The accumulation no cat model sees
You are writing AI liability as if each policy fails on its own. They don’t. Every insured’s AI leans on the same three or four frontier models — so the book is one correlated position wearing the costume of a diversified one.
The number you don’t compute today
What percent of your automated decisions share a single model dependency? That figure is your true PML concentration — and right now nothing in the stack produces it. A signed drift receipt per run does: it records where each agent’s work landed on a fixed 144-cell lattice, recomputes byte-identical for any auditor, and fires before a claim exists. It is the forward sensor that builds the accumulation dataset going forward — the one you can’t reconstruct from a past that didn’t log it.
A drift receipt is a countable event, not yet a loss-linked one. Whether the drift we measure predicts your correlated losses is an empirical question — and only a house with a real loss book can answer it. That’s the proposal: a joint validity study mapping our leading drift signals against your actual incident record. Not a product sale. A measurement partnership on the one exposure that could end a book.
Then: elias@thetadriven.com · a 20-minute conversation on the study.
The lattice diagram is illustrative. The measurement is open source (MIT); the financialization is licensed. See /brokers · /agent-year · ThetaDriven · US Patent App. 19/637,714.