Terence Tao argues AI could bring division of labor to math for the first time in history
Mathematician Terence Tao explains how AI could reshape math research by enabling division of labor. Until now, mathematicians had to do everything themselves: framing problems, building strategies, executing them, verifying results, and writing them up. Unlike industry or the natural sciences, specialization was never an option in math, Tao explains.
AI and formal verification could change that by filling skill gaps in collaborations, Tao says. But if AI generates strategies without verifying them, the result is a flood of untested ideas. A new style of math only works when automation advances across several areas at once. Tao sees humans as essential because AI performance is uneven—a principle that likely applies to many other fields, too.
The level of automation and AI power that you can profitably use before it becomes slop is roughly proportionate to how stringent your verification is.
Terence Tao
It seems like the field is moving toward Tao’s vision of “industrial mathematics”: instead of solo researchers grinding away for years, large AI-supported teams could pursue broader but shallower research. AI crunches billions of data points, while humans make “inspired guesses” from a handful of observations.
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