A recent study from Oregon State University reveals a troubling gap: developers using GitHub Copilot write code 50% faster but gain zero comprehension. Researchers found students completed brownfield tasks quicker and passed more tests, yet couldn't explain or modify the code they shipped. This creates comprehension debt—code that works but nobody understands, accumulating silently in codebases.

The crisis emerges because AI breaks the traditional coupling between writing and understanding code. Developers accept AI suggestions without building mental models, leaving no evolutionary trail in git history. When a senior developer leaves, teams inherit working systems they can't safely modify. Unlike legacy code that loses understanding over time, AI-generated code never builds it, creating a maintenance paralysis.

Organizational incentives accelerate this problem. Velocity metrics reward shipping features quickly, while comprehension remains invisible. Developers face pressure to accept AI suggestions, even when they don't understand them. This creates a tragedy of the commons where rational individual choices—accepting code for sprint velocity—collectively destroy long-term maintainability. The debt compounds across team generations, degrading knowledge transfer.