That's about as interesting as saying that a Taylor series can approximate any analytic function arbitrarily well given time and space. Or that a lookup table can approximate any function arbitrarily well given time and space: see also the Chinese room example.

The first question is whether that neural network is learnable. Sure, some configuration of neurons may exist. Is it possible given enough time and space to discover what that configuration is, given a set of inputs and outputs?

The second question is whether "enough time and space" means "beyond the lifetime and resources of anyone alive," in which case it seems perfectly reasonable to me to call it a limitation. I generally want my software to work within my lifetime.