

>> Yes.

>> And it's very rare to have that in a

CMO, but when you have that, that is the

world-class CMO. And that's magical.

>> Yeah.

>> Uh that particular description I

actually sometimes have used for my

board members for the whole deeply

analytical, but he's very intuitive. And

I think that's how he makes investment

that happens to be very successful. So,

hopefully similarly we're 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 Laney,

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.

>> I think it's actually uh that'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 also often

interview the world's most successful

founders, and they all say I say, "What

do you wish you'd known when you

started?" And they all say, "I wish I'd

known that it would all work out, and I

wish hadn't 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, Laney didn't hit the quarter.

Laney didn't set the urgency in the

sales team. Laney didn't hire those

extra people cuz you didn't know the

access to mom would be there. The

paranoia drives the success.

It's a really interesting one. Can I ask

you, Brendan at McQuaid 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?

>> 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, similarly,

I am 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 the laughing stock 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 5 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?

>> 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?

>> Consistently.

And in fact, I actually do view the role

of leadership to be

and 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 a I have a

small sense of pride in the way my

career has panned out over the past 6

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 6 years

all my core team members never left.

Then we all move from one project to the

next to the next together.

And now when I said, "Hey, I want to do

this thing and build a similarly." I was

able to somehow convince Michael and

Percy who are 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's

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.

>> Can I ask you a really important

question for me, which is that I see a

lot of amazing people in research and

academia who are considering or starting

a company in the same way that you did.

And I always worry that I'm going to

finance a science project cuz science

projects, although great and although

interesting, and intellectually

satiating,

don't always make great companies.

You've been able to do that incredibly

well in the last, you know, 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?

>> The thing I actually would look for is

are they married to a problem

or are they married to impact?

>> Mhm.

>> Sometimes researchers are very much uh

focused on a problem, and something

about that problem fascinates them. But

often times 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.

>> So, talk to me. It was Shardul that

introduced us. Uh huge thanks to Shardul

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 about it?

>> For sure.

So,

uh we raised our $100 million round uh

about 5 months ago.

Um

and soon after um

we were pre-empted uh fairly recently

um by insiders. So, Shardul at Index uh

led our previous round, and including

Shardul and some of the other insiders,

we're looking at the market, and Shardul

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's quite never seen this kind of

traction, this kind of pull.

When that is paired with a technological

progress that has been made, and also

the amount of computer that we can also

leverage to even further accelerate our

progress,

that's what 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.

Uh we were not planning on raising at

that particular moment, but there are a

couple of 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 New Enterprise 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 a 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

not running a process, so if you'd be

interested in joining, uh

you know, we have 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 6 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 the team. Sholto has

been a fantastic uh partner. We actually

have a lot of Index connection uh at

Semilac. Uh our seed actually was led by

Mike Volpi, who runs now his own firm.

Uh and Sholto and along with uh

Neil and Greenoaks team, along with

Patrick and who is the partner.

>> You didn't need the money, I take it.

You raised $100 million 6 months ago.

Was there a consideration of we don't

need the money, why would we take $200

million now?

>> There was certainly that consideration.

where we netted out was

the money 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 you 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.

>> Totally get that.

What did you not know about fundraising

coming from a world of academia or

research that you now know having been

through three rounds?

>> Well, one actually here was coming in,

I was actually fairly skeptical

what the roles of VCs actually were.

>> [laughter]

>> What do they actually do?

>> We all are.

>> What do they actually do? How do they

help? And I would 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 some 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-to-day basis is new.

If there's someone I can trust,

then that's an amazing boost. Uh

initially, um I started to work um with

uh Ventures, P, and we also had another

firm, uh Astar, who also helped uh lead

our seed. And these funds and the team,

and we also work with the shiny 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 suddenly one is

the VCs can actually

help

in some magical ways and 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'd 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.

>> Do you worry that the market is so

frothy that it can get ahead of itself?

Like when you announce this fund raise

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?

>> I do think there's parts of market that

is actually quite frothy.

>> Yeah.

>> For sure. There's 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, where 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

and 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

Similarly as well.

>> Do the unit economics vary for you on a

per simulation basis? And what I mean by

that is like, you know, if you look at,

say, Anthropic and OpenAI and model

routing, 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?

>> 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 actually

understand all the 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 cost going up and up and

up, and we now have to these thinking

models that are thinking for like half

an hour, a day, and actually start to

spending like token maxing and, you

know, spending, you know, a lot of money

on just running this process.

I actually do think simulation could

actually be the next frontier of that.

Where

in my vision, I think there's a world in

which in about 2 3 years, we're running

a single simulation session

that's going to take 10, 20 million

dollars to run

a single session,

but it's going to be so valuable

that people will pay 100 million dollars

for it.

That's where I see it go.

>> And that would be for the world's

largest enterprises. That'd be for a

government or whatever that may be.

>> Especially on the sort of the high end

of the spectrum, that's what it would

be.

>> What cannot be simulated today that you

think will be possible in 3 years?

>> 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

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 that 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, Mike 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 evaluate 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 bridged every day. A huge part

of it also is getting, you know, models

to be better, creating better

simulation, making the system more

scalable. All that becomes a part of

this.

>> Can we play a time machine game with me

and you?

>> Let's do it.

>> In 10 years time, what is the craziest

thing that you can see happening?

>> 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 these one

language model that's really large,

that's very smart, that can do very

complex reasoning tasks.

That's like CPU.

What I see coming and what I think

simulation as a field can offer is the

GPU of intelligence unit. As I mentioned

before,

Simulate 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 thought of a lot of things. I want to

make sure that the model that represents

me feels the same way.

But the beautiful part about people

is individually, we have so much

diversity, so much different tastes in

our world that makes individuals so

interesting. But also when they come

come together as a large collective, the

emerging 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.

>> 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. 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 and

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.

>> 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.

sign a million dollar contract

with you and get unbelievable insight.

Yeah.

>> Maybe Simily will actually own a small

hedge fund down the line.

>> That's a cool idea.

Would you be down to do that?

>> Well, it turns out we actually do have

quants

in our firm. Um

So some of the members who have joined

actually do have more quant background.

And I think right now they're joining

that because they actually want to start

a quant firm at Simily. They actually

joined because they actually see the

vision of Simily 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.

>> Is there a world again, I'm saying 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 Simily's hedge fund or similar

providers and actually it is not a fair

marketplace?

>> I think it especially with

obviously if we were to assume that

we're going to have some form of AGI and

if we were to assume some form of

perfect simulator, I think a lot of the

world a lot of the things that we assume

to be true about our world, I think will

change.

Certainly, one of these could actually

be the stock market.

>> What else do you think we assume to be

true today that you think won't be in 5

years time?

>> What I think we generally assume to be

true about the world that we live in is

that it is fundamentally impossible to

get everyone's perspective.

Therefore, 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.

>> Is the future of love

not also similarly? 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've got a girlfriend and so she was

watching and but I feel if you could

date 100 people at the same time,

>> Yeah.

>> for the first date, of course, not not

onwards. Uh you're going to get in a lot

of trouble for that, June. Um

>> [laughter]

>> 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?

>> I I get that. Well, look, I think love

comes in different forms and I think

just like as we discussed today,

people have such degree of diversity.

Uh

personally, I I am a bit of a romantic,

I'll 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 had 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 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

that would matter.

>> Oh, you're so romantic.

>> Unfortunately,

>> Do you know what I what I'm thinking of,

dude? I'm a content person. I'm a

content person and an investor. Weird

mindset actually in in both ways.

There's a a show called Married at First

Sight. Um 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 Simily if you

could do the perfect marriage at first

sight because of simulations that've

been run before. But, you know, I'm just

leaving you with pearls of wisdom that I

think would be great. Uh we're going to

do a quick fire answer. I say short

statement. Well, who's the most

underrated AI researcher today?

>> 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.

>> What area of AI do you think is

particularly overheated today?

>> I do think new labs without a clear

vision for how they're going to impact

the world, I do genuinely think there's

some risk that they will turn out to be

interesting research project, but not a

viable company.

>> 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.

>> I fundamentally believe that

for AI companies in the future, you have

to have interesting data strategy. Do

you have access to data that no one else

has 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,

I'll say 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 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.

>> Who do you think those are?

>> The recently edged uh came out of uh

their stealth uh

quite bullish on their team. I think

they're going to be exciting.

Uh so that's one.

>> Final one for you. What's the kindest

thing that anyone's ever done for you? I

think it's a nice note.

>> Uh 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 wasn't 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

didn't know 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 Wootters. She's a theory

professor. I 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 a 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, truly for

their own kindness.

And I'm very grateful that they did.

>> Never forget the first believer.

>> [snorts and gasps]

>> Uh June from Quantum Funds to Simulated

Worlds to Love. Uh this has taken many

different twists and turns, but thank

you so much for joining me.

>> Thank you for having me.