Loudoun County, Virginia, once known for its pastoral scenery and proximity to Washington, DC, has earned a more modern reputation in recent years: The area has the highest concentration of data centers on the planet.

Ten years ago, these facilities powered email and e-commerce. Today, thanks to the meteoric rise in demand for AI-infused everything, local utility Dominion Energy is working hard to keep pace with surging power demands. The pressure is so acute that Dulles International Airport is constructing the largest airport solar installation in the country, a highly visible bid to bolster the region’s power mix.

Data center campuses like Loudoun’s are cropping up across the country to accommodate an insatiable appetite for AI. But this buildout comes at an enormous cost. In the US alone, data centers consumed roughly 4% of national electricity in 2024. Projections suggest that figure could stretch to 12% by 2028. To put this in perspective, a single 100-megawatt data center consumes roughly as much electricity as 80,000 American homes. Data centers being built today are gearing up for gigawatt scale, enough to power a mid-sized city.

For enterprise leaders, energy costs associated with AI and data infrastructure are quickly becoming both a budget concern and a potential bottleneck on growth. Meeting this moment calls for a capability most organizations are only beginning to develop: energy intelligence. The emerging discipline refers to understanding where, when, and why energy is consumed, and using that insight to optimize operations and control costs.

These efforts stand to address both immediate financial pressures and longer-term reputational risks, as communities like Loudoun County grow increasingly concerned about the energy demands associated with nearby data center development.

In December 2025, MIT Technology Review Insights conducted a survey of 300 executives to understand how companies are thinking about energy intelligence today, as well as where they’re anticipating challenges in the future.

Here are five of our most notable findings:

• Energy intelligence is becoming a universal business priority. One hundred percent of executives surveyed expect the ability to measure and strategically manage power consumption to become an important business metric in the next two years.
• AI workloads are already driving measurable cost increases, and the surge is just beginning. Two-thirds of executives (68%) report their companies have faced energy cost increases of 10% or more in the past 12 months due to AI and data workloads. Nearly all respondents (97%) anticipate their organization’s AI-related energy consumption will increase over the next 12-18 months.
• Mounting costs are the top energy-related threat to AI innovation. Half of executives (51%) rank rising costs as the single greatest energy-related risk to their digital and AI initiatives. Most companies currently tracking and attempting to optimize data center energy consumption are motivated by cost management.
• Organizations are responding through infrastructure optimization and energy-efficient partnerships. To address mounting energy demands, three in four leaders (74%) are optimizing existing infrastructure, while 69% are partnering with energy-efficient cloud and storage providers. More than half are also implementing AI workload scheduling (61%) and investing in more efficient hardware (56%).
• Closing the measurement gap is the next frontier. Most enterprises still lack the granular data needed for true energy intelligence. This gap is especially pronounced for companies relying on third-party cloud providers and managed services for their data compute and storage needs, where 71% say rising consumption-based costs originate, yet energy metrics are often opaque.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.