Machinery, Meaning, and Agency: Data Engineering for AI

For decades, data engineering has focused on the data engineering lifecycle: building pipelines, orchestrating workflows, managing storage and compute, and overseeing the undercurrents - security, architecture, data governance, and more. But as AI transitions to systems that take action, infrastructure is no longer the main bottleneck. Meaning and clarity in data are.

When machines take actions, semantic ambiguity becomes catastrophic. This talk explores the shift from machinery to meaning and why the discipline's future depends on a renaissance in data modeling. We will examine why rigid dogmatism and unstructured chaos both fail modern systems, and how adopting Mixed Model Arts - a pragmatic, multi-paradigm approach to modeling context, semantics, and relationships - provides the foundation machines need to understand our world and act responsibly within it.