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Notes -
I would say only partial.
Recommendation algorithms were generally clustering approaches and support vector machines, I think. Neural networks were regarded as a potential (but not very good) way to do clustering for the most part, and one of the clusterings they were trying to do was vision (image class analysis). The breakthrough moment in my opinion was the publication of AlexNet in 2012, which demonstrated that neural networks weren't just a way to do clustering and image analysis, but that if done right they were a better way to do it and produced a step change in performance on a task usually considered impossible.
Transformers came out of google's work on translation, which has a fairly different heritage. That's less about clustering and more about sequence analysis using autoencoders, recurrent neural networks, etc. Data-driven neural networks were already pretty much considered SOTA in that field if I remember rightly (Google's lead researcher famously said, 'our translation performance goes up 10% for every linguist I fire') but benefited from the massive increase of interest in neural networks, hardware for driving neural networks, collecting big data pools, and general theoretical work on representations/efficiency/etc.
I don't know that AlexNet et al were truly breakthroughs in and of themselves so much as a logical extension of existing NN stuff enabled by the step-change in GPU hardware -- as I recall LeCun was doing similar work in the 1980s, and I myself did a project with a simple one-layer LSTM network in the late 90s in which I had to work at night because the server load involved was bringing down other (so-called) "important workloads" at my department (at a major university) during the day.
None of this was really feasible, even as research, until pretty near 2010 -- the hardware just wasn't there yet.
They weren't, really, I agree. "Deep learning" as in Massively-Multilayer Neural Networks was known to be a good thing ages ago, we needed the hardware to do it. I would say AlexNet was a breakthrough in the since of marking a step change from, 'hey guys if you gave us some really massive computers I bet this toy would do something useful' to 'holy shit guys, see what I can do'.
All credit to the neural network guys for sticking to their guns until the hardware caught up, of course.
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RankBrain (2015) was pre-Transformer and was used in production search. Basically a learned embedding model for semantic relevancy.
AlexNet was the turning point, I agree.
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