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Comment by fooster | Hacker News Reader

Where is your evidence of this "massive cost"? Inference is massively profitable for both anthropic and openai. Training is not.

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The evidence is that quotas exist, as seen here, and are low enough that people are hitting them regularly. When was the last time you hit your quota of Google searches? When was the last time you hit your quota of StackOverflow questions? When was the last time you hit your quota of YouTube videos? Any service will rate limit abuse, but if abuse is indistinguishable from regular use from the provider's perspective, that's not a good sign.

It's also kind of interesting that they don't think they can do what an economy would normally do in this situation, which is raise prices until supply matches. Shortages generally imply mispricing.

There's a lot of angles you take from that as a starting point and I'm not confident that I fully understand it, so I'll leave it to the reader.

the sales pitch is that you can keep throwing more and more tokens at a problem to solve it.

if the prices dont keep going down, the pitch falls apart, that you need a specialist to come in and make it work

Great point.

The parent's argument is that the marginal cost of inference is minimal. However, the fundamental flaw is that he's separating inference from the high cost frontier models. It's a cross-subsidy that can't be ignored.

Without any insider knowledge on the economics of these companies, I suspect it's that the amount of infrastructure you have to build is determined by peak usage rather than average usage. If peak usage is much higher for a small part of one day a week (say on Monday morning as software developers across the US get back to work) the cost of fulfilling demand at all times can be insane. That's why companies are implementing batch/standard/priority pricing for the API.

Check out this article from today [0].

It sounds like it's more of a profit maximization function (and not just demand) with GPU rental prices increasing 48% since Feb.

> Renting one of Nvidia’s most-advanced Blackwell generation of chips for one hour costs $4.08, up 48% from the $2.75 it cost two months ago, according to the Ornn Compute Price Index.

[0] https://www.wsj.com/tech/ai/ai-is-using-so-much-energy-that-...

This article convinced me otherwise https://www.wheresyoured.at/the-subprime-ai-crisis-is-here/

This is a great article, thanks for sharing

good article!

You're assuming they can just stop training. For the entirety of these companies' existence, they have done training. It is part of their price. They must keep pushing out better and better models. That's like saying Nvidia can just stop making new GPUs, they're obviously making so much money with their current models now.

The majority of accounts are free - these are profitable?

IMO they need as many users before their IPO - then the changes will really begin.

Inference for API or subscriptions? There is a massive price difference between the two.

Inference cannot happen without training the model first, so the distinction is quite pointless.

source?

After googling https://www.reddit.com/r/singularity/comments/1psesym/openai...

>OpenAI's compute margin, referring to the share of revenue excluding the costs of running its AI models for paying users

Huh?

The reddit summary comment makes no sense. How are they getting revenues without ads or paying customers?

"After" makes more sense.

FTA:

>The company has yet to show a profit and is searching for ways to make money to cover its high computing costs and infrastructure plans.

I've seen sources like this before. It's all hearsay and promo. I was asking for any publicly available verifiable information regarding the cost of inference at scale. I haven't seen any such info personally which is why I asked.

I'm dying to see S-1 filing for Anthropic or OpenAI. I don't actually think inference is as cheap as people say if you consider the total cost (hardware, energy, capex, etc)

Well they're not public yet so you'll have to put up with rumors. But the numbers are available for companies like DeepSeek say they have an 80% profit margin, so it stands to reason OAI etc would do similar numbers considering they charge much more.

AFAIK,

1. the 80% margin from 2025 was theoretical,

2. they're relying on distillation/synthetic data for training,

3. and have been very opaque about cross-subsidization of R&D with their models.

The distillation alone adds a big asterisk for comparisons.

Talking nonsense.

> you'll have to put up with rumors.

> But the numbers are available for companies like DeepSeek

You'd rather trust self-reported figures? LMAO

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