I definitely find your last point is true for me. The more work I am doing with AI the more I am expecting it to do, similar to how you can expect more over time from a junior you are delegating to and training. However the model isn't learning or improving the same way, so your trust is quickly broken.

As you note, the developer's input is still driving the model quite a bit so if the developer is contributing less and less as they trust more, the results would get worse.

> However the model isn't learning or improving the same way, so your trust is quickly broken.

One other failure mode that I've seen in my own work while I've been learning: the things that you put into AGENTS.md/CLAUDE.md/local "memories" can improve performance or degrade performance, depending on the instructions. And unless you're actively quantitatively reviewing and considering when performance is improving or degrading, you probably won't pick up that two sentences that you added to CLAUDE.md two weeks ago are why things seem to have suddenly gotten worse.

> similar to how you can expect more over time from a junior you are delegating to and training

That's the really interesting bit. Both Claude and Codex have learned some of my preferences by me explicitly saying things like "Do not use emojis to indicate task completion in our plan files, stick to ASCII text only". But when you accidentally "teach" them something that has a negative impact on performance, they're not very likely to push back, unlike a junior engineer who will either ignore your dumb instruction or hopefully bring it up.

> As you note, the developer's input is still driving the model quite a bit so if the developer is contributing less and less as they trust more, the results would get worse.

That is definitely a thing too. There have been a few times that I have "let my guard down" so to speak and haven't deeply considered the implications of every commit. Usually this hasn't been a big deal, but there have been a few really ugly architectural decisions that have made it through the gate and had to get cleaned up later. It's largely complacency, like you point out, as well as burnout trying to keep up with reviewing and really contemplating/grokking the large volume of code output that's possible with these tools.

Your version of the last point is a bit softer I think — parent was putting it down to “loss of talent” but yours captures the gaps vs natural human interaction patterns which seems more likely, especially on such short timescales.