# Mathpocalypse

**URL:** <https://discourse.julialang.org/t/mathpocalypse/139898>\
**Category:** Offtopic\
**Tags:** math, ai\
**Created:** [October 8, 2026, 10:06am UTC](https://discourse.julialang.org/t/mathpocalypse/139898 "2026-10-08T10:06:01Z")\
**Posts on this page:** 8\
**Page:** 1

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**Author:** ![foobar\_lv2](https://avatars.discourse-cdn.com/v4/letter/f/ee59a6/32.png) [@foobar\_lv2](https://discourse.julialang.org/u/foobar_lv2)\
**Post date:** [October 8, 2026, 10:06am UTC](https://discourse.julialang.org/t/mathpocalypse/139898/1 "2026-10-08T10:06:01Z")

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By now most of us have heard about the “[mathpocalypse](https://news.ycombinator.com/item?id=49984923)” giant drop of AI-generated unreadable slop-papers by open-AI. Many with attached lean formalization, which prove correctness of the result beyond the shadow of a doubt, and suggest more or [or less](https://arxiv.org/abs/2610.08144) that the natural language proofs are correct as well.

Among that, a lot of very important results! Unique games ~~conjecture~~ theorem, incredible progress on the Riemann hypothesis, faster fast fourier transform (and integer multiplication) in O(n\,\mathrm{log}^{1-\varepsilon}n), faster fast matrix multiplication in O(n^{2.25}), and much more. Much bigger than the already incredible Navier-Stokes moment.

Every maths-adjacent community needs to grapple with this. We should have a thread for talking about that. And not just about the technical aspects: Emotional outbursts and mutual support explicitly invited.

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**Author:** ![rafael.guerra](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/rafael.guerra/32/216610_2.png) [@rafael.guerra](https://discourse.julialang.org/u/rafael.guerra)\
**Post date:** [October 8, 2026, 10:38am UTC](https://discourse.julialang.org/t/mathpocalypse/139898/2 "2026-10-08T10:38:08Z")

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> [@foobar\_lv2](#):
>
> faster fast fourier transform (and integer multiplication) in O(n\,\mathrm{log}^{1-\varepsilon}n)

See OpenAI’s [preprint reference](https://github.com/openai/math/blob/main/preprints%2FAn-explicit-power-saving-for-the-exact-discrete-Fourier-transform-September-25-2026%2Fmain.pdf).  
Given the tiny value of epsilon reported, is there any practical use?

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**Author:** ![foobar\_lv2](https://avatars.discourse-cdn.com/v4/letter/f/ee59a6/32.png) [@foobar\_lv2](https://discourse.julialang.org/u/foobar_lv2)\
**Post date:** [October 8, 2026, 10:58am UTC](https://discourse.julialang.org/t/mathpocalypse/139898/3 "2026-10-08T10:58:35Z")

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> [@rafael.guerra](#):
>
> Given the tiny value of epsilon reported, is there any practical use?

No, it appears to be [impractial](https://en.wikipedia.org/wiki/Galactic_algorithm). But it’s very WTF, and opens the surprising hypothetical possibility that future progress may find something practical.

Same appears to apply for the matrix multiplication. (heck, even good old Strassen is barely practical!)

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**Author:** ![langestefan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/langestefan/32/207923_2.png) [@langestefan](https://discourse.julialang.org/u/langestefan)\
**Post date:** [October 8, 2026, 11:23am UTC](https://discourse.julialang.org/t/mathpocalypse/139898/4 "2026-10-08T11:23:17Z")

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> [@foobar\_lv2](#):
>
> Every maths-adjacent community needs to grapple with this. We should have a thread for talking about that. And not just about the technical aspects: Emotional outbursts and mutual support explicitly invited.

Great, put some label on it so I can easily mute it all…

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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [October 8, 2026, 11:23am UTC](https://discourse.julialang.org/t/mathpocalypse/139898/5 "2026-10-08T11:23:41Z")

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In the short term, it seems like a bigger problem for pure math than applied math.

In pure math, almost the entire value really comes from the process, the understanding and the new abstractions that are generated in the course of grappling with a problem. The actual answers to these grand challenges may have little or no practical utility, but the byproducts of the process are often useful — especially the generations of creative mathematicians who are trained as a result, capable of taking on many sorts of problems with a wider range of applications. But the career incentive structure in pure math requires “breakthrough results” in order to decide whom to hire, and now that incentive structure seems in danger of being irrevocably broken. In its wake, a whole generation of students may be deterred from pursuing a career in pure mathematics, its rich culture of deep intellectual challenges replaced by a [“radioactive wasteland”](https://leanprover.zulipchat.com/#narrow/channel/113488-general/topic/The.20role.20of.20AI.20companies.20in.20large.20formalisation.20projects/near/580123783) of incomprehensible proofs of unexplained origins ([“mathslop”](https://davidbessis.substack.com/p/the-fall-of-the-theorem-economy)) that disincentivize further study.

Whereas, in applied mathematics, the mathematical results are usually a means to an end, so in the short term we may be able to solve many practical problems more quickly, just as software engineers can now produce useful software more quickly in many cases.

But in the long run, we may all be poorer for the loss of trained mathematicians and mathematical culture. How will you learn to do mathematics when all homework problems, and even many research-level questions (once they are carefully chosen and clearly defined, which takes experience), have solutions that are a click away? Just as it is not clear to me how the next generation of software engineers will be trained. How many people will want to devote years of their life to learning these things? We are already seeing severe strain on systems of higher education. So who will be able to guide and evaluate the results of these tools in 20 years?

Not to mention the resulting concentration of power and information in the hands of a wealthy few.

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**Author:** ![rafael.guerra](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/rafael.guerra/32/216610_2.png) [@rafael.guerra](https://discourse.julialang.org/u/rafael.guerra)\
**Post date:** [October 8, 2026, 12:01pm UTC](https://discourse.julialang.org/t/mathpocalypse/139898/6 "2026-10-08T12:01:35Z")

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Like poetry, mathematics is a creative pursuit. If the creative process is automated, people may feel their vocation is devalued.  
School curricula, including math, often compound the problem by neglecting the history and human struggle behind the ideas, so students meet finished results without the people who made them.  
AI may deepen this, with theorems that arrive with no human story at all.

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**Author:** ![mihalybaci](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mihalybaci/32/13528_2.png) [@mihalybaci](https://discourse.julialang.org/u/mihalybaci)\
**Post date:** [October 8, 2026, 12:39pm UTC](https://discourse.julialang.org/t/mathpocalypse/139898/7 "2026-10-08T12:39:55Z")

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> [@stevengj](#):
>
> How many people will want to devote years of their life to learning these things? We are already seeing severe strain on systems of higher education. So who will be able to guide and evaluate the results of these tools in 20 years?

I came across a video that was very much click-bait, and the opening line was “Millenials may be the smartest generation”, including all future generations.

While I didn’t watch the video, the basic premise was that every generation of students was taught based on the knowledge built from the combined work of all previous ones – [standing on the shoulders of giants](https://en.wikipedia.org/wiki/Standing_on_the_shoulders_of_giants). And to get good jobs, a person had to possess that previous knowledge **and** have the ability to go further.

But now Gen Z and Gen Alpha can turn to LLMs for answers. And if job security in today’s market means devoting your energy to creating the best LLM-prompts, then there is automatically less incentive and less _time_ someone can devote to learning the subject matter itself.

I doubt that the “human intelligence has peaked” will come to fruition, but it is at least something worth contemplating.

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**Author:** ![greatpet](https://avatars.discourse-cdn.com/v4/letter/g/e495f1/32.png) [@greatpet](https://discourse.julialang.org/u/greatpet)\
**Post date:** [October 8, 2026, 12:44pm UTC](https://discourse.julialang.org/t/mathpocalypse/139898/8 "2026-10-08T12:44:12Z")

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Two unrelated casual questions: 1. Since LLMs are incredibly good at literature search, why can’t OpenAI spend a few percent of their math budget on generating proper citations? 2. Should grant agencies like the NSF generously support pure mathematicians in purchasing LLM token credits or local AI inference hardware, at a level comparable to supercomputer costs of computational sciences like CFD and lattice QCD?