OpenAI 宣称透过数千个 AI 代理人解决了困扰数学界数十年的「纳维-斯托克斯存在性与平滑性问题」，引发学术界广泛关注与反思。传统数学研究更接近艺术探索，重视严谨推演与深刻洞察，而非单纯追求解答的速度或实用价值；然而 OpenAI 仰赖暴力计算推导出的证明，却缺乏透明的引证与清晰的逻辑解释，使得人类难以真正理解其背后的数学本质。

包括多位菲尔兹奖得主在内的数学家发表联合声明，忧心这种大规模自动生成证明的模式将「解题」与「理解」彻底割裂，进而削弱人类的创造力。许多具突破性的数学概念（例如虚数）往往源于人类遭遇艰难计算时的深层反思，倘若将运算完全外包给 AI，数学家恐将错失开创全新理论框架的契机。

此外，AI 大幅消耗运算资源来攻克现有问题，可能会排挤传统师徒制中年轻学者与研究生的培育空间，剥夺他们磨练思维的机会。讽刺的是，支撑现代 AI 发展的晶片技术与神经网络架构，正是奠基于数百年来人类纯粹出于好奇心所建立的数学理论，如今技术热潮却可能反过来侵蚀这片滋养创新的学术沃土。

OpenAI announced that it had solved the decades-old Navier-Stokes existence and smoothness problem using thousands of AI agents, sparking widespread debate within the mathematical community. Unlike conventional mathematics, which prioritizes deliberate artistic exploration and conceptual understanding over speed or immediate utility, OpenAI's brute-force approach bypassed the traditional reflective process without offering clear citations or transparent explanations, leaving mathematicians struggling to comprehend how the solution was achieved.

Prominent mathematicians, including multiple Fields Medalists, issued a declaration warning that mass-producing computer-generated proofs risks detaching solutions from human understanding and stifling creativity. Historically, foundational concepts like imaginary numbers emerged directly from the intellectual struggle over difficult calculations; outsourcing this laborious process to AI could prevent mathematicians from inventing entirely new definitions, conjectures, and mathematical fields.

The massive computational costs poured into such AI achievements also threaten the traditional apprenticeship model by depriving young researchers and students of essential developmental opportunities. Ironically, the very algorithms, chip fabrication techniques, and neural networks powering modern AI were born from centuries of human mathematical curiosity, which now risks being undermined by the commercial drive of the tech industry.