World Models

World models are learned representations of how an environment behaves, including its objects, physics, dynamics, constraints, and possible interactions.

Key points

• Hassabis describes video-generation progress as moving toward models of the mechanics and physics of the world, not just prettier media outputs ^[src-063].
• Veo is discussed as a sign that models can infer enough structure from video to render materials, liquids, lighting, and short physical sequences ^[src-063].
• The next step is interactivity: moving from generated clips to environments a user can enter, steer, and play inside ^[src-063].
• Hassabis connects world models to AGI because a general system needs an internal model of how the world works, not only text prediction ^[src-063].
• Games are a natural testbed because open-world simulations already combine rules, agents, environment state, and player co-creation ^[src-063].
• Fan gives the robotics version: video models can act as next-world-state simulators, but robotics needs to align those simulations with actions so physically useful futures, not just plausible pixels, guide behavior ^[src-082].
• Dream Dojo extends the idea into neural simulation: given continuous action signals, a learned model predicts future RGB frames and sensor states without a classical graphics engine or explicit physics equations ^[src-082].

Related entities

• Demis Hassabis
• Google DeepMind
• Veo
• AlphaGo
• Jim Fan
• NVIDIA

Related concepts

• Intuitive Physics In AI
• Learnable Natural Systems
• Embodied Reasoning
• AI For Science
• World Action Models
• Physical AI
• Robotics Data Loop

Source references

• ^[src-063] Lex Fridman – "Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games | Lex Fridman Podcast #475" (2025-07-23)
• ^[src-082] Sequoia Capital — "Robotics' End Game: Nvidia's Jim Fan" (2026-04-30)