Just because I can run near frontier level open weight models doesn’t mean I can continue to train models of equal or superior performance, doing that requires massively more hardware. And even if I did, that doesn’t mean I can serve those models to millions or billions of users.
My point is that I would rather have 1000 labs training and serving inference than 2 because that would distribute the wealth creation more broadly rather than allowing OpenAI and anthropic to capture all the value, it would drive more innovation as a broader set of experiments are pursued in parallel.
How am I shifting my argument. They are corollary arguments. Massive data processing is necessary for frontier model training and inference. If OpenAI and anthropic are behaving irresponsibly they we can limit their access the level of compute to train and serve models. That doesn’t imply individuals can’t run frontier level models or should be prohibited from doing so. That’s a coherent position across a spectrum of possible scenarios: I look at the behavior of the people running the model and would limit their ability to train and run models if they act in unsafe ways.
My point is that I would rather have 1000 labs training and serving inference than 2 because that would distribute the wealth creation more broadly rather than allowing OpenAI and anthropic to capture all the value, it would drive more innovation as a broader set of experiments are pursued in parallel.