Large language models are running into limits in domains that require an understanding of the physical world — from robotics to autonomous driving to manufacturing. That constraint is pushing investors toward world models, with AMI Labs raising a $1.03 billion seed round shortly after World Labs secured $1 billion. Large language models (LLMs) excel at processing abstract knowledge through next-token prediction, but they fundamentally lack grounding in physical causality. They cannot reliably predict the physical consequences of real-world actions.  AI researchers and thought leaders are increasingly vocal about these limitations as the industry tries to push AI out of web browsers and into physical spaces. In an interview with podcaster Dwarkesh Patel, Turing Award recipient Richard Sutton warned that LLMs just mimic what people say instead of modeling the world, which limits their capacity to learn from experience and adjust themselves to changes in the world. This is why models based on LLMs, including vision-language models (VLMs), can show brittle behavior and break with very small changes to their inputs.  Google DeepMind CEO Demis Hassabis …