I'd actually say that you end up needing to think more in the first example.

Because as soon as you realize that the output doesn't do exactly what you need, or has a bug, or needs to be extended (and has gotten beyond the complexity that AI can successfully update), you now need to read and deeply understand a bunch of code that you didn't write before you can move forward.

I think it can actually be fine to do this, just to see what gets generated as part of the brainstorming process, but you need to be willing to immediately delete all the code. If you find yourself reading through thousands of lines of AI-generated code, trying to understand what it's doing, it's likely that you're wasting a lot of time.

The final prompt/spec should be so clear and detailed that 100% of the generated code is as immediately comprehensible as if you'd written it yourself. If that's not the case, delete everything and return to planning mode.

> I'd actually say that you end up needing to think more in the first example.

Yes, but you are thinking about the wrong things, so the effort get spent poorly.

It is usually much more efficient to build your own mental model than to try to search for a solution that solves exactly what you need from externally. Without that mental model it is hard to evaluate whether the external solution even does what you want, so its something you need to do either way.

Depends how complex the task is. Sometimes I’m handed tasks so simple but tedious that AI has meant I can breeze through these instead of burning myself out on them. Sure, it doesn’t speed things up much in terms of time, but I’m way less burnt out at the end because it’s doing all the fiddly stuff that would tire me out. I suspect the tasks I get aren’t that typical though.