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AI is increasing infrastructure demand in two connected places: data centers need far more electricity, cooling and high-capacity equipment, while telecom networks need more uplink capacity, edge computing and software-controlled resources. The International Energy Agency projects global data-center electricity use to rise from 485 TWh in 2025 to about 950 TWh in 2030. At the same time, AI applications such as multimodal assistants, connected cameras and AI glasses can send more data upstream through mobile networks. The result is not one universal shortage, but intense local competition for grid connections, equipment, spectrum, sites and skilled operators.
What exactly is AI changing in infrastructure?
AI changes infrastructure demand by making computing more concentrated, more power-intensive and more geographically distributed. Training large models generally runs in specialized data centers with accelerated servers. Inference—the process of answering user requests—can run in those same facilities or move closer to users at network-edge locations. Telecom networks then connect people, sensors and devices to the computing resources.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThat creates two feedback loops. First, denser AI servers increase the electricity and thermal load of each data-center hall. Second, AI-native applications can alter mobile traffic patterns, including the balance between downloads and uploads. Infrastructure owners therefore have to plan power, cooling, connectivity, radio capacity and computing together rather than as separate projects.
How much electricity will AI data centers use?
Global data-center electricity consumption is projected to roughly double between 2025 and 2030, from 485 TWh to 950 TWh, according to the International Energy Agency’s 2026 outlook. For context, the IEA estimated 415 TWh in 2024, equal to about 1.5% of global electricity consumption that year. These are data-center totals, not a separately measured “AI-only” total; AI is a major driver of the growth through accelerated computing and larger workloads.
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| Measure | Figure | How to interpret it |
|---|---|---|
| Global data-center electricity, 2024 | 415 TWh | IEA estimate; about 1.5% of global electricity use |
| Global data-center electricity, 2025 | 485 TWh | IEA projection |
| Global data-center electricity, 2030 | 950 TWh | IEA projection; approximately double the 2025 level |
| U.S. data-center share in 2030 | 11.8% | Lawrence Berkeley National Laboratory central estimate; scenarios range from 9.5% to 15.3% of total U.S. electricity use |
The U.S. percentage is a national average. A facility’s local effect can be much larger because data centers cluster in particular utility territories and transmission zones. The IEA says the United States and China together account for nearly 80% of projected global data-center electricity growth through 2030.
Why are AI data centers harder to power and cool?
AI servers put more demand into each rack and each electrical room than conventional enterprise computing. The IEA reports that AI-server power density increased 11-fold between 2020 and 2025, with another fourfold increase expected by 2027. That trend changes the physical design of facilities even when the building’s total floor area stays the same.
Power delivery must handle higher density
Higher rack loads require more capacity from medium- and low-voltage distribution, switchgear, transformers, uninterruptible power supplies, batteries and backup generation. A project can have enough land and server demand yet still wait for the utility equipment and grid connection needed to energize it. Delivered electricity cost also depends on tariffs, connection charges, contracted generation and any storage used to manage peaks.
Cooling becomes a design constraint
Servers account for roughly 60% of electricity use in modern data centers, while storage uses about 5% and networking up to 5%, according to the IEA. Cooling ranges from about 7% in efficient hyperscale facilities to more than 30% in less-efficient enterprise sites; the share varies by facility type and operating conditions. As rack density rises, air cooling can become less effective for some deployments, increasing interest in liquid or hybrid systems. The evidence does not establish one cooling method as best for every site: water availability, retrofit difficulty, maintenance, climate and operating efficiency all matter.
Grid connection and permitting can delay capacity
Data centers compete for transmission and distribution capacity with factories, housing and other large loads. Long interconnection queues, slow permitting, land constraints and water approvals can bind before global electricity supply is exhausted. A site with a favorable wholesale-power price is not necessarily buildable if the local substation, transmission path or permitting process cannot support the requested load.
Resilience must include rapid load changes
AI workloads can create fast changes in power demand. The IEA identifies batteries, other storage and flexible operation as possible ways for data centers to interact with the grid. Flexibility can include shifting suitable training or batch workloads across time or sites, while keeping latency-sensitive inference available continuously. The right balance depends on recovery objectives, workload tolerance and the reliability requirements of the users being served.
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Equipment supply chains become strategic
Transformers, switchgear, advanced chips and high-bandwidth memory can all become bottlenecks. The IEA reports tightening constraints in chips and electrical equipment. These constraints can lengthen schedules or raise delivered project costs even when financing and customer demand are already secured.
Will AI create a data-center power shortage?
AI can create serious local power shortages or connection delays without exhausting global electricity supply. The most important distinction is between total generation and deliverable capacity at a specific site, at a specific time and under a specific utility’s rules.
- Global supply: The IEA’s projection is a rapid increase in total electricity consumption, not proof that the world will run out of power.
- Regional grids: Clusters of large facilities can consume available transmission and distribution capacity, creating queues or requiring expensive upgrades.
- Permits and water: A project may be electrically feasible but delayed by land-use, environmental or water approvals.
- Equipment: Transformer, switchgear, chip and memory availability can determine the schedule independently of generation.
- Price exposure: Local tariffs and congestion can raise the delivered cost of power even when national averages look manageable.
For households and businesses, the practical question is therefore not only “How much power will AI use?” but also “Where will the load be connected, who pays for upgrades, and how are those costs reflected in rates or taxes?” Those answers vary by country, state, utility territory and project agreement.
How is AI changing telecom networks?
AI is pushing telecom infrastructure toward more balanced traffic, more spectrum planning and tighter coordination between network and computing resources. Ericsson’s June 2025 Mobility Report says only applications with both high adoption and high data-rate requirements will materially change global mobile traffic. Where those conditions occur, operators may need additional mid-band and centimetric-band spectrum, radio upgrades and differentiated connectivity.
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Uplink capacity matters more
Mobile networks historically carried more downstream video and web content than upstream data. AI-native use cases can reverse some of that asymmetry. Multimodal assistants, AI or augmented-reality glasses, connected cameras, autonomous systems and cloud gaming may send images, video, sensor streams or user context to remote models. Ericsson says projected net-new traffic growth will require careful planning and may require additional spectrum to accommodate increasing uplink requirements.
There is no single reliable multiplier for future uplink traffic. The outcome depends on adoption, how much processing occurs on the device, the resolution and frequency of sensor data, compression, privacy settings and whether applications respond continuously or only when prompted.
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Radio and spectrum assets need different planning
Operators may need to examine uplink performance at cell edges, not just peak downlink speed near a base station. Additional mid- and centimetric-band spectrum can provide capacity, but propagation, site density, device support and backhaul determine how much usable capacity reaches customers. AI services may also require differentiated connectivity so that latency-sensitive or high-reliability traffic receives predictable treatment.
Networks become more programmable
The International Telecommunication Union’s 2025 telecom GenAI work identifies network knowledge, data collection, processing, storage, resource coordination and agent collaboration as requirements for AI-enabled telecom operations. Potential uses include planning, deployment, management and orchestration. In practice, this means operators need software and APIs that can expose network conditions, allocate resources and coordinate applications with compute locations.
Should AI inference happen in the cloud or at the edge?
Neither location is universally superior. Centralized cloud facilities generally offer scale and access to large accelerator clusters; edge locations can reduce latency, limit movement of sensitive data and keep services closer to devices. The choice should follow the application’s response-time, privacy, bandwidth and reliability requirements.
| Decision factor | Centralized data center | Network edge |
|---|---|---|
| Compute scale | Best suited to large shared accelerator pools and model serving at high volume | More limited local capacity; useful for targeted or time-sensitive workloads |
| Latency | Can be higher when users or sensors are far from the facility | Shorter physical path can support faster responses |
| Data movement | More raw data may traverse access and transport networks | Local processing can reduce some backhaul and exposure of raw data |
| Power and cooling | Benefits from purpose-built, efficient facilities but concentrates load | Distributes load across many sites, each with its own power and cooling limits |
| Operations | Fewer sites to manage, with larger individual failure domains | More locations, coordination points and maintenance requirements |
GSMA describes compute moving beyond centralized data centers toward network-edge locations as operators pursue cloud and network integration, edge computing, AI, open radio access networks and network APIs. A hybrid design is often the practical result: edge sites handle latency-sensitive preprocessing or inference, while centralized facilities train models and serve less time-critical requests.
How should a company compare infrastructure strategies?
A sound comparison uses the same criteria for every candidate site or architecture. The following framework applies to a new data center, an edge deployment or a telecom upgrade.
| Criterion | Questions to answer |
|---|---|
| Power availability and delivered cost | How many megawatts are available, when can they be connected, what tariffs and upgrade charges apply, and can contracted generation or storage reduce exposure? |
| Density and cooling | What rack power is required, can the building support liquid or hybrid cooling, how much water is available, and is a retrofit practical? |
| Latency and locality | How far are users and data sources from the compute site, what response time is required, and are privacy rules location-sensitive? |
| Traffic direction | What is the expected uplink-to-downlink mix, which spectrum is available, and how does performance change at the cell edge? |
| Resilience | What backup power, network redundancy, geographic diversity and recovery objectives are required? |
| Deployment and regulation | What permits, interconnection queues, land, water approvals and community decisions could control the schedule? |
| Workload flexibility | Can training or batch inference move across sites or time, or must every request run locally and continuously? |
Using this matrix prevents a common mistake: selecting the cheapest server location while ignoring the cost and timing of power delivery, cooling, transport links and network upgrades.
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What does AI infrastructure growth mean for electricity customers?
The effect on a household or small business depends on local regulation and utility planning rather than on the global TWh figure alone. A large new load may lead to substation or transmission investment, revised tariffs, special connection agreements or changes in a utility’s resource plan. Those outcomes are not automatic, and the available data does not establish a universal bill increase.
Consumers who want to follow the issue can examine three public signals in their area:
- Utility filings that describe large-load interconnection requests, rate cases or transmission upgrades.
- Planning and permitting documents that identify proposed data-center campuses, water use and backup generation.
- Telecom filings and coverage plans that show spectrum, small-cell, backhaul or edge-compute investment.
For investors or business owners, the relevant exposure is similarly local: power availability, equipment lead times, cooling requirements, network quality and the ability to shift workloads can matter more than a headline global growth rate.
What remains uncertain about AI infrastructure demand?
Forecasts are scenarios, not fixed commitments. Data-center electricity estimates can change with chip efficiency, model size, workload mix, financing, permitting, grid access and the speed at which new facilities are built. The IEA’s figures should therefore be read with their year and geography attached.
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Telecom uncertainty is even more use-case dependent. Ericsson conditions major traffic effects on both adoption and data-rate requirements. GSMA identifies several forces that can increase or reduce traffic, including local processing, compression and the location of inference. No authoritative source establishes one future percentage increase for global mobile traffic or uplink demand.
The most durable conclusion is directional: AI raises the value of high-capacity power systems, advanced thermal management, flexible workloads, stronger uplinks, edge compute and programmable network coordination. The exact mix will differ by site, operator and application.
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What should planners do first?
Start with a workload and location map, then size infrastructure around the actual constraints rather than an assumed average.
- Classify workloads: Separate model training, batch inference and real-time inference; record latency, privacy and availability requirements.
- Estimate traffic direction: Measure expected device uploads and downloads, including sensor frequency, resolution and compression.
- Secure power evidence: Obtain utility capacity, interconnection timing, tariff terms and upgrade responsibilities in writing.
- Model thermal options: Test air, liquid and hybrid designs against rack density, water availability, climate and retrofit limits.
- Build flexibility: Identify workloads that can move across sites or time and pair them with storage or flexible operating plans where appropriate.
- Stress-test delays: Include permitting, spectrum, equipment lead times, chip availability and network redundancy in the schedule.
This sequence links AI demand to the physical and financial decisions that determine whether capacity arrives on time.
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Frequently Asked Questions
How are data-center electricity figures measured?
They are reported in terawatt-hours, a measure of energy consumed over a year, rather than instantaneous megawatts of capacity. A national or global TWh estimate combines many facilities and does not show the connection limit, peak demand or local grid congestion at an individual site.
Can renewable generation by itself solve AI data-center constraints?
Not necessarily. Generation is only one part of delivery; transmission, transformers, switchgear, cooling, permitting and water can still limit a project. A renewable contract may reduce the energy’s emissions profile without removing a local interconnection queue or the need for backup power.
Why might two AI applications create very different telecom loads?
One application may process data on the device, upload only occasional prompts and receive small responses. Another may continuously stream high-resolution video or sensor data to a remote model, creating much heavier uplink demand; adoption and data-rate requirements therefore matter together.
What is the difference between edge computing and a small data center?
Edge computing describes placing processing near users or data sources, which can occur in telecom facilities, distributed sites or other local locations. A small data center is a physical facility description; it may or may not be positioned close enough to deliver the latency, locality or network coordination benefits associated with an edge architecture.
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