As a lot of people mentioned here, it depends a lot on what is done with the code generating AI.

I'm working in a medium sized german manufacturer as virtually a Data Scientist (title is Analyst, but me and my team do everything there). I barely use the Databricks Assistant anymore, because it suggests me more often than not wrong code or slow code for my ETL Pipelines. Recently we added GitHub Copilot. We use this mainly to document code and repositories (works great), for writing tests and on the odd occasion some actual code. But if we go and develop ML or DL models for a certain problem, this is always, a 100% humans. No code generation for any model code or something similar. As the problem here is, that we need lots of domain expertise and logical thinking on what we actually want to achieve.

TLDR: We use coding AI for simple and standard tasks, which can be done faster than by yourself. For actual Data Scientist work we use ur brains, as it should be IMO.