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].
  • LeCun adds an agent-planning definition: a world model predicts the next state from the current state plus an imagined action, so an agent can anticipate consequences before acting [src-102].
  • The source distinguishes representation-space prediction from pixel-level generation: useful world models should ignore unpredictable detail and predict abstract state that supports planning [src-102].

Related entities

Related concepts

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)
  • [src-102] Vivatech / Yann LeCun – "Beyond Language Models: Building AI that Understands the World" (2026-06-17)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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