Show Notes
Insurance for AI agents: what happens when the machine screws up
In January 2026, something happened that almost nobody in tech noticed and everybody in tech should have.
The biggest names in commercial insurance walked into statehouses across America and asked permission to stop covering artificial intelligence. Berkshire Hathaway. Chubb. Travelers. AIG and WR Berkley followed. Regulators approved more than 80% of the requests. Florida, Connecticut, and Maryland moved fastest. Three ISO endorsements went live on January 1st, and the silent AI coverage era ended without a single press release.
Meanwhile, you spun up four more agents this quarter.
Dr. Charles Higgins saw the gap coming before the paperwork landed. He and Dr. Sophia Kalanovska met inside the UKRI Safe and Trusted AI programme, both doing PhDs in AI at King's College London. Halfway through, they built Tromero, a GPU aggregation platform that turned crypto mining rigs into cheap machine learning compute. BlueYard Capital led a £1.5 million seed in 2023. They sold the platform to a cloud infrastructure company and went back to consulting.
And then the same conversation kept happening. Every enterprise they talked to was moving from chatbots to agents. Agents that book things. Agents that route trucks. Agents that pay invoices. And every single conversation collapsed into the same word: risk. Not "will it work." Not "what does it cost." Risk. Higgins describes projects dying over a 0.001% chance of catastrophe, because a rounding error on a probability is still a career-ending headline for whoever signed off.
They were doing risk assessment for free. Then they realized they were describing a product that already exists everywhere else in the economy. Somebody hands you a piece of paper. If it goes wrong, they pay. That product is insurance.
Neither of them knew anything about insurance. A data scientist and a software engineer walking into one of the most closed, credential-obsessed industries on earth. They gave themselves six months to figure out who was writing AI cover and why. The answer: nobody, because there was no data.
Insurance runs on history. Actuaries price the future by counting the past. There is no past here. The failure modes for agentic AI are, in Higgins's framing, close to inconceivable right up until the moment they happen. So Redberry Labs built an underwriting engine that uses AI itself as the simulation layer, running an agent against thousands of scenarios to estimate how likely it is to fail and how expensive that failure gets. He argues this style of underwriting was impossible before large models were flexible enough to play out the scenarios.
Redberry is now a managing general agent. They write the policy, assess the risk, find the client, and sell it. They just do it on a reinsurer's checkbook.
The part of the conversation founders need to sit with is the murky middle. Pure AI failure is easy: the agent did it, the policy pays. Pure human failure is easy: your general liability handles it. But your inbox agent drafts an email that's 90% right, you change three words, you hit send, and it triggers a lawsuit. Who caused that? Your D&O? Your tech E&O? Your cyber? Higgins spends most of his time in that gap, and he's honest that there is no clean answer yet.
Redberry Labs: https://www.redberrylabs.com
Charles Higgins on LinkedIn: https://www.linkedin.com/in/charles-higgins-70a996170/
Ryan Estes on LinkedIn: https://www.linkedin.com/in/estesryan/
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