As the world rapidly embraces AI and 5G, the energy impact of these technologies is beginning to reveal its full weight. While AI and 5G are undeniably transformative, they also contribute to a significant rise in energy demands—a critical issue for telecom networks and data centers. Today, these areas account for roughly 1% of global electricity consumption. Though it may sound small, the absolute energy required is substantial. And with data traffic growing at a rate of 30% annually, the strain on fixed and mobile networks is only set to increase.

Rising Data and the Strain on Infrastructure

Telecom networks and hyperscalers are already experiencing significant pressure from expanding 5G networks and the increasing workload demands from enterprises. This shift is expected to intensify with the energy requirements for large language model (LLM) training and AI-driven applications like video, gaming, and enterprise solutions. Projections from GSMA Intelligence suggest that by 2030, cloud energy consumption could rise between 30% and 60%, pushing its global energy share to around 1.5% to 2.0%.

Scaling the AI Energy Challenge

The scale of AI’s energy impact is staggering. The incremental energy demand driven by AI through 2030 could be as much as the total annual consumption of Egypt, a populous and high-growth country. This comparison illustrates the urgency for solutions that mitigate the energy and environmental costs that accompany AI advancements.

How AI Can Drive Energy Efficiency in Telecoms

Fortunately, AI isn’t only adding to the energy burden; it’s also creating opportunities to enhance efficiency in the telecom sector. For instance, telecom operators are already deploying AI-driven technologies such as Radio Access Network (RAN) shutdowns and dynamic spectrum management to cut down on energy costs. Equipment vendors actively integrate AI to improve efficiency, helping operators manage their operational and capital expenses more effectively. This AI-enabled efficiency is expected to help balance telecoms’ rising energy demands over the next three years as operators continue investing in AI solutions.

The Dual Role of AI: ‘AI for Telco’ and ‘Telco for AI’

In the context of telecom, AI plays a dual role: not only does it serve the industry by improving network efficiency (referred to as "AI for telco"), but it also enables enterprises to leverage AI-powered solutions more effectively ("telco for AI"). For companies to fully benefit from AI in their operations, they’ll need enhanced network capacity. Telecoms will play a crucial role in scaling their infrastructure to support the growing data loads and computational demands that AI requires, pushing capacity and efficiency upgrades across the sector.

The Long-Term Outlook: Toward an AI-Native, Energy-Conscious Model

Looking toward the future, AI is set to influence telecom operations in three broad phases. Early adopters are already using AI to optimize various functions. At the same time, a more extensive portion of the industry is expected to follow suit, eventually moving towards an AI-native model by the late 2020s. Such a model will see AI and automation deployed across telecoms’ functions to offset additional energy consumption through downstream efficiencies.

Achieving energy neutrality will require rigorous benchmarking to ensure that AI’s benefits outweigh its energy costs. Operators will need to measure the efficiency gains from various AI applications, from LLM training to inference, to ensure AI’s overall energy footprint is managed effectively.

Final Thoughts

The relationship between AI, 5G, and energy consumption in the telecom sector is complex and evolving. While the energy requirements are significant, AI has the potential to offset its demands through efficiency gains and smarter infrastructure planning. Telecom operators must remain vigilant about tracking and balancing AI-driven energy usage to ensure a sustainable and energy-efficient future.

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