A single PR doesn't really "prove" anything. Optimization passes on well-tested narrowly scoped code are something that LLMs are already pretty good at.
It is able to figure out some things that I know do not have much training data at all.
It is looking at the manual and figuring things out. "That doesn't make sense. Wait, that can't be right. I must have the formula wrong."
I just seen that in the chain of thought.
Sure, LLMs can improve but they're ultimately still bound by the constraints of the type of data they're trained on and don't actually build world models through a combination of high bandwidth exploratory training (like humans) and repeated causal inference.