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This paper explores the implications of large language models (LLMs) in mathematics, highlighting their ability to generate theorems and proofs while emphasizing that true mathematical progress lies in human understanding and the collaborative journey of discovery. It argues that the current focus on the "residue" of mathematical work鈥攃ountable outputs produced by AI鈥攐vershadows the essential process of nurturing collective comprehension. The authors call for a reevaluation of institutional priorities to ensure that the human journey of mathematical discovery is preserved and funded, as it cannot be mass-produced like AI-generated results.
AI is driving the cost of mathematical outputs to zero, but the true value lies in the human journey of understanding that remains underfunded and at risk.
Large language models have begun refuting long-standing conjectures and, for a few thousand dollars of tokens, solving long-open problems (OpenAI, August 2026). The introspection this has prompted about the future of mathematical discovery is overdue, and the anxiety accompanying it legitimate -- but both are attached to the wrong loss. What machines now produce is the countable part of mathematics -- theorems, proofs, refutations -- which was always the \emph{residue} of the work, not its product. The product is human understanding: not a stock of results but a collective, hard-won way of deciphering the world and acting upon it. The two are arcs of a single loop -- looking produces the residue; taking it up again, one journey at a time, is what rebuilds the shared understanding. Machines are strong on the countable arc, absent from the one that feeds it. The peril is to leave the loop open. AI did not create the confusion between residue and product; it has called a bluff long on the books, driving the cost of the residue towards zero and making the scarce thing visible at last. The pressing questions are therefore institutional: who can check the claims of AI companies, what the work becomes for the next generation of researchers, and whether the one thing that cannot be mass-produced -- the journey that nourish a shared understanding -- continues to be funded. Mathematics, we argue, is uniquely placed among the sciences on the first question -- a proof answers to no one's permission -- and uniquely exposed on the last: the journey has never had a price our institution knew how to pay. The decision is ours.
