  We establish a scale separation of Kolmogorov width type between subspaces of
a given Banach space under the condition that a sequence of linear maps
converges much faster on one of the subspaces. The general technique is then
applied to show that reproducing kernel Hilbert spaces are poor
$L^2$-approximators for the class of two-layer neural networks in high
dimension, and that multi-layer networks with small path norm are poor
approximators for certain Lipschitz functions, also in the $L^2$-topology.
