AI Is Getting Good at Math. Here’s Why.

The essay argues that recent AI math results, such as OpenAI's counterexample to Erdős's unit distance conjecture and proofs of the Cycle Double Cover and Navier-Stokes problems, follow a pattern: models search depth-first, pursue many branches cheaply, and use automated verifiers plus failed attempts as negative hints to prune. It frames problem difficulty as the number of insights needed (k) and the branching factor (b), predicting AI excels where conceptual leaps are few and remaining work is tedious, while humans retain an edge on deep, highly branching search trees.