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.

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.

A major hurdle in robotics is the laborious collection of real-world training data. Atlas accelerates this by creating high-fidelity simulations from sparse real-world images ("real-to-sim"), enabling rapid training and randomization of robotic policies without extensive data capture.

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.

Waabi's CEO explains that for physical AI, world models must go beyond just creating realistic simulations. The critical feature is 'controllability'—the ability to precisely generate and manipulate specific, safety-critical scenarios for testing. This is a fundamental difference from world models used for generating creative media or games.

Traditional simulators are rule-based, programmed with physics equations. World models pioneer "neural simulation," which learns the physics of the world implicitly from massive datasets of visual observations. It's a pattern-recognition approach to predicting outcomes, rather than one based on pre-defined rules.

Instead of using traditional, rule-based simulators, Comma AI trains its driving agent inside a learned "world model." This generative model creates photorealistic, diverse driving scenarios and, crucially, responds accurately to the agent's simulated actions—a key requirement for effective robotics training.

World Labs posits that "world models"—AI focused on visual and physical understanding—represent a new general-purpose platform, similar to LLMs for text. These models can generate, simulate, and reconstruct physical worlds, with applications spanning from robotics and construction to entertainment and VR.