AI lab General Intuition uses video game data to train AI that understands space, time, and human action. This is a richer dataset for building 'world models' than the text-based data used for LLMs, with applications far beyond the gaming industry itself.

Vision Language Action models (VLAs) have not yet produced a 'ChatGPT moment' for robotics. Consequently, investor enthusiasm and capital are increasingly flowing towards the alternative 'World Model' approach, which learns physics from video, even though it has yet to demonstrate superior tangible results.

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.

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.

The concept of a 'world model' is evolving from action-conditioned video predictors to single, multimodal models like Google's Omni. Omni demonstrates a deep, scalable understanding of the world, shown through nuanced video editing, representing a more practical approach than traditional, computationally expensive architectures.

A "world model" transcends simple video generation. It is defined by three key capabilities: real-time responsiveness to user input (e.g., mouse clicks), long-horizon consistency over minutes or hours, and interactivity via multiple modalities like keyboard and voice.

Current multimodal models shoehorn visual data into a 1D text-based sequence. True spatial intelligence is different. It requires a native 3D/4D representation to understand a world governed by physics, not just human-generated language. This is a foundational architectural shift, not an extension of LLMs.

Demis Hassabis sees video generation as more than a content tool; it's a step toward building AI with "world models." By learning to generate realistic scenes, these models develop an intuitive understanding of physics and causality, a foundational capability for AGI to perform long-term planning in the real world.