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Comment by sharmajai | Hacker News Reader

Capital allocation to advance mathematics and sciences is undoubtedly a good thing. But this does beget a few questions:

1. How much did it cost?

2. Would an equivalent allocation to incentivize human effort have borne the same results?

3. Post IPO, will Open AI continue allocating similar levels of capital to further these causes?

#2 is particularly important to answer, given research grants have been getting cut elsewhere: see U.C.L.A's research grant cuts from last year and Terrence Tao's attempt to raise funds (about $60k) by collaborating with Open AI earlier this year [1].

[1] https://www.nytimes.com/2026/10/16/us/math-ai-terence-tao.ht...

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As for #2, personally I feel like OpenAI's results are superhuman at this point. I don't think there's enough human talent alive right now to produce these sorts of results within a few years, no matter the budget.

There are lots of reasons I believe this. One is that frontier AIs are very clearly superhuman on shorter problems (as can be seen on benchmarks like FrontierMath, or just by playing with them yourself if you have a source for difficult problems)--so these achievements are "just" scaling that advantage to research-sized problems. Second, AI is also clearly superhuman in its knowledge across fields, so it can make connections and combine techniques that humans won't be able to access. Third, these problems are famous and have withstood efforts of many of the best humans, in many cases being considered unapproachable.

Altogether it seems like AI is not only smarter but also more cost efficient that humans for math-adjacemt problem solving. I'm not saying that AI is superior at everything in math, eg theory building, but I think it's really unlikely you could get any group of humans that could compete with OpenAI's latest results.

They first do have to show results that are actually useful to any human, though.

Right now, they just created busy work for real mathematicians while snatching away their feeling of accomplishment and discovery.

The only "use" for these results people can claim currently, all rest on the idea that surely better results will follow. Which is why it's being called strip-mining, because you can only keep that up for so long.

What if they just start churning out Lean proofs forever, and the only job left for mathematicians is turning them into human readable proofs, this is definitely a net negative for humanity, because then true progress of mathematics will slow down, because there are more people that like doing mathematics for discovery and accomplishment, than there are people that like doing math homework for the sake of it.

Also I would prefer a world where humans get to have this sense of discovery and accomplishment, instead of what they are trying to turn mathematics into.

Coding used to be a super creative and rewarding thing for me, and now I do 0 of it. Does this mean I produce less code, or hate my job because I can no longer "do the problem solving" that gave me so much joy? No -- it's the opposite on both accounts and I can actually tackle much larger problems now.

If anything, proof abundance should speed up discovery, and mathematics will be fine. What is happening now is just a massive shock to the system so understandably people are upset.

(It's also understandable why AI companies are strip mining open maths problems. "Solve P=NP" is crazy hard, but still a simple prompt, with a verifiable solution. You don't get that this often outside of maths.)

`Superhuman` does not imply subservience or beneficence to humanity, it's just a nice to have for us if it pans out that way.

I think, AI is not waiting for humans to understand new science. While humans are busy understanding the new proofs, AI can already use the new findings to create 10 more, and based on those 10 more, some agents will create 100 more. Until the gap between human understanding and AI understanding is so large that unless we are using some research agents, we will not be able to contribute meaningfully on the scale we do it now. Agents will build some research database where they can built upon another, and our traditional peer review system with humans in the loop will be partially replaced, because requiering human understanding would slow down progress.

Well long story short, the tech bros are right and we will have super intelligence some time soon!

It depends on the definition of superhuman. For example my car is superhuman as it allows me to get in one hour to a place where it would take me ten hours to go by walking, and can I walk ten hours non stop? However it is not superhuman enough to carry me where I could not walk too. And there are places where I can walk to but I cannot reach by car. Maybe that applies to AIs as well. A plane is in the same category of superhumanity of cars, with more speed. But I could still get to Australia by walking and rowing with my hands on a log (and probably die, I know.)

However there are truly superhuman vehicles, spaceships and submarines, that can carry humans where no human could go alive.

I don't know if these AIs are superhuman in the way of planes or in the way of spaceships. I also don't know if they can do everything we can do by talking or if they have some limits.

I mean, a plane can fly but not reach a branch and stay still on it. A small drone, almost. My coding agents sometimes can't do the obvious but is it because of some intrinsic limitation or because my instructions are bad? So far my experience is that they are superhuman like cars or trains, not yet as planes.

Man I would love the superhuman math AI when I was at uni, there were some problems I couldn't even grasp and this always bothered me.

Disproving big conjectures can be useful to mathematicans because it means they don’t have to waste time proving results that assume that conjecture.

Sometimes the very attempt to disprove a conjecture will produce new insights along the way. To get comparable value when a machine simply spits a counterexample, you'd need to at least mine the intermediate reasoning traces to see its false starts.

>1. How much did it cost?

>2. Would an equivalent allocation to incentivize human effort have borne the same results?

Unlike their initial massive brute-force run that solved the Navier–Stokes Millennium Prize Problem, most of their subsequent breakthrough results in mathematics have come from a single prompt to a single AI agent, using approximately 3 hours of ChatGPT Pro-equivalent thinking compute per result on average.

> solved the Navier–Stokes Millennium Prize Problem

as far as I know they still haven't claimed the prize, but rather stopped just short of it

they produced a Lean certificate for a math problem, but it's not "solving a Millennium Prize Problem" until they get a human readable version approved and published in a Math journal.

that's the rules for the Milennium Prize Problems.

I would personally sooner say they ruined the Navier-Stokes Millennium Prize Problem, because the last part is important. Nobody in fluid dynamics research has any use for the result specifically because the breakdown singularity happens at a scale smaller than molecules, and they were dealing with that already. The Millennium Prize Problems exist to encourage mathematics, not to pull the rug on it.

They will never claim the prize for any Millennium Prize problems because the blowback for asking for the prize would be 10x worse than anything they've received about the proof efforts so far. This leads to the joke about the Clay Institute continuing to be on track to never having to pay out the prize money (after the previous drama of the first problem fully solved by humans resulting in the same).

As a result, I also doubt they'll ever bother to submit these to a journal when they already have the attention and ongoing review of leading mathematicians from around the world.

Perhaps the most salient part of the Millenium puzzles though is the 2 year acceptance period, where we give a time for that review and consensus (the part that matters, not journals or prize claims) to settle before claiming it solved.

> most of their subsequent breakthrough results in mathematics have come from a single prompt to a single AI agent, using approximately 3 hours of ChatGPT Pro-equivalent thinking compute per result on average.

but that would mean anyone could do it, right?

then why are these results coming from OpenAI all the time?

are you saying they're just better at prompting?

Results come from an internal model that has not been publicly released. And there are lesser problems and conjectures ordinary folks have solved with Astra/Sol.

There have been a lot of ppl finding some new things with current frontier models already... I think all it took to find the PHP bug was one 1 line prompt.. I guess ppl just don't try it that much..

How would a single agent work that long, the context window would overflow pretty quickly, no?

Find me a Grand Unified Theory, make no mistakes

1. some bad hack for the switch2 took 2 weeks of non stop work for an AI, so most certainly this was expensive.

2. most probably no, the AI doesnt rest and its very very fast.

3. also no, they are doing this for aura farming which then will result in more money for them, the money will dry as soon as this stops working.

The problem with point 2, is that person couldn't then work on other things. And there's a limited supply of people of that capability in the world.

We're now at stage where we can turn energy into thought to generate expertise on demand.

I think you are interpreting 2 as offering some large amount of money as a prize for discovering a solution. But I think it was meant more as funding a team of mathematicians to work on the problem.

Yes, that's what I meant, thanks for clarifying!

If you want to draw from history there are multiple successful examples of this in science and engineering at least: Bletchley Park, the Manhattan Project, Moon Landings.

Making the mRNA Covid Vaccinnes.It's truly incredible what the world can do, when it decides to focus on a singular goal.

I suppose the point I was making, was that the Engineers and Physicists and others, while working on the Manhattan project couldn't be doing other things.

And to develop those people takes years of education and training, and most people don't have the cognitive ability to do that kind of work. The pipeline to produce more of those workers, is skinny indeed.

Whereas now, we're limited by how many electrons we can put to work. The question then becomes, "Could our electricity be put to better use?"

> 3. Post IPO, will Open AI continue allocating similar levels of capital to further these causes?

I think the scarier alternative may be OpenAI forever gating their super-frontier models, capturing a significant portion of the sciences and leveraging it for equity in businesses that build on scientific advances. e.g. "Hey CATL, how about you guve us 5% equity in exchange for a battery chemistry breakthrough our AI has found?".

It's roughly equivalent to the playbook Palantir had for the public sector when they marketed themselves as a company that had cracked data science. At least that's what they've been pitching back then. I can't find a lot on public disclosed deals based on that.

> I think the scarier alternative may be OpenAI forever gating their super-frontier models, capturing a significant portion of the sciences and leveraging it for equity in businesses that build on scientific advances. e.g. "Hey CATL, how about you guve us 5% equity in exchange for a battery chemistry breakthrough our AI has found?".

surely if they were able to produce economically relevant breakthroughs, they wouldn't be burning compute on all of this

Presumably the more "real world" a breakthrough is the more integrated real world validation mechanism are required for an agentic loop to validate it's hypothesis (unless you have reliable mechanisms for simulation and throw even more compute at simulations).

At least that's roughly what the hypothesis of https://www.discoveryloop.com/ is.

1. it comes from VCs philanthropy, who cares?

2. it wouldn't happen so who cares?

3. no, but models will be better, it'll be cheaper to do

It's not a snark. The scale of VC philanthropy is unprecedented and it fuels itself. All of it will eventually, one way or another, become open. Long story short – there is nothing to complain about.

It's not philanthropy if you do it expecting a payout.

or it will fail spectacularly (also not sure philanthropy is the correct term)

success and failure of anthropic are both good for open weight models

Why is #2 important to answer?

Was the question of if horses and carriages are important a thing we had to answer? No, it just fell out of it. If human maths is pointless, what is the issue with defunding it?

if it's pointless then there is no issue with defunding it, but that is the question being asked.

if a similar investment on humans would have similar or superior results then human maths is not pointless and directing the investment to AI instead is arguably a loss.

the same question can be made for other areas AI is advancing. My AI budget is over 3 times my salary. If instead of providing AI tooling the company were to raise everyone's salary by 3x, what type of results would it get? Surely it would not get 10x the amount of PRs and LoC but maybe it would get superior value in other ways.

Humans are bound by communication overhead and non-market-based incentives. Adding 3x engineers does not do anything. On an average workplace, you have 80-90% of dead weight who shuffle pointless information around and play social games to keep their children fed.

Giving the 10% access to these tools is a crazy value multiplier, because they can run at lightning speed with their mental models and the right incentives for the company.

Just wanted to say that these questions would indeed be great to have answered, or at least discussed. It's weird how cultish some people get.

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