We can be pretty confident that these services are not subsidized. There are dozens of companies offering these services. Pretty much every single company has published open-weights models that you can run yourself. These open models, you could make money selling them for the same prices Google Gemini costs, while renting on-demand GPU instances from Google Cloud. It actually seems very implausible that Google is losing money on their proprietary models hosted on their own infrastructure. And OpenAI knows they have to compete with Google, which owns its own chips, OpenAI isn't going to be selling things at a loss. They cannot win that fight no matter how much Saudi money they get.
Again, I agree that it sounds plausible, but it doesn't guarantee anything, and the lack of hard data usually indicates things aren't as confidently profitable as you believe. Otherwise the companies would be bragging about it.
Probably in the end it'll be profitable for the companies somehow, but exactly how or what the exact prices will be, I don't think anyone know at this point. That's why I'm reserving my "Developing countries can now affordably use AI too" for when that's reality, not based on guesses and assumptions.
Google publishes their profits quarterly, but they only do that because they are required to by law. They would prefer people assume they're offering these services at a loss so nobody attempts to compete with them.
But again, it's not a guess or assumption - you can run the latest DeepSeek model renting GPUs from a cloud provider, and it works, and it's affordable.
There are two (three technically) ways that AI can be used.
> 1. renting gpu instances per minute from (you mention Google cloud) but I feel like some other providers can be cheaper too since new companies are usually cheaper, We get the lowendhosting of AI nowadays is usually via a marketplace-like thing (vast,runpod,tensordock)
Now vast offers serverless per minute AI models so checking it for something like https://vast.ai/model/deepseek-v3.2-exp or even glm 3.6 basically every of these turns out to be $.30 cents/minute or 18$ per hour
As an example GLM 4.6/ (now 4.7) have a YEARLY pricing of around 30 bucks iirc so now compare the immense difference in pricing
2. Using something like openrouter-based pricing :- Then we are basically on the same model of pricing similar to Google Cloud.
Of course AI models are reaching frontier and I am cheering for them but I feel like long term/even short term, these are still pretty expensive (even something like openrouter imo)
Someone please do genuine maths about this and I can be wrong, I usually am but I expect a 2-3x price (conservative side of things) increase if things arent subsidized
These are probably 10s of billions of dollars worth of gpu's so I assume that they would be barely profitable on the current rate but they get around 100s of billions in some cases worth of tokens generations so they can probably work via the 3rd use case I mention
Now coming to the third point which I assume is related to the 2nd/1st is that usually, the companies providing these GPU computes provide such compute, usually they can make money via providing by large term contracts.
Even huggingface provides consulting services which I think is the biggest profit to them and Another big contender can probably be European GPU compute providers who can provide a layer of safety or privacy for EU companies.
The large labs (OpenAI, Anthropic) and Hyperscalers (Google, Meta) currently are not trying to be profitable with AI as they are trying to capture market share. They may not even try to have positive gross margins, although the massive scale limits how much they can use per inference operation.
Pure inference hosters (Together, Fireworks etc.) have less capital and are probably close to zero gross margins.
There are a few things that make all of this more complicated to account for. How do you depreciate GPUs (I have seen 3 years to 8 years), how do you allocate cost if you do inference during the day and train at night etc.
The challenge with doing this yourself is that the market is extremely competitive. You need massive scale (as parallelism massively reduces cost), you need to be very good in negotiating cheap compute capacity and you need to be cost-effective in your G2M.
Opinions are my own, and none of this is based on non-public information.
So basically all of these are probably running in zero/net negative turns and they require billions of $'s to be spent and virtually there isn't any moat/lock-in (and neither there has to be)
So I guess the only reasonable business feels to me is private AI for large businesses who genuinely need it for their business (once again the MIT study applies) but that usually wouldn't apply to us normal grade consumers anyway and would be actually really expensive but still private and would be so far off from us normal people.
TLDR: The only ones making money are/ are gonna be B2B but even those are gonna dwindle if the AI bubble bursts because imagine an large business trying to explain why its gonna use AI if 1) the MIT study shows its unprofitable and 2) the fear around using AI etc. and all the financial consequences that the bubble's explosion might cause
So that all being said, I doubt it. I think that these prices are only till the bubble lasts which is only as strong as its weakest link which is openAI right now with trillions promised and a net lose making company whose CEO said that AI market is in a bubble and whose CFO openly floats the idea that OpenAI should be bailed out by the US govt if need be
So yeah..... Honestly Even local grade gpu's are expensive but with the innovations of open weights models, I feel like they would be the way to go for 90% of basic use cases being run inside them and probably there are very few cases of moat (and I doubt the moat existing in the first place)
