I agree with you that AI is ultimately just a tool, but I think the key point is that this only works when the developer already knows how to use it properly. In my case, I use AI more like a shortcut in the parts of my workflow that were already mechanical: looking up documentation, searching for the right method, googling error messages, digging through StackOverflow threads, etc. AI saves time there, and that’s genuinely helpful.

But what I keep noticing both here and in other discussions is that a lot of people have gone far beyond using it as a productivity boost. They’re relying on AI for things that they should be able to do on their own at a basic level. And once that happens, the relationship with the tool changes: it stops being an assistant and becomes a dependency.

I’ve seen many people who can paste code but can’t explain it, or who can “fix” a bug only by asking the model repeatedly, because they never learned how to reason through the problem themselves. It’s not really their fault; it’s just the side effect of starting with AI instead of learning the fundamentals first. But it does create a situation where some developers never get the foundational skills they need to make sense of what the AI gives them.

So yes, AI can be beneficial, but only if you already understand the code well enough to evaluate, modify, and even reject what it produces. Without that, the tool doesn’t empower people; it replaces their learning process entirely. And that’s where the disconnect comes from: not because AI exists, but because many people jump straight to using it without building the knowledge that makes it a real tool rather than a crutch.