The existence of a universal function approximator or function representation is not particularly unique to neural networks. Fourier transforms can represent any function as a (potentially) infinite vector on an orthonormal basis.

What would be particularly interesting is if there were a proof that some universal approximators were more parameter efficient than others. The simplicity of the neural representation would suggest that it may be a particularly useful - if inscrutable approximator.