There's an uncomfortable truth emerging in engineering organizations that have adopted AI coding assistants: the bottleneck has shifted.
Writing code used to be the slow part. Now it's instant. You prompt, AI generates, you ship a PR. But the review process hasn't gotten any faster—and suddenly there's a lot more code to review.
The Flood
Consider a typical scenario. Before AI tools, a developer might write 200 lines of code per day. After adopting Copilot or Cursor, they might produce 400 lines. Sounds like a win.
But those extra 200 lines don't review themselves. The reviewer who used to spend 45 minutes on a PR now needs 90 minutes. And if every developer on the team doubled their output, the review queue grows exponentially.
We're seeing this pattern across our customer base:
- PR size up 35% in organizations with high AI adoption
- Review pickup time up 60% as reviewers struggle to keep pace
- Review depth down 25% as overwhelmed reviewers start skimming
The last point is the dangerous one. When reviewers skim, bugs slip through. Technical debt accumulates. The codebase degrades.
The "Rubber Stamp" Problem
In Velocinator, we track Review Depth: the average number of comments and interactions per PR. Healthy code review has a natural rhythm—questions asked, suggestions made, discussions had.
When Review Depth drops to zero—PRs approved with no comments—that's a red flag. It usually means one of two things:
- The code is so trivially simple that no feedback is needed
- The reviewer is rubber-stamping without actually reading
With AI-generated code, we're seeing a surge in the second pattern. Reviewers are overwhelmed, so they approve quickly to clear the queue. They trust that the code "probably works" because it was AI-generated.
This is backwards. AI-generated code often needs more scrutiny, not less, because:
- The author may not fully understand what was generated
- AI optimizes for "works" not "maintainable"
- AI can introduce subtle bugs that look correct at a glance
What's Actually in Those PRs?
We analyzed thousands of PRs across our customer base to understand what AI is generating. The patterns are instructive:
Boilerplate and Scaffolding
Tests, API clients, data models, configuration. This is AI's sweet spot—repetitive code with clear patterns. Review burden here is low because the code is predictable.
Feature Implementation
Business logic, algorithms, data transformations. This is where AI struggles with context. The generated code might work but not fit the architecture. Review burden should be high, but often isn't.
"Close Enough" Code
Code that solves the stated problem but in a suboptimal way. It works, passes tests, but creates technical debt. This is the hardest to catch in review because there's nothing obviously wrong.
Measuring Review Quality
How do you know if your team is maintaining review standards? Velocinator tracks several signals:
Review Depth Score
Comments per PR, weighted by PR size. A 500-line PR with 0 comments is suspicious. A 50-line PR with 0 comments might be fine.
Target: At least 0.5 comments per 100 lines of code, on average.
Review Time vs. PR Size
Time spent reviewing should scale with PR size. If large PRs are getting approved as quickly as small ones, reviewers are skimming.
Signal: Strong correlation between PR size and review time indicates thorough review. Weak correlation indicates rubber-stamping.
Rework After Merge
Code that gets modified within 21 days of merging often indicates issues missed in review. Track rework rate by reviewer—if certain reviewers' approvals correlate with high rework, they may need coaching.
Target: Less than 15% of code should require rework within 3 weeks.
Follow-on Bug Rate
Are bugs being filed against recently merged PRs? Cross-reference Jira bugs with PR merge dates to identify correlation.
Strategies for Sustainable AI Adoption
1. Enforce PR Size Limits
AI makes it easy to generate large PRs. Fight back with hard limits.
Set a team policy: PRs over 400 lines require explicit justification. PRs over 800 lines get automatically flagged for review by a senior engineer or architect.
This forces developers to break work into reviewable chunks, regardless of how fast they can generate code.
2. Require Author Attestation
Before submitting a PR with AI-generated code, require authors to attest:
- "I have read and understand every line of this code"
- "I can explain why this approach was chosen"
- "I have considered alternative implementations"
This sounds bureaucratic, but it forces developers to engage with AI output instead of blindly shipping it.
3. Allocate Review Time Explicitly
If your team is generating 35% more code, your review capacity needs to increase by 35%. That time has to come from somewhere.
Options:
- Hire more engineers (expensive)
- Reduce feature velocity (unpopular)
- Dedicate explicit review blocks (sustainable)
We recommend the third option. Schedule 2 hours of protected review time per developer per day. Treat it like a meeting—it's not optional.
4. Invest in Automation
Every minute a human spends reviewing code that a machine could catch is wasted.
Maximize automated checks:
- Linters and formatters for style consistency
- Static analysis for common bug patterns
- Test coverage requirements
- Security scanning for known vulnerabilities
AI-generated code is often particularly susceptible to automated checks. Let the machines review the machines.
5. Track and Surface Review Health
Put review metrics on your team dashboard:
- Current review queue depth
- Average review pickup time
- Review depth trend over time
- PRs waiting >24 hours
Make the problem visible. When everyone can see the queue growing, help flows naturally.
The New Normal
AI coding assistants aren't going away. They're getting better, more integrated, more ubiquitous. The generation speed advantage will only grow.
That means the review bottleneck will only get worse—unless teams adapt.
The organizations that thrive will be the ones that recognize code review as a critical skill that needs investment, not a chore to be minimized. They'll measure review quality, not just velocity. They'll balance the speed of AI generation with the wisdom of human oversight.
Velocinator helps you see the balance. Because shipping fast is only valuable if what you're shipping is worth keeping.
