Comment by black3r

> Knowledge comes basically from manual trial/error almost daily.

This is the important statement, although I'd swap the word "knowledge" for "experience" here. You can gain "knowledge" from books, but only trial & error will give you experience to know "which" knowledge to use in which situations.

And what's important about this in the context of working with AI is the "error" part.

You have to experience errors to become truly experienced. And part of the experience is to recognize when you're about to make an error - to avoid it.

AI-driven processes mess up our natural trial & error learning curve in multiple ways:

- the AI push forces us to ship features faster (cause if we don't, our competitors will), reviews are sloppier, we discover errors later on, the feedback loop gets longer...

- using AI to debug and fix errors means we spend less time understanding what the error was about, which means we learn less about how to avoid the error in the first place...

- AI itself sounds overly confident, so reading its outputs without previous experience you may be less likely to recognize when it's making an error, which makes it harder for you to recognize when you're making an error trusting it...

On the other hand, this last point I tried to make is also why I don't think avoiding AI completely is a good strategy. Whether we like it or not, AI is becoming a part of developer's workflow. And as such, we also need to learn the trial & error process of using AI - what makes AI make errors and how to prompt it to avoid that.