Thanks for the detailed critique.
I think we might be talking past each other on the "super-manager" term. I defined it as a hybrid of EM + IC roles, not pure management, though I can see how that term invited misinterpretation.
On the false dichotomy: fair point that I painted two archetypes without acknowledging the complexity between them or the many other archetypes. What I was trying to capture was a pattern I've observed: some skills from managing and reviewing others' work (feedback, delegation, synthesizing approaches) seem to transfer well to working with AI agents, especially in parallel.
One thing I'm curious about: you said my framing overlooks "the real benefit of going through the process of authoring a change." But when you delegate work to a junior developer, you still need to understand the problem deeply to communicate it properly, and to recognize when their solution is wrong or incomplete. You still debug, iterate, and think through edge cases, just through descriptions and review rather than typing every line yourself. And nothing stops you from typing lines when you need to fix things, implement ideas, or provide examples.
AI tools work similarly. You still hit edit-compile-test cycles when output doesn't compile or tests fail. You still get stuck when the AI goes down the wrong path. And you still write code directly when needed.
I'm genuinely interested in understanding your perspective better. What do you see as the key difference between these modes of working? Is there something about the AI workflow that fundamentally changes the learning process in a way that delegation to humans doesn't?