as you’ve listed, there are answers within reach but switching costs are enormous and the labor market is locked into a set of known technologies that generate salaries, a low energy state that would require substantial energy investment in reskilling, but there’s no slack energy in the system for that and even if there was, the transition will have winners and losers, redistribute jobs, and if there’s one thing the labor mkt is terrified of it’s losing one’s job. disruptive technology shifts can be career ending for a lot of people … bottom line is that getting paid to shovel python slop has been historically lucrative
I think you’re right to point out the inertia in the labor market and the structural energy required for paradigm shifts.
What worries me most, though, is that because of exactly the reasons you’ve described — and now, with the rise of GenAI that makes generating boilerplate imperative code even easier — we might be permanently missing the opportunity to invent a better, more elegant solution.
Instead of challenging the underlying paradigm, we’re accelerating its replication — just with fewer keystrokes. GenAI is helping us pave cow paths, not design highways.
My fear is that the convenience of generating "good enough" code at scale will solidify suboptimal workflows, and the space for reimagining them might quietly close forever.
life finds a way