Is AI the End of Math As We Know It?
T hroughout the summer of 2026, there were plenty of signs of what was to come.
2026 年整个夏天，有很多迹象表明即将发生的事情。
Every week, it seemed, artificial intelligence models were offering proofs of decades-old mathematical conjectures.
人工智能模型似乎每周都在提供数十年前的数学猜想的证明。
So what happened on September 8 shouldn’t have been a complete surprise.
因此，9 月 8 日发生的事情并不完全令人意外。
And yet to many it felt sudden, almost violent.
然而对许多人来说，这感觉很突然，几乎是暴力的。
OpenAI announced that its models had solved one of math’s most famous problems — and immediately sparked a controversy over authorship and standards.
OpenAI 宣布其模型已经解决了最著名的数学问题之一，并立即引发了关于作者和标准的争议。
Mathematics would never be the same.
数学永远不会一样。
“In whatever years I have left, I don’t expect that I’ll ever again prove a theorem because I’m actually needed to prove it,” Scott Aaronson of the University of Texas, Austin wrote on his blog .
德克萨斯大学奥斯汀分校的斯科特·阿伦森在他的博客上写道：“无论我剩下多少年，我都不希望我再次证明一个定理，因为我实际上需要证明它。”
“Human mathematicians are forevermore dethroned as the main theorem-proving entities on planet earth.” Other online missives expressed optimism, but many revealed some combination of grief, confusion, and fear.
“人类数学家作为地球上主要定理证明实体的地位永远被废黜。”其他网上留言表达了乐观情绪，但许多留言都透露出悲伤、困惑和恐惧。
I n philosophy, “qualia” refers to the subjective qualities of our experience: what it’s like for Alice to see blue or for Bob to feel delighted.
在哲学中，“感受性”指的是我们体验的主观品质：爱丽丝看到蓝色或鲍勃感到高兴是什么感觉。
Qualia are “the ways things seem to us,” as the late philosopher Daniel Dennett put it.
正如已故哲学家丹尼尔·丹尼特所说，感受性是“事物在我们看来的方式”。
In these essays, our columnists follow their curiosity, and explore important but not necessarily answerable scientific questions.
在这些文章中，我们的专栏作家追随他们的好奇心，探索重要但不一定可以回答的科学问题。
Two days after the announcement, I found myself in a classroom at the University of California, Berkeley, surrounded by some 150 students, postdocs, and professors.
消息发布两天后，我发现自己身处加州大学伯克利分校的一间教室里，周围有大约 150 名学生、博士后和教授。
The mathematician Ken Ono , who took a leave of absence from the University of Virginia to work at an AI start-up called Axiom Math, was scheduled to give a talk.
数学家小野肯 (Ken Ono) 从弗吉尼亚大学请假前往一家名为 Axiom Math 的人工智能初创公司工作，他原定将发表演讲。
“You might be graduating into a profession that might not even exist, or that will be very different than what you expected,” Ono told them.
小野告诉他们：“你毕业后可能会进入一个甚至不存在的职业，或者与你的预期有很大不同。”
“You need to brace.”
“你需要做好准备。”
The audience responded with anger and frustration.
观众的反应是愤怒和沮丧。
There were whispers and exchanged glances; Ono couldn’t make it through a single slide without a fresh wave of questions.
大家窃窃私语，交换眼神。小野无法在没有新一波问题的情况下完成一张幻灯片。
“I’m not entirely sure what our takeaway is supposed to be,” one student said.
“我不完全确定我们的外卖应该是什么，”一名学生说。
Another asked how Ono and his start-up would take responsibility in light of “the shameful way that AI companies are treating mathematics.”
另一位网友问道，考虑到“人工智能公司对待数学的可耻方式”，小野和他的初创公司将如何承担责任。
When Ono said that mathematicians would do their “very best” to avoid the bleak future the students were envisioning, a third retorted, hands trembling: “What are you doing?
当小野说数学家将尽最大努力避免学生们所设想的黯淡未来时，第三个人双手颤抖地反驳道：“你在做什么？
What is your ‘very best’?” The talk, including time for questions, had been allotted 50 minutes; with the extended Q&A, it went on for more than two hours.
你的‘最好的’是什么？”演讲时间（包括提问时间）为 50 分钟；加之加长的问答，持续了两个多小时。
Many students lingered afterward, to vent and console one another.
许多学生随后留下来，互相发泄、安慰。
To me, the reasons for these emotional reactions were obvious.
对我来说，这些情绪反应的原因是显而易见的。
The young mathematicians recognized that their field would have to change or risk extinction.
年轻的数学家认识到他们的领域必须改变，否则就有灭绝的危险。
What did surprise me was that when I returned to New York and brought it up in conversations with friends or family, they didn’t see what the problem was.
令我惊讶的是，当我回到纽约并在与朋友或家人的谈话中提出这个问题时，他们并不明白问题出在哪里。
All sorts of amazing discoveries are around the corner, they imagined.
他们想象，各种令人惊奇的发现即将到来。
Wasn’t that the point?
这不是重点吗？
These conversations exposed a long-standing problem: Many people don’t know what mathematics really is, or why mathematicians do it.
这些对话暴露了一个长期存在的问题：许多人不知道数学到底是什么，也不知道数学家为什么要这样做。
Now mathematicians need to answer that question for themselves and explain it to the rest of the world — quickly.
现在，数学家需要自己回答这个问题，并迅速向世界其他地方解释。
How they answer will determine what happens next.
他们如何回答将决定接下来会发生什么。
As an undergraduate, I studied both mathematics and literature.
作为一名本科生，我学习了数学和文学。
I always found that “pure math” — the study of mathematical concepts for their own sake, without a care for real-world applications — existed somewhere between the sciences and the arts.
我总是发现“纯数学”——为了数学概念本身而研究数学概念，而不关心现实世界的应用——存在于科学和艺术之间。
It prizes logic and certainty, but at its core lie fuzzier notions of beauty, intuition, and depth.
它重视逻辑和确定性，但其核心是美、直觉和深度的模糊概念。
“Math is either the most science-y humanities or the most humanities-type science, depending who you ask,” said Marcel Goh , a doctoral student at McGill University.
麦吉尔大学博士生马塞尔·吴 (Marcel Goh) 表示：“数学要么是最具科学性的人文学科，要么是最具人文性的科学，这取决于你问的是谁。”
Usually it’s utterly useless, yet time and again its abstractions demonstrate “ unreasonable effectiveness ” in applications to physics and beyond.
通常它是完全无用的，但它的抽象在物理学及其他领域的应用中一次又一次地证明了“不合理的有效性”。
In mathematics, it’s not a cliché that the journey matters more than the destination.
在数学中，旅程比目的地更重要，这并不是陈词滥调。
Problems are posed not so much because their answers will be practical and important, but because they represent interesting journeys.
提出问题并不是因为它们的答案实用且重要，而是因为它们代表了有趣的旅程。
The hope is that as mathematicians struggle to solve a problem, they’ll come up with intriguing tools and connections, stumble on novel ideas and directions, take fruitful detours, and answer new questions they never would have thought to ask.
我们希望，当数学家努力解决问题时，他们会想出有趣的工具和联系，偶然发现新颖的想法和方向，采取富有成效的弯路，并回答他们从未想过要问的新问题。
“It was never about the theorems,” Goh said.
“这从来都不是关于定理的，”吴说。
“It’s to carry on a tradition that has benefited society despite not having any market value.
“这是为了继承一种造福社会的传统，尽管它没有任何市场价值。
To reason deeply, to understand the world …
深入思考，理解世界……
and to spread that knowledge.”
并传播这些知识。”
It breaks our system.
它破坏了我们的系统。
It just takes it to its absolute limit and destroys it.
它只会把它带到它的绝对极限并摧毁它。
Tasmin Chu, California Institute of Technology
Tasmin Chu，加州理工学院
AI models skip to the end.
AI模型跳到最后。
At the moment, their proofs are nearly impossible to read.
目前，他们的证明几乎无法阅读。
They elide salient details and waste pages on irrelevant concepts.
他们省略了重要的细节，并在不相关的概念上浪费了页面。
They fail to demonstrate how the new work builds on or connects to other results in the mathematical literature.
他们未能证明新工作如何建立在数学文献中的其他结果之上或如何与数学文献中的其他结果联系起来。
Mathematicians know that the proofs are true — they’re verified by a system that checks their logic — but have no idea why.
数学家知道这些证明是正确的——它们是由检查其逻辑的系统验证的——但不知道为什么。
What’s missing is understanding, the currency that mathematicians trade in.
缺少的是理解，这是数学家交易的货币。
If there is a journey, humans are not invited along for the ride.
如果有一段旅程，人类不会被邀请一起同行。
It’s as if you were teleported to the peak of a tall mountain.
就好像你被传送到了一座高山的顶峰。
Surrounded by fog, you have no idea where you are, or what’s around you.
被雾气包围，你不知道自己在哪里，也不知道周围有什么。
You do not know how your mountain connects to others, and you have no equipment to help you explore, no way to help someone else join you.
你不知道你的山是如何与其他山相连的，你没有设备来帮助你探索，也没有办法帮助别人加入你。
If you had climbed the mountain yourself, you would have experienced how the human body adapts to altitude and changes in oxygen levels.
如果你亲自爬过这座山，你就会体验到人体如何适应海拔高度和氧气水平的变化。
You might have had to invent tools to navigate, to climb steep cliffs, or to make a shelter.
你可能不得不发明工具来导航、攀登陡峭的悬崖或建造庇护所。
You might have encountered a fellow explorer, gotten lost together in a hidden valley, and found a plant that could be turned into a life-saving medicine.
你可能遇到了一位探险家同伴，一起迷失在一个隐蔽的山谷中，并发现了一种可以变成救命药物的植物。
Instead you’re perched on the peak but in the dark, while the maker of the teleportation machine tells you that it can explore the wilderness better than any human.
相反，你栖息在黑暗的山峰上，而传送机的制造商告诉你，它比任何人类都能更好地探索荒野。
Mathematicians have always known that understanding is more valuable than an answer.
数学家一直都知道理解比答案更有价值。
So why should an AI answer machine feel like such a threat?
那么为什么人工智能答录机会让人感到这样的威胁呢？
In part, it’s because of the way mathematics has evolved.
部分原因在于数学的发展方式。
For centuries, proving theorems and understanding those proofs have gone hand in hand.
几个世纪以来，证明定理和理解这些证明一直是齐头并进的。
If someone solves a problem that the mathematical community has decided is central and important and hard, that solution is considered a reflection of deep insight and creativity.
如果有人解决了数学界认为是核心、重要和困难的问题，那么该解决方案就被认为是深刻洞察力和创造力的体现。
So mathematical culture has developed almost entirely around problem-solving.
因此，数学文化几乎完全围绕解决问题而发展。
Graduate students cut their teeth on simpler versions of big, complex problems, then seek to get their work published, find good jobs, and earn credit and recognition — all of which are assessed in terms of the theorems they prove.
研究生先研究复杂大问题的简单版本，然后寻求发表他们的作品，找到好工作，并获得学分和认可——所有这些都是根据他们证明的定理进行评估的。
The incentive structures that power the mathematical community are not set up for a world where solving problems is easy and understanding their answers remains hard.
为数学界提供动力的激励结构并不是为一个解决问题很容易但理解问题的答案仍然困难的世界而建立的。
“It breaks our system,” said Tasmin Chu , a doctoral student at the California Institute of Technology.
“它破坏了我们的系统，”加州理工学院的博士生 Tasmin Chu 说。
“It just takes it to its absolute limit and destroys it.”
“它只会把它带到它的绝对极限并摧毁它。”
Over the past few months, I’ve heard from lots of mathematicians, students and senior researchers alike, about the shape of this destruction.
在过去的几个月里，我从许多数学家、学生和高级研究人员那里听到了关于这种破坏的情况。
Companies such as OpenAI and Anthropic are competing directly against mathematicians (and poaching many of them ) to quickly prove theorems, while putting little effort into writing clear papers to explain those results or put them in context.
OpenAI 和 Anthropic 等公司正在直接与数学家（并挖走了其中的许多人）竞争，以快速证明定理，而很少花精力撰写清晰的论文来解释这些结果或将它们放在上下文中。
Most mathematicians have no idea what these companies have even proved at this point; OpenAI has announced that it’s keeping more than 100 important results secret .
大多数数学家都不知道这些公司目前已经证明了什么； OpenAI 宣布对 100 多项重要结果保密。
(This has the further effect of discouraging mathematicians from working on questions that might already be moot.)
（这进一步阻碍了数学家研究可能已经没有实际意义的问题。）
“What is happening is a huge damage to open science,” Chu said.
“正在发生的事情对开放科学造成了巨大损害，”朱说。
The only possible value of this is shareholder value.
唯一可能的价值是股东价值。
It completely overlooks the central mathematical enterprise — and, even worse, it seeks to overwrite it.
它完全忽视了核心的数学事业——更糟糕的是，它试图覆盖它。
Many mathematicians have stopped posting open conjectures and potential ideas at the end of their papers, for fear that they’ll be scraped by bots and fed into AI models.
许多数学家已经停止在论文末尾发表公开的猜想和潜在的想法，因为担心它们会被机器人抓取并输入人工智能模型。
Others are posting their papers before they’re ready in order to avoid getting scooped.
其他人在准备好之前就发布论文，以避免被抢先。
Journals and the online preprint server arxiv.org have been overrun with AI-assisted or AI-generated proofs that no one can properly digest or check, which forces researchers to do the hard, currently thankless work of rewriting the proofs so they can actually benefit the mathematical community.
期刊和在线预印本服务器 arxiv.org 已经充斥着人工智能辅助或人工智能生成的证明，没有人能够正确消化或检查，这迫使研究人员必须做艰苦且目前吃力不讨好的重写证明的工作，这样他们才能真正使数学界受益。
(Even as models improve and those papers get easier to read, their sheer volume will overwhelm current systems for sorting through new work and deciding what’s interesting and important.
（即使模型得到改进并且这些论文变得更容易阅读，它们的庞大数量也将压垮当前用于分类新工作并决定哪些有趣和重要的系统。
In the meantime, arxiv.org has started limiting authors to two submissions per month .) When an editor of Transactions on Machine Learning Research reached out to the authors of 10 papers slated for rejection, most either did not respond or could not answer basic questions about work they’d claimed as their own.
与此同时，arxiv.org 已开始限制作者每月提交两次论文。）当《机器学习研究汇刊》的编辑联系 10 篇即将被拒绝的论文的作者时，大多数人要么没有回应，要么无法回答有关他们声称自己的作品的基本问题。
At the same time, MathOverflow , an online forum where mathematicians answer each other’s questions — connecting famous researchers with young students and leaving years of exchanges for future generations to read and learn from — is seeing a massive decline in activity, as students privately ask AI models their questions.
与此同时，MathOverflow 是一个数学家互相解答问题的在线论坛——将著名研究人员与年轻学生联系起来，并为后代留下多年的交流内容供后代阅读和学习——但活动却大幅下降，因为学生们私下询问人工智能模型他们的问题。
Others don’t want to share ideas in another space exposed to bots.
其他人不想在另一个有机器人的空间中分享想法。
The situation is heartbreaking.
情况令人心碎。
Part of what is so wonderful about mathematics is its open and honest collaboration, and its independence from political or corporate concerns.
数学如此美妙的部分原因在于它开放和诚实的合作，以及它独立于政治或企业的关注。
That’s not to say that there haven’t been other problems with the culture of mathematics — inequity and lack of access, among other things — but what’s special about that culture is at great risk without a signal change in the field’s incentive structure.
这并不是说数学文化不存在其他问题——不平等和缺乏机会等——但如果该领域的激励结构没有发生重大变化，这种文化的特殊之处就面临着巨大的风险。
“The existing equilibrium has broken,” said Daniel Litt , a mathematician at the University of Toronto.
“现有的平衡已经被打破，”多伦多大学数学家丹尼尔·利特说。
“We’ll have to find a new one.”
“我们必须找到一个新的。”
If AI proves all possible theorems, and no mathematicians know about these theorems, it cannot be part of culture, because culture is inherently human.
如果人工智能证明了所有可能的定理，并且没有数学家知道这些定理，那么它就不可能成为文化的一部分，因为文化本质上是人类的。
Pavel Etingof, Massachusetts Institute of Technology
帕维尔·埃廷戈夫，麻省理工学院
There’s a range of possible futures based on how mathematicians collectively respond — or don’t respond — to this moment.
根据数学家对这一时刻的集体反应（或不反应），存在一系列可能的未来。
In one, “once the profession turns over, you have a collection of mathematicians who are trained in pushing a button without reading the outputs,” Litt said, rather than being trained to ask interesting questions and engage with the results.
利特说，其中一个问题是，“一旦这个行业发生转变，你就会看到一群数学家，他们接受的训练是在不阅读输出的情况下按下按钮，”而不是接受训练来提出有趣的问题并参与结果。
“There’s a bad future where if we don’t adapt, there’s just no more math in 50 years.
“如果我们不适应，未来会很糟糕，50 年后就不会再有数学了。
No one is doing it, not even the machines.
没有人这样做，甚至机器也没有这样做。
“I’m not saying it’s likely,” he continued.
“我并不是说这有可能，”他继续说道。
“We’re not going to sleepwalk into that.
“我们不会梦游到这个地步。
But I think the world where we change nothing probably looks like that.”
但我认为我们什么都不改变的世界可能就是这样。”
As Matilde Marcolli of Caltech wrote in an essay for the London Mathematical Society Newsletter , “If we insist on focusing our entire profession solely on the tackling of conjectures as the unique goal and reward, then we may be out of business quite fast, not because of the end of mathematics but because of the end of our imagination.”
正如加州理工学院的马蒂尔德·马科利（Matilde Marcolli）在《伦敦数学会时事通讯》的一篇文章中所写的那样，“如果我们坚持将整个职业的重点仅放在解决猜想上，作为唯一的目标和回报，那么我们可能很快就会破产，不是因为数学的终结，而是因为我们想象力的终结。”
There is a more optimistic alternative — assuming a new incentive structure that disengages problem-solving and theorem-proving from jobs, advancement, and recognition.
还有一种更乐观的选择——假设一种新的激励结构，将问题解决和定理证明与工作、晋升和认可分开。
“Mathematicians will be spending less time trying to peck on the permafrost, getting out mathematical gems with great effort,” said Pavel Etingof of the Massachusetts Institute of Technology.
麻省理工学院的帕维尔·埃廷戈夫（Pavel Etingof）表示：“数学家将花更少的时间去啄永久冻土，并付出巨大的努力来获取数学宝石。”
“They’ll be sourcing that kind of pecking more to AI and trying to interpret and understand and learn the results that will come from that.
“他们将把这种啄食更多地交给人工智能，并试图解释、理解和学习由此产生的结果。
There will be a lot more mathematics to make sense of.
将会有更多的数学知识需要理解。
And there will be a lot of work for mathematicians — maybe different work from before, but definitely interesting work.”
数学家将会有很多工作——也许与以前不同，但绝对是有趣的工作。”
That work will involve, to some extent, studying proofs written by AI — the way every undergraduate learns the known proofs in their textbooks.
这项工作在某种程度上将涉及研究人工智能编写的证明——就像每个本科生在教科书中学习已知证明的方式一样。
Many mathematicians expect AI-generated arguments to graduate from slop to more elegant writing soon enough; perhaps within a few years, Litt said, “we’re reading beautifully written expositions of highly interesting results.
许多数学家期望人工智能生成的论证能够很快从草率的文字变成更优雅的文字。利特说，也许几年之内，“我们就会阅读到关于非常有趣的结果的精美的阐述。
To me, that’s the dream.
对我来说，这就是梦想。
That’s why I got into it.”
这就是我参与其中的原因。”
These future mathematicians will have much to do.
这些未来的数学家将有很多事情要做。
After all, Litt added, “there’s no amount of well-writtenness that lets you understand math.” They’ll have to rewrite AI proofs, find more elegant arguments, come up with theories and frameworks and definitions, ask questions, formulate new conjectures, develop a sense of what’s interesting and important.
毕竟，利特补充道，“再好的文字也无法让你理解数学。”他们必须重写人工智能证明，找到更优雅的论点，提出理论、框架和定义，提出问题，提出新的猜想，培养对有趣和重要的认识。
They’ll be the ones building research programs, creating communities around certain ideas, and passing that knowledge on to others.
他们将建立研究项目，围绕某些想法创建社区，并将这些知识传递给其他人。
(I am reminded of the literary theorist Roland Barthes’ famous 1967 essay “The Death of the Author,” in which he argues that it is the reader, and not the writer, who has ultimate control and agency in actively creating a work’s meaning.)
（我想起了文学理论家罗兰·巴特 1967 年发表的著名文章《作者之死》，他在文中认为，在积极创造作品的意义方面，拥有最终控制权和代理权的是读者，而不是作者。）
“If AI proves all possible theorems, and no mathematicians know about these theorems, it cannot be part of culture, because culture is inherently human,” Etingof said.
“如果人工智能证明了所有可能的定理，并且没有数学家知道这些定理，那么它就不可能成为文化的一部分，因为文化本质上是人类的，”埃廷戈夫说。
“Those papers will only become a part of mathematical culture when some human reads them and understands them and gets something out of them.”
“只有当有人阅读它们、理解它们并从中有所收获时，这些论文才会成为数学文化的一部分。”
The question is whether the next generation of mathematicians will want to.
问题是下一代数学家是否愿意这样做。
Or even be able to.
或者甚至能够。
Mathematicians are already rethinking how they’ll train their students, accept papers for publication (they’re currently experimenting with requiring accompanying talks), and make hiring decisions.
数学家们已经在重新考虑如何训练学生、接受论文发表（他们目前正在尝试要求附带演讲）以及做出聘用决定。
There aren’t easy answers for more nebulous skills such as understanding, communication, and vision.
对于理解、沟通和愿景等更模糊的技能，没有简单的答案。
But the stakes are high enough that I think they’ll succeed.
但赌注足够高，我认为他们会成功。
I’m less certain about whether new generations of mathematicians will be attracted to this remade discipline.
我不太确定新一代数学家是否会被这个重塑的学科所吸引。
I think there is a good future where we come out way ahead of where we are now, where we learn a huge amount of interesting math.
我认为，我们有一个美好的未来，我们会远远领先于现在，我们会学到大量有趣的数学。
Daniel Litt, University of Toronto
丹尼尔·利特，多伦多大学
Many people are drawn to math because they want to solve problems, explore frontiers, and be the first to discover some new truth.
许多人被数学所吸引，因为他们想解决问题、探索前沿并成为第一个发现新真理的人。
Will they be willing to put in decades of training for the job of interpreter?
他们愿意为口译员工作投入数十年的培训吗？
They’ll still read and translate proofs and expect to rewrite them.
他们仍然会阅读和翻译校样并期望重写它们。
But it seems less likely that they will frequently experience the same moments of clarity or sense of creative ownership.
但他们似乎不太可能经常经历同样的清晰时刻或创造性所有权感。
“A lot of the reason why people do pure math, even though it pays so much less than industry, is because you get to author your own creative contributions and then give them to a community that appreciates them for their intellectual beauty,” Chu said.
“人们做纯数学的很多原因，尽管它的报酬比工业界低得多，是因为你可以创作自己的创造性贡献，然后将它们交给一个欣赏他们的智力之美的社区，”楚说。
“This environment where mathematical discourse is centered around machines talking and humans listening, I don’t think it’s a very attractive one.”
“数学话语以机器说话和人类倾听为中心的环境，我认为这不是一个非常有吸引力的环境。”
And AI is itself undermining the kind of education that would be necessary for that role.
人工智能本身正在破坏这一角色所必需的教育。
This came up at the Berkeley colloquium, too.
这个问题也在伯克利研讨会上出现过。
“There’s a serious risk that 20, 30, 40 years from now, there will be no people around who have the same classical mathematics training,” one student said.
“存在一个严重的风险，即 20、30、40 年后，周围将没有人接受过同样的经典数学训练，”一名学生说。
“I’m worried about a world where the full body of mathematical knowledge is inaccessible.”
“我担心这个世界无法获得完整的数学知识。”
The result could be a lost generation.
结果可能是迷失的一代。
Goh is one of many young mathematicians struggling with the angst such a future is producing, and with what he sees as a lack of effort on the part of working mathematicians (at least until very recently) to remake the incentives to avoid it.
吴是许多年轻数学家中的一员，他们正与这样的未来所产生的焦虑作斗争，他认为在职数学家（至少直到最近）缺乏努力来重新制定避免这种未来的激励措施。
“There’s something sick about the culture,” he told me.
“这种文化有些病态，”他告诉我。
“I didn’t think so, but now I do.”
“我以前不这么认为，但现在我这么认为了。”
Goh is alarmed by how some have taken up AI without enough critical thought, and he feels pressured to use it so that he doesn’t get left behind.
Goh 对一些人在没有足够批判性思考的情况下就开始使用人工智能感到震惊，他感到有压力使用它，这样他就不会被抛在后面。
“I was just very depressed about that,” he said.
“我对此感到非常沮丧，”他说。
“That doesn’t feel inspiring to me.
“这对我来说并没有什么启发。
…
……
It didn’t have to be this way.”
事情本来不必是这样的。”
He likens it to Saruman from The Lord of the Rings , corrupted by the promise of unlimited knowledge and power.
他将其比作《指环王》中的萨鲁曼，被无限知识和力量的承诺所腐蚀。
Goh and a growing number of mathematicians are starting to consider themselves AI “vegetarians,” consciously measured in their use of the technology.
吴和越来越多的数学家开始认为自己是人工智能“素食主义者”，有意识地衡量他们对这项技术的使用。
There’s nothing rewarding or interesting or fun about offloading their cognition to a machine, they argue.
他们认为，将认知转移到机器上并没有任何回报、有趣或乐趣。
Goh is determined and would rather leave the field than use AI in his creative work.
Goh 态度坚决，宁愿离开这个领域，也不愿在他的创造性工作中使用人工智能。
“I think the ship is sinking,” he admitted, “but I’ve already made my decision to stay on board as long as I can.”
“我认为这艘船正在下沉，”他承认，“但我已经决定尽可能长时间地留在船上。”
There is, through all of this, reason for hope.
纵观这一切，我们仍有理由抱有希望。
Mathematicians see these problems and feel a collective sense of concern (if not panic), so they are working now to create attractive conditions for people in their field — both those interested in using AI and those who don’t want to.
数学家看到这些问题并感到集体担忧（如果不是恐慌的话），因此他们现在正在努力为自己领域的人们创造有吸引力的条件——无论是那些对使用人工智能感兴趣的人还是那些不想使用人工智能的人。
Chu and others have formed the Association for Human Mathematics , for example, because “what we need right now actually is not more mathematics but policy proposals and tools …
例如，朱和其他人成立了人类数学协会，因为“我们现在需要的实际上不是更多的数学，而是政策建议和工具......
to deal with this crisis and change our ecosystem.”
应对这场危机并改变我们的生态系统。”
It will be hard.
这会很难。
Inertia is powerful.
惯性是强大的。
But mathematics could come out of this stronger than before.
但数学可能会因此变得比以前更强大。
Forced to name and prioritize what people love and value about the field, current and future mathematicians can emphasize skills that have previously been deprioritized: the ability to communicate and share knowledge, to develop a broad vision, to experiment with original ideas without feeling the need to publish theorem after theorem.
当前和未来的数学家被迫命名并优先考虑人们对该领域的热爱和重视，现在和未来的数学家可以强调以前被忽视的技能：沟通和分享知识的能力，发展广阔的视野，试验原创想法的能力，而不需要发表一个又一个定理。
To think deeply.
要深入思考。
“I think there is a good future where we come out way ahead of where we are now, where we learn a huge amount of interesting math,” Litt said.
“我认为，我们有一个美好的未来，我们将远远领先于现在，我们将学到大量有趣的数学知识，”利特说。
“We have people doing the coolest stuff they’ve ever done.
“我们的员工正在做他们曾经做过的最酷的事情。
We have a really vibrant community of people who deeply understand stuff.”
我们拥有一个非常充满活力的社区，由深刻理解事物的人组成。”
If we can’t adapt, he added, “whose fault is that?”
他补充道，如果我们无法适应，“那是谁的错呢？”
Editor’s note: Scott Aaronson is a member of Quanta Magazine ’s advisory board.
编者注：斯科特·阿伦森 (Scott Aaronson) 是《Quanta》杂志顾问委员会的成员。
Correction: October 6, 2026 The original version of this article stated that Ken Ono left academia for a start-up, rather than taking a leave of absence.
更正：2026 年 10 月 6 日这篇文章的原始版本称 Ken Ono 离开学术界去创业，而不是休假。
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《Quanta》杂志对评论进行审核，以促进知情、实质性、文明的对话。
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