What does this look like in practice?
My hunches are that the to-and-fro of ideas as you discuss options or make corrections leads to a context with competing “intentions”; and that they can’t tell the difference between positive and negative experiences when it comes across each successive token in the context.
But I don’t make LMMs, so this is pure guesswork.
The rabbit hole problem is pretty rare. Usually it happens when the model flips into "stupid mode", some kind of context poisoning. If you are experienced, you know to purge the context when that happens.
In personal projects I avoid manual editing as a form of deliberate practice. At work, I only edit when it is a very small edit. I can usually explain what I want more concisely and quickly than hand editing code.
I probably would use more hand editing if I had classic refactoring tools in the IDEs similar to intellij/pycharm. Though cli based tools were a pleasant surprise once I actively started using them.
And then I waste 20 minutes bashing my head against the wall trying to write three paragraphs meticulously documenting all the key gotchas and lessons from the "good" context window with the magic combination of words needed to get Claude back in the right head space to one-shot again.
At least if I pull that off I can usually ask Claude to use it as documentation for the project CLAUDE.md with a pretty good success rate. But man it leaves a bad taste in my mouth and makes me question whether I'm actually saving time or well into sunk cost territory...
And then quit after accepting a new job that pays them their modified value, because tech companies are particularly bad at proactive retention.
And not at all for you, because you're unlikely to retain them for long. Which makes this immaterial - AI or human, you're only going to delegate to n00bs.
(This is distinct from the question of which benefits society more, which is a separate discussion.)
But this is literally what senior engineers do most of the time? Have juniors write code with direction and review that it isn't buggy?
I mentor high-school students and watch them live write code that takes a completely bizarre path. It might technically be intentional, but that doesn’t mean it’s good or useful.
Given the nature of the statistics in question, the line between the two is extremely blurry at this point.
Some people really are going to hang on until the better end (and beyond) eh?
"AI can't code like me!" people are going to get crushed.