Math and code got there first

Quanta Magazine has a piece this week on how AI has changed mathematical research — AlphaEvolve, LLMs as collaborative partners, problems that used to take months solved in days.

The structural reason mathematics and software development got there first is worth pausing on. Both have fast automated verification built in — proof assistants like Lean for math, test suites and type checkers for code. The loop closes in seconds. Drug discovery has never had that — the verification step is a wet lab experiment that takes weeks or months.

That gap is getting shorter. A dynamic flow system at NC State, published last year in Nature Chemical Engineering, generates ten times more experimental data than previous approaches by monitoring reactions in real time rather than waiting for steady state. Exscientia has been running closed design-make-test-learn cycles in its Oxford robotics facility since late 2024. Periodic Labs, which launched last October with a $300M round from founders of ChatGPT and GNoME, is building explicitly toward this for materials discovery.

The distinguishing factor between disciplines where AI has already transformed research and those where it hasn’t isn’t the AI. It’s the speed of the verification loop. Mathematics and software development had that built in. Experimental science is engineering its way to the same place.

The Quanta piece reads like a preview.