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On October 3, 2026, Li Xinyi, associate professor at the Beijing International Center for Mathematical Research at Peking University and an alumnus of Peking University's Mathematics Department (Class of 2007), publicly criticized OpenAI and Anthropic for using AI to "solve" difficult mathematical problems. He believes that such results are "solved but not understood at all," and that the accompanying publicity will scare off young people who want to study mathematics and mislead funding decisions. Li Xinyi also revealed that a postdoc on his team's draft on an obscure conjecture highly overlapped with a cross-disciplinary paper generated by AI on arXiv, ultimately ending in joint authorship; he maintains that academic credit should only go to those who contribute to understanding. In addition, he pointed out that work in applied mathematics of the "combining A with B" type is more easily replaced by AI, whereas in fields such as geometry and topology that rely on spatial intuition, AI currently does not learn well. At present, this criticism was reported by Huxiu, and no other sources have cross-verified it.
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Oct 3
Huxiu
Li Xinyi, a PKU Class of 2007 mathematics alumnus, criticized OpenAI and Anthropic for harming the mathematics community by using AI to solve problems
Li Xinyi, an associate professor at Peking University's Beijing International Center for Mathematical Research and a Class of 2007 mathematics alumnus, criticized OpenAI and Anthropic for using AI to "solve" difficult mathematical problems, arguing that such results are "solved but not understood at all," and that the accompanying publicity will scare off young people who want to study mathematics and mislead funding decisions. He revealed that a postdoc on his team's draft on an obscure conjecture highly overlapped with a cross-disciplinary paper generated by AI on arXiv, ultimately ending in co-authorship, and argued that academic credit should only go to those who contribute to understanding. He also noted that work in applied mathematics of the "combine A with B" type is more easily replaced by AI, while in fields such as geometry and topology that rely on spatial intuition, AI currently learns poorly.