[lumost]

Capex was the theoretical moat, same as TSMC and similar businesses. DeepSeek poked a hole in this theory. OpenAI will need to deliver massive improvements to justify a 1 billion dollar training cost relative to 5 million dollars.

[usef-]

I don't know if you are, but a lot of people are still comparing one Deepseek training run to the entire costs of OpenAI.

The deepseek paper states that the $5mil number doesn't include development costs, only the final training run. And it doesn't include the estimated $1.4billion cost of the infrastructure/chips Deepseek owns.

Most of OpenAI's billion dollar costs is in inference, not training. It takes a lot of compute to serve so many users.

Dario said recently that Claude was in the tens of millions (and that it was a year earlier, so some cost decline is expected), do we have some reason to think OpenAI was so vastly different?

[lumost]

Anthropic’s ceo was predicting billion dollar training runs for 2025. Current training runs were likely in the tens/hundreds of millions of dollars USD.

Inference capex costs are not a defensive moat as I can rent gpus and sell inference with linear scaling costs. A hypothetical 10 billion dollar training run on proprietary data was a massive moat.

https://www.itpro.com/technology/artificial-intelligence/dol...