"software engineers are going to have to get better at thinking in larger abstractions"
........Math was first on my list.
I don't know how else to say that.
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.
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.
Yep, exactly. The failure to realize that you mean different things when talking about "larger abstractions" is exactly the kind of miscommunication that software people will need to navigate better in the future.
I’m not sure that my colleagues who I think of as “good at math” and “good at thinking in larger abstractions” are necessarily the same ones, but there’s definitely a lot of overlap.