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points by Rudybega 3 days ago | 0 comments

To give some context:

Kakeya / local smoothing (math): The result would resolve the Kakeya conjecture and Stein’s restriction conjecture across all dimensions together. Mathematicians had been making progress one dimension at a time, so this would settle a whole family of central harmonic analysis problems at once.

ETH is false (computer science): ETH has been used as a computational hardness premise in hundreds of papers, so a disproof would undercut a large body of existing complexity results. It would also change what researchers think is achievable for efficient computation.

QCD existence (physics): It would give a rigorous mathematical foundation to the theory of the strong force, which physicists have used for decades without proving it’s well-defined. It also bears on a Millennium Prize Problem.

[–] emil-lp 3 days ago

I would guess ETH has been assumed in thousands of papers. I have several myself.

On the positive side, suddenly there's a whole bunch of open problems!

[–] dekhn 3 days ago

Is there any obvious path from these results to any sort of physics, chemistry, or biology breakthrough? From what I can tell: no (but I lack the physics and chemistry expertise to say anything about the QCD finding).

Turning the question arouhnd: what would be the most impactful thing for OpenAI or Anthropic to do to show large breakthroughs?

[–] Kotlopou 3 days ago

From the physics, not without more breakthroughs, and even then I can't imagine what that would look like. The particle physics that you can use industrially has long been understood, and the field has long been long stuck because the only things we don't understand are also insanely expensive to experimentally access. Maybe once we move up the Kardashev scale, though.

ETH sounds more like there's a path it could become important? In general, the recent complexity results suggest that a) our intuitions about that world are unreliable, and b) the truth seems to be insanely complicated. I mean, new faster multiplication algorithms in 2026, what the hell? How can we say anything about the full space of all possible algorithms?

[–] dekhn 3 days ago

I wonder if the best statement we can make for future AI discoveries is that, ideally, they will allow us to move up the Kardashev scale faster.

The new faster multiplication algorithms are both galactic, are they not?

It does not seem surprising to me at all that AI can find new galactic algorithms that are not of any direct use (at best they can guide us to find better solutions that are non-galactic).

What would be surprising and interesting is if AI can find a new floating point multiply-add that is 2X faster per operation than the current fastest chip implementations. That would immediately have billion+ dollar implications. The teams who already work on this have excellent approaches and invest enormous amounts into tiny improvements already; there are probably not much low-hanging fruit there (proponents of using AI for chip design improvements have impressive claims, but unclear that any of those really worked out in reality).

[–] redox99 3 days ago

If we had really good understanding of physics, couldn't we find out how to make a room temperature superconductor (or prove it can't exist)?

Or is such understanding reliant in experimentation because there are "hidden constants" we can't figure out with math alone?

[–] teamonkey 3 days ago

We already do have a very good understanding of QCD, it’s a very successful model with 70 years of experimental results behind it. This proof may offer new insight but most likely will just be confirming what we already knew.

[–] dekhn 3 days ago

This is where I start to get curious.

Why haven't these theories had more impact on materials science? We use DFT and other greatly simplified approaches (with tremendous results) but still I suspect we are massively underperforming in terms of producing materials with useful properties.

[–] hershkumar 3 days ago

QCD simulations (like lattice QCD) are extremely expensive (exponential in the number of constituent quarks) and usually things like material properties require bulk physics, in large system sizes. The separation of scale between a theory like QCD and something like a simple metal makes it very difficult to practically do a first principles computation.

[–] stratos123 2 days ago

Anthropic already has a wet biolab for AI research and IIRC got some boring results out, so my guess is that non-boring results are only a few months away. I doubt they'd be related to the achievements in math, except in the sense that all AI-achieved results are related to each other because they all signify an improved capability for general research.

[–] demibabs 3 days ago

Solve p = np with an example algorithm maybe?

[–] dekhn 3 days ago

Huh? I'm not sure that makes any sense.

[–] jlev1 3 days ago

Thanks Claude!

[–] Rudybega 3 days ago

Glad to help (I only really understand the implications of ETH well enough to summarize myself without assistance haha). I figured if I wanted simplified context, others might too.

[–] perching_aix 3 days ago

was it the "bears on" that you caught? :)