Evan then goes on to try to use the complexity of the simplest member of each model class as an estimate for the size of the classes (which is probably wrong, IMO, but I’m also not entirely sure how he’s defining the “complexity” of a given member in this context)
[Low importance aside]
I think this is equivalent to a well known approximation from algorithmic information theory. I think this approximation might be too lossy in practice in the case of actual neural nets though.