Unpacking the True Cost of Cognitive Load in Software Engineering Teams

A recent discussion on the ‘Modern Software Engineering’ channel, featuring Manuel Pice, co-author of ‘Team Topologies,’ delved into the multifaceted challenge of cognitive load in software engineering teams. Pice, drawing on research conducted with Dr. Laura Vice, emphasized that cognitive load extends far beyond the inherent complexity of the problem being solved. Identified drivers fall into four clusters: team composition (size, skill sets, role clarity), clarity of work and goals, and the working environment (tools, physical space). The ‘Team Topologies’ methodology, a modeling approach, aims to manage this load by fostering autonomous, focused teams, reducing inter-team coupling, and intentionally designing internal platform teams to offer ‘golden paths’ that simplify workflows for product teams.

The cost of unmanaged cognitive load is substantial, directly impacting key DORA metrics such as lead time for changes and mean time to recovery, leading to slower delivery and increased system instability. Beyond organizational metrics, high cognitive load is strongly correlated with increased burnout and anxiety among engineers. Effective management isn’t about eliminating cognitive load, but rather balancing it strategically; temporary increases for learning new skills (e.g., AI) can be an investment, while unnecessary burdens from poorly planned reorgs or processes should be mitigated. Pice also highlighted AI’s dual nature: while offering productivity gains, it can paradoxically increase cognitive load through context switching or workflow disruptions if not thoughtfully integrated. A survey is currently underway by the Team Topologies team to further assess AI’s precise impact on team cognitive load and overall effectiveness.