AI Products Break on the Data Layer — Not on the Next Model Release

This article argues that AI product failures in production often stem from data layer issues—ingestion, retrieval, and memory lifecycle—rather than inherent model weaknesses. It advocates for applying data-engineering discipline to harden this layer, ensuring reliable AI behavior.