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AI is increasing demand for electricity, cooling, and high-density equipment in data centers while changing what telecom networks must carry and where computing happens. The pressure is not uniform: data-center power demand is concentrated in particular locations, and the effect of AI on mobile traffic depends on which applications people adopt and how much data they send. The result is a linked infrastructure challenge: securing power and cooling for computing, and building networks that can support more uplink traffic, low latency, and computing closer to users.
What is AI changing in data centers and telecom networks?
AI’s infrastructure effects show up in two connected systems. Data centers need more electricity and thermal management as AI uses accelerated servers and denser racks. Telecom networks, meanwhile, may need to carry more data from users and connected devices to computing resources, and may host some of those resources closer to the network edge.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Those are related but not interchangeable demands. A data center can be constrained by power delivery or cooling even when there is enough electricity globally. A mobile network can face an uplink or latency challenge without every AI application producing a large increase in total traffic. Planning therefore needs to account for where workloads run, where users and data are located, and whether a given application mostly receives information or sends it.
How much electricity will AI data centers use?
The best available figures describe electricity use by data centers as a whole, not an AI-only total. The International Energy Agency (IEA) estimated that data centers used 415 terawatt-hours (TWh) in 2024, about 1.5% of global electricity consumption. In its 2026 projection, the IEA estimated global data-center electricity use at 485 TWh in 2025 and projected roughly 950 TWh in 2030—about double the 2025 estimate.
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| Measure | Figure | Source and qualification |
|---|---|---|
| Global data-center electricity use in 2024 | 415 TWh; about 1.5% of global electricity consumption | IEA, 2025 estimate |
| Global data-center electricity use in 2025 | 485 TWh | IEA, 2026 projection |
| Global data-center electricity use in 2030 | 950 TWh | IEA, 2026 projection |
| U.S. data centers’ share of national electricity use in 2030 | 11.8%; scenarios range from 9.5% to 15.3% | Lawrence Berkeley National Laboratory (LBNL), 2026 estimate |
These estimates should not be read as a precise forecast of electricity used by AI alone. Data centers serve multiple kinds of computing, and projections can change with workload mix, efficiency improvements, chip supply, financing, permitting, and access to grid connections. The IEA’s figures nevertheless show why AI is an important part of the infrastructure question: AI training and inference use accelerated servers, and the IEA reports that AI-server power density rose 11-fold from 2020 to 2025, with a further fourfold increase expected by 2027.
Why national totals can hide local bottlenecks
Data centers tend to cluster, so a global or national supply figure does not tell a project developer whether power is available at a particular site. Transmission and distribution capacity, connection queues, permitting, land, water, and local electricity prices can become constraints before global electricity supply is exhausted. The IEA identifies the United States and China as responsible for nearly 80% of projected global data-center electricity growth to 2030, underscoring how concentrated the demand can be geographically.
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Why does AI make each data center more demanding?
A data center is more than its servers. It combines computing, storage, networking, cooling, and power equipment, and the balance among those systems varies by facility. AI changes the mix by increasing demand for accelerated servers and by concentrating more computing power in individual racks.
| Facility component | Approximate share of electricity use | Qualification |
|---|---|---|
| Servers | Roughly 60% | Average for modern data centers described by the IEA |
| Storage | About 5% | Share varies by facility |
| Networking | Up to 5% | Share varies by facility |
| Cooling | About 7% to more than 30% | About 7% in efficient hyperscale facilities; more than 30% in less-efficient enterprise facilities |
Because these are approximate shares and facility types differ, they are not a universal design recipe. They do make clear that adding computing capacity affects more than the server line item: the power system must deliver electricity, and cooling must remove the resulting heat.
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Power delivery and thermal design
Higher rack and hall loads can increase the need for switchgear, transformers, uninterruptible power supply systems, backup generation, batteries, and on-site or contracted generation. These components must be planned as part of the facility’s ability to serve the workload, not as afterthoughts once servers are selected.
More power concentrated in a rack also creates a harder thermal-management problem. Air cooling may be less effective for some high-density deployments, increasing interest in liquid or hybrid cooling. The evidence supports rising thermal demand, but does not establish one cooling technology as best for every facility. The choice depends on the density being deployed and the site’s design and operating conditions.
Grid access, resilience, and supply chains
Interconnection queues and slow permitting can delay projects even when a company has funding and equipment. The IEA reports growing policy activity around data-center integration, alongside long connection queues and permitting delays. Storage and flexible operation are possible ways for data centers to interact with the grid, particularly when workloads can be scheduled rather than served immediately.
Equipment availability is another constraint. The IEA identifies tightening constraints in chips and electrical equipment; advanced chips and high-bandwidth memory can also affect how quickly capacity can be deployed. A plan that accounts only for the building and headline electricity demand may therefore miss dependencies that determine when a site can actually operate.
Will AI create a data-center power shortage?
AI-driven growth can create serious power constraints in particular places, but the available figures do not establish that the world will run out of electricity for data centers. The practical risk is a mismatch between fast-growing, concentrated loads and the grid, equipment, permits, and generation that can serve them at a given location.
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A project may be delayed or become more expensive if transmission capacity is limited, an interconnection queue is long, electrical equipment is scarce, or local permitting and water requirements are difficult to meet. Those limits can arise while national or global electricity supply remains available elsewhere. Likewise, rapid growth in data-center forecasts does not by itself show that every proposed facility will be built or that the highest-demand scenario will occur.
For a community, utility, or operator evaluating a proposed site, the useful checks are concrete:
- Is there enough deliverable grid capacity at the site, and when can a connection be made?
- Which transmission, distribution, transformer, and backup-power upgrades are needed?
- How do local tariffs, generation contracts, and storage affect delivered cost and reliability?
- Can the facility meet its cooling and water requirements under local conditions?
- Can flexible workloads be shifted across time or sites without violating response-time needs?
- Are the chips and electrical systems needed for the planned build available on the intended schedule?
How will AI change telecom networks and mobile traffic?
AI applications can make mobile networks more uplink-intensive, because some send sensor readings, images, or other user-generated information toward cloud or edge computing resources. Ericsson’s June 2025 Mobility Report says that only AI applications with both high adoption and high data-rate requirements are likely to materially change global mobile traffic. It does not set one settled multiplier for future traffic growth.
Why the uplink matters
Many familiar mobile uses—such as consuming video or browsing pages—are largely downlink-oriented: the network sends more information to a user than the user sends back. Some AI-native uses can shift that balance. Multimodal assistants, AI or augmented-reality glasses, connected cameras, autonomous systems, and cloud gaming may send more sensor or user-generated data upstream for processing.
The consequence is not simply “more mobile data.” Operators need to understand the direction, timing, location, and latency needs of traffic. A network designed around downlink-heavy use may need more balanced uplink resources if high-adoption, data-intensive applications become common.
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Spectrum and network capabilities
Ericsson says projected net-new traffic growth will require careful network planning and may require additional mid- and centimetric-band spectrum to accommodate increasing uplink requirements. The need depends on the adoption and data-rate demands of applications, so it is not a guarantee that every network or market will need the same spectrum changes.
AI also increases the value of differentiated connectivity: the ability to plan network resources around different application requirements rather than treating all traffic as identical. How useful that is depends on real service needs, network capabilities, and the operator’s spectrum and deployment position.
More programmable networks
Telecom infrastructure is not just radio equipment. The International Telecommunication Union’s 2025 TR.GenAI-Telecom work identifies network knowledge, data collection, processing, storage, resource coordination, and agent collaboration as requirements for telecom GenAI systems. Its use cases include network planning, deployment, management, and orchestration. In practical terms, network operators need systems that can coordinate application, computing, and network resources rather than optimizing each in isolation.
The business motivation is also broader than adding capacity. GSMA reports that operators are prioritizing revenue and customer experience while pursuing cloud and network integration, edge computing, AI, open RAN, and network APIs. These directions are ways of adapting operator services and infrastructure; they do not, on their own, guarantee a particular commercial return.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should AI inference run in the cloud or at the network edge?
There is no universal answer: centralized data centers and edge locations serve different needs. Centralized infrastructure can pool computing resources, while edge locations can put compute closer to users or data sources when latency, privacy, or locality matter. GSMA describes compute moving beyond centralized data centers toward network-edge locations.
| Consideration | Centralized data center | Network-edge location |
|---|---|---|
| Compute location | Consolidated in a central facility | Distributed closer to users or data sources |
| Potential fit | Workloads that can use centralized computing resources | Workloads where latency, privacy, or locality can matter |
| Planning emphasis | Facility power, cooling, grid access, and workload flexibility | Coordination of application, compute, and network resources |
This is a placement decision, not a contest in which one location replaces the other. A service can use central resources for some tasks and edge resources for others. The right arrangement depends on response-time targets, where data originates, privacy requirements, and available compute, power, and network capacity. The supplied evidence supports the direction of movement toward edge compute, not a claim that all inference will move there.
How should infrastructure planners compare strategies?
Whether the decision is where to build a data center, how to upgrade a mobile network, or where to place inference, the same planning discipline applies: compare the workload’s requirements with the infrastructure that can serve it. The following criteria help expose trade-offs before a commitment is made.
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- Power availability and delivered cost: Check grid capacity, connection timing, tariffs, contracted generation, and storage—not just the nominal electricity supply in a region.
- Density and cooling: Match expected rack power to the thermal design, water use, operational efficiency, and difficulty of retrofitting a facility.
- Latency and locality: Determine how close compute must be to users and data sources, and account for privacy and response-time requirements.
- Network directionality: Estimate the expected uplink-to-downlink mix, spectrum position, and cell-edge performance for the intended applications.
- Resilience: Evaluate backup power, network redundancy, geographic diversity, and recovery objectives for the service.
- Deployment speed and regulation: Include permitting, interconnection queues, land, water, and community acceptance in the schedule and site decision.
- Workload flexibility: Separate training, batch inference, and real-time inference. Identify which work can move across sites or time and which must be close to users or immediately available.
These checks can change the apparent best option. A central facility may have an attractive computing design but face a long grid connection queue. An edge deployment may better fit a latency-sensitive use case but require coordination among application, compute, and network resources. The decision should follow the workload and site constraints, not a blanket assumption that every AI task needs the largest possible facility or the nearest possible server.
What remains uncertain about AI infrastructure demand?
Forecasts are sensitive to factors that can shift quickly: efficiency gains, workload mix, chip supply, financing, permitting, and grid access. For mobile networks, the uncertainty is especially pronounced because there is no single authoritative traffic-growth multiplier for AI. Ericsson conditions its assessment on adoption and data-rate requirements, and GSMA describes several channels that can affect traffic depending on how applications are used.
That uncertainty is a reason to plan for scenarios rather than treat one forecast as a guarantee. A useful forecast identifies the geography, year, and scope of its figures; distinguishes total data-center electricity use from AI-only use; and makes clear whether a number is an estimate, projection, or scenario. For telecom, it should distinguish overall traffic from uplink demand and explain which application assumptions drive the result.
Frequently Asked Questions
Does every AI feature on a phone send data to a data center?
No. The infrastructure impact depends on where a feature performs its computation and what information it transmits. A device can handle some work locally, while other services rely on cloud or edge resources; the article’s network implications apply to applications that send data over telecom networks.
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Why do AI training and inference matter differently for infrastructure planning?
They are distinct workload types, and planning should not assume they have identical timing or placement needs. Training and batch inference may have more opportunity to move across sites or time than real-time inference, which can have tighter response-time requirements.
Can a data center use batteries to avoid grid constraints?
Storage is one potential way for a data center to interact with the grid, and flexible operation can help when workloads are movable. Storage does not by itself establish that a site has the grid connection, generation, or permitting needed to serve its full demand.
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