Dario Amodei from Anthropic has made the claim that if you looked at each model as a separate business, it would be profitable [1], i.e. each model brings in more revenue over its lifetime than the total of training + inference costs. It's only because you're simultaneously training the next generation of models, which are larger and more expensive to train, but aren't generating revenue yet, that the company as a whole loses money in a given year. Now, it's not like he opened up Anthropic's books for an audit, so you don't necessarily have to trust him. But you do need to believe that either (a) what he is saying is roughly true or (b) he is making the sort of fraudulent statements that could get you sent to prison. [1] [this video]
Amodei's claim that each model is profitable on its own
Inference costs scale linearly with usage. R&D expenses do not. That's not to mention that Dario Amodei has said that their models actually have a good return, even when accounting for training costs [0]. [0] [this video]
Dario Amodei has said that their models actually have a good return, even when accounting for training costs [0]. They lose money because of R&D, training the next bigger models, and I assume also investment in other areas like data centers. Sam Altman has made similar statements, and Chinese companies also often serve their models very cheaply. All of this makes me believe them when they say they are profitable on API usage. Usage on the plans is a bit more unknown. [0] [this video]
if you're referring to [this video] he doesn't actually say that each model has been profitable. He says "You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish cartoon example that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model is actually, in this example is actually profitable. What's going on is that at the same time" notice those are hypothetical numbers and he just asks you to assume that inference is (sufficiently) profitable. He doesn't actually say they made money by the EoL of some model.
[this video] is on such but he doesn't actually say that each model has been profitable. He says "You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish cartoon example that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model is actually, in this example is actually profitable. What's going on is that at the same time" importantly you'll notice that he's talking revenue, and assumes that inference is cheap enough/profitable enough that 100M + Inferance_Over_Lifetime < 200M
Video list and Hacker News comment labels from HackerTube (prompted by @karpathy), released under ODC-By 1.0, built on the open-index/hacker-news archive. Hacker News data is provided by Y Combinator; comments remain the work of their authors. Video titles, channel names and thumbnails belong to their creators and are shown via YouTube. HackerTube Watch is an unofficial edition. Data export of 2026-10-31.
Plays via YouTube's privacy-enhanced embed
5 Hacker News comments
From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.
Read more
Read more
Read more