[mrbungie]

Wouldn't say math loses usefulness after trig. But rather, due to how it is (usually, my experience being in Chile) teached it rapidly becomes too abstract, decouples from its "real world" use cases and then it is easy to forget the forest for the trees.

The Mathematics for Machine Learning book[1] exposes this as a top-down vs bottom-up problem. While both approaches have pros and cons, a sweet spot may lay somewhere in the middle and that needs you to embrace some inevitable backtracking (i.e. college curricula should not forget to add some courses where world modelling using the math and throughfully explaining why that underlying theory and math is actually useful in describing and/or predicting reality).

PS: I also think there is still a lot of focus in resolving problems manually.

[1] https://mml-book.github.io/book/mml-book.pdf, page 13.