We’ve officially crossed the threshold where AI (Claude, Cursor, Copilot) writes more code than we do. But there’s a massive hidden cost emerging: Verification Debt.

Generation speed has outpaced verification accuracy. While we can prompt a feature into existence in seconds, reviewing that code for subtle logic errors—especially cross-file dependencies—is becoming a full-time job.

The Problem

AI bugs are "confidently wrong." They look correct at a glance, but:

• They might break a service three directories away.
• They often miss edge cases in business logic.
• Reviewing an AI PR can take more effort than reviewing human code because you can't assume "intent."

Let’s Discuss

I’m curious how other teams are scaling their quality control as AI output grows:

1. How do you verify AI code beyond a simple diff review?
2. Do you trust AI to write its own unit tests? (Does that feel like the blind leading the blind?)
3. Have you found any tools that actually understand your whole codebase context during review?