AI coding agents boost code volume 30% but not software output
More code is not the same as more software. A new Harvard University study makes that distinction painfully clear for any team betting on AI coding agents to raise output.
What the Study Found
Researchers Fiona Chen and James Stratton analyzed aggregated data from Jellyfish, covering 300 million individual work events across more than 700,000 employees at over 700 software development firms from 2021 through March 2026. That is a large enough sample to take seriously.
After firms introduced AI coding agents, the raw production numbers jumped sharply:
- 30 percent increase in total lines of code generated
- 20 percent rise in total commits
- 23 percent increase in pull requests
The problem: none of that translated into more software actually shipping. Resolution rates for Issues and Epics tracked in tools like Jira showed no statistically significant change after AI tools were introduced. The researchers also found no shift in the size or complexity of those tracked issues.
Where the Time Goes
The study identifies human code review as the bottleneck. AI-generated code requires more review time, pull requests are more likely to require revisions, and reviewers leave more comments. The efficiency gains at the coding stage get absorbed downstream before they reach the finish line.
The authors conclude there is little evidence that firms increase software output or reduce employment by using AI coding tools. The coding phase speeds up; everything after it slows down to compensate.
The Operator Takeaway
If you’re a solo developer or a small team using AI coding agents, the constraint is probably not code generation. It’s your own review capacity. Shipping faster requires either raising your review throughput or shrinking the blast radius of each AI-generated pull request. The firms in this study didn’t solve that. Most won’t by default.
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