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AI’s Energy Appetite: Can Data Centers and the Power Sector Keep Up?
AI’s Energy Appetite: Can Data Centers and the Power Sector Keep Up?

July 1, 2025

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Artificial Intelligence is no longer a futuristic concept—it’s a present-day powerhouse driving innovation across industries. But behind every AI breakthrough lies a less glamorous reality: a massive and growing demand for energy. As AI models become more complex and data-intensive, the infrastructure supporting them—particularly data centers—faces mounting pressure to deliver more power, more efficiently, and more sustainably.

The Power Behind Intelligence

AI workloads, especially those involving large language models and real-time analytics, require immense computational resources.

According to Berkeley Lab, U.S. data center annual energy use remained stable between 2014–2016 at about 60 TWh. U.S. data center energy use has continued to grow at an increasing rate, reaching 176 TWh by 2023, representing 4.4% of total U.S. electricity consumption. This annual energy use range would translate to a total energy demand for data centers between 325 to 580 TWh in 2028. This annual energy use also represents 6.7% to 12.0% of total U.S. electricity consumption forecasted for 2028.

Gridlock: Infrastructure Struggles to Keep Up

Access to power has become a critical factor in driving new data center builds.

In major U.S. data center hubs like Northern Virginia, Santa Clara, California, and Phoenix, the time required to secure new power connections has been steadily increasing. These delays are often due to overloaded grids, permitting bottlenecks, and long lead times for electrical equipment. For developers and operators, this means longer timelines and higher costs—both of which can stall AI-driven innovation.

The challenge isn’t limited to the United States. Internationally, cities such as Amsterdam, Dublin, and Singapore have taken more drastic measures. In recent years, these locations have implemented moratoriums on new data center construction, citing insufficient power infrastructure as the primary reason. These restrictions reflect a growing tension between digital growth and energy availability, especially in regions where the grid is already under strain or where sustainability goals limit expansion.

According to JP Morgan, in Europe, data centers will add 112 TWh of power demand by 2030. Countries offering tax incentives and lower electricity costs are seeing a clustering of installations, which further stresses local grids.

This global bottleneck underscores a critical truth: the future of AI isn’t just about chips and algorithms—it’s also about transformers, substations, and smart energy planning.

The Road Ahead: Collaboration is the Key!

1. T&D Infrastructure Is Critical to AI’s Future- The explosion in data volume is fueling the expansion of data centers worldwide, which in turn is driving up demand for reliable and scalable power delivery. To keep pace, the transmission and distribution (T&D) sector must evolve. Investments in T&D infrastructure are no longer optional—they are essential. Upgrading these systems will enable more efficient delivery of electricity to high-density compute environments, reduce latency, and support the deployment of advanced storage and compute technologies.

2. Greening AI- Investment in renewable energy- The momentum behind renewable energy projects is expected to remain strong, helping to sustain the elevated power purchase agreement (PPA) rates seen in recent years. A wide range of technologies—including offshore wind, geothermal, gas with carbon capture, and clean fuels hold promise for the medium to long term.

3. Support forward-thinking policy frameworks: Governments and regulators play a vital role in enabling sustainable digital infrastructure. Incentives for clean energy adoption, streamlined permitting for grid upgrades, and support for innovation can accelerate progress.

AI’s energy appetite is real—and growing. But with smart planning, innovative technologies, and cross-sector collaboration, the data center and energy industries can rise to the challenge. The future of AI depends not just on algorithms and data, but on the power systems that keep them running.


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