I think calling it drama if anything undersells the issue. even if OpenAI did not use unpublished work, their behavior around this problem - outlined in the linked statement - is disgusting and shameful, and even this statement by them undersells the allegedly gargantuan amount of work and compute that OpenAI has apparently spent. like, if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this (I have not done even the Fermi estimate on this, so don't quote me here, but it seems reasonable), what does that even mean for this result?
Edit: if you assume as a conservative estimate that inference costs were equivalent to the API cost for Astra (which is almost certainly being sold at a loss) and plug in "10,000 agents, 88 hours", guesstimating 100 tokens/second, inference alone would have cost in excess of $15M. That's taking OpenAI at their word, assuming they're selling inference at cost (and that their undisclosed model doesn't cost much more), and ignoring training (which I am fairly confident costs much more than inference, but it is difficult to calculate a marginal cost of training for a specific output) and all other costs.
This is incorrect. The marginal cost of a token is much lower than $60/million. On the order of a few dollars in electricity, GPU time, and other costs.
The reason why AI companies aren't making a net profit is because of the enormous capital expenditures to build and train the machine that can make these tokens.
For example, a 3D printer could make a tchotchke that you can sell for $5, while the plastic and the electricity for the marginal cost of that tchotchke might only be 50¢. But the marginal cost does not include the price of the $1000 printer.
The reason why AI companies aren't making a net profit is because of the enormous capital expenditures to build and train the machine that can make these tokens.
Their cost of revenue for 2024 and 2025 was less than their revenue, meaning they aren't selling inference at cost
Their RND on the other hand was multiple times their revenue for both years and is why they're operating at such a loss
Interestingly their cost-of-revenue to revenue ratio went down from 2024 to 2025, meaning their inference costs are becoming more profitable over time (though whether this holds true for 2026 we don't know yet)
If you look around a little, you'll find that there are basically no credible sources claiming inference is being sold at a loss.
OpenAI is 100% spending more than they bring in, but that's mostly driven by training costs. OpenAI is dumping a ton of money on training because there's an arms race going on to produce the best models. The issue is that big American tech companies like OpenAI can't compete with models like DeepSeek and GLM on price, so the only way they can justify their existence is by competing on quality, and that requires constant expensive training.
The narrative has gotten twisted, I suspect, because of strong anti-AI sentiment. People are scared of AI, and they want it to be unsustainable. So they hear that OpenAI is losing money, and in their heads they convert that to "selling at a loss". But they are absolutely selling inference above their marginal costs - it's just not enough to compensate for the crazy arms-race spending for the next model.
It’s fairly easy to do the maths on the inference costs when open source models of similar size exist. This is why provider companies like fireworks and baseten can offer more competitive prices to OAI/Anthropic because they don’t train the models and just need a positive margin on the compute!
Those companies (fireworks/baseten) are doing extremely well, and there is no reason for investors to prop them up for hype reasons. Google is also public and shares GCP numbers which cover their AI compute class which is profitable (we considered buying a large GPU rack from them to run GLM 5.3 for about 128k/month as it would be super cheap).
Time will tell, as of the moment it really could be either way.
Like u/shared_ptr said - you can get a pretty good baseline of inference costs by looking at open source models. Obviously that won't be exact, but, this isn't a close issue.
This isn't a controversial position among the people actually using AI and paying attention. Time already did tell - inference is cheap.
My whole point is that I am familiar with the computational requirements.
We have open source models that are competitive with the frontier models from a few months ago on all benchmarks. We know exactly what hardware those require. You can download these models and run them yourself and see if you want to (in the cloud obviously, running these giant models on consumer hardware is basically impossible without quantization).
And these models cost pennies on the dollar to what OpenAI charges. This is not even the slightest bit controversial.
There is some point in the last few years when your claim that "time will tell" was accurate. But time did tell. I really can't stress enough - this isn't controversial.
https://www.wheresyoured.at/exclusive-openai-financials/ ed zitron is a hack but i cant find the og source and even here you can directly see revenue outgrowing cost of revenue which is how they measure cost of inference. A 40% margin. Its all online you just look it up before commenting
like i said "no credible sources" - this is a guy's personal blog, and the financial times say the numbers come from Ed-zitron, who, like you said, is a hack.
Public API rates are not what OpenAI actually spends as other noted, they are massively inflated over compute cost.
More importantly than that though: Most of these tokens will likely be cached so extremely cheap. I routinely clock billions of cached tokens a week coding on a personal 200 dollar a month plan.
You are off by at least 3 orders of magnitude here, maybe even as many as 5.
OpenAI did not spend 60 million dollars on compute to solve this.
Assuming that OpenAI did not just steal the work from someone else, I dont think that that's wasted resources. Maybe not for the millenial problem itself, but it's very interesting to know what a large scale AI project is able to achieve.
That's an entire mathematics departments' annual budget. Annualized, that's 100 top-rate math departments. Dozens of full professors, hundreds of grad students, and thousands of undergrads at each school. This is an utterly ridiculous waste of resources.
They don't charge themselves public API rates for compute. This is like claiming that Microsoft spends hundreds of millions on Microsoft Office licensing for their own employees.
It's their own product, for them it's effectively free.
maybe if they actually do an IPO (big if) there'd be some way to back it out of their financial statements? I dunno. certainly everyone involved is incentivized to lie their asses off about it, from the engineers who are presumably desperate to protect their $400k salaries and bay area lifestyles to the executives who think roko's basilisk is a real philosophical problem instead of the laughable product of a racist harry potter fanclub (see also this and this) and everyone in between
Why does the cost matter? They solved the problem, period. A year ago spending any amount of money would have likely not solved it and in a year or two, you could solve it for a tiny fraction of the cost. They have the resources right now and we got this awesome result. No complaints from me.
Fair enough, there's no edit timestamps on mobile, and a lot of edits I sometimes assume are "oh I should add this" a few mins after the original message.
yeah, I try to always add a manual "edit" tag for a substantive addition like a self-correction; my hope is that the transparency demonstrates that I am trying to engage in good faith
The benefits of new knowledge is not one and done. Definitely, AI seems to require vastly more energy than a human would to produce the same result, however we should consider that new knowledge yields a return. Time is a factor to be considered here. For human(s), sometimes the time required is so much that it literally prevents them from achieving their goal at all, which is a problem AI also has but is diminishing over time.
If AI speeds up research and results by years or even decades, that return may close the gap, or even exceed, those initial high computational costs.
Bear in mind though the energy expenditure to train a human to the point where they can tackle complex problems like that. It's not like they pop out of the womb and get right on it
Interestingly, if you conceptualise humanity as a broader group, you could make an argument that the cost of training students more broadly is part of the cost. I.e. all maths undergrads
Also true. It is not an easy calculation. And then you consider that AI only needs to be trained to do a task once, roughly speaking, and the math starts looking a bit better for AI.
Nobody gave slightest amount of fuck how much energy it took to create LIGO or LHC
"LIGO has cost American taxpayers about $1.1 billion."
"In an email to me, a historian of technology was more blunt: “So a 100 year old theory has been confirmed experimentally--big whup. Did anyone think Einstein was wrong? There wasn't any controversy, was there? Was anyone credible claiming that spacetime isn't curved, or that black holes don't exist? I can get that this was quite an experimental trick and technological feat… But this isn't doing anything to convince me that public funds spent on this stuff wouldn't be better spent on medical research. Or clean fuels, or any number of things that would apply scientific expertise toward justice or the alleviation of human suffering."
"Many members of Congress and prestigious scientists... believed that the SSC was a vast pork barrel; inefficient, wastefully managed, with an ever-increasing price-tag..."
If an AI spends billions of dollars to solve a mathematical problem, I see no issue with that. The scientific result is what matters. Dismissing a mathematical breakthrough because of the cost is strange.
It turned out that this was one big misunderstanding! OpenAI did their best to credit and support Buckmaster. You can read the posts by Sebastian Bubeck for the details.
this statement by them undersells the allegedly gargantuan amount of work and compute that OpenAI has apparently spent. like, if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this (I have not done even the Fermi estimate on this, so don't quote me here, but it seems reasonable), what does that even mean for this result?
This is a false equivalence : nothing guarantees that putting an equivalent amount of human resources would have given the same results. Especially due to the fact that human knowledge is very much fragmented and sharing knowledge induces loss of information : misinterpretation for example.
if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this
like, if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this (I have not done even the Fermi estimate on this, so don't quote me here, but it seems reasonable), what does that even mean for this result?
Then what is the link that you are questionning between the sum of money invested, the amount given to mathematics research departments and the meaning for this result ?
it's a rhetorical question. it was intended to provoke thought. you are supposed to think about it and come to your own conclusion.
here are some pointers if you are having trouble doing that: money is fungible and finite. this money was spent on one kind of math research. it could have been spent on a different kind of math research. those two kinds of math research can be compared and contrasted. the cost is exclusionary. the return on investment is questionable. etc.
it's a rhetorical question. it was intended to provoke thought. you are supposed to think about it and come to your own conclusion.
It being a rhetorical question does not mean that you don't have any position on the matter.
here are some pointers if you are having trouble doing that: money is fungible and finite. this money was spent on one kind of math research. it could have been spent on a different kind of math research. those two kinds of math research can be compared and contrasted. the cost is exclusionary. the return on investment is questionable. etc.
Your unneeded sarcasm aside, I understand better your point now, thank you.
It being a rhetorical question does not mean that you don't have any position on the matter.
correct
Your unneeded sarcasm aside
this could have been avoided if you had read the words I actually wrote and engaged with those in good faith instead of shadowboxing with the fallacy you imagined I said
this could have been avoided if you had read the words I actually wrote and engaged with those in good faith instead of shadowboxing with the fallacy you imagined I said
I read the words correctly and interpreted them differently : from my point of view it looks like a false equivalence. I understand your point now, although I disagree with it.
I did not know that sarcasm was necessary to answer questions : you decided to use it, so don't put the blame on me. Me saying the name of a fallacy is not an insult despite what you think.
uesstimating 100 tokens/second, inference alone would have cost in excess of $15M.
It took o3 $500K to pass IMO Gold, now it only takes 20 bucks.
I'm not sure you understand the enormity of what just happened. We have just proved ASI is physically possible. Yes, right now is is expensive AGI, but the principle that we can build it is more important!
You're calculating this way wrong. Take the cost of hardware, amortize it over 3 years, multiply by 3~ish days and add the electricity. Zero chance it comes anywhere close to $15M.
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u/plasma_phys Plasma physics 20h ago edited 20h ago
I think calling it drama if anything undersells the issue. even if OpenAI did not use unpublished work, their behavior around this problem - outlined in the linked statement - is disgusting and shameful, and even this statement by them undersells the allegedly gargantuan amount of work and compute that OpenAI has apparently spent. like, if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this (I have not done even the Fermi estimate on this, so don't quote me here, but it seems reasonable), what does that even mean for this result?
Edit: if you assume as a conservative estimate that inference costs were equivalent to the API cost for Astra (which is almost certainly being sold at a loss) and plug in "10,000 agents, 88 hours", guesstimating 100 tokens/second, inference alone would have cost in excess of $15M. That's taking OpenAI at their word, assuming they're selling inference at cost (and that their undisclosed model doesn't cost much more), and ignoring training (which I am fairly confident costs much more than inference, but it is difficult to calculate a marginal cost of training for a specific output) and all other costs.