AI Engineering Needs Platform Controls

This article discusses the essential platform controls needed to scale AI-assisted engineering effectively, drawing parallels to cloud platform management. It emphasizes that success depends on boring but critical controls like ownership, cost visibility, sensible defaults, observability, policy, repeatable workflows, and feedback loops. The author highlights challenges in tracking AI costs across diverse usage patterns (IDE, chat, agents, models) and advocates for team-level metrics, quotas, and tagging. Practical guidance includes using GitHub Copilot usage metrics and FinOps principles to align AI spend with business outcomes, ensuring governance without slowing teams down.