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there are question marks that make me support the labs a bit, which is like these Claude proxies and these model proxies in China where there's a ton of tokens that are used and there's intermediaries and they have tons of accounts and even cases where Kimmy was serving Claude in their products to users.
if I were to have access to Claude's reasoning trace when I was building Olmo, I'm actually like Claude Opus, Claude Mythos, whatever, I'm not sure that it would have actually made our model a lot better.
But there are question marks that make me support the labs a bit, which is like these Claude proxies and these model proxies in China where there's a ton of tokens that are used and there's intermediaries and they have tons of accounts and even cases where Kimmy was serving Claude in their products to users. And I'm just like, don't really understand how this fits into distillation as a common technical tool in post-training. And like, yes, we should watch it to like, there's other weird stuff w
OpenAI and Anthropic spend way more to build out this function of a data industry.
I think those two companies in particular have been the most forward-looking and the most willing to spend egregious amounts to make a step towards a direction that people think is impossible.
Anthropic has been like very focused on the types of things the models are going to be able to do. And then their reward was this cloud code explosion with their focus on coding for so long.
if somebody wants to work at the frontier of AI, I mostly tell them to work at Anthropic and OpenAI, which is the easiest bet.