The thing about these layers of abstraction is that they add load and thus increase the demand for people and teams and organizations that command these lower levels. The idea that, on a systemic level, higher abstraction levels can diminish the importance, size, complexity or expertise needed overall or even keep it at current levels is entirely misguided.
As we add load on top, the base has to become stronger and becomes more important, not less.
It's not hopeless though, it feels like that in the past decade, some of the smartest minds working at the lower levels of abstractions have come up with great new technologies. New programming languages that push the envelope of performance and security while maintaining good developer experience, great advancements in microchip technologies, that kinda thing.
It's important to maintain access to universities and higher education, where people who have the interest and mindset can learn and become part of this base that powers the greater software market.
Sure, they will enable even more people proportionally to not think about those low level systems. But my argument is that the need for that low level expertise has always expanded and will keep expanding.
Automation entails tonnes of complexity that need to be managed. It doesn't just evaporate. More automatic systems will demand more people and teams to learn low level systems in great detail and at high levels of accuracy.
So part of this is just another abstraction. But another part, which I agree with, is that abstracting how you learn shit is not good. For me, I use AI in a way that helps me learn more and accomplish more. I deliberately don’t cede my thinking process away, and I deliberately try to add more polish and quality since it helps me do it in less time. I don’t feel like my know-how is useless — instead, I’m seeing how valuable it is to know shit when a junior teammate is opening PRs with critical mistakes because they don’t know any better (and aren’t trying to learn)