High dimensionality does not necessarily mean more complex. In fact, it has been known for quite a while that going to higher dimensions makes various problems easier; non-linearly separable datasets suddenly become separable in higher dimensions for example. Turning this to 11, you basically get kernel machines. Kernels embed the data into potentially infinite dimensional spaces, and that has been very successful before deep learning took over.