Are Wang Hong and Wei Dongyi and their peers also about to be "slain" by AI?

"On Tuesday night, I cried and woke up my daughter."

On October 8, at an online conference, French mathematician Hugo Duminil-Copin talked about his experience. He is a 2022 Fields Medal winner and had long stood at the pinnacle of mathematical research. What made him cry was a large batch of mathematical research results just announced by OpenAI.

Among them, 16 problems were directly related to his research.

In a subsequent radio interview, he said AI was like a bulldozer, flattening the problems he was working on all the way through. In just 0.1 seconds, people who often work for a living empathized with this top mathematician's workplace predicament. It is equivalent to—

You spend many years studying and accumulating, and finally find a business opportunity that could make you rich, when suddenly someone tells you:

This entire business model was implemented by AI at zero cost half a day ago.

Hugo Duminil-Copin is of course not the only victim.

OpenAI released 722 mathematics papers at once, involving hundreds of mathematical results and covering number theory, geometry, theoretical computer science, and many other fields.

On the famous preprint platform arXiv, a person can upload only 2 papers per month. To upload all these papers, at least more than 300 accounts would be needed.

Some of these quite difficult problems have troubled scientists for a century.

For example, the Riemann Hypothesis, proposed in 1859, has been unresolved for 167 years. OpenAI said they had made important progress this time and given a stronger "zero-free region" result;

Whether the Catalan constant is irrational has also troubled mathematicians for more than 100 years. OpenAI said they had successfully proved that it is indeed an irrational number;

The Kakeya conjecture, which won Wang Hong the Fields Medal this year, was proposed in 1917, and only in 1971 was a two-dimensional proof given. In 2025, Wang Hong gave a three-dimensional proof. Only a year has passed, and AI has given a four-dimensional proof.

Among the results listed this time, there are many other heavyweight breakthroughs like these.

Even more devastating is that these mathematical problems that have troubled humanity for a century were solved by OpenAI with astonishing speed:

The average computation consumed per result is equivalent to ChatGPT Pro thinking for 3 hours.

Although we still cannot understand those complex formulas, that feeling of "I worked hard for so many years and suddenly fell behind" is something ordinary people understand all too well.

Over the past decade or so, we stayed up late for exams and worked overtime; mathematicians stayed up late for exams and proofs. Everyone once simply hoped that their efforts could exchange for a stable future.

Whether judged by the number, breadth, or speed of breakthroughs, this is an unprecedented event in the history of mathematics.

Of course, OpenAI does not care about human feelings; they just keep dropping bombs on the mathematics community.

The first to sound the horn was Anthropic.

On July 19, the night of the World Cup final, a researcher at Anthropic posted a tweet on X, roughly meaning that he had disproved the Jacobian conjecture.

The Jacobian conjecture is no nobody; it is an important problem that has troubled mathematicians for more than 80 years.

The problem statement is just one sentence: if the Jacobian determinant of a polynomial mapping is a nonzero constant, then the mapping must have a polynomial inverse.

This problem is simple, but the answer is rather difficult. The simple riddle once attracted countless mathematicians into the pit, and then they never climbed out again.

Over the decades, mathematicians have repeatedly announced that they had solved this problem, but later it was pointed out that their proofs were wrong...

Including Chinese mathematician Zhang Yitang, many mathematicians have been left with PTSD. After tormenting mathematicians for decades, this conjecture was thus lightly disproved by a single tweet...

Competitor OpenAI, unwilling to be outdone, also joined this mathematics "arms race."

Within a few months, the mathematical problems the two sides claimed to have solved included but were not limited to:

This may be the most brute-force months in the history of mathematics.

However, brute force is not necessarily a good thing, and many mathematicians have expressed deep concern about it.

For example, this time OpenAI produced a batch of results in operator algebras. A mathematician in a related field concluded after reading them:

AI is right; humanity has wasted 30 years in the wrong direction.

Some people therefore face more realistic livelihood problems.

Some found that the direction they were working on had been blown up, and they did not know where the project funding would go; others no longer needed to finish writing half-completed papers, and whether they could get their degrees was now uncertain.

On Reddit, a doctoral student who claimed to study operator algebras left a message saying that he and his classmates had already solved a special case of a certain problem and had discussed the next step with their advisor, planning to develop the more general case into a doctoral dissertation. After OpenAI announced its results, they began rushing to organize their existing work, hoping to publish it as independent research. He believed the topic still had research value, but the original excitement had been greatly diminished.

Grand technological progress, when it lands on specific individuals, always makes people sigh.

After creating this big news, OpenAI CEO (Sam Altman) finally stopped sitting stunned and instead looked up at the stars.

Last year AI could not tell whether 9.8 or 9.11 was larger; this year it can already challenge the Riemann Hypothesis.

What it wants to do next year, I dare not imagine.

When Wang Hong and Deng Yu won their awards, some people said this might be the last Fields Medal ceremony of pure humans.

The answer is that the two sides are not enemies at all. On the contrary, major model companies are seriously recruiting mathematical talent.

Jacob Zimmerman, who just won the Fields Medal this year together with Wang Hong and Deng Yu, has now joined OpenAI and is involved in AI safety work.

Alpöge, who cracked the Jacobian conjecture a while ago, is his student and currently works at Anthropic.

Zimmerman once said: I do mathematics mainly because I enjoy it, and I am good at it.

Whichever company stops to catch its breath may be eliminated outright. It's not that you fall behind if you don't advance; it's that advancing too slowly counts as falling behind.

Anthropic is sprinting toward an IPO

From GPT3 to GPT6, the iteration time for each major version was 3 years, 2 years, and 1 year respectively, and for minor versions they wish they could update weekly.

Google's Gemini, because it fell behind on just one or two key versions, had its title stripped by netizens and was demoted to the American soybean bun, and to this day it still hasn't returned to the first tier.

This is also why Altman is constantly having atomic bomb explosions and sitting paralyzed in shock every few days; Anthropic's CEO Dario also acts mysterious and jumpy.

Mathematics is a field that abounds in hard problems.

A few percent improvement in reasoning ability, a few percent reduction in token consumption—it's hard for users to feel intuitively, but cracking a problem that has troubled mathematicians for decades is an achievement anyone can understand.

If you don't believe it, go take a look at the civilian science forum; 90% of civilian math enthusiasts are attacking the Goldbach conjecture.

Solving problems is also a way to flex muscles.

In September, OpenAI heard that the neighboring company A was challenging the NS equations, so it immediately concentrated all its resources in that direction, organized ten thousand AI agents to work in parallel, and finally preempted company A by publishing its own results first.

The NS equations are one of the directions Wei Dongyi focuses on

Mathematics has now become a battlefield of the gods, and any stray shockwave can stun the humans in the arena.

Before mathematicians felt crushed, people in other industries had already been crushed several rounds over.

The first thing that made the whole world sweat was Go.

After 2016, AlphaGo defeated Lee Sedol and Ke Jie one after another, and since then Go players have gone from a profession symbolizing human intelligence to a job of fighting AI every day.

At that time, humans could still understand this failure as the loss of a special skill—machines being good at chess doesn't mean they're better than humans at everything, right?

Later, the battlefield moved from the chessboard to the office desk.

Midjourney and Stable Diffusion began to take the rice bowls of illustrators; ten years of learning a craft may be no match for one minute of AI output;

The progress in coding ability quickly lowered Vibe coding to the difficulty of Lego bricks (not that hard, actually);

A solo debut wasn't enough; just recently, Mango TV's AI lineup was officially announced and launched. Although there are only 5 of them, gazing at their faces you can see a sea of people.

We used to believe: the more a job requires knowledge, creativity, and professional training, the less easily it can be replaced.

But these skills often leave behind a large amount of text, images, and code, which happen to be exactly what models can repeatedly learn from and quickly generate.

Instead, it's the trivial chores we most want machines to take over that haven't yet disappeared from our lives so smoothly.

For example, doing housework.

We originally expected machines to take over tedious labor so that people would have more time to think and create. Now the situation is reversed:

But if so much work can be completed faster, why is the first thing we feel pressure rather than finally being able to relax a little?

03

Is simply living day to day humanity's only choice?

We have always been preparing for "not being eliminated." When we were young, we worked hard to get into good schools; when we graduated, we chose promising industries; after starting work, we got certifications, did projects, and accumulated experience.

Not everyone necessarily wants to become the top of the industry; more often we just think about gaining a bit of bargaining power and irreplaceability by honing some skill of our own.

Ten years ago, a type of motivational chicken soup was very popular: "When the era abandons you, it won't even say hello." The real meaning of this sentence is that the era actually does say hello before abandoning you, but many people didn't notice.

The conclusion is that we must keep up with the era and not be the ones abandoned.

When DeepSeek first became popular, someone put together a teaching guide and listed it on WeChat Reading, and it once rose to number one in popularity.

AI now understands human speech better than 80% of employees and 99% of leaders in this world.

Could it be that this time the era really isn't planning to say hello to us?

On October 11, Yau Shing-Tung led the establishment of the Mathematics and Artificial Intelligence Laboratory Alliance, joining eight mathematics research institutions, preparing to independently develop models dedicated to mathematical science.

No one knows where these attempts will ultimately lead, but to say mathematics is already over is probably premature.

More than 30 years ago, someone also concluded that history had ended, and later it proved to be merely a comma in history.

In an era of great change, thought is always especially active, and we don't need to rush to write our own ending.

According to incomplete statistics, this year has successively seen X repression theory, great water theory, great sleep theory, thin muscle theory... the Hundred Schools of Thought have reappeared in the world.

Whether it's combining work and play, or cutting costs and adding laughs, in the face of a future full of uncertainty, experts and ordinary people are about the same level.

At least there are still some certainties we can grasp.

For example, starting tomorrow, care about food and vegetables, drink great water, sleep great sleep, and be a pure day-to-day person.

Text/Wang Dong

Editor/Zhang Yu

This article comes from the WeChat public account "Phoenix Weekly" (ID: phoenixweekly), author: Wang Dong, published by 36Kr with authorization.