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AI infrastructure is not merely a server investment. It is a tightly coupled physical and financial system in which billions of dollars of GPUs, electrical equipment, buildings, cooling plant, software and contracted computing revenue can depend on the same substation, fuel supply, network route or customer. For insurers and investment committees, concentration is the first-order issue: a single site can create an outsized loss from a failed UPS, cooling loop, transformer, generator, grid connection, fire, cyber-physical event, supplier delay or a sudden drop in demand even when the GPUs themselves are undamaged.

The right question is therefore not “How reliable are the GPUs?” It is “Can this site deliver contracted compute through correlated power, cooling, network, supply-chain and technology shocks, and does the insurance structure respond across the asset’s life?” The framework below shows what to verify, how to model downtime and obsolescence, and where portfolio limits are needed.

Why AI data centres create an unusual insurance concentration

AI clusters combine exceptional asset density with exceptional power density. Swiss Re Institute says construction costs for a single AI data-centre location can reach USD 20 billion, while its 2026 analysis links about USD 450 billion of projected 2026 hyperscaler capital expenditure to physical AI infrastructure such as servers and GPUs. That value is often concentrated in one campus, one electrical interconnection and a small number of vendors.

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“The data centres powering today’s AI infrastructure are growing in scale and complexity, challenging the insurance industry’s ability to provide the cover demanded by financing.”

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Swiss Re Institute, Insuring AI: data centre value accumulation risks, 2026

The insured loss may start outside the compute equipment. A substation fire can remove power to an intact cluster; a cooling-loop leak can force an emergency shutdown; a ransomware event can prevent manual failover; a delayed transformer can postpone energisation for months; or a customer demand shortfall can leave expensive GPUs underutilised. Those pathways make property damage, equipment breakdown, business interruption, cyber, liability and construction covers interdependent.

What belongs in the value at risk

Value layer What to identify Why it changes the loss
Real estate and civil works Land, buildings, fire protection, security and site works May survive an outage while revenue stops; replacement timing can differ from equipment timing.
Electrical infrastructure Utility interconnection, substations, transformers, switchgear, UPS, batteries and generators A single failed component can interrupt an entire hall or campus and may have a long replacement lead time.
Cooling and water systems Direct-to-chip or immersion systems, pumps, heat rejection, controls, water rights and reclaimed-water connections High-density GPUs can exceed safe temperatures quickly; water or control failures can damage hardware and void service commitments.
IT equipment GPUs, CPUs, memory, storage, networking, racks, firmware and spares Values can fall rapidly as new accelerator generations arrive, even when physical condition is good.
Contracted earnings Reserved capacity, utilization, prices, service-level agreements and customer concentration Business-interruption and service-credit exposure can exceed physical repair costs.

Set separate sums insured and valuation methods for these layers. A replacement-cost basis for a building does not establish the current value or replacement lead time of a GPU fleet, and a property limit does not automatically fund lost compute revenue.

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Power is a chain, not a utility invoice

“Firm power” is only as strong as the weakest dependency between the grid and the GPU rack. Obtain a one-line electrical diagram and a dependency register that follows energy from the transmission connection through the campus, not just the monthly tariff.

Documents and specifications to require

  • Binding interconnection agreement, energisation date, contracted capacity, curtailment rights and queue position.
  • Ownership and maintenance responsibilities for the substation, transformers, switchgear and protection systems.
  • UPS topology, module ratings, bypass arrangements, battery chemistry, autonomy, replacement schedule and test records.
  • Generator make, capacity, paralleling controls, black-start sequence, fuel type, on-site storage, replenishment contracts and tested autonomy.
  • Power-quality records covering voltage sags, harmonics, frequency excursions and transfer events.
  • Maintenance logs, open defects, spare transformers and switchgear, and the time required to obtain each critical component.
  • Dependencies on cooling controls, network rooms, building-management systems and cloud orchestration.

Howden projects roughly 10 GW of behind-the-meter generation at US data centres by 2030. That trend can improve resilience but introduces fuel, emissions, mechanical, permitting and environmental risks. Howden also attributes 45% of major data-centre outages primarily to UPS failures, making UPS design and maintenance a pricing issue rather than a technical footnote.

Failure scenarios that should be modelled together

  1. Grid loss with successful backup: measure transfer time, ride-through performance, fuel burn and the portion of load that is shed.
  2. Grid and backup loss: assume a substation or switchgear event coincides with a generator, battery or control-system failure.
  3. Power-quality event: test a voltage sag or harmonic disturbance that trips protection without causing visible fire damage.
  4. Fuel interruption: model blocked deliveries, contaminated fuel, generator derating during a heat wave and competing demand for local fuel.
  5. Common-control failure: include ransomware or a building-management fault that prevents automatic transfer and requires a manual sequence.

The output should be an outage-duration distribution, not a single “N+1” claim. Record which equipment must be repaired, which spare is available, who can work on it, and what external dependency controls restoration.

Cooling and water can become the loss trigger

AI racks generate far more heat per footprint than conventional enterprise halls. Map the complete heat-removal path: cold plates or immersion tanks, pumps, manifolds, heat exchangers, chillers, dry coolers or cooling towers, controls, leak detection and discharge arrangements.

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Swiss Re identifies cooling failures as an emerging contributor to loss. Underwriting should therefore establish the temperature-excursion limits for each GPU and the time between a pump or control failure and an automatic shutdown. Ask whether a leak can reach energized equipment, whether isolation valves allow a hall to be segmented, and whether the water chemistry and filtration regime is documented.

Water and heat-rejection checks

  • Confirm water rights, municipal supply terms, drought restrictions and reliance on reclaimed water.
  • Test the effect of extreme heat on chiller or dry-cooler capacity and generator derating.
  • Verify redundant pumps, controls and power feeds are physically separated rather than sharing one room or cable route.
  • Review leak sensors, automatic isolation, drainage capacity and incident response times.
  • Check whether a cooling failure affects GPU warranties, maintenance contracts or customer service-level agreements.
  • Include heat discharge, noise and environmental-permit conditions in liability and business-continuity analysis.

GPU value can change before the policy or loan matures

Physical damage is only one way an accelerator can lose economic value. A new generation may deliver materially better performance per watt, a customer may move workloads to another architecture, or a vendor may stop supporting firmware and spare parts. Underwrite the cash flows and the technology roadmap separately from the replacement-cost schedule.

Questions for the equipment and revenue model

  • What utilization is contractually committed, what is merely forecast, and how quickly can customers reduce or migrate workloads?
  • Are prices fixed, indexed, or exposed to spot-market competition?
  • How concentrated are the GPUs by chip generation, vendor, board design and networking fabric?
  • What are the warranty exclusions, firmware dependencies, repair-versus-replace rules and replacement lead times?
  • What resale value is assumed at the end of the investment or policy horizon, and is there an observable secondary market for that exact configuration?
  • How many spare boards, power supplies and network components are held on site, and can the vendor service them in the relevant region?

Stress a simultaneous utilization decline and replacement shock. A site can suffer a business-interruption loss because it cannot deliver contracted capacity, then face a balance-sheet loss because the damaged or stranded GPUs are worth less than their insured or financed value.

Location determines correlated physical loss

Overlay the actual campus, substations, fuel routes and network entry points with natural-catastrophe and climate data. Do not rate the data hall in isolation from the infrastructure that feeds it.

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Swiss Re finds that more than one-quarter of US data-centre capacity may be in locations with at least three large-hail days per year, and that over 40% is in significant-to-very-high tornado-day zones. The relevant exposure includes roof and cladding damage, substations, cooling towers, backup generation, access roads and regional contractor availability.

Site-screening questions

  • Is the campus exposed to flood, wildfire, wind, hail, tornado, seismic activity or extreme heat, and are the substations and fuel depots exposed to the same event?
  • Can employees, fuel trucks and heavy-repair crews reach the site after a regional event?
  • Are two “independent” utility feeds actually on the same transmission corridor or in the same floodplain?
  • Does the hazard model reflect future climate conditions and the operating temperature assumptions of the cooling system?
  • Are catastrophe deductibles, waiting periods and annual aggregate limits aligned with the concentration at the campus?

Downtime can cost more than physical damage

High-density computing earns revenue only while the service is available. An outage can therefore trigger lost compute charges, service-level credits, customer migration, contingent business interruption, extra expense and reputational damage even when no GPU is destroyed.

Reported primary cause of major outages Howden 2026 share Underwriting implication
UPS failures 45% Review topology, battery testing, maintenance quality and common-mode controls.
Cooling failures 14% Model heat-excursion time, leak isolation and seasonal capacity.
Networking failures 11% Map carrier, cross-connect, optical and software-defined dependencies.
IT systems failures 9% Include orchestration, firmware, storage and recovery dependencies.

Build business-interruption values from contracted utilization and service obligations, then test customer concentration and contingent outages. A single hyperscaler or model provider may account for a large share of revenue; its own cloud, network or cyber incident can interrupt the insured site without any local damage. Define the waiting period, indemnity period, service-credit treatment, denial-of-access trigger and proof-of-loss requirements before binding.

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Construction and commissioning are separate risk phases

Construction risk does not end when the building is weather-tight. Verify the engineering, procurement and construction schedule, permits, grid-queue position, long-lead equipment, liquidated damages, testing protocol and phased acceptance.

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Howden reports that 58% of all data-centre capacity built since 1990 was delivered between 2020 and 2025. That compressed build cycle increases the chance that many projects compete for the same transformers, switchgear, generators, specialist technicians and insurers at once.

Controls before operational cover begins

  1. Confirm factory-acceptance tests for transformers, switchgear, UPS modules, generators, batteries and cooling plant.
  2. Require integrated-system testing under full and partial load, including loss of utility, transfer to generation, failback and manual recovery.
  3. Document punch-list items, temporary systems, software versions, open permits and who bears delay risk.
  4. Obtain evidence that spare parts, fuel contracts, specialist contractors and communications links are available at the energisation date.
  5. Use phased acceptance so the insured value, occupancy, liability and business-interruption periods match the portion actually placed in service.

Portfolio aggregation is easy to underestimate

Several apparently diversified facilities can fail together. Track common utilities, transmission corridors, metropolitan hazards, cloud customers, chip vendors, cooling technologies, fuel sources, EPC contractors and network carriers. Swiss Re identifies concentration, supply-chain bottlenecks and network dependencies as accumulation channels in the AI capital-expenditure cycle.

Set limits by grid node, metro area, hazard zone, hyperscaler, GPU vendor, EPC contractor and energy source. Use facultative reinsurance or syndication for peak sites when an occurrence at one campus could consume a disproportionate share of capital. Aggregation reports should show both insured values and business-interruption values, because a shared dependency may produce simultaneous revenue loss without simultaneous physical damage.

A practical investment and underwriting workflow

1. Underwrite dependencies first

Start with the one-line diagram and dependency register. For every critical function, record the primary path, alternate path, physical separation, ownership, maintenance provider, last test, spare availability and restoration estimate. Missing evidence is itself a risk signal.

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2. Run correlated scenarios

At minimum, model a regional heat wave, grid curtailment, substation fire, transformer delay, generator-fuel interruption, cooling-loop leak, ransomware combined with manual failover, and a chip-supply or replacement shock. Calculate gross loss, insured loss, uninsured loss, time to partial service, time to full service and the capital needed to operate during recovery.

3. Price downtime explicitly

Build the value from committed megawatts or GPU-hours, contracted prices, utilization, service-level penalties and extra expense. Apply customer and cloud-provider concentration factors and include contingent business interruption where a third party controls the dependency.

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4. Match the policy structure to the lifecycle

Construction, commissioning, operational property and equipment breakdown, cyber, liability, environmental impairment and business interruption have different triggers, exclusions, waiting periods and sublimits. Coordinate them before binding so a failure during handover is not left between construction and operational wordings.

5. Set portfolio guardrails

Define maximum exposure to one grid node, transmission corridor, metro, hazard zone, hyperscaler, chip vendor, EPC contractor and energy source. Escalate exceptions to the investment committee and document the reinsurance or syndication plan for peak accumulations.

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6. Monitor after close

Require quarterly reporting on utilization, power quality, outage minutes, near misses, maintenance completion, spare capacity, customer mix, technology refresh, permit status and compliance changes. Re-rate when utilization, customer concentration, GPU generation or energy architecture changes materially.

Policy wording, liability and community exposure

Review environmental permits, generator emissions, water consumption, thermal discharge, noise, physical security, war and terrorism wording, cyber controls and responsibility for customer data and model services. A facility can face liability without a property loss if emissions exceed a permit, water use triggers restrictions, heat discharge harms a third party, or a cyber event exposes customer data.

Litigation is also becoming more material. Howden reports that major lawsuits and arbitrations involving data centres rose from four in 2021 to 14 in the first half of 2026. Allocate responsibility among the owner, operator, EPC contractor, utility, cloud customer and equipment vendor, and check indemnities, additional-insured status, subrogation rights and contractual caps.

Governance for insurers using AI in these decisions

NAIC’s AI Model Bulletin, adopted in December 2023, states that insurers remain responsible for complying with applicable insurance laws, regulations, insurance standards and consumer-protection rules when AI supports decisions. The same principle applies whether a model helps select an infrastructure investment, set a limit, price an outage or triage a claim: maintain explainability, validation, data governance, human oversight, bias testing and records sufficient for a regulator or policyholder to challenge an outcome.

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“When insurers use AI, they remain responsible for complying with insurance laws, regulations, insurance standards, and consumer protection rules.”

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National Association of Insurance Commissioners, Insurance Topics: Artificial Intelligence, updated 2026

What the current insurer market is doing

BlackRock’s 2025 survey of 463 insurance executives found 73% investing in AI-related software and technologies. The leading cited use cases were security selection and evaluating investment opportunities at 70%, and insurance-risk underwriting at 68%.

NAIC reports AI use, planned use or exploration by 88% of auto insurers, 70% of home insurers, 58% of life insurers and 92% of health insurers. NAIC also reports that about 95% of insurer bond investments in infrastructure are in energy, with roughly two-thirds in downstream electric-power generation, transmission and distribution. That exposure makes grid reliability and data-centre power demand relevant to both underwriting portfolios and investment portfolios.

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“The greatest long-term opportunities are expected to lie in power supply … and outage, where one of the industry’s largest protection gaps creates significant scope for product innovation.”

Howden Group Holdings, Insuring the data centre supercycle, 2026

Due-diligence checklist for an investment committee or underwriter

Area Evidence to obtain Decision test
Power Interconnection agreement, capacity, curtailment terms, one-line diagram, UPS and generator tests, fuel autonomy Can the site sustain the contracted load through simultaneous grid and backup stress?
Cooling and water Thermal design, redundancy, leak controls, water rights, drought plan, temperature limits How quickly does a single failure force GPU shutdown, and what hardware or SLA damage follows?
GPUs and network Bill of materials, chip and vendor mix, warranties, firmware support, spares, lead times What is the replacement cost and resale value after a technology refresh or supply shock?
Revenue Customer contracts, utilization history, pricing, SLA credits, cloud dependencies What is the insured downtime value and how concentrated is it?
Natural catastrophe Campus and substation hazard overlays, access routes, contractor capacity Can one regional event damage the site and delay restoration resources?
Construction EPC schedule, permits, factory tests, integrated-system tests, liquidated damages Is the project genuinely ready for the proposed operational policy start date?
Aggregation Grid, metro, vendor, customer, EPC, carrier and energy-source maps What common cause could create a portfolio loss across “separate” facilities?
Governance and liability AI-use controls, cyber programme, environmental permits, security, contracts and indemnities Are model decisions auditable and are third-party responsibilities clear?

How to make the final risk-adjusted decision

Proceed only when the project’s expected cash flow remains acceptable after applying correlated outage, catastrophe, construction-delay, customer-concentration and GPU-obsolescence scenarios. A strong opportunity has firm and demonstrably redundant power, physically separated dependencies, resilient cooling and water arrangements, diversified customers and hardware, tested recovery procedures, mature construction controls and policy wording that responds across handover and operations.

When those conditions are not met, the remedy is not simply a higher premium. It may be a lower limit, a larger deductible, a shorter indemnity period, a construction milestone condition, a technology-value adjustment, a portfolio cap, facultative reinsurance or a decision not to invest. Treating the data centre as an interdependent system produces a more defensible price and a clearer answer to whether insurance capacity can support the financing.

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