Those who think the challenge is to overreach the limitations imposed by programming languages and types, etc, to do seemingly "magical" things. These are ones who would never understand why you might want higher-kinded types, the relative pros and cons of the functional vs imperative approaches, or even of type systems in general. They also tend to think the code written, in itself, is the valuable output of their work.
And then there are those who think the challenges is to model problem domain so well, using the language features, types and data structures, etc, that the solution (and further adjustments to it) becomes obvious and straightforward. They also tend to think the analysis and model that results is what is valuable.
Another way to see this difference: what impresses them? it is a hack that uses arcane knowledge to bypass software and hardware "limitation" or structures? Or is it code that is simple but still effective at solving complex problems in elegant ways, that may look simple but reflects a huge amount of knowledge, skill, and architectural intuition built up over many years?
I of course think that the second group are more effective and are doing proper programming, but many programmers and the general public think otherwise, or can't event tell the difference. I once worked with some colleagues could never see the light, despite a lot of support from me.
So here is the kicker, the dissonance in response to AI, I think, is kind of analogous to these two groups. The first groups likes that the AI will not stop them, but rather get them much quicker to their "magic", whereas the second group is more skeptical and sees many issues with AI not seeing the big picture and understanding deeper intent.