One last point is that a lot of the progress I've noticed in the tech world right now seems to be in the world of DevOps, which is what I believe is being referred to in point 2; a minimal number of systems administrators and developers are needed now to due to advances in deployment and debugging automation. Case in point: Google's servers broke and fixed themselves. Do we still need workers to do these tasks now? Definitely. 10 years from now? Not so sure, and flooding the job market with a bunch of "coders" certainly won't make matters better.
not amenable to analysis using classical methods.
Care to explain how this is true? I think I have an idea, but using "a healthy combination of certain areas of comp-sci (databases, machine learning, NLP, AI), statistical methods, and, quite often, improvisation" seems to be an even more obtuse approach than going about it the old-fashioned way. I'd much rather hear that people are using what we already know or (still better, but probably not as plausible) the latest mathematical advances regarding nonlinear systems rather than just ad-hoc'ing methods because... computers! I believe that this is at least partiallly Nassim Taleb's objection to the entire field of data science as well. How many 'results' coming from data science are the product of sound and rigorous methodologies, and how many are just due to chance/data dredging?
If you want to stay out of France, France will not miss you.
Otherwise, I have seen plenty of rich people who were also pretty bad with money.
Namely Donald Trump. Who is revered by American society for... absolutely no good reason whatsoever.