In this episode, I explore a fast-emerging concept in artificial intelligence known as “world models.” Unlike today’s large language models, which primarily learn from text, world models are designed to learn from real-world experiences—images, motion, physics, and interactions with physical environments.

This shift could unlock a major advance in robotics. By building internal models of how the world works, AI systems may eventually predict outcomes, understand cause and effect, and navigate complex environments more reliably. The result could be far more capable robots in factories, warehouses, and other real-world settings, marking an important step beyond today’s software-only, text-intensive AI.