I know Phd.s at {MSFT, Google, Uber, Deepmind, GE, P+G} research divisions. I don't know anyone who works at those who does not have a doctorate. So I would say this applies to more than 'a handful of domains' -- If you want to do research, you should plan on having a doctorate, even in industry. The majority of data scientists I know have doctorates, even if that strikes me as (generally) overkill.
Otherwise, I agree with the rest of your statements.
I like programming languages and functional programming and haven't had trouble finding things to learn and people to help me, mostly through the Haskell community. You have everything from completely disorganized but perfectly accessible help like IRC channels and mailing lists to things like type theory meetups and study groups (if you live near enough people with those interests). It'll be easier in the Bay Area or New York, but moving here has lots of other benefits and isn't nearly as constrained as going to grad school.
Once you know enough on your own you can parlay that into a "sexy" job if you're so motivated. I interned at a company that had their own internal language, for example, and one of the main people working on their compiler had a philosophy degree and (unless I'm misremembering) no grad school whatsoever. It's more difficult because interesting jobs are relatively rare and a lot of places (especially ones close to academia) are still disproportionately concerned with credentials but it's possible.
As long as you can concretely demonstrate your capabilities, you have a chance at progressive companies without credentials. This still isn't true within academia itself, at many government roles or at big rules-bound corporations, but it is possible within the "core" tech industry.
From a degree-less "researcher" whose published in neuroimage, i'd say, wherever you can and as you learn more you get better at knowing where to start when searching/who to talk to/who to work with.
>Academia undoubtedly sucks if one mainly takes money or career prospects into account.
Or tired of trying to get your lab to work on more on the cutting edge of the technology vs the grant hamster wheel and status games or sucking up to people who clearly don't have any understanding of modern technology/physics/mathematics/techniques on a intimate level and squandering resources because they can, and figure you can get what you want done faster with more resources you can more directly allocate.
>…learning without the help of a community and a mentor is hard.
It's going to be "hard" no matter what path you take imo, but that what makes the journey of knowledge fun, because of that small chance you might actually figure something out you never knew before is worth ones time?
For credibility, I was a Computer Science PhD candidate 4 years ago.
Hard to answer without knowing your background and exactly what you want to learn, but generally speaking...
(1) Try to find some people who are good at something advanced you want to learn. Figure out a way to work with them.
(2) Be active in online communities. Ask and answer questions, write something and open source it, or contribute to another project.