An engineer shipped seven features in a single sprint. DORA metrics looked immaculate. The promotion packet practically wrote itself. Six months later, an architectural change required modifying those features. No one on the team could explain why certain components existed or how they interacted.

Code has become cheaper to produce than to perceive. AI-assisted development decouples the parallel processes of production and absorption that traditionally occurred together. When engineers write code manually, typing forces engagement and the friction of implementation creates space for reasoning. AI tools generate hundreds of lines in seconds, but the cognitive work of truly understanding what was built remains bounded by human processing speed.

This gap between output velocity and comprehension velocity is cognitive debt. Unlike technical debt, which surfaces through system failures, cognitive debt remains invisible to velocity metrics. The code works, the tests pass, the features ship. The deficit exists only in the minds of the engineers who built the system, manifesting as uncertainty about their own work. By the time reliability metrics signal a problem, the comprehension deficit has already compounded.