In computer vision at least, deep learning has been a revolution. More than half of what I knew in the field became obsolete almost overnight (it took about a year or two I would say) and a lot of tasks received an immediate boost in term of performances.

Yes, neural networks have been here for a while, gradually improving, but they were simply non-existent in many fields where they are now the favored solution.

There WAS a big fundamental paradigm shift in algorithmic. Many people argue that it should not be called "neural networks" but rather "differentiable functions networks". DL is not your dad's neural network, even if it looks superficially similar.

The shift is that now, if you can express your problem in terms of minimization of a continuous function, there is a new whole zoo of generic algorithms that are likely to perform well and that may benefit from throwing more CPU resources.

Sure it uses transistors in the end, but revolutions do not necessarily mean a shift in hardware technology. And, by the way, if we one day switch from transistors to things like opto-thingies, if it brings a measely 10x boost on performances, it won't be on par with the DL revolution we are witnessing.