Most 'traditional' search engines are likely to already be 'AI' search engines anyway - there is a good chance you are transforming your query into a vector in

Most 'traditional' search engines are likely to already be 'AI' search engines anyway - there is a good chance you are transforming your query into a vector in a latent space, and searching for documents that are themselves annotated with a vector approximately close to your search vector (so they are already semantic search). The difference is more just what output format they give you the results in - bespoke text, or a set of links most relevant to the query. The extra layer of text2text processing probably doesn't add much if the top result already answers your query. What people really want to do though, is to set custom criteria that synthesise data across multiple different sets to draw novel conclusions that aren't in any webpage. That is probably very expensive computationally though, hence there still being a need for bespoke sites that semantically index data for certain types of queries.