It was a week that left mathematicians reeling. Hot on the heels of a flurry of cases of artificial intelligence furthering the field, OpenAI declared a major scalp: its latest AI model had cracked a Millennium Prize Problem, a puzzle with a $1m reward that had defied human brains for decades.

The achievement bore little resemblance to how mathematical problems normally fall. A near-trillion dollar private company had unleashed 10,000 agents – AI systems that carry out tasks autonomously – on the problem. The bill was estimated at $15m.

The trajectory towards AI capable of superhuman maths has long been clear, but to nail such a substantial problem so swiftly sent shock waves through the field. Mathematicians are asking what will be left for themif works in progress are hoovered up and claimed by others, and how they should train the next generation when even fiendish assignments can be solved at the press of a button.

“I feel slightly shell-shocked,” said Prof Colva Roney-Dougal, the head of pure mathematics at the University of St Andrews. At a recent public lecture, she said she believed that AI was unlikely to do anything remarkable soon. “Three months later, I’m totally wrong,” she says. “We’re all just waiting to see what happens. It’s coming so fast.”

Prof David Silvester, a mathematician at the University of Manchester, said the field felt “very unstable” given the pace of change. “All the open maths problems could fall with enough resources,” he said. “This is irreversible. This is not going to change.”

Prof James Robinson, a mathematician at the University of Warwick, said hard problems were the fuel of mathematics, driving creative approaches across successive generations. “It’s frustrating to see these big tech companies burning this fuel up just so that they can show off about how great their latest model is,” he said. “It seems like immature playground boasting writ large, underpinned by billions of dollars and the potential for significant environmental damage in an age when climate change is probably the biggest challenge we face.”

One reason Silvester went into maths was the rush that comes from cracking a problem. For many mathematicians it is the weeks, months and years spent circling a problem, breaking it down, trying one approach after another, and never quitting that appeals. “Without that, the subject seems completely different to me,” Silvester said.

Mathematicians already devote considerable time to verifying each other’s proofs. It was this kind of review that found a gap in Andrew Wiles’s work on Fermat’s Last Theorem in 1993, prompting a year of further effort to fix the flaw. Silvester suspects mathematicians might find themselves poring over ever more proofs dashed out by AI. “It’s more like becoming an accountant, you’re auditing, you’re checking,” he said.

There are knock-on effects throughout the field. Mathematicians are recruited on the strength of their published papers, but problems they have been working on for months might now be solved by AI in days. “There’s a real sense of ‘I could spend the next month thinking about something and somebody else does it by pushing a button’,” says Roney-Dougal. “That’s horrible, and I think it’s going to be horrible for a few years.”

It’s also a headache for teaching undergraduates. Universities routinely set students problems and quizzes to work through at home. That kind of coursework is now dead. “There’s no point in doing it because we can’t vouch for its authenticity,” says Silvester. Lecturers are now having to tell students not to use AI for some problems, while ensuring they can use it for others: after all, AI-assisted maths is the future.

For all the upheaval, Prof Alexander Paseau, who studies the philosophy of mathematics at the University of Oxford, believes maths will not lose much of its appeal. “The challenge still remains, even if AI is eventually better than humans at research,” he said. “You’ll still want to be able to understand the mathematics yourself. And we’ll still enjoy it and appreciate its beauty: the sheer enjoyment and the beauty of a mathematical proof will always be there. AI is not going to take any of that away.”

A lot of AI maths does not solve problems from scratch, but builds on work by humans, he added. The OpenAI breakthrough, for example, relied heavily on work by the Madrid-based mathematicians Diego Córdoba and Luis Martinez-Zoroa.

OpenAI’s work described a solution to the Navier-Stokes problem which involves equations that predict how fluids, and even the weather, behave. It is one of seven Millennium Prize Problems published by the Clay Mathematics Institute in 2000. The company’s announcement on Tuesday has led to further concerns in the community. OpenAI set its latest model to work after hearing rumours that two Millennium Problems had been solved by mathematicians. Prof Tristan Buckmaster at New York University and Levent Alpöge at Anthropic were doing related work on Navier-Stokes and had used OpenAI’s products in the process. They suspected OpenAI’s model had learned from their work-in-progress. After an investigation, OpenAI denied this was the case.

Some mathematicians are still wary, however. “The big story now in mathematics is that nobody wants to share anything,” Buckmaster told the Guardian. “Mathematics is different today than it was only a few days ago. We have to decide what to do about that.”