I think there's a vast ocean of different software engineers and the type of code they write on a daily basis, which is why you get such differing views on AI's effectiveness. For me, AI has only ever been useful for very basic tasks and scripts: If I need a quick helper script for something in Python, a language that isn't my daily drier, AI usually gets me there in a couple of prompts. Or maybe I am writing some powershell/bash and forgot the syntax for something and AI is quicker (or has more context) than a web search.

However, my main job is trying to come up with "elegant" architectures for complex business logic that interacts with an existing large code base. AI is just completely out of its depth in such cases due to lack of context but also lack of source material to draw from. Even unit tests only work with the most basic of cases, most of the time the setup is so complex it just produces garbage.

I've also had very little luck getting it to write performant code. I almost have to feed it the techniques or algorithms before it attempts to write such code, and even then it's usually wrong or not as efficient as it could be.