Adding black boxes on top of black boxes is not a good way to abstract complexity. Helm does nothing more than any template engine does, yet requires me to trust not only the competency of some random chart author but also that they will correctly account for how my k8s environment is configured.
When I inevitably have to debug some deployment, now I'm digging through not only the raw k8s config, but also whatever complexity Helm has added on to obfuscate that k8s config complexity.
Helm is an illusion. All it does is hide important details from you.
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.
You are right that typing speed isn't the bottleneck, but wrong about what AI actually accelerates. The 10x engineers aren't typing faster they're exploring 10 different architectural approaches in the time it used to take to try one, validating ideas through rapid prototyping, automating the boring parts to focus on the hard decisions.
You can't evaluate a small sample size of people who are not exploiting the benefits well and come to an accurate assessment of the utility of a new technology.
Skill is always a factor.