Simulating Humanity

Joon Sung Park’s headline claim is that simulation has its own scaling law: “The more data about humans and more compute you ingest, you start to get predictable gains in model performance” when simulating and predicting people. Simile post-trains its own models and is seeing “an early glimpse” of this curve.

The core moat argument: Park contrasts generative models’ emphasis on “super-rational, objective machines” with Simile’s need for models “as dumb as I am”—models that make the same mistakes humans make. Web-trained models contain fundamentally self-exposed attitudinal data, with some behavioral data sprinkled in, rather than the “dark knowledge of humanity”—what people actually do. On niche populations, Park says frontier-model behavior prediction falls to 20–30% (50–60% on the general population), versus Simile’s validated benchmark of replicating people’s attitudes and behaviors with 85% accuracy, “about as accurately as people could replicate their own.”

The validation asset is the “Generative Agent Simulations of 1,000 People” paper: 1,000 representatively sampled Americans, two hours of data collection, digital twins tested two weeks later against surveys, Big Five, behavioral-economics games, the General Social Survey and published RCTs. A follow-up showed post-training on tens of thousands of preregistered experiments from the Open Science Foundation platform delivers significant further gains—causal, randomized-controlled-trial data is the scarcest and most valuable input because “the world is our ground truth, but it happens once.”

The product pitch is simulation as a tool for shaping outcomes, not predicting them: “It doesn’t really help you to hear that your sales are going to tank in two quarters… What they want to know is, well, what do we need to do now to avoid that future?” His Foundation/psychohistory discussion—the counterintuitive first move of exiling the scientists to Terminus—illustrates why step-by-step causal simulation beats point forecasts, e.g., an EV marketing plan that lifts EV sales but makes overall auto sales go down.

On TAM, Park explicitly rejects the $100B market-research framing: “Simulation is not a tool for market research. Simulation is a tool for human decision-making.” Current deployment includes concept testing, focus groups, simulated earnings calls for public companies, and a Gallup strategic partnership; Simile collects data from tens of thousands of people weekly and has panel partnerships reaching tens of millions globally. Collected panelists are reusable across studies because traits like risk tolerance “don’t really change over time.”

Maturity marker for investors: Park says the simulation industry feels like where GPT-3.5 and GPT-4 were for the AGI saga—powerful enough to do real damage in current verticals, with aggressive scaling still ahead. His hunch is that simulations will eventually “cost as much as training a foundation model,” and of a society-scale climate-change run he says, “I would raise the money right now just to run that.” In a host exchange, swyx says an Africa UBI study returned “no” and floats a roughly $14M cost over five years; Park does not confirm the figure and questions whether implementation was the issue.

Park describes Simile as both a research lab and a product company: about 60 people, an SF headquarters at Mission Rock plus a new New York office, co-founded with Michael Bernstein, Percy Liang (who coined “foundation model”) and Lainie Ellen, with roughly 15–20% of headcount drawn from Park’s lab. His closing market frame: “You look at any advanced civilization in science fiction, and there are two twin-pillar technologies. One’s AGI in some form, and the other is simulation.”

🔗 Original source & video: Simulating Humanity: from Generative Agents to 8 Billion Digital Twins — Joon Sung Park, Simile AI