I would have stated this a bit differently: No amount of running or testing can prove the code correct. You actually have to reason through it. Running/testing is merely a sanity/spot check of your reasoning.
If LLM-generated code has been "reasoned-through," tested, and it does the job, I think that's a net-benefit compared to human-only generated code.
Net-benefit in what terms though? More productive WRT raw code output? Lower error rate?
Because, something about the idea of generating tons of code via LLMs, which humans have to then verify, seems less productive to me and more error-prone.
I mean, when verifying code that you didn't write, you generally have to fully reason through it, just as you would to write it (if you really want to verify it). But, reasoning through someone else's code requires an extra step to latch on to the author's line of reasoning.
OTOH, if you just breeze through it because it looks correct, you're likely to miss errors.
The latter reminds me of the whole "Full self-driving, but keep your hands on the steering wheel, just in case" setup. It's going to lull you into overconfidence and passivity.
This is actually a trick though. No one working on self driving actually expects people to actually babysit it for long at all. Babysitting actually feels worse than driving. I just saw a video on self-driving trucks and how the human driver had his hands hovering on the wheel. The goal of the video is to make you think about how amazing self-driving rigs will be, but all I could think about was what an absolutely horrible job it will be to babysit these things.
Working full-time on AI code reviews sounds even worse. Maybe if it's more of a conversation and you're collaboratively iterating on small chunks of code then it wouldn't be so bad. In reality though, we'll just end up trusting the AI because it'll save us a ton of money and we'll find a way to externalize the screw ups.