I didn’t expect the math vs AI debate to blow up as it has. Until very recently, math had always been a niche subject, even compared to physics. Hardly anyone cared about it online, although there would be the occasional viral post on “how to self-learn math,” which always circled back to the same advice of “read a lot of books and do many problem sets.” The top two math Reddit subs, r/mathematics and r/math, would go weeks before all the posts on their front pages were replaced with new ones; now it’s one or two days, thanks to AI.
I remember that from early 2023, after Elon bought Twitter, to early 2026, I saw hardly any discussion on the platform about math, aside from the occasional post about training models or in the context of machine learning. Even when news broke of OpenAI models scoring well on Olympiad benchmarks, it didn’t generate anything close to the debate seen now. The assumption was that models would always fall short of actual mathematicians. That all changed in the summer of 2026 with a key development: an AI-assisted disproof of the Jacobian conjecture in dimensions three and higher, which had resisted resolution for nearly 90 years. Then everything exploded.
Whatever one thinks of the debate, the benefits of AI are clear. AI has made it much easier to learn math. You no longer have to rely entirely on textbooks and problem sets. You can either ask the AI to create a curriculum or just “learn as you go.” As someone who “does math,” I think I am qualified to offer my assessment. Math has become so difficult and specialized that you’re forced to use AI to be competitive. AI can do what no human can, reasoning through huge search spaces and making non-obvious connections.
An agent can expedite what would otherwise be months of calculations. I can give the AI an idea, and it can perform a full literature review, assess the idea’s feasibility and novelty, or even attempt to solve the problem outright. There is nothing even comparable to it at this point. If you’re not using it, you’re at a huge disadvantage to those who are. It’s analogous to taking steroids in professional bodybuilding.
AI expands the frontier of knowledge by finding results that otherwise would have remained undiscovered, that can be of value to researchers. If given a choice between not knowing something and adhering to some unwritten rule of “math purity,” versus taking a shortcut, I think the latter is an acceptable tradeoff, provided AI usage—including the specific roles AI assisted with—is disclosed, and relevant authors/sources are cited appropriately. Sometimes you just want to know something, and yeah, if this means invoking the “nuclear option” of AI, so be it. I think this is an honest or fair compromise.
Consider the Navier Stokes claimed “blowup” solution. Its proof, despite being AI generated and controversial, has provided significant food for thought for the mathematics community: knowledge has been created, clouds of uncertainty have been lifted. And new knowledge will be built on top of this, with or without AI, which is how math progresses. But I agree with critics authors who use AI need to be upfront and transparent as to where their results came from or originated, such as prompts used or literature consulted. Just saying “AI was used” is not good enough–what is also needed is the thought process that went into the result–and I can understand why the math community is peeved when such details are not specified.
I think mathematicians have resigned themselves to the inevitability of AI. I think this is why their opinions have been tepid, calling for moderation, rather than outright banning AI. AI is here to stay; people are going to be using AI, so this calls for moderation in the sense of having a debate about how math, as a profession, has to evolve. This is the nuance that is lost online. They are not acting out of selfishness to protect their jobs, but rather to figure out how the “profession of math” goes forward.
In the future, math will be more like fine art. Journals will act as galleries. Math as a profession will exist, but it will be harder to make it a career when anyone with some background knowledge and a pro Astra or Opus subscription can “fire up” new results: compose a prompt, and at the press of a button, new math is produced. Professional mathematicians will be competing with amateurs and labs with access to computing power. And this means more supply, and consequently, important results possibly being overlooked and higher journal rejection rates.
What I mean is, despite AI, math taste still exists, much like taste in art. There’s a category I call “slopmaticians”: people who use AI to produce math that’s technical but uninteresting, doesn’t really engage with the literature, is poorly motivated, or is aimed at a very narrow audience. This kind of math is heavy on calculations and may interest a few people, but it can still be rejected by arXiv or a journal. Institutional filters still exist to keep this on the outside, just as they always did in the pre-AI era.
Just as Uber disrupted the “medallion cartel” and allowed people to earn money driving part time, AI helps democratize math. But at the same time, I still see hierarchies (e.g., academia, journals) persisting. Moreover, many problems are not amenable to AI. For every problem that falls to AI, there are presumably many others that don’t budge. The OpenAI team, which solved the Navier–Stokes Millennium Prize Problem, had “swarms” of 10,000 agents, a proprietary model, and computing resources far beyond those available to most researchers or universities.
As for criticism, math has always occupied a position above reproach. Other professions are constantly subjected to criticism. Law is associated with ambulance chasers. Sales is associated with scams or deception. Healthcare is associated with quackery. There are entire categories of jokes about lawyers and doctors. Politics is associated with corruption. Tech executives are constantly blamed for everything wrong with society, such as teen mental health problems, social media addiction, or even subverting democracy itself. In physics, even string theorists have come under criticism for their ideas being “untestable.”
Math places undue weight on competition, assigning worth to a result by how many people have failed to solve a problem rather than by other aspects of the mathematics itself. I think this is a valid criticism. The popularity of AI, even among mathematicians, shows the lengths people will go to to be competitive, even if it means outsourcing the actual process of doing math to a machine, or not understanding the results well enough to appraise their accuracy. Bitter disputes over priority and credit, such as concerns about “prompt theft,” resemble legal battles over copyright infringement. In that sense, mathematics is less about proving results than about proving who “owns” a result.
Few are above this. Just as teens may be addicted to smartphones, academia is addicted to credit. Producing papers is a lot of work, jobs are few and far between, and people justifiably want to be acknowledged for that effort.
But imagine if mathematicians were not allowed to take credit for results: each paper would be published under a new, untraceable alias, similar to anonymous 4chan posts. (This sorta actually happened.) Things like tenure would stop working, because how does a university award a promotion to a pseudonym? Output would certainly fall, but at the same time, the true mathematicians, who do it for the love of math and not credit, would remain.
Overall, I see the merging of math with AI as a “net positive,” by expanding the frontier of knowledge. I predict math as an institution will survive AI without making significant concessions. Hierarchies and signifiers of status such as journals and tenure will continue. But I don’t think math is above criticism, and criticizing it is not inherently anti-intellectual. Just as the “taxi monopoly” was disrupted by the much better Uber, maybe math needs a similar change or upgrade, which AI provides.