In 30 seconds
- AI can catch common issues fast as a first pass.
- AI-written code needs closer human review, not less.
- Humans focus on intent, design and risk.
AI as reviewer
- Spots common bugs, style issues and missing tests
- Summarizes large changes for human reviewers
- Flags possible security problems
Treat its comments as suggestions, not verdicts.
Reviewing AI-written code
- Check it solves the actual problem, not just the prompt
- Look for invented APIs and outdated patterns
- Read the tests: do they check behaviour, or just mirror the code?
- Ask whether the team could maintain it
Splitting the work
| AI first pass | Human review |
|---|---|
| Style, lint, obvious bugs | Intent and requirements fit |
| Missing tests, simple security checks | Design, architecture, risk |
See code reviews and human-in-the-loop review.
Myth vs reality
Myth · AI review means humans can skip it.
Reality · Humans still own the decision.
Myth · AI code is cleaner, so review is faster.
Reality · It can look clean while being wrong.
Myth · AI reviewers are consistent.
Reality · They can miss or flag different things each run.
FAQ
Should AI comments block merges?
Usually no; use them to inform humans.
How do we label AI-written code?
Many teams note it in the pull request description.
Does pair programming still help?
Yes, especially for design and knowledge sharing.