Simile AI获20亿美元B轮融资，探索人类行为模拟新范式

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI’s $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups.

Time to catch up on why this Second Summer of simulation is working!

From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today’s frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.

We go deep on Simile’s approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.

We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.

We discuss:

• How Smallville and Generative Agents led to Simile
• Why Joon’s team asked: “What if we can just recreate the world that we live in?”
• Why useful personal agents require deep models of their users
• Memory architectures, Markdown files, and the limits of prompting
• “Social physics” and behavioral foundation models
• Why web data captures what people say more than what they actually do
• Interviews, transactions, observational data, and randomized controlled trials
• Why predicting the future matters less than understanding how to shape it
• How Simile creates representative simulated populations
• Simulation versus prediction and the connection to Foundation’s psychohistory
• How to evaluate simulations instead of simply stacking LLM hallucinations
• Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy
• Why frontier models can struggle to reproduce real human behavior
• Why good simulations need to reproduce human biases and mistakes
• Post-training models on randomized controlled trials
• Population-level versus individual-level simulation
• Scaling laws for human simulation
• The long-term ambition to simulate all 8 billion people on Earth
• Whether simulations could help solve climate change or detect collapsing democracy
• Thomas Schelling and the history of agent-based modeling
• Why future simulations could require an entire data center
• Multi-agent simulations and what happens when simulated people interact
• Replacing expensive human panels with synthetic populations
• Why market research is only the starting point for simulation
• Why Joon sees simulation as surprisingly similar to painting
• Using simulation to study questions like UBI
• Whether we are already living in a simulation
• Why AGI and simulation may be the twin technologies of advanced civilizations

Joon Sung Park

• LinkedIn: https://www.linkedin.com/in/joonspark
• X: https://x.com/joon_s_pk
• Website: https://www.joonsungpark.com
• Simile: https://www.simile.com

Timestamps

00:00:00 Introduction and Joon’s Path from Art to AI

00:01:46 Smallville, Generative Agents, and the Origins of Simulation

00:05:03 “Let’s Just Create a World” and the Future of Personal Agents

00:09:53 Social Physics and Behavioral Foundation Models

00:14:08 Prediction vs. Simulation: How Do You Shape the Future?

00:16:59 How Simile Models Real People and Populations

00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy

00:30:23 Post-Training Models to Reproduce Human Behavior

00:40:04 Scaling Laws and Simulating 8 Billion People

00:43:10 From Schelling to Society-Scale Agent Simulations

00:46:13 The Cost and Economics of Simulating the World

00:52:05 Real-World Use Cases, Synthetic Populations, and the Market

00:57:27 The Future of Simulation, Painting, and UBI

01:04:23 Are We Already Living in a Simulation?

01:06:08 Building Simile and Hiring

Transcript

Introduction: Joon Sung Park, Simile, and the Story So Far

Vibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?

Joon [00:00:13]: Yeah, for sure. I’m really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children’s Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.

Vibhu [00:00:49]: Painting.

Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that’s what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn’t a hobby. It was like, “Hey, let’s make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.

Smallville, Generative Agents, and the 2023 Breakout Paper

Swyx [00:01:46]: So there’s a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.

Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.

Joon [00:02:10]: Yeah, it’s a good question. How many people have read it, I’m not sure.

Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.

Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.

Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.

Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you’ve read recently?” It’s this one.