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...
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.
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.
>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.
> 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?
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.
> 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.
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
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.