Why do high-dimensional spaces behave counterintuitively in linear algebra?

Distances and volumes behave differently as dimensions increase.

In high dimensions, vectors become almost orthogonal and volume concentrates in corners. Linear algebra operations like dot products become less intuitive, impacting ML model behavior.