Can AI solve the energy problem it created?

It costs about $5 and takes roughly 15 minutes to train an AI model that can help determine where America’s next data centers should be built. That’s not a rounding error. The world is debating hundred-billion-dollar AI investments and warning that we need fleets of new power plants, yet one of the most important pieces of the puzzle costs about as much as a pack of gum. Before we build another plant, we should figure out how much of the grid we’re already leaving on the table. Much has been made of AI’s power requirements, and for good reason. Bloomberg projects that data centers will consume up to one-fifth of all power in the U.S. by 2035-up to 200 gigawatts-with much of that demand landing in regions where power is already constrained. Power generation wait times can stretch past five years on a grid where much of the infrastructure is 50 to 70 years old. The pressure is real, and it’s arriving faster than utilities can plan for it with current systems and processes. What most people don’t realize is that our existing grid massively overproduces the energy we need, and much of it sits idle. Power systems are sized for the handful of hours a year when demand peaks, the hottest afternoons and the coldest mornings. The rest of the time, that capacity sits there, already built and already paid for. Across most hours, we use only about half of it. It doesn’t take a math degree to appreciate that we already have the power we need; we just need to ensure the power that would go wasted is put to good use. This is a planning problem, and planning problems are exactly what AI is good at. New models can evaluate where and when the grid has unused power, along with the costs and timelines required to build the infrastructure that reroutes it, which I call capacity mining. It lets the AI revolution advance at pace while optimizing longer-term backbone investments and minimizing backlash. This doesn’t require any new hardware or a scientific breakthrough. It requires asking the right questions with the right, highly efficient, and inexpensive models. Here’s the sequence that will make this work for everyone involved. First, apply AI models to match data center demand against the headroom that already exists, citing locations and operating profiles where there’s available power and operational flexibility rather than forcing utilities to build new generation and transmission capacity around a site chosen for other reasons. Second, where matching alone isn’t enough, apply AI to optimize the expansion of transmission lines and battery systems to route power to where it’s needed or store it until it is. New power plants, which are costlier, slower to build and often polluting, are the last resort. In most cases they’re avoidable. The result is a buildout that moves at the speed of software, not steel and permitting. Getting that order right also helps utilities deliver on their recent White House pledge to shield consumers from rising electric bills tied to data center growth. Capacity that already exists doesn’t need to be paid for twice, and finding it costs relatively nothing. But the larger point is that data centers don’t have to raise bills at all, and can actually lower them. At one large investor-owned utility, we’ve calculated that large loads, including data centers, can generate roughly $1 million per megawatt per year in new revenue, about $1 billion per gigawatt. Structured correctly, that revenue offsets fixed grid costs every other customer currently shoulders alone. Reliability works the same way. A buildout done right pulls investment into stronger transmission, better monitoring, and improved protection and control systems, and those upgrades serve every household on the line, not just the hyperscaler at the end of it. I also believe hyperscalers will gladly cover more of the cost in exchange for faster access to power. Think of a data center as a mini-utility. Its buildout should put money back into the community it lands in, both in quality local jobs and in more modern, reliable infrastructure for everyone on that grid. There’s no good reason to make regular people pay for electricity that data centers use, and deflating that argument will do more to cool the backlash than any ad campaign. This is a crossroads for utilities, which are not historically growth businesses and have been flat for decades. If they do data centers right, they can drive the most revenue growth in a generation while optimizing their grids for every other customer. Get it wrong, and they’ll create stranded assets and raise rates, push hyperscalers to build behind-the-meter generation themselves, lose that revenue entirely, and miss the biggest modernization opportunity they’ll ever get. We don’t have to choose between winning the AI race and protecting communities from unnecessary costs. The better question is whether AI can solve the problems AI is creating. On this one, it can, and with tools that are ready today. We have all the power we need; we just need to unlock it. The early-rate deadline for the Most Innovative Companies Awards is Friday, September 4, at 11:59 p.m. PT. Apply today.