CodeRabbit CEO: Using OpenAI to Review AI-Generated Code in Practice

When AI coding assistants can automatically generate hundreds of pull requests, the new bottleneck is not writing code—it's reviewing it. In an interview, CodeRabbit co-founder and CEO Harjot Gill shared how he built an AI code review service using OpenAI models, along with real-world lessons from internal red teaming to personal productivity automation.

The Bottleneck Shifts from "Writing Code" to "Reviewing Code"

Gill is himself a heavy Codex user, revealing that he pushed roughly 2,500 to 3,000 pull requests over the past 30 days. That number isn't enviable in itself—it reveals a structural shift: once coding agents are deployed at scale, the first bottleneck any company hits is code review.

In traditional development workflows, writing and reviewing workloads are roughly balanced; in AI-generated code scenarios, generation speed far outpaces human review speed. Gill's assessment is blunt: "The first bottleneck every company hits once they deploy these coding agents is code review." That's precisely the market gap CodeRabbit targets—becoming the scale provider of AI code review.

Pull Request is a core collaboration mechanism in distributed version control systems (such as Git): after a developer completes changes on a separate branch, they initiate a merge request to the main branch, where other members review the code diff, offer feedback, and decide whether to merge. It serves both as a quality gate for code and as a vehicle for team knowledge sharing. In traditional workflows, the number of pull requests is roughly proportional to team size and commit frequency, and reviewers can typically keep up within reasonable timeframes. But when AI coding agents generate code in bulk, pull request output can increase by an order of magnitude while human reading and judgment speed remains nearly unchanged—so the queue length balloons rapidly. This is the mechanism behind "review becomes the new bottleneck": the bottleneck isn't in the toolchain but in human attention, an unscalable resource.

Reasoning Models Deliver a Step Change in Review Capability

CodeRabbit's collaboration with OpenAI began at the company's founding. Gill emphasizes that every model release—especially iterations of reasoning models—has substantially improved the product's ability to find code issues at scale.

The logic here is straightforward: code review isn't just pattern matching—it requires understanding context, inferring intent, and judging boundary conditions. Progress in reasoning models on these tasks translates directly into the review tool's ability to uncover deep issues. Gill didn't disclose specific accuracy figures, but his wording of "massive improvements" suggests model iteration has a decisive impact on product competitiveness.

Reasoning Model refers to a class of large models that perform explicit multi-step thinking before producing a final answer, typically characterized by longer internal reasoning chains to decompose problems, check assumptions, and self-correct. Compared with traditional language models that rely primarily on pattern matching, reasoning models perform more reliably on tasks requiring causal inference, boundary condition enumeration, and cross-file context association. Code review falls squarely into this category: judging whether a null pointer might be triggered, whether a concurrent modification introduces a race condition, or whether a refactor breaks caller contracts all require tracing execution paths rather than simple comparison. Therefore, each iteration in model reasoning capability directly expands the coverage of deep defects that review tools can detect—which explains why vendors treat reasoning model progress as a decisive variable in product competitiveness.

Using the Sharpest Model to Fortify Your Own Fences First

The most tension-filled part of the interview is Gill's assessment of "cyber models." He says they're "pretty scary"—good enough to effectively hack and penetrate existing systems. After getting early access, CodeRabbit didn't rush to package them as a product for customers; instead, they first used them for penetration testing and security hardening of their own platform.

"We have to fight fire with fire," Gill says, pointing to a reality: AI security tools themselves are amplifiers of offensive-defensive asymmetry. Finding your own vulnerabilities with the strongest models first, then offering it as a service externally—this sequence is almost mandatory in the security product space. It also indirectly shows that OpenAI's release cadence for sensitive capability models has a direct impact on downstream security vendors' product strategies.

Red Teaming originates from military exercises and refers to organizing dedicated adversarial teams that simulate real attackers' perspectives to discover system weaknesses. In the AI security context, red teaming includes both having models attempt to break through their own safety alignment (e.g., inducing harmful content generation) and using models as attack tools to probe target systems for vulnerabilities. Using powerful capability models for penetration testing of one's own platform falls into the latter category: attackers and defenders use the same strongest tools, exposing problems before external attackers can. This practice has become standard in the security industry, but it also creates a governance challenge—the same model capability can be used for both defense and offense, and vendors' caution in release cadence directly affects whether downstream security products can get adequate hardening windows.

Personal Productivity Automation: Letting Codex Write Daily Reports

Beyond the company-level code review business, Gill also applies automation to his own daily management. He configured a Codex-driven workflow that sends daily reports to his executive assistant, recording all emails, activities, and how the day should be scheduled.

This detail may seem trivial, but it reflects an application pattern: the value of AI coding agents isn't limited to writing code—they can serve as general-purpose automation engines that take over repetitive information processing work for individuals or teams. For managers, this is closer to real time savings than "AI helps me write functions."

Observations from Multi-Source Cross-Validation

It should be noted that the core information in this article comes from Gill's own public interview—a single source. The claim of "roughly 2,500 to 3,000 pull requests in 30 days" is a self-reported personal usage intensity that cannot be independently verified; the points that "reasoning models bring massive improvements" and "cyber models possess strong penetration capabilities" align with general industry understanding of reasoning models and security capability models, but specific degrees remain based on vendor claims. Readers should distinguish between "directional consensus" and "specific numerical claims" when evaluating.

Implications for Development Teams

CodeRabbit's case offers three actionable insights. First, before deploying coding agents, assess the throughput capacity of your code review process—otherwise review becomes the new queue point. Second, security teams should prioritize using the strongest models for internal red teaming rather than waiting for external attacks to expose problems. Third, reframe AI agents from "code writing tools" to "automation executors"—personal daily reports are just the starting point; there's much more room for workflow orchestration.

The larger the scale of AI-generated code, the more pronounced the leverage effect of review and security. Gill's practice proves at least one thing: in the AI coding chain, the review step is transforming from a cost center into a product opportunity.

One-line Summary

As AI coding agents proliferate, code review becomes the new bottleneck—CodeRabbit responds with reasoning models and internal red teaming.

Paragraph Summary

Based on an interview with CodeRabbit co-founder and CEO Harjot Gill, the article notes that after large-scale deployment of AI coding agents, the first structural bottleneck for enterprises has shifted from writing code to reviewing it. Gill reports pushing roughly 2,500 to 3,000 pull requests in 30 days, with generation speed far exceeding human review speed. CodeRabbit has partnered with OpenAI since its founding, and reasoning model iterations have significantly enhanced its ability to find deep code issues at scale. On security strategy, the company first uses models with strong penetration capabilities for red teaming and hardening its own platform before offering services externally. Gill also uses Codex to automatically generate daily reports for his executive assistant to schedule his day. The article also notes that core information comes from a single source, and personal usage figures and capability assessments should be treated with caution.