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Nice read, but I'd add an objection here: even if models don't improve any more, and they raise the standard subscription to 100$/month, I'd still buy it (and a lot of other people, I guess) because I'd extract far more value from it.
Does that get them to the TAM they need to justify current valuations though? I'd guess not.
> At this point, it's becoming obvious that it is not profitable to provide model inference, despite Sam Altman recently saying that OpenAI was. Except the authors own provided data says it cost them $2B in inference costs to generate $4B in revenue. Yes training costs push it negative, but this is like tech growth 101, debt now to grow faster leads to larger potential upsides in the future.
But training will have to stay forever, right? Otherwise the LLM will be stuck with outdated information...
The people writing all of these "AI is unprofitable" pieces are doing financial journalism similar to analyzing the dot-com bubble by looking at pets.com's burn rate. The infra overspend was real as well as the bankruptcies, but it existentially foolish for a business to ignore the behavioral shift that was taking place.
I have to constant remind myself to stop arguing and evangelizing about AI. There is a growing crowd who insists that AI is a waste of money and that AI cannot do things I'm already doing on a daily basis.
Every minute spent explaining to AI skeptics is a minute not spent actually capitalizing on the asymmetry. They don't want to hear it anyway and I have little incentive to convince anyone otherwise.
The companies bleeding money to serve AI below cost prices won't last, but thats all more the reason use them now while they're cheap.
The cost can be significantly reduced immediately and drastically if OpenAI or Anthropic were to choose to do so. By simply stopping the training of new models, profitability can be achieved on the same day. With the existing models, we have already substantial use cases, and there are numerous unexplored improvements beyond the LLM, tailored specifically to the use case.
But then they stop being up-to-date with... the world, right?
> total revenue: $4B > compute for training models: -$3B > compute for running models: -$2B > employee salaries: -$700M Though not really representative of what users of said models may experience financially, at this point the question should be raised: if AI compute is 7x more expensive than developer salaries, what's the point? I thought the whole idea was to save money on human resources...
Someday (probably), a model will be trained once that is better than a human at coding, and it will only need trained once. It can then be used indefinitely for the cost of inference, which is cheap and will continue getting cheaper.
Nice read, but I'd add an objection here: even if models don't improve any more, and they raise the standard subscription to 100$/month, I'd still buy it (and a lot of other people, I guess) because I'd extract far more value from it.
That's also what I do not get. The companies are unprofitable because of competition, not because what they do cannot be profitable.
The big labs have 50+% margins on serving the models, the training is where they lose money. But every new model boosts OpenAI's revenue growth which is unheard of at their size (300+% YoY). Therefore it's completely reasonable to keep doubling down and making bigger bets. Most people miss that they have almost a billion free users that are waiting to be monetized. Google makes 400B a year and it's crazy to think Ope…
> In fact, even if you remove the cost of training models from OpenAI's 2024 revenues (provided by The Information), OpenAI would still have lost $2.2 billion fucking dollars.
"Please don't waste your breath saying "costs will come down." They haven't been, and they're not going to." Yes, every new technology has always stayed exorbitantly priced in perpetuity.
The first mobile phone, the Motorola DynaTAC 8000X, was launched in 1984 for $3,995 (more than $12k in 2025 dollars). So we should expect a 12x cost reduction in LLMs over 40 years.
> Please don't waste your breath saying "costs will come down." They haven't been, and they're not going to. Cost to run a million tokens through GPT-3 Da-Vinci in 2022: $60 Cost to run a million tokens through GPT-5 today: $1.25
An unknown to me: are the less powerful models cheaper to serve, proportional to how much less capable they are than frontier models? One possible explanation for why e.g. OpenAI was eager to retire GPT 4 is that those older models are still money losers.
Cursor burning cash to subsidize Anthropic's losses to subsidize Amazon's compute investments is their problem, not mine. The people writing all of these "AI is unprofitable" pieces are doing financial journalism similar to analyzing the dot-com bubble by looking at pets.com's burn rate. The infra overspend was real as well as the bankruptcies, but it existentially foolish for a business to ignore the behavioral shif…
Obviously the goal of these companies is to generate as much profit as possible as soon as possible. They will turn the tables eventually. The asymmetry will go in the opposite direction, maybe to the extend that one takes advantage of the current asymmetry.