Why Governing World Models Is AI's Next Big Policy Challenge As artificial intelligence moves beyond language into the physical world through "world models," Stanford researchers warn that policymakers face an even steeper governance challenge than with large language models—and the window to get ahead of the technology is closing fast. If crafting effective regulations for large language models has proven difficult, governing the next wave of AI will be exponentially more complex. A new policy brief from the Stanford Institute for Human-Centered AI (HAI) marks the first comprehensive examination of how to govern “world models”—AI systems that don't just process language but build working representations of physical environments to predict how they change in response to action. World models are already emerging from research labs into commercial applications, from crisis response systems to autonomous vehicles to robotic manufacturing—with profound implications for everything from privacy to national security. In this conversation, HAI Associate Director and Hoover Senior Fellow Amy Zegart, HAI Founding Director Fei-Fei Li, and HAI Executive Director Russell Wald, three of the brief’s authors, discuss why world models demand urgent policy awareness and what makes governing them fundamentally different from current AI oversight efforts. What exactly is a world model, and why should policymakers care about it now? Fei-Fei Li: Spatial intelligence is the ability to perceive, reason about, and act in three-dimensional space, and world models are the foundation for achieving it. A world model is an AI system that builds an internal representation of an environment—like a city, a factory floor, or a disaster zone—and uses that representation to predict what happens when you take action in that environment. Unlike a language model that predicts the next word, a world model predicts physical consequences: what happens if you move this object, open that door, or reroute traffic