🔗 It’s time to rethink how we measure engineering productivity

In an era where AI assists coding, traditional measures of engineering productivity, like lines of code, have become obsolete. Sriramprabhu Rajendran emphasizes that more code doesn’t equate to faster delivery. Instead, the focus should shift to validating software effectively. As teams experience increased coding output with new tools, they often face longer review cycles and hidden complexities. Rajendran advocates for separating tasks into two categories: AI-accelerable tasks with low validation burden and strategically complex tasks that require human validation. This nuanced approach improves estimations and project roadmaps by prioritizing validation time over mere code production. Engineering teams will thrive by recognizing that generating confidence is now the core constraint, and adapting leadership strategies accordingly will be crucial for success in the evolving tech landscape.