Over the last 12 months I’ve integrated AI copilots (GitHub Copilot, Tabnine, etc.) into my daily workflow. They speed up boilerplate, suggest one-line fixes, and even refactor entire functions on demand.

But I’ve noticed something unsettling:

* Shallow Understanding: I sometimes accept suggestions without fully understanding them. * Problem-Solving Rust: On hard problems, I feel less confident in reaching a solution independently. * Onboarding New Devs: Junior engineers rely on AI outputs without questioning edge cases, leading to subtle bugs.

Questions for the community:

* Have you experienced skill atrophy or decreased ownership since adopting AI tools? * What practices help you preserve deep understanding while still leveraging AI speed? * Should we treat AI copilots as “draft generators” or as true programming partners?

I’d love to hear anecdotes, strategies, or hard data. Let’s figure out how to use these powerful assistants without becoming their apprentices.