I agree with many aspects of your assessment, but when you say, "The concept of an easy to use, fire-and-forget, scalable search engine that gives you great relevance out of the box isn't new. Yet the bag-of-words model remains the standard across the sector ...", I think you have to weigh the emergence of transformer-based neural networks around 2017 more strongly.

In 2018, BERT, the first demonstration of a pretrained large language model (LLM), exceeded human performance on Stanford's Question Answering Dataset [1]. Nobody in 2010 predicted such rapid progress.

Between 2017 and 2020, I worked with several teams managing very complex search systems. In one case a single LLM obviated dozens of hand-tuned relevance signals developed over the better part of a decade.

One of the main effects of neural search adoption will be raising the baseline quality of search; a second will be reduction in the overall cost and complexity of search impementations.

For example, it's not easy to configure a keyword system to find "works fine, We have two Roku's [sic] in other televisions which are working fine" in response to "does it work with different tvs?". But neural search finds this result directly, without any tuning or configuration [2].

Thank you for sharing the video and the article!

[1]: https://www.nytimes.com/2018/11/18/technology/artificial-int...

[2]: https://www.youtube.com/watch?v=Tn7AqmY9yaY&t=112s