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AI data centers increase electricity demand and can increase both on-site cooling water use and water consumed by power plants. They also add emissions when their electricity comes from fossil-fuel generation. Electricity-price effects are not automatic: prices depend on local supply, new grid and generation costs, tariffs, and how predictable or flexible the data-center load is.
National and global estimates show the scale of the change, but they cannot by themselves tell you what a particular facility will do to a household bill, river basin, or local emissions total. Those outcomes depend on the site, cooling system, workload, grid mix, and cost-allocation rules.
How much electricity are data centers using?
A data center’s electricity load includes servers, networking, storage, cooling, power conversion, lighting, and other building systems. AI is a major source of new demand because accelerated servers used for model training and inference consume substantial power, but published totals generally cover all data-center workloads rather than AI alone.
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The International Energy Agency (IEA) estimates that all data centers used about 415 terawatt-hours (TWh) of electricity worldwide in 2024, approximately 1.5% of global electricity consumption. In its Base Case, the IEA projects roughly 945 TWh in 2030. Accelerated servers, primarily associated with AI, grow faster than conventional servers and account for almost half of net global data-center electricity growth through 2030 in that analysis.
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United States outlook
The latest Lawrence Berkeley National Laboratory (LBNL) update, published in 2026, models a wide range for U.S. data-center electricity use in 2030. Its reference case is 649 TWh, equivalent to 11.8% of projected U.S. electricity consumption. Across the modeled scenarios, the estimate is 9.5% to 15.3%, with compounded uncertainty bounds of 521 to 843 TWh.
| Measure | Estimate | What it means |
|---|---|---|
| Global data-center electricity, 2024 | 415 TWh (about 1.5% of global electricity) | IEA estimate for all data-center workloads |
| Global data-center electricity, 2030 | 945 TWh | IEA Base Case projection, not an observed result |
| U.S. data-center electricity, 2030 | 649 TWh; 11.8% of U.S. electricity | LBNL 2026 reference case |
| U.S. 2030 scenario range | 9.5%–15.3%; 521–843 TWh uncertainty bounds | Model outcomes affected by adoption, equipment, efficiency, utilization, cooling, supply chains, and construction pace |
The LBNL model is bottom-up: it combines equipment shipments, device power use, cooling-performance simulations, facility types, and locations. The range is therefore not a promise that every outcome is equally likely. It shows how strongly the result changes when assumptions about AI adoption, server efficiency, utilization, cooling, infrastructure, and construction timing change.
Do AI data centers raise electricity prices?
There is no universal “yes” or “no.” The IEA summarizes the issue this way: “The impact of electricity demand growth on electricity prices depends on a combination of fundamental factors and policy choices.” A large new load can raise costs in one power system and improve asset utilization in another.
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- Tight supply: If generation and transmission are already near their limits, a new data center can require expensive generation, substations, lines, or storage. Those investments can increase system costs.
- Concentrated growth: A facility that appears quickly in one constrained grid zone may trigger upgrades that would not have been needed without it.
- Uncertain build-out: Developers may request more interconnection capacity than they initially use. Utilities can face the risk of building for a projected load that arrives slowly or never reaches its requested level.
- Cost allocation: If tariffs or contracts do not make the data center pay the costs it causes, some costs can be spread across other customers.
When new load can reduce pressure on rates
- Available capacity: In a system with excess generation or underused network assets, additional predictable demand can spread fixed costs across more kilowatt-hours.
- Steady operation: A high-capacity-factor data center can help power plants and grid equipment operate closer to the level for which they were built.
- Flexible computing: Some training and batch workloads can shift in time or location. Flexibility can reduce operation during expensive peak periods, although not every AI workload can be moved without affecting performance or service commitments.
Why a national statistic cannot answer a household-bill question
Customer bills are determined by a specific utility’s generation costs, transmission and distribution spending, rate design, regulatory decisions, and other demand. The available evidence describes mechanisms and planning options, not a causal nationwide increase attributable solely to AI data centers. To establish an effect in a named territory, examine utility filings, rate cases, regulator orders, or a credible empirical study for that territory.
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How utilities assign data-center costs
Large-load agreements can address the risk that ordinary customers pay for facilities that use less capacity than forecast. Common tools include minimum-demand commitments, financial guarantees, phased interconnection, demand charges, and rules requiring the customer to fund dedicated network upgrades. The exact protections vary by jurisdiction and must be evaluated in the utility’s tariff and regulatory record.
LBNL’s 2026 rate-design review focuses on managing two opposite risks: insufficient supply if a very large load arrives, and stranded or underused investment if the expected load does not materialize. A contract can change who bears those risks, but it does not eliminate the physical need for generation or transmission where the grid is constrained.
How much water do AI data centers use?
“Water use” combines two different pathways that should not be added together without explanation:
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- Direct site consumption: Water consumed at the facility, commonly through evaporative cooling and other building uses.
- Indirect electricity-related consumption: Water consumed at power plants that generate the electricity the data center buys.
Water consumption means water removed from the immediate water cycle, such as evaporation. It is different from water withdrawal, which can include water returned to a source. Power-plant water intensity varies with fuel, cooling technology, efficiency, location, and operating conditions.
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| U.S. measure | Amount | Boundary and year |
|---|---|---|
| Direct data-center consumption | About 66 billion liters | LBNL estimate for 2023; water consumed at data-center sites |
| Projected direct consumption | About 145–275 billion liters | LBNL projection for 2028 |
| Indirect electricity-related consumption | Nearly 800 billion liters | LBNL estimate for 2023; water consumed by electricity generators |
The indirect figure is much larger in this estimate because it includes the water footprint of electricity generation across the grid. Comparing it with a facility’s cooling-water meter without matching the accounting boundary produces a misleading result.
Why there is no universal “water per AI prompt” number
A 2025 LBNL review reports more than 10,000-fold variation in workload-level water use across the conditions it evaluated. The main determinants include server efficiency, utilization, inactive-server share, refresh cycle, cooling technology, infrastructure efficiency, climate zone, and the water-consumption factor of the supplying grid.
A workload on efficient, highly utilized equipment in a cool climate with low-water electricity can have a very different footprint from the same workload on older servers in a hot region using water-intensive generation. There is no single cooling or operating recipe that minimizes water in every location; site constraints determine the appropriate combination.
What emissions are associated with data centers?
Electricity supply and location largely determine data-center emissions. The LBNL estimate for the United States is 61 billion kilograms of carbon-dioxide equivalent (CO2e) associated with data-center electricity use in 2023. That estimate reflects regional generation and power interchange rather than a universal per-server factor.
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The IEA estimates around 180 million metric tons (Mt) of indirect CO2 from global data-center electricity use in 2024, excluding backup generation. That represents about 0.5% of global fuel-combustion emissions at the time. The figure includes every data-center workload, with AI as a subset.
What the IEA scenarios imply through 2035
In the IEA Base Case, electricity-related data-center emissions rise toward approximately 320 Mt CO2 around 2030, then decline gradually to about 300 Mt by 2035. The trajectory depends on how quickly low-carbon generation and grid connections become available.
Renewables provide nearly half of additional global data-center electricity demand through 2030 in the Base Case. Natural gas and coal also contribute, while nuclear generation grows later. In the higher-demand Lift-Off Case, long grid-connection queues leave more demand beyond 2030 supplied by fossil fuels. These are scenario findings, not guaranteed outcomes.
Important accounting limits
LBNL reports an average of 0.34 kilograms of CO2e per kilowatt-hour for the electricity mix serving U.S. data centers in 2023, alongside an average 4.52 liters per kilowatt-hour of indirect water consumption. Both averages vary among balancing authorities. LBNL’s method assigns impacts using regional generation and interchange and does not incorporate an individual facility’s power-purchase agreement or behind-the-meter generation. A contract for renewable electricity may change procurement accounting, but it does not automatically change the physical generation and transmission impacts on the interconnected grid.
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Which design choices change the footprint?
Two facilities with the same computing output can have very different effects. Compare them on these dimensions:
- Grid and geography: Local generation mix, transmission congestion, climate, and water stress matter more than a national average.
- Cooling: Air cooling, direct liquid cooling, evaporative systems, and hybrid designs have different electricity and water requirements.
- Server efficiency and utilization: More efficient hardware and higher utilization can reduce energy and water per unit of useful computation; inactive servers still consume infrastructure resources.
- Timing and flexibility: A steady load has different grid effects from a sharp coincident peak. Deferrable training can sometimes be scheduled when lower-carbon or lower-cost power is available.
- Infrastructure and refresh cycle: Power-conversion efficiency, cooling overhead, and replacement schedules affect total demand over the equipment’s life.
- Emissions boundary: Check whether estimates include only purchased electricity, backup generators, construction, or embodied hardware emissions. The cited IEA global figure excludes backup generation.
How to evaluate a proposed facility in your area
Use a local, like-for-like assessment rather than a generic “AI is bad” or “AI is clean” label.
- Identify the load: Find the requested megawatts, expected ramp-up schedule, annual energy use, peak demand, and whether the facility can curtail or shift workloads.
- Map the interconnection: Determine which substation, transmission upgrades, generation additions, or storage projects are required and who pays for each item.
- Check the tariff: Read minimum-demand rules, demand charges, guarantees, exit fees, and provisions for costs if the project is delayed or underbuilt.
- Separate water pathways: Request annual direct withdrawal and consumption, cooling technology, reclaimed-water use, and the supplying grid’s indirect water factor. Keep withdrawals and consumption distinct.
- Use the local grid mix: Apply the relevant balancing-authority or utility emissions factor and state whether backup generation is included.
- Test scenarios: Compare full build-out, delayed build-out, high-utilization, low-utilization, flexible-load, and constrained-grid cases. Label each as a scenario rather than a measured outcome.
- Review safeguards: Look for drought limits, heat-wave operating plans, noise and air permits, renewable-energy claims, and monitoring requirements in public filings.
Common misconceptions
“Every new AI data center raises everyone’s bill.”
Not necessarily. A constrained system may require costly new infrastructure, while an underused system may benefit from predictable demand. The result depends on local conditions and cost allocation.
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“The water number is the water number.”
Direct cooling consumption and indirect power-plant consumption describe different pathways. A credible comparison states the boundary, year, geography, and whether it measures withdrawal or consumption.
“Renewable power makes the facility impact-free.”
Renewables can lower operating emissions, but they do not erase construction, transmission, cooling, water, or peak-capacity effects. Grid congestion and the timing of demand still matter.
“A global AI estimate describes my local project.”
Global IEA totals include all data centers and many regions. Local effects require site-specific load, grid, water, and tariff information.
What the evidence supports
AI data centers are a fast-growing, geographically concentrated electricity load. Their environmental and price effects are conditional rather than fixed: the same computing demand can produce different costs, water use, and emissions under different cooling systems, climates, utilization rates, grid mixes, and contracts. The most reliable judgment combines a facility’s measured or contracted load with local infrastructure plans, water accounting, emissions factors, and transparent rules for assigning costs.
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