Convergence of Software Engineering Productivity in Changing Organisations
Intermediate — engineering leader or senior IC who has lived through reorgs and tech migrations
Scope
Why does software engineering productivity tend to converge toward a stable baseline regardless of disruptions — new technologies, reorganisations, scaling, mergers? What determines that baseline, and how can leaders influence it?
In scope:
- The convergence phenomenon itself — why productivity stabilises
- The J-curve pattern during disruption and recovery
- Team structures and cognitive load as structural determinants of the baseline
- How new technologies get absorbed without permanently shifting productivity as much as promised
- How different org changes (mergers, scaling, pivots) create different disruption/recovery signatures
- Practical leadership strategies for accelerating convergence and raising the baseline
Out of scope:
- Detailed measurement framework tutorials (DORA/SPACE as tools — referenced but not the focus)
- Platform engineering deep-dives
- Individual developer productivity tips
- Specific technology comparisons
After completing this plan, you will be able to:
- Explain why engineering productivity tends to converge and what forces drive that convergence
- Recognise the J-curve pattern in their own organisation's response to change
- Identify how team structures and cognitive load set the productivity baseline
- Predict how different types of org change will affect productivity and plan accordingly
- Apply practical frameworks for accelerating convergence and raising the baseline during transformation