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.
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.
Google already has a huge competitive advantage because they have more data than anyone else, bundle Gemini in each android to siphon even more data, and the android platform. The TPUs truly make me believe there actually could be a sort of monopoly on LLMs in the end, even though there are so many good models with open weights, so little (technical) reasons to create software that only integrates with Gemini, etc.
Google will have a lion‘s share of inferring I believe. OpenAI and Claude will have a very hard time fighting this.
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.
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Training frontier models takes months, costs billions, and the model is outdated in six months.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.
They can totally lose money on subscriptions despite the costs of inference, because research costs have to be counted too.
Of course they are losing money when you factor in R&D. Everybody knows that. That is not what people mean when they say that they "lose money" on subscriptions.
I don't really think that view is as widespread as you believe.
But this is how every subscription works. Most people lose money on their gym subscription, but the convenience takes us.
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)
For small personal projects it’s great value for money. Cheapest subscription was like 3$ during new years, token quota is acceptable to me (my guess it’s about 50-100M tokens per 5h)
Dunno how it would be with big projects, but with “personal project” things it feels to me that GLM-4.7 is 80-90% of Claude Opus 4.5. Just a tiny bit of more hand holding for GLM.
You can buy a GPU that's been used to mine bitcoin for 5 years with zero downtime, and as long as it's been properly taken care of (or better, undervolted), that GPU functions the exact same as a 5 year old GPU in your PC. Probably even better.
GPUs are rated to do 100%, all the time. That's the point. Otherwise it'd be 115%.
You don't run your gaming PC 24/7.
The only reason they're "perishable" is because of the GPU arms race, where renewing them every 5 years is likely to be worth the investment for the gains you make in power efficiency.
Do you think Google has a pile of millions of older TPUs they threw out because they all failed, when chips are basically impossible to recycle ? No, they keep using them, they're serving your nanobanana prompts.
Why do people keep saying inference is cheap if they're losing so much money from it?
And cost of inference tripled from $3B in 2024 to $10B in 2025, so cost of revenue linearly grows with number of users, i.e. it does not get cheaper.
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?
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.