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Comment by burnerRhodov3 | Hacker News Reader

The internal metric that they are using is they get $2 for every $1 in inference. So, if you discount model development costs, they are selling the tokens for more than it costs them to generate a token. And this is before they start integrating their advertisment networks into the model. They see something as high as $8-11 per $1 of inference once purchasing and advertisments are incorporated.

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The “but ads” logic fails basic sense checks. There isn’t enough ad money to go around to make up the gaps. With the big ad players like Google, Meta, and Amazon all shutting Anthropic out of their ecosystem there’s very little evidence that Anthropic can steal away enough of that as revenue to save itself.

In the “but they’ll slow down training and so will become more profitable” thinking is in big trouble given the pace of open models. They’ll then be even less competitive with free options and so even less reason to buy from Anthropic.

There's not enough ad money? What? lol. there will always be ad money as long as a click costs below my profit. Every single one of those companies you listed was excluded from the Ad system of the ones before it. They built their own.

> So, if you discount model development costs, they are selling the tokens for more than it costs them to generate a token.

If you discount my expenses I am actually swimming in cash too.

It is absolutely moronic to discount the value of developing the thing from the profitability of the thing. Models to depreciate with time.

For sure, models definitely depreciate but so does CapEx. but you don't include CapEx, you have an amortization schedule over it's useful lifetime. However, when you start mixing CapEx and model depreciation, you get an interesting curve. As CapEx increases, model development is spread across multiple generations across that same CapEx. So, assuming you've amortized your CapEx, successive model generation eventually has a horizontal assimtote at OpEx and depreciation.

The real question is "Are frontier training costs growing faster than the productive lifetime value of the GPU infrastructure used to create those models?" Which is a hard question to answer and is something we'll find out it early 2030.

Also, Anthropic is known to lie through their teeth to make themselves look better, so any claims that they make a profit on inference should be treated as false until proven otherwise.

Any “non-GAAP” measure of “profit” should generally be viewed with a giant red flag.

In nearly all cases these metrics just say “if you ignore all the reasons why we’re horribly unprofitable, we’re profitable.” WeWork’s “community adjusted EBITDA” was a textbook example of this.

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