Google Grid Deal Helps Sate AI Energy Demands

Google advanced one of its initiatives Monday to address the burgeoning demand for energy by its data centers, driven by power-hungry artificial intelligence applications.

The company announced agreements with Indiana Michigan Power and the Tennessee Valley Authority to throttle energy demand from machine learning workloads at its data centers in those regions.

“This builds on our successful demonstration with Omaha Public Power District (OPPD), where we reduced the power demand associated with ML workloads during three grid events last year,” Michael Terrell, Google’s head of advanced energy, explained in a company blog.

Google began managing grid demand at its data centers by shifting non-urgent computing tasks — like processing a YouTube video — during specific periods when the grid is strained. Through its partnerships, it leveraged those demand management capabilities to help grid operators maintain reliability during peak demand periods.

“As AI adoption accelerates, we see a significant opportunity to expand our demand response toolkit, develop capabilities specifically for ML workloads, and leverage them to manage large new energy loads,” Terrell wrote. “By including load flexibility in our overall energy plan, we can manage AI-driven growth even where power generation and transmission are constrained.”

Pete DiSanto, senior vice president of data centers at Enchanted Rock, an electrical resiliency-as-a-service company in Houston, added, “Demand-side solutions are absolutely critical for aligning growth with grid reliability.”

“Without demand-side solutions, the grid simply won’t be able to keep up with the scale and speed of AI data center growth, especially in regions already facing capacity and interconnection challenges,” he told TechNewsWorld. “These tools are the key to enabling rapid expansion without breaking the grid.”

AI Strains U.S. Power Capacity

Demand-side power management solutions are expected to play a crucial role in meeting the growing electricity demands of data centers driven by AI in the coming years. According to Morningstar Research Services, U.S. data center power capacity is expected to approximately triple to 80 gigawatts by 2030, driven by the growth of data centers utilizing generative artificial intelligence.

However, Morningstar acknowledges that its projections are less bullish than those of other prognosticators, which see capacity reaching 100 gigawatts during the same period. “We believe such forecasts overlook the practical limitations associated with building large-scale infrastructure and also underestimate the long-term rising energy efficiency of AI chips,” it noted in a report titled “Powering Tomorrow’s AI Data Center” released in July.

“We don’t have sufficient generating capacity for both the existing energy loads and AI data centers,” said Rob Enderle, president and principal analyst with the Enderle Group, an advisory services firm in Bend, Ore.

“It is critical that demand-side solutions be created to mitigate what would otherwise be long periods of brownouts or full outages to keep the grid from failing catastrophically, requiring much of it to be rebuilt,” he told TechNewsWorld.

Mark N. Vena, president and principal analyst with SmartTech Research in Las Vegas, agreed. “As AI workloads explode, the electrical grid simply can’t keep up unless demand can flex in real time,” he told TechNewsWorld. “Demand-side strategies like shifting compute loads or pausing non-urgent processes help avoid blackouts while still meeting data center needs.”

Flexibility Essential for Data Centers

Demand-side solutions aren’t just important; they’re becoming a prerequisite for growth, maintained Wyatt Mayham, head of AI consulting at Northwest AI Consulting (NAIC), a global provider of AI consulting services. “The narrative of simply building more power plants is too slow,” he told TechNewsWorld. “Grid interconnection queues are already years long.”

“Demand-response agreements allow data centers to act like a virtual power plant, providing grid stability that utilities desperately need,” he said. “For the data center, it’s a new revenue stream and, more importantly, a ticket to the front of the line for power allocation. We’re going to see these agreements become table stakes for any large-scale AI deployment.”

Ezra Hodge, senior managing partner and global practice group leader for artificial intelligence for EMA Partners, an international executive search and leadership advisory firm, agreed that demand-side flexibility will soon be table stakes, but added, “It’s not just about software or load shedding.”

“The real unlock is talent — leaders who understand both AI infrastructure and energy markets,” he told TechNewsWorld. “Without that dual fluency in the room, demand-side solutions remain theory instead of action.”

“What’s needed now are cross-disciplinary operators — VP-level and above — who can translate AI workloads into energy language and grid constraints into data center architecture,” he said.

Managing Power Without Performance Loss

While a reduction in power to accommodate peak demands on the grid has the potential to degrade service, savvy management of energy in the data center can avert that problem. “Reduced power means reduced compute. That’s physics. There’s no way around the laws of physics,” acknowledged Rick Bentley, CEO of Hydro Hash, a crypto mining company that focuses on clean energy and high-efficiency operations in Albuquerque, N.M.

“That said, the data center could turn to local or onsite generation from their backup systems during this time to maintain power availability, and therefore compute, while not burdening the grid,” he told TechNewsWorld.

Everett Thompson, founder and CEO of WiredRE, a Las Vegas–based advisory firm focused on cloud, colocation, and data center infrastructure, explained that every data center starts with its own onsite utility.

“That utility may consist of diesel generators and batteries, but it’s still an onsite utility all the same,” he told TechNewsWorld. “What’s changing is the relationship between the end user and the grid, with end users taking on more responsibility as grid resources become constrained.”

There may be some risk of service degradation, but it’s manageable, contended Enchanted Rock’s DiSanto. “When designed well, demand-side strategies allow AI data centers to support grid stability without sacrificing critical performance,” he said. “It’s all about engineering for flexibility from the start.”

“Behind-the-meter natural gas generation and battery energy storage systems play a key role here, providing reliable, dispatchable power during grid peaks so compute performance remains uninterrupted, even when utility supply is constrained,” he said.

“Companies like Google are designing systems that prioritize flexible, non-latency-sensitive tasks for curtailment first,” added SmartTech’s Vena. “As long as critical inference and low-latency applications are protected, most users won’t notice a thing.”

Grid Challenges Go Beyond Power Generation

The key to avoiding degradation is understanding that all AI workloads are not the same, noted NAIC’s Mayham. “A demand-response event doesn’t mean shutting down customer-facing services like search or generative AI chats,” he said. “It means pausing non-urgent, resource-intensive workloads — like training a future model or running a massive batch analysis. These tasks are highly schedulable.”

“The ability to shift these massive loads to off-peak hours or pause them for an hour is precisely the ‘load flexibility’ Google is talking about,” he continued. “It’s a challenge of sophisticated software, not a fundamental technical barrier.”

He noted that the real bottleneck isn’t power generation but the physical grid. “This is the part of the story most people miss,” he said. “You can have a contract for hundreds of megawatts, but it’s useless if you can’t get it to your racks.”

“We’re seeing multi-year delays for critical hardware like high-voltage transformers,” he continued. “Some data center projects are being stalled not by a lack of power generation, but by a shortage of basic electrical components. Google’s strategy is realistic because it works around this constraint by better utilizing the infrastructure that already exists, reducing the need for new, time-consuming grid buildouts.”

“The future of AI power isn’t just one solution,” he added. “It’s an ‘all-of-the-above’ arms race. Beyond demand response, major players are investing in everything from direct-to-chip liquid cooling to reduce energy waste, to signing long-term deals with nuclear plants, to funding the development of small modular reactors to power data center campuses directly, bypassing the grid altogether.”