An AI lab with no R&D. Truly a hacker news moment

▲ nl 7 days ago | parent | next [-]

The unspoken context there is that the inference isn't the thing causing the losses.

▲ gitremote 7 days ago | parent [-]

Inference contributes to their losses. In January 2025, Altman admitted they are losing money on Pro subscriptions, because people are using it more than they expected (sending more inference requests per month than would be offset by the monthly revenue).

https://xcancel.com/sama/status/1876104315296968813

▲ aurareturn 7 days ago | parent [-]

So people find more value than they thought so they'll just up the price. Meanwhile, they still make more money per inference than they lose.

▲ oblio 7 days ago | parent | next [-]

This assumes that the value obtained by customers is high enough to cover any possible actual cost.

Many current AI uses are low value things or one time things (for example CV generation, which is killing online hiring).

▲ aurareturn 7 days ago | parent [-]

  Many current AI uses are low value things or one time things (for example CV generation, which is killing online hiring).

We are talking about Pro subs who have high usage.

	▲	oblio 7 days ago \| parent [-]
		True. At the end of the day, until at least one of the big providers gives us balance sheet numbers, we don't know where they stand. My current bet is that they're losing money whichever way you dice it. The hope being as usual that costs go down and the market share gained makes up for it. At which point I wouldn't be shocked by pro licenses running into the several hundred bucks per month.

▲ gitremote 7 days ago | parent | prev [-]

Currently, they lose more money per inference than they make for Pro subscriptions, because they are essentially renting out their service each month instead of charging for usage (per token).

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aurareturn 7 days ago | parent [-]

Do you have a source for that?

	▲	gitremote 6 days ago \| parent [-]
		When an end user asks ChatGPT a question, the chatbot application sends the system prompt, user prompt, and context as input tokens to an inference API, and the LLM generates output tokens for the inference API response. GPT API inference cost (for developers) is per token (sum of input tokens, cached input tokens, and output tokens per 1M used). https://openai.com/api/pricing/ https://azure.microsoft.com/en-us/pricing/details/cognitive-... (Inference cost is charged per token even for free models like Meta LLaMa and DeepSeek-R1 on Amazon Bedrock. https://aws.amazon.com/bedrock/pricing/ ) ChatGPT Pro subscription pricing (the chatbot for end users) is $200/month https://openai.com/chatgpt/pricing/ "insane thing: we are currently losing money on openai pro subscriptions! people use it much more than we expected." - Sam Altman, January 6, 2025 https://xcancel.com/sama/status/1876104315296968813 Again, this means that the average ChatGPT Pro end user's chattiness cost OpenAI too much inference (too many input and output tokens sent and received, respectively, for inference) per month than would be balanced out by OpenAI receiving $200/month in revenue from the average Pro user. The analogy is like Netflix losing money on their subscriptions because their users watch too much streaming, so they ban account sharing, causing many users to cancel their subscriptions, but this actually helps them become profitable, because the extra users using their service too much generated more costs than revenue.

▲ hn92726819 7 days ago | parent | prev [-]

I think you maybe have misunderstood the parent (or maybe I did?). They're saying you can't compare an individual's cost to run a model against OpenAI's cost to run it + R&D. Individuals aren't paying for R&D, and that's where most of the cost is.