Traced

What if mechanistic interpretability succeeded — not as research curiosity but as regulatory mandate — and the tools built to make AI systems transparent became the most powerful attack surface in existence? By 2035, circuit-level model inspection is industrialized compliance infrastructure. The EU requires interpretability audits for high-risk AI systems. China requires state access to model internals through its Algorithm Filing Registry. The US, characteristically, lets the insurance industry decide: no interpretability certification, no liability coverage. Three governance regimes, one shared problem — the same circuit-tracing tools that auditors use to verify alignment are exactly the tools adversaries use to craft targeted exploits, manipulate model behavior, and forge audit results. Meanwhile, software engineering has undergone a quieter extinction. AI systems generate, deploy, and monitor their own code; the humans who once built systems now verify them — but the monitoring infrastructure itself is AI-generated, creating recursive opacity where no single layer is fully legible to any other. The world's central horror is not that AI systems are opaque. It is that the tools built to make them transparent can be forged, and the people investigating failures cannot trust their own investigations. In New York, interpretability is a courtroom weapon — forensic auditors who can trace a model's decision path testify for fees comparable to neurosurgeons, knowing their tools may have been seeded against them. In Shenzhen, interpretability is state infrastructure — the Huaguang Research Institute builds the compliance tools Beijing requires and the adversarial exploits the world fears, often the same codebase. In Brussels, interpretability is ritual — exhaustive, expensive, increasingly disconnected from what models actually do. The question is not whether AI systems are transparent. It is who gets to look, what they see, and whether either can be trusted.