Metrics

Technical interviews have begun measuring verification instead of output. That is real progress, but it stops one layer short of what matters. I had the pleasure of corresponding via email with Andy Nian, cofounder and head of growth at PracHub, a site that catalogs technical interview questions reported by candidates. He wrote to me in August asking whether I would cite his AI-assisted question set in a piece I had written about why interviews should measure judgment rather than output. ...

There is a pattern in senior resumes and LinkedIn profiles that makes me more uncomfortable every year. Someone “grew revenue by 38%.” Someone else “reduced costs by $4.2M.” Another “increased engineering productivity by 60%.” Sometimes I know the work behind these lines well enough to know that the link between the person and the number was much weaker than the sentence suggests. It is tempting to call this a problem of dishonest people. My view is that it is worse than that. I believe we built a market that rewards the performance and punishes the truth. In my experience, the candidate who writes “grew revenue by 38%” is more likely to get the interview than the one who honestly describes a shared effort. When the reward for exaggeration is high and the chance of being checked is low, I do not see exaggeration as a personal failure. I see it as the rational response to the system we built. ...

Your team closed 47 tickets last sprint. Deployment frequency is up. Lead time is down. The sprint review looked clean. And you have no idea whether your team is getting better at what matters. Every metric on that dashboard measures motion. None of them measures whether the team made the right decision. In an era where AI generates the first draft of almost everything, code, architecture proposals, incident summaries, and product specs, that gap is no longer academic. It is the difference between a team that compounds in judgment and a team that accelerates toward the wrong destination. ...