AI Now Writes 1 in 4 Pull Requests: How to Review It Without Slowing Down

AI-generated code has gone from 1% to 27.6% of all pull requests in a year. Code generation got 27x faster; review capacity did not. If your review process hasn't changed to match, you're either rubber-stamping more than you realise or you've quietly become your own delivery pipeline's bottleneck.

Reviewing AI Code Is a Different Job Than Reviewing Human Code

Human-written bugs tend to cluster around genuine misunderstanding or edge cases the author didn't think of. AI-generated code fails differently — it's often locally correct and confidently written, but can miss broader architectural context, introduce subtly wrong assumptions about how a system behaves, or solve the literal prompt while missing the actual intent. Reviewing for 'does this look right' is not enough; you need to review for 'does this fit the system.'

A Practical Process That Scales

• Require the same test coverage bar for AI-generated PRs as human ones — do not let velocity trade away your quality gate
• Flag PRs above a certain size or touching critical paths (auth, payments, data migrations) for mandatory senior review regardless of who or what wrote them
• Use AI-assisted review tools to do the first pass (style, obvious issues, test coverage gaps) so human reviewers spend their time on architecture and intent, not syntax
• Track review turnaround time separately from PR volume — if volume is up 27x and turnaround is flat, you're either understaffed on review or under-scrutinising

The Real Fix Is Upstream: Better Issues, Better Output

The clearest lever for reducing bad AI-generated PRs isn't more review — it's better-specified issues going in. An agent given a vague ticket produces a vague, often wrong solution just as reliably as a confused junior developer would. Investing in issue-writing discipline pays off more than almost any downstream review process change.