Great post! I think some extra noodling on the goals of US labs vs the Chinese labs might be worth some convo.
Main issue is the US labs are chasing superintelligence as a winner-take-all game (because of the “recursive learning runs at machine speed so you can’t catch up” hypothesis) which shifts their cost structure, R&D approach, and business model significantly away from the Chinese labs. Massive bet underway in the US that LLMs lead to superintelligence and all of its prizes go to whoever gets their first (this, as you point out, is even more existential to Anthropic’s ideology though Sam was openly racing to be king until last year).
The Chinese labs are chasing superintelligence also but currently have to engineer their way to it more than brute force it with compute so it’s a totally different game and played in a much more Darwinian pond (with a much meaner regulatory axe waving over their heads).
There is a pretty good argument forming that the US lab approach is wasting a ton of money and if the winner-take-all approach does not score then it’s going to be way more harmful to US AI progress than Chinese competition.
One last thought, there is evidence building that sharing your data with AI labs may be harmful to your future economic prospects. If that builds then open weight models have an even stronger reason to exist. Intelligence as a service is not yet settled as a sustainable business model.
@stratechery Great article! It seems hard, maybe mathematically impossible, to truly treat intelligence as fungible a priori. I don’t think this changes the bulk of the analysis though
@stratechery Frontier labs will not be fine. They incinerate cash, have no moats, no switching costs, easy to reverse engineer.
WTF did we decide to spend trillions on LLMs again, Ben?
@rabois Spot on! US models would cook and not open us to being the victim of a massive Chinese Trojan horse. Am I thinking about this the right way?