Data center backlash could slow CIOs’ AI plans

A growing backlash against building new data centers in the US may have huge cost implications for CIOs planning to expand their organizations’ AI initiatives.

Protests against building new data centers were organized in 42 states 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 data center construction moratoriums in place, and eight other states had pending legislation, according to datacenterbans.com.

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 Arif Gasilov, a partner in the natural resources and built environment division of sustainability advisory firm Gasilov Group.

IT leaders need to calculate the backlash into their planning for the compute and other IT infrastructure needs that new data centers would meet, he says.

“What this means for CIOs is that power cost assumptions built in 2023 are wrong in close to half the country,” Gasilov says. “A CIO planning an AI deployment that depends on colocation or cloud capacity in any of these states should be asking their provider what the rate structure looks like under the new tariffs and recalculating economics.”

In some cases, it may be possible to go smaller to avoid the moratoriums or tariffs on large data centers, but some state regulations target facilities close to each other as opposed to individual data centers, he notes.

Deployment challenges

If the backlash continues, IT leaders may need to rethink the way they deploy AI, says Chuck Girt, 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 Kevin Surace, CEO of biometric security vendor TokenCore.

“Compute capacity is becoming as strategically important as electricity, semiconductors, and network connectivity,” he says. “Fewer data centers mean less available capacity, reduced geographic redundancy, longer provisioning times, and greater dependence on a small number of cloud providers and locations.”

Organizations that have not secured capacity could find that their AI strategy is technically sound but physically impossible to execute on schedule, he suggests.

Surace, also an AI and green energy expert, 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 massive water use of older data centers, he notes, and some are using renewable energy generation. Nuclear power will become an electricity option soon, he adds.

Cost pressures rising

In the meantime, IT leaders should expect higher costs for compute and other IT infrastructure provided through data centers, Surace says.

“Demand for AI compute is accelerating, so constraining the supply of facilities, electricity and high-density capacity will place upward pressure on cloud pricing, colocation, accelerator access, and long-term capacity contracts,” he adds.

Organizations that have the capacity will should be able to protect themselves through multiyear agreements and dedicated infrastructure, he suggests. Smaller organizations, startups, and universities could face the greatest percentage increases and may simply be priced out of leading-edge AI capabilities, he adds.

Therefore, Surace advises CIOs to treat compute and energy as strategic supply-chain risks. Organizations should secure capacity as soon as they can, avoid dependence on one cloud or one geographic region, and use smaller and more efficient AI models 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.”