Maybe because it isn’t needed. That’s the reason we have experts and professionals, because it’s more economical to use them than for everyone to start from scratch. It’s easier to go to a mechanic shops to fix my engine block than to try to do it myself. I’ve heard that a lot of shops charge more if you said an amateur try to fix things first.
It’s a lot of stuff, but that’s not how you experiment for an hypothesis.
The ai doesn't have the same incentives as a human. It can also be applied to the repro where it exists.
Your mechanic analogy is a bit inapt. Sure they're experts and can replace your engine much better than you can. But tracking down a non obvious system level bug like this one is much less repeatable and much harder because the problem has already been filtered through a lot of reviews and testing. As a system becomes mature, the bugs become harder and harder individually to track down.
> As a system becomes mature, the bugs become harder and harder individually to track down.
What I'm talking about is that experts knows the system mechanism more than amateurs, so any hypothesis and experimental setup will be more focused and thus more economical than any amateurish one. And there's the matter of knowledge not present in some docs or other forms, such as past experiences.
Even with LLM tooling, we've seen the rises of harnesses and helper tools instead of relying on generation for everything. Who would you trust to build such harness, a domain experts or some random guy off the street?