Study finds AI-assisted developers slower on some tasks, prompting a book on agentic software development

The author argues that a single measurement showing a productivity gain or loss should not be taken as a verdict on AI coding tools, since the full body of results forms a trend that slopes in a particular direction. The piece distinguishes between evidence grades, noting that weakly supported claims can motivate practices but cannot establish them, and that small studies generalize only to similar groups. It draws on the Lindy effect to argue that Parnas's 1972 information-hiding principles remain relevant because they limit the errors an AI agent can make. The author also notes that AGENTS.md, a configuration convention roughly eighteen months old, is promising but not yet proven to be lasting infrastructure. The post introduces a book on the Agentic Development Lifecycle and invites readers to consider how they would ship AI-generated code safely.