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Simile builds foundation models of human behavior to simulate complex social and market dynamics, shifting the focus from simple prediction to causal reasoning. By creating high-fidelity agent-based simulations, organizations can test counterfactuals and evaluate the downstream implications of strategic decisions, effectively preventing costly errors. Unlike traditional large language models trained on web data, these simulations rely on representative behavioral data and randomized control trials to capture human values, preferences, and biases. This approach functions as a "GPU of intelligence," allowing for the modeling of collective human behavior and emergent phenomena at scale. As these models evolve, they provide a more granular and accurate alternative to static survey panels, enabling leaders to simulate entire market launches and policy outcomes before implementation, ultimately transforming how society allocates capital and makes high-stakes decisions.