The main worry about ML is that I can see a parallel with the infamous problem where people created advanced Excel spreadsheets containing small but significant errors and it became Garbage-In/Gospel-Out. I can't blame The Great Recession on that, since I was unwittingly part of the cause, and the fragile valuations we computed for Mortgage-Backed Securites were not done using any sort of AI or spreadsheet, but I've heard rumors enough on what a bad spreadsheet could do to a major corporation.
And the fatal flaw with ML is that it cannot extend beyond what it knows. I've failed at least one "programming aptitude test" because I knew more than the
test creators and knew more than one "correct" answer while knowing the weaknesses of what they considered "correct".
The thing to realize about ML is that it's less about math (exepting perhaps statistics) and more about data. There are plenty of publicly-available ML engines out there. In fact, in front of my I have a PCB that has TensorFlow Lite built into it and it cost me about $16. Someday I plan to train it to send me an alert when its onboard microphones hear the "victory songs" of my laundry-room appliances.
What's really valuable and proprietary are the data sets. A good ML system needs LOTS and LOTS of data and the holders of that data would like to be paid for the effort of collecting it. Or, in cases like financial trading sets, want to have a competive secret for their machinations.
The secret of how to be miserable is to constantly expect things are going to happen the way that they are "supposed" to happen.
You can have faith, which carries the understanding that you may be disappointed. Then there's being a willfully-blind idiot, which virtually guarantees it.