2 posts tagged with "ai-tools"

Ask any VP of Engineering whether AI coding tools are a productivity win and they'll say yes. Ask the same question to a staff engineer who lives in a ten-year-old codebase with six undocumented data models and a deployment process held together with shell scripts, and you'll get a different answer.

The productivity story for AI coding tools is bifurcated in a way that most organizations haven't fully processed. Junior engineers are seeing 27–39% gains in completed weekly tasks. Experienced developers are, in a controlled study of real-world issues, taking 19% longer to finish tasks when they have AI assistance than when they don't. Both results are consistent with how these tools work — and they lead to a management trap that's playing out quietly on engineering teams right now.

The new hire ships a feature on day three. Everyone on the team is impressed. Three weeks later, she introduces a bug that a senior engineer explains in five words: "We don't do it that way." She had no idea. Neither did the AI that wrote her code.

AI coding assistants have collapsed the time-to-first-commit for new engineers. But that speed hides a trade-off that most teams aren't tracking: the code-reading that used to slow down junior engineers was also the code-reading that taught them how the system actually works. Strip that away, and you get engineers who can ship features they don't understand into architectures they haven't internalized.

The problem isn't the tools. It's that we haven't updated onboarding to account for what AI now does — and what it no longer requires engineers to do themselves.