The growing use of artificial intelligence (AI) is driving up energy consumption and costs, according to Mark McNees, a professor at Florida State University’s Jim Moran College of Entrepreneurship. McNees, who directs the Social and Sustainable Enterprises program, points to a significant increase in electricity prices for consumers living near new data centers built to handle AI workloads.
Wholesale electricity costs have risen by as much as 267% over the past five years in some areas with high concentrations of data centers. A single ChatGPT query now uses about ten times more energy than a traditional Google search.
“As AI adoption accelerates and data centers proliferate to support this demand, we’re facing significant upward pressure on electricity prices that most consumers don’t yet realize is coming,” McNees said.
McNees has written extensively on energy sustainability and efficiency. He mentors students at Florida State University and hosts the InNOLEvation Mindset Podcast.
Discussing the long-term outlook, McNees said: “It’s not sustainable without fundamental changes to how we finance energy infrastructure. The current system was designed for an era when electricity demand grew only modestly year over year. That world ended when ChatGPT launched.”
He explained that utilities are building expensive infrastructure based on projected data center demand, but when projects do not materialize, residential customers often bear the cost. For example, AEP Ohio has received requests for 30 gigawatts of new connections from data centers—enough to power 24 million homes—but developers frequently explore multiple locations before committing.
McNees cited research from Lawrence Berkeley National Laboratory suggesting that states with higher electricity demand growth have sometimes seen smaller retail price increases because fixed costs are distributed across more kilowatt-hours. He noted that outcomes depend on planning and cost allocation: “In northern Virginia, large data center customers cover roughly 9% of transmission costs, helping keep residential transmission rates below the national average. In Mississippi, data center revenue has funded grid modernization without raising household rates. The model works when implemented thoughtfully. The crisis emerges when it isn’t.”
On potential solutions so consumers do not bear rising energy prices due to AI-driven demand, McNees pointed out some tech companies’ initiatives: “Microsoft recently announced it will request to pay higher electricity rates in areas where it’s building data centers — specifically to prevent residents from subsidizing its AI infrastructure.” He added: “But voluntary corporate goodwill isn’t a policy framework.”
McNees outlined several structural solutions:
– Require data centers to build their own generation using solar or battery storage.
– Reform interconnection rules so utilities avoid speculative investments.
– Invest in distributed energy resources like residential efficiency upgrades.
– Expand renewable energy paired with storage as a fast way to meet capacity needs.
He concluded by referencing testimony from industry executives who argue renewables combined with batteries offer reliable power for AI operations more quickly than new gas turbines can be deployed.
