The idea of removing model training from your costs is a little wild tbh.
The profitability of being able to serve a query wasn't really under question (nor is the margin expected to be anything less than 80%+) I think.
Yeah, I didn't believe they'd claim something like that. But yes indeed, from the article:
> Anthropic's gross margins are above 80% before accounting for revenue shared with distribution partners, including Amazon (AMZN.O), opens new tab, and the cost of training its model,
Is this how all AI companies calculate if they're profitable or not, by removing the highest costs? What a circus.
Note this claim is about „operating profit“, which commonly is the revenue - operating expenses (COGS, rent, payroll). This does not include RnD cost.
>Anthropic's gross margins are above 80% before accounting for revenue shared with distribution partners, including Amazon (AMZN.O), and the cost of training its model, the newspaper said.
Gross margin is typically (revenue - COGS) / revenue. Thus, both statements above seem generally in line with commonly accepted accounting standards.
It’s also easier to strip it out of the picture to think about how much it costs to serve the next token. If you can have great economics to serve the next token (profitable) you can always figure out ways to further reduce your R&D costs.
Now they are absolutely intertwined but I don’t think this is ever as big of an issue that people make it out to be. Replace token with any widget, this is how businesses measure themselves.
1) gross margins are 80% w/o revenue sharing.
2) gross margins are positive w/ revenue sharing.
So that means Anthropic is making money on every token, and customers are willing to pay 80% margins (some of which might go to e.g. Bedrock to serve the model).
The real circus is commenters on HackerNews thinking this number means Anthropic is cooking the books or that it's actually profitable. Gross margin is meaningless for an AI company, since most of their expenses are R&D and infrastructure (the two things excluded from gross margin), but they have to report it anyway.
HN had long debates about whether AI inference could even be affordable from a compute perspective.
In one sense, yes, but I do see people question it regularly.
It is certainly not above them to play accounting tricks to pretend to be anywhere near profitable.
If you create a machine that can turn a dollar into 5, you don't dillute ownership of the machine, you use your fabulous profits to expand production. Anthropic, on the other hand, raises money like crazy, and seems desperate to IPO.
It's one of several metrics and tries to estimate steady-state profitability. It's the only one being leaked because it's the most sensational one. But don't assume cash-flow profitability is negative just because you don't know it.
Which unfortunately probably hides the real truth. That large labs do have potential problems with long term profitability.
And either way, the training of new base models will eventually slow from the current frantic pace.
But people are saying its not part of the calculation to generate the cost of a query...
>And either way, the training of new base models will eventually slow from the current frantic pace.
Sure, maybe.