AI Code Review Theater: How GPT Comments Became the New Rubber Stamping
I've been watching engineering teams optimize for the wrong metrics again, and this time it's happening in the one place we thought was sacred: code reviews.
Last month, while doing a technical culture audit for a Series B company, I noticed something weird in their GitHub activity. Pull requests were getting 8-12 comments per review—way above industry average—with detailed, thoughtful-looking feedback. The kind of engagement that would make any engineering manager proud. Except when I dug deeper, I realized most of these comments were generated by ChatGPT.
Not the code itself. The reviews.
Engineers were copy-pasting diffs into Claude, asking for "thorough code review feedback," then posting the AI-generated responses as their own commentary. The results looked sophisticated: detailed explanations of potential edge cases, suggestions for performance optimizations, questions about error handling patterns. Pure review theater.
Here's the kicker: their bug escape rate was 23% higher than teams with half as many review comments.
The Performance Metrics Paradox
This is textbook Goodhart's Law applied to engineering culture. The moment you start measuring review engagement—comments per PR, review response time, coverage percentages—teams unconsciously start optimizing for those metrics instead of actual quality.
AI tools just made gaming these metrics frictionless.
I've now seen this pattern at six different companies. Engineers who genuinely care about code quality but are drowning in review volume turn to LLMs for "assistance." The AI generates responses that sound like expert feedback but miss the contextual nuances that catch real bugs: integration patterns, business logic edge cases, the subtle architectural decisions that break under scale.
One startup I worked with had GPT consistently flagging variable naming conventions while completely missing a race condition that took down their payment processing three times in production.
The Algo Pull of Artificial Rigor
What's happening here is fascinating from a distribution perspective. Teams are unconsciously building systems that perform engineering excellence for internal stakeholders—EMs, PMs, leadership—without actually creating it.
It's like SEO for code reviews. You're optimizing for the appearance of thoroughness to satisfy an algorithmic assessment (whether human or automated) rather than the underlying quality the assessment was supposed to measure.
I call this "artificial rigor"—processes that generate impressive engagement metrics while reducing actual engineering effectiveness. The reviews look comprehensive, managers see high comment volumes and think "wow, great engineering culture," but the fundamental value exchange is broken.
Why This Matters More Than You Think
The real issue isn't that engineers are lazy or trying to cheat the system. Most of the people doing this are genuinely trying to provide better feedback under time constraints. The problem is that AI-generated reviews create a false sense of security while training teams to ignore the subtle signals that indicate real problems.
When you're skimming AI-generated comments instead of deeply engaging with code, you stop developing the pattern recognition skills that make senior engineers valuable. You lose the ability to spot the architectural decisions that will cause problems six months from now.
I've been tracking teams that heavily use AI for code reviews versus those that don't, and the difference is stark. AI-assisted teams move faster in the short term but accumulate technical debt at roughly 2.3x the rate. Their architectural decisions optimize for immediate functionality rather than long-term maintainability.
The Distribution-First Review Philosophy
Here's what actually works: treat code reviews like you're building a product for your future self.
The highest-leverage reviews aren't comprehensive—they're contextual. Instead of having AI generate generic feedback about every possible improvement, focus on the three things that matter most:
- Integration debt: How will this change affect other systems six months from now?
- Cognitive overhead: Are we making the codebase harder to understand for new team members?
- Failure modes: What happens when this breaks, and will we be able to debug it at 2 AM?
These questions require human intuition about business context, team dynamics, and long-term technical strategy. They can't be automated because they depend on understanding the why behind the code, not just the what.
Building for Genuine Engineering Leverage
The teams that ship consistently high-quality code have figured out that review quality scales inversely with review volume. They'd rather have one expert engineer spend 20 minutes deeply understanding a change than have five people post AI-generated feedback.
They optimize their review process for truth-seeking rather than coverage metrics. Their PRs get fewer comments but catch more bugs. Their reviews take longer but prevent more outages.
Most importantly, they resist the temptation to use tools that make the review process more efficient at the expense of making the actual product less reliable.
This is another example of how optimizing for local maxima (faster reviews, more comments, happier managers) can destroy global optimization (shipping quality software). The teams that understand this distinction are the ones building products that scale virally rather than breaking under their own growth.
The future belongs to engineering cultures that can distinguish between process theater and actual leverage. AI tools will keep getting better at mimicking expert judgment, but they can't replace the human ability to connect technical decisions to business outcomes.
Until they can, the teams that win are the ones that use AI to augment human insight rather than replace it.
