Tools: Open Source Cognitive Debt: When Velocity Exceeds Comprehension

The 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. The engineer who built them stared at her own code like a stranger’s.

Code has become cheaper to produce than to perceive.

When an engineer writes code manually, two parallel processes occur. The first is production: characters appear in files, tests get written, systems change. The second is absorption: mental models form, edge cases become intuitive, architectural relationships solidify into understanding. These processes are coupled. The act of typing forces engagement. The friction of implementation creates space for reasoning.

AI-assisted development decouples these processes. A prompt generates hundreds of lines in seconds. The engineer reviews, adjusts, iterates. Output accelerates. But absorption cannot accelerate proportionally. The cognitive work of truly understanding what was built, why it was built that way, and how it relates to everything else 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 or maintenance costs, 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.

The debt is not truly invisible. It eventually appears in reliability metrics: Mean Time to Recovery stretches longer, Change Failure Rate creeps upward. But these are lagging indicators, separated by months from the velocity metrics that drive quarterly decisions. By the time MTTR signals a problem, the comprehension deficit has already compounded.

Engineering performance systems evolved to measure observable outputs. Story points completed. Features shipped. Commits merged. Review turnaround time. These metrics emerged from an era when output and comprehension were tightly coupled, when shipping something implied understanding something.

The metrics never measured comprehension directly because comprehension was assumed. An engineer who shipped a feature was presumed to understand that feature. The presumption held because the production process itself forced understand

Source: HackerNews