![[D] AI safety might fail because we’re protecting the wrong layer](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8552-300x171.png)
AI safety efforts often focus on aligning the model's internal behavior, but this approach may be insufficient. Instead of relying on AI's "good intentions," real-world engineering practices suggest implementing hard boundaries at the execution level, such as OS permissions and cryptographic keys. By allowing AI models to propose any idea, but requiring irreversible actions to pass through a separate authority layer, unsafe outcomes can be prevented by design. This raises questions about the effectiveness of action-level gating and whether safety investments should prioritize architectural constraints over training and alignment. Understanding and implementing robust safety measures is crucial as AI systems become increasingly complex and integrated into society.
