Data center backlash could slow CIOs’ AI plans

A in mid-July, with participants concerned about new facilities driving up electricity and water costs and using large swaths of land.

As of mid-July, 10 states, including Florida, Georgia, and Virginia, had active .

In addition, as of May, 23 states had approved large-load tariffs that require data centers to pay the full infrastructure cost for their facilities, says , CTO at fiber-optic network provider FiberLight.

With fewer options for AI compute power, organizations would have less flexibility in where they deploy AI workloads, he suggests.

“I don’t think the rate of data center construction changes the direction AI is headed, but it could influence how organizations deploy and access AI at scale,” he says. “Most enterprises aren’t going to build this infrastructure themselves; they’re going to rely on cloud and data center environments to provide the compute AI requires.”

A lack of data center options could put many organizations in a bind, says , is concerned that generalized fear about older data center designs is turning into blanket opposition to new construction. Modern facilities have cut down on the where appropriate, he recommends.

He also suggests that CIOs ask data center providers several hard questions:

• Where does the water come from?





• Is the cooling loop closed?





• Who pays for new grid infrastructure?





• What percentage of power is generated onsite?





• What environmental monitoring is publicly reported?

Data centers can mitigate some of the community concerns, he says. “Transparency and early community engagement are far less expensive than lawsuits, project cancellations, and moratoriums,” he adds.

Backlash against inefficiency

While protests are likely to continue, some don’t see the concerns about data centers as a condemnation of AI. Instead, the problem is with inefficient AI deployments, says Anurag Gurtu, cofounder and CEO of agentic AI platform provider Airrived.

“Enterprises don’t actually want more data centers; they want more intelligence per watt, per GPU, and per dollar,” he says. “The winners won’t be those with the biggest infrastructure footprint, but those extracting the most value from every unit of compute.”

Limitations on data centers will impact companies only if their AI strategies depend on nearly unlimited infrastructure, he adds.

“The next generation of AI will be constrained by compute, power, and economics,” Gurtu says. “Organizations that optimize models, deploy domain-specific AI, and leverage hybrid architectures will continue to innovate, while those relying solely on scaling hardware will face diminishing returns.”

While limited compute options could lead to higher prices, the solution is to focus on efficiency, he adds.

“Rising infrastructure costs also accelerate innovation in model optimization, inference efficiency, and intelligent orchestration,” Gurtu says. “History shows constraints often become the catalyst for the next wave of breakthroughs.”