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Infrastructure can support growing AI demand more efficiently by improving software, servers, cooling and other facility systems together, while planning around the power and equipment available at each site. Efficiency can reduce the energy needed for a given amount of computing, but it does not guarantee lower total consumption: demand can grow faster than those savings. The practical question is therefore how to meet more computing demand with less energy per service, while keeping facilities and local grids reliable.
How quickly is data-centre electricity demand growing?
The International Energy Agency (IEA) estimated that data centres used about 415 TWh of electricity in 2024, roughly 1.5% of global electricity consumption. That figure covers data centres generally, not AI alone. The IEA’s 2026 update estimates that data-centre demand rose 17% in 2025 to about 485 TWh, while electricity use at AI-focused data centres grew 50% that year.
| Measure | IEA estimate or projection | Scope and qualification |
|---|---|---|
| Data-centre electricity use in 2024 | About 415 TWh; roughly 1.5% of global electricity | IEA estimate published in 2025; all data-centre workloads, not AI alone |
| Data-centre electricity use in 2025 | About 485 TWh; 17% growth from 2024 | IEA estimate in its 2026 update; global total |
| Data-centre electricity use in 2030 | About 945 TWh in the 2025 Base Case; about 950 TWh in the 2026 update | IEA projections, not measured outcomes; the later estimate remains close to the earlier trajectory |
| AI-focused data-centre electricity use | Projected to triple from 2025 to 2030 | IEA 2026 global projection; not a forecast for every facility or region |
These figures describe different years and, in the case of the 2030 values, different IEA outlooks. They should not be read as a precise prediction for an individual operator. The IEA’s 2025 sensitivity cases illustrate the uncertainty further: for 2035, data-centre electricity demand ranges from around 700 TWh in the Headwinds case to more than 1,700 TWh in the Lift-Off case; the High Efficiency case is around 970 TWh. These are scenario outcomes shaped by assumptions about AI adoption, efficiency and energy-system constraints, not guaranteed endpoints.
Which parts of infrastructure drive the increase?
AI is an important source of additional computing demand, but not all data-centre electricity use is AI-related. In the IEA’s 2025 Base Case, the mix of workloads and infrastructure contributions to growth is uneven:
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| Category | Share of net data-centre electricity growth, 2024–2030 | How to interpret it |
|---|---|---|
| Accelerated servers | Almost half | Includes the faster-growing class of servers used for workloads such as AI; this is a share of net growth, not total consumption |
| Conventional servers | About one fifth | A separate contribution to net growth, not an estimate of all conventional-server electricity use |
| Cooling and other infrastructure | About one fifth | Facility systems also add to net growth as data-centre demand expands |
| Other IT equipment | About one tenth | A further contribution to net growth in the Base Case |
The categories explain why focusing only on the computing chips or servers can miss part of the efficiency opportunity. A server’s energy use is only one part of the infrastructure needed to deliver computing; cooling and other facility systems also matter. Conversely, a facility-wide efficiency gain does not erase the extra electricity required if the volume or intensity of computing rises substantially.
How can infrastructure keep up with growing AI demand while using energy more efficiently?
Use a portfolio approach rather than treating efficiency as a single equipment upgrade. The IEA’s High Efficiency case assumes stronger progress across software, hardware and infrastructure. In that 2025 modelled case, energy savings are more than 15% relative to the comparable outlook. This is a scenario result, not a guaranteed saving for a particular operator, project or year.
Match software and computing demand
Software efficiency belongs in the same assessment as hardware and facilities because the energy required depends partly on the computing workload being delivered. Assess the workload mix—such as AI or other accelerated computing versus conventional server workloads—and track energy alongside useful computing output. A change that reduces energy for one workload does not necessarily produce the same result for another.
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Assess servers and facility systems together
Compare server and infrastructure improvements within a common boundary. A server-focused measure may reduce energy in the IT equipment while leaving cooling and other facility demand unchanged; a facility improvement may reduce supporting energy without changing the workload itself. The IEA’s growth breakdown shows why both sides belong in planning, but it does not establish a universal best upgrade or a site-specific savings figure.
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Measure comparable workloads, not just headline capacity
IEA 4E’s Total Energy Model 4.0 uses computational-capacity and efficiency metrics, including standardised SERT measurements, and segments data-centre types and workloads. That kind of workload-aware measurement helps avoid comparing unlike facilities or treating a rise in computing capacity as an efficiency improvement by itself. For a project decision, define the workload and measurement boundary before comparing alternatives.
Why can a global efficiency gain fail to solve a local power problem?
A global share is not a map of local grid stress. In the IEA’s 2024 estimate, the United States accounted for 45% of data-centre electricity consumption, China 25% and Europe 15%. The IEA also notes that US capacity is concentrated in five regional clusters. A concentrated new load can make grid integration difficult even when data centres remain a relatively small share of electricity use worldwide.
The IEA’s 2026 update identifies slow grid connections and constrained supplies of equipment such as transformers and power electronics. Those constraints affect whether and when a project can connect; better energy efficiency alone cannot make unavailable grid capacity or equipment appear. Assess a proposed site against its actual interconnection conditions, local supply and equipment availability rather than assuming a global forecast applies uniformly.
AI loads can also swing rapidly. The IEA identifies load variability as a reliability consideration and notes that storage or flexible operations may support reliable supply and provide grid value. Whether either option is useful depends on the facility’s workload, operating requirements and local grid conditions; the global outlook does not establish a universal configuration.
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What should an operator evaluate before choosing an efficiency path?
Compare options against the conditions that determine their actual energy and grid effect. There is no single efficiency measure that can be selected from a global percentage alone.
- Workload: Identify the expected mix of AI or accelerated computing, conventional servers and other IT equipment.
- Efficiency boundary: State whether the comparison covers software, servers, cooling, other infrastructure or the whole facility.
- Comparable measurement: Use workload-aware capacity and efficiency measures; do not treat unlike workloads or facility types as direct comparisons.
- Reliability and flexibility: Account for rapid load swings and test whether storage or flexible operations can fit the operational needs.
- Site and grid: Check local concentration, interconnection timing, equipment availability and regional electricity supply.
This framework separates two questions that are often conflated: how much energy a facility needs to deliver a defined computing service, and whether the site can reliably obtain the power required as demand grows.
What do data-centre emissions figures include?
The IEA estimates that all data-centre electricity use produced around 180 million tonnes of indirect CO2 emissions in 2024, excluding backup generation. This is an estimate for data centres as a whole, with AI as one subset of their workloads; it is not an AI-only emissions figure. The emissions associated with a facility also depend on the electricity supply serving it, so the global total should not be applied directly to a particular site.
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