Many engineering teams are purchasing AI code review tools, but are they solving the problem they think they are?
Over the past few months, I've had eye-opening conversations with dozens of engineers from Series-B and Series-C startups. As the founder of Vibinex, I'm naturally curious about their code review processes, especially when they mention it's a pain point.
A common pattern emerges: their most valuable engineers are spending significant time reviewing pull requests, or worse, fixing issues in previously shipped features, instead of building new features. This bottleneck slows down the entire development cycle, leading to product delays and frustration across teams.
flowchart LR
start((("`**Author Raises pull request**`"))) --> ci["`CI/CD/CT`"]
ci --> r1["Author makes fixes for automated feedback & requests human review"]
r1 --> r2{"Reviewer reads issue description, reads code changes line-by-line, identifies bugs/issues & tests locally and provides feedback"}
r2 --> |Requests changes|f1["Author makes fixes"]
r2 --> |Approves|last((("`**PR merged**`")))
f1 --> r2
To address this challenge, many have turned to AI-powered code review tools like CodeRabbit, CodeAnt, Greptile, or Ellipsis, hoping to accelerate their review process.
When I ask these teams how they evaluated these tools before purchase, they invariably mention the quality of PR comments – bug detection, security vulnerabilities, style recommendations, and readability improvements. These are all valuable capabilities focused on code quality.
But here's where it gets interesting: when I ask them to walk me through their actual review steps with these tools in place, a different picture emerges.
Well, I get a Slack notification with the PR link, open the PR, look at the AI comments, but then I still need to go through the code myself to understand the changes and their implications.
They're still performing every step they did before adopting these tools.
flowchart LR
start_((("`**Author Raises pull request**`"))) --> ci_["`CI/CD/CT &
**AI Code Review**`"]:::highlighted
ci_ --> r1_["Author makes fixes for automated feedback & requests human review"]
r1_ --> r2_{"Reviewer reads issue description & **AI comments**, reads code changes line-by-line, identifies bugs/issues & tests locally and
provides feedback"}:::slowed
r2_ --> |Requests changes|f1_["Author makes fixes"]
r2_ --> |Approves|last_((("`**PR merged**`")))
f1_ --> r2_
classDef highlighted fill:#c0ffcb,stroke:#25a500,stroke-width:2px,color:#000000
classDef slowed fill:#ffe0e0,stroke:#d95050,stroke-width:2px,color:#000000
This reveals a fundamental misalignment between expectations and reality. Teams purchase these tools hoping to reduce the time senior engineers spend on reviews, but they're actually getting tools that primarily help authors write better code.
Don't get me wrong – improving code quality before review is tremendously valuable. Catching bugs, formatting issues, and potential performance problems early saves time for everyone.
But if your primary goal is to reduce the review bottleneck, there's a disconnect between the problem you're trying to solve and the solution you've implemented.
This insight helped me realize an important distinction: most AI code review tools on the market today are fundamentally author-focused, not reviewer-focused.
They excel at:
But they don't fundamentally change how reviewers experience the review process. Reviewers still need to read the code changes line-by-line to:
When engineers tell me they've adopted AI code review tools to "save time on reviews," there's often confusion about which side of the equation they're optimizing for.
I used to emphasize that Vibinex wasn't competing with these AI code review tools. But I've changed my perspective. We're all trying to solve the same problem – making the development cycle more efficient – just from different angles.
Author-focused tools improve code before review, while reviewer-focused tools make the review experience itself more efficient. The ideal solution likely includes both approaches.
Rather than viewing these as competing solutions, engineering teams should consider how they complement each other:
flowchart LR
start_((("`**Author Raises pull request**`"))) --> ci_["`CI/CD/CT &
**AI Code Review**`"]:::highlighted
ci_ --> r1_["Author makes fixes for automated feedback & requests human review"]
r1_ --> r2_{"`⏩
Reviewer **quickly understands logic & design changes** to identify issues and provide feedback`"}:::highlighted
r2_ --> |Requests changes|f1_["Author makes fixes"]
r2_ --> |Approves|last_((("`**PR merged**`")))
f1_ --> r2_
classDef highlighted fill:#90EE90
As development teams face increasing pressure to ship faster while maintaining quality, examining the entire PR lifecycle becomes critical. The goal shouldn't be eliminating human review, but making it as efficient and valuable as possible.
By understanding the distinct needs of authors and reviewers, teams can build a more holistic approach to code reviews that truly accelerates development cycles.
The next time you evaluate tools in this space, consider which part of the problem you're truly trying to solve:
Clarifying this distinction will help you make more informed decisions about which tools will actually deliver the outcomes you're seeking.
I'd love to hear about your experiences with code review processes and tools. What challenges is your team facing, and what solutions have you found effective? Connect with me on LinkedIn or check out Vibinex if you're interested in solutions specifically designed for the reviewer experience.