The InfoWorld article “Small language models: Rethinking enterprise AI architecture” argues that as large language models (LLMs) hit limits of scale, cost, and risk, enterprises are shifting toward small language models (SLMs) that are faster, cheaper, and more private for well-defined, repetitive tasks. It highlights three primary advantages: division of labor between small and large models, radical economic efficiency for high‑volume workloads, and improved privacy at the edge when models run on devices or on‑premises. The underlying message is that competitive advantage will increasingly come from how well organizations architect and orchestrate multiple models around their own data, workflows, and governance.