No you're right. I initially thought you were wrong but it is sus.

My intuition for a priori cut something along the lines of, "Even if you had the entire source code in your head at once, there's limits to reasoning about it". Computability is one hard result. You also have to interact with the real world on a wide variety of hardware systems, or even just a wide variety of systems if you create an API - how do you reason past the abstraction boundary reliably without actually having tests and interacting with systems and getting feedback? Not really possible unless LLM's control everything. More philosophical questions (such as "is our 'correct' actually the right thing?") we grant the easy case that everybody's in consensus - the "easier" problems show up either way.

But getting to the point of "understanding in principle every piece of linux" is pretty undefined and practically doesn't seem possible for a singular LLM or a human. This also seems really hairy for smuggling in whatever implicit premises you want to swing the issue either way.

But personally I (and many other people) have seen late 2025 models get extremely good, and that precisely is because they actually started doing deep tooling and like, actually running and testing their code. I was not getting nearly as much value out of them (still a decent amount of value!) prior to the tooling explosion, not even MCPs were good. It was when they actually started aggressively spawning subshells and executing live tests. But I guess using a priori/posterioi isn't really a useful split here?

Yeah, maybe you are right. But is doing math and reasoning about Turing machines a priori? If so, then it seems plausible to me that reasoning about a codebase (without running it) is also ‘a priori’.