The next major leap in AI may come from "world models," which aim to give LLMs an experiential, physical understanding of concepts like space and physics. This mirrors the difference between knowing facts from a book and having real-world experience.

To build generalist robots, the most effective approach is pre-training foundation models on internet-scale video datasets, not just simulation or tele-operated data. This vast, diverse data provides a deep, implicit understanding of physics and object interaction that is impossible to replicate in controlled environments, enabling true generalization.

Google's Project Genie, which generates interactive virtual worlds from prompts, is not just a gaming or media tool. It's a foundational part of Google DeepMind's strategy to achieve AGI by creating simulated environments where AI can learn about physics, actions, and consequences.

By training on a trillion action tokens from video game controller and keyboard inputs, General Intuition is creating AIs that can operate any system with a similar interface. This novel approach allows their models to control robots and industrial machines as if they were playing a video game.

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.

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.

GI's founder argues game footage is a superior data source for spatial reasoning compared to real-world videos. Gaming directly links visual perception to hand-eye motor control ("simulating optical dynamics with your hand"), avoiding the information loss inherent in interpreting passive video, which requires solving for pose estimation and inverse dynamics.

To protect user privacy, GI's system translates raw keyboard inputs (e.g., 'W' key) into their corresponding in-game actions (e.g., 'move forward'). This privacy-by-design approach has a key ML benefit: it removes noisy, user-specific key bindings and provides a standardized, canonical action space for training more generalizable agents.

The push toward physical AI and spatial intelligence is primarily a strategy to overcome data scarcity for training general models. By creating simulated 3D environments, researchers can generate the novel, complex data that is currently unavailable but crucial for advancing AI into the real world.