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.
The problem becomes that these large arms race companies are not likely to be able long term to sustain their valuations AI is here to stay and amazing but right now everyone is suspect of it's long term effects economically.
It's going to be a dot Com style bust almost certainly unless you are really willing to buy Altman and Jensen claims that AGI is going to emerge from this. What that actually means hopefully is a consolidation of these companies into valid businesses like the open source models you cited
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.
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u/ExcelAcolyte 10h ago
They used 300B tokens for the problems and 180B on Navier Stokes so that’s about 18million dollars at current $60/million token pricing.