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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAI data centers may have enough electricity available across a region and still be unable to connect where they are being built. The emerging risk is not just whether power can be generated, but whether local substations, transformers, cables and other grid equipment can deliver it to clusters of large facilities in time. That risk varies by place; the available evidence does not establish a binding bottleneck in every city or identify current feeder-level constraints for specific cities.
Why AI data centers need so much electricity
AI computing depends on servers running in data centers, and those servers draw electricity both to process workloads and to support the facility systems that keep them operating. The International Energy Agency (IEA) reported that global data-center electricity demand grew 17% in 2025, compared with 3% growth in global electricity demand overall.
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In its 2026 central outlook, the IEA estimates global data-center electricity consumption at 485 terawatt-hours (TWh) in 2025 and projects 950 TWh by 2030. It also projects AI-focused data-center consumption to triple over that period. These are global estimates and projections, not measured totals for any city.
Higher server power density adds pressure inside facilities and at their grid connections. The IEA says AI-server power density increased 11-fold between 2020 and 2025 and projects a further fourfold increase by 2027. By 2027, one rack in an advanced data center could have peak power demand equivalent to that of 65 households, according to the IEA; this comparison is for a rack at peak demand, not a whole data center.
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How large could the U.S. share be?
For the United States, Lawrence Berkeley National Laboratory’s June 2026 update estimates data centers could use 11.8% of U.S. electricity in 2030. Its modeled scenarios range from 9.5% to 15.3%, reflecting uncertainty in equipment shipments, device electricity use, cooling performance, facility types and locations.
| Measure | Estimate or outlook | Scope and date |
|---|---|---|
| Data-center electricity demand growth | 17% in 2025 | Global; IEA, 2026 |
| Overall electricity demand growth | 3% in 2025 | Global; IEA, 2026 |
| Data-center electricity consumption | 485 TWh in 2025; 950 TWh in 2030 | Global central outlook; IEA, 2026 |
| Data-center share of electricity use | 11.8% in 2030; scenarios range from 9.5% to 15.3% | United States; LBNL model estimate, June 2026 |
Why a city can run short of grid capacity when generation exists elsewhere
Electricity has to travel through a connected system, and a large new customer needs a viable connection and enough capacity along the relevant route. A constraint at a transformer, substation, feeder or cable can prevent a project from receiving the power it needs even if generation is available elsewhere in the region. Adding generation alone does not necessarily fix a local delivery constraint.
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Data centers often cluster in and around major cities. The IEA’s 2025 World Energy Outlook said most facilities in its project pipeline were larger than 200 megawatts (MW), and that data centers were concentrated around large cities, primarily in the United States, China and Europe. Multiple large projects seeking connections in the same area can therefore compete for local network capacity and the equipment needed to expand it.
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Building a facility can also move faster than planning, permitting and constructing the grid upgrades needed to serve it. Connection studies and queues, equipment supply, and utility planning all affect when a project can actually receive power. In its 2025 report, the IEA described average U.S. data-center connection queue times of one to three years, up to seven years in northern Virginia, and waits reported as high as seven or ten years in the United Kingdom and parts of Europe. Those are observations reported in 2025, not verified queue status for October 2026. The same IEA report estimated that grid constraints and related bottlenecks could put around 20% of projected data-center additions by 2030 at risk of delay globally; that is not a probability for any particular city or project.
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Why AI loads add equipment and reliability challenges
Higher rack power density increases the demands placed on the facility’s electrical equipment and its connection to the wider grid. AI workloads may also create large, rapid swings in demand. The IEA’s 2026 update says these conditions test supplies of transformers and power electronics and make storage relevant to reliable supply. They can complicate planning as well as equipment procurement.
Storage, onsite generation or workload flexibility may help manage certain operating conditions, but none automatically removes a grid constraint. A battery does not create unlimited connection capacity; onsite generation still raises questions about cost, emissions, reliability and the ability to follow variable AI loads. Flexibility also depends on whether workloads and service requirements allow operations to shift or reduce demand when the grid is under pressure.
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What utilities, grid operators and data-center operators can do
No single measure fits every constraint. The appropriate response depends on where capacity is lacking, how quickly it can be added, what reliability the facility requires, and who bears the cost. Options range from expanding the network to using existing capacity more effectively.
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| Response | What it can address | Key trade-offs and questions |
|---|---|---|
| Expand network capacity and procure equipment | Adds capacity where planning studies identify a need, including through grid and equipment upgrades. | Construction, permitting and equipment supply take time. Costs and their allocation need to be addressed. |
| Coordinate connections and forecasts earlier | Gives utilities, grid operators, regulators and developers a chance to plan around large loads before they arrive. | Proposed projects vary in certainty. How should planners distinguish committed demand from speculative plans and avoid double-counting? |
| Use monitoring, forecasting and grid-enhancing tools | Can help operators monitor networks and make safer, more effective use of existing capacity. | These tools can improve utilization, but do not replace physical upgrades where the network lacks capacity. |
| Use storage or flexible workloads | May help manage rapid demand swings or reduce demand during system peaks when operations permit. | Depends on workload and service requirements, incentives, and the storage system’s operating limits and costs. |
| Consider onsite generation | Could provide another source of electricity where grid connections are delayed. | Requires scrutiny of cost, reliability, emissions and whether generation can follow the facility’s variable load. |
The IEA’s September 2026 report on grid modernisation describes digital monitoring, forecasting and grid-enhancing approaches across transmission and distribution. These are ways to improve operation of existing networks, not a guarantee that an overloaded local connection can serve every proposed project. Its 2026 update also notes that data centers can be more flexible with appropriate incentives, while identifying storage as relevant to reliable supply.
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Who pays for upgrades, and what should communities ask?
New data-center demand can prompt investment in generation and grid infrastructure. That creates an affordability question: which costs should be paid by the facility, shared across customers, or recovered through other arrangements? The available evidence does not establish that ratepayers in a particular city are already paying a specific amount for data-center upgrades.
Forecasts also require care. A project pipeline is not the same as a set of facilities certain to be built and connected. Planners and regulators need to consider how committed each proposed project is, whether forecasts count speculative projects more than once, and whether facilities can reduce or shift demand during system peaks. These are policy questions, not proof that a particular community has already incurred a particular cost.
The IEA’s 2025 World Energy Outlook reported a pause on new data-center requests in Dublin through 2028. That is a dated example from the report, not confirmation that the pause remains in force in October 2026. Local claims about a current connection constraint, queue or moratorium need current evidence from the relevant utility, grid operator or regulator.
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How to tell whether the bottleneck is local
National forecasts show the scale of possible demand growth; they cannot by themselves show whether a specific city has enough capacity at a particular connection point. To assess a local claim, look for current information from the relevant utility, grid operator or regulator about the connection process and planned network upgrades. Check whether a reported delay concerns generation, a transmission route, a substation, a feeder or another part of the system. Those are different problems and may call for different responses.
The central issue is timing and location: a data center needs power delivered at its site, not merely available somewhere in a broad region. As the IEA puts it in its 2025 report, “There is no AI without energy – specifically electricity for data centres.” Its 2026 grid-modernisation report describes the two-way relationship: data centers can add demand and worsen grid congestion, while AI applied to energy systems could improve reliability and efficiency. Whether that potential helps a particular city depends on the network, the project and the decisions made by the organizations responsible for connecting and operating it.
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