[ben_w]

Computer science is indistinguishable from sufficiently advanced maths.

The AI can already do that part.

The abstraction that matters going forward, is understanding why the abstraction chosen by the AI does or doesn't match the one needed by the customer's "big picture".

The AI is a bit too self-congratulatory in that regard, even if it can sometimes spot its own mistakes.

[ndriscoll]

A lot of studying math is just learning jargon and applications for what are actually pretty straightforward concepts, which lets you better communicate with the computer. You get higher bandwidth communication and better ability to know all of the nuances in things it might propose. You can propose things and understand when it replies with nuances you missed.

Like intro differential geometry is basically a deep dive into what one actually does when reading a paper map. Something everyone (over 30?) is familiar with. But it turns out there's plenty to fill a graduate level tome on that topic.

Linear algebra is basically studying easy problems: y=ax. Plenty to write about how to make your problem (or at least parts of it) fit that mould.

I suspect and think I've seen others say that you get better outputs from LLMs when using jargon. Essentialy, its pattern matching tells it to say what an expert would say when using the terminology experts use.