> "just a few dollars per million tokens"

Google AI Pro is like $15/month for practically unlimited Pro requests, each of which take million tokens of context (and then also perform thinking, free Google search for grounding, inline image generation if needed). This includes Gemini CLI, Gemini Code Assist (VS Code), the main chatbot, and a bunch of other vibe-coding projects which have their own rate limits or no rate limits at all.

It's crazy to think this is sustainable. It'll be like Xbox Game Pass - start at £5/month to hook people in and before you know it it's £20/month and has nowhere near as many games.

I’m not familiar with the Claude Code subscription, but with Codex I’m able to use millions of tokens per day on the $200/mo plan. My rough estimate was that if I were API billing, it would cost about $50/day, or $1200/mo. So either the API has a 6x profit margin on inference, the subscription is a loss leader, or they just rely on most people not to go anywhere near the usage caps.

I wonder how many people have a subscription and don’t fully utilize it. That’s free money for them, too.

What walled garden man? There’s like four major API providers for Anthropic.

Except all those GPUs running inference need to be replaced every 2 years.

"They're not losing money on subscriptions, it's just their revenue is smaller than their costs". Weird take.

> They are not losing money on subscription plans. Inference is very cheap - just a few dollars per million tokens. What they’re trying to do is bundle R&D costs with inference so they can fund the training of the next generation of models.

You've described every R&D company ever.

"Synthesizing drugs is cheap - just a few dollars per million pills. They're trying to bundle pharmaceutical research costs... etc."

There's plenty of legit criticisms of this business model and Anthropic, but pointing out that R&D companies sink money into research and then charge more than the marginal cost for the final product, isn't one of them.

the path is by charging just a bit less than the salary of the engineers they are replacing.

My crude metaphor to explain to my family is gasoline has just been invented and we're all being lent Bentley's to get us addicted to driving everywhere. Eventually we won't be given free Bentley's, and someone is going to be holding the bag when the infinite money machine finally has a hiccup. The tech giants are hoping their gasoline is the one that we all crave when we're left depending on driving everywhere and the costs go soaring.

how do I understand what is the sustainable pricing?

From what I understand, they make money per-token billing. Not enough for how much it costs to train, not accounting for marketing, subscription services, and research for new models, but if they are used, they lose less money.

Finance 101 tldr explanation: The contribution margin (= price per token -variable cost per token ) this is positive

Profit (= contribution margin x cuantity- fix cost)

Does a GPU doing inference server enough customers for long enough to bring in enough revenue to pay for a new replacement GPU in two years (and the power/running cost of the GPU + infrastructure). That's the question you need to be asking.

If the answer is not yes, then they are making money on inference. If the answer is no, the market is going to have a bad time.

Because they're not saying they are making a profit

No, it's a confession they have no moat other than trying to hold onto the best model for a given use case.

I find that competitive edge unlikely to last meaningfully in the long term, but this is still a contrarian view.

More recently, people have started to wise up to the view that the value is in the application layer

https://www.iconiqcapital.com/growth/reports/2026-state-of-a...