Claude in its current Opus 5.5 form has made some wild reaches in response to reasonable-seeming prompts. For instance: "this error is getting swallowed, make sure the real termination message exposes the root cause" -> discovering that the issue is actually in a third party library that isn't exposing the original error except so through logs, so doing some hackery to inject a log-capturer that gets the stdout, buffers it, and looks for the message in order to re-throw based on that...
And it can demonstrate that working, but it's added some fragile and/or unintended consequences like (a) string matching on a particular WARN log message of a particular version of the lib, and (b) buffering those logs such that there were now cases where they would never make it to the actual output stream before the program terminating.
It's certainly capable of being used smarter than that, but it's also really easy to end up with messy fragile stuff if you get lazy about reviewing the output.
Without judgement, I think this illustrates a fundamental objection the anti-AI crowd raises.
The example prompt requires no understanding of what's happening, before suggesting action. Where I work, process requires that a PR review for a solution, has to have a corresponding explanation of why, or it's marked needs work
I think there's some nuance here though between "fully anti-AI" and "be careful with that power saw."
That example I mentioned didn't make it to production. It got noticed and fixed more properly by the person who submitted the first prompt before it even went to PR.
But a lot of times things in that same vein can be solved sufficiently with a "dumb" prompt like that, so a one-line prompt to get a PR drafted in the background for the dumb stuff can be a pretty good time saver.
The art is not skimping on your attention to the bigger or more important stuff so much that all the time you save on first getting code written is lost by cleaning up stuff like that...
Opus's first proposal for a fix was to spawn multiple Python processes and build a load balancer between them in Python. At first I asked it why it wasn't using haproxy and it said that haproxy would be an added dependency and that the overhead for this kind of thing in Python would be negligible. I knew this was idiotic but wanted to test this out, so had Opus run two experiments in parallel. Sure enough an haproxy load balancer was much, much better on HTTP load balancing than Python code was.
This is the kind of thing that if you're unfamiliar with how networking code works, the models can autopilot into bad decisions that are verifiably correct (higher throughput than the original CPU-bottlenecked implementation) but much short of best practice.