AI Can Write Code in Seconds—But Who is Checking for Bugs?
The volume of code written by AI coding tools has increased significantly since the emergence of ‘vibe coding’. Both developers and non-developers can now churn out code at an unprecedented rate using tools like GitHub Copilot, Cursor, and other AI-powered assistants. However, while code generation has accelerated, other aspects of the software development lifecycle—particularly code reviews—have not kept pace, potentially creating bottlenecks and quality concerns. The Bottleneck in Code Reviews “As AI generates more of our code, the bottleneck shifts from writing to reviewing. This new reality makes AI-powered code review valuable and essential for modern development teams,” Harjot Gill, CEO at CodeRabbit, said. Traditionally, code reviews require manual scrutiny by peers, where senior engineers meticulously examine pull requests line by line. This process is both time-consuming and prone to human error. Missed bugs and overlooked inefficiencies can lead to costly business downtime and engineering distractions when these issues manifest in production. “Engineering leaders often find that their senior engineers are stretched thin,” said Aravind Putrevu, Director of Developer Experience at CodeRabbit and an AI-driven code reviewer. “They not only write and maintain their code but also spend significant time reviewing junior engineers’ work. This becomes a bottleneck, slowing down the overall software development process.” While AI has been instrumental in generating code, its unchecked proliferation raises concerns. More code does not necessarily mean better code. The sheer volume of AI-generated code necessitates a more efficient review process. “If AI code assistants like Cursor focus on helping developers write code, CodeRabbit acts as a reviewer on the other side,” Putrevu explained. “It serves as the first line of defense, eliminating obvious errors and ensuring that only refined code reaches human reviewers.” CodeRabbit uses LLMs to automate reviews, identify potential issues, and provide actionable feedback. This process allows human reviewers to focus on architectural and business-critical decisions rather than spending time on trivial mistakes. More Code, More Problems? With AI enabling developers to generate and ship code faster, a key question emerges: Is quality being sacrificed in pursuit of speed? “We’re seeing a new pattern emerge: developers using AI to write 80% of their code in minutes, then spending days debugging subtle integration issues and architectural misalignments. That’s why intelligent code review is becoming the critical path to deployment,” Gill further said. “Earlier, a developer might spend 20% of their workday writing code. Now, with AI, they can generate significantly more in the same amount of time,” Putrevu noted. “But this increased output also means the review process becomes a much bigger bottleneck.” With industry leaders claiming that AI can soon write 95% of the code, the problem of reviewing and debugging becomes even more prominent. This has also led to the birth of vibe debugging. Unchecked AI-generated code can lead to maintenance hazards. If poor-quality code makes its way into production, organisations could find themselves dealing with bloated, inefficient, and difficult-to-maintain systems. Simply having more code does not always equate to better software—it must be reviewed, refined, and optimised. Should you be worried about AI reviewing your code? Just like people have been sceptical about using AI coding tools within their organisations, they are also now sceptical about AI code reviewers for similar reasons. But CodeRabbit is emerging as a viable solution to this challenge as it ensures that code is not only syntactically correct but also adheres to best practices and organisational guidelines. “Organisations can set predefined rules and quality metrics that AI reviewers enforce,” Putrevu added. “Even if AI agents are generating the code, CodeRabbit ensures that it meets a certain quality threshold before merging into production.” This approach provides a scalable solution to the problem. AI-driven reviews complement traditional tools like SonarQube and Codacy by offering real-time suggestions and generating fixes, reducing the need for manual intervention. “We’re not replacing human reviewers. We’re augmenting them, helping developers ship faster without compromising quality,” Putrevu clarified. Why Companies are Turning to AI for Code Reviews Organisations from Fortune 100 companies like Visa and Mastercard to digital-native enterprises like Flipkart and emerging Y Combinator startups are adopting AI code reviewers. These companies recognise the need to maintain high-quality standards while accelerating development cycles. For startups, where senior engineers may be scarce, AI-assisted reviews ensure that code quality is not compromised despite limited resources. Indie developers also benefit from an automated second opinion that provides insights they might otherwise miss. “Developers don’t want to rely solely on static analysis reports or post-mortem quality checks,” Putrevu emphasised. “They need real-time feedback that helps them iterate quickly and efficiently.” AI-generated code has the potential to revolutionise software development, but it also introduces new challenges. Without robust code review processes, organisations risk shipping subpar code that is difficult to maintain. “More AI-generated code doesn’t necessarily mean better software,” Putrevu concluded. “The key is ensuring that the review process keeps pace with the speed of development. AI-driven reviewers like CodeRabbit are stepping in to bridge this gap, ensuring that teams can move fast without breaking things.”
Traditionally, code reviews require manual scrutiny by peers, where senior engineers meticulously examine pull requests line by line.
Traditionally, code reviews require manual scrutiny by peers, where senior engineers meticulously examine pull requests line by line.
["AI Developers", "AI in Coding", "CodeRabbit", "Top Trend", "Vibe Coding"]
["Top Trend", "AI Developers", "TPU", "AI coding", "AI", "Scala", "Git", "AI in Coding", "CodeRabbit", "Vibe Coding", "Aim", "GAN", "GitHub", "R", "startup"]
["AI", "Aim", "TPU", "R", "Scala", "Git", "GitHub", "GAN", "startup", "AI coding"]
Mohit writes about AI in simple, explainable, and often funny words. He's especially passionate about chatting with those building AI for Bharat, with the occasional detour into AGI.