I just wanted to comment on the "out of distribution" solution the author proposes, partly for the young grads on this forum.
Going "out of distribution" in abilities also means your job prospects go "out of distribution". When you specialize, so too does the kind of position you'd be the better fit for. This can mean radically fewer possibilities, and strong geographic restrictions.
To give an example, my PhD topic concerned something "that's everywhere" but, when you look at things more closely, there's only < 10 labs (by lab, I mean between 1 and 3 permanent researchers and their turnover staff) in the world working on it, and around that many companies requiring skills beyond gluing existing solutions together, in which case they'd just as well hire a cheaper (and more proficient) generalist with some basic notions.
This isn't even a very abstract, very academic field, it's something that gets attacked within academia for being too practical/engineering-like on occasion.
I understand the "belly of the curve" gets automated away, but consider that the tail end of the curve - producing knowledge and solutions to novel problems - has been for a long time, since Gutenberg's invention of the printing press, if not oral communication. The solutions scale very well.
A researcher's job is, almost by definition, to work themselves out of a job, and this has been the case since long before AI. Once the unknown has been known, a new unknown must be found and tackled. There's very, very few places in the world that truly innovate (not implementing a one-off novel solution produced in some academic lab) and value those skills.
I don't mean to be overly bleak, but it doesn't necessarily follow from this automation that the freed salary mass will go towards higher-level functions; just as likely (if not more), this goes towards profits first.
Seems like the hope, for OOD workers, is that matching weird employer needs with weird employee capabilities is a belly-of-the-curve problem that's about to get automated away.
Going "out of distribution" in abilities also means your job prospects go "out of distribution". When you specialize, so too does the kind of position you'd be the better fit for. This can mean radically fewer possibilities, and strong geographic restrictions.
To give an example, my PhD topic concerned something "that's everywhere" but, when you look at things more closely, there's only < 10 labs (by lab, I mean between 1 and 3 permanent researchers and their turnover staff) in the world working on it, and around that many companies requiring skills beyond gluing existing solutions together, in which case they'd just as well hire a cheaper (and more proficient) generalist with some basic notions.
This isn't even a very abstract, very academic field, it's something that gets attacked within academia for being too practical/engineering-like on occasion.
I understand the "belly of the curve" gets automated away, but consider that the tail end of the curve - producing knowledge and solutions to novel problems - has been for a long time, since Gutenberg's invention of the printing press, if not oral communication. The solutions scale very well.
A researcher's job is, almost by definition, to work themselves out of a job, and this has been the case since long before AI. Once the unknown has been known, a new unknown must be found and tackled. There's very, very few places in the world that truly innovate (not implementing a one-off novel solution produced in some academic lab) and value those skills.
I don't mean to be overly bleak, but it doesn't necessarily follow from this automation that the freed salary mass will go towards higher-level functions; just as likely (if not more), this goes towards profits first.