For more than two thousand years, research in pure mathematics has retained basically the same structure. Euclid began with definitions and axioms, and from them he proved theorems through logical arguments. Essentially, we still do the same today. Of course, this does not mean that mathematical research has been static since Euclid: we now have more efficient notation, thousands of specialized journals, computers, databases, and symbolic computation software. Yet the core of the activity had remained almost untouched: a person would think for days, months, or years until they found a new idea. They would then write a proof and try to convince other mathematicians that it was correct.
Pressing a Button
New artificial intelligence models such as GPT‑5.6 Sol and Claude Fable 5 are no longer mere calculators or search engines. They can work on research problems, propose definitions, find examples and counterexamples, connect ideas from different fields, and construct original proofs. Now it is becoming possible to enter a problem, press a button, and receive a new proof that would have taken a researcher months to find.
This does not happen every time. AI systems still make mistakes, misunderstand some questions, and occasionally produce arguments that appear convincing but contain errors. Their answers must be checked carefully. Nevertheless, it is becoming increasingly common for AI-generated proofs to be correct, and this is set to change the rules of mathematical research completely.
We have all read news reports about recent AI models solving important open problems. But this revolution is not confined to headlines or to the most powerful systems operating far beyond the reach of ordinary researchers. This is something that any of us can experience firsthand simply by interacting with recent models such as GPT‑5.6 Sol or Claude Fable 5. In their combination of speed, breadth of knowledge, and ability to explore many strategies at once, these systems are beginning to surpass what any human being can do. A mathematician may still notice something the machine missed, correct one of its mistakes, or solve a particular problem that the model could not. But no person can compete with an artificial intelligence that draws on an enormous amount of information, and explores several paths simultaneously.
The End of Handcrafted Mathematics
Until now, an important part of the value of a piece of research came from the effort required to produce it. A theorem could be the result of years of work, countless failed attempts, and long conversations with colleagues. Finding the proof was a craft.
That kind of research will probably disappear as the dominant way of producing mathematics. Solving a problem without AI assistance will be like performing by hand a calculation that a computer can complete in seconds. People will still be able to do it for pleasure, training, or educational purposes, but it will no longer be the most efficient way to work at the frontier of knowledge.
These changes may be painful for may of us. Our professional identity is closely tied to our ability to solve problems. We have devoted much of our lives to developing intuition, learning techniques, and finding proofs. Now a tool has emerged that can do in minutes what would have taken us months.
But the end of handcrafted mathematics does not mean the end of mathematics. When proofs become abundant, understanding will become scarce. Someone will have to decide which questions matter, distinguish a deep idea from a superficial observation, verify that the arguments are correct, and explain why a result deserves our attention.
The role of the mathematician will change. It will no longer consist primarily of producing every step of a proof, but of reviewing, discerning, digesting, and communicating ideas generated by AI. It will also be necessary to organize thousands of results, determine which of them form a coherent theory, and translate complex proofs into concepts that human beings can understand.
In other words, AI will be able to produce mathematics much faster than we can absorb it. The bottleneck will no longer be finding new results, but understanding them.
What Chess Can Teach Us
Something similar has already happened in the world of chess. For centuries, grandmasters were the ultimate authorities over the chessboard. They discovered new strategies, prepared openings, and found combinations hidden in positions that other players could not understand. Their prestige stemmed, to a large extent, from seeing things that no other human being could see.
Today, no human plays better than the most advanced computers. And yet chess did not die. On the contrary, its community has flourished. Millions of people play online, study games, follow tournaments, and enjoy the explanations of masters and commentators. Computer programs discover extraordinary moves, but we still need a human being to explain why those moves are extraordinary.
No one stops enjoying a game because a computer could have played it better. What interests us is understanding the ideas, experiencing the tension of the position, and sharing the game with others.
Something similar may happen with mathematics. Artificial intelligence may discover most theorems, but human beings will still be able to enjoy the moment when we understand a proof, recognize an unexpected connection, or find an elegant way to explain a theory. The source of the ideas will change. Our capacity to marvel at them need not disappear.
Teaching Will Remain Human
Artificial intelligence will also transform teaching. Every student will be able to have a tutor available at any time, capable of offering explanations adapted to their level, generating examples, and answering questions without ever becoming tired.
Even so, teaching mathematics will remain a primarily human activity. We learn not only because we receive information, but because we belong to a community. We are motivated by a teacher’s enthusiasm, the recognition of our classmates, and the satisfaction of explaining something to another person. We often persevere with a difficult problem because someone believes that we can solve it.
A good teacher does more than transmit definitions and theorems. A good teacher also communicates curiosity, patience, mathematical taste, and a way of approaching the unknown. They help students tolerate frustration, formulate better questions, and discover that they are capable of understanding ideas that initially seemed inaccessible. AI will be an extraordinary tool in the classroom. But human connection will remain what gives learning its meaning.
A new industrial revolution
The industrial revolution mechanized physical production. Machines could manufacture goods faster, and on a scale that no individual artisan could match. The AI revolution is doing something similar with intellectual production. This comparison also reveals why the present transformation can feel so unsettling. Industrialization did not merely provide artisans with better tools; it challenged the economic value and social status of their skills. In the same way, AI does not simply help mathematicians calculate more quickly. It reaches into the activity many of us regard as our deepest contribution.
However, I am convinced that mathematics will not disappear, just as material production did not disappear. But its methods, institutions, and professional roles may change just as profoundly.
Note: This text was written with the assistance of AI. All the central ideas are the author’s own, and the author takes full responsibility for them.
Received 11 August 2026.
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