For me, most software development is about finding something boring and laborious. We get a computer to do the work so humans can level up and work on something requiring actual thought. That requires getting a deep understanding of the actual work.

Some of that definitely happens in well-run ML projects. But there's a bunch of Silver Bullet Syndrome stuff going on, where ML's shiny results and magazine articles lead to inflated expectations and inflated claims of success. A fellow nerd says, "I did an algorithm!" Some turns that into an impressive presentation with claims of X% gains in the Key Business Metric, hallowed be its name. In reality, it's more plus or minus X% when you account for externalities, natural variation, and actor adaptation. But that's ok, because by the time anybody finds out, attention is elsewhere.

That's not to really blame ML for that. For a period years ago, I kept getting asked, "Can we use a wiki for that?" I would start an explanation of what it actually takes to make a wiki work (hint: it's not the software). Their eyes would glaze over in short order, because they realized that it would take actual work. So many people want the silver bullet, the magic pill. Especially people in the managerial caste, as the reigning dogma there is that management is a universal skill. Details are for the little people.