Comment by paxys
4 days ago
I don't think it's a secret that AI companies are losing a ton of money on subscription plans. Hence the stricter rate limits, new $200+ plans, push towards advertising etc. The real money is in per-token billing via the API (and large companies having enough AI FOMO that they blindly pay the enormous invoices every month).
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.
Banning third-party tools has nothing to do with rate limits. They’re trying to position themselves as the Apple of AI companies -a walled garden. They may soon discover that screwing developers is not a good strategy.
They are not 10× better than Codex; on the contrary, in my opinion Codex produces much better code. Even Kimi K2.5 is a very capable model I find on par with Sonnet at least, very close to Opus. Forcing people to use ONLY a broken Claude Code UX with a subscription only ensures they loose advantage they had.
> "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.
OpenAI only released ChatGPT 4 years ago but…
Google has made custom AI chips for 11 years — since 2015 — and inference costs them 2-5x less than it does for every other competitor.
The landmark paper that invented the techniques behind ChatGPT, Claude and modern AI was also published by Google scientists 9 years ago.
That’s probably how they can afford it.
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I can see it to be £18.95 from the UK, which is almost double that. I guess this is an oversight from your part or maybe quoting from memory.
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 $1200/mo. So either the API has a 6x profit margin on inference, the subscription is a loss leader, or they just rely on most people not to go anywhere near the usage caps.
I use GLM lite subscription for personal use. It is advertised as 3x claude code pro (the 20$ one).
5h allowance is somewhere between 50M-100M tokens from what I can tell.
On 200$ claude code plan you should be burning hundreds of millions of token per day to make anthropic hurt.
IMHO subscription plans are totally banking on many users underusing them. Also LLM providers dont like to say exact numbers (how much you get , etc)
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It's the latter. It's the average use that matters. Though I suspect API margins are also probably higher than people think.
Inference might be cheap, but I'm 100% sure Anthropic has been losing quite a lot of money with their subscription pricing with power users. I can literally see comparison between what my colleagues Claude cost when used with an API key vs when used with a personal subscription, and the delta is just massive
I wonder how many people have a subscription and don’t fully utilize it. That’s free money for them, too.
The trick is that the jump goes from 20 to 100 Dollar for the Pro to Max subscription. Pro is not enough for me, Max is too much. 60 would be ideal, but currently at 100 it's worth the cost.
But this is how every subscription works. Most people lose money on their gym subscription, but the convenience takes us.
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Of course they bundle R&D with inference pricing, how else could you the recoup that investment.
The interesting question is: In what scenario do you see any of the players as being able to stop spending ungodly amounts for R&D and hardware without losing out to the competitors?
In the scenario where that market collapses, ie when we stop making significant gains with new models. It might be a while, though, who knows.
> 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.
I’m not saying charging above marginal cost to fund R&D is weird. That’s how every R&D company works.
My point was simpler: they’re almost certainly not losing money on subscriptions because of inference. Inference is relatively cheap. And of course the big cost is training and ongoing R&D.
The real issue is the market they’re in. They’re competing with companies like Kimi and DeepSeek that also spend heavily on R&D but release strong models openly. That means anyone can run inference and customers can use it without paying for bundled research costs.
Training frontier models takes months, costs billions, and the model is outdated in six months. I just don’t see how a closed, subscription-only model reliably covers that in the long run, especially if you’re tightening ecosystem access at the same time.
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Didn't OpenAI spend like 10 billion on inference in 2025? Which is around the same as their total revenue?
Why do people keep saying inference is cheap if they're losing so much money from it?
When you have 800–900 million active users, no matter how cheap it is, your costs will be in the billions.
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What walled garden man? There’s like four major API providers for Anthropic.
For example, OpenAI’s agent (Codex) is open source, and you can use any harness you want with your OpenAI subscription. Anthropic keeps its tooling closed source and forbids using third-party tooling with a Claude subscription.
Except all those GPUs running inference need to be replaced every 2 years.
Why?
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"They're not losing money on subscriptions, it's just their revenue is smaller than their costs". Weird take.
It means the marginal cost to sell another subscription is lower than what they sell it for. I don't know if that's true, but it seems plausible.
The secret is there is no path on making that back.
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.
Why? Computers and anything computer related have historically been dropping in prices like crazy year after year (with only very occasional hiccups). What makes you think this will stop now?
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I like this analogy.
I also think we're, as ICs, being given Bentleys meanwhile they're trying to invent Waymos to put us all out of work.
Humans are the cost center in their world model.
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the path is by charging just a bit less than the salary of the engineers they are replacing.
After hearing this 10 times a day for the last 5 years I'm starting to get a bit tired. Do you have a rough time for when this great replacement is coming? 1 year? 2? 5? If it's longer than that can we shut up about it for a few years please.
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how do I understand what is the sustainable pricing?
Depends on how you do the accounting. Are you counting inference costs or are you amortizing next gen model dev costs. "Inference is profitable" is oft repeated and rarely challenged. Most subscription users are low intensity users after all.
I agree; unfortunately when I brought up that they're losing before I get jumped on demanding me to "prove it" and I guess pointing at their balance sheets isn't good enough.
The question I have: how much are they _also_ losing on per-token billing?
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)
Do they make enough to replace their GPUs in two years?
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Why do you think they're losing money on subscriptions?
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
That doesn’t mean that the subscription itself is losing money. The margin on the subscription could be fine, but by using that margin to R&D the next model, the org may still be intentionally unprofitable. It’s their investment/growth strategy, not an indictment of their pricing strategy.
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But why does it matter which program you use to consume the tokens?
The sounds like a confession that claude code is somewhat wasteful at token use.
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...
But if more users use your service you get an advantage. Letting the users chose their tool for that would be a good thing.
Honestly I think I am already sold on AI, who is the first company that is going to show us all how much it really costs and start enshitification? First to market wins right?