Two Years of AI Coding Tools Taught Me That Speed Is the Wrong Metric
AI coding tools are being sold as a productivity multiplier, but unchecked adoption is silently degrading the code review skills and engineering judgement that keep production systems safe—teams that don't build explicit AI audit and constraint mechanisms are accumulating invisible technical debt at speed.
After rolling out AI-assisted development across a team for two years, one technical lead found that the tools' real effect is exposing where developers are weak, not just making them faster. Speed masks a decline in design depth: engineers accept AI-generated code without scrutinizing performance at scale, race conditions, or dependency health. Code review ability degrades because reviewing requires seeing concurrency, boundary cases, and security holes—exactly what AI omits on the Happy Path.
True seniority now means knowing when to turn AI off: during domain modeling, online incident response, and technology selection, where only human context and judgement apply. In interviews, the author now tests troubleshooting reasoning without AI, engineering mechanisms that constrain AI output, and the ability to spot silent debt in seemingly correct generated code.
The core paradox: AI tools help most in the areas where skilled engineers already excel, while junior developers just produce garbage faster. The metric that matters is not output speed but the reflex to ask "why" after AI gives an answer.
The productivity narrative around AI coding tools is missing a cost side: speed lowers the cost of mistakes but raises their probability because developers stop scrutinizing output they don't fully understand.
AI tools invert the skill-leverage curve—they amplify the output of already-competent engineers while accelerating the production of low-quality code from those who lack fundamentals.
The interview practices described here signal a market split: employers will increasingly filter for engineers who can audit and constrain AI, not just prompt it, making AI-proof reasoning a hiring differentiator.
The 'silent debt' concept is under-discussed: AI-generated code that passes tests and runs can still embed failure modes that only surface years later, and the generator provides no warnings.