On a blistering hot day in Phoenix, Arizona, as a million air conditioners drove up demand on the power grid, a cluster of energy-hungry artificial intelligence servers bucked the trend. They actually helped. For three hours, these AI computers at an Oracle data center dropped their power consumption by 25 percent to provide perfectly timed relief during that day's peak demand. And critically, the advanced Nvidia chips continued to meet the stringent performance requirements of their tasks. Training, fine tuning and using AI large language models.

Our team at Emerald AI orchestrated this first-of-a-kind demonstration of flexible AI computing. And we're not alone. Google has also made impressive strides. Scaling up our technologies across the country and around the world could help solve one of the biggest challenges of our time: powering the AI revolution, while also advancing a more reliable, affordable and clean power grid. Far from undermining it, AI could actually help save the grid.

To understand why, we need to reimagine the challenge of powering AI. And so I've reinvented my own career. For 15 years as an energy executive and as America's lead clean energy diplomat, I focused on building more clean energy. But energy supply is just half the equation. And so I founded Emerald AI to focus on the other half, demand, helping AI intelligently use energy, support grids and unlock massive stranded power capacity that already exists.

Without this capability, we face an impending crisis, a historic collision between two multi-trillion dollar networks: the network of AI data centers that's rapidly growing, and an aging electricity grid, utterly unprepared for all this new demand. That's bad news, folks, for multiple reasons.

First, America risks falling behind in AI. In Virginia, the data-center capital of the world, it takes up to seven years to connect new data centers to the grid. Second, power prices are soaring for communities. Just in 2025, as we built new grids and new power plants, data center demand drove up the average annual household power price in Columbus, Ohio, by 240 dollars. And this is just the beginning. As data centers surge from four percent of US power demand today to 12 percent by 2030. That's It's like adding another Germany to the US power grid.

And third, fossil fuels are set to power the boom in AI data centers, which require reliable power supplies today. In the United States, natural gas is powering most AI growth, and countries like India will see rising coal use increasing global carbon emissions.

But it doesn't have to be this way. The biggest new user of electricity could actually be our grid's greatest ally. The key lies in something deceptively simple. Flexibility. That's distinct from efficiency or using less energy overall. Rather, if AI were just a little more flexible in when it uses energy, it could consume vast amounts of otherwise stranded power on today's grids.

Think of our electric power system as a superhighway that faces peak rush hour just a few hours per month. Think of that hottest day of the summer in Phoenix, Arizona, when air conditioning demand peaks. On those days, grids risk being overwhelmed by these massive new data centers that may soon consume more than a gigawatt, or more energy than the state of Vermont consumes. But most of the time, power plants are running well below their full capacity, and transmission lines are carrying less power than they could. Just like that highway. On average, throughout the year, half of the power system's capacity goes unused.

Here's a graph of the Arizona public service utility's grid over the last year, which averaged four gigawatts of load and only approached nine gigawatts once in the summer. What if, during those peak rush-hour periods, when the grid is truly stressed, AI data centers could dynamically reduce their power consumption and take advantage otherwise of all that spare capacity throughout the year. It would be like briefly taking 18-wheelers off of that road to let the remaining traffic flow smoothly.

Well, it turns out that if AI data centers were just modestly flexible, just less than two percent of the year, trimming demand by a quarter, just a couple hours at a time, America could fit up to 100 gigawatts of new data centers on existing power grids across the country. That's four trillion dollars of AI investment unlocked today without waiting years for new infrastructure.

Now, to be sure, America will need even more energy to power our growing economy as data centers, factories and other users of electricity join. But by making AI data centers flexible, we can prudently expand our grid and buy ourselves time to build clean nuclear or geothermal power plants.

And what's more, with flexible AI data centers acting as giant shock absorbers on the grid, we can integrate intermittent but cheap solar and wind power, driving down the cost of energy for AI.

So that's what I do. My team and I are building the software brain to give AI data centers this crucial flexibility. It's an AI for AI. We call it the Emerald Conductor. It works by harnessing something we call spatiotemporal flexibility. That's a fancy term for a simple idea. Let's break it down.

First, temporal flexibility. Not all AI jobs are created equal. Some workloads, like training or fine-tuning an AI model, conducting deep research or running a massive scientific simulation are what we call “batchable.” They're incredibly important, but they don't have to be completed right this second. Software can intelligently pause or slow these workloads briefly, when the grid is stressed, and then speed them back up when there's plenty of power available.

Then there's spatial flexibility. Think of your query to a generative AI chatbot. You can't pause the job of responding to that query, but you can move it across the country at the speed of light. So even as we struggle to build electric power transmission, we can take advantage of virtual transmission or the network of fiber optic cables that crisscrosses the country and the planet to move AI workloads from a data center in a city where the grid's currently strained, let's say, Phoenix on a hot day, to a data center in a region where there's presently abundant power, say, the wind-swept Great Plains. The AI workloads get done, but the grid gets a break right when it needs it most. And the user never even notices, because behind the scenes, there’s an AI orchestrating AI. Data centers become smart, cooperative partners to the power grid.

And we know it works. Remember that demo I told you about? It happened. In May 2025, in Phoenix, Arizona, we took a cluster of 256 GPU servers, and we ran a mix of AI workloads, some highly flexible, others entirely inflexible, and many in between. One hot afternoon, our software received a signal that the local utility was going to reach its peak demand, and so Emerald Conductor gracefully reduced the AI computational power load by 25 percent for the exact three hours requested by the grid.

On the right hand side, you can see the performance of the AI jobs. The far right, the highly inflexible ones performed at 100 percent, and the flexible ones achieved a performance above their acceptable thresholds. We proved that AI data centers can flex when the grid is tight and sprint when users need them to.

But proving the technology was just the first step. The hardest part will be to convince the enormous energy and AI industries to cooperate and to change the way that they operate. For over a century, electric power utilities have assumed that their users can't simply reduce their power consumption when the grid faces peak rush hour. Sure, in limited situations, a utility may request homes to adjust their thermostats or large industrial loads to dial down consumption, but these interventions are typically tiny and marginal.

But AI data centers are fundamentally different, with a transformative potential to be flexible. They're massive energy users compared with tiny household loads that need to be aggregated. They respond faster and more gracefully than large manufacturing facilities, and they can move their workloads around the country at the speed of light, which no other energy user can do.

That's why I'm so excited about initiatives that bring together the energy and technology industries, like EPRI's DCFlex. In upcoming demonstrations in the United States and with National Grid in the United Kingdom, Emerald will showcase how AI workloads can flex and move across regions, and will prove that software like Conductor can orchestrate a symphony of AI workloads in concert with on-site energy equipment like batteries, to deliver even more flexibility to power grids.

And with our partner Nvidia, we're building a reference design for next generation data centers or AI factories to be power flexible so that utilities that see the certification can more swiftly connect a grid-friendly AI factory.

So where does this all leave us? Well, it means rather than wait years for grid upgrades, we can build all the AI infrastructure we need right now to sharpen our competitive edge. And far from crashing the grid, flexible AI data centers can provide relief before the grid hits a breaking point, avoiding rolling blackouts. Rather than increasing power prices, they could actually go down as flexible AI data centers more effectively utilize the existing energy infrastructure, deferring expensive upgrades.

And rather than goose demand only for fossil fuels, AI's soaring energy needs could encourage more clean energy onto the grid at home and abroad. Solar today is the cheapest, fastest-growing power source on the planet. Imagine flexible AI data centers capable of ramping their energy consumption to match daytime solar peaks, or shifting their loads so that they better integrate clean energy onto the grid.

The AI revolution is here. And I believe we can have it all. Breakneck innovation, massive investments in AI and abundant, affordable, reliable and clean energy for all. An AI for flexible AI infrastructure could be a linchpin for our future energy system. Thank you. (Applause)