“I realized we couldn’t build our way out of this. We needed intelligent demand,” Sivaram told Fortune.

In a bid to address this need, Sivaram founded a software company called Emerald AI to develop grid flexibility for data centers—essentially reducing power consumption at times of peak load demand on the grid during the hottest or coldest days each year—without harming AI operations.

In addition to heightened energy efficiency, the goal is to speed up the time for AI factories and their power generation to connect to the grid while maintaining “the five nines”—the industry term for 99.999% reliability.

Let’s call it the Disney FastPass approach—now known as the Lightning Lane—for quickly moving ahead in the grid queue.

“We call it flexible-load fast track,” Sivaram said, correcting the Disney reference with a laugh.

Emerald AI’s pitch quickly won financial backing and support from Nvidia, which has helped fast-track the company’s growth and deployment of its AI software. “An AI for AI,” he said.

On March 31, Emerald AI announced the completion of a $25 million strategic funding round with Nvidia’s NVentures, Eaton, GE Vernova, Radical Ventures, Salesforce, Samsung, Siemens, and more, including IQT, the venture capital arm of the CIA and other U.S. intelligence agencies. The round was led by Energy Impact Partners. That brings total funding to $68 million in 16 months since Emerald’s founding.

Last week, Emerald and Nvidia partnered with leading U.S. power producers, including AES, Constellation Energy, Invenergy, NextEra Energy, and Vistra.

And later this year, once a series of pilots prove successful, Emerald and Nvidia will open the first power-flexible, commercial AI factory, Nvidia’s 96-megawatt Vera Rubin AI Factory Research Center, in Virginia.

“The advent of the AI revolution meant that this idea should face primetime because, suddenly, AI factories don’t have enough power,” Sivaram said. “Historically, the data centers had no problem getting power. They’ve been less than 5% of the grid, but now they’re headed toward 25% of the American power supply over the course of a decade.”

As Constellation Energy CEO Joe Dominguez said: “We don’t have a supply problem; we have a peak problem.”

And Emerald’s “grid-friendly AI factories” aim to solve that problem.

The Nvidia fast pass

While Emerald’s software aims to fast-track AI factories, it was Nvidia’s early support that fast-tracked Emerald.

“We’re just excited for the opportunity to commercialize this and push it out there in a bigger way,” said Marc Spieler, Nvidia senior managing director for global energy. “The pilots have been highly successful. We believe this will unlock the potential for getting more AI factories onto the grid faster, utilizing more of the untapped electrons on the grid.”

The longer-term goal is for power-flexible AI factories to unlock up to 100 gigawatts of extra grid capacity from the existing U.S. power grid thanks to increased efficiencies. For context, 100 gigawatts can power roughly 75 million homes.

A grid interconnection study can take years of regulatory reviews, but if you can offer power flexibility at peak demand times, developers may get almost immediate grid hookups, Spieler told Fortune. “Our goal is to have as much connected to the grid as possible and not go behind the meter, not being islanded, by being flexible,” he said. “You can really think of it as highly reactive, demand response at scale.”

And Nvidia was happy to support Emerald’s potential. It’s far from NVentures’ only support announced March 31. ThinkLabs, which has AI focused on compressing power grid studies from years to minutes, announced a $28 million Series A financing round also led by Energy Impact Partners.

“We’re an ecosystem company. We go to market through partners. It doesn’t matter if they’re a Fortune 100, or Fortune 10 company, or an AI startup,” Spieler added. “If somebody has the right idea and is able to execute, we’re going to get behind them and fill the gap.”

How it works

Eight years ago, Sivaram wrote Taming the Sun: Innovations to Harness Solar Energy and Power the Planet.

In it, he documented Microsoft’s work moving workloads between multiple locations to “chase” more clean energy. And Google later worked to move more computational work overnight to utilize wind power at its strongest.

“I thought, ‘Wouldn’t it be nice if, instead of trying to move electrons to where the bits are, if bits could move to where the electrons are?’ Or the bits could be virtually controllable—slowed down or paused,” Sivaram said.

From that idea came the Emerald Conductor platform to “orchestrate” on-site energy resources alongside computational flexibility so projects can connect faster and support the power grid.

“We found that there is inherent flexibility that we can tap into because some AI workloads can be delayed a little bit, and the customers are okay with that,” he said. “Some AI workloads can be shifted from one location to another with latency that is acceptable for customers.

“And there may be resources on the site of a data center, such as a [storage] battery or a [backup] generator, that we can also recruit. Emerald AI finds ways to recruit all these different flexibility levers to provide back to the grid a very precise response,” Sivaram added.

And through tests and pilots, customers’ critical tasks continued to function without degradation, he said. “They kept chugging along at 100% performance.”