Karen Clark on AI, Frequency Perils, and Why Reinsurers Stopped Trusting the Models

Karen Clark, founder and CEO of Karen Clark & Company, returns to Risky Science to discuss KCC's new white paper on artificial intelligence in catastrophe models — where AI genuinely improves the science, and where the industry is overstating what it can do.

Clark built the first commercial catastrophe model in the 1980s. In this conversation she argues that the biggest step change in the field wasn't AI at all, but the shift from statistical to physical, dynamical models — and that AI's real value is narrower than the hype suggests. It sits almost entirely in the hazard component, sharpening intensity footprints for frequency perils like severe convective storm and winter storm, where high-resolution atmospheric data is abundant. The vulnerability and financial components remain largely untouched.

We also get into why she thinks a $100 billion aggregate loss year isn't as significant as the industry treats it, given that a single Category 5 hurricane into Miami would cause over $200 billion on its own. She explains KCC's daily live events process, which has been ingesting 30 gigabytes of atmospheric data and producing hail and tornado footprints every day since 2018, and why she believes that testing regime is what makes the models credible. She makes a pointed claim about Winter Storm Uri: KCC had the 2021 Arctic air outbreak at a 1-in-75-year return period, while she says other modelers didn't have it inside 10,000 years.

Recorded ahead of the Monte Carlo Rendez-Vous, the conversation closes on hyperscale data centers — an emerging exposure where the modeling question is less about the buildings than about the data going into the models, and where Clark reframes tornado risk in a way that cuts against how most CEOs are thinking about it.