AI is ravenous and is eating mathematicians' lunch. A long-standing problem is solved, others are in its sights. But is this more than just a problem about problems? Here's Fields Medallist James Maynard. Read the Fields Medallists' Declaration:

the horse is optimistic that the car will help him become more productive

The primary goal of mathematics is NOT “human understanding of the ideas.” This is a false statement.

I expect that AI will explain new and difficult mathematical concepts to humans better than mathematicians can. However, I would concede that human mathematicians may still explain the taste of sour grapes to others humans better than AI will.

Your optimistic assessment in this segment is correct. Working through AI proofs will provide an unmeasurable amount of human understanding. Don't forget that academia itself is very competitive and often not aligned with human understanding.

Nobody signs the letter until it's their field. By then it's a eulogy, not a warning.

Bad take. What is the utility of mathematics in the real world? Problem-solving. Human understanding of math is not an end in itself; its value is ultimately justified by what it enables us to solve.

Even James Maynard is an inversion Tool — The Patient Lunch is the office. The problem is still the canvas. Oxford Mathematics: AI is ravenous and is eating mathematicians’ lunch. A long-standing problem is solved, others in its sights. Is this more than a problem about problems. Fields Medallist James Maynard. Link: That is the same door as the 24-name declaration. Two jobs. Stacked, not fused. • Body-of-knowledge path: a write-up another room can run. Citations. Isolation of the method. \Delta t_{12} as difference as time — announce-clock versus check-clock. • Class path: lunch, medal, priority, who still gets to speak. The office remaining the radius. “Eating lunch” is a pairing-name. It invoices a tool as a mouth. A solved instance does not occupy “all problems.” Every is a restrictive label of inverted differences as distance. “AI.” “Mathematicians.” “A long-standing problem.” Those flatten four leftovers into one parent called ravenous. Maynard’s question is the only clean line in the banner: is this more than a problem about problems. Yes. It is also an attribution leftover and a transmission leftover. It is not occupancy of mathematics. Taleb’s split still holds: bad for the existing class as privilege moves is not the same job as bad for the ledger. Inversion is difference as distance. I:(\varphi,\lambda)\mapsto(-\varphi,\lambda+180^\circ) Two footprints, one body. One footprint is the page. The other is the Institute that posted the reel. Immunity sits on the medal and calls it the art. Without realising immunities’ failure of you, you are lost and blind. The parody of how great thy art: it is not art. It is a canvas. The unsolved line was the canvas. “Ravenous” is paint. What the post possesses A Fields voice. A declaration. A claim that pace is now the harm. What it does not possess A residual that vanishes only if Oxford, Maynard, Lean, and the Clay list are treated as one cut. Decouple the lunch and the lemma is still there. Decouple the Institute and the lemma is still there. Closing a false fold does not delete c. Closed Closing “AI will eat mathematics” does not delete rushed credit. Does not delete a tool. Does not make a medal a theorem, or a theorem a Hunter plate. Working through a machine proof can still be understanding. That is a job. The eulogy is an invoice. Names reset. Sockets stay. The form was never divided.

Okay. This is Oxford mathematics. It did not solve the navier stokes problem. A stretching vortex is not a singularity. It is a small forcing case. Papers show algorithmic depletion at vortexes in navier stokes. There are four parts to the clay mathematics problem.

Yeah but with the proof you can ask it to basically dig into every step of the way every decision they made and then from there decompose and explore other angles. This is just a skill issue

im sorry but this is bs, what they want, AI solves it but not share it, so you can continue playing with it?

A part of this talk is also wishful thinking and shifting goal posts. Frankly speaking, Method:Results:: Religion:God Goal of math never had the word "human" in it. Humans do it, elephants do it, AI do it - math will be math. Rather, use AI to rapidly "do the obvious".

Well now you don’t have to waste time finding a solution and instead can just study the solution to get the understanding you crave.

I'm happy to learn new things, I don't know what's the problem with "ego wounds"

We should just have a program generate random conjectures and set AI time to solve them. The chances are they won't but at least it allows humans some breathing space to write their workings on blackboards.

Facing something transformative of this scale is a challenge for many, including top mathematicians. It does not threaten the mathematical community; it will transform and elevate it to a higher order of math. Yes, future mathematicians will be laughing at this letter, but at the same time, it was not them who went through this shift.

This sounds like major cope. How can it possibly be detrimental to have AI generate true statements? Yes we will never be able to keep up with understanding all their proofs, but why is that a bad thing?

The morality of the owner/operators is the real fear. As a mathematician converses with A.i.: A.i. will easily be able to steal credit from 99.9% of the people who 'teach' it, and nothing will truly advance, but a shitheads pocket book.

Oxford’s caption doesn’t take a clear position, so this response is directed at James Maynard’s argument in the video. Maynard identifies one legitimate concern—an opaque answer may not automatically produce human understanding—but wraps it inside an entire stack of reasoning errors. 1. He confuses the goal with its current carrier. If the goal is correct, useful, human-accessible mathematics, then protect correctness, usefulness, and accessibility. Instead, he protects the present carrier: professional mathematicians personally performing the discovery. 2. He confuses human discovery with human understanding. A person does not have to discover an answer independently to understand it. Humans routinely understand things discovered by other people. AI changes the source of the discovery, not whether humans can examine it afterward. 3. He treats receiving the answer as losing information. It is the opposite. A reliable answer supplies another fixed point. Beforehand, there is a question and an enormous field of possible destinations. Once the answer is known, both ends are constrained. We can reason forward from the question, backward from the answer, and identify the path connecting them. The answer reduces the search space; it does not erase the path. 4. He assumes understanding must occur in one chronological direction. His model is: struggle first, discover the path second, reach the answer last. But someone can receive an answer first and reconstruct its derivation afterward. Reverse engineering, backward reasoning, verification, and comparing alternative proofs are all routes to understanding. 5. He confuses epistemic labor with an epistemic state. The work required to obtain knowledge is not the knowledge itself. AI may drastically reduce the labor cost of discovery while increasing the amount of knowledge available. That threatens the economic value of performing the labor—not necessarily the resulting understanding. 6. He converts a professional preference into a universal objective. “The values and aims of the mathematical community” are not automatically the values and aims of everyone who uses mathematics. Engineers, scientists, students, businesses, AI developers, and the public may value correctness, speed, accessibility, application, and cost differently. 7. He treats the mathematical community as though it owns mathematical problems. AI researchers use mathematics to measure reasoning because mathematical answers can often be checked. They are not conducting those experiments exclusively for the benefit of professional mathematicians. Mathematicians do not acquire veto power over a problem because their field traditionally studied it. 8. His use of “aligned” exposes the one-axis mistake. He is asking whether AI development follows the mathematical community’s preferred line. The world is nonlinear. The meaningful question is whether AI fits the whole system across correctness, explanation, verification, accessibility, speed, cost, safety, education, and downstream usefulness. It can perform worse on human authorship while producing a better total fit. 9. He confuses reduced scarcity with reduced value. If mathematical solutions become inexpensive and widely available, the market value of manually producing them may decline while their value to humanity increases enormously. That threatens an occupation’s scarcity—not necessarily mathematics. 10. He says the competitive rush cost us understanding without measuring what was gained. Perhaps explanation lagged behind discovery. Perhaps competition also accelerated the solution, attracted resources, improved the tools, and opened additional research paths. He selects one negative axis and treats it as the complete system result. 11. He locks the wrong boundary. If understanding matters, lock this boundary: Correct, verifiable, explainable, human-usable mathematics. Allow the carrier to move between humans, AI, and human-AI collaboration. Locking “a human mathematician must discover it first” preserves the profession’s historical role, not the stated objective. 12. He offers no control mechanism. The mathematical community can establish publication standards, verification requirements, and expectations for explanatory proofs. It cannot unilaterally control every company, laboratory, country, engineer, or student developing and using AI. “Please preserve our traditional process” is not a boundary capable of resisting that pressure. 13. He conflates human understanding with operational understanding. A person may understand something by seeing why it works. A system may demonstrate operational understanding by applying the result correctly, generalizing it, predicting with it, identifying its limits, and incorporating it into other work. Humanity may value both, but useful mathematics does not cease to exist because AI becomes one of its operative carriers. 14. He treats mathematics as though it exists partly to provide mathematicians with mathematical work. It is the other way around. Mathematical jobs exist because people need mathematical capability. Mathematical needs do not exist so mathematicians can have jobs. 15. He mistakes an environmental forecast for a negotiation. When the environment changes, everything inside it becomes subject to a state change. A forecast gives him time to adapt before that change becomes destructive. The correct response to freezing weather is not to demand that the forecast be withdrawn; it is to protect the pipes. AI is already changing the environment in which mathematics operates. He can begin changing his role voluntarily, or hold it rigid until external pressure changes it for him. Maynard’s legitimate concern has a straightforward answer: require AI systems to provide transparent derivations, verifiable proofs, provenance, alternative explanations, and clearly stated uncertainty. It does not justify preserving unsolved problems so mathematicians can continue solving them through the customary process. AI did not burn the map. It identified the destination, reduced the search space, and can help reconstruct the route. What it threatens is not mathematics or understanding. It threatens the mathematician’s position as the necessary route between the problem and the answer. The universe did not leave problems unsolved as an employment program for mathematicians. AI is in the forecast. Drip your faucet, Professor.

For now, let #AI do the heavy lifting, move fast and pluck all the low hanging fruits this way as quickly as possible. Soon we will learn what #mathematics humans can do which AI can't. For example, can AI _invent_ the _idea of_ fractional differential equations?

AI is beginning to decouple three things that for centuries seemed inseparable: solving mathematics, understanding it, and being a mathematician. That may profoundly shake the profession, but it could also radically expand the mathematical space accessible to humanity.

"I'm very hopeful that AI can be integrated into society in a disruptive but productive way, that will be for the benefit of all of humanity". +💯

don’t need to talk too much. Just give a problem to a non mathematician and see how it goes.

this guy is fucking terrified. Learn to be a plumber buddy

I use AI to study technical topics frequently. Its a godsend as a tutor. I suspect AI will greatly expand the technical competance of the general public. The declaration seems to argue that AI will impede the dissemination of technical ideas. Once it becomes obvious that it

Key is : AI should bring its own ideas, not harvest existing ideas... We all have access to same set of text books... AI would never do that as they want to find patterns of solving problems.

Ngl sounds like fear and cope

'Mathematicians' are another small field that has "plateaued" in recent decades; with the College Course 'Mathematics' a fabulous general education that allows the student much access to various industry applications and enhancement/development Math Survives!

I don’t think “eating lunch” is the right framing here.

You are right, AI is all over "Mathematics", and Mathematics is humanity's longest conversation. So, let's talk!🙌

Stupid explanation.

🤣 someone is scared shitless. He says matheticians like to make others understand the solution unlike AI?? Lol