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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Gartner forecast in November 2024 that power availability could operationally constrain 40% of existing AI data centers by 2027. That is a warning about limits on expansion and electricity access—not a prediction that 40% of facilities will shut down or suffer blackouts. Current projections reinforce the underlying concern, but the risk is uneven: local grid connections, equipment, permitting and reliable power matter as much as total electricity supply.
Where did the 40% figure come from?
The number comes from a Gartner forecast published November 12, 2024. Gartner said 40% of existing AI data centers could be operationally constrained by power availability by 2027. It also estimated that incremental demand from AI-optimized servers would reach 500 terawatt-hours (TWh) annually in 2027.
This was Gartner’s forecast, not a government finding, a measured share of facilities already affected, or a confirmed outcome for 2027. “Operationally constrained” can mean a facility cannot add planned racks or draw more power, must limit workloads, or faces a delay getting new electricity. It does not, by itself, mean a facility loses all power or closes. Gartner cited the years it can take to bring transmission, distribution and generation projects online as a reason for concern.
Why is AI data-center demand growing so quickly?
AI systems use clusters of specialized accelerators, creating high-density electricity demand. Large facilities also concentrate that demand in particular locations, where a single project can require substantial new capacity. Training, inference and conventional cloud computing do not all have the same load pattern; their timing and power needs vary. Cooling and other facility systems add to the total.
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The International Energy Agency (IEA) uses a different analysis and estimates global data-center electricity use will rise from 485 TWh in 2025 to 950 TWh in 2030, with AI-focused consumption tripling over that period. The IEA also estimates that an advanced AI server rack could have peak power demand equivalent to about 65 households by 2027. That comparison is about peak demand, not the rack’s average consumption. The IEA and Gartner figures have different definitions, models and time frames; they should be read as separate projections, not combined into one total. See the IEA’s energy-and-AI executive summary.
What kind of power shortage can constrain a data center?
A project may be short of deliverable power even when the wider country has enough electricity in aggregate. The constraint can occur at several points between a power plant and a server rack:
- Generation: The region lacks enough electricity-producing capacity to meet demand, especially during peak periods.
- Transmission: Electricity is available elsewhere but cannot be moved to the site because lines or grid capacity are insufficient.
- Distribution and interconnection: Local substations, feeders or connection studies cannot accommodate the proposed load on the project’s schedule.
- Electrical equipment: Transformers, uninterruptible power supply (UPS) systems, switchgear and power electronics may be delayed or in short supply.
- Reliability and flexibility: A system must balance demand as it changes and maintain reliable service during stress, which can require reserves, storage or flexible loads.
- Permitting and construction: Approvals, land, community concerns and project timelines can delay new infrastructure even when the engineering solution is known.
Equipment can be a bottleneck in its own right. A Johns Hopkins energy-institute analysis modeled unmet demand by 2027 of 14.1 gigavolt-amperes (GVA) for data-center transformers and 22.1 GVA for UPS equipment under its high-growth scenario. Those are scenario results, not confirmed global inventory shortages. GVA measures apparent electrical power capacity, rather than energy consumed over time. The analysis is available from the Johns Hopkins energy institute.
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AI workloads can also create large, rapid swings in electricity demand, according to the IEA. That can make balancing harder than a simple annual energy total suggests. Batteries and other flexibility measures may help, but they do not automatically replace sustained generation or a missing transmission line.
Where is the pressure most visible?
The issue is global, but the immediate constraint is often local. A region may have ample generation and still lack the transmission, substation capacity, equipment or completed interconnection needed at a particular site. Conversely, locations with available grid capacity may compete for land, labor, cooling resources and permits.
In the United States, the Energy Information Administration (EIA) identifies ERCOT, which serves most of Texas, and PJM, a regional grid operator covering parts of the Mid-Atlantic and Midwest, as areas where electricity load is expected to grow especially quickly through 2027. Its February 2026 analysis forecasts U.S. load growth of 1.9% in 2026 and 2.5% in 2027. These are modeled forecasts, not guaranteed growth rates. The EIA analysis says most regions could accommodate higher demand in its scenarios, while Texas showed more pronounced modeled price stress.
In a higher-demand scenario, the EIA modeled additional natural-gas generation and a $37 per megawatt-hour increase in ERCOT’s 2027 wholesale price relative to its reference case. That is a scenario result, not a forecast of the actual market price. The agency also reported that natural gas supplied 40% of U.S. electricity generation in 2025.
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How are data-center operators responding?
No single option solves every constraint. A strategy that helps a site waiting for a grid connection may not fix a regional generation shortage or make an emissions-intensive power source acceptable. Common responses include:
Choose sites and contracts around available capacity
Operators can prioritize locations with available generation and transmission, negotiate long-term electricity contracts, seek guaranteed supplies, or reserve capacity well ahead of a facility’s opening. Gartner reported that major power users were seeking long-term supplies independent of other grid demand. A contract alone does not prove that a site’s interconnection is complete or that power will be available on every schedule or under every condition.
Build or procure on-site generation
Natural-gas turbines or engines, fuel cells and combined heat and power can provide power at or near a site; developers are pursuing on-site gas generation where grid connections are slow. Such systems bring their own fuel, permitting, operating-cost and emissions questions. The IEA warns that rapid changes in AI loads may stretch the technical capabilities of on-site gas plants.
Nuclear, including proposed small modular reactors, and advanced geothermal are also part of the longer-term supply discussion. A proposal or power-purchase agreement does not equal an operating plant. The IEA reports that conditional data-center offtake agreements with small modular reactor projects grew from 25 GW at the end of 2024 to 45 GW in 2026. These are pipeline agreements, not generating capacity already serving data centers.
Pair renewable power with storage and grid access
Renewable power-purchase agreements can support new wind or solar generation, but an agreement does not necessarily provide continuous physical electricity at the data center every hour. Storage, a sufficiently connected grid, overbuilding generation or another balancing source may be needed to match variable output with round-the-clock operations. Batteries can smooth short-duration swings, provide bridging power, reduce peaks or support grid services; their value depends on duration and charging supply. A battery sized for seconds or hours cannot cover a prolonged shortage by itself.
The IEA says technology companies accounted for around 40% of corporate renewable power-purchase agreements signed in 2025. That is an indicator of contracting, not proof that all associated generation is already operating or dedicated to data centers.
Use electricity and compute more efficiently
More efficient accelerators, better server utilization, liquid cooling and improved thermal management can reduce electricity needs for a given amount of computing. Operators can schedule flexible work around grid conditions, use smaller or specialized models where suitable, avoid unnecessary retraining and move some inference closer to users through edge computing. Gartner specifically recommends efficiency measures, alternative lower-power approaches, edge computing and smaller language models. The savings depend on workload and deployment choices; efficiency does not guarantee that total demand falls if usage expands.
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Who may pay for the power constraint?
Higher power costs can affect data-center operators directly and may feed into the prices of colocation, cloud computing or AI services. Utilities may also need to invest in substations, lines and other upgrades. Who ultimately bears those costs depends on local market rules, utility rate design, contracts and how infrastructure costs are allocated among new and existing customers.
There is no basis for assuming that every data-center project automatically raises every household’s electricity bill. The IEA says higher data-center demand does not necessarily raise electricity prices if infrastructure investment and policy are suitable. In practice, consumers and businesses may be affected differently by region and rate structure, while some costs may be absorbed by operators or cloud providers rather than passed through directly.
What are the emissions trade-offs?
When grid power is unavailable, on-site fossil-fuel generation or delayed retirement of existing fossil plants can increase emissions or make emissions targets harder to meet. Gartner warned that rising demand could keep fossil plants operating beyond planned retirement dates. In its higher-demand scenario, the EIA modeled substantial additional natural-gas generation; natural gas supplied 40% of U.S. generation in 2025.
Renewable contracts, storage, nuclear, geothermal, efficiency and flexible demand may help limit emissions, but project timing matters. The IEA’s reported renewable-contract and small-modular-reactor figures describe agreements and pipelines, not a guarantee of near-term, around-the-clock clean electricity at a particular site. On-site gas can address some reliability or timing needs, but it is not a universally clean solution.
How can you tell whether a particular data center is power-constrained?
The 40% forecast does not identify which facilities will be constrained. To assess a specific project, distinguish a request for power from capacity that is contracted, deliverable and ready when needed. Useful questions include:
- Is the site physically connected to the grid, or is it still awaiting an interconnection?
- What is its contracted firm capacity, as distinct from the amount it has requested?
- Is the limiting factor generation, transmission, local distribution, equipment, permitting or some combination?
- When are the interconnection and any required upgrades expected to be complete?
- Does the operator have a reliable fuel supply if it depends on on-site generation?
- Can it sustain its planned load during heat waves, low renewable output or grid emergencies?
- Does its electricity contract allow curtailment, and how would that affect operations?
- Are batteries intended for ride-through, peak shaving or multi-hour backup, and are they sized for that purpose?
- Are environmental permits and community approvals complete?
What could make the 2027 outlook better or worse?
Demand could undershoot current forecasts if AI adoption slows, projects are canceled, capital becomes harder to obtain, or more efficient chips and models reduce power per unit of work. Improved utilization, workload scheduling and the opening of new regions with available capacity could also ease pressure.
The outlook could worsen if adoption accelerates, especially if inference demand grows faster than expected, or if transmission, generation and equipment projects fall behind. Storage and flexible-load programs could help connect some facilities without requiring full firm power at every moment, but they do not remove the need for dependable infrastructure. The direction of the risk is credible; the timing and severity will differ by site and grid.
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