Two mathematicians used large language models to discover counterexamples to long-standing conjectures, automating hypothesis generation and testing with minimal guidance. The article calls this “brute intelligence,” where AI runs iterative search loops to tackle any problem framed like a math exercise. It argues we’ll need to reshape tasks into testable, calculable formats for AI to industrialize discovery across fields.

• Tao got an LLM (Anthropic's Fable) to produce a counterexample to the Jacobian conjecture over a weekend, calling it "a massive miracle" unlikely via manual search.
• Rybin disproved another long-standing conjecture using ChatGPT alone with just four prompts and no expert steering.
• The pattern works because these problems admit short, verifiable counterexamples—AI can find them if it can check its own work.
• The real implication is reframing fields (code, drug discovery, finance) into testable, formalized "math-like" tasks so AI can brute-force solutions via fast, parallel iteration.