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