Vector Advantage

In 2013, the word2vec paper introduced the idea that words could be represented as vectors—mathematical points in a high-dimensional space where semantic relationships are captured as geometric relationships. "King" minus "man" plus "woman" equals "Queen." It was a parlor trick that hinted at something profound. A decade later, vector embeddings have become the foundational infrastructure of modern AI. Every major AI application—semantic search, RAG, recommendations, clustering, anomaly detection—depends on embeddings. The vector database market, which didn't exist in 2019, is projected to reach $4.5B by 2028.