How AI Data Centers Are Changing U.S. Energy Demand in 2026

AI data centers are facilities that contain the servers, networking equipment, data storage systems, and cooling equipment needed to develop and operate artificial intelligence. They use substantial amounts of electricity because AI workloads often require specialized chips, dense groups of servers, fast data connections, and continuous cooling.

The term “AI data center” does not describe a completely separate type of facility. Traditional data centers support cloud computing, file storage, websites, video streaming, and business software. Modern data centers may support these services while also handling intensive AI applications.

Some facilities operate on a much larger scale. Hyperscale data centers contain thousands of servers and support extensive cloud and digital services. Technology companies may build these hyperscale facilities to serve users across several regions.

Companies have accelerated data center construction during the AI boom. New data centers increasingly include specialized equipment for deep learning, large language models, image generation, and other machine learning use cases.

When you ask an AI assistant to summarize a report or create an image, your device does not usually complete the entire task. It sends the request to remote servers, which process the information and return the output. Millions of requests can require substantial computing capacity when people and businesses use these tools throughout the day.

U.S. data center energy consumption has increased as companies have expanded cloud services, data storage, and AI infrastructure. Researchers expect further growth, although the size and timing remain uncertain.

The Lawrence Berkeley National Laboratory’s 2025 update, published in June 2026, estimates that U.S. data centers could consume 649 terawatt-hours of electricity in 2030 under its reference case. That amount would equal approximately 11.8% of total U.S. electricity use.

The report also provides a range of possible outcomes:

These figures are projections, not guaranteed results. Researchers calculated the range by changing assumptions about equipment shipments, specialized AI chips, server use, chip operating life, and other factors.

The range shows why one forecast cannot provide a final answer about future AI energy consumption. Companies may adopt AI faster or more slowly than expected. Chip manufacturers may improve computing efficiency, and data center operators may change how they design and use their facilities.

The International Energy Agency provides a broader view. It estimates that data centers consumed about 485 terawatt-hours worldwide in 2025. Its central projection places consumption near 950 terawatt-hours in 2030, or approximately 3% of global electricity consumption.

The IEA also expects electricity use from AI-focused data centers to grow faster than total data center demand. However, AI will not cause all future growth. Cloud computing, streaming, online services, and data storage will also contribute.

The U.S. Government Accountability Office has warned that researchers cannot yet isolate the exact share of data center electricity used for generative AI. Technology companies do not consistently disclose detailed energy and water information about their AI systems. GAO’s assessment of generative AI’s environmental effects therefore recommends caution when interpreting AI-only estimates.

AI systems require several layers of physical IT infrastructure. Servers perform the calculations, networking equipment moves information, storage systems hold data, and cooling equipment removes the heat that the hardware produces.

AI Servers Require Intensive Data Processing

Traditional business software often follows a defined set of instructions. Machine learning systems must process large datasets and complete many calculations at the same time.

Data center operators often use graphics processing units and other AI accelerators for deep learning and large language models. These chips can complete many calculations in parallel, which makes them suitable for training and operating AI systems. However, groups of high-performance chips can draw much more power than conventional business servers.

Operators also place more IT equipment into each server rack to increase capacity. This higher equipment density concentrates power requirements and heat within a smaller area.

The IEA notes that an AI-focused facility may use as much electricity as some power-intensive factories. However, actual power usage varies based on the facility, equipment, workload, and number of users.

Training and Inference Both Use Electricity

AI training and inference are different processes.

• Training allows developers to build or update a model by processing large datasets and adjusting its internal parameters.
• Inference occurs when a trained model responds to a request, classifies information, creates an image, or completes another task.

Training a large model may require significant computing power over a limited period. Inference can create continuing demand because people and businesses may send millions of requests after a company releases the model.

The total amount of AI power consumption depends on the model’s size, hardware, request length, number of users, and data center energy efficiency. Different use cases also require different levels of computing power. Generating a high-resolution video may require more processing than classifying a short piece of text.

Cooling Systems Add to Power Usage

Servers convert part of the electricity they use into heat. Data center operators must remove that heat to protect the equipment and maintain reliable performance.

Facilities may use fans, pumps, chillers, cooling towers, liquid cooling, or combinations of these systems. The most suitable method depends on the equipment, climate, building design, and available water.

Operators increasingly use liquid cooling for dense AI servers because liquids can transfer heat more effectively than air in some applications. However, the complete effect depends on the design. A system can reduce cooling electricity while introducing new equipment, maintenance, or water requirements.

Data center development does not occur evenly across the United States. Site selection depends on access to a reliable power supply, fiber-optic connections, suitable land, tax incentives, water resources, and major markets.

The U.S. Energy Information Administration’s Annual Energy Outlook 2026 projects that data center server energy use will grow fastest in the South Atlantic and West South Central regions. These regions include Virginia and Texas, two major areas for data center construction.

Northern Virginia has one of the world’s largest concentrations of data centers. Its facilities benefit from extensive fiber connections, established power infrastructure, and proximity to government and business customers.

The effect is already visible in state electricity data. The EIA reported in May 2026 that commercial electricity sales in Virginia increased by nearly 30 million megawatt-hours between 2019 and 2025. The agency identified data centers as a major cause of that growth, along with electric vehicles and building electrification.

PJM Interconnection, which operates the regional power grid across parts of 13 states and Washington, D.C., expects the Dominion zone in Virginia to experience the largest absolute increase in summer peak demand within its territory from 2026 through 2030. PJM expects data center load to cause much of that increase.

Texas has also attracted new data centers because it offers land, energy resources, and a large technology market. However, rapid development can make forecasting difficult. Proposed hyperscale data centers may request grid connections but never begin operating, while other projects may require more power than developers initially estimated.

Utilities must separate confirmed projects from speculative requests before they build expensive data center infrastructure.

Large data centers can operate throughout the day and require hundreds of megawatts of electricity. Utilities must provide enough generation, transmission, and distribution capacity to serve this continuous load.

A new facility may require:

• Additional power generation
• New transmission lines
• Larger substations
• Grid connection studies
• Backup power systems
• More accurate demand forecasts
• Agreements about infrastructure costs
• Emergency plans for periods of grid stress

The U.S. power grid does not operate as one uniform system. Regional grid operators, utilities, and state regulators manage different resources and constraints. A data center may cause little difficulty in an area with an available power supply and transmission capacity. The same facility could require major upgrades in an area with limited capacity.

EIA projects that U.S. electricity consumption will continue increasing through 2050, with data center servers serving as a major source of growth. The agency also expects natural gas, solar, and wind power to supply much of the additional electricity across its modeled scenarios.

However, new power plants alone may not solve every problem. Utilities must connect those plants to customers through transmission and distribution systems. Planning, permitting, equipment procurement, and construction can take several years.

Continuous Demand Changes Grid Planning

Homes and businesses usually use different amounts of electricity throughout the day. Data center servers can create a more consistent load because operators keep them running around the clock.

This continuous demand can help utilities spread some fixed costs across more electricity sales. It can also require new generating capacity and raise operational costs. The effect on your energy costs depends on how regulators and utilities allocate those expenses.

A data center does not automatically raise residential rates. Special tariffs, infrastructure agreements, and direct investments from developers can protect other customers from some expenses. Poor cost allocation, however, could leave households or smaller businesses paying for infrastructure built mainly for a large customer.

Regulators must determine who benefits from each project and who should pay for the required upgrades.

A large data center can create opportunities and concerns for the community that hosts it. The final result depends on the project, location, tax agreement, utility structure, and available resources.

Potential Economic Benefits

Data center projects may provide:

• Construction jobs
• Permanent technical and operations roles
• Property and local tax revenue
• Investment in power and communications infrastructure
• Business for local contractors and suppliers
• Support for a region’s technology sector

The U.S. Department of Energy’s Data Center Resource Hub reports that Virginia’s data center industry supports jobs, labor income, and state economic activity. It also reports that Loudoun County received more than $875 million in data center tax revenue during one year.

These benefits require context. A large facility can generate substantial tax revenue while employing fewer permanent workers than a factory or office complex of a similar size. Communities should compare the expected benefits with the value of tax incentives, public infrastructure, land, and other resources committed to the project.

Possible Community Concerns

Residents and local officials may raise concerns about:

• Electricity costs
• Water availability
• Land use
• Noise from cooling equipment
• Emissions from backup generators
• New transmission lines
• Construction traffic
• The number of permanent jobs
• Limited public information about resource use

Local governments need project-specific evidence to assess these concerns. A national estimate cannot show how one facility will affect a town’s water system or utility rates.

Developers should provide clear information about expected power demand, water use, backup generation, construction schedules, employment, and tax arrangements. Communities can then compare the promised benefits with the project’s long-term requirements.

Some data centers use water to remove heat from servers and supporting equipment. Power plants may also consume water when they generate the electricity that data centers use.

Researchers describe these as direct and indirect water use:

• Direct water use occurs at the data center, often through cooling equipment.
• Indirect water use occurs when power plants generate electricity for the facility.

Not every data center uses the same cooling design. Some facilities use evaporative cooling, which can consume water as it releases heat. Others use closed-loop liquid systems, air-based systems, or combinations of several methods.

A system that uses fewer gallons of water may require more electricity, especially during hot weather. A system that reduces electricity use may consume more water. Operators must evaluate both resources instead of treating them as separate issues.

Location also matters. A facility’s water demand may create little concern in a water-rich area with sufficient infrastructure. The same demand may become more serious in a drought-prone region or a community with limited water capacity.

The GAO found that public estimates of generative AI water consumption remain limited. Companies often do not provide enough detail to calculate the water used for a specific model or service. Readers should therefore be cautious with claims that assign one fixed amount of water to every AI request.

The environmental impact of a data center depends on its electricity source, cooling system, location, equipment, and operating practices. A facility that receives much of its electricity from fossil fuels may produce more indirect carbon emissions than one supplied by low-carbon sources. Backup diesel generators can also produce local air pollution when operators test or use them.

Data center construction requires land and building materials. Operators must also replace servers and other IT equipment as technology changes. Poor equipment management can increase electronic waste and operational costs.

Climate change adds another planning concern. Higher temperatures can increase cooling demand, while drought can limit water availability in some regions. Extreme weather can also interrupt the power supply or damage grid infrastructure.

Many technology companies have established sustainability goals that cover renewable energy, carbon emissions, water use, or waste. These targets can guide investment, but companies must report their methods and results clearly. A broad environmental claim does not show how one facility operates during every hour of the year.

Renewable energy can supply part of the electricity that AI data centers require, but operators must also plan for times when wind and solar generation are unavailable.

Technology companies use several approaches:

• Power-purchase agreements with renewable-energy projects
• On-site solar generation
• Wind power contracts
• Battery energy storage
• Nuclear power agreements
• Geothermal energy
• Natural gas generation
• Grid electricity from several sources
• Flexible scheduling for some computing tasks

An annual renewable-energy agreement does not always mean that a facility receives carbon-free electricity during every hour of operation. A company may purchase enough renewable electricity to match its annual consumption while the local grid uses natural gas, coal, nuclear, or other sources at particular times.

Energy storage can move some renewable electricity from one part of the day to another. However, a large facility may still need grid power or another reliable source during extended periods of low renewable generation.

Data center operators use several measurements to evaluate energy management. One common metric is power usage effectiveness, or PUE.

PUE compares the total energy used by a facility with the energy used by its IT equipment. The calculation is:

PUE = Total facility energy use ÷ IT equipment energy use

A PUE of 1.0 would mean that all facility energy goes directly to computing equipment. Real facilities also require cooling, lighting, power conversion, and other supporting systems, so their PUE remains above 1.0.

A lower PUE generally indicates that a facility uses less supporting energy for each unit consumed by its servers. The Department of Energy reports that some national laboratory computing facilities have achieved a PUE of 1.03.

However, PUE does not measure the complete environmental impact of a facility. It does not show:

• Whether servers perform useful work
• How much water the facility consumes
• Which sources provide the electricity
• How much carbon the electricity produces
• Whether the operator uses equipment efficiently
• How much electronic waste the facility creates

A data center can report a low PUE while its total power usage continues to rise. Operators should combine PUE with measurements for water, carbon emissions, server utilization, and energy used for each computing workload.

Data center operators can improve energy efficiency without reducing every service they provide. The most effective approach depends on the equipment and facility.

Operators can:

• Use more efficient AI chips and servers.
• Improve equipment utilization.
• Remove or replace unused servers.
• Select efficient cooling systems.
• Reuse heat where local buildings or industries can use it.
• Schedule flexible computing tasks during lower-demand periods.
• Locate facilities where power and water are available.
• Report electricity, water, and emissions consistently.
• Invest in new generation and grid infrastructure.
• Reduce unnecessary model requests and calculations.
• Match each workload with the least complex suitable AI model.
• Monitor PUE and other performance measurements.
• Include energy management in site selection and expansion plans.

Your company can also influence demand. Before you add an AI service, determine whether it solves a measurable problem. Select the least complex model that can complete the task reliably. A smaller or specialized model may require less computing power than a large general-purpose system.

Businesses can review American Wired’s guide to the uses, benefits, and risks of AI for small businesses before expanding their use of AI. Companies should also compare the best AI productivity tools for work based on business value, data protection, and actual employee needs.

Business leaders, utilities, regulators, and communities can use these seven questions when they assess a proposed data center:

1. How much electricity will the facility require?
Identify its expected average demand, peak demand, and plans for future expansion.

2. What infrastructure will support it?
Determine whether the project requires new power plants, transmission lines, substations, water systems, or roads.

3. Who will pay for the upgrades?
Review how the utility and developer will divide construction, operating, and maintenance costs.

4. How much water will the facility use?
Separate direct cooling water from the water associated with electricity generation.

5. What benefits will remain in the community?
Compare tax revenue, permanent employment, construction work, and local purchasing with the incentives and resources involved.

6. How will the operator report its effects?
Require consistent information about energy, water, emissions, jobs, and operating changes.

7. Can the facility reduce demand during grid stress?
Identify workloads that operators could delay and backup systems that could support safe demand reduction.

These questions do not determine whether a project should proceed. They give decision-makers a consistent way to compare its expected benefits, costs, and risks.

Your company may never build or operate a data center, but these facilities can still affect your work. Cloud software, AI assistants, online storage, analytics tools, and cybersecurity services depend on remote computing and data center infrastructure.

Higher infrastructure and energy costs may influence software prices. Regional power constraints may affect where technology providers build facilities and how quickly they expand. New reporting rules may also require providers to disclose more information about energy, water, and carbon emissions.

Reliability and security remain important. A facility needs enough power to operate, but it must also protect the information that its servers process. American Wired’s guide to small-business cybersecurity explains how access controls, employee training, software updates, and incident planning can protect your systems and sensitive data.

When your business evaluates an AI provider, ask:

• Where does the provider process your information?
• What security controls protect the data?
• Can the provider explain its energy and sustainability claims?
• Does the service produce enough business value to justify its cost?
• Can your company change providers or stop using the service?
• Who remains responsible for reviewing the AI’s output?

These questions connect the physical infrastructure behind AI with the decisions you make as a customer.

The infrastructure behind AI will continue to affect American businesses, energy systems, and communities. Follow American Wired for evidence-based coverage of the technologies, risks, and business decisions shaping how Americans work and live.

Written by

American Wired Editorial Team

writter

The American Wired Editorial Team delivers trusted coverage of technology, business, AI, and innovation with a commitment to accuracy, insight, and relevance.