The people with experience also aren't necessarily the ones that end up teaching - which isn't to say the same information can't be conveyed necessarily (e.g. good academics keep up to date with the field in industry) but there is powerful focus that practical experimentation brings.
The ones with experience are most often not the ones teaching. You know what they say, those who can’t do… teach.
Some people like to teach
I know. I love to teach too. But I’d never take it up as a profession. The vast majority of successful people who have a yearning to pass on their knowledge hand select a few protégés or write a book.
I have had one teacher who was like that. He’d been involved in the development of nuclear weapons before he retired. Incredibly smart guy. Unfortunately, he couldn’t teach physics worth a dime. He had the highest drop out rate of any physics teacher at my college.
Those two scenarios cover the vast majority of cases.
The reason a lot of the theoretical basis is taught is because you need to get the skills to learn why things work, when to use them, when they fail, when not to use them, and __most importantly__ their limitations. The problem is, most of this isn't explained explicitly. Maybe just this process happening for a few decades and momentum. Or that teaching isn't a priority and so no one tries to fix it. (there are exceptions to this. You've all probably met professors that are outstanding and make boring things seem fascinating)
But what you're talking about is part of this "when to use, what to use" part. It is also why those classes are so boring, because they aren't properly motivated. But it is also why we're running into so many problems: because evaluation is fucking hard. You see models perform really well on research papers but not in the real world but you'll also see researchers evaluating papers purely on singular benchmarks. "In reality" you're forced to come to terms with the limitations of the limitations of datasets, as datasets are just proxies and what you are about is the actual generalization. But if we're not discussing and evaluating on actual generalization in research then we get this dichotomy.
There's definitely more efficient (tractable) posterior estimators that work at large scale but just a lot of stuff isn't really known unless you're in that niche yourself. Statistics is often taught from the reference of "here's a bunch of tools and when to use them" rather than "here's the problems, our assumptions, and the main tool we use to solve them. It looks different in different settings, but they are actually the same thing." So it is kinda problematic, but then again, to get there requires a lot more work and most people aren't going to bother with things like metric theory. So a middle ground approach is taken and it gets jumbled.