There's a delusion that what America's AI companies are doing is "best"; the chinese should realize that the forefront is bloated and there's likely hundreds of speed ups viable. Pushing open weights will continue to grind down the bloat.
> One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.
endlessly knowing about pokemon is not delivering value proposition
cancer also grows carelessly.
There is nothing about the bitter lesson that says just be dumb and pour money into a hole, you still have to invent the methods to scale well, and being under immense pressure with constraints seems likely to produce that research.
I’m not sure how well the analogy holds up, or if there’s anything to be learned from it though.
Certainly applies more general imho. Constrained by some resource -> invest resources elsewhere, and/or invest in reducing the constraint(s) encountered.
But we have not been maximally efficient, we keep gaining efficiency. If we keep gaining efficiency, why should we assume it is impossible to gain more?
Not sure if the word "delusion" is the correct word here? It has not been proven in either direction. We can all see lots of possible issues with it, but it is also possible that it could be what is needed to unlock key capabilities.
We can see that the Chinese models have been getting better, but OpenAI is out there supporting 10 million active users with their frontier models, and now we know that Deepseek can't even get what they need to properly train models.
Thanks to the import restrictions, I expect Chinese GPU hardware to be competitive within a few years.