Study: AI coding agents write more code, but firms don't ship more software
Harvard researchers Fiona Chen and James Stratton analyzed aggregated Jellyfish engineering analytics covering roughly 300 million work events (such as commits and pull requests) across more than 700,000 employees at over 700 software development firms from 2021 through March 2026. They found that while AI coding tools speed up code generation, that gain is absorbed by downstream constraints: code review takes significantly longer, pull requests are more likely to need revisions, and reviewers leave more comments. The authors report little evidence that firms increase software output or reduce employment by using these tools.
It uses large-scale engineering data to suggest AI coding gains can be swallowed by review and collaboration bottlenecks rather than showing up as shipped output.