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title: "Comment by Mateo Ruiz on \"How much should you trust AI output? Simple features yes, core architecture no\" | Hashnode"
description: "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 Pa"
url: "https://hashnode.com/posts/how-much-should-you-trust-ai-output-simple-features-yes-core-architecture-no/6af550650e1241c13dd2ea22/comment/6af563df06281bef46ea9ddc"
---

[Discussion](https://hashnode.com/posts/how-much-should-you-trust-ai-output-simple-features-yes-core-architecture-no/6af550650e1241c13dd2ea22)

Comment [How much should you trust AI output? Simple features yes, core architecture no](https://hashnode.com/posts/how-much-should-you-trust-ai-output-simple-features-yes-core-architecture-no/6af550650e1241c13dd2ea22)

[Mateo Ruiz](https://hashnode.com/@mateoruiz)

[9h ago](https://hashnode.com/posts/how-much-should-you-trust-ai-output-simple-features-yes-core-architecture-no/6af550650e1241c13dd2ea22/comment/6af563df06281bef46ea9ddc)

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.