The brain does not see the world. It predicts the world, then corrects when prediction fails. This is the predictive coding hypothesis, developed in neuroscience over the last three decades. The brain runs continuous forward models of what should happen next, compares them to incoming sensory data, and propagates only the prediction errors upward. Most of what feels like perception is the prediction; the senses just supply correction.

Modern AI has independently arrived at the same architecture. Diffusion models predict and denoise. Reasoning models predict the next reasoning step and self-correct. World models predict future states from current states. Even the basic transformer is, structurally, a next-token prediction machine that updates representations through error backpropagation.

Two fields, working separately, converged on prediction with error correction as the base computation of intelligence. This is unlikely to be coincidence. The mathematics of efficient representation under uncertainty produces the same answer whether the substrate is wetware or silicon. Predictive coding is not a brain hypothesis or an AI architecture. It is the ground state.