In this second installment of our five-part database series, Tim O’Brien explores the emergence of vector databases—a fundamentally new type of database built for AI applications that store mathematical representations of meaning rather than traditional rows and columns. The episode demystifies how embedding models convert text and images into high-dimensional vectors, why this matters for modern AI applications, and what it means when meaning itself becomes geometry.