I think the key word is ability, and I fully agree with that. Using GenAI as a teaching aid can supercharge learning, especially as it makes it very easy to learn by doing. The problem is that people use GenAI to do and hence don't learn.

(The preliminary research so far supports this: using AI to do the hard assignments produces poor learning outcomes, but using AI as a tutor, or even just for help with the hard assignments, produces slightly better learning outcomes.)

I think what you're seeing is the effect of the incentives of the system. The system uses simplistic numbers like grades as proxies for actual learning, and these grades heavily influence students' job prospects, and so you're simply seeing Goodhart's Law in action. Given how easy current methods of skill assessment are to game with AI, my guess is the entire system has to be overhauled.

Yes, I agree, the skills are orthogonal. Digital typesetting is vastly quicker than manually putting down metal type, and since you’re exposed to more type you have the opportunity to learn faster. But getting good at typography with digital tools will help you very little if you need to lay out type manually.

I wonder how much of this is due to poor incentives at the university level?

I've seen this work well at a job when there's a feedback loop for juniors that incentivized them to learn with more scope and compensation