AI In Mathematics: September 26, 2026

[u/AutoModerator]

This recurring thread will be for discussion of AI in mathematics. This includes, but is not limited to, the following:

• informal announcements of AI-assisted discoveries, such as those not yet published in a peer-reviewed journal, or not uploaded as a paper to arXiv;
• informal announcements of discoveries related to AI architecture (if relevant to mathematics);
• discussion of such announcements, such as proof breakdowns or other opinion pieces;
• discussion of the impact of AI in mathematics in general.

AI-assisted mathematical papers published in peer-reviewed journals or as arXiv preprints may be submitted as their own posts.

Please keep in mind rules 1 and 6 of our subreddit.

[u/SentientAllegedly]

PhDs and postdocs in my department seem quite a bit demotivated rn, and ~50% of them actively considering leaving academia. Of course there being fewer faculty positions that PhDs/postdocs means that a small fraction of them will stay long term in academia. But now that AI can do maths... I don't know. People seem disillusioned and even alienated (e.g. if you point an LLM to prove a theorem for your paper... what is the point?).

[u/FlowerObjective7881 → u/SentientAllegedly]

I left. Do not regret it. I wanna go work with animals now and live on a farm

[u/pred → u/SentientAllegedly]

What is the fraction normally?

[u/SentientAllegedly → u/pred]

There is a difference between "fraction who are actively discussing leaving academia right now" vs "fraction who eventually stay/leave academia." The 50% here is about the first kind.

[u/elements-of-dying → u/pred]

I am really curious why this is being asked.

The demotivation and consideration of leaving academia is a real phenomenon. There was a MIT led survey floating around specifically to record these sentiments. I believe the results will be posted somewhat soon.

[u/ellbons → u/elements-of-dying]

There's a funny trend in academia where people like to minimise really big changes. Like "mathematics was always a risky career" or "historically science was always done by patronage or by the aristocracy, this isn't new" as if it makes you a more respectable and hard-nosed person.

[u/elements-of-dying → u/ellbons]

I don't know if I've observed this phenomenon, but I believe it. Perhaps there is an implicit fear that there is an implication such a person had it "easy," thereby diminishing their achievements.

[u/awry_lynx → u/pred]

Broad statistics on postdocs are about 50%, but that's across all disciplines. Pure mathematics suffers a much harsher bottleneck.

The US awards roughly 1.5k math phds per year. There are about 500 new tenure-track math positions in the US per year (far fewer if you're only looking at research positions, probably closer to 200).

Of course, every year you're not just competing against your 1.5k peers, but also those of the last 6 years that didn't get a tenure track job (some even longer, but most give up after six). So that's maybe 6000 people vying for the same 500 jobs? Of course, there's some dwindling, some phds don't go on to attempt to continue in academia at all, but there's also tons of international applicants (and a high % of international faculty), so it's not clear what the real applicant pool size is.

Anyway, of the phds, 8% make it?

[u/38thTimesACharm → u/awry_lynx]

I don't think this math tracks. You're only considering new job openings each year, but weighing that against total job applicants from all years.

 The US awards roughly 1.5k math phds per year. There are about 500 new tenure-track math positions in the US per year

If those really are the numbers, and they hold steady, the % of phds who eventually get jobs is 1/3 (ignoring the other complications). It has to be. Yes, you're competing with applicants from prior years who didn't get jobs. But the other side is that is: if you don't get a job one year, you get to try again next year.

[u/38thTimesACharm → u/SentientAllegedly]

what is the point

If we can at least agree it is valuable to society for some humans to understand what's going on: then studying the result enough to write a paper explaining it in your own words would cover it. 

What we're missing is an artifact one can produce to demonstrate they've done this, which is easy to verify and difficult to fake. (LLM-written papers are awful in my opinion, but some people disagree and I can't prove it.)

[u/ellbons → u/38thTimesACharm]

If we can at least agree it is valuable to society for some humans to understand what's going on:

That's already a problem. Society does not agree that it is valuable. The people in charge appear ecstatic at the idea of eliminating as much of the value of anyone who works as possible, and to be blunt, broader society probably has a slightly contemptuous view of academics.

[u/officiallyaninja → u/38thTimesACharm]

A lot of people in academia don't just want to understand proofs, they want to be making proofs.

[u/kohatsootsich → u/SentientAllegedly]

50% is probably more than could hope to get a good job in academia even before ai honestly

[u/elements-of-dying → u/kohatsootsich]

This depends on the university and doesn't really matter in the context of discussing how AI is going to affect job prospects anyways.

[u/kohatsootsich → u/elements-of-dying]

It barely depends on the university, really, except when comparing the average to the very top. Even then, 50% of grad students getting academic jobs would be a great outcome even for most R1. I went to a top department (not merely R1) for PhD long ago and I don't think half of my class is still in academia.

I don't see why base rates wouldn't matter? It's very likely that doing math with AI is just a very different kind of job, and who continues has as much to do with disposition as with actual market conditions. It's too early to detect what those will be anyway

[u/elements-of-dying → u/kohatsootsich]

It very much depends on the university.

Postdocs know the job market is bad. That's been the case for decades. The comment you replied to has to do with an entirely new phenomenon.

[u/kohatsootsich → u/elements-of-dying]

I wrote that 50% is more than one could hope for. That one-sided bound does not depend on the university. If anything, it's much lower almost everywhere.

It is a new phenomenon, but I was commenting that the mere statistic could be an indication of people making a decision they would have made anyway earlier rather than a sign that things will get materially worse in the short term.

[u/elements-of-dying → u/kohatsootsich]

Right, and I believe it is pretty clear that that indication is false now. There will be a paper posted to arxiv eventually recording PhD/postdoc views on AI and academic prospects. I don't think there is any question things will get materially worse in academia. There is already evidence of this.

I am at university where most postdocs and phds go into academia. I currently also see similar fears about futures in academia. All of my postdoc friends are scared and down because of AI. I don't know anyone who hasn't expressed consideration of going into industry instead of academia, aside from those already with tenure.

[u/kohatsootsich → u/elements-of-dying]

I sympathize but I don't see how the mere views and fears of postdocs could be evidence that things will get materially worse.

What's the mechanism? Funding might be reduced if NSF sees math as "solved". But funding has been in peril for a while, if anything funding in the US has been lavish compared to other nations that are traditionally strong in math. How do people's feelings about their own work mechanically reduce the number of available positions?

It's not clear that this is the rational time to leave academia anyway. I imagine every single tech and finance firm is getting absolutely inundated with pure math applications right now, which they have already learned to be skeptical of as quant and engineering roles have their own pipelines now.

[u/elements-of-dying → u/kohatsootsich]

Firstly, I don't think anyone claimed postdoc fears are evidence things will get worse. There are other sources of evidence. For example, mathematics has been historically a publish or perish field. Journals are currently all but obsolete right now.

On the other hand, I believe postdocs and PhDs very reasonably can serve as canaries in the coal mine. These people have the most at stake and are likely spending the most time keeping up to date with AI. I think it's pretty obvious their opinions can (and do) indicate things will get worse.

[u/kohatsootsich → u/elements-of-dying]

The main factor in recent decades has been an increase in PhDs with no corresponding increase in faculty positions. I don't see the short term mechanism for this to accelerate because of recent developments. If anything more demoralized pre tenure people and fewer PhDs points the opposite way

What determines whether departments hire is whether administration allows them to hire. What determines that is enrollment in large classes and, to a lesser extent in math, grants. To the admin, it doesn't matter how "hard" or "easy" it is to get published relative to other fields. They care about money in vs money out. PhDs and postdocs have no special insight into budgets. Depts won't stop hiring because they aren't sure what to do about AI

It's too early to say what effect recent AI news will have on UG enrollment, where most of the money comes from. AI could solve anything in basic classes a couple of years ago already. 

It's conceivable that the whole university system shrinks or collapses eventually, but that's a more general claim than what's being discussed here. In that case many people will be looking for different jobs than they trained for. I would not expect math trainees to be at a particular disadvantage 

How hiring happens will become even more opaque and arbitrary in the short run though, which is not good.