I'm not disagreeing that it's good to keep humans in the loop, but the systems I've worked on give domain experts new information they could not get before -- for example, non-invasive in-home elder care monitoring, tracking "mobility" and "wake ups" for doctors without invading patient privacy.
I think at its best, ML models give new data-driven capabilities to decision makers (as in the example above), or make decisions that a human could not due to the latency of human decision-making -- predictive maintenance applications like detecting impending catastrophic failure from subtle fluctuations in electrical signals fall into this category.
I don't think automation inherently "de-skills" humans, but it does change the relative value of certain skills. Coming back to agentic coding, I think we're still in the skeuomorphic phase, and the real breakthroughs will come from leveraging models to do things a human can't. But until we get there, it's all speculation as far as I'm concerned.
But I agree that the space is wide enough that different interpretations arise depending on where we stand.
However, I still find it good practice to keep humans (and their knowledge/retrieval) as much in the loop as possible.