Embeddings

Plain English. Turning text (or images, or code) into lists of numbers — vectors — arranged so that similar meanings land near each other. Embeddings are the substrate under semantic search, RAG, recommendation and most "the AI understands your data" claims.

Why it moves money. An entire database category — vector databases — is priced on embeddings being indispensable. Two findings complicate that. In at least one careful study, plain text search (grep) beat vector retrieval on accuracy, with the harness mattering more than either. And embeddings turn out to be translatable across model spaces with no paired data, which means a leaked vector index can leak the meaning of what it encodes — a security property investors in the category should understand.

What to watch. Whether the vector-database layer defends its position against boring retrieval plus better harnesses, and how vendors respond to the inversion-attack literature.

From the signals. A Cornell-led paper showed embeddings translate across model spaces, exposing vector databases. grep beat vector retrieval — and the harness beat both.