The next major leap in AI may come from "world models," which aim to give LLMs an experiential, physical understanding of concepts like space and physics. This mirrors the difference between knowing facts from a book and having real-world experience.

To build generalist robots, the most effective approach is pre-training foundation models on internet-scale video datasets, not just simulation or tele-operated data. This vast, diverse data provides a deep, implicit understanding of physics and object interaction that is impossible to replicate in controlled environments, enabling true generalization.

While language models understand the world through text, Demis Hassabis argues they lack an intuitive grasp of physics and spatial dynamics. He sees 'world models'—simulations that understand cause and effect in the physical world—as the critical technology needed to advance AI from digital tasks to effective robotics.

Startups and major labs are focusing on "world models," which simulate physical reality, cause, and effect. This is seen as the necessary step beyond text-based LLMs to create agents that can truly understand and interact with the physical world, a key step towards AGI.

GI discovered their world model, trained on game footage, could generate a realistic camera shake during an in-game explosion—a physical effect not part of the game's engine. This suggests the models are learning an implicit understanding of real-world physics and can generate plausible phenomena that go beyond their source material.

A key application of world models is to simulate physical environments. This allows developers to test AI "policies" (e.g., a self-driving car's decision-making model) virtually, drastically speeding up development cycles and reducing reliance on expensive, slow, and risky real-world testing.

The paradigm shift with AI is not an abandonment of physical laws. Instead of using supercomputers to approximate solutions to physics equations, AI learns the patterns governed by those laws directly from historical data. The ultimate goal is to forecast direct impacts, not just variables.

Large Language Models are limited because they lack an understanding of the physical world. The next evolution is 'World Models'—AI trained on real-world sensory data to understand physics, space, and context. This is the foundational technology required to unlock physical AI like advanced robotics.

The AI's ability to handle novel situations isn't just an emergent property of scale. Waive actively trains "world models," which are internal generative simulators. This enables the AI to reason about what might happen next, leading to sophisticated behaviors like nudging into intersections or slowing in fog.