I think we're talking about two different things here. I'm just talking about the fundamental prerequisites to do research in the field. I get the feeling that you're talking about something other, more ephemeral "thing".
if you do research in sorting/searching algorithms, even improving the performance of the best algorithm by a tiny bit would require quite a "special level of insight, or creativity, or whatever." Making a "BIG" advance would require genius level abilities, think a few dozens of people in the entire world.
Perhaps so, but we're not talking about research on sorting/searching algorithms. We're talking about AI. I would argue that the bar for publishing novel research is lower here, as the field is broader and correspondingly less developed (in areas).
I mean, right now, ML in particular is heavily based on empirical observation and experiment and lacks theory in many areas. So just trying some new combination of hyper-parameters that look interesting, achieving a "better than the latest published result" in some benchmark, and writing up a paper can constitute original research in this area. No, it's not the same as a major breakthrough in theoretical understanding, but then again not everything is. Which is at least part of my overall point.
It's probably also worth nothing that not all "research" happens in academic settings and not all researchers publish (or are driven to publish to the extent that academics are... especially tenure track folks). I'm thinking of corporate settings where simply achieving a useful result that helps the company make more money is considered a win.
All of that said, I'm open to the possibility that there are, indeed, fewer than 10k people who can do AI research, as I'm starting to wonder if there are fewer than 10k people who can write FizzBuzz successfully. But acquiring the basic tools necessary to do research in the AI/ML domain isn't so hard as to be inaccessible to a large number of people.