AI Insurance Is Expensive. Here's How to Underwrite What Actually Matters
Extinction pays no claims. The real exposure for insurers is the messy middle: AI that disrupts markets, injures people, infringes IP at scale, or triggers correlated failures across clients - while courts remain open and plaintiffs' lawyers stay busy.
Capacity is thin because much of the risk is systemic and correlated. If the same model is embedded across thousands of clients, one flaw can turn into an aggregation event. Traditional cyber and tech E&O were not built for model-layer concentration risk or copyright class actions fueled by model training and outputs.
What the market is signaling
- Carriers are capping limits and carving out the worst tails (broad IP, data scraping, and "model failure" exclusions).
- Large AI providers are exploring self-insurance and captives, then stacking fronted paper and reinsurance above it.
- Reinsurers are wary of "systemic, correlated, aggregated" losses - the exact profile of widely deployed foundation models.
What to insure (and what to avoid)
- Focus on intermediate scenarios with real payable loss: IP claims, privacy breaches, bias/discrimination suits, bodily injury/property damage from AI-enabled physical systems, business interruption from widespread model outage or bad outputs.
- Be explicit on what's excluded: existential catastrophe, war/terror triggers, and broad "model collapse" unless tightly scoped.
- Split coverage by hazard: cyber (security, data), tech E&O/media (outputs, IP), product liability (physical harm), and recall where relevant. Avoid catch-all wording.
Underwriting checklist for AI model providers
- Architecture: model type, third-party components, and fine-tuning stack. Who controls updates and rollback?
- Deployment footprint: industries, geos, user counts, and criticality (consumer vs. public sector/defense/infra).
- Controls: red-teaming cadence, evals, kill switches, rate limits, content filters, retrieval isolation, human-in-the-loop.
- Observability: immutable logs, model/version pinning, output tracing, prompt/response retention, incident forensics.
- Copyright/data: training data provenance, opt-out handling, filtering, indemnities from data vendors, DMCA process.
- Bias/safety: documented testing, regulatory mapping (EEOC, FDA-like claims if any), audit history.
- Contracts: customer SLAs, disclaimers, caps, indemnities, and carve-outs that shift or retain risk.
- Concentration: percent of revenue tied to one model family or one cloud provider; dependency on single vendor safety tools.
- Aggregation controls: customer segmentation, update rings, feature flags to limit global blast radius.
- Governance: safety committee minutes, incident postmortems, insurance purchasing strategy, captive plans.
How to price what you can't observe yet
- Use scenario bands, not point estimates. Price coinsurance and aggregates based on deployment breadth and interdependence.
- Set sublimits for IP and output liability. Apply higher retentions for "model error" claims vs. classic cyber.
- Define events clearly. Borrow cyber-cat style wording for "widespread model failure" (time and space bounds matter).
- Buy reinsurance with explicit systemic wording and event definitions aligned to your policy forms.
Captives and "Right-Way" Risk
As AI firms scale, liability scales with them. Captives can warehouse volatility, align incentives, and create better data for pricing. The stack usually looks like: captive layer (retained), fronted paper for admitted needs, plus quota share/excess treaties.
- Use corridors and aggregate stop-loss for years 1-2 while data matures.
- Lock down claims-made wording, prior acts, and reporting duties to prevent "silent AI" carryover.
- Require verifiable telemetry as a condition of coverage (or make it a rating credit).
M&A Forecasts, Two Sets of Numbers, and Your D&O Book
Recent deal chatter shows a classic tension: bullish projections for buyers, cautious projections for boards and fairness opinions. That is a recipe for shareholder suits, especially when management comp spikes on close.
For carriers, expect Side A/B/C pressure if projections diverge, sales processes look rushed, or disclosures shift late in the game.
D&O underwriting checklist on sale processes
- Projection governance: who built them, update cadence, and documented changes between drafts.
- Fairness opinion assumptions vs. buyer's model - are they consistent or strategically different?
- Comp triggers: change-of-control payouts and optics vs. timing of negative guidance.
- Process quality: auction breadth, go-shop, board minutes, independent committee use.
- Disclosure risk: safe-harbor language, reconciliation of non-GAAP, and analyst communications.
- Litigation history and activist posture around the issuer.
Crypto Narrow Banking: Life Insurers Funding Bitcoin Loans
One Bermuda-regulated life insurer is taking premiums in Bitcoin and lending Bitcoin at duration to large institutions. That's narrow banking logic applied to crypto: stable, long-dated liabilities funding long-dated loans.
The risk is in counterparty credit, rehypothecation, and collateral that can gap move. Throw in policy loans against the Bitcoin value and you add liquidity strain during drawdowns.
Checklist for crypto-linked life products
- ALM: duration match between liabilities and Bitcoin lending book; stress tests at high-vol volatility.
- Counterparty controls: over-collateralization, margining, rehypothecation bans, tri-party custody.
- Liquidity: policy loan rules, LTV caps, margin call timing, and gates in stress.
- Regulatory posture: Bermuda framework, disclosures, and reinsurance structure.
- Tax communications: precise, conservative disclosures around basis, loans, and distributions.
- Concentration: institution and venue concentrations; clear stop-outs for market structure shocks.
Investment Book Watchouts
Macro matters for your asset side. Policy and central-bank commentary points to the chance of an abrupt correction if AI expectations overshoot fundamentals. That hits equity-heavy and growth-tilted portfolios first.
- Run AI-shock scenarios on equity, credit (especially tech and data-center capex plays), and structured exposures.
- Revisit liquidity ladders and collateral terms for derivatives tied to tech benchmarks.
IMF Global Financial Stability Report
Policy Architecture You Can Place Today
- Layered program: primary with tight definitions and telemetry requirements; excess with systemic sublimits and event definitions.
- Separate IP/media tower with defense-inside and panel counsel, plus ADR provisions to control costs in class actions.
- Parametric add-ons for defined AI service outages (measured by objective uptime/latency of named APIs) with low limits.
- Captive fronting for model-error band, surrounded by quota share to reinsurance with explicit aggregation terms.
Broker talking points for AI clients
- Your biggest discount is proof of control: logs, kill switches, red-team reports, and rollback plans.
- Accept coinsurance and aggregates; you're buying claims handling and balance-sheet time, not a blank check.
- Consider a captive now; bring reinsurers into the data room early.
Upskill the team
Your pricing and wording improve with better model literacy. A short path is curating roles-based training for underwriters, claims, and risk engineers focused on AI systems and failure modes.
Regulatory context for crypto-insurance players
For teams evaluating Bitcoin-denominated life insurance, start with the local rulebook. Bermuda's digital asset framework is a good reference point for governance and custody expectations.
Bermuda Monetary Authority: Digital Asset Business
Bottom line: Sell what you can price and model today, structure against correlation, and force telemetry into every policy. Let captives take the first shock. And keep your wording tight - AI doesn't forgive vague definitions.
