Most of the AI productivity stories I hear sound like they're optimizing for the wrong metric. Writing code faster doesn't necessarily mean shipping better products faster. In my experience, the bottleneck is rarely "how quickly can we type characters into an editor" - it's usually clarity around requirements, decision-making overhead, or technical debt from the last time someone optimized for speed over maintainability.
The author mentions that real 10x engineers prevent unnecessary work rather than just code faster. That rings true to me. I've seen more productivity gains from saying "no" to features or talking teams out of premature microservices(or adopting Kafka :D) than from any coding tool.
What worries me more is the team dynamic this creates. When half your engineers feel like they're supposed to be 10x more productive and aren't, that's a morale problem that compounds. The engineers who are getting solid 20-30% gains from AI (which seems realistic) start questioning if they're doing it wrong.
Has anyone actually measured this stuff properly in a production environment with consistent teams over 6+ months? Most of the data I see is either anecdotal or from artificial coding challenges.