well, be careful with "more capable" - the universal approximation theorem shows that a NN with one hidden layer is able to represent any function (in the limit

well, be careful with "more capable" - the universal approximation theorem shows that a NN with one hidden layer is able to represent any function (in the limit of how wide that layer is, at least). So there's nothing fundamental about "more capable" or "less capable." It's only about which representations are easy to train and which aren't.