First of all, this is actually how this part of the conversation started:

> If the US companies need trillions to barely beat Chinese companies spending billions, despite a multi year head start...

Because they're talking about the cost difference of distilled model development and ground-up trained model development.

And second, the answer is that OpenAI and Anthropic had to do all the research into how to train models. Then they had to acquire all the data, curate and filter it. Then they had to design all the ways to iterate on training and antagonize it to be better - because there's not actually an enormous corpus of aligned, human stream-of-thought data. Then over half a decade they've been refining these methods.

There's no way you don't understand that if it was not easier and cheaper to distill a model, the institutions in question would be training their own models from scratch.