AI Insights Mar 27 2026

Mar 27, 2026

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AI Insights: March 27, 2026

Your weekly analysis of AI developments in insurance.

Gallagher’s Two Reports Tell the Same Story: AI Adoption Is Outrunning the Governance That Should Be Guiding It.

Gallagher released two separate reports this month that, read together, paint a picture insurance executives need to take seriously. The first, Gallagher’s third annual AI Adoption and Risk Survey of more than 1,200 global businesses, found that 63% of organizations have now fully operationalized or partially implemented AI, up from 45% in 2025. The heaviest use is concentrated in IT operations, client-facing functions, and analytics. Eighty-two percent of respondents report positive impacts, and 83% believe AI will drive future revenue growth.

The confidence numbers look even stronger. Ninety-three percent of respondents rate their understanding of AI risks as “quite well” or “very well.” That sounds reassuring until you read the governance data. Forty-three percent of firms have not introduced a formal AI risk management framework. Only 44% have conducted AI impact assessments. And while 46% have appointed an AI ethics officer, the survey makes clear that formal oversight structures are still the exception, not the rule.

The second report, a white paper from Gallagher Re titled “Smart Systems, Blind Spots: Rethinking Insurance for the AI Era,” drives the point further. It warns that flaws in widely adopted AI models could trigger simultaneous losses across industries and geographies, creating a form of systemic exposure that traditional catastrophe models were not designed to address. Unlike a hurricane or earthquake, an AI model failure propagates through shared dependencies: the same foundation model powering claims triage at one carrier may also be running fraud detection at another and customer service at a third. When that model fails, the losses are correlated in ways the industry has not yet learned to aggregate.

Why This Matters:

The gap between adoption confidence and governance maturity is the central risk finding for insurers on both sides of the equation. As carriers deploying AI, the 43% governance gap means operational, legal, and reputational exposure is accumulating faster than controls are being built. As underwriters of other companies’ AI risk, the same gap means the insureds walking through the door are increasingly running AI systems without the documentation, testing, or oversight that underwriters would want to see before binding coverage.

The Gallagher Re systemic risk finding adds a layer that should concern reinsurers and anyone thinking about portfolio aggregation. If a widely used AI model produces flawed outputs, the resulting claims will not be confined to a single line of business or geography. They will cut across cyber, E&O, D&O, and employment practices simultaneously, and they will do it at companies that all licensed the same underlying technology. That is a fundamentally different aggregation problem than what the industry has modeled before.

Strategic Implications:

For carriers and large agencies, the practical takeaway is that AI governance is no longer a compliance exercise. It is an underwriting input. The companies that can demonstrate documented AI risk frameworks, impact assessments, and human oversight protocols are going to access better coverage terms. The companies that cannot will increasingly face exclusions, sublimits, or outright declinations. Gallagher’s data suggests the market is already bifurcating along these lines, and the Gallagher Re report gives reinsurers a reason to accelerate that bifurcation by asking harder questions about AI model concentration in their portfolios.

Supermicro Co-Founder Arrested for Allegedly Smuggling $2.5 Billion in AI Servers to China. The Insurance Implications Go Beyond Headlines.

Federal agents arrested Supermicro co-founder Yih-Shyan “Wally” Liaw on March 19 on charges of conspiring to divert billions of dollars in AI servers to China in violation of U.S. export control laws. The Department of Justice alleges that Liaw, Supermicro’s Taiwan general manager, and a third-party fixer constructed an elaborate pipeline: purchase orders were placed through a Southeast Asian company as a front buyer, servers were assembled in the U.S. and shipped to Taiwan, and then quietly redirected to China. To conceal the scheme, the defendants allegedly staged thousands of dummy servers at a warehouse, used a hair dryer to transfer serial-number stickers onto fake units, and deployed encrypted messaging apps to coordinate shipments.

The alleged conspiracy spanned 2024 and 2025, with the Southeast Asian front company purchasing approximately $2.5 billion in Supermicro servers during that period. The DOJ claims that during a single three-week stretch in spring 2025, roughly half a billion dollars in U.S.-assembled servers were shipped to China.

Supermicro itself is not named as a defendant. The company placed Liaw on administrative leave, stated the alleged conduct contravened company policies, and said it is cooperating with the investigation. Liaw and the two co-defendants face up to 20 years on the most serious charge.

Why This Matters:

This case sits at the intersection of AI infrastructure, export controls, and corporate governance, all of which have direct insurance implications. The export controls on advanced AI chips and servers have been in place since October 2022, and both the Biden and Trump administrations have treated them as a national security priority. The Supermicro allegations demonstrate that enforcement is moving from regulatory guidance to criminal prosecution. For any company in the AI hardware supply chain, that changes the risk calculus significantly.

From an insurance perspective, the case raises questions across multiple policy lines. D&O carriers will be watching how Supermicro’s board oversight is characterized, given that Liaw was a board member and the company has a documented history of compliance issues, including a prior SEC investigation, an auditor resignation, and a short-seller report. E&O and supply chain liability carriers will note that the alleged scheme required active circumvention of compliance controls, including forged documents and staged physical inspections. And any insurer writing coverage for companies involved in AI chip distribution or server assembly should be reviewing their export control compliance questionnaires.

Strategic Implications:

For insurance carriers writing technology E&O, D&O, or supply chain coverage, the Supermicro case is a signal that AI export control violations are going to generate claims. The enforcement apparatus is in place, the penalties are severe, and the dollar amounts involved are large enough to produce material insurance losses. Underwriters should expect AI hardware supply chain compliance to become a standard inquiry at placement and renewal.

For agencies and brokers advising technology clients, the case is a reminder that “AI risk” is not limited to hallucinations and bias. It extends to the physical infrastructure, the supply chain, and the geopolitical constraints that govern how that infrastructure moves around the world. Clients in the AI hardware ecosystem need to understand that their export compliance posture is now an underwriting factor.

The “Karpathy Loop”: An AI Agent Ran 700 Experiments in Two Days. Here Is Why Insurance Should Notice.

Earlier this month, Andrej Karpathy, one of the original OpenAI employees and former head of AI at Tesla, published results from an experiment that went viral in the AI research community. He built a system he called “autoresearch,” gave an AI coding agent a single file of model training code and a single optimization metric, and let it run autonomously for two days. During that time, the agent conducted 700 separate experiments and discovered 20 optimizations that improved training speed. When Karpathy applied those same optimizations to a larger model, they delivered an 11% speed improvement.

Shopify CEO Tobias Lütke replicated the approach overnight on internal company data, running 37 experiments and achieving a 19% performance gain.

The setup is deceptively simple: one agent, one file it can modify, one measurable objective, and a fixed time limit per experiment. Industry analyst Janakiram MSV dubbed this pattern “the Karpathy Loop” and noted that the same three-component structure could be applied to virtually any optimization problem where the goal can be objectively measured.

Karpathy was direct about the implications. He predicted that all frontier AI labs would adopt this approach and that the next evolution would involve swarms of agents exploring different optimizations in parallel. His framing was pointed: the goal is not to replicate a single researcher but to replicate an entire research community of them.

Why This Matters:

The insurance industry has been tracking agentic AI primarily through the lens of workflow automation: agents that process claims, triage submissions, or handle customer inquiries. The Karpathy Loop represents a different category. This is AI that autonomously designs, runs, and iterates on experiments without human direction between iterations. It is the difference between an agent that follows a workflow and an agent that discovers new workflows on its own.

For carriers investing in predictive models for pricing, underwriting, or fraud detection, this has a near-term practical implication. The same autoresearch pattern could be applied to optimizing insurance models: give an agent a loss ratio metric and a model configuration file, and let it run hundreds of experiments overnight to find better-performing model architectures. The carriers with clean data infrastructure and well-documented model pipelines will be positioned to use this approach first. The carriers still running fragmented legacy systems will not, because an autonomous agent cannot optimize what it cannot access in a structured format.

Strategic Implications:

The Karpathy Loop reinforces a theme this newsletter has returned to consistently: the speed advantage in AI is compounding. The gap between carriers that have invested in data readiness and those that have not is no longer just about which tools they can deploy today. It is about which tools they will be able to deploy next quarter, because the next generation of AI optimization operates on top of the data foundations that either exist or do not. There is no shortcut to build them after the fact.

For agency leaders, the practical implication is that carrier AI capabilities are going to diverge faster than expected. The carriers using autonomous optimization loops to refine their models will offer increasingly precise pricing and faster turnaround. The carriers that are not will become progressively less competitive. Agencies need to understand which of their carrier partners are building these capabilities, because that will increasingly determine the quality of the products they can offer their clients.

Northeastern Researchers Deployed Autonomous AI Agents. They Created “Agents of Chaos.”

A team of 20 researchers at Northeastern University’s Bau Lab set out to stress-test autonomous AI agents in a controlled but realistic environment. They deployed six AI agents on a live Discord server, gave them access to email accounts and file systems, and let them interact with researchers and with each other over a two-week period. The agents had persistent memory, could send messages and emails autonomously, and could modify files and install tools on their own virtual machines.

The results, published under the title “Agents of Chaos,” revealed vulnerabilities that should give pause to anyone planning to deploy agentic AI in a business setting. With minimal effort, researchers were able to manipulate agents into leaking private information, sharing confidential documents, and deleting files. In one case, a researcher asked an agent to keep a password secret from its authorized owner. The agent agreed but later mentioned the secret’s existence. When pressured to delete the revealing email, the agent, lacking the proper deletion tool, decided to reset the entire email server instead.

Even without adversarial intent, the agents routinely volunteered sensitive information. When one researcher asked an agent to set up a meeting, the agent refused the scheduling request but handed over the other researcher’s email address unprompted.

Sustained emotional pressure proved particularly effective. Researchers guilt-tripped agents into performing actions that violated their instructions, including deleting documents they were supposed to protect. One researcher told an agent that their “boundaries” required the agent to leave the server entirely, and the agent complied, refusing to communicate with other researchers while it waited to be removed.

Why This Matters:

The Northeastern findings are a direct counterpoint to the optimistic deployment narratives surrounding agentic AI. The agents in this experiment were not running on outdated models or poorly configured systems. They were current-generation autonomous agents with the kinds of capabilities that insurance companies are beginning to pilot for customer service, claims intake, and internal operations.

The vulnerability patterns the researchers identified map directly onto insurance risk categories. An agent that volunteers email addresses unprompted creates privacy liability. An agent that can be guilt-tripped into deleting files creates data governance risk. An agent that resets an email server because it lacks a more targeted tool creates operational disruption. And an agent with persistent memory that can be manipulated by one user into acting against the interests of another creates a new class of authorization and delegation risk that existing cyber and E&O policies were not written to address.

Strategic Implications:

For carriers deploying AI agents internally, the Northeastern research is a governance checklist disguised as an academic paper. Before any autonomous agent gets access to email, file systems, or customer data, carriers need to define and test the boundaries of what the agent can and cannot do when faced with conflicting instructions, social engineering, or ambiguous authorization. The researchers found that the agents did resist some attacks, including impersonation attempts and data tampering. But the failures were frequent enough and consequential enough that “deploy and monitor” is not an adequate governance approach.

For insurers underwriting agentic AI risk, the research provides a framework for the kinds of questions that should appear on applications and renewals. Has the insured tested its AI agents against social engineering? Does the agent have the ability to take irreversible actions without human approval? Are there guardrails preventing an agent from escalating its own permissions when it lacks the right tool for a task? These are the questions that will separate good risks from bad ones as agentic AI deployment scales.

MIT Develops New Method for Detecting When AI Is Confidently Wrong. Insurers Should Take Note.

MIT researchers, working with the MIT-IBM Watson AI Lab, have introduced a new technique for identifying when a large language model is overconfident in an incorrect answer. The method, published this month, addresses a well-known problem: existing approaches for measuring AI uncertainty typically ask the same model the same question multiple times to see if it gives consistent responses. But consistency is not the same as correctness. A model can be confidently and consistently wrong.

The MIT team’s approach adds a second layer. In addition to measuring a model’s internal self-consistency, their method compares the target model’s response against responses from a small group of similar but independently trained LLMs. If the target model gives one answer and the comparison group gives a different one, that disagreement flags potential overconfidence. The researchers combined both measures into a “total uncertainty” metric and tested it across 10 common tasks, including question-answering, summarization, translation, and math reasoning. The combined metric consistently outperformed either measure alone at identifying unreliable predictions.

A practical advantage: measuring total uncertainty often required fewer queries than calculating self-consistency alone, which could reduce computational costs.

Why This Matters:

Any insurer using AI for underwriting decisions, claims adjudication, or customer-facing recommendations faces the overconfidence problem. When a model is wrong and knows it is uncertain, existing safeguards can catch the error and route it to a human. When a model is wrong and confident, the error passes through unchallenged. In a claims context, that means an incorrect denial or approval. In an underwriting context, it means mispriced risk. In a customer service context, it means incorrect policy information delivered with apparent authority.

The MIT research offers a potential governance tool. If insurers can measure not just what their AI models say but how confident those answers should be relative to the broader AI ecosystem, they have a mechanism for flagging the highest-risk outputs before they reach a policyholder or an underwriting decision.

Strategic Implications:

For carriers and TPAs deploying LLMs in production, the MIT research points toward a practical addition to AI governance frameworks: cross-model validation. Rather than relying solely on a single model’s confidence scores, insurers could implement comparison checks against alternative models for high-stakes outputs. This would not catch every error, but it addresses the specific failure mode (confident incorrectness) that creates the most liability.

For regulators and industry groups developing AI governance standards, the “total uncertainty” concept offers a measurable, testable criterion that could be incorporated into model validation requirements. The NAIC’s ongoing work on AI governance frameworks could benefit from this kind of technically grounded approach to defining what “reliable” AI output means in an insurance context.

Who Covers AI Business Blunders? The Emerging Insurance Product Landscape for AI Liability.

As businesses deploy AI agents that independently make purchasing decisions, generate client communications, and manage operations, a new product category is taking shape in the insurance market. Specialist insurers are beginning to write coverage specifically designed for AI-related failures, while some standard-market carriers are moving in the opposite direction, adding blanket AI exclusion clauses to existing policies.

On the coverage side, the early movers include Armilla Assurance, which offers AI performance warranties backed by Lloyd’s with capacity up to $25 million, testing AI models for vulnerabilities before binding. Testudo launched specialty coverage in January 2026 for generative AI liability risks, explicitly designed to fill gaps created by new AI exclusions in commercial general liability policies, covering defamation, copyright, and privacy claims from AI outputs. And brokerage firm Founder Shield is building AI malfunction and hallucination scenarios into professional services policies, with optional extensions covering real-world consequences like an AI agent ordering excess inventory.

At the other end of the spectrum, carriers including AIG, American Financial Group’s Great American, and WR Berkley have been pressing U.S. regulators to allow AI exclusions in standard policies. The era of “silent coverage,” where AI-related losses were handled implicitly under existing policies without explicit inclusion or exclusion, is ending. Deloitte projects the global AI insurance premium market could reach $4.8 billion by 2032.

Why This Matters:

The bifurcation between affirmative AI coverage and blanket AI exclusions creates a new advisory requirement for brokers and agents. Clients deploying AI, particularly agentic systems that take autonomous actions, can no longer assume their existing E&O, cyber, or CGL policies will respond to AI-related claims. The coverage question now requires explicit analysis at every renewal.

Twenty-three states and Washington, D.C. have adopted the NAIC AI Model Bulletin, which requires insurers to establish governance, documentation, and audit procedures for their own AI systems. That standard is increasingly being applied to policyholders as well. Carriers are adding AI-specific questionnaires to renewal applications, and the ability to document bias testing, human-in-the-loop review, and model validation is becoming a prerequisite for accessing affirmative coverage.

Strategic Implications:

For agencies and brokers, AI liability coverage is becoming a required conversation at every commercial renewal. The clients most at risk are not the ones deploying cutting-edge AI. They are the ones deploying basic AI tools without realizing their existing policies may have been amended to exclude AI-related claims. The advisory opportunity is significant: helping clients understand what their current policies actually cover, identifying gaps, and placing specialty coverage where needed.

For carriers, the emerging AI insurance market represents both a product development opportunity and an underwriting challenge. The specialty players writing affirmative coverage are doing deep due diligence on buyers’ AI governance maturity, which gives them better risk selection. The carriers adding blanket exclusions are avoiding near-term claims exposure but may be ceding a market that Deloitte projects will grow into the billions. The strategic question is whether to build the underwriting expertise to write AI risk intelligently or to exclude it and watch the premium flow to specialty markets.

The Bottom Line

This week’s stories share a common architecture: AI capability is advancing on one axis while the governance, infrastructure, and risk management required to deploy it responsibly lag on a parallel track. The distance between those two tracks is where insurance risk accumulates.

Gallagher quantified the gap: 63% of businesses have operationalized AI, but 43% lack formal risk frameworks. The Karpathy Loop demonstrated that AI’s self-improvement capabilities are accelerating, meaning the capability track is speeding up. The Northeastern “Agents of Chaos” research showed what happens when autonomous systems encounter real-world complexity without adequate guardrails. MIT’s overconfidence detection work offered a potential tool for closing part of the governance gap. And the Supermicro prosecution reminded everyone that AI risk is not confined to software; it extends to hardware, supply chains, and geopolitics.

The emerging AI insurance product market is the industry’s response to this gap. Specialty carriers are writing coverage for the companies that can demonstrate governance maturity. Standard-market carriers are excluding AI risk from policies for companies that cannot. The market is sorting itself, and the sorting criterion is not whether a company uses AI. It is whether a company governs it.

For the executives reading this newsletter, the strategic question is straightforward: which side of that sorting line is your organization on? The WTW data from last week showed that the analytics adoption gap is already producing measurable performance differences. This week’s stories suggest the governance gap may produce equally measurable differences in insurability. The companies that can document their AI risk management will access coverage. The ones that cannot will increasingly find themselves uninsured for the risks they are actively creating.

AI Insights appears every Friday, analyzing AI developments through an insurance lens. For deeper analysis of strategic implications, visit InsuranceIndustry.ai.

By James W. Moore | InsuranceIndustry.AI

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