Unlike credit rating agencies which lacked direct financial consequences for bad ratings, this model creates "skin in the game." By structuring as a managing general agent (MGA), the auditor's compensation is tied to the profitability of the insurance policies, creating a powerful incentive to maintain rigorous, honest standards.
AI audits are not a one-time, "risk-free" certification but an iterative process with quarterly re-audits. They quantify risk by finding vulnerabilities (which can initially have failure rates as high as 25%) and then measuring the improvement—often a 90% drop—after safeguards are implemented, giving enterprises a data-driven basis for trust.
The model combines insurance (financial protection), standards (best practices), and audits (verification). Insurers fund robust standards, while enterprises comply to get cheaper insurance. This market mechanism aligns incentives for both rapid AI adoption and robust security, treating them as mutually reinforcing rather than a trade-off.
Insurers lack the historical loss data required to price novel AI risks. The solution is to use red teaming and systematic evaluations to create a large pool of "synthetic data" on how an AI product behaves and fails. This data on failure frequency and severity can be directly plugged into traditional actuarial models.
Drawing from the nuclear energy insurance model, the private market cannot effectively insure against massive AI tail risks. A better model involves the government capping liability (e.g., above $15B), creating a backstop that allows a private insurance market to flourish and provide crucial governance for more common risks.
While foundation models carry systemic risk, AI applications make "thicker promises" to enterprises, like guaranteeing specific outcomes in customer support. This specificity creates more immediate and tangible business risks (e.g., brand disasters, financial errors), making the application layer the primary area where trust and insurance are needed now.
Existing policies like cyber insurance don't explicitly mention AI, making coverage for AI-related harms unclear. This ambiguity means insurers carry unpriced risk, while companies lack certainty. This situation will likely force the creation of dedicated AI insurance products, much as cyber insurance emerged in the 2000s.
The approach to AI safety isn't new; it mirrors historical solutions for managing technological risk. Just as Benjamin Franklin's 18th-century fire insurance company created building codes and inspections to reduce fires, a modern AI insurance market can drive the creation and adoption of safety standards and audits for AI agents.