My colleagues in math are now frightened about the (very expensive) mathematical theorem proving ability of these AIs, and many of them really do think that if they can do math, they can do all cognitive tasks. Running a store like this should be so easy! Every single conversation about AI with them has become more frustrating. They are so confused when I still say that the AI companies will die a painful death. When I give my usual points about their expense and their failures in other domains, I am given the usual spiel of “it’ll get better in other areas” and “it’ll get cheaper”.

Unlike them, I have actually been paying attention to this stuff from the beginning. What they think is going on is AI solving math first and shortly getting around to all the other stuff, but what I’ve seen is that AI labs had already tried all the other stuff first and only managed to win the booby prize of theorem proving, which doesn’t pay the bills. And what’s the point of spending thousands or millions to output random blobs of Lean that technically compile if there is no one around to bother making sense of them?

One example I gave is when Anthropic vibe coded an entire C compiler from scratch back in February, which turned out to be a pile of shit. I’ve said that if AI had made similarly rapid progress on software engineering, we would have seen Anthropic continue to put out these demonstrations, and they would have become truly high quality. They would release a compiler more efficient than gcc one week, and a browser better than Chrome the next. (OpenAI’s actual attempt at a browser didn’t go so well.) And if they could do this, they would actually have a shot of making money!

If they could do this, they would have already. The theorem proving stuff actually works (for certain things, in certain ways, at enormous expense), and look at how OpenAI and Anthropic do not hesitate to snipe mathematicians for results rather than being content as tool vendors. But lately I haven’t heard of any software demonstrations. Silence is much louder than noise. More Millennium prize problems bashed with tens of millions in compute costs are not going to change my mind very much.

The counterargument I got was that AI can already one-shot most programming tasks and I shouldn’t be cherry-picking the failures. I am far too tired to argue at this point.

Despite all the doomer propaganda that has been swirling around for many years, it’s still easier to imagine the end of the world than the end of capitalism.

Signed.

Terry Tao talks about how he used to try to cooperate with the AI industry to achieve a positive outcome, but now he finally sees their true colors. Link

During this event, OpenAI requested an interview concerning my vision of the future of AI and mathematics. I accepted, and spoke with them for perhaps an hour. I had done similar interviews in various venues, and I assumed that, as with these other cases, they would eventually post the entire interview online, which talked about both the possibilities and risks of AI much as I have done in these other interviews. As it turned out, they only used a few snippets of that interview for that infamous advertisement instead. In retrospect, I should have pushed back harder on their decision; but I decided at the time that even a selective release of my commentary would help raise awareness of the potential for AI, and in particular on the possibility of the “best of both worlds”.

Since then, the situation has deterioriated markedly. Many of the people in the industry that shared my views have left or become sidelined, with most major tech companies now increasingly focused on the race to develop extremely powerful, autonomous AI technologies regardless of their actual value to society. The current drama surrounding the Navier-Stokes global regularity problem is the most dramatic and visible instance of this, but there have been multiple other such examples, and much of my commentary in the last few months has been aimed that the increasingly severe divergence between the current objectives of the AI industry, and of mathematics in general.

Much respect to artists for seeing all this coming from the very beginning, and holding the line.

One detail that seems to have not spread around: the announcement was so rushed that when the 165 page output was released, it only included 16 citations, and those citations did not even include the work of Diego Córdoba and Luis Martínez-Zoroa, who introduced the overarching framework for attacking the problem (that Buckmaster-Alpöge and OpenAI used). After the announcement and after these redditors pointed it out, they threw in a few more citations to include them, but come on.

Of course there are people trying to find a silver lining to this by conjuring up the hypothetical scenario where a student only uses the AI to aid in learning the material instead of just doing all the work.

First, any convenience in learning the material just reduces your ability to learn it. The friction involved with learning may seem like an inconvenience to be smoothed away, but it turns out that the friction is how learning happens. It’s called engaging with the material. This has been the case with previous technologies: handwriting is better for retaining memory than typing (https://pmc.ncbi.nlm.nih.gov/articles/PMC11943480/), although it seems like AI is on an entire new level. (I guess there is some commentary about the sadly common worldview that life is about avoiding inconveniences. I feel like this mindset draws a lot of people to AI.)

Second, there is a very thin line between “helping” you learn the material and just doing the work for you. The temptation to cut corners is always there, and when you have the Corner Cutting Machine at your disposal, you are kidding yourself if you think you will have perfect discipline. Tools influence behavior.

Glad to see that OpenAI has not changed in their scummy ways. Despite all that has changed in the meantime, they have kept their time-honored tradition of passing off other people’s work as their own.

One of OpenAI’s math announcements a month ago claimed that their results cost only $2000 worth of tokens, which frustrated me because they were likely sweeping away many inconvenient details and almost certainly misrepresenting their true costs. But people took this as a gotcha. This is the same bullshit as the water usage arguments. We are literally seeing city council members signing motherfucking NDAs about this, and you think that water usage numbers provided by the tech companies themselves are going to sway me?

I am also questioning OpenAI’s strategy of strip-mining math for PR, since it seems like advances in math do not actually register that well in the public. From what I remember, the Hugging Face incident got a lot more press than any of the math results.

Is this the post you’re referring to? https://davidbessis.substack.com/p/the-fall-of-the-theorem-economy

https://www.math.brown.edu/reschwar/Stories/AI.pdf