I think the bigger risk isn't AI writing a complex feature incorrectly; it's the team gradually losing the ability to reason about the system it ships.

At IT Path Solutions, one principle we consider important when working with AI-generated code is that every architectural decision needs a clear owner, even when AI handles much of the implementation. AI can generate a working feature, but maintainability depends on understanding its assumptions, dependencies, failure paths, and trade-offs.

I'd also distinguish between code review and architectural review. Reviewing whether the code works is one thing; understanding whether it belongs in the system that way is a different level of judgment. AI makes implementation faster, but that second responsibility becomes even more important.