“I have thee not, and yet I see thee still.” Macbeth’s dagger hung in the air, menacing, dissolving the moment he reached for it. With AI, insurers face a threat just as intangible. They are rapidly withdrawing from the field as a result.
Insurance functions on three fundamental requirements: quantifiable probability, bounded liability, and uncorrelated risk. Marine disasters and earthquakes meet these criteria; AI systemic risk does not.
For ships and earthquakes, underwriters have over three centuries of maritime loss records and millennia of geological and seismic data. Even extreme “black swan” natural catastrophes can be modelled using Realistic Disaster Scenarios (RDS) and historical frequency curves. With the leading AI models there is no stable baseline. Capability jumps are non-linear and evolving faster than empirical claims data can accumulate. You cannot compute an expected loss curve when the underlying mechanism changes fundamentally with every model iteration.
Correlation is another deeper problem: sinking a container ship in the Atlantic does not simultaneously sink every cargo vessel globally. An earthquake in Tokyo may cause catastrophic localised damage, but it does not trigger a simultaneous fault rupture in London or San Francisco. Lloyd’s syndicates can pool and distribute capital across geographically independent risks.
Current AI models, on the other hand, exhibit high architectural concentration. Millions of applications, enterprises, and infrastructure layers depend on the same handful of foundation model APIs, cloud providers, or algorithmic paradigms. A critical agentic intrusion might trigger correlated, simultaneous failure across thousands of policyholders globally. Cyber insurers already price correlated cloud-provider failures. Witness the 2024 CrowdStrike incident. A single faulty kernel update took down ~8.5 million Windows devices. Parametrix estimated direct financial losses for US Fortune 500 firms at $5.4 billion, but estimated insured losses at only $540m to $1.08bn (a protection gap of 80% to 90%). AI systemic risk differs in degree and kind: the failure surface is not a single software update but the underlying reasoning architecture shared across every downstream application, and the loss mechanisms (autonomous decision-making across financial, infrastructural, and legal domains) might propagate faster than any claims-adjustment process can contain.
The physical assets affected in a ship going down or an earthquake have finite values. A hull, its cargo, and secondary port damages have hard financial ceilings that can be stated on a policy schedule. Some AI harms are bounded and already insurable: copyright infringement, for instance, has a countable claimant pool and calculable statutory damages. The genuinely unbounded risks are those involving autonomous decision-making at scale: a trading system triggering cascading market failure, an infrastructure-control agent mismanaging a power grid, a security tool propagating a vulnerability across every connected enterprise. It is this second class, where a single correlated event’s upper bound exceeds the solvency reserves of the global reinsurance market, that defeats current underwriting.
Finally, with ships and earthquake losses, the proximate cause is clear. Physical wreckage, global ship tracking, or seismograph records must demonstrate whomis to blame. Attribution has never been trivial in complex-liability insurance: pharmaceutical side-effect cases and medical-device failures generate decades of litigation. AI compounds the complexity problem. A single adverse outcome may involve the foundation model’s training data, the fine-tuning corpus, the system prompt, a third-party plugin, a user data source, or their own instructions. Untangling this causal chain will be great for lawyers, not so much everyone else.
Lloyd’s syndicates will underwrite narrow, bounded enterprise software errors (e.g., standard cyber liability with tight exclusion clauses and sub-limits). What they cannot underwrite is broad, systemic autonomy and catastrophe risk. Insurance can distribute risk; it cannot manufacture loss data where none exists. That absence is a solvable problem in principle. The question is whether it will be solved before the first systemic event forces the issue.
A handful of specialist products exist. Munich Re has offered limited AI-error-and-omission cover since 2018; certain Lloyd’s syndicates write narrow algorithmic-liability endorsements for defined use cases such as autonomous-vehicle testing or medical-diagnostic software. These are real but bounded: they cover specific, tested deployments with known failure modes, not open-ended generative-model autonomy. This still leaves a significant gap.
The gap might close: aviation was considered uninsurable before 1930; nuclear liability is handled today via government-backed pools such as the Price-Anderson Act. Cyber cover did not exist as a commercial line in 1995. In each case, the market adapted once loss data accumulated, actuarial models matured, and, in the US, the state agreed to provide a backstop. The question for AI is not whether adaptation is conceivable, but whether the pace of capability change outstrips the pace at which loss data, actuarial models, and government backstops can mature. With ships, that process took decades. With AI model generations measured in months, the timing is fraught.
The adaptation has not yet begun in earnest. In the meantime, the market is contracting. Since January 2026 the standard ISO forms that underpin the great majority of American commercial general liability policies have offered ready-made generative-AI exclusions. Several major underwriters have gone further, attaching absolute AI exclusions to directors’ and officers’ and errors-and-omissions lines. The few specialist endorsements that remain typically cap payouts at levels that are trivial beside the scale of potential systemic disruption. Actuaries can still price a North Sea storm; they have no loss history for an autonomous execution loop making unpredictable calls across unmonitored infrastructure.
Today’s leading artificial intelligence laboratories face perils just as catastrophic as earthquakes, yet the commercial insurance market will not touch them. To bridge the gap, the purveyors of foundation models have constructed contractual defences, off-balance-sheet vehicles, and other tools - all designed to ensure that when a model misbehaves, the customer foots the bill.
Vendors currently cap their total exposure at the fees paid by the client over the preceding twelve months - a trivial sum compared with the existential fallout of an algorithmic failure in a trading system or hospital. These protections evaporate the moment the customer puts the model to work. Fine-tuning a model, supplying a system prompt, or connecting proprietary data via retrieval-augmented generation (RAG) is legally construed as a user modification. In the eyes of the contract, the customer ceases to be a mere consumer and becomes the system’s sole operator, leaving the vendor as nothing more than a supplier of raw compute.
This reclassification trick relies on framing large language models as probabilistic text experiments rather than deterministic enterprise software. By deploying sweeping “as-is” clauses and broad waivers of consequential loss, Silicon Valley transfers the entire duty of care downstream. What residual liability the tech giants cannot contract away is ring-fenced within balance-sheet reserves and offshore captive insurance units designed to fund legal defence and regulatory settlements, not to absorb a systemic shock.
The implications for insurability are paradoxical. If strict-liability doctrines do pierce vendor disclaimers, they create something the market currently lacks: a legally defined, financially accountable party at the root of the causal chain. Insurers can underwrite a known defendant with a balance sheet. What they cannot underwrite is a diffuse web of contractual exemptions that leaves no single entity answerable.
The EU’s trajectory suggests this is the direction of travel. The AI Act’s conformity-assessment regime, combines with the revised Product Liability Directive’s explicit inclusion of software and AI systems within the definition of “product”. This points toward a future in which foundation-model providers carry non-delegable duties. Whether that future arrives before or after the first systemic AI loss event will determine whether the insurance gap is bridged by markets or by legislatures.
If a model triggers a genuine systemic crisis, judges may well decide that the model provider cannot simply disclaim responsibility for the inherent nature of its product - leaving the architects of AI holding the very liabilities they thought they had outsourced: the dagger they hoped not to have to grasp.