But they can still compete on price. Migrant farm labor does that successfully in the present.
That's why it's different to the old luddite fallacy. Machinery and robotic factories throughout the ages have cost millions to replace 100's of jobs. Every time you want to build a factory, it costs money.
With software that is not necessarily the case. You write it once and then distribute everywhere at little to no cost.
Automation requires really understanding the process and formalizing it. It requires knowing all possible failure points, or at least having a "A Bad Thing Has Happened" failure point of last resort and learning the details of those over time. This will annoy people no end.
The cost of bad software can be substantial.
Nothing fancy. "machine learning" has a lot of space taken up by fancy filtered "running averages" and other curve fit ... things. Then there are Kalman filters. I can't even categorically say that what I did was or was not a Kalman filter.
Once you do that, then there are degenerate and ... overflow cases that propagate like fruit flies. You keep talking about "use cases" in the presence of people for whom that is foreign.
The hard part is then accountability. One "thing" can take two smart people who know this stuff well hours to hammer out.
Never say never, but because of observer bias and path dependence, it doesn't feel like that gets any better. At some point, a GA has to admit it is powerless in the face of its own inadequacy and ask a grownup what to do. So what you're doing is distilling the set of ... opportunities for difficulty into an increasingly higher proof.
I tend to classify a lot of learning computer problems as pre- or post-general AI. As in, would it essentially take a human equivalent AI to solve this problem or can we do it sooner with shortcuts and good programming?
ie. I used to think that computer vision was completely in this post-AI field, but the recent huge strides that have been made in deep learning etc. have changed my mind considerably. Human-like ability to recognize and differentiate objects is a major step forward in replacing humans around the workplace.