I disagree. There are lots of advancements that DL has yet to fully realize with even the current technology. You're focused on commercial applications but applying neural network models, especially CV models to many types of scientific research has yet to be explored due to lack of funding.
I'd like to think I put my comments "as potential problems" since I can't claim to follow everything that's done as deep learning.
Still, to continue the devil's advocate position. Deep learning comes up with a lot of things that are suggestive but not tight enough in their approximation to be useful.
I would guess there are huge number of correlations that seems plausible but aren't really causations. You can apply employ a monster stream of sort of intelligent seeming claims and predictions and find they don't yield any progress in any firm scientific domain. The application of deep learning to finding cancer and related diagnosis processes has been "exciting and promising" for a long time but effectively yielded nothing so far because "quite accurate in highly controlled situations" turns out to seldom be that useful, at least not so far.
