"Useful and transformative" doesn't mean "financially successful".
A single LLM provider might have been able to get great margins and capture a significant fraction of the total economic output of (currently e.g. junior grade software engineering), but collectively they're in an all-pay auction for the hardware to train models worth paying for, and at the same on questionable margins because they need to compete with each other on cost.
They can all go bankrupt, and leave behind only trained models that normal people won't be able to run for 5 years while consumer-grade stuff catches up. Or any single one of them might win, which may not be OpenAI. Any or all may get state subsidies (US, Chinese, European, whatever).
Paid/API LLM inference is profitable, though. For example, DeepSeek R1 had "a cost profit margin of 545%" [1] (ignoring free users and using a placeholder $2/hour figure H800 GPU, which seems ballpark of real to me due to Chinese electricity subsidies). Dario has said each Anthropic model is profitable over its lifetime. (And looking at ccusage stats and thinking Anthropic is losing thousands per Claude Code user is nonsense, API prices aren't their real costs. That's why opencode gives free access to GLM 4.7 and other models: it was far cheaper than they expected due to the excellent cache hit rates.) If anyone ran out of money they would stop spending on experiments/research and training runs and be profitable... until their models were obsolete. But it's impossible for everyone to go bankrupt.
That's more of "cloud compute makes money" than "AI makes money".
If the models stop being updated, consumer hardware catches up and we can all just run them locally in about 5 years (for PCs, 7-10 for phones), at which point who bothers paying for a hosted model?
Comments
"Useful and transformative" doesn't mean "financially successful".
A single LLM provider might have been able to get great margins and capture a significant fraction of the total economic output of (currently e.g. junior grade software engineering), but collectively they're in an all-pay auction for the hardware to train models worth paying for, and at the same on questionable margins because they need to compete with each other on cost.
They can all go bankrupt, and leave behind only trained models that normal people won't be able to run for 5 years while consumer-grade stuff catches up. Or any single one of them might win, which may not be OpenAI. Any or all may get state subsidies (US, Chinese, European, whatever).
All kinds of outcomes are possible.
Paid/API LLM inference is profitable, though. For example, DeepSeek R1 had "a cost profit margin of 545%" [1] (ignoring free users and using a placeholder $2/hour figure H800 GPU, which seems ballpark of real to me due to Chinese electricity subsidies). Dario has said each Anthropic model is profitable over its lifetime. (And looking at ccusage stats and thinking Anthropic is losing thousands per Claude Code user is nonsense, API prices aren't their real costs. That's why opencode gives free access to GLM 4.7 and other models: it was far cheaper than they expected due to the excellent cache hit rates.) If anyone ran out of money they would stop spending on experiments/research and training runs and be profitable... until their models were obsolete. But it's impossible for everyone to go bankrupt.
[1] https://github.com/deepseek-ai/open-infra-index/blob/main/20...
I don’t think the current industry can survive without both frontier training and inference.
Getting rid of frontier training will mean open source models will very quickly catch up. The great houses of AI need to continue training or die.
In any case, best of luck (not) to the first house to do so!
That's more of "cloud compute makes money" than "AI makes money".
If the models stop being updated, consumer hardware catches up and we can all just run them locally in about 5 years (for PCs, 7-10 for phones), at which point who bothers paying for a hosted model?