Mathematician Terence Tao finds AI doesn't speed up his core problem-solving but makes his papers "richer" by adding complex plots and deeper literature searches. Tasks that were previously infeasible are now easy. AI expands the scope and quality of work rather than just shortening the timeline for existing tasks.

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.

Expert mathematicians adopt formal tools like Lean not primarily to catch errors, but to offload tedious, low-level deductions. This automation allows them to operate at a higher level of abstraction and focus their cognitive energy on creative intuition and problem-solving strategy.

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.

Philosopher Nick Bostrom suggests that even if AI surpasses human intellect in fields like theoretical mathematics, it won't eliminate human engagement. Instead, these pursuits may evolve into hobbies appreciated for the process and social connection, much like chess has remained popular despite computer dominance.

Unlike other sciences, mathematics has historically lacked a strong experimental branch. AI changes this by enabling large-scale studies—for example, testing a thousand different problem-solving approaches on a thousand problems. This creates a new, data-driven methodology for a field that has been almost entirely theoretical.

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.

The AI narrative should shift from job replacement to labor abstraction. AI automates menial work (e.g., a banking analyst moving logos) to free up humans for higher-value, more fulfilling tasks that require judgment and storytelling, ultimately increasing overall productivity and skill.