Top AI models are now solving major open problems in mathematics, leading some in the field to feel their core purpose is being automated away. This isn't just about tools; it's a profound identity crisis for a discipline built on human ingenuity and the pursuit of solving theorems.

An AI model disproved a mathematical conjecture not through a flash of creative genius, but by methodically applying a known technique from a different math subfield. This highlights AI's current strength: synthesizing vast, disparate human knowledge rather than generating truly novel, alien ideas. It's an exhaustive librarian, not an intuitive genius.

As AIs automate theorem proving and even explanation, the role of human mathematicians will shift. Instead of being creators, they will act as curators, using their taste and social connection to guide others through the vast, AI-generated landscape of mathematical ideas. Their value will lie in providing motivation and a human-centric narrative.

OpenAI's Astra model solving major open problems in mathematics has led to a profound sense of despair among some experts. The sentiment, described as "The dark night of mathematics," reflects a fear that AI is not just automating tasks but devaluing a deeply human field of intellectual discovery.

Future AI-driven mathematical discoveries will likely follow two paths. One is finding 'lightning bolt' connections between existing, disparate fields (e.g., number theory and physics). The other, more profound path, is 'mountain building'—constructing entirely new theoretical frameworks, a skill signifying a much higher level of general intelligence.

Even if AI could instantly prove any mathematical claim, it wouldn't end the field. The truly creative and valuable work in mathematics lies in higher-level tasks AI can't do: asking interesting questions, identifying fruitful problems, and inventing entirely new branches of mathematics like calculus or information theory.

A major frontier for AI in science is developing 'taste'—the human ability to discern not just if a research question is solvable, but if it is genuinely interesting and impactful. Models currently struggle to differentiate an exciting result from a boring one.

The ultimate skill of a great scientist isn't performing calculations but identifying the most fruitful questions to pursue. While AI is becoming superhuman at answering well-posed problems, the human role of taste and strategic direction-setting remains paramount for breakthroughs.

Professors often assign solvable but challenging problems to new PhD students to help them build research skills. As AI can now "crush" these problems, academia faces a crisis in how to train the next generation of scientists without these traditional rites of passage.