Developers report a new frustration: AI-generated code that's almost correct. Stack Overflow's survey of 90,000 developers found 66% cite this as their top issue, while 45% say debugging it takes more work than it's worth. The problem creates a dangerous category of bugs that pass tests but fail in production.

Unlike traditional errors that fail loudly, these subtle bugs slip through to staging. A developer spent three hours chasing a phantom pagination feature that Claude added without being asked. This mismatch reveals AI's core weakness: it excels at pattern completion but struggles with debugging, which requires understanding system context and business logic.

Microsoft Research quantified the gap, testing nine models on SWE-bench Lite. The best performer, Claude 3.7 Sonnet, solved only 48.4% of real-world debugging tasks. Meanwhile, a METR trial found developers using AI were actually 19% slower, yet believed they were 24% faster. The productivity illusion masks accumulating technical debt.

The solution isn't abandoning tools but changing how we use them. Treating AI suggestions like code from a confident junior developer—reviewing every line—becomes essential. Some skills, like rigorous testing and understanding system history, resist automation. They require sharpening, not replacing.