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Boaz Barak @boazbaraktcs · 2h Terry Tao 2 years ago on working with o1: "The experience seemed roughly on par with trying to advise a mediocre, but not completely incompetent, graduate student." Think of what other fields AI is currently at that level.
Tweet by Boaz Barak quoting Terence Tao's two-year-old assessment of o1 as a mediocre-but-not-incompetent graduate student, and suggesting other fields where AI is currently at that level.
Ash Jogalekar @curiouswavefn
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Bogdan Ionut Cirstea reposted Ash Jogalekar @curiouswavefn · 20h I am guilty of this myself. I am about to submit an AI-derived mathematical proof to arXiv that I don't completely understand but which addresses a problem in theoretical biology with a neat solution. I understand the problem, the approach and the result, but I don't understand all the details of the proof. It's been an exciting experience that has occupied the better part of the last month for me. I am transparent about my limitations in understanding the proof and welcome human review that proves it right or wrong (you learn something important about AI in either case). In general, I consider it an entirely good thing for people who are not specialized to use AI for solving problems in their field using tools from other fields that they don't fully understand. But I agree that it is placing a serious burden on human verifiers. Unless, of course, the AIs become so good at verifying that we don't need humans. [quoted tweet] Andrew Akba… @Andrew_Akb… · Aug 19 Terence Tao, one of the most well known mathematicians, speaks up on AI in mathematics in his new paper: "What if an AI tool generates a lengthy proof th... [link card, cut off at bottom] arXiv [Submitted on 17 Aug 2026] Mathematics in the age of AI Terence Tao
Ash Jogalekar (@curiouswavefn), reposted by Bogdan Ionut Cirstea, on submitting an AI-derived proof he does not fully understand and the verification burden this places on humans, quoting a post about Terence Tao's arXiv paper "Mathematics in the age of AI" (submitted 17 Aug 2026).
ai math resultsterence taoverification burdenarxivai-derived proofsmathematics
rohit @krishnanrohit
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rohit @krishnanrohit · 5h I've been calling this the "prompting paradox" concept for about a year now. LLMs can solve pretty much any problem you specify well enough, and the entire idea now is to help teach it how to specify things better for itself ! [Quoted tweet] xjdr @_xjdr · Jul 26 i saw Terrence Tao use sol med to answer a lot of very complex problems in one of his chat logs. i became curious. i had a particularly sticky problem that was in my 'ai cant do this yet' pile that i was only very recently able to get sol ultr...
Rohit Krishnan describes his 'prompting paradox' concept — LLMs can solve almost any well-specified problem, so the frontier is teaching the model to specify problems better itself — quoting xjdr's account of seeing Terence Tao use a model called 'Sol Med' (likely 'Sol' family, e.g. Sol Ultra) to solve very hard problems.
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Terence Tao @tao@mathstodon.xyz I doubt that anything resembling genuine "artificial general intelligence" is within reach of current #AI tools. However, I think a weaker, but still quite valuable, type of "artificial general cleverness" is becoming a reality in various ways. By "general cleverness", I mean the ability to solve broad classes of complex problems via somewhat ad hoc means. These means may be stochastic or the result of brute force computation; they may be ungrounded or fallible; and they may be either uninterpretable, or traceable back to similar tricks found in an AI's training data. So they would not qualify as the result of any true "intelligence". And yet, they can have a non-trivial success rate at achieving an increasingly wide spectrum of tasks, particularly when coupled with stringent verification procedures to filter out incorrect or unpromising approaches, at scales beyond what individual humans could achieve. This results in the somewhat unintuitive combination of a technology that can be very useful and impressive, while simultaneously being fundamentally unsatisfying and disappointing - somewhat akin to how one's awe at an amazingly clever magic trick can dissipate (or transform to technical respect) once one learns how the trick was performed. But perhaps this can be resolved by the realization that while cleverness and intelligence are somewhat correlated traits for humans, they are much more decoupled for AI tools (which are often optimized for cleverness), and viewing the current generation of such tools primarily as a stochastic generator of sometimes clever - and often useful - thoughts and outputs may be a more productive perspective when trying to use them to solve difficult problems.