What It Means to Be a Mathematician When AI Does the Math
IEEE Spectrum AI Benjamin Skuse
AI systems have progressed from basic mathematical computation to solving unsolved problems and producing publishable research, with Google DeepMind's Aletheia achieving Ph.D.-level results and OpenAI's system disproving a conjecture in combinatorial geometry. Large language models combined with proof assistants are automating the formalization of mathematical proofs, removing a bottleneck that previously required mathematicians to manually translate informal proofs into machine-readable code. As AI increasingly handles mathematical problem-solving, mathematicians must reconsider whether their value lies primarily in obtaining answers or in the deeper satisfaction found in the struggle to understand complex ideas.
Why it matters
In the mid-noughties, when music by the Killers and Franz Ferdinand blared out of every pub and nightclub I passed, I spent my days and nights struggling through a Ph.D. in applied mathematics. My research focused on simulating how special light waves interact in liquid crystals and using simple equations to approximate and understand those interactions. When I look back at my thesis now, liquid crystal technology is old hat, and I imagine my work could be completed with AI assistance in a matter of days—maybe hours. But the same cannot be said for the work of the pure mathematics Ph.D. students with whom I shared a cramped office at the University of Edinburgh. At the time, I felt sorry for these colleagues, who day after day sat at their desks, seemingly tearing their hair out and making no progress. (Though I was struggling too, I was at least always making some headway.) When we finished and went our separate ways, some hadn’t even published a paper.Now, in hindsight, I finally und
