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AI is driving a major build-out of physical infrastructure in two connected systems. In data centers, accelerated servers are raising electricity use, rack power density and cooling requirements, concentrating pressure on grids, equipment supply chains and local permitting. In telecom networks, multimodal AI, smart glasses, cameras and autonomous systems can make traffic more uplink-heavy, while latency-sensitive inference pushes computing closer to users at the network edge.
The scale is substantial but not a single fixed number. The International Energy Agency (IEA) projects global data-center electricity consumption to rise from about 485 TWh in 2025 to 950 TWh in 2030. Outcomes for telecom depend on how quickly high-data-rate AI applications are adopted. The practical response is to plan power, cooling, spectrum, uplink capacity, edge locations and programmable network controls together rather than treating AI as only a software upgrade.
Why is AI increasing infrastructure demand?
Generative AI workloads use specialized accelerated servers rather than ordinary general-purpose machines. Training runs can operate thousands of processors in parallel, and inference serves model responses continuously to users, applications and devices. Each accelerated server consumes more power than a conventional server, and many are installed in tightly packed racks. The result is a physical infrastructure problem: more electricity must reach each hall, more heat must be removed, and more network capacity must connect distributed users and data sources.
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The same pattern appears in telecom. AI applications can collect video, audio, sensor readings and user context before sending that information to a model. A conventional mobile workload is often dominated by downloading video or web content; an AI glasses session, connected camera or autonomous machine can send a much larger share upstream. If inference is placed near the user to meet response-time or privacy requirements, operators also need computing, storage and orchestration in many locations rather than in a few centralized cloud facilities.
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These are linked investments. Central data centers provide large-scale training and model serving. Metro and network-edge sites can handle time-sensitive inference. Radio, transport, core-network and application systems must coordinate where data is collected, processed and stored.
How much electricity will AI data centers use?
Global data-center electricity consumption was estimated at 415 TWh, about 1.5% of worldwide electricity use, in 2024 in the IEA’s 2025 analysis. The IEA’s 2026 outlook projects consumption to roughly double from 485 TWh in 2025 to 950 TWh in 2030. These are data-center totals, not a separately measured “AI-only” load; AI is a major reason the trajectory is rising.
| Measure | Figure | Qualification |
|---|---|---|
| Global data-center electricity use | 415 TWh in 2024 | IEA estimate; approximately 1.5% of global electricity consumption |
| Projected global data-center use | 485 TWh in 2025 to 950 TWh in 2030 | IEA 2026 projection; includes all data-center workloads |
| U.S. data-center share | 11.8% of national electricity use in 2030 | Lawrence Berkeley National Laboratory estimate; scenarios range from 9.5% to 15.3% |
| AI-server power density | 11-fold increase from 2020 to 2025 | IEA measure; a further fourfold increase is expected by 2027 |
The U.S. estimate illustrates why national averages can be misleading. A country can have adequate generation overall while a particular metropolitan area lacks transmission capacity, substations or a permitted site for a new campus. The IEA expects the United States and China together to account for nearly 80% of global data-center electricity growth through 2030, so local effects in those markets can be especially pronounced.
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Forecasts remain scenarios, not guarantees. Efficiency improvements, model design, chip availability, financing, permitting and grid access can all change the amount and timing of construction. A project developer should attach every load estimate to a year, location, workload mix and interconnection assumption.
What equipment inside a data center is affected?
AI changes the balance among the systems that make a facility operate. The IEA describes a data center as a combination of servers, storage, networking, cooling and power equipment. In modern facilities, servers average roughly 60% of electricity demand, storage about 5% and networking up to 5%. Cooling can represent about 7% in efficient hyperscale sites and more than 30% in less-efficient enterprise facilities; the shares vary by facility type.
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Power delivery and backup
Higher rack densities require more capacity in switchgear, transformers, busways, uninterruptible power supplies (UPS), batteries and backup generators. A hall designed for conventional racks may have enough floor space but insufficient electrical distribution, so retrofits can require new conductors, protection equipment and cooling loops. Designers must also account for the power quality and rapid load changes associated with large accelerator clusters.
Cooling and heat rejection
More watts in the same rack produce more heat per square metre. Air cooling can remain suitable for some deployments, but its limits become more important as density rises. Liquid or hybrid systems can remove heat more directly, yet they introduce plumbing, leak detection, water-management and maintenance requirements. The available evidence supports rising thermal demand; it does not establish one universally best cooling technology. Selection should follow rack power, climate, water availability, retrofit constraints and operating expertise.
Grid connection and on-site flexibility
Large campuses compete for transmission and distribution capacity. Long interconnection queues, slow permitting and community concerns about land, water and noise can delay a project after the servers have been ordered. The IEA identifies storage and flexible operation as potential ways for data centers to interact with the grid. Workloads that can be scheduled can sometimes move to another site or a different time, reducing the need to size every connection for the absolute peak.
Supply-chain dependencies
Accelerator chips, high-bandwidth memory and electrical equipment can all become bottlenecks. A utility connection that is ready on paper does not guarantee that transformers, switchgear or servers will arrive on the same schedule. Procurement plans should therefore track equipment lead times alongside permits and interconnection milestones.
Will AI create a data-center power shortage?
AI can create shortages at specific locations even when global generation is sufficient. The binding constraint is often the deliverable megawatts at a permitted site, not the world’s total electricity supply.
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- Connection queues: Transmission and distribution upgrades may take longer than the data-center build.
- Permitting: Local approvals for land, substations, generators, cooling systems and water use can arrive on different schedules.
- Peak demand: A cluster of dense halls can create a large coincident load that a local feeder was never designed to serve.
- Equipment availability: Transformers, switchgear, batteries and advanced chips may have long lead times.
- Price exposure: Scarce capacity can raise local electricity prices or require contracted generation and storage.
For a site decision, compare delivered power cost and timing rather than quoting only a national electricity forecast. Include transmission upgrades, tariff structure, backup-fuel rules, water constraints, geographic diversity and the ability to shift flexible workloads.
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AI will not automatically multiply mobile traffic by a fixed factor. Ericsson’s June 2025 Mobility Report says material global effects occur when AI applications achieve both high adoption and high data-rate requirements. Under those conditions, projected net-new traffic growth requires careful planning and potentially additional mid- and centimetric-band spectrum, especially to accommodate increasing uplink requirements.
Why uplink becomes more important
Multimodal assistants, AI or augmented-reality glasses, connected cameras, autonomous systems and cloud gaming can send sensor feeds or user-generated content to a model. Networks historically optimized for downlink-heavy video and web use may therefore need a more balanced capacity plan. Engineers should model cell-edge uplink performance, device transmit power, scheduling overhead, backhaul and the proportion of traffic that is pre-processed locally.
Spectrum and radio changes
Additional mid-band and centimetric-band spectrum can provide capacity, but higher-frequency coverage is more sensitive to distance, blockage and indoor penetration. Operators may combine new spectrum with denser sites, improved antennas, differentiated connectivity and traffic policies for demanding AI applications. The correct mix depends on geography, device capabilities and the application’s latency and data-rate target.
Transport and core-network requirements
Uplink growth must be carried beyond the radio access network. Fiber backhaul, mobile transport, packet core capacity and data-center interconnects need sufficient headroom, while routing systems must direct requests to an inference location that can meet the response-time objective. Monitoring should distinguish model requests, sensor uploads and ordinary consumer traffic so that capacity investments follow the real bottleneck.
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Should AI inference happen in the cloud or at the edge?
Neither location is universally superior. Centralized cloud sites offer scale and efficient accelerator utilization; edge sites reduce distance to users and can keep sensitive data local. The right placement depends on latency, privacy, data volume, resilience, power availability and how much of the workload can be moved.
| Consideration | Centralized data center | Network-edge location |
|---|---|---|
| Best fit | Large training jobs, batch inference and workloads that tolerate network delay | Real-time control, interactive applications and processing close to sensors or users |
| Capacity economics | High utilization and shared accelerators are easier to achieve | Smaller pools may be less efficient but avoid some transport demand |
| Latency and locality | Longer path for distant users; centralized data handling | Shorter path and potential local-data processing |
| Resilience | Fewer, highly engineered sites require geographic redundancy | Many sites require consistent power, security, maintenance and failover |
| Network impact | More traffic crosses transport links to reach the model | Can reduce backhaul for locally processed data, but adds edge interconnects |
GSMA describes compute moving beyond centralized facilities toward network-edge locations, while the International Telecommunication Union (ITU-T) identifies coordination of application, computing and network resources as essential for telecom generative-AI systems. A practical architecture can use a central model for training and a tiered inference design: local processing for urgent or private data, regional sites for interactive sessions, and central capacity for heavy or delay-tolerant jobs.
What does an AI-native telecom network need?
AI-native operation requires more than installing a model in the network operations center. ITU-T’s telecom generative-AI work identifies requirements for network knowledge, data collection, processing, storage, resource coordination and agent collaboration. Those capabilities support planning, deployment, management and orchestration.
Common building blocks
- Observability: Telemetry from radios, transport, core functions, edge servers and applications in a form that models can interpret.
- Policy controls: Guardrails that limit automated changes, define approval thresholds and preserve rollback paths.
- Resource orchestration: A controller that can place compute, bandwidth and storage together instead of optimizing each domain separately.
- Network APIs: Programmable interfaces that expose connectivity characteristics to authorized applications.
- Security and privacy: Identity, isolation, data-retention controls and protection against model or agent misuse.
GSMA reports that operators are pursuing cloud and network integration, edge computing, AI, open radio access networks and network APIs while prioritizing revenue and customer experience. The business case is strongest when automation improves a measurable outcome such as energy use, fault recovery, installation time or application quality.
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Use a common scorecard for every candidate data-center, edge or network design. A low-cost site can fail if it cannot obtain power on schedule; a fast edge deployment can fail if utilization is too low to support its operating cost.
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| Decision area | Questions to answer |
|---|---|
| Power | How many megawatts are available, when will they be delivered, what is the tariff, and can storage or contracted generation cover peaks? |
| Density and cooling | What rack power is required, can the building support the thermal design, and are water use and retrofit risks acceptable? |
| Latency and locality | Where are users and data sources, what response time is required, and must data remain in a jurisdiction or facility? |
| Traffic direction | What uplink/downlink ratio is expected, 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 | How long will land, permits, interconnection, fiber and community approvals take? |
| Workload flexibility | Can training, batch inference or noncritical processing move across sites or time periods? |
What can companies do now?
- Profile the workload. Separate training, batch inference, interactive inference and sensor preprocessing. Record accelerator type, rack power, data volume, latency target and expected growth.
- Map physical constraints. For each location, document available grid capacity, interconnection milestones, transformer and switchgear lead times, cooling options, water limits, fiber routes and permitting requirements.
- Model telecom directionality. Estimate uplink and downlink by application, including concurrent devices, sensor frame rates and cell-edge conditions. Do not substitute a generic “AI traffic multiplier” for an application-level model.
- Choose a placement tier. Keep delay-tolerant or compute-intensive work centralized; place latency-sensitive or privacy-sensitive functions regionally or at the edge when the added operational complexity is justified.
- Design for flexibility. Add workload scheduling, storage and geographic failover so noncritical jobs can move when a site, circuit or power block is constrained.
- Automate cautiously. Build telemetry and APIs first, then introduce AI-assisted optimization with human approval, audit logs and tested rollback procedures.
- Review assumptions quarterly. Recheck adoption, model size, chip availability, electricity prices, spectrum plans and permitting dates because any of them can change the investment case.
What remains uncertain about AI infrastructure demand?
Data-center forecasts can shift as model efficiency improves, workloads change, financing conditions move and grids approve or delay connections. A more efficient model may reduce energy per response while a larger user base increases total responses. Similarly, edge processing can reduce backhaul for one application but add thousands of small sites for another.
Telecom uncertainty is especially high. Ericsson conditions its conclusions on adoption and data-rate requirements, and GSMA identifies several channels that can either increase or reduce traffic depending on the use case. Therefore, capacity plans should present ranges and trigger points rather than one promised traffic number. State the geography, year, adoption assumption and workload characteristics whenever presenting a forecast.
Frequently Asked Questions
What is the biggest physical bottleneck for a new AI data center?
It is often the local power connection rather than the global supply of electricity. Transmission upgrades, transformers, permits and cooling infrastructure can determine when a site can actually operate, so developers should validate those dependencies before ordering accelerator servers.
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No. Cooling choice depends on rack density, climate, building design, water availability and retrofit constraints. Higher-density accelerator racks make liquid or hybrid approaches more attractive in some facilities, but the available evidence does not support one technology for every deployment.
Why are AI applications expected to increase mobile uplink traffic?
Many AI-native devices send cameras, microphones, sensor readings or user-created content to a model instead of only downloading finished content. The effect is significant only when those applications reach high adoption and require high data rates; there is no universal traffic multiplier.
When does edge inference make more sense than centralized inference?
Edge placement is most useful when response time, privacy or local operation matters enough to justify distributing compute and maintaining many sites. Centralized capacity is generally better for large training jobs, batch work and requests that can tolerate network delay.
How can a telecom operator introduce AI automation without increasing outage risk?
Start with network-wide telemetry, clear policy limits and human approval for changes. Test recommendations in a controlled environment, retain audit logs and provide a reliable rollback path before allowing automated orchestration across production systems.
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