Measuring skill at a single, static task (like chess or Go) is a dead end for measuring true intelligence. It just measures specialization.

A better measure is skill-acquisition efficiency: How quickly can an agent learn new things from limited experience?

This efficiency in humans comes from adaptive world models—actively building and refining internal simulations of the world.

Formal proof now shows this isn't just a nice idea: any general agent must contain a world model. There is no shortcut.

Therefore, the future of AI research must be in creating and testing systems that can actively induce these models in novel, unknown environments.