How Major Reasoning Models Converge to the Same “Brain” as They Model Reality Increasingly Better

Research from MIT and other groups suggests that as AI models scale up, they independently converge toward the same internal representation of reality — regardless of whether they were trained on text, images, or audio. This 'Platonic Representation Hypothesis' proposes that sufficiently capable models stop memorizing tasks and instead build a statistical model of reality itself. Three forces drive this convergence: task generality (there's only one optimal world model), model capacity, and a simplicity bias in deep networks. The phenomenon parallels how human brains integrate multimodal inputs into a unified perception of the world, and has implications for robotics and AGI.