Too early to say that with a straght face yet.
"Deep learning" produced some amazing generative art, and some very flashy research papers, but it failed to drive business decisions. (And not for the lack of trying, that's for sure.)
You don’t have to fall for the hype and think deep learning will lead us to AGI in the next 5 years, but dismissing it as generative art and flashy papers isn’t any more accurate.
As for Alexa, et al - these things fall into the "generative art" bucket. The search results they give aren't any better than the Eliza-tier expert systems of yore. They just feel much better and more human when you use them.
(Which is also important, but doesn't drive business decisions, except as part of a marketing strategy.)
I personally own a startup that uses deep neural networks as part of its core product functionality and our business decisions would be drastically different if it weren’t for modern machine learning. We could technically ship some similar products, but they would either be far inferior or take orders of magnitude longer to engineer by hand.
Deep learning is a cool feature to differentiate Alexa from competing home electronic gadgets.
But if you want to forecast Alexa sales, or understand market segmentation, or the portrait of a typical Alexa buyer then you need something other than deep learning because neural nets utterly failed in this domain.
The business metrics problem is vastly, vastly more important than the "making cool gadgets" problem, and huge resources were poured into making deep learning a thing in this space. The money was mostly wasted. (A negative result is still a result, but still the misallocation of resources is staggering.)
FYI, deep learning isn’t a differentiating feature for the Alexa, it powers essentially all modern voice applications.
https://ai.googleblog.com/2020/11/improving-on-device-speech...