Twenty-five leading mathematicians signed an open letter arguing that AI labs are threatening their intellectual work as they seek to one-up each other with solutions to famous math problems. Each signatory has been awarded the Fields Medal, considered the most prestigious prize in mathematics.
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While the ability of AI models to solve the world’s outstanding mathematical challenges could be a boon to humanity, the signatories of the new letter argue that will only be the case if those solutions can be understood and communicated by the math community and, ultimately, the rest of the world.
“Often these solutions are announced in a rush, leaving no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others,” they wrote — and OpenAI’s proof remains unverified. “As in all creative professions, this raises severe attribution and plagiarism questions. Moreover, without the willing mathematicians who must take care of their development and integration into the mathematical canon, AI-conceived ideas would never become fully alive and the crucial human transmission chain between mathematicians would be lost.”
With other mathematicians growing paranoid and wondering if their Codex use was in turn fed into OpenAI’s new models, there is real fear that the culture of open research will be threatened. Today, if frontier labs see a useful path to a discovery, they can spend tens of millions of dollars using LLMs to beat the original researchers to a proof — a dynamic that will incentivize secrecy.
This letter follows the Leiden Declaration, released by a working group of mathematicians in June. That document also grapples with the ways that LLM proofs will change their work and offers a set of recommendations for mathematicians, institutions, and policymakers.
As with software engineering and other areas where AI tools are changing workflows, mathematicians find a justification in the work around the work: The value in math isn’t just the proofs and who gets credit, but the intellectual super-structure that nourishes students, finds new questions and ideas, and integrates them into broader human civilization.
And if you don’t particularly care about the cutthroat world of high-stakes mathematical proofs, don’t forget: Your field of interest is next.
“The issues the mathematical community faces now are similar to issues that other scientific and creative professions are facing, and indicate issues that all of humanity might face: how to make sure that, as AI changes the way work is done, we do not lose sight of what that work was meant to achieve in the first place,” they wrote.
this post was submitted on 13 Sep 2026
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Im not an AI lover, but this critique that it is wrong 50% of the time is simply not true for newer models.
It's still consistent with my experience, but in certain contexts, can be corrected if the output can be processed in some context that can auto detect wrong output. For example if you have a unit test to pass, then it can be wrong several times but doesn't matter because it auto runs and gets directed to try again.
In contexts without the possibility to objectively measure correctness, it is still pretty bad.
Incidentally that's why I don't let it go crazy on anything without tests. Shit, it could rerun the test 10 times if it needs to, I don't really care what the intermediate steps are, just that the end result is correct and it isn't polluting my codebase with 7 line comments and docstrings for helper methods that are simpler than left-pad. If it then successfully fixes a difficult-to-find (but often easy to fix) bug that stems from changes to a large upstream codebase 9 times out of 10, then as far as I'm concerned, the accuracy of the tool I'm using is 90%. Don't much care if the underlying model's accuracy is technically only 50% or maybe even less. Still haven't found a definitive solution for the claudespeak comments. They're annoying and no matter how and how many times I tell it to stop adding them, it will fucking not.
Which is ALSO why I think asking a chatbot for advice on anything you're not knowledgeable about yourself is a horrible idea. The accuracy is OK if it can iterate against validation.
I still remember trying to get it to solve a problem I had with nested multi-level navigation bars in an enterprise app, and it's JavaScript solution was like:
Hey LLM, I got an error;
multiLevelNavBardoes not exist. What the fuck?It's good at filling in the blanks. But if the blanks are too big, all you get is filler
Id say its better than majority of my colleagues. Can you imagine if we fact check people in the same way?
Depends. It is way often more right than it is wrong, however if you have a long enough chat, the chance that it produces a falsehood approaches 100%. Of course humans produce errors too.
Too some extent I agree with you. However, most of the time you really dont need to have "long" chats. In some ways I think some of the Flasehoods derive from the prompts themselves. Lots of bad/false/incorrect info is available on the web.
Can you imagine if we fact checked long conversations with people? How often do thing bad/false info would surface?
Do you not fact check your conversations?
If one person in the group has any sniff of not true, someone pulls out their phone to check.
Friends dont let friends spread misinformation. Fact check today.
Most of the conversations I'm talking about are not a quick whip out the phone and check...
Yeah, if you pay, you're right. But how often would you say the newer models are incorrect? Even if it's 1%, that still hold true that you can't check them.
Id say its accurate more than 90% of the time. I see erroneous facts added to the response that are wrong or not correctly represented the most.
Do you apply this same logic to the "experts" that you talk to? I talk with alot of SMEs from different domains. I could never fact check them all. Way too broad of subject matters. Similar to many of my indepth AI interactions
Yes, I do. Especially that they can have their shit peer reviewed outside of academia.
Usually when I do this after the fact it comes back as "oh, that specific context wasn't given" or "oh, I misunderstood what you were asking", etc. Seems like the same...
Humans can't check their work though? That's the issue. Science is the process of checking the work. AI just assumes that you're getting the correct version with no future checking. Think Fermilab or Cern.