this post was submitted on 30 Aug 2026
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(Credit and/or blame to David Gerard for starting this.)

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[–] rook@awful.systems 9 points 23 hours ago (2 children)

Terence Tao observes that an llm may solve a complex mathematical problem without usefully improving the state of mathematics, because the intermediate results of the process themselves can have significant value.

https://mathstodon.xyz/@tao/117207849921390904

But such an incomprehensible proof would not be the primary value of the exercise. The process of starting with one ansatz, discovering the precise obstruction preventing it from working, adjusting the ansatz to (partially) eliminate that obstruction, and then iterating, would almost certainly reveal important new insights about fluid mechanics that would not have been feasible to obtain by other means. Crucially, this iteration would only work well at producing such insights if the iterator did not have access to the final ansatz in advance, as this naturally inhibits the exploration of alternate routes to the ansatz that are superficially "dead ends", but in fact end up being highly instructive in the nature of their failure.

But there is now a scenario in which an autonomous AI harness, backed by an enormous amount of computational resources, performs this entire iteration internally, and ends up producing the final ansatz, and thence the solution to the Navier-Stokes regularity problem, while the AI company running the harness keeps the process to arrive at that ansatz almost completely out of public view. Technically, one of the most prominent open problems in mathematics would now be solved; but there would be almost no value added to mathematics as a consequence. It is theoretically possible that with some herculean (and heavily AI-assisted) additional effort by a third party, some portion of the process could be reverse-engineered to recover some actual insight and understanding from the solution; but this would be a far less efficient process than if the solution had been obtained via a diverse combination of both human mathematicians and machine assistance as mentioned above.

https://mathstodon.xyz/@tao/117207855800042681

He’s not the first to have pointed this out, but I think this was a better summary than similar posts that I’ve come across and at least partially understood.

[–] blakestacey@awful.systems 8 points 20 hours ago (1 children)

We don't need more of mathematics to look like the Four Color Theorem ("proved", but with no insight gained), with the added problems that the "proofs" are now far more laborious to check even for technical correctness, theorem-proving software is less reliable, and oh yeah, we're holding the population of Memphis to the exhaust pipe of a diesel bus to make it work.

[–] rook@awful.systems 4 points 7 hours ago

There was blogpost by a different author (that I’ve since lost 😞) who observed that big academic and cultural changes are going to have to come to the field of mathematics, because merit used to be measured by producing proofs (loosely speaking) but now if the proof-extruder can do it for you over the weekend, you need to demonstrate your mathematical chops some other way, and I don’t think anyone really knows what that looks like yet.

Another triumph for moving fast and breaking things, I guess.

[–] sc_griffith@awful.systems 10 points 23 hours ago* (last edited 23 hours ago) (2 children)

terry tao grimly climbing into the hotdog suit

[–] fnix@awful.systems 3 points 10 hours ago (2 children)

Was he promoting LLMs earlier on?

[–] sc_griffith@awful.systems 5 points 4 hours ago* (last edited 4 hours ago)

he's probably done more work promoting llms in math than anyone else has, and probably by a lot

[–] gerikson@awful.systems 4 points 5 hours ago

yeah basically

[–] gerikson@awful.systems 6 points 21 hours ago

Monkey paw curls