[emil-lp]

Remember that OpenAI is subsidized from here to the highway.

A better way to model this, since you seem interested is the following:

How much would it cost you to start such a service for, say, 10k users?

Any other internet service has had virtually Zero cost, $0. Google, Facebook, youtube, Wikipedia, you name it. They all went into the dumpster to pick up a thrown away desktop computer, and they could serve up towards 100k if not a million users.

How much would it cost you to serve, say, 10k simultaneous users with a SOTA model? And if you wanted to go cash positive after a year, how much would each user have to pay?

[simianwords]

> How much would it cost you to serve, say, 10k simultaneous users with a SOTA model? And if you wanted to go cash positive after a year, how much would each user have to pay?

My post has this same argument - we have multiple third party companies running open weight models. They are obviously not subsidised. And people are willing to pay for it. And these models are as good as the SOTA models from last year. So this kinda proves my point that SOTA is sustainable.

[emil-lp]

I didn't find the answer there, that's why I asked.

What hardware is needed, how much of it, cooling, and what does it all cost you?

Or are you saying I can take my old desktop and serve Deepseek v3.2 to 10k users simultaneously and it would cost me about $1 per megatoken?

[simianwords]

I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.

Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.

So there's nothing inherent about inference that makes it too costly or whatever.

[oblio]

> I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.

> Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.

> So there's nothing inherent about inference that makes it too costly or whatever.

Do we have audited GAAP financial data for any of these companies? If we don't, all these are... vibes, man.

[simianwords]

Good questions. The post you are replying to is also based on vibes. So how about we make a bet and come back after IPO filings?

But isn't it okay to suggest that random 3rd party hosting companies are ... not losing money? Why?

[oblio]

I'll bet 50€ that big AI companies (OpenAI, Anthropic, xAI) have bad regarding their core financials. Excluding one time deals such as renting hardware, external cash infusions, the core AI model business (training models and selling inference) is unprofitable and will be be at the expected scales (50 billion € or more) for the next 5 years at least.

I bet that within 5 years they will be sold for scrap to bigger companies and will become divisions inside them.

[emil-lp]

Since you couldn't answer, I asked ChatGPT.

It said: upfront investment: $3M to $6M.

Customers should pay $25k per month.

Checks out

[simianwords]

I pasted everything you wrote and ChatGPT said

>The "$25k per month" figure is almost certainly the result of ChatGPT making assumptions, not a fact derived from any known business model.

https://chatgpt.com/share/6a28193b-6ec0-8333-a1af-d07e8d89ef...

Your whole calculation is also ridiculous - I think you assumed what revenue per month is required to pay off hardware within a year? Why would I use hardware within a year?

I would suggest consulting with ChatGPT and coming up with a better and more coherent argument.

[emil-lp]

I reran the numbers, break even after four years. 10k users.

Minimum upfront: about $15M

Comfortable upfront: $20M–$25M

Monthly revenue needed: $900k–$1.5M

Required price per 10k individual customers: $99–$149/month

API-equivalent output price: usually $8–$20/M output tokens, unless utilization is very high.