The centralized choke point of web search is getting relaxed now. Unlike search engines and social networks, you can download a LLM and run it. A small one, but capable of using a library of search stubs to directly fetch information from hubs, feeds and other search engines. You can own the agent who can solve the web search part for you.

Imagine you have a 4B model and keep an equal size corpus of search stubs, small MD documents linking to hubs, feeds and search engines for millions of topics. You can use the LLM to read the stub and perform the search for you, all orchestrated locally, with greater privacy and independence. You can dis-intermediate the search chokepoint now. You can set the criteria for what to include, exclude, how to rank and present the results.

This works because good entry points for any topic change slowly over time. The construction of search stubs is trivial with existing AI agents, and can be shared as open source. A few GB for the model, a few for the search routing layer, and you got a sovereign local agent.

If this holds, access control shifts from whatever Google thinks maximizes profit to whatever the community thinks has value.