When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a must-have infrastructure layer for the gen AI era. Billions of venture dollars flowed, developers rushed to integrate embeddings into their pipelines and analysts breathlessly tracked funding rounds for Pinecone, Weaviate, Chroma, Milvus and a dozen others.The promise was intoxicating: Finally, a way to search by meaning rather than by brittle keywords. Just dump your enterprise knowledge into a vector store, connect an LLM and watch magic happen.Except the magic never fully materialized.Two years on, the reality check has arrived: 95% of organizations invested in gen AI initiatives are seeing zero measurable returns. And, many of the warnings I raised back then — about the limits of vectors, the crowded vendor landscape and the risks of treating vector databases as silver bullets — have