Software engineers have debated productivity metrics for decades, starting with lines of code. But as the new generation of AI coding agents delivers more code than ever, what their managers ought to be measuring is less clear. Enormous token budgets ' essentially, the amount of AI processing power a developer is authorized to consume ' have become a badge of honor among Silicon Valley developers, but that's a very weird way to think about productivity. Measuring an input to the process makes little sense when you presumably care more about the output. It might make sense if you're trying to encourage more AI adoption (or selling tokens), but not if you're trying to become more efficient. Consider the evidence from a new class of companies operating in the 'developer productivity insight' space. They're finding that developers using tools like Claude Code, Cursor, and Codex generate a lot more accepted code than they did before. But they also find that engineers have to return to...
learn more
