Funny story to build on this. I spent a couple of days last week building a Powershell script for database refreshes (backups + restores). The process was iterative (typical), and AI helped me solve all the problems we've ran into historically here. It helped me write tests for all the gotchas I had stored in my head from watching various approaches fail over the last couple of years. I ended up with pre and post hooks on every side, and at a high level I'd vastly improved the time to delivery because (as I initially stated) I knew EXACTLY which problems I needed to solve there, and AI helped me get there in a fraction of the time. The end result was a common .psm1 file, 2 ps1 scripts (the orchestrator and executor script), and a total of about 2k lines of Powershell, which I hadn't combed through line-by-line because the tests were solid and I tested it in non-prod prior to releasing to production.

The first run in production ended up causing an unhealthy db in RECOVERING state because there was a tiny logic bug where the restored db wasn't verified as ONLINE before it set it MULTI-USER (which our db state monitors caught within the couple seconds of that being true).

I think AI has enabled us to solve problems much quicker without being intimate with every tiny detail of the code itself. The funny thing about the story is that this isn't a novel problem. Coming in behind a peer who wrote the thing, or even coming in to debug a script I'd written myself a year ago is the same. AI has just pushed up the time to lacking knowledge of precise details of logic, so you can ship something and be unaware of EXACTLY what it does the same day, instead of 6+ months later.

I'm not sure if this is right or wrong, but debugging it was 3 minutes of presenting the issue back to AI instead of 2 days of combing through 2k lines of Powershell that I may or may not have written.