Comment by abathologist
Comment by abathologist 15 days ago
One clever ingredient in OpenAI's secret sauce is billions of dollars of losses. About $5 billion dollars lost in 2024. https://www.cnbc.com/2024/09/27/openai-sees-5-billion-loss-t...
Comment by abathologist 15 days ago
One clever ingredient in OpenAI's secret sauce is billions of dollars of losses. About $5 billion dollars lost in 2024. https://www.cnbc.com/2024/09/27/openai-sees-5-billion-loss-t...
You hit the nail on the head. Just gotta add the up to $10 billion investment from Microsoft to cover pretraining, R&D, and inference. Then, they still lost billions.
One can serve a lot if models if allowed to burn through over a billion dollars with no profit requirement. Classic, VC-style, growth-focused capitalism with an unusual, business structure.
Due to batching, inference is profitable, very profitable.
Yet undoubtedly they are making what is declared a loss.
But is it really a loss?
If you buy an asset, is that automatically a loss? or is it an investment?
By "running at a loss" one can build a huge dataset, to stay in the running.
Imagine pipelineing lots of infra-scale GPU's, naive inference would need all previous tokens to be shifted "left" or from the append-head to the end-of-memory "tail", which would require a huge amount of data flow for the whole KV cache etc. Instead of calling GPU 1 the end-of-memory and GPU N the append-head, you keep the data static and let the role rotate like a circular buffer. So now for each new token inference round, the previous rounds end-of-memory GPU becomes the new append-head GPU. The highest bandwidth is keeping data static.
they would be break-even if all they did was serve existing models and got rid of everything related to R&D
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).
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.
they are not the only player so getting rid of R&D would be suicide
That's all different now with agentic which was not really a big thing until the end of 2024. before they were doing 1 request, now they're doing hundreds for a given task. the reason oai/azure win over locally run models is the parallelization that you can do with a thinking agent. simultaneous processing of multiple steps.