A few years ago, when I first started talking about mathematics in public, a professor of algebraic number theory pulled me to one side and gave me some sharp words of warning: “The world fears mathematicians, Hannah. Let’s keep it that way.”

He was being, to put it mildly, a bit of a snob. Maths had an intimidating mystique to maintain, and I was apparently jeopardising it by titting around on the internet making videos and podcasts, rather than focusing on a perfectly respectable research career.

I have thought a lot about that comment since. Partly because I have spent the intervening years doing everything I can to completely ignore him (and getting a  column feels like a rather lovely reward for doing so).

But buried beneath his snobbery, I have come around to the idea that he was right about something. And I have never felt that more than in the past few weeks, since a swarm of AI agents have been busy solving our most famous long-standing problems.

I’m talking, of course, about OpenAI’s announcements over the past few weeks that it has found proofs for hundreds of mathematics problems that had previously resisted human attempts to solve them.

The furore began, most notably, with a proof (by way of finding a counterexample) that the Navier-Stokes equations can break apart. This particular gem was such a big deal to the mathematical community that at the turn of the millennium, the Clay Mathematics Institute promised they’d give $1 million to whoever managed to solve it.

Those Navier-Stokes equations are what I did my PhD on. They are basically Isaac Newton’s second law of motion: F=ma, but applied to fluids. They are, in my not entirely unbiased opinion, staggeringly beautiful, able to articulate everything from the drip of a water droplet on a leaf and the churn of an entire atmosphere to the gases swirling around in a galaxy in the sky.

The thing is, these equations have a tricky little bit that pops out of the acceleration part, which contains a feedback loop within it. The faster fluid flows, the faster the gradient can get between two adjacent streams, and so the faster fluid flows. It’s a bit like the fluid version of putting a microphone next to a speaker. The feedback loop can get louder and louder until everything ends up breaking. Or, in the case of the Navier-Stokes equations, the speed of the flow heads off to infinity.

To be clear: no one was ever worried about fluid dynamics tearing open a black hole when you stir your tea or anything. It was just that the weird behaviour of this little term left the possibility that the equations themselves would break.

On the flip side, there’s another term in the equation – the viscosity – that acts as a brake and calms everything down, stopping the flow from getting completely out of control. So, the question was: does the brake always kick in?

If you have been following the story, then by now you will know what happened. Various groups of mathematicians had been using AI to circle in on this problem, finding special cases where the feedback loop goes crazy, without quite getting to the full Navier-Stokes.

But supposedly, once OpenAI heard that others were getting close, they threw everything at it: 10,000 AI agents, sending each other 3 million messages over 88 hours, costing millions of dollars in tokens, to find a single example where everything blows up. The Navier-Stokes equations do indeed fall. And an AI proved it.

This should have been a glorious moment of scientific exposition, but instead, mathematicians have spent the last few weeks having a massive collective wobble.

Part of the reason is that nobody yet understands the answer. The OpenAI solution is almost hilariously ugly. It is a proof that’s 166 pages long, accompanied by hundreds of thousands of lines of formal code – almost impenetrable symbols – slapped down on the table and left for the mathematicians to pick apart, while the AI labs move on to the next thing. They came for the glory without giving much back.

So, we are left with a proof that almost no one wants to read, a $1 million prize that no one will want to collect, and no one who can stand at a blackboard and explain why this particular vortex makes everything break, when nothing else we tried ever did.

And now, to top it off, the new tranche of proofs that landed last week claim to have polished off hundreds of other important, unsolved mathematical problems, including a big result in the biggest trophy in all of mathematics: the Reimann Hypothesis. All tipped onto the code-sharing website GitHub for someone else to check.

So, mathematicians are having their own existential crisis about their role and purpose, like Lee Sedol did after AlphaGo, Gary Kasparov before him with Deep Blue and probably every literature graduate, musician and artist since ChatGPT was launched.

Please don’t get me wrong. I want to use this column, at least in part, to talk about the incredible, world-changing things that are happening with AI in science. I know that there is some inevitability to machines outpacing the humans, and I know that at its best, AI has a great deal to offer.

But at its worst, AI is an engine of extraction. Not just text, or images or equations. The real sting in the tail is that OpenAI set its sights on mathematics because the prestige, the respect and the glory was just another thing that could be mined.

It didn’t need to be that way.

They so easily could have been open and collaborative. But the moment when one of humanity’s greatest intellectual traditions became vulnerable, they chose conquest over stewardship.

And so, I am left thinking about that professor again. He was right that there is a special mystique to mathematics, but wrong about who would puncture it. And I’m sorry to say that, while the world might fear mathematicians, the machines certainly don’t.