On the flip side, if the rest of the process becomes easy and in some sense standardized (the art/craft of code is replaced with back-prop), then maybe the complements like data cleaning, model bias/error measuring, and interpretability become “the work” that the high-paid scientists/engineers focus on?
I can particularly imagine a regulatory environment much more rigorous than software has got away with thus far, for example strict requirements around certifying that your model doesn’t exhibit X Y Z biases according to standard frameworks of evaluation.
Not if the ladder gets pulled up behind us. If there's nowhere to train people with 0 years of experience to do this work effectively, then new developer creation will drop by 10x which means five years in we'll be missing 40% of the workforce.
What often divides the excellent engineers from the poor ones is how well they can think about corner cases, and tests are mostly about writing down the corner cases in a sustainable way (vs half-assedly writing down half of the corner cases and writing 0.5% of tests as asserting a bug.)
The main problem I saw with DevOps and QA automation was that if people don't write code all day, having them write code that gates release of software to production does not result in good outcomes. Sooner or later developers have to inject some engineering practices.
If the engineers are running AI generated code, nobody knows how to do that job, and you will get a long string of permanently damaged brand names in the aftermath.