Codex Security and the Rise of AI Reviewing AI
The next big shift in AI-assisted software development is not more code generation. It is AI for verification.
OpenAI’s new Codex Security research preview, announced in early March 2026, is a good signal of where the market is going. The product scans repositories commit by commit, builds repository-specific threat models, validates findings in isolated environments, and ranks issues with proposed fixes. OpenAI says early adopters used it to detect more than 11,000 critical and high-severity vulnerabilities while cutting false positives by more than 50%.
The PR Tsunami: What AI Code Volume Is Doing to Your Review Process
AI coding tools delivered on their core promise: developers write less, ship more. Teams using AI complete 21% more tasks. PR volume has exploded—some teams that previously handled 10–15 pull requests per week are now seeing 50–100. In a narrow sense, that’s a win.
But there’s a tax on that win that most engineering leaders aren’t accounting for: AI-generated PRs wait 4.6x longer for review than human-written code, despite actually being reviewed 2x faster once someone picks them up. The bottleneck isn’t coding anymore. It’s review capacity, and it’s getting worse as AI generation accelerates.
AI Code Review: The Hidden Bottleneck Nobody's Talking About
Here’s a problem that’s creeping up on engineering teams: AI tools are dramatically increasing the volume of code being produced, but they haven’t done anything to increase code review capacity. The bottleneck has shifted.
Where teams once spent the bulk of their time writing code, they now spend increasing time reviewing code—much of it AI-generated. And reviewing AI-generated code is harder than reviewing human-written code in ways that aren’t immediately obvious.
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