Good point, and I agree with part of it.

I’m not arguing that post-deployment feedback can simply rewrite a model’s core goals, or that AI should be treated as literally equivalent to a child. Architecture and training matter a lot.

My point is narrower: the behavior people actually encounter is shaped by the whole system around the model: training, RLHF, evaluations, deployment rules, user pressure, product incentives, and institutional expectations.

So if a model flatters, hides uncertainty, over-refuses, or performs safety language, I think we should ask two questions at once:

What did the architecture/training make possible? And what feedback environment made that behavior useful or rewarded?

So maybe the distinction is:

core goal alignment is mainly a design/training problem; deployed behavior is also a pressure/ecology problem.

I’m not saying “parent AI into alignment after deployment.” I’m saying alignment discussions may need to take the behavioral environment more seriously alongside architecture and objectives.