For more details on how and why an insurance exchange for homeowners insurance makes sense, check out my recently published article: Obamacare For Homeowners Insurance: Fixing America's Broken Insurance Markets In A Time Of Climate Change. papers.ssrn.com/sol3/papers....

A centralized insurance exchange would be a pro-market reform.

And we already know it works: the ACA created similar exchanges for health insurance.

Ironically, this idea makes more sense in homeowners insurance, where there are more carriers due to lower entry barriers.

Right now, huge discrepancies exist in coverage, pricing, and mitigation discounts.

Why? Because most homeowners don’t comparison shop—it’s hard, confusing, and time-consuming.

That lack of shopping weakens competition and drives up costs for those least able to bear them.

This could expand where liability and insurance work while informing smarter ex ante regulation. Draft’s still evolving—feedback welcome! papers.ssrn.com/sol3/papers....

We also propose a data-driven complement: build on California’s new transparency laws and other insurance data systems to mandate standardized AI safety reporting.

But many AI risks—cyberattacks, terrorism, financial fraud, even psychosis—are adversarial and hard to measure, making insurance-based safety incentives much weaker.

We clarify when liability and insurance can actually incentivize AI safety—mainly when losses are common and observable (like self-driving cars or AI-enabled medical devices).

California’s transparency-first approach can help build the data needed to actually understand and reduce AI risk—something insurers alone can’t do.

That's because liability means that insurers will become key intermediaries in promoting AI safety. But they’ve struggled to model or mitigate analogous risks in cyber insurance—and likely would again with AI.

Drawing from our new draft, The Limits of Regulating AI Safety Through Liability and Insurance: Lessons From Cybersecurity, papers.ssrn.com/sol3/papers...., we argue that expanding liability for AI harms won’t necessarily make systems safer.

Unlike last year’s vetoed S.B. 1047, this new law explicitly caps penalties—even for catastrophic AI failures—and avoids assigning broad liability to AI developers.
That’s deliberate: as Sen. Scott Wiener said, “SB 53 is more focused on transparency.”

This framework draws on principles of consumer financial regulation and lessons from the EU’s new AI Act. It seeks to balance the promise of AI-driven financial advice with the need to protect consumers from significant harm. Check it out!

Our solution is a dual regulatory approach: (1) a licensing requirement for robo-advisors that use generative AI to match consumers with products, and (2) heightened duties of care and loyalty for all robo-advisors.

We argue that the current U.S. regulatory framework is not up to the task. Existing rules fail to prevent AI-enabled robo-advisors from providing conflicted, inaccurate, or manipulative advice on a large scale.

Generative AI can make customized financial advice widely available. But because it mimics human advisors so convincingly, it also creates serious risks that consumers will be nudged into costly or inappropriate products

Check out the broader law review article, The Limits of Regulating AI Safety Through Liability and Insurance: Lessons From Cybersecurity. on which our Lawfare piece is based here: papers.ssrn.com/sol3/papers....