Five worthy reads: The next big insurance market no one is talking about yet
Amid the ongoing AI boom, have you ever stopped to think about who pays when AI gets something wrong?
It's a straightforward question, but a loaded one: with AI systems increasingly trusted to give medical, legal or financial guidance and news summaries their confident errors— hallucinations— can cause harm to the real world. The term might sound strange because hallucination is typically associated with human perception not machines. Yet conceptually, the phenomenon is surprisingly familiar. Just as a person experiencing a hallucination perceives something that isn't real, an AI hallucination occurs when the model gives us information that might seem credible but in reality it's false information.
AI hallucinations are emerging as a major enterprise risk, with real-world incidents ranging from fabricated legal citations to incorrect customer policies and costly business errors. AI doesn't say "I'm guessing", it states the same as a matter of fact.
In 2023, United States District Court Judge P. Kevin Castel found that lawyers had submitted a legal brief that had false citations in the Mata v. Avianca, Inc. case after relying on ChatGPT for legal research. The fabricated citations included fake case names, fake docket numbers, and fake legal reasoning.
The lawyers and the firm were fined $5,000 under Federal Rule of Civil Procedure Rule 11. There is no harm in using AI to conduct research or seek guidance, the real risk lies in accepting AI-generated information without verifying it.
The damage is real, the source is invisible. So shouldn't there be something built around that risk?
The question is no longer whether AI can get things wrong, it can. As AI becomes more deeply embedded, hallucinations are no longer just a technical flaw they are a potential business liability. And when a risk becomes real, measurable and costly, the question of insurance inevitably follows.
To explore this question further, here are five articles that explore this possibility, examining the challenges of assessing and ultimately insuring against the risks created when AI gets things wrong.
Traditional Insurance Leaves Enterprises Exposed as AI Liability Claims Surge
Between 2021 and 2025 generative AI-related lawsuits in the US grew 978%. The most common claims include: Patent infringement (11.9%), Copyright infringement (11.2%), and Privacy and personal injury claims (10.2%) which often involve the misuse of personal data or AI-generated outputs. These figures indicate that AI-related legal disputes are moving beyond incidents and becoming a mainstream business risk. This article concludes that conventional insurance products do not adequately cover AI-specific risks because they were developed before widespread enterprise AI adoption.
AI hallucinations cover launched in first wave of new insurance products for AI
When an AI confidently gives wrong answers, someone pays the price. Armilla is betting it should be the insurer, and not the victim. The company launched an insurance policy that covers legal claims from businesses or third parties caused when AI systems go wrong and not just dramatic failures, but the slow quiet kind. The kind where a model that worked fine last year starts drifting, because the world evolved and training data didn't.
Because the internet is flooded with AI generated content, it makes it difficult to find clean, human generated data to learn from. This is what the insurance industry calls model drift. Originally, it was called a coverage blind spot. AI-related insurance already existed but it typically had low limits and did not cover losses caused by model drift—which is the specific gap Armilla's product targets.
Florida Supreme Court Posts New Rule on AI Hallucinations in Court Filings
As generative AI adoption accelerates across industries, regulators and institutions are introducing stronger safeguards to ensure responsible use of AI. In June 2026, the Florida Supreme Court established new requirements reaffirming that attorneys remain accountable for the accuracy and integrity of AI-assisted court filings. This development underscores mounting concerns about AI hallucinations and signals a broader shift toward enhanced governance and human accountability as organizations deploy AI in critical decision making and professional context.
This article highlights several real-world incidents where AI hallucinations caused problems across different industries, highlighting how these errors can result in financial loses, legal issues and reputational damage when AI-generated content is not properly verified.
How to Stop AI Hallucinations by Teaching It to Say "I Don't Know"
Here is the thing about AI models: When they don't know something, they don't say so. Instead they're built to be helpful, which usually means giving you an answer, even a wrong one, rather than admitting "I'm not sure". It's a strange side effect of how they're trained. This article highlights how one can fix that essentially by training AI to be comfortable saying "I don't know" instead of guessing.
Put together, this isn't a string of isolated stories. It's the shape of a market forming in real time. Armilla's early bet on hallucination coverage won't stay a lone experiment for long. As more businesses lean on AI for decisions that carry real legal and financial weight, the question won't be if hallucination insurance becomes standard, but how fast the rest of the industry catches up. The bigger fix, of course, is technical; teaching models to say "I don't know" instead of confidently guessing. But that's a long game, and businesses don't have the luxury of waiting for it to play out. Until AI gets better at knowing its own limits, someone has to insure against the risk of it being wrong. That gap is exactly where this next insurance market is built.
The only real question left is: who gets there first and who ends up playing catch-up?