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No. AI training and deployment still rely mainly on data centers, and the latest International Energy Agency outlook projects global data-center electricity use to nearly double from 2025 to 2030. What is changing is the kind of facility the industry needs: AI workloads require denser computing, more capable cooling and reliable access to much larger amounts of power.
Why hasn’t AI made data centers obsolete?
AI runs on physical infrastructure. Training and deploying models require servers, storage, networking, cooling, power equipment and grid connections, usually brought together in data centers. AI may change the workload and the hardware inside a facility, but it does not remove the need for those systems.
The IEA’s 2025 report put global data-center electricity use at about 415 terawatt-hours (TWh) in 2024, or 1.5% of global electricity consumption. Its 2025 base case projected about 945 TWh in 2030. The agency’s 2026 update estimates 485 TWh in 2025 and about 950 TWh in 2030, with electricity consumption by AI-focused data centers tripling over that period.
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These are estimates and projections for data centers overall, not a measure of electricity used only by AI. The figures come from different IEA outlook vintages and use different starting years; read them as evidence of continued growth, not as directly interchangeable measurements.
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What changes as AI workloads grow?
Higher power density
AI servers use energy-intensive accelerators and can concentrate more computing power in a smaller space. The IEA reports that AI-server power density increased elevenfold from 2020 to 2025 and could rise another fourfold by 2027. A conventional data center typically uses 10–25 megawatts (MW), while a hyperscale AI center can exceed 100 MW.
More demanding cooling and power systems
Higher rack power means more heat to remove and greater demands on the electrical equipment serving the facility. Operators planning for AI must consider whether a site can support the required server density, cooling capacity and reliable power—not merely whether there is room for more racks. A facility designed for lower-density equipment may need upgrades to serve some AI workloads.
Closer ties to energy supply
As data-center loads grow, operators and utilities have to consider grid interconnection, generation, storage and reliability together. The IEA identifies renewables, natural gas, nuclear power, storage and efficiency as parts of the potential supply response. No single generation source eliminates the need for the data-center facilities that run AI.
How much electricity could AI data centers use?
The available global outlook figures measure data-center electricity use as a whole. They do not provide a single, directly comparable forecast for AI alone. The IEA’s 2026 update says consumption by AI-focused data centers will triple from 2025 to 2030, but that is a change in AI-focused consumption, not a claim that all data-center electricity use will triple.
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| Outlook | Starting estimate | 2030 estimate | What it describes |
| IEA, 2025 | About 415 TWh in 2024; 1.5% of global electricity | About 945 TWh | Global data-center electricity use in the IEA’s 2025 outlook |
| IEA, 2026 update | 485 TWh in 2025 | About 950 TWh | Global data-center electricity use; the update also projects AI-focused consumption will triple by 2030 |
For a separate U.S. estimate, the U.S. Department of Energy says data-center electricity use could reach 325–580 TWh by 2028. That range is a U.S. projection, not a global figure or an estimate for AI alone.
What happens to existing data centers?
Existing facilities do not all become obsolete at once. Their future depends on whether their power, cooling, network connections and physical layout suit the workloads they need to serve. Some may be upgraded for denser equipment; others may continue to run workloads that do not require the newest AI hardware. A site that cannot meet the power or cooling needs of a particular AI deployment may be a poor fit for that deployment without being useless for every other purpose.
That makes the transition a mix of new construction and adaptation, rather than a simple replacement of every existing facility. The IEA notes that a data center can be operational in two to three years, while energy infrastructure takes longer to plan and build. The mismatch can limit how quickly new capacity comes online even when operators are ready to build.
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Could AI data centers become stranded assets?
Yes. A planned or operating facility can lose economic value if it cannot secure affordable electricity, obtain a grid connection, acquire the necessary equipment, raise financing or attract enough demand. A building’s technical capacity is not a guarantee that it will be competitive or fully used.
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That risk is different from saying data centers as a category are obsolete. The IEA’s 2025 and 2026 outlooks both project strong growth in overall data-center electricity use, while recognizing uncertainty around how quickly AI adoption and infrastructure expansion proceed. Efficiency gains, slower adoption, supply constraints, financing conditions and grid bottlenecks could delay projects or make some uneconomic without eliminating the underlying need for data centers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What will determine which data centers succeed?
For operators, utilities and communities evaluating a project, the most useful questions are practical:
- Demand: Does the project depend on a base-case outlook, faster AI adoption or a more cautious scenario?
- Power: Is there a viable path to grid connection, dependable supply and manageable energy costs?
- Density and cooling: Can the facility support the target AI servers and remove the resulting heat?
- Timing: Can power and grid infrastructure be delivered when the data center is ready to operate?
- Efficiency and emissions: Can hardware, software and facility efficiency improve, and what generation mix will supply the load?
The answers vary by site and project. A data center can be built faster than the broader energy infrastructure needed to serve it, so power access and timing may matter as much as the building itself.
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