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Companies are still spending on data centers because AI and cloud applications need more computing capacity, and much of that demand now comes from running models in production—not just training them. The boom is uneven, however. The available forecasts combine hyperscalers, cloud providers and other buyers, so they do not measure ordinary enterprise-owned facilities alone. Spending is also being limited by electricity availability, cooling requirements, hardware costs and tight technology budgets.
What the spending figures actually measure
Gartner’s July 2026 forecast puts worldwide spending on data-center systems at $822 billion in 2026, up 62.5% from $506 billion in 2025. This is a global market estimate covering broad buyer categories; it is not a tally of construction and equipment purchased only by corporate data-center departments.
Gartner also forecasts $287 billion of worldwide infrastructure-as-a-service (IaaS) spending in 2026, up 29.3% from $222 billion in 2025. IaaS is rented computing infrastructure supplied by cloud providers, not physical data-center systems purchased by the customer. Both figures can rise at the same time because organizations are expanding capacity through a mix of owned equipment and services bought from providers.
These are market forecasts, not audited totals of spending already realized. Each figure should therefore be read as an estimate for a defined category and year, rather than as proof that every company is building a facility.
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AI is the main reason capacity is expanding
Gartner identifies data-center systems and IaaS as leading IT growth segments because of investment in AI infrastructure, cloud platforms and intelligent applications. Traditional technology categories are not expected to grow at the same pace.
Inference is becoming a continuous workload
Gartner forecasts $42.276 billion in worldwide AI-optimized IaaS spending in 2026, a 96.4% increase from 2025, and $66.143 billion in 2027. Within the 2026 figure, it expects $23.3 billion for inference and $19 billion for training. Inference—the process of producing outputs from a trained model—is forecast to account for 55% of this category’s spending.
Training can be organized as periodic, large-scale jobs. Inference often runs continuously in customer support, search, fraud detection, software features, industrial systems and internal workflows. Moving from experimentation to production therefore creates an ongoing requirement for accelerators, storage, networking and reliable power. The forecast does not establish that every AI project will be profitable or that every company needs to own a facility.
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Why companies do not simply “use the cloud”
Many companies do use the cloud. The important distinction is who buys and operates the physical infrastructure.
| Approach | Where spending appears | What it can provide | What the available figures do not establish |
|---|---|---|---|
| Owned data-center systems | Data-center systems spending | Direct control over equipment, deployment schedules, security design and placement | That ownership is always cheaper or delivers a faster return |
| Rented IaaS capacity | Worldwide IaaS spending | On-demand or contracted compute without constructing the customer’s own facility | That renting is always cheaper, has unlimited capacity or removes exposure to power constraints |
| Hyperscaler capacity | Provider capital expenditure and cloud-service revenue | Large shared regions serving many customers | How much of a global market forecast is attributable to ordinary enterprises |
Cloud use shifts the physical buildout to providers; it does not eliminate the need for buildings, servers, networks, electricity or cooling. A company may rent general-purpose capacity, reserve specialized AI instances, operate equipment in a colocation site or build its own systems. The suitable choice depends on workload duration, utilization, control, location and available capital. The cited forecasts do not provide a universal total-cost comparison.
Power is now a buildout constraint
Gartner’s June 2026 power forecast estimates worldwide data-center electricity consumption at 565 terawatt-hours (TWh) in 2026, up from 447 TWh in 2025, a 26% increase. It separately estimates worldwide data-center power demand at 132 gigawatts (GW) in 2026, up from 104 GW in 2025, and projects 290 GW by 2030.
GW describes power capacity at a point in time; TWh describes energy consumed over a period. They are different measures and should not be treated as interchangeable.
Gartner says compute-intensive AI workloads are driving the increase and that AI capacity is constrained by power availability. A project can have financing and equipment plans yet still wait for a grid connection, substation work or sufficient local generation. That makes power security a location and scheduling issue, not merely an operating expense.
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Gartner forecasts electricity used by cooling and other data-center infrastructure at 195 TWh worldwide in 2026, up from 159 TWh in 2025. It estimates AI-optimized servers will consume 175 TWh in 2026, compared with 195 TWh for conventional servers. By 2027, it projects AI-optimized server consumption at 258 TWh and conventional-server consumption at 200 TWh.
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AI servers can therefore alter the facility design itself: rack density, cooling technology, electrical distribution and water or heat-rejection systems become material planning decisions. Gartner estimates AI-optimized servers will account for 31% of data-center power consumption in 2026 and will consume more than conventional servers in 2027.
Where new U.S. capacity is being built
Synergy Research Group’s 2026 analysis of 21 major cloud and internet companies says Texas and the U.S. Midwest together represented 33% of operational U.S. hyperscale capacity at the end of 2025. The same regions represented 53% of identified capacity expected to come online over the following few years.
The shift is associated partly with power availability. Northern Virginia remains the largest single U.S. concentration, while Texas is the most prominent state in the future pipeline. Synergy also identifies Wisconsin, Indiana, Michigan and Missouri as Midwestern states with multiple major projects.
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The 53% figure describes planned pipeline capacity, not completed facilities. Projects can be delayed, redesigned or canceled, so pipeline shares should not be read as guaranteed operating capacity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why spending can rise while budgets feel tighter
AI-related infrastructure is attracting capital, but that does not mean every technology budget is expanding equally. Gartner cites inflation, shortages, higher hardware and memory costs, AI funding initiatives and shifting priorities as pressures on IT spending. Organizations may approve large AI or cloud commitments while delaying unrelated upgrades, consolidating vendors or reducing lower-priority projects.
This concentration also explains why headline growth rates can coexist with cautious procurement. A cloud provider or hyperscaler may be adding capacity for thousands of customers, while an individual enterprise may rent that capacity, postpone a private build or limit its deployment to a small production workload.
What this means for an enterprise decision
- Start with workload behavior: continuous inference, periodic training and ordinary business applications have different utilization and hardware requirements.
- Separate capacity from ownership: determine whether the requirement is best met by owned systems, colocation, reserved IaaS or on-demand services.
- Check power and cooling early: a site’s grid access, delivery timetable and cooling design can determine whether a project is feasible.
- Account for location: latency, data-residency rules and network paths may justify regional capacity even when another site has cheaper or more available power.
- Stress-test costs: include accelerators, memory, networking, facilities, electricity, cooling and utilization—not just server purchase prices.
The current evidence supports a clear direction: AI and cloud workloads are increasing demand for data-center capacity, with inference becoming a larger share of AI infrastructure use. It does not support a blanket conclusion that every enterprise should build, that cloud capacity is unlimited or that all announced projects will be completed.
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