🚀 Measuring Developer Productivity in the Age of AI

Tracking developer productivity has always been tricky. With the rise of AI-assisted coding, it’s even harder to separate meaningful insights from vanity metrics.

Many teams still focus on raw activity metrics like lines of code (LOC) or pull requests (PRs) per engineer per week. But does writing more code really mean being more productive?

📊 The Right Metrics for Developer Productivity

Instead of looking at a single number in isolation, stacking and correlating key metrics gives a clearer picture of how teams are actually performing. Here are some of the most insightful ones:

🔹 Avg PRs per Engineer per Week – A baseline for developer activity, but only useful when paired with other insights.
🔹 Lines of Code per PR – Can indicate AI assistance or complexity, but shouldn’t be the sole measure of productivity.
🔹 Cycle Time – How long it takes to move from the first commit to a merged PR. Shorter cycles often mean more efficient workflows.
🔹 Time to Merge – Measures how quickly PRs make it through the pipeline, helping spot bottlenecks in code review or deployment.
🔹 Time to First Review – A leading indicator of engineering velocity and collaboration.
🔹 Revert Rate per PR – Helps track code quality. If AI-assisted code is frequently reverted, is it actually helping?
🔹 Total PRs per Team – Gives insight into overall throughput, but without context, can be misleading.

🔍 Correlating Metrics for Better Insights

One metric alone rarely tells the full story. The key is understanding the relationships between them:

✅ A high PR count with low cycle time could indicate efficiency—but if reverts are high, quality might be suffering.
✅ Faster time to merge and first review time might suggest streamlined processes—but do those PRs still require lots of back-and-forth?
✅ AI-generated code might inflate LOC per PR, but does it actually reduce cycle time and improve quality?

By stacking metrics on top of each other, engineering leaders can get a more accurate picture of productivity, rather than relying on outdated or misleading numbers.

🔥 Rethinking Developer Productivity

True developer productivity isn’t about just writing more code—it’s about delivering high-quality work efficiently.

Tracking the right things means:
🚀 Spotting bottlenecks in workflow
📈 Understanding AI’s impact on engineering teams
🛠️ Balancing speed with quality

What does your team track? Are your metrics driving better outcomes, or just creating more noise?