On a micro scale, yes I see this regularly at work: some devs blindly put up ai generated PRs that are fundamentally flawed or don’t actually complete the work in the ticket. It’s often trivial to check this. Similarly some tickets are written by ai that make incorrect assumptions about our product, resulting in nonsensical requirements.

You could argue that these are just examples of laziness, but I think these are both tasks that require critical thinking, and it’s easier to hand them off to an llm that at least purports to do this for you.

I’d argue that both setting requirements, and self code review are both things that need careful attention. (What _exactly_ do you need doing? Does your work correctly do the thing that needs doing?).

I have no fundamental issue with using ai, but there’s no doubt that applying critical thinking to both these tasks directly, and to the broader question of “should I use ai for this job” should result in a different outcome.

Personally I would also not agree with your take that working with llms has allowed me to do “acute critical thinking”. It’s sped up my development time, sure, but I would argue that my evaluation of, say a new sdk now using Claude is far less detailed than it would’ve been in the past when I did it by reading docs. As a side note, I’ve been rereading Douglas Hofstadter’s GEB recently in paperback, and I’ve found a lot of joy in wrestling with paragraphs and writing notes in the margins. This is way more focussed attention than I really give anything when working with llms.