AI hasn't made engineering productivity unmeasurable. It's made the easy metrics dangerous, inflating commits and lines of code automatically, widening the gap between feeling fast and being fast, and hiding real costs downstream. Here's what breaks, why, and what to measure instead.
Two teams can post identical delivery numbers while one thrives and the other burns out. Psychological Productivity Engineering measures the human substrate beneath the output: a structured survey, a DevSat score, and a feedback loop that catches problems before they cost you people.
In today's competitive business landscape, engineering productivity goes far beyond technical efficiencies - it directly shapes your organization's success. Leading companies understand that aligning engineering practices with strategic business goals
AI isn’t replacing product development - it’s compressing it. Learn how teams use AI across discovery, roadmap alignment, PRDs, user research, engineering scoping, and delivery to ship better products faster without sacrificing ownership or quality.
In a comprehensive new study published in January 2025, where I was also one of the researchers, some valuable insights rose from deploying an AI coding assistant across the engineering organization of 400+
