They are universal approximators, although the term approximator means there's an upper bound on the accuracy of how well they emulate a given function (based on the size of the network). However, just because they are universal approximators doesn't mean that you can automatically infer the optimal number of connections and weight for each of those connections in order to minimize the loss over some dataset (derived from some function). Being able to be a universal approximator does not imply you can automatically learn the best approximation of a given function. It's the difference between being capable of learning something, and having learned it. If that makes sense.