I think it's quite plausible that Anthropic is bleeding out ~100/month on token costs per $20/month user, and even at 80% margin, this is just merely breakeven. Their limited capacity also means that they are _losing_ the opportunity to sell the same capacity at a per-token marginal profit. I think the only plausible endgame here is that Anthropic uses the usage data to RL-finetune Claude Code to the point where it is actually worth a $200/month subscription.
Enjoy the $20/month Claude Pro plan while it lasts; I don't really see it sticking around for more than a year at best.
> By default, Anthropic does not train generative models using code or prompts that are sent to Claude Code.
> We aim to be fully transparent about how we use your data. We may use feedback to improve our products and services, but we will not train generative models using your feedback from Claude Code.
[...]
> If you choose to send us feedback about Claude Code, such as transcripts of your usage, Anthropic may use that feedback to debug related issues and improve Claude Code’s functionality (e.g., to reduce the risk of similar bugs occurring in the future). We will not train generative models using this feedback. Given their potentially sensitive nature, we store user feedback transcripts for only 30 days.
For understanding what value they place on that data, they do have a program where you can opt-in to have your data be used for training[1] in exchange for a discount on the API rates.
[0] https://docs.anthropic.com/en/docs/claude-code/data-usage
[1] https://support.anthropic.com/en/articles/11174108-about-the...
Here's one way they could get around their own privacy policy: keep track of what % of Claude-generated code is retained in the codebase over time (as an indicator of how high-quality / bug-free the code was); A/B test variations of Claude Code to see which variations have higher retention percentages.
No usage data is retained, no code is retained, no data is used (other than a single floating point number) and yet they get to improve their product atop your usage patterns.
Here's another idea: use a summarization model to transform your session transcript into a set of bits saying "user was satisfied/dissatisfied with this conversation", "user indicated that claude was doing something dangerous", "user indicated that claude was doing something overly complicated / too simple", "user interrupted claude", "user indicated claude should remember something in CLAUDE.md", etc. etc. and then train on these auxiliary signals, without ever seeing the original code or usage data.
I am very confused why this means Anthropic is bleeding out. The most important thing is that Anthropic has a thing people love. And can raise the price just fine.
Their real fear should be that Google has a signficant cost edge in AI inference and will be able to offer Gemini CLI at a much cheaper rate than they will even if Gemini CLI isn’t quite as good.
In any case: author does not factor in Anthropic's potential gross margins on the tokens via the API. He assumes if $10,000 is consumed via the Claude Code API tokens, then Anthropic must be losing $10,000.
We don't know. What's their cost of inference?
Ed acknowledges that we don't know their inference costs in the article. But unless they made a Deepseek level of breakthrough in serving through API, they are either breaking even or losing money at each call.
There is a race to the bottom and survival of the deepest pockets right now in the field. And this "subsidy" funded by investors will not last.
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That’s what they said about Uber, too, but it’s still around.
Well, they are around because they increased the prices for customers, reduced the pay for gig-workers and added ads.
Comparing Uber to Anthropic is not correct, because their cost models are not the same. Uber has mainly labor cost which is low-skill and high-volume, followed by service costs which are high-skill but low-volume. Which leaves a lot of room for optimization.
Anthropic has very big bills to pay, and those bills will only go up as they scale. In depth analysis of frontier model companies is difficult, since they are private and secretive.
It is "unpopular" to say this, especially this bluntly, but low-skill labor can be made as cheap as you want it to be. If my numbers aren't wrong, average Uber/Lyft worker works for less than hourly local minimum wage (don't say tips, ~80% of Uber customers don't tip). But they accept it because of lack of opportunity, flexibility of gig jobs, and potential for working many jobs.
There's absolutely a floor at which point drivers will revolt, especially since they know how much the rider is paying.
There is also a fundamental floor how cheap compute can be: infrastructure costs, hardware depreciation, maintenance. Realistically, in next 5 years, we will not reach negligible compute costs. You can ask crypto-currency community about the limits of compute costs.
So, I am not sure it is as bad as the article says. And there is a huge advantage to capturing developer mindshare worth burning millions on.
And isn't Anthropic backed by Amazon?
But I don’t blame the foundation model platforms for giving users a taste and then rolling back the features / quantity of tokens per $ after giving them a taste. They needed to get started somewhere and they were able to offer an amazing product for an affordable price.
It’s not until after the heaviest users start to abuse the subscription tier they are on that the platform understands how their product scales.
He warned a while ago that a $20/month sub was not going to be sustainable, and now what do we have? $100, $200, and $300 dollar tier plans. And now he’s warning even this isn’t going to be enough.
It might seem like breathless hyperbole to you or me, but I think he’s giving a pretty fair warning in advance to anyone who is hitching their wagon to these services in a big way and might get caught out badly if they aren’t lucky
Who knows how things will work out, but there might be a scenario where Microsoft gets 2% more of OpenAI and controls the whole thing.