AI coding boom widens debugging gap

The sharpest point here is the “85% right” danger zone: AI coding tools are good enough to accelerate output, but not reliable enough to replace engineering judgment when systems break.

• –The real skill premium is shifting from writing boilerplate to evaluating AI output, tracing failures, and knowing when a plausible answer is wrong
• –Debugging-focused models matter more than codegen demos because production incidents expose whether engineers actually understand dependencies, state, and failure modes
• –Teams that optimize only for AI-assisted throughput risk creating brittle code ownership, especially when juniors skip the mental-model-building that used to happen during manual implementation
• –This fits a broader industry worry that AI boosts short-term velocity while pushing architecture understanding, incident response, and root-cause analysis into a smaller pool of senior engineers