Private Governance

Hadfield’s core proposal is to make AI regulation an outcome market: government decides acceptable risk, while competing, approved specialists discover, implement, and verify the technical controls. Instead of legislators freezing today’s red-teaming, data, or process requirements into statute, regulatory-services providers would adapt them as technology changes. The wager is that markets are better “information processing and discovering engines,” provided government retains muscular oversight.

California’s SB 813 would bootstrap that market by making independent certification meaningful evidence that an AI company met its duty of care. The current concept is a rebuttable presumption, not blanket immunity: injured parties could still sue and introduce evidence of negligence. For developers facing unsettled agentic-AI liability, that converts tort law’s “potential energy” into an immediate reason to purchase credible oversight.

The investable bottleneck is not demand for AI-safety services but institutional credibility around who certifies, how performance is measured, and who can revoke approval. Certifiers would need to show that covered vehicles crash less, chatbots cause fewer harms, or other specified outcomes improve—not merely that forms were completed. Hadfield’s non-negotiable backstop is that government must be able to “yank your license.”

A race to the bottom remains the proposal’s central execution risk because developers may select whichever certifier is cheapest and most permissive. Hadfield therefore favors an expert commission, scrutiny of certifiers’ funding, and proof that each can financially survive denying certification; an auditor that must approve four of five customers to stay alive is structurally compromised. Multi-state or international approval could add redundancy when one government “takes its eye off the ball.”

Well-designed certification could reduce rather than deepen big-tech concentration by giving startups a proportionate route to institutional trust. A 10-person developer serving a limited application should face a different program from software entering 10,000 vehicles, while static thresholds such as FLOPs will age poorly. Without trusted validation, Freedman argues, only incumbents can afford to prove to banks and other enterprises that their systems are safe.

Neither insurance nor expanded liability eliminates the need to build the underlying regulatory intelligence. Insurers cannot rationally price novel AI risks without loss histories, duties, standards, and evidence about which controls work; near-miss liability might meanwhile discourage reporting and red-team discovery. Insurance can become a powerful complementary carrot once certifiers supply the missing risk structure, but it should not decide society’s acceptable bioweapons or systemic-finance risk.

The model deliberately does not claim to solve catastrophic externalities, where after-the-fact damages may be meaningless. Bioweapons, market collapse, or harms so large that “who cares that you followed some rules” may require separate ex-ante restrictions and explicit carve-outs. Hadfield’s closing call is pragmatic: society needs “the MVP of new approaches on regulation,” because static rulemaking still has its “shoelaces tied on the starting line.”

🔗 Original source & video: Private Governance: Creating a Market in AI Regulation, with Dr. Gillian Hadfield & Andrew Freedman