Season 1. Episode 7. Unscripted with Rajiv Dattani

Rajiv Dattani helped run METR, the organization that evaluated whether OpenAI's and Anthropic's frontier models were too dangerous to release, before leaving to build something stranger: insurance for AI, underwritten through Lloyd's of London.

 

In this conversation, he tells Jeff how METR measured model risk in human hours, meaning how long it would take a person to complete the most complex task an AI model can now do, and why that number matters for tracking how close AI gets to automating its own research and development. He walks through the historical playbook insurers have used to get ahead of new technology risk before regulation catches up, from Benjamin Franklin's fire insurance audits to Progressive's early seatbelt and airbag discounts, and explains why AI insurance can, for the first time, be priced using simulated red-team evaluations instead of waiting years for real loss data to accumulate.

 

He pushes back on the panic around the viral stat that 95% of AI pilots fail, arguing that's a healthy sign of experimentation rather than a warning, and says most failed pilots are management failures, not model failures, because leaders often don't understand their own processes well enough to know where AI should even go. The conversation closes on what worries him most: not one catastrophic AI failure, but the lack of infrastructure to catch small, compounding errors before they cascade into systemic economic risk once AI is embedded across banking and payments infrastructure.