The problem with search is not that our answers aren't summarized well, it's that the quality of information returned for those searches is getting increasingly worse, and we are getting increasingly worse at categorizing or filtering that information in any useful way. And LLMs pulling information in and summarizing it for me is... not helpful? It's summarizing the same garbage, except now sometimes it also summarizes it wrong.
But it's not even an issue with the quality (although the quality of information from LLMs is also pretty over-hyped I think). Conceptually, I don't know that this is a product that I would ever want. I can't think of any time where I've sat down to do a search on Google or DuckDuckgo and thought, "You know what I want? I want these results presented to me in a less structured format using natural language and with less granular knowledge about where each specific statement is coming from."
At least Bing seems to be trying to do inline citations in some of its answers, which is a step up over Google's AI announcement, I guess?
Maybe I'm just in the minority on that. Users seem to like this a lot. But my ideal version of the Internet is one that decreases the number of abstractions and layers and summaries between myself and primary data rather than increasing them. My ideal Internet is a tool that makes it easier for me to actually find things, not a tool that increases the layers between me and the raw source/information that I'm looking for. I already have enough trouble needing to double-check news summaries of debates, events, and research. Getting another summary of the summaries doesn't seem helpful to me?
I can think of some ways where I might use an LLM in search, even really exciting ways where maybe it could help with categorization or grouping, but it doesn't seem like Google/Bing are interested in pursuing any of that. I look at both the Bing and Google announcements and just think, "why are you making it worse?" But who knows, maybe the actual products will sell me on the concept more.
The first is a little more mundane: LLM embeddings. OpenAI currently offers an API that turns sentences into coordinates for a point in some 1536-dimensional conceptual space such that two points are close together if they are conceptually close together. This is insanely powerful. For example, you can generate captions for a bunch of images and store the embeddings for them. Then, you can look for a "picture of a rabbit eating a carrot" by turning that phrase into a 1536-dimensional point and looking for the nearest points around it. Basically, it blows open search technology for everyone. You no longer have to deal with synonyms, idiomatic phrases that mean similar things, misspellings etc - the problems you'd run into when trying to implement simple text search using traditional techniques. It all gets simplified to generating coordinates in some hyperspace and looking for nearest neighbors. This is a total game changer.
The second direction is ChatGPT. Sure, if you want to read a detailed analysis of the demographic situation in China, you'd prefer an article written by an expert. You would still use a search engine, pick a search result and do things the way you do them today. However, there's an entire collection of things that can be answered directly by ChatGPT. For example "how many mins should I hard boil an egg" or "Can I take NyQuil when I'm stoned" or anything else where you really just want a single sentence answer. Today, you launch a browser, search for what you want, skip past the first 10 advertisements, look for a site that seems reasonably reputable, click through all the GDPR warnings, scroll past the banner ads and the SEO optimizing bullshit text to find that one sentence that you wanted all along. Or, you could ask ChatGPT and get an answer instantly. (assuming chatGPT is good enough eventually).
It's hard to predict which of these two technologies will disrupt the current status quo in search. Neither might. But we haven't ever been this close to a level playing field in search since the 1990s. The excitement is hard to resist.