The author, Kevin Buzzard of the Xena Project, observes that recent advances in artificial intelligence—particularly language models solving problems beyond human reach—have elicited strong emotional responses within the mathematical community. He applies Elisabeth Kübler‑Ross’s five stages of grief to characterize these reactions, noting that many mathematicians are experiencing denial, anger, bargaining, or depression in response to what they perceive as a threat to a cherished human endeavor. In the denial stage, he cites the Association for Human Mathematics, which seeks to protect mathematics as a human activity and offers an “AI‑free” pledge, mentioning Fields Medallist Peter Scholze’s refusal to use AI. Anger appears on platforms like the “proofs and prompts” website, where posts by mathematicians such as Vlad Lazic express frustration. The bargaining stage is represented by the Advisory Group on Mathematics and Artificial Intelligence, which released recommendations urging AI labs to cease testing advanced problems on proprietary models and to release results responsibly while supporting human understanding. Depression is illustrated through personal anecdotes: a faculty member finding Navier–Stokes news “extremely depressing,” a post‑doc considering leaving research, and a PhD student questioning the purpose of their work after a chatbot solved a lemma in one shot.
Beyond the grief framing, Buzzard highlights a deeper divide about what mathematics is for. He contrasts Thurston’s view—that the field’s value lies in human understanding gained from proofs—with Hardy’s perspective of mathematics as art and a desire for lasting fame. The author himself admits to being excited by AI’s potential but confesses that his motivation leans toward the theorem tally rather than deep understanding, and he calls on OpenAI to publicly release the theorems their models have generated, arguing that transparency would allow the community to verify and build upon the results rather than duplicate effort. He acknowledges uncertainty about whether AI will soon also provide explanations, leaving open the question of how the community will adapt if machines prove both capable of solving and elucidating mathematical truths.
Buzzard concludes with an optimistic outlook, noting that time will tell how well his opinions age, and reiterates his open letter urging OpenAI to simply dump their collection of theorems upon the world. He suggests that once the results are made public, humans can reverse‑engineer and understand them, eliminating the need to reprove what machines already know. This stance reflects his belief that the value of a proof lies in its utility and the ability to build new knowledge, rather than in any solitary act of comprehension, and it underscores the ongoing tension between preserving traditional mathematical practices and embracing the transformative power of AI.