As the rapid rise of artificial intelligence transforms industries, it’s straining the energy-hungry data centers that power it. These facilities, essential to running complex AI models and digital services, face mounting pressure as electricity demand surges.

This demand raises urgent questions about sustainability, grid reliability, and the viability of future growth.

Interconnection queues for new projects can stretch to seven years in high-density areas like Northern Virginia, driving data center operators to look for other options. The intersection of AI-driven growth and energy infrastructure modernization poses a complex challenge, but emerging solutions could help data centers ease the strain on the grid and even actively contribute to its stability.

The AI Data Center Landscape

Data centers account for approximately 4.4% of total U.S. electricity consumption, a figure projected to rise to as much as 12% by 2028. These facilities don’t just power AI training—they also facilitate AI inference processes that make tools like ChatGPT or recommendation algorithms functional.

Many data centers are clustered geographically in regions like Northern Virginia, Silicon Valley, and Dallas–Fort Worth, exacerbating pressure on local grids. Dominion Energy has warned that Northern Virginia’s demand will grow by 5.5% annually and will double by 2039, with billions of dollars in infrastructure investment required to accommodate this growth.

And because end-use energy efficiency is paramount, the rising demand requires the production of far more renewable energy to decrease reliance on fossil-fueled electricity.

Meeting this unprecedented challenge requires major investments in technology and selective changes in utility business models.

AI Demand and Grid Infrastructure

The electrification of AI-ready data centers is a double-edged sword. The U.S. power grid enables AI innovation but struggles to accommodate the rapid rise in AI-driven demand. Without proactive investments, the grid could fall behind the demands of AI expansion, heightening the risk of localized energy crises emerging in high-demand regions.

There are some solutions at the utility level. Digital substations can improve grid capacity by 10% to 30% through precise monitoring, and energy-intensive facilities such as data centers can boost efficiency in end use.

Advanced monitoring, AI-driven analytics, and modern energy management systems can deliver significant efficiency and cost savings. Implementing these technologies, alongside investments in interregional transmission and renewable energy integration, can reduce pressure on the grid while fostering sustainable AI growth.

Four potential scenarios encapsulate potential outcomes of these changes:

1. Sustainable AI. The industry adopts advanced energy efficiency using AI-powered analytics and hardware optimization. Efficiency gains stabilize electricity demand, allowing AI to grow without straining the grid. Technologies such as microgrids and onsite distributed energy resources (DERs) play a pivotal role. Investments in sustainable computing architectures and renewable energy integration reinforce progress.

2. Limits to growth. Constraints in electricity availability and infrastructure inhibit AI expansion. Long permitting delays and underinvestment in DERs restrict the scalability of data centers, forcing the industry to reduce power consumption, potentially preventing energy crises but also slowing AI innovation.

3. Abundance without boundaries. Exponential AI growth without sufficient energy planning leads to demand outpacing supply. Infrastructure struggles to keep up, resulting in increased reliance on fossil fuels and inefficient computing infrastructure. Environmental impacts are significant, and residential energy costs rise as competition for resources intensifies.

4. Energy crisis. This worst-case scenario includes localized blackouts, economic disruptions, and public backlash against AI-driven energy use—embodying the chaos caused by insufficient planning. The grid’s inability to meet peak demand forces data centers to scale back operations, and policymakers scramble to enforce restrictive regulations.

Each scenario requires specific interventions. Success depends on aligning industry practices with grid modernization efforts.

Solving for Stability

Several proven solutions can address both the generation and consumption sides of the power equation:

1. Data center infrastructure management (DCIM). These monitoring and control systems provide real-time visibility into energy consumption patterns and enable dynamic adjustments based on grid conditions and asset utilization. Ideally, they don’t rely on proprietary software or hardware components.

2. Microgrids. Self-serving onsite power systems can optimize grid power and DERs to keep costs down while preserving or even enhancing reliability.

3. Liquid cooling. Now the industry standard for high-density data centers, liquid cooling is up to 3,000 times more efficient than air cooling, allowing more energy to flow to compute versus cooling applications.

4. High-efficiency power distribution. The onsite power distribution system itself is subject to energy losses. Using the most efficient uninterruptable power sources, automatic transfer switches, and other devices minimizes network losses.

5. New baseload options. Small modular reactors and advanced fuel cells offer promising sources of always-on power.

All these technologies increase energy efficiency, reduce demand, and relieve stress on the surrounding utility grid. But data centers can do more than simply optimize their own operations—they can become true grid partners by participating in demand response (DR) programs.

The problem is that these schemes were not designed with data center operating parameters in mind. While demand response could reduce total U.S. peak demand by up to 20%, few data centers engage in these programs, because they require nearly instantaneous changes in power use. That’s too fast for data centers, and it keeps them on the sidelines despite their having highly flexible loads.

Adjusting DR program parameters to allow data centers time to shift workloads while integrating cybersecurity measures would establish a strong incentive for participation. If some of the largest consumers of power could curtail their demand, utilities would have a potent new tool for keeping the grid in balance.

Solving the Energy Challenge

The energy challenges AI-ready data centers pose are daunting but solvable.

By prioritizing efficiency, embracing innovative technologies, and fostering collaboration between grid and data center industry leaders, the U.S. can manage AI’s electricity demand while supporting sustainable growth. But critical hurdles remain, such as incompatible DR program rules, long interconnection timelines, and the slow pace of renewable energy deployment.

Meeting these challenges head-on requires coordinated efforts across sectors to ensure the data center industry evolves as a partner to the grid—not just a consumer. When data centers transform into active contributors to grid stability through renewable integration and demand response, the promise of sustainable AI becomes attainable.

With strategic investments and forward-thinking policies, the U.S. can power the next era of digital transformation without compromising its environmental or economic future.

Gain a benchmark perspective on the intersection of AI and energy—explore the key drivers, risks, and strategies for sustainable growth in Schneider Electric’s industry-shaping report.