What I Learned From Analyzing Google’s AI Mode Patent

If you’re not familiar with embeddings, think of them as mathematical representations of meaning. Instead of storing your literal search history, Google converts your behavior into numbers that capture relationships between concepts. 

Basically, it’s search history as vector math. This is a direct application of semantic search, and it’s not brand new. Folks like Dan Hinckley have shown how Open AI’s patent highlights the importance of semantic SEO to chunk content, embed it into vector space, and match it against intent.

What’s new is how Google applies it to users themselves. Each person ends up with a kind of semantic fingerprint, similar to a dynamic, multidimensional snapshot that includes explicit queries, implicit signals, and past interactions.

A user is no longer just a single query, but a constantly evolving semantic embedding that represents Google’s holistic understanding of their intent, context, and knowledge. 

Yes, it’s giving The Matrix.