Researchers in robotics, language models, and game-playing AI are all arriving at the same conclusion.

In robotics, world models address the sample efficiency crisis. A physical robot cannot afford millions of trials. Build a model from modest real experience, then practice extensively in imagination.

In language models, Yann LeCun left Meta in late 2025 to found AMI Labs, focused on building explicit world models that learn in abstract representation spaces. By March 2026, AMI Labs had raised over a billion dollars.

In game AI, MuZero mastered chess, Go, shogi, and Atari without being told the rules. It learned an internal model of the game dynamics and planned within that model.

Three different fields, three different methods, one shared conclusion: agents that model the world outperform agents that merely react to it.

The advantage grows with task complexity and reward sparsity.