Post #4066495

2026-07-24 15:35 UTC

long rant about math The recent big AI results in math have left me in quite a bad mood. I believe the main ingredient is Lean, which is a formal language resembling a programming language. Math proofs written in Lean can be verified deterministically with a computer, which really helps mitigate the hallucination problems of LLMs. Back in the days of pure scaling LLMs and Sam Altman talking about Dyson spheres, I was skeptical that LLMs would do math, but I did think that perhaps in the future, techniques using these formal languages could contribute to math. Well, it seems like OpenAI and Anthropic had the same idea and I underestimated their limitless checkbooks. Many of the biggest results were announced by mathematicians directly working for them (and presumably being paid a handsome amount). For what it’s worth, after the last of these big announcements, I decided to try one of these AIs on one of my small problems that I couldn’t figure out. The AI did give a solution. That is, until I checked it thoroughly and realized that the it had a subtle but severe mistake that made it useless. I reprompted it, it failed again, and I ran out of tokens. I’m sure someone will tell me to shell out $200/mo for a pro subscription. In the math and computer science research community, this is all anyone can really talk about right now. Honestly, after watching this whole AI bubble starting from the very beginning, I think the AI companies want to use marketing to stoke fear that all mathematicians will be replaced. But now, I am just too tired to argue. The amount of alarm and the extraordinary social pressure to use LLMs has soured me to this whole research thing. If becoming a researcher will one day require supporting these evil AI companies, I would rather just not. My dream job now is Factorio developer. A lot of annoying people in technical areas view the world in terms of an intelligence hierarchy: the smartest people do math and physics, the slightly less smart people do coding, and the dumb people do everything else. So if AI can do math then it can do anything else. But, as an example, it is abundantly obvious now that AI is not replacing filmmaking. The techbros might be moved by arguments about how hilariously expensive video generation is, and how all these videos are 2 second clips stitched together so you won’t feel the uncanny valley. But the real reason is that nobody wants to watch slop made with no intention or feeling. Also, nobody wants to support the AI companies, which could not act more evil even if they tried. The mania in math right now quite resembles the mania in software engineering back in December-February, when Claude Code definitely solved all coding. I don’t think the boosters expected that by April, everyone would be complaining about how expensive it all was while seeing an endless parade of vibe coding disasters (and no increase in productivity). Even if math research works out perfectly well (which is a still big if), it’s not going to pay the bills. They would need to find a use case in the real world, where hallucinations can cause serious damage and cannot be formally prevented. And they have certainly tried. Math will not change the fact that all of this will collapse.

Replies (5)

@flaviat@awful.systems 2026-07-24 17:25

I just entered university for math and even though this is all very demotivating, it’s just what I’m good at. math.andrej.com/…/how-to-review-formalized-mathem… The AI people’s cry of "no don’t look at the code! it’s in lean so it’s correct! does give me a bit of hope (hi bitofhope if you’re here) that it’s bullshit that will fall over

Open ##4068501
@BioMan@awful.systems 2026-07-24 19:17

Am I right in understanding that almost all the big name results in LLM-derived math recently come from big publicity projects in which someone spent ungodly amounts of money to have the thing nondeterministically fuzz huge numbers of independent random sees of outputs around a topic, putting out simulacra of ideas which could be then deterministically algorithmically checked? In fields where something like finding one counterexample to a conjecture would be a big deal, or where you just need to try a huge number of possible solutions until you happen to hit on one that works, rather than follow a long train of logic?

Open ##4070525
@BigMuffN69@awful.systems 2026-07-24 23:58

“The mania in math right now quite resembles the mania in software engineering back in December-February, when Claude Code definitely solved all coding. I don’t think the boosters expected that by April, everyone would be complaining about how expensive it all was while seeing an endless parade of vibe coding disasters (and no increase in productivity). Even if math research works out perfectly well (which is a still big if), it’s not going to pay the bills.” ^MBAs at Open AI desperately trying to figure out who is willing to buy a counter example for 100 billion USD . pee en gee

Open ##4075650
@BlueMonday1984@awful.systems 2026-07-25 17:30

A lot of annoying people in technical areas view the world in terms of an intelligence hierarchy: the smartest people do math and physics, the slightly less smart people do coding, and the dumb people do everything else. So if AI can do math then it can do anything else. But, as an example, it is abundantly obvious now that AI is not replacing filmmaking. Going by those annoying peoples’ logic, filmmakers are smarter than coders, because LLMs can (allegedly) program, but they can’t make a good film. I have no wider point to this, I just find this really, really funny

Open ##4093314
@rook@awful.systems 2026-07-25 20:57

I reprompted it, it failed again, and I ran out of tokens. I’m sure someone will tell me to shell out $200/mo for a pro subscription. One of the things that’s never clear from the reporting on ai successes is exactly how much actual paid human time went in to achieving those successes. This was especially notable in the fable-based security work… a huge amount of person-hours went into turning fable-detections into actual meaningful vuln reports. A lot of demonstrably clever and capable people are involved with the llms-for-maths work, and a lot of money was spent on their time and supporting their work. Replicating it without your own stable of mathematicians and computer scientists and all the tokens they can eat is probably impractical. I believe the main ingredient is Lean, which is a formal language resembling a programming language. Math proofs written in Lean can be verified deterministically with a computer, which really helps mitigate the hallucination problems of LLMs. Fwiw, lean is a general purpose programming language, though despite microsoft’s efforts no-one uses it for that. I think its popularity with mathematicians came as a bit of a surprise. Anyway, the other important thing that didn’t get reported on is that building the formal definition of the problem is not trivial! Obviously I don’t need to tell you that, but from the reporting you’d think that an llm solved all these problems, when in fact it was an llm in the hands of some very capable people who absolutely did not just prompt the thing in plain english. Anyone hoping for self-marking homework here is going to be disappointed… lean slop confirming to formal spec slop is just expensive slop. Reviewing regular genai code is awful, even the thought of reviewing genai dependently-typed code makes me want a new career.

Open ##4103452