That's mostly tautological: the networks you call "deep" are the ones that use more layers. Unless you need funding or media attention, in which case you call everything "deep".
But you're making it sound like shallow networks are a thing of the past. I would compare this to the field of NLP, where it seems we don't have a good general idea what to do with deep networks, and the things that work so far are mostly shallow.
word2vec is one layer plus a softmax. GloVe is similar to a one-layer network. char-rnn seems to do as well with two layers as it does with three. All the gradient-descent classifiers out there are equivalent to one-layer networks.
But you're making it sound like shallow networks are a thing of the past. I would compare this to the field of NLP, where it seems we don't have a good general idea what to do with deep networks, and the things that work so far are mostly shallow.
word2vec is one layer plus a softmax. GloVe is similar to a one-layer network. char-rnn seems to do as well with two layers as it does with three. All the gradient-descent classifiers out there are equivalent to one-layer networks.