Stop Using Vector Databases for AI Memory

Most AI engineers reach for a vector database when they need memory. It feels obvious. Store embeddings, retrieve by similarity, done. It is the wrong architecture. In this video we break down why semantic similarity is not the same as relevance, why larger context windows make the problem worse, and what a properly designed AI memory layer actually looks like in 2026. We cover OpenHuman, an open-source project building a billion-token memory engine called Neocortex that runs on a MacBook Air CPU with zero LLM dependency, and a continuously running subconscious system modelled on neuroscience. What we get into: * Why vector retrieval fails at scale * The four-tier memory model that mirrors how biological memory works * Knowledge graphs vs flat text storage * How a background intelligence layer runs 10,000 times a day for under $1 * What model-agnostic memory actually means for your stack Voice generated by AI