> 1) search engines will be replaced with monolithic large language models
This is already well under way. It's called vector search[1]. Google, Bing, Facebook, Spotify, Amazon, etc etc already use this to power their search and recommender systems.
There are even a bunch of companies popping up (I work for one[2]) that let everyone else get in on the fun.
Check out this video with the creator of SBERT / SentenceTransformer explaining how vector search is used in combination with language models to power semantic search: https://youtu.be/7RF03_WQJpQ
IMO vector search is pretty much a solved problem with simple to use, open source libraries like Faiss offering incredible performance out of the box for most commercial use cases.
A much harder problem is creating accurate vectors to begin with. Even the most advanced language models today create word/sentence embeddings that leave a lot to be desired. Not to mention this is slow and GPU intensive.
Creating an end-to-end solution for embedding/searching/ranking (of which vector search is just one component, the other one should be some kind of keyword based search to increase precision) is what would be very valuable to offer as a service.
Well under way? Some variation of a vector space model is what pretty much every IR model since the .com bubble has been based upon. Even before Google, Excite's technology was based upon this. PageRank was based on spectral graphs essentially.
This is already well under way. It's called vector search[1]. Google, Bing, Facebook, Spotify, Amazon, etc etc already use this to power their search and recommender systems.
There are even a bunch of companies popping up (I work for one[2]) that let everyone else get in on the fun.
Check out this video with the creator of SBERT / SentenceTransformer explaining how vector search is used in combination with language models to power semantic search: https://youtu.be/7RF03_WQJpQ
[1] https://www.pinecone.io/learn/what-is-similarity-search/
[2] https://www.pinecone.io