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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsData centers house the servers, storage, networking, and support systems that store and move data and provide computing capacity. AI companies use that capacity to train models and run them for users. Keeping the equipment operating also requires substantial electricity, cooling, and systems that help maintain service.
What a data center does
A data center is a working technical facility, not simply a building full of computers. The International Energy Agency defines data centers as facilities for servers, storage systems, networking equipment, and associated components installed in racks and arranged in rows. Those systems process, store, and move data for digital services.
The equipment works together: servers perform computing tasks, storage retains data and model-related information, and networks connect machines inside the facility and link them to users and other systems. Power and environmental-control infrastructure supports the computing equipment and helps keep services available.
Why AI companies need data centers
AI models require computing capacity both to train and to operate. Data centers provide a place to house the servers and accelerators that perform that work, along with the storage and networking needed to supply data and deliver results. As a result, AI companies rely on data-center capacity whether they build or operate facilities themselves or use capacity provided by another operator.
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AI workloads can be particularly demanding because they use accelerated computing equipment. The International Energy Agency’s 2025 analysis projects that electricity use by accelerated servers, driven mainly by AI adoption, will grow 30% annually in its Base Case. For conventional servers, the corresponding Base Case projection is 9% annual growth. These are scenario projections, not measured growth rates that apply to every company or facility. (IEA, Energy and AI: Energy demand from AI)
What is inside a data center
Servers and accelerators
Servers perform general computing work. Accelerators are used for demanding computing workloads, including AI. Together, these systems supply the processing capacity used to train models and generate outputs when models are in use.
Storage and networking
Storage systems retain data and model-related information. Networking equipment moves information between servers and storage within the facility, and connects the facility to users and other systems.
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- Keep critical network equipment secure: glass door and side panels are lockable to prevent unauthorized access; Front door can be installed on either side of the front of the cabinet to satisfy your door swing orientation preference
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Power, cooling, and continuity equipment
Computing equipment produces heat while operating, so facilities use cooling and environmental controls to manage temperatures. They also need electrical infrastructure and continuity equipment. The IEA identifies uninterruptible power-supply batteries and backup generators as examples that help maintain service when power is disrupted.
How much power do AI data centers use?
There is no single power figure for an AI data center: facilities differ in size, equipment, workload, and design. The IEA gives broad indicative figures of 10–25 megawatts for traditional data centers and says demand by hyperscale AI centers can exceed 100 megawatts. These are facility-level comparisons, not specifications for every site or estimates of the electricity used by an individual AI request. (IEA, Artificial Intelligence topic page)
Megawatts describe power capacity or demand at a point in time; they are not the same as electricity consumed over a period. Actual consumption depends on how much equipment is operating and for how long. The IEA’s figures should therefore be read as broad scale indicators, not as a direct measure of a particular service’s energy use.
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Why cooling and efficiency matter
Cooling is necessary because operating servers and other equipment generate heat, but cooling does not consume the same share of electricity in every facility. The IEA reports that cooling accounts for about 7% of total consumption in efficient hyperscale facilities, compared with over 30% in less-efficient enterprise facilities. The contrast illustrates how facility type and efficiency can affect the energy used to manage heat; neither figure is universal. (IEA, Energy and AI: Energy demand from AI)
“Hyperscale” describes very large facilities, while enterprise facilities are operated for an organization’s own needs. Facility scale, workload, operator, and efficiency all matter when comparing data centers. An enterprise facility is not automatically equivalent to a hyperscale one, and AI-heavy sites are not all built to the same design.
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- They show why infrastructure matters: AI depends on physical computing systems, and larger or more intensive workloads can require substantial facility capacity.
- They do not establish a universal cost per query: the facility-level power ranges do not tell you how much electricity one prompt or model use consumes.
- They do not establish a universal water requirement: the cited figures do not specify a water requirement for each facility, model, or query.
- They are not a forecast for every operator: the IEA’s server-growth figures are Base Case projections, while actual facilities and future demand can differ.
For a personal-finance reader, the practical distinction is between the digital service you use and the infrastructure behind it: data centers are the facilities that make large-scale computing possible, while their power and cooling demands are operational issues that vary across sites. The available figures describe facility categories and scenarios, not a per-user bill or a prediction of how a particular company will price its service. The European Commission also publishes a summary of data-center energy performance and electricity use at Energy performance of data centres.
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