AI tools accelerate code generation, but the cognitive burden of reviewing this code shifts to developers. Without proper context, reviewers may inadvertently accumulate 'cognitive debt' by accepting functional code without fully understanding its underlying architecture or potential failure scenarios.
DORA research highlights that reviewing is a distinct cognitive task from creation. When generation speed outpaces the ability to review, the review process becomes a bottleneck. This creates a scenario where the team's velocity is limited by the slowest component, which is often manual code review.
To manage this, teams can model their throughput using the formula: team velocity ≈ min(human review velocity, automated review velocity). By reducing the time spent on individual reviews (C) or decreasing the proportion of changes requiring manual intervention (p), teams can increase their overall delivery capacity.
Strategies to reduce cognitive load include defining clear contracts before implementation, documenting system behavior, and specifying failure scenarios. Automating repetitive checks allows developers to focus manual efforts on complex architectural assessments, preventing the accumulation of technical and cognitive debt.
