AI broke code review. Two experts disagree on what replaces it.

Ask two experienced engineers about how to handle the flood of AI-generated code in their review queues, and you’ll get two different answers.

The debate remains very much unsettled. And on Tuesday, September 29, two industry leaders will join a live event to hash out what to do.

John Bristowe, Principal Developer Advocate at Octopus Deploy, will join Viktor Farcic, the platform engineering voice behind DevOps Toolkit, for the live conversation we’re calling “Human Review vs. Verified Pipelines: What Catches Bugs in the Age of AI Code.”

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Here are the facts: Developers have adopted AI en masse. According to the 2026 DORA report, 90% of developers now use AI at work. The result? Developers are merging 98% more pull requests than they managed in the pre-AI era.

But all that AI-generated code is leaving a mess. Bugs per developer are up 54%, and one analysis of 10,000 developers found that incidents per pull request have climbed a staggering 243%. Octopus Deploy’s own AI Pulse report found that while AI usage enables faster code creation, it can “degrade overall performance” because coding agents write large code updates that humans struggle to fully understand.

Part of the problem is that developers have adopted automated code generation faster than they have adopted automated code review, effectively moving the human bottleneck further down the software creation chain without removing it entirely. And AI code review may have the same shortcomings as the coding agents.

Bristowe argues that code review has quietly become little more than theater. No human reviewer can quickly audit a 40,000-line, agent-created pull request, since they were not part of the reasoning that produced it and cannot realistically understand everything it may change.

What does Bristowe recommend? Moving the quality gate off the humans’ desks and into the delivery pipeline itself. Does that mean more AI? Not necessarily, with the developer advocate arguing that building robust “policy-as-code” rules into deployment standards can flag only what goes against those policies. Humans can handle those exceptions, without pretending they are “reviewing” the entire package.

Expect Farcic to press Bristowe on how well a policy-as-code setup can truly absorb judgment, and whether we’re simply creating another accountability sink in software development. The conversation will also explore the plight of the junior engineer, who can no longer expect to join a team of humans writing code that other humans review and discuss.

The debate kicks off at 2:30 p.m. Eastern/11:30 a.m. Pacific on Tuesday, September 29. It’s free to attend, and attendees will receive a companion resource built from Octopus Deploy’s AI Pulse data, available immediately for participants who show up live. Register today.

What you’ll take away:

• Why AI-generated code broke the assumptions code review was built on, and why more review isn’t the fix
• Why using AI to review AI’s own code doesn’t close the gap (same training data, same blind spots)
• How to build a pipeline that verifies every deployment against a defined set of rules, no matter who or what wrote the code
• Where code review still earns its keep, and where it needs to step aside for the pipeline

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