* People using it as a tool, aware of its limitations and treating it basically as intern/boring task executor (whether its some code boilerplate, or pooping out/shortening some corporate email), or as tool to give themselves summary of topic they can then bite into deeper.
* People outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are interested only in results, and are not interested in knowing more about the topic or honing their skills in the topic
The second group is one that thinks talking to a chatbot will replace senior developer
From my perspective the distinction is more on the supply side and we have two generations of AI tools. The first generation was simply talking to a chatbot in a web UI and it's still got its uses, you chat and build up a context with it, it's relying heavily on its training data, maybe it's reading one file.
The second generation leans into RAG and agentic capabilities (if you can glob and grep or otherwise run a search, congrats you have v1 of your RAG strategy). This is where Gemini actually scans all the docs in our Google Workspace and produces a proposal similar to ones we've written before. (Do we even need document templates anymore?) Or where you start a new programming project and Claude can write all the boilerplate, deploy and set up a barebones test suite within a couple of minutes. There's no doubt that these types of tools give us new capabilities and in some cases save a lot more time than just babbling into chatgpt.com.
I think this accounts for a lot of differences in terms of reported productivity by the sane users. I was way less enthusiastic about AI productivity gains before I discovered the "gen 2" applications.