Mathematicians Hate AI. They Can’t Quit It
Mathematicians Hate AI. They Can’t Quit It
数学家讨厌人工智能，但他们离不开它
Mathematician Tristan Buckmaster believes OpenAI used his work to rush ahead and beat him to solving a legendary math problem with a $1 million bounty. But that’s not been enough for him to stop using the company’s models—and he’s not the only mathematician that feels that way. 数学家特里斯坦·巴克马斯特（Tristan Buckmaster）认为，OpenAI 利用了他的研究成果，抢在他之前解决了一个悬赏 100 万美元的传奇数学难题。但这并不足以让他停止使用该公司的模型——而且他并不是唯一有这种感觉的数学家。
“Even if you don’t agree with any of this, you’re kind of stuck. With AI being so useful, it’s hard to completely prevent oneself from using it,” Buckmaster tells WIRED. “These companies have a monopoly, and there is not much choice,” he adds. “即使你不同意这一切，你也陷入了困境。由于人工智能非常有用，很难完全阻止自己不去使用它，”巴克马斯特告诉《连线》（WIRED）。他补充道：“这些公司处于垄断地位，我们没有太多选择。”
In the week and half since the New York University professor accused OpenAI of copying his approach, Buckmaster has been using the company’s coding agent Codex to tidy up his research papers. When he has time to do math (which he says is rare, since the fallout thrust him into the spotlight), the tool has been helping him understand the logical steps OpenAI’s agents might have taken to get from his earlier workings to the final proof. 在这位纽约大学教授指责 OpenAI 抄袭其研究方法后的一个半星期里，巴克马斯特一直在使用该公司的编码代理 Codex 来整理他的研究论文。当他有时间做数学研究时（他说这种情况很少见，因为这场风波让他成为了焦点），该工具一直在帮助他理解 OpenAI 的代理可能采取了哪些逻辑步骤，才从他早期的研究成果推导出了最终证明。
Buckmaster had used Codex as well as Anthropic’s competing Claude to work on what’s known as the Navier-Stokes existence and smoothness problem, alongside Anthropic researcher Levent Alpöge. OpenAI deployed tens of thousands of agents to reach the solution, but only after it learned the equation was close to being solved, Buckmaster says. 巴克马斯特曾与 Anthropic 的研究员莱文特·阿尔珀格（Levent Alpöge）合作，使用 Codex 以及 Anthropic 的竞争产品 Claude 来研究所谓的“纳维-斯托克斯存在性与光滑性问题”（Navier-Stokes existence and smoothness problem）。巴克马斯特表示，OpenAI 部署了数以万计的代理来得出解决方案，但那是在得知该方程即将被解决之后才进行的。
When Buckmaster went public with his claims, it ignited a firestorm about artificial intelligence and whether it would make human mathematicians obsolete. It also led OpenAI to do an investigation and amend its announcement about solving Navier-Stokes to say it “confirmed that Buckmaster’s Codex prompts over the two months preceding this announcement and paper on September 8, 2026, could not have influenced the system in any way, including through training.” The company pointed WIRED to its announcement in an email. 当巴克马斯特公开他的指控时，这引发了一场关于人工智能是否会使人类数学家过时的激烈争论。这也促使 OpenAI 进行了调查，并修改了关于解决纳维-斯托克斯问题的公告，称其“确认巴克马斯特在 2026 年 9 月 8 日公告和论文发布前两个月内输入的 Codex 提示词，不可能以任何方式（包括通过训练）影响该系统。”该公司在电子邮件中向《连线》指出了这一公告。
Showing that AI was pushing the boundaries of mathematics was “more important than the result,” Buckmaster says. But churning out solutions to long-standing math problems without fully crediting the human work undergirding them—especially ahead of major IPOs—is irresponsible and “childish,” he says. 巴克马斯特说，展示人工智能正在推动数学的边界“比结果本身更重要”。但他表示，在没有充分归功于支撑这些成果的人类工作的情况下，就匆忙产出长期数学难题的解决方案——尤其是在重大 IPO 之前——是不负责任且“幼稚”的。
Other mathematicians have raised similar concerns. Only a handful of people on the planet understand the techniques in geometric group theory that German mathematician Andreas Thom has dedicated the last two decades to developing. So when OpenAI said in August that its Astra model had used them to prove a long-standing problem he had been working on, “I was amazed,” says Thom. “And of course I was wondering, how did they learn about it?” 其他数学家也提出了类似的担忧。全球只有少数人理解德国数学家安德烈亚斯·托姆（Andreas Thom）过去二十年来致力于发展的几何群论技术。因此，当 OpenAI 在 8 月份表示其 Astra 模型利用这些技术证明了他一直在研究的一个长期难题时，托姆说：“我感到很震惊。当然，我也在想，他们是怎么知道这些的？”
So he says he asked OpenAI researchers Mark Sellke and Sébastien Bubeck. In an August email, he pointed out that the firm’s assertion that “no progress” had been made on the problem in the last decade overlooked a 2019 paper of his, as well as other mathematicians’ work. The company amended its press release. He and a colleague had been using ChatGPT to assist their work on the problem in the months running up to the result, but when he asked if their interactions had been fed into training data, Thom says Sellke replied: “That did not happen.” 他说，他询问了 OpenAI 的研究员马克·塞尔克（Mark Sellke）和塞巴斯蒂安·布贝克（Sébastien Bubeck）。在 8 月份的一封电子邮件中，他指出该公司声称过去十年在该问题上“没有进展”，这忽略了他 2019 年的一篇论文以及其他数学家的工作。该公司随后修改了新闻稿。在得出结果前的几个月里，他和一位同事一直在使用 ChatGPT 辅助研究，但当他询问他们的互动是否被输入到训练数据中时，托姆说塞尔克回答道：“这种情况没有发生。”
“I set it aside,” Thom recalls. “I’m not so much interested in these political things; I want to work on mathematics.” “我把它放在一边了，”托姆回忆道。“我对这些政治性的事情不太感兴趣；我想做数学研究。”
While Thom has seen OpenAI’s statement that Buckmaster’s prompts couldn’t have influenced the system, he says he doesn’t trust this and concedes he will probably never know whether his work actually fed the result. “AI really kills this entire idea that you could trace back who contributed what,” he says. “That is probably over.” 虽然托姆看到了 OpenAI 关于巴克马斯特的提示词不可能影响系统的声明，但他表示并不信任这一点，并承认他可能永远不会知道他的工作是否真的促成了该结果。“人工智能确实扼杀了那种‘你可以追溯谁贡献了什么’的理念，”他说。“那个时代可能已经结束了。”
It’s a big change from how science is typically done, with academics subjecting their findings to peer review and building on each others’ work with credit. Beyond upending attribution, the fact that humans still don’t fully understand each step OpenAI’s agents took to arrive at the Navier-Stokes result also poses existential questions for mathematicians, who see their field slipping from their understanding. 这与传统的科学研究方式发生了巨大变化，传统方式下，学者们会将研究成果提交给同行评审，并在相互认可的基础上建立研究成果。除了颠覆归属权之外，人类至今仍无法完全理解 OpenAI 的代理得出纳维-斯托克斯结果的每一步，这也给数学家们带来了存在主义的质疑，他们眼睁睁看着自己的领域超出了他们的理解范围。
“If I want to make a contribution to mathematics, how do I do that as just a human nowadays when these trillion-dollar companies are in on the game?” says Cornell mathematician Alex Townsend, the coauthor of a forthcoming book on the field’s evolution. “如果我想为数学做出贡献，在这些万亿级公司参与其中的今天，我作为一个普通人该怎么做？”康奈尔大学数学家亚历克斯·汤森德（Alex Townsend）说，他是即将出版的一本关于该领域演变书籍的合著者。
Since August, Townsend has seen many of his colleagues start to ask what they need to know about the technology and how they can set up subscriptions to access higher-powered models. “I feel both excited and nervous simultaneously,” Townsend says. “Excited because I can achieve things that I couldn’t achieve without it, and nervous because I’m questioning: ‘OK, what’s my purpose here?’” 自 8 月以来，汤森德看到许多同事开始询问他们需要了解哪些关于该技术的信息，以及如何订阅以获取更强大的模型。“我感到既兴奋又紧张，”汤森德说。“兴奋是因为我能实现以前无法实现的目标，而紧张是因为我在质疑：‘好吧，我在这里的目的是什么？’”
Thom accepts that his area of study is changing, and has continued to use ChatGPT—with updated privacy settings to stop his work being used to train the data—to speed up writing papers because it is “extremely efficient.” (OpenAI’s offerings through universities and at the enterprise level default to not training models on users’ data.) 托姆接受了他的研究领域正在发生变化的事实，并继续使用 ChatGPT——通过更新隐私设置来阻止他的工作被用于训练数据——以加快论文写作速度，因为它“极其高效”。（OpenAI 通过大学和企业层面提供的服务默认不会使用用户数据来训练模型。）
“If a human had actively done that, then I would be very, very angry,” he says of someone using his work without attribution. But if information was pulled into the model through a back door, by an algorithm which nobody fully understands, “I could probably live with that,” he says. “如果是一个人类主动那样做，我会非常、非常生气，”他谈到有人在不署名的情况下使用他的工作时说。但如果信息是通过后门被算法拉入模型的，而这种算法没人能完全理解，“我大概还能接受，”他说。
Some mathematicians are less forgiving. Twenty-five Fields medalists wrote in an open letter that AI companies and mathematicians are “severely misaligned.” More than 4,000 people have signed the Leiden Declaration, which has a series of recommendations for how mathematicians, funders, and politicians can ensure that AI doesn’t swallow the field. 一些数学家则没那么宽容。25 位菲尔兹奖得主在一封公开信中写道，人工智能公司与数学家之间存在“严重的错位”。超过 4000 人签署了《莱顿宣言》（Leiden Declaration），其中提出了一系列建议，旨在指导数学家、资助者和政治家如何确保人工智能不会吞噬数学领域。
Buckmaster fears the possibility of using AI to “clean up some of your grammar, and suddenly your years of work [could be] gobbled up in user data and sold to another mathematician or grad student. That’s what I think most of the mathematicians tend to be worrying about, and I think it’s a real issue.” 巴克马斯特担心，使用人工智能来“润色一下语法，结果你多年的心血可能就被吞进用户数据中，并被卖给另一位数学家或研究生。我认为这正是大多数数学家所担心的，而且我认为这是一个真正的问题。”
With no oversight on the horizon and AI further engraining itself in the field, some mathematicians are left wondering what the future holds. 由于目前看不到任何监管措施，且人工智能进一步深入该领域，一些数学家不禁开始思考未来将会怎样。