# Developer Productivity in the AI4SE Era: Why Traditional Metrics Are Failing
Measuring AI4SE-era developers by lines of code, story points, or PR counts? These metrics are not just useless — they actively encourage the wrong behavior. We need a new framework.
[dev-productivity][measurement]
The Disaster of Old Metrics
Old Metric
Why It Fails Under AI4SE
Lines of code
Agents can generate huge volumes of code — that doesn’t mean value
Story points
Agents change completion speed — relative estimation breaks down
PR count
Encourages splitting into tiny PRs to pad numbers — ignores review burden
Commit frequency
Agents auto-commit — no longer a signal of human initiative
Adapting the SPACE Framework for AI4SE
Dimension
What AI4SE Should Focus On
Satisfaction
Developer satisfaction with agent collaboration
Performance
End-to-end outcomes (not coding speed)
Activity
Volume of HITL review + decision participation
Communication
Spec quality + review feedback frequency
Efficiency
End-to-end flow efficiency from spec to merge
The Core Shift
From “how much was produced” to “how much was verified”
Don’t look at how much code the agent generated — look at how much passed independent review
Don’t look at how fast PRs merged — look at how many regressions followed
Don’t look at time spent using tools — look at the closed-loop cycle time from spec to delivery