Harvard–Jellyfish study: AI coding tools raise code volume but not completed work due to review bottlenecks
A Harvard study using Jellyfish data (≈300 million work events across ~700 firms, Jan 2021–Mar 2026) finds AI coding assistants modestly increase code output (lines +12%, commits +9%, PRs +5%; only commits statistically significant) while AI agents produce larger increases (lines +30%, commits +20%, PRs +23%). Despite more code, assistants did not increase resolved Jira issues and agents raised resolved issues by only 12%, with significant downstream impacts: average PR review time +49%, comments per PR +53%, share of PRs requiring changes doubled, and share of engineers performing reviews +14%—suggesting review and other complementary stages are bottlenecks.