I should have loved electrical engineering

EE encompasses a lot of "engineering that takes hard math" at a professional and research level (similar to "hard CS," just different fields of math), so it is very hard to do as an undergrad, when your background in complex analysis and E&M is weak.

Early classes on circuits in EE will usually take shortcuts using known circuit structures and simplified models. The abstraction underneath the field of analog circuits is extremely leaky, so you often learn to ignore it unless you absolutely need to pay attention.

Hobbyist and undergrad projects thus usually consist of cargo culting combinations of simple circuit building blocks connected to a microcontroller of some kind. A lot of research (not in EE) needs this kind of work, but it's not necessarily glamorous. This is the same as pulling software libraries off the shelf to do software work ("showing my advisor docker"), but the software work gets more credit in modern academia because the skills are rarer and the building blocks are newer.

Plenty of cutting-edge science needs hobbyist-level EE, it's just not work in EE. Actual CS research is largely the same as EE research: very, very heavy on math and very difficult to do without studying a lot. If you compare hard EE research to basic software engineering, it makes sense that you think there's a "wall," but you're ignoring the easy EE and the hard CS.

>Early classes on circuits in EE will usually take shortcuts using known circuit structures and simplified models.

Might just be me, but I found it all clicked when we started learning the fundamentals underneath these abstractions. For me it was harder in the first classes because it's about memorizing poorly understood concepts, my brain prefers logically deriving complex concepts as a learning method.

My biggest criticism of EE pedagogy is that it tends to proceed from abstractions and then derive the whole world. This makes it a bit of a slog for a lot of students. I’d like to see an application-first approach that builds up principles from observed behavior. Like, measure the slip in an induction motor and then work out what’s going on there, instead of deriving motors from Maxwell’s equations.