The Verge 深度报道：OpenAI 以 88 小时解决 Navier-Stokes 引发数学界信任危机

The Verge 采访十余位数学家，梳理 OpenAI 用约 10,000 个 agent、数千万美元算力和 88 小时解决 Navier-Stokes 千禧年大奖难题后引发的争议。

两位身处近期争议核心的数学家向 The Verge 讲述了 AI 巨头们“幼稚”的竞争如何正在颠覆这一领域。

过去几年，OpenAI 在数学这片日益艰险的疆域上不断插旗。本周，它宣称拿下了迄今最大的战利品之一：一个传奇性千禧年大奖难题的解答。在正常情况下，这本应被当作一项历史性成就来庆祝。

然而，许多数学家却以日益不安的心情注视着 OpenAI 的步步紧逼。在他们看来，这家公司与其说是一位热情的新来者，不如说是一个资源雄厚到不可思议的外来闯入者，横冲直撞地扑向他们毕生致力于研究的问题，却似乎对长期以来的规范、以及被它甩在身后的人所承受的后果漠不关心。这种不安的核心，是一种感觉：OpenAI 做数学的动机不同。数学家想推动这一领域前进，OpenAI 想赢。

数学通常不会这么戏剧化，那么事情是怎么变得如此糟糕的？

本周，The Verge与十多位数学家进行了交谈，其中包括 Tristan Buckmaster 和 Andreas Thom，他们正处于近期围绕 OpenAI 在该领域工作的争议中心。即便是对那些最严重指控持怀疑态度的人，也描述了一个被这家科技巨头的行径所震动的领域，并对其在决心击败竞争对手的过程中接下来可能做出什么感到担忧。

Buckmaster 指责 OpenAI 未能充分说明他通过其工具 Codex 所做的工作是否可能对其近期的成功有所贡献。在给 The Verge 的一份声明中，OpenAI 发言人 Laurance Fauconnet 强烈否认他的提示词中的材料发挥了任何作用：“我们可以明确地说，Buckmaster 博士在过去两个月里的 Codex 提示词不可能以任何方式影响该系统，包括训练。”

Buckmaster 仍不信服。“鉴于他们迄今为止的行为，人们应当对此类声明抱有极大的怀疑，”他说。

数学通常不会如此戏剧化，那么事情怎么会变得如此糟糕？仅仅一则谣言就足以让紧张局势彻底爆发。

OpenAI 表示，它听说一些研究人员在千禧年大奖难题上取得了进展，于是决定看看其一款尚未发布的先进模型是否也能有所突破。结果证明它可以。OpenAI 称，它动用了大约 10,000 个智能体、数千万美元的计算资源，仅用 88 小时就找到了 Navier-Stokes 问题的一个解，该问题涉及流体的流动。

该公司还发现，自己正在与谁赛跑：纽约大学教授 Buckmaster，以及其最强劲的竞争对手之一 Anthropic 的研究员 Levent Alpöge。在若干条研究路线中，这两人正在攻克 Navier-Stokes，尽管尚未完成证明。接下来发生的事情有些细节存在激烈争议，但双方对事件的基本经过大体上一致。有一点尤其清楚：Alpöge 的参与对 OpenAI 来说是个麻烦，尽管他声称这属于“个人合作”，独立于他在 Anthropic 的工作。

即便是对那些对最严重指控持怀疑态度的人来说，他们也形容这个领域被这家科技巨头的行径所震动，并担心它为了击溃对手，接下来还可能做出什么事。

Buckmaster 表示，在得知该公司已经察觉到他们的进展、并正竞相拿出自己的解法之后，他联系了 OpenAI。他说，与 OpenAI 研究员 Sébastien Bubeck 的讨论变得充满火药味，在他看来甚至带有威胁意味，但公司为他提供了一条前进路径——一条将 Alpöge 排除在外的路径。Buckmaster 说，对方提出给他几乎“不受限的算力”来完成他自己的工作，并让他有机会成为 OpenAI 宣布这一突破的论文的唯一作者，而这篇论文当然会为其工具记上一功。

“我所要做的就是把 Levent 推出去当替罪羊，”Buckmaster 在接受 The Verge 电话采访时说。他表示自己断然拒绝了 Bubeck 的提议，在他看来那是一种“贿赂”，同时他也开始质疑 OpenAI 是否可能从他使用 Codex 中获益——Codex 是该公司的 AI 工具之一，他一直在用它来攻克这个问题。OpenAI 否认任何人——或任何智能体——访问过他的具体用户数据，直到其更近期的表态中才承认，无法排除从他使用这些产品中衍生的数据被用于改进模型的可能性。

Buckmaster 最终决定将他的研究成果以及他对 OpenAI 行为的叙述一并公之于众。他说，他的办公室已经变成了一个类似“作战室”的地方，同事们帮忙审查他的数学推导、协调对外联络，甚至联系律师。

Bubeck 拒绝了 Buckmaster 在社交媒体上以及在一次采访中对《纽约时报》对这些对话的描述。他承认曾提出向 Buckmaster 提供 OpenAI 的资源，以帮助他完成自己的证明，或让他接手撰写该公司的证明。引人注目的是，Bubeck 表示 OpenAI 与其他数学家也做过类似的安排，但没有透露他们的身份。

但他的叙述仍然表明，Alpöge 与 Anthropic 的关联是一个症结所在。他告诉《泰晤士报》：“从我们的角度来看，我们怎么能让一名 Anthropic 员工参与 OpenAI 的内部项目呢？”

如果目标是在数学领域竞争，那么几乎没有比解决千禧年大奖难题更大的战利品了。这七个问题由克莱数学研究所于2000年提出，被广泛认为是该领域最艰巨的挑战之一。每一个问题的解决者都能获得100万美元的悬赏。在这些奖项设立之时，许多问题已经经历了数十年的严密审视。在此后的四分之一世纪里，只有一个问题被攻克——那就是庞加莱猜想，一个关于三维球面的拓扑学问题。

对于一家想要证明其模型在数学方面最强的AI公司来说，这些难题是无法抗拒的目标。对于Buckmaster以及The Verge采访的许多其他数学家来说，这有助于解释为什么OpenAI在听说其他方正在逼近时行动如此猛烈——尤其是当一家竞争对手AI公司似乎也参与其中时。

对Buckmaster而言，这一事件强化了他此前与Google DeepMind合作时就已深信的一点：“所有这些科技人都痴迷于”解决重大的著名问题，并且“痴迷于抢首发”，他说。“一切都是关于竞争。”

Tristan Buckmaster说，在那个世界里，人们对声望、名声、争第一以及被公认为第一有着强烈的执念。“那是唯一的硬通货，”他说。

他说，当各家公司竞相去解决那些著名难题时，常常被“忽视”的，正是数学家本身——不仅是那些累积的工作让这些突破成为可能的人，还有他们最初从事数学研究的理由。诚然，有些人可能追求声望，但大多数人根本不是追逐奖杯的人。牛津大学数学教授 Andras Juhasz 将数学描述为一门优雅的学科，一半是科学，一半是艺术，驱动着投身其中的人有着许多不同的动机。“

往往没有立竿见影的实际应用，”他说，“他们做数学是因为它美。他们享受其中。那是发现的感觉。那是自然而然的事。”

与课堂层面的数学练习不同，前沿数学很少有一条通往答案的既定路径。研究人员可以从任意多种角度攻克问题，有些角度截然不同，这使得通向解决方案的那些想法——以及是谁提出了它们——变得尤为重要，或许比解决问题本身还重要。数学家极其看重这种传承脉络，因为正是它让这个领域得以扩展，新的技巧和方法往往比它们原本要解决的问题更具深远影响。

在 Buckmaster 看来，他与 Bubeck 的交流典型地体现了研究数学界与大型科技公司之间的鸿沟，也凸显了在研究中对什么才算有价值的认知差异。他一边读着与 Bubeck 聊天时做的笔记，一边说，当他拒绝公司提出的让他署名的提议时，这位 OpenAI 研究员明显愣住了。“我能看到 Sébastien 的表情。当我说我不在乎千禧年大奖难题时，他很震惊，”他回忆道。

Buckmaster 说，他此前在技术研究人员中也遇到过类似的心态。他说，在那个世界里，人们对声望、名声、争第一以及被视为第一有着强烈的执念。“那是唯一的硬通货，”他说。

Buckmaster 并不是唯一一位在与 OpenAI 打交道后对其动机感到担忧的数学家。德国德累斯顿工业大学的教授 Andreas Thom 上个月发现自己身处一场争议的中心，起因是 OpenAI 宣布了一项令人瞩目的数学成果，而该成果大量建立在他和同行研究员 Gábor Kun 的工作之上。该公司悄然修改了公告，承认了两人的贡献，却没有宣布或公开披露这一改动。

Thom 将这段经历描述为“一段不太愉快的体验”，但他告诉The Verge，在 Buckmaster 公开爆料之前，他基本上已经把这件事抛在脑后了。Buckmaster 的指控促使 Thom 重新审视一个关于 OpenAI 这项突破的未解问题：他和同事们与 ChatGPT 就这项研究进行的对话，是否可能被用来帮助改进那些最终攻克了他多年钻研的问题的模型。

Thom 说，只有 OpenAI 掌握着回答这个问题所需的信息。“说实话，我怀疑他们自己都不知道。”他说，训练模型并用它们产出数学结果的人“是另一类人”。在他看来，这很难成为这种不确定性的借口。“因为这实际上意味着他们根本不在乎，对吧？”

“与那些资源远超学术研究机构的强大 AI 公司竞争的前景，可能会让他们更加不愿去追求雄心勃勃的问题。”

这种紧张关系与其他地方已经上演的争斗如出一辙。作家、音乐人、艺术家和媒体公司都曾就 AI 公司利用大量人类创作成果构建系统一事提出质疑，而这些成果往往未经许可、未获署名或未予补偿。虽然数学看似是另一个世界，但根本问题是相同的：公司对那些其积累成果被用来构建系统的人，究竟欠了什么？

在 Thom 的案例中，这个问题仍未得到解决。OpenAI 未回应 The Verge 的提问：Thom 及其同事与 ChatGPT 的对话数据，是否可能促成了该公司基于其工作所构建的解决方案。

更广泛地说，Thom 表示，他对一种他眼中的失察感到不满：未能认可“整个社区为所有这些研究成果所付出的集体努力”——而这些成果正是 AI 近期数学进展的基础。他说，公司“现在只是在利用[它]，就好像它根本不算什么。”

“我觉得这里面有一种我不喜欢的态度，”他说。

数学家对竞争并不陌生——研究者极为看重优先权，数学史上充斥着关于谁先谁后的激烈争执——但尽管竞争并不排斥合作，这些绝非普通的竞争者。从历史上看，在数学领域抢先发表相对困难，原因显而易见：很少有人具备足够的专业能力，能够迅速切入某项发现。AI 公司的运作规模则截然不同。研究者担心，它们可能把抢先发表变成某种工业化流程，数学家几乎无力反击——一旦有突破即将到来的消息传开，它们就能迅速调动成千上万个智能体和海量算力。

在 Buckmaster 看来，OpenAI 本可以轻松地与研究者合作，而不是与他们竞速争夺结果。事实上，这家公司似乎完全愿意与他合作。问题出在 Alpöge 身上，或者更具体地说，出在他与 Anthropic 的关系上。

“他们如此急于发表，急于击败 Anthropic，”他说。他们几乎没有注意到夹在中间的研究者。

尽管一切如此匆忙，OpenAI 在数年内都不会知道自己是否赢得了解决 Navier-Stokes 的千禧年大奖。克莱数学研究所要求一项结果发表后须经过两年时间，在此期间它必须“在全球数学界获得普遍接受”。目前，Navier-Stokes 处于一种奇特的悬置状态：该研究所已将其从悬而未决问题清单中移除，但尚未宣布它已被解决。“这一过程刻意从容不迫，”该研究所在一份声明中表示。

与此同时，OpenAI 已经翻篇了。在给 The Verge 的一份声明中，OpenAI 的 Fauconnet 表示，“自 Navier-Stokes 完成以来，我们在另一个千禧年大奖难题上取得了实质性进展”，并补充说，公司正在“研究如何审慎地分享这些成果”。

具体是哪个问题仍不清楚。社交媒体上未经证实的 猜测认为，这可能是霍奇猜想，粗略地说，它涉及如何用更简单的构件来理解复杂的几何形状。还有 传言称，Anthropic 也正在逼近一个属于自己的千禧年大奖难题。

随着这两家 AI 巨头竞相收割更多数学奖杯，它们发现，仅凭惊人的成果并不足以赢得它们正在改造的这个社区的信任。

数学家们开始反击。The Verge 采访的许多人，甚至是该领域最热衷于 AI 的支持者，都担心这些公司正在 对研究产生寒蝉效应，迫使数学家对未完成的工作更加保密，唯恐有人突然杀出、抢先一步。有几位表示，此前曾整理过重要未解问题清单的同事正在重新考虑这一做法，他们担心，原本意在为该领域提供有用资源的清单，反而可能变成 AI 公司的目标清单。

抵制正变得越来越公开。今年 6 月，数学家们发布了《莱顿宣言》，这是一套关于在数学中负责任地使用 AI 的原则，已获得国际数学联盟的背书，并有近 3900 人签署，比我上次在 8 月中旬报道时增加了近 500 人。它敦促政策制定者、政府、媒体和其他群体不要轻信那些“夸大其产品能力”的公司所制造的“炒作”。在千禧年大奖难题争议愈演愈烈之际，OpenAI 撤回了对加州理工学院一场本科生数学黑客松的赞助，此前该赞助遭到了激烈的反对，反对者谴责企业利益的侵入，并担心该活动会催生大量低质量的“垃圾数学”。

“他们根本不把我们作为一个共同体放在心上。这一切都只是两家市值万亿美元的公司之间像小孩子一样耍性子的无聊闹剧。”

中国清华大学数学教授、哈佛大学荣休教授、著名菲尔兹奖得主丘成桐（Shing-Tung Yau）对The Verge表示，他担心这对年轻研究人员可能造成的影响。“研究难题对博士生和青年教职人员来说本就风险不小，”他说，“与资源远超学术研究团队的强大 AI 公司竞争的前景，可能会让他们更加不愿去追求那些雄心勃勃的问题。”

丘成桐拒绝就研究人员的工作可能被 OpenAI 使用的指控发表看法，但表示他支持进行独立审查以查明真相。更广泛地说，他担心缺乏透明度以及急于抢先发布，可能会掩盖某项突破背后的知识谱系。他说，数学上的功劳应当反映知识贡献，而不是谁拥有最多的算力预算或谁发布了最响亮的公告。

丘成桐还指出了另一个问题。OpenAI 和其他 AI 公司处于一种特殊的位置——既是重要研究工具的提供者，又在某种意义上也是研究者。他说，这“引发了严重的利益冲突担忧”，尤其是因为它们可能从特权访问客户未完成和未发表的工作中获益。

“这种担忧值得得到实质性的回应，”他说。“不应简单地将其视为普通竞争而予以驳回。”

这种日益加剧的不安感，很大程度上归结于信任问题。数学家们不必接受针对 OpenAI 最爆炸性的指控，就足以对一家既提供他们研究工具、同时又与他们竞争的公司感到担忧。

“是的，说实话，我不认为某个数据安全公告之类的真能解决这个问题，”Thom 说。“我并不怎么信任他们。”Buckmaster 也有同感：“我们凭什么要相信他们说的任何话？”

Buckmaster 表示，同事们对他公开表态的反应绝大多数是正面的。但底下还潜藏着另一种情绪：恐惧。他告诉 The Verge，他最初打算感谢那些在他公开自己的经历时支持他的人。但在许多人表示不愿让自己的名字被公开关联之后，他决定不这么做了。“现实是，数学家们其实很怕它们，”Buckmaster 说，他指的是那些 AI 公司。

而恐惧很难成为构建一个富有成效且健康的研究社区的坚实基础。Buckmaster 并不认为这对涉及其中的那些公司有多大影响。“他们根本不把我们作为一个社区放在心上，”他说，“这一切都只是两家市值万亿美元的公司之间像小孩子一样的小家子气闹剧。”

Two mathematicians at the heart of recent controversies tell The Verge how AI giants’ ‘childish’ rivalries are upending the field.

OpenAI has spent the last few years planting flags across the increasingly difficult terrain in mathematics. This week, it claimed one of its biggest prizes yet: a solution to a legendary Millennium Prize problem. In normal circumstances, this would have been celebrated as a historic achievement.

Instead, many mathematicians have watched OpenAI’s relentless advance with growing unease. To them, the company appears less like an enthusiastic newcomer than an impossibly well-resourced interloper, charging into problems they have dedicated their lives to studying with little apparent regard for long-standing norms or the consequences for those left in its wake. At the heart of that unease is a sense that OpenAI is doing mathematics for different reasons. Mathematicians want to advance the field. OpenAI wants to win.

Mathematics is not normally this dramatic, so how did things get this bad?

This week, The Verge spoke with more than a dozen mathematicians, including Tristan Buckmaster and Andreas Thom, who are at the center of recent controversies surrounding OpenAI’s work in the field. Even those skeptical of the most serious allegations described a field shaken by the tech giant’s conduct and fearful of what it might do next in its determination to trounce its rivals.

Buckmaster has accused OpenAI of failing to adequately explain whether work he did through its tool Codex could have contributed to its recent successes. In a statement to The Verge, OpenAI spokesperson Laurance Fauconnet strenuously denied that material from his prompts had played a role: “We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.”

Buckmaster remains unconvinced. “Given their behavior up until this point, one should take such statements with great skepticism,” he said.

Mathematics is not normally this dramatic, so how did things get this bad? A rumor was all it took for tensions to boil over.

OpenAI says it heard some researchers were making progress on Millennium Prize problems and decided to see whether one of its advanced, unreleased models could make headway too. It turns out it could. OpenAI says it took roughly 10,000 agents, tens of millions of dollars of compute, and just 88 hours to find a solution to the Navier-Stokes problem, which concerns the flow of fluids.

The company had also discovered who it was racing against: Buckmaster, an NYU professor, and Levent Alpöge, a researcher at one of its fiercest rivals, Anthropic. Among several lines of research, the pair were pursuing Navier-Stokes, though had not yet completed a proof. Some details of what happened next are fiercely contested, but the two sides broadly agree on the basic sequence of events. One thing is particularly clear: Alpöge’s involvement was a problem for OpenAI, despite his saying it was a “personal collaboration” independent of his work with Anthropic.

Even those skeptical of the most serious allegations described a field shaken by the tech giant’s conduct and fearful of what it might do next in its determination to trounce its rivals.

Buckmaster said he contacted OpenAI after learning the company had become aware of their progress and was racing toward a solution of its own. He said discussions with OpenAI researcher Sébastien Bubeck grew contentious and, in his view, threatening, but the company offered a path forward for him — one that excluded Alpöge. Buckmaster said he was offered practically “unlimited compute” to finish his own work, and the opportunity to be the sole author of OpenAI’s paper announcing the breakthrough, which would of course credit its tools.

“All I had to do was throw Levent under the bus,” Buckmaster told The Verge in a phone interview. He said he flatly rejected Bubeck’s offer, which he viewed as a “bribe,” and also began questioning whether OpenAI may have benefited from his use of Codex, one of the company’s AI tools he had been using to tackle the problem. OpenAI has denied that anyone — or any agent — accessed his specific user data, and until its more recent comments acknowledged it could not rule out the possibility data derived from his use of the products was used to improve the model.

Buckmaster ultimately decided to go public with both his work and his account of OpenAI’s conduct. His office, he said, had been transformed into something of a “war room,” with colleagues helping scrutinize his mathematics, coordinate outreach, and even get in touch with lawyers.

Bubeck has rejected Buckmaster’s characterization of the conversations on social media and in an interview with The New York Times. He acknowledged offering OpenAI’s resources to help Buckmaster complete his own proof or to have him take over the writing of the company’s. Strikingly, Bubeck said OpenAI had made similar arrangements with other mathematicians, though did not identify them.

But his account nevertheless makes clear that Alpöge’s affiliation with Anthropic was a sticking point. “From our perspective, how can we have an internal OpenAI project with an Anthropic employee?” he told the Times.

If the goal is to compete in mathematics, there are few bigger trophies than solving a Millennium Prize problem. The seven problems, set out by the Clay Mathematics Institute in 2000, are widely considered among the most formidable challenges in the field. Each carries a $1 million bounty for whoever solves it. Many had already endured decades of intense scrutiny by the time the prizes were established. In the quarter-century since, only one — the Poincaré conjecture, a topological problem concerning three-dimensional spheres — has fallen.

For an AI company looking to prove that its models are the best at mathematics, then, they are irresistible targets. To Buckmaster and many other mathematicians The Verge spoke to, that helps explain why OpenAI moved so ferociously when it heard others were closing in — particularly once a rival AI company appeared to be involved.

For Buckmaster, the episode reinforced something he already believed strongly from a previous spell collaborating with Google DeepMind: “All these tech people are obsessed” with solving big famous problems and are “obsessed with scooping,” he said. “Its all about competition.”

In that world, Tristan Buckmaster said there is an intense fixation on prestige, fame, being first, and being seen to be first. “That’s the only currency,” he said.

He said what often gets “lost” when companies race to solve famous problems are the mathematicians themselves — not just the people whose accumulated work makes these breakthroughs possible, but the reasons they do mathematics to begin with. Yes, some may pursue prestige, but most are simply not trophy hunters. Andras Juhasz, a professor of mathematics at the University of Oxford, described mathematics as an elegant discipline that is part science, part art, with many different motivations driving those working there. “Often there is no immediate practical application,” he said. “They do it because it’s beautiful. They enjoy it. It’s the sense of discovery. It’s natural.”

Unlike classroom-level mathematical exercises, frontier mathematics rarely has a prescribed route to an answer. Researchers can attack problems from any number of angles, some radically different, which makes the ideas that lead to a solution — and who developed them — especially important, perhaps more so than solving a problem itself. Mathematicians care deeply about this lineage because it is how the field expands, with new techniques and methods often proving more consequential than the problem they were designed to solve.

To Buckmaster, his exchanges with Bubeck typify the chasm that separates the worlds of research mathematics and Big Tech, and highlight the differences between what is considered valuable in research. Reading from notes he took while chatting with Bubeck, he said the OpenAI researcher was visibly taken aback when he rejected the company’s offer to take credit. “I could see Sébastien’s face. He was shocked when I said I don’t care about the Millennium Prize,” he recalled.

Buckmaster said he had encountered a similar mentality among tech researchers before. In that world, he said there is an intense fixation on prestige, fame, being first, and being seen to be first. “That’s the only currency,” he said.

Buckmaster isn’t the only mathematician to come away from an encounter with OpenAI concerned about the company’s motivations. Andreas Thom, a professor at the Technical University of Dresden in Germany, found himself at the center of a controversy last month after OpenAI announced an impressive mathematical result that built heavily on work by him and fellow researcher Gábor Kun. The company quietly amended its announcement to acknowledge the pair’s contribution without announcing or publicly disclosing the change.

Thom described the ordeal as “not a very pleasant experience,” but told The Verge he had largely put it behind him until Buckmaster went public. His allegations prompted Thom to revisit an unresolved question about OpenAI’s breakthrough: whether conversations he and his colleagues had with ChatGPT about the research could have been used to help improve the models that ultimately cracked the problem he’d spent years working on.

Only OpenAI has the information needed to answer that question, Thom said. “To be honest, I suspect that they don’t even know.” The people training the models and using them to produce mathematical results are “a different kind of people,” he said. To him, that’s hardly an excuse for the uncertainty. “Because it effectively means that they don’t really care, right?”

“The prospect of competing with powerful AI companies, whose resources far exceed those available to academic research groups, could make them even more reluctant to pursue ambitious questions.”

The tension echoes fights already playing out elsewhere. Writers, musicians, artists, and media companies have all challenged AI companies over systems built from vast stores of human-created work, often without permission, recognition, or compensation. While mathematics may seem a world apart, the underlying question is the same: What do companies owe to the people whose accumulated work they ingested to build their systems?

In Thom’s case, the question remains unresolved. OpenAI did not respond to The Verge’s question on whether data from conversations Thom and his colleagues had with ChatGPT could have contributed to the company’s solution that built on his work.

More broadly, Thom said he resents what he sees as a failure to recognize the “the communal effort that this entire community has put into all the research results” underpinning AI’s recent mathematical advances. Companies, he said, “are just now using [it] as if it was kind of nothing.”

“I think there is a certain attitude that I don’t like in that,” he said.

It’s not that mathematicians are strangers to competition — researchers care deeply about priority and bitter disputes over who came first litter mathematical history — but while competition does not preclude cooperation, these are no ordinary competitors. Scooping in mathematics has historically been relatively difficult for obvious reasons: Very few people have the specialized expertise to swoop in on a discovery at speed. AI companies operate on a different scale. Researchers worry they could turn scooping into something of an industrial process mathematicians would have little chance of fighting back against, rapidly spinning up thousands upon thousands of agents and enormous amounts of compute whenever word spreads that a breakthrough is close.

To Buckmaster, OpenAI could have easily collaborated with researchers rather than race them to results. Indeed, the company seemed perfectly willing to work with him. The problem was Alpöge, or, more specifically, his ties to Anthropic.

“They were in such a rush to publish, to beat Anthropic,” he said. They barely took note of the researchers caught in the middle.

For all the rush, OpenAI won’t know whether it has won the Millennium Prize for solving Navier-Stokes for years. The Clay Mathematics Institute requires a period of two years to have passed since a result was published, during which it must have “received general acceptance in the global mathematics community.” For now, Navier-Stokes occupies a peculiar limbo: The Institute has removed it from its list of unsolved problems, though hasn’t yet declared it solved. “The process is deliberately unhurried,” the Institute said in a statement.

OpenAI, meanwhile, has already moved on. In a statement to The Verge, OpenAI’s Fauconnet said that “since the completion of Navier-Stokes we have made substantial progress on another Millennium Prize problem,” adding that the company is “working through how to share these results thoughtfully.”

Which problem remains unclear. Unconfirmed speculation on social media suggests this could be the Hodge conjecture, which concerns, very roughly speaking, how complex geometric shapes can be understood in terms of simpler building blocks. Rumors are also circulating that Anthropic is closing in on a Millennium Prize problem of its own.

As the two giants of AI race to collect yet more mathematical trophies, they are discovering that astonishing results alone are not enough to earn the trust of the community they are transforming.

Mathematicians are beginning to push back. Many The Verge spoke to, even the most enthusiastic proponents of AI in the field, worried the companies were having a chilling effect on research, pushing mathematicians to be more secretive about unfinished work for fear someone may swoop in and beat them to it. Several said colleagues who had previously compiled lists of important unsolved problems were reconsidering the practice, concerned that what was intended as a useful resource for the field could instead become a list of targets for AI companies.

Resistance is becoming increasingly public. In June, mathematicians published the Leiden Declaration, a set of principles for the responsible use of AI in mathematics that has been endorsed by the International Mathematical Union and signed by nearly 3,900 people, an increase of nearly 500 people since I last covered it in mid-August. It urges policymakers, governments, the media, and other groups to not buy into “the hype” created by companies who “overstate the capabilities of their products.” As the Millennium Prize controversy raged, OpenAI withdrew its sponsorship of an undergraduate mathematics hackathon at Caltech following fierce opposition decrying the intrusion of corporate interests and worries the event would create a deluge of low-quality “slop mathematics.”

“They don’t care anything about us as a community. It’s all about this petty drama between two trillion-dollar companies that are acting like children.”

Shing-Tung Yau, a professor of mathematics at China’s Tsinghua University, an emeritus professor at Harvard, and a recipient of the prestigious Fields Medal, told The Verge he worries about the potential effect on young researchers. “Working on hard problems already carries considerable risk for Ph.D. students and junior faculty,” he said. “The prospect of competing with powerful AI companies, whose resources far exceed those available to academic research groups, could make them even more reluctant to pursue ambitious questions.”

Yau declined to weigh in on allegations that researchers’ work may have been used by OpenAI, but said he is in favor of an independent review to establish what happened. More broadly, he worries that a lack of transparency and rush to announce first could obscure the intellectual lineage behind a breakthrough. Mathematical credit, he said, should reflect intellectual contributions, not who had budget for the most compute or made the loudest announcement.

Yau pointed to another problem, too. OpenAI and other AI companies occupy a peculiar position as both the providers of important research tools and, in a way, researchers. It “raises a serious conflict-of-interest concern,” he said, particularly as they could benefit from privileged access to customers’ unfinished and unpublished work.

“That concern deserves a substantive response,” he said. “It should not simply be dismissed as ordinary competition.”

A lot of this growing sense of unease comes down to trust. Mathematicians do not have to accept the most explosive allegations against OpenAI to worry about a company that both provides their research tools and, simultaneously, competes with them.

“Yeah, quite honestly, I don’t think that some data security announcement or whatever will really solve it,” Thom said. “I don’t really trust them.” Buckmaster felt similarly: “Why should we trust anything they said?”

Buckmaster said the reaction from colleagues to his going public had been overwhelmingly positive. But there was an undercurrent of something else too: fear. He told The Verge he initially intended to thank those who supported him when he went public with his experiences. He elected not to after many expressed discomfort at the idea of having their names publicly attached. “The reality is that mathematicians are actually scared of them,” Buckmaster said, referring to the AI companies.

And fear is hardly a solid foundation on which to build a productive and healthy research community. Buckmaster isn’t convinced it matters much to those companies involved. “They don’t care anything about us as a community,” he said. “It’s all about this petty drama between two trillion-dollar companies that are acting like children.”

来源：The Verge：AI（RSS）· theverge.com