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.

Instead of reacting to its environment, ONE X's world model AI allows its robots to 'think' forward and simulate potential outcomes of an action. Like a human anticipating spilling hot coffee, the robot can identify risks and select the safest trajectory, which is critical for operating in a home.

Vision-Language-Action architectures that predict abstract environmental representations bake human design bias into intermediate states. In accordance with the bitter lesson, predicting future raw camera inputs and actions simultaneously preserves maximum environmental information for generalization.

The cutting edge of physical AI involves more than just programming a robot's response to a stimulus ("policy"). It also requires a "world capability"—a virtual twin that simulates and predicts outcomes, allowing the physical robot to choose intelligent actions based on those predictions.

Large language models are insufficient for tasks requiring real-world interaction and spatial understanding, like robotics or disaster response. World models provide this missing piece by generating interactive, reason-able 3D environments. They represent a foundational shift from language-based AI to a more holistic, spatially intelligent AI.

To perform complex edits like 'knock over this water glass,' a model must understand physics, causality, and object relationships. This requirement inadvertently builds a form of visual intelligence that serves as a precursor to more sophisticated world models for applications like robotics.

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.

Robots can generalize skills to new hardware by using an intermediate 'thinking' step. The model generates an image of the next desired milestone (e.g., a half-folded shirt). This visual goal is embodiment-agnostic and easier to create than new motor commands, allowing the robot to then solve for the actions to match the image.