World Models Need Causality, Not Pretty Pixels — Christopher Manning, Moonlake AI

This talk argues that current generative video models, while capable of simulating observations, lack the semantic understanding necessary for planning and true embodied intelligence. The speaker, Christopher Manning, proposes a shift towards building action-conditioned world models in code, derived from single photos or short videos. These models allow for direct manipulation of objects and can augment their knowledge by researching objects online. A key technique involves a loop inspired by Claude Code, which compares rendered simulations against reality to minimize the sim-to-real gap, with the ultimate goal of replacing thousands of hours of teleoperation with simulation. The talk also touches upon the historical evolution of AI, from early cybernetics and robotics to modern LLMs, framing embodied AI as the future North Star.