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More AI servers increase a data center’s electricity demand, and high-density AI racks concentrate more of that demand in less space. Because nearly all electricity used by computing equipment ultimately becomes heat, facilities must also remove more heat. The added cooling electricity is not a fixed one-to-one increase: the International Energy Agency (IEA) estimates that cooling and environmental control account for about 7% of electricity use in efficient hyperscale data centers, compared with more than 30% in less-efficient enterprise facilities.
How AI server demand raises data center power use
More computing means a larger electrical load
Servers are the largest electricity-consuming component in modern data centers. The IEA estimates they account for around 60% of electricity use on average, although the share varies by facility type. As operators add servers to run AI workloads, the computing load rises along with the electricity needed to operate the facility. The IEA’s 2025 analysis of energy demand from AI explains the components behind that demand.
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AI increases the role of high-performance servers
AI adoption is driving more use of accelerated servers, including systems equipped with GPUs. These servers can draw substantially more power in a compact space than conventional equipment, so a facility’s load can rise even without a proportional increase in its floor area. The IEA reports that AI server power density increased 11-fold between 2020 and 2025 and projects a further fourfold rise by 2027. These are changes in power density, not a claim that every AI rack has the same power draw.
Why higher rack power makes cooling harder
Almost all electricity consumed by computing equipment eventually becomes heat. A higher-powered rack therefore presents a greater heat-removal challenge in the space where it is installed. The issue is not simply the total heat across a building: concentrating more power in a rack can make it harder to keep equipment within operating conditions.
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Cooling electricity does not rise in lockstep with server electricity. The amount depends on factors such as the facility’s cooling design, climate, equipment utilization, and overall efficiency. The IEA’s estimate—about 7% of electricity use for cooling and environmental control in efficient hyperscale facilities versus more than 30% in less-efficient enterprise facilities—shows why there is no single cooling percentage that applies to every data center.
What the global demand figures do—and do not—show
The IEA’s central outlook puts data center electricity consumption at roughly 485 terawatt-hours (TWh) in 2025 and 950 TWh in 2030, equivalent to around 3% of global electricity demand by 2030. These are scenario-based projections, not guaranteed outcomes; AI adoption, hardware and software efficiency, and infrastructure constraints can change the trajectory. The IEA’s 2026 executive summary on energy and AI sets out the outlook and its key uncertainties.
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The IEA also reports that data center electricity demand grew 17% in 2025, while electricity use from AI-focused data centers grew 50%. Those figures describe global changes over that year; they do not predict the growth rate of an individual facility or indicate whether a particular local grid can serve new demand. A global energy projection cannot substitute for a site-specific assessment of grid capacity, cooling design, or water use.
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Annual electricity consumption, available power at a given moment, backup capacity, and a site’s grid connection are separate planning questions. AI training and model use can produce large, rapid changes in demand, making it important to manage not just how much energy is available over a year but also whether supply remains reliable as load changes.
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The IEA says that “AI training and model use induce large and rapid power swings,” and describes energy storage as critical to reliable supply. It also reports bottlenecks in equipment supply, grid connections, and approvals. These constraints can affect how quickly data center capacity is built, even when projected demand is clear. The IEA’s 2026 account of demand growth and infrastructure bottlenecks discusses these pressures.
What to compare when evaluating data center cooling
A cooling approach should be assessed against the planned rack density and the conditions at the specific site, rather than chosen from a generic claim about AI workloads. Useful comparison points include:
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- Electricity use: the cooling system’s share of facility consumption, evaluated for the facility type and operating conditions.
- Heat-removal capability: whether the design can manage the expected heat at the planned rack density.
- Reliability: how well the system supports continuous operation and responds to changing loads.
- Local constraints: the site’s available power and water resources and any limits on grid connection.
The cited IEA material provides broad electricity-share and power-density figures, not a head-to-head test of air cooling, direct liquid cooling, immersion systems, or specific vendors. It therefore cannot establish which technology is best for a particular facility.
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How to interpret the rack-to-household comparison
The IEA says an advanced data center rack could have peak power demand equivalent to that of 65 households by 2027. This is a comparison of peak power, not annual energy use, and should not be read as a typical rack specification. Actual requirements depend on the equipment and the facility.
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