Data center campuses like Loudoun are springing up across the country to accommodate the insatiable appetite for AI. But this construction involves huge costs. In the US alone, data centers consumed roughly 4% of national electricity in 2024. Estimates suggest this figure could increase 12% by 2028. To put this in perspective, a 100-MW data center consumes approximately the same amount of power 80,000 American homes. The data centers being built today are preparing for this gigawatt scaleEnough to power a medium-sized city.
For enterprise leaders, the energy costs associated with AI and data infrastructure are increasingly becoming both a budget concern and a potential drag on growth. Meeting this moment requires a capability that most organizations are just beginning to develop: energy intelligence. The emerging discipline is concerned with 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 long-term reputational risks, as communities like Loudoun County are 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 anticipate challenges in the future.
Here are our five 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 a key business metric over the next two years.
- AI workloads are already driving measurable cost increases, and the increase is just beginning. Two-thirds of executives (68%) reported that their companies have faced an increase in energy costs of 10% or more in the last 12 months due to AI and data workloads. Nearly all respondents (97%) anticipate that their organization’s AI-related energy consumption will increase over the next 12-18 months.
- Rising costs are the biggest energy-related threat to AI innovation. Half of executives (51%) consider rising costs the biggest energy-related risk to their digital and AI initiatives. Most companies currently attempting to monitor and optimize data center energy consumption are motivated by cost management.
- Organizations are responding through infrastructure optimization and energy-efficient partnerships. To address growing 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 detailed data needed for true energy intelligence. This gap is particularly pronounced for companies that rely on third-party cloud providers and managed services for their data compute and storage needs, where 71% say this generates increased consumption-based costs, yet energy metrics are often opaque.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes writing surveys and collecting data for the surveys. The AI tools that may have been used were limited to secondary production processes that underwent thorough human review.
