hn.today

AI coding agents generate more code, but not more software

arstechnica.com10 points0 comments
Screenshot of AI coding agents generate more code, but not more software

Harvard researchers Fiona Chen and James Stratton used aggregated Jellyfish analytics covering 300 million engineering “work events” across more than 700,000 employees at over 700 software firms (2021-Mar 2026) to measure how AI coding assistants and AI coding agents changed engineering output. Using difference-in-differences regressions and GitHub activity to time tool introductions, they show agentic coding produces far more raw code - about 30% more lines, 20% more commits, and 23% more pull requests - yet yields no statistically significant increase in resolved Jira-tracked Issues or Epics and no compositional shift in their size or complexity.

The reason is a downstream bottleneck in human review: average time from pull request submission to merge rises 49%, the share of PRs requiring changes nearly doubles, and comments per PR increase about 35%. Firms respond by shifting 14% more workers into code review, but overall employment doesn’t change significantly. AI-assisted review remains limited - by March 2026 agents generated roughly 23.3% of review comments and touched 10.8% of PRs - so efficiency gains in code generation are largely absorbed by increased human review effort, leaving net software output unchanged for most firms.

Read on arstechnica.com0 comments on Hacker News

Summary generated by AI from the linked article. hn.today is not affiliated with Hacker News or Y Combinator.

More in Programming

The daily digest

Today's best Hacker News stories, summarized and screenshotted, one email a day.