It seems like everyone is reading this article to be about public web search. But what the article is really about is how products are using new AI tech to add

It seems like everyone is reading this article to be about public web search. But what the article is really about is how products are using new AI tech to add shiny features instead of solving existing, core problems like search and information retrieval in new and innovative ways, especially for personal and private data. The reason this is happening is pretty easy to explain: the generative AI and chat demos are a sufficient leap beyond what was previously possible that people are excited to be on the frontier of new applications, not just new implementation of previously known use cases. Not to mention that some of the demos have people excited about the "singularity" being closer than they might have previously thought (though this can be debated...) and that VCs will shovel money to you if you want to play around with generative AI even without a proven use case (slight exaggeration but not much) I personally believe that transformers and LLMs do unlock a ton of new applications, especially when applied in interesting ways to interesting data, like what is private-to-you or private-to-your company. For example, LLMs can be used to not just generate content, but plan sequences of actions including searches, summarizations, and even calculations (see LangChain agents https://langchain.readthedocs.io/en/latest/modules/agents.html https://langchain.readthedocs.io/en/latest/modules/agents.ht... for an example of how to do this). And this can have real value for existing, known problems like search. People just have to choose to focus on these less-sexy but core problems ... PS I'm currently working on a project towards this goal, and if anyone is interested I'd love to talk (see link in profile). I believe we can solve much of the author's desire by simply hooking up the right tech to the right data sources, and doing it in a privacy preserving way (for example we're running most of our ML including vector DB, summarization, etc on device) and then present that info at the right time (ie in your OS)