The 14% coding time figure is one of those stats that sounds surprising until you actually track your own time. When I started building a coding agent with persistent state, I realized how some days are spent with minimal actual typing, most of it is design, reading code, debugging, problem solving, and context-switching.

But I'd push back on one thing the article implies that AI is automatically a productivity win. It's not. Some days I've shipped two months of work in a few days with AI. Other days, like today, I've burned a whole day and gotten almost nothing done because the proper research was not done by me or multiple agents.

The bottleneck for AI can be the human understanding of how to optimally use the tool. While the bottleneck for the human can be not maximizing multiple agents, or the input the user enters, then the retention of the output. If the user's input is lost, the output falters. If the user doesn't understand what the AI output is, there is going to be a problem eventually.

The article touches on adoption barriers (Myth 7), but it doesn't really get into the ego piece. There's still a wave of experienced devs who either refuse to adopt AI, or use it quietly and don't share what they're doing. That slows the whole team's learning curve. At this point, I think it's pretty much understood that you should be using AI as a dev — not to replace your skills, but to accelerate them. That means still learning new languages, still writing code, still troubleshooting. The tools change, but the craft doesn't.

I think the article is right that the real leverage is organizational, not individual. The teams that succeed with AI aren't the ones giving everyone a license — they're the ones rethinking how they review, test, and maintain code.

What I'm still uncertain about is how to measure whether AI is actually making systems better, not just faster. Lines of code is clearly a bad metric, but I haven't seen a good alternative yet. What metrics are people actually using that feel meaningful?