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USA Data Center Electricity Demand Projected to Triple

U.S. data center electricity consumption is projected to more than triple from 2021, the last full year without ChatGPT, to 2030, according to data sent to Rigzone by the International Energy Agency (IEA) recently. The IEA, which described U.S. data center electricity demand figures as the closest proxy available for the energy consumption of artificial […]

U.S. data center electricity consumption is projected to more than triple from 2021, the last full year without ChatGPT, to 2030, according to data sent to Rigzone by the International Energy Agency (IEA) recently.

The IEA, which described U.S. data center electricity demand figures as the closest proxy available for the energy consumption of artificial intelligence (AI), modelled that U.S. data center electricity demand came in at 120.65 Terawatt hours (TWh) in 2021, IEA data sent to Rigzone showed. In other data sent to Rigzone, which is available in the IEA’s Energy and AI report released earlier this year, the IEA forecasts that this consumption will rise to well over 400 TWh in 2030, in its base case.

In data sent to Rigzone, the IEA modelled that U.S. data center electricity consumption stood at 108.41 TWh in 2020, 134.07 TWh in 2022, 154.07 TWh in 2023, and 182.61 TWh in 2024. The IEA projected in its AI report that this demand will rise to well over 200 TWh in 2025, more than 250 TWh in 2026, over 300 TWh in 2027, around 350 TWh in 2028, and a little bit under 400 TWh in 2029.

A chart included in the IEA report showing electricity generation for data centers by fuel in the U.S., in the IEA’s base case, from 2020 to 2035, outlined that natural gas has had, and will continue to have, the biggest slice of the pie.

According to this chart, natural gas generated around 50 TWh of electricity for data centers in the U.S. in 2020. This figure rose slightly in 2021 and 2022, and came in a little under 100 TWh in 2024, the chart showed. The IEA chart projects that this figure will come in slightly over 100 TWh this year, go well over 100 TWh in 2026, come in at around 150 TWh in 2027, and keep pushing up towards 200 TWh in 2028. By 2029, this figure is projected to stand a little over 200 TWh and by 2030, it’s expected to go well above this figure, the chart outlined.

In a statement posted on its website on November 30, 2022, San Francisco, California, based OpenAI, which describes itself as an A.I. research and deployment company whose mission is to ensure that artificial general intelligence benefits all of humanity, introduced ChatGPT.

“We’ve trained a model called ChatGPT which interacts in a conversational way,” that statement announced.

A BofA Global Research report sent to Rigzone back in December 2024 stated that ChatGPT “was the game-changer of the past 24 months”.

Several AI models followed the introduction of ChatGPT, with Mountain View, California, based Google introducing Bard (now known as Gemini) in February 2023, San Francisco, California, based Anthropic introducing Claude in March 2023, Redmond, Washington, based Microsoft announcing Copilot in September 2023, and Palo Alto, California, based xAI announcing Grok in November 2023.

What Is a Data Center?

In its report, the IEA noted that AI model training and deployment occur mainly in data centers. It added that data centers are facilities used to house servers, storage systems, networking equipment, and associated components that are installed in racks and organized into rows.

The IEA highlighted in its report that this IT equipment, and a range of auxiliary equipment required to keep it in working order, comprises servers, storage systems, networking equipment, cooling and environmental control equipment, uninterruptible power supply (UPS) batteries and backup power generators, and other infrastructure, like lighting and office equipment for on-site staff.

“The share of these different components in data center electricity consumption varies greatly by type of data center, depending on the nature and efficiency of the equipment they have installed,” the IEA pointed out in its report.

Data Centers and Energy

The IEA went on to state in its report that data centers, “at least at the scale seen today, are relatively new actors in the energy system at the global level”. 

“Today, electricity consumption from data centers is estimated to amount to around 415 TWh, or about 1.5 percent of global electricity consumption in 2024. It has grown at 12 percent per year over the last five years,” it added.

The IEA noted in its report that the rise of AI is accelerating the deployment of high-performance accelerated servers, leading to greater power density in data centers.

“Understanding the pace and scale of accelerator adoption is critical, as it will be a key determinant of future electricity demand,” the IEA highlighted in its report.

“The key input to our modelling is therefore near-term industry projections for server shipments, considering the outlook for demand and supply constraints,” it added.

The report noted that the U.S., China, and Europe are projected to remain the largest regions for data center electricity demand over the coming years.

“China and the United States are the most significant regions for data center electricity consumption growth, accounting for nearly 80 percent of global growth to 2030,” the IEA revealed in its report.

“Consumption increases by around 240 TWh (up 130 percent) in the United States, compared to the 2024 level. In China it increases by around 175 TWh (up 170 percent). In Europe it grows by more than 45 TWh (up 70 percent),” it added.

Chevron, Exxon Data Center Power

In a statement posted on its website earlier this month, Chevron said it expects to maintain capital and cost discipline while investing to extend cash flow growth into the next decade. In line with these objectives, the company noted in the statement that it expects to deliver its first AI data center power project in West Texas, “targeting first power in 2027”.

“We are excited about our new power business, where we have an early-mover advantage and look forward to providing the power required to support U.S. leadership in Artificial Intelligence,” Jeff Gustavson, President of Chevron New Energies, said in that statement.

In a statement posted on its website back in February, Chevron said AI data centers are “hungry for power” and added that Chevron is “tapping into natural gas to help nourish that appetite”.

“Working with GE Vernova and Engine No. 1, Chevron is aiming to deliver affordable, reliable energy to U.S. data centers as demand for AI grows,” Chevron said in that statement.

“Together, the companies plan to deliver a total of up to four gigawatts (GW) of power – enough to power as many as 3.5 million U.S. homes for one year. By doing so, the companies are leveraging natural gas abundance to drive the nation’s AI leadership,” it added.

Back in July, ExxonMobil noted on its website that energy demand for AI “is at an all-time high” and said the company is “a part of the solution”.

“Artificial intelligence is transforming all aspects of modern life – from virtual assistants to the data centers that power it. With the evolution and outgrowth of AI comes increasing demand for reliable energy – and lots of it,” Exxon added.

“As the largest producer of oil and natural gas in the United States, ExxonMobil is uniquely positioned to meet this challenge,” it continued.

Exxon said in this statement that it is uniquely positioned to help power low carbon data centers by using natural gas power generation, abated by carbon capture and sequestration (CCS).

“We own and operate the largest integrated CCS system in the U.S., spanning more than 1,000 miles of pipeline connecting customers and permanent storage sites along the U.S. Gulf Coast,” it pointed out.

In a statement posted on its site back in December last year, Exxon said it was working to expand its customers to include data centers to support AI growth.

To contact the author, email [email protected]

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NextDecade contractor Bechtel awards ABB more Rio Grande LNG automation work

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Equinor begins drilling $9-billion natural gas development project offshore Brazil

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Woodfibre LNG receives additional modules as construction advances

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ExxonMobil begins Turrum Phase 3 drilling off Australia’s east coast

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Schneider Electric Maps the AI Data Center’s Next Design Era

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SoftBank’s 10 GW Ohio Campus Marks a Turning Point for AI Infrastructure

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Q1 Executive Roundtable Recap

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

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Executive Roundtable: The AI Infrastructure Credibility Test

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International Data Center Day: Future Frontiers 2030-2070

In honor of this year’s International Data Center Day 2026 (Mar 25), Data Center Frontier presents a forward-looking vision of what the next era of digital infrastructure education—and imagination—could become. As the media partner of 7×24 Exchange, DCF is committed to elevating both the technical rigor and the human story behind the systems that power the AI age. What follows is not reportage, but a plausible future: a narrative exploration of how the next generation might learn to build, operate, and ultimately redefine data centers—from tabletop scale to lunar megacampuses. International Data Center Day, 2030 The Little Grid That Could They called it “Build the Cloud.” Which, to the adults in the room, sounded like branding. To the kids, it sounded literal. On a gymnasium floor somewhere in suburban Ohio (though it could just as easily have been Osaka, or Rotterdam, or Lagos) thirty-two teams of middle school students crouched over sprawling tabletop worlds the size of model train layouts. Only these weren’t towns with plastic trees and HO-scale diners. These were data centers. Tiny ones. Living ones. Or trying to be. Each team had been given the same kit six weeks earlier: modular rack frames no taller than a juice box, fiber spools thin as thread, micro solar arrays, a handful of millimeter-scale wind turbines, and a small fleet of programmable robotic “operators”—wheeled, jointed, blinking with LED status lights. The assignment had been deceptively simple: Design, build, and operate a self-sustaining data center campus. Then make it come alive. Now it was International Data Center Day, 2030, and the judging had begun. The Sound of Small Machines Thinking If you stood at the edge of the gym and closed your eyes, it didn’t sound like a science fair. It sounded like… something else. A low hum of micro-inverters stepping

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Superconducting the AI Era: Rethinking Power Delivery for Gigawatt Data Centers

For the data center industry, the AI era has already rewritten the rules around capital deployment, site selection, and infrastructure scale. But as the build cycle accelerates into the gigawatt range, a deeper constraint is coming into focus; one that sits beneath generation, beneath interconnection queues, and even beneath permitting. It is the physical act of moving power. The challenge is no longer simply how to procure energy, but how to deliver it efficiently from the grid edge to the campus, across buildings, and ultimately into racks that are themselves becoming industrial-scale power consumers. In this emerging reality, traditional copper-based distribution systems are beginning to show signs of strain not just economically, but physically. In the latest episode of the Data Center Frontier Show Podcast, MetOx CEO Bud Vos frames this moment as a structural turning point for the industry, one where superconducting technologies may begin to shift from theoretical to practical. “When you start looking at gigawatt-type campuses,” Vos explains, “you find three fundamental constraints in the power distribution problem: the grid interconnect, the campus distribution, and then delivery inside the data hall.” Each of these layers compounds the difficulty of scaling infrastructure in a copper-based world. More capacity means more cables, more trenching, more materials, and more complexity in an exponential expansion of the physical systems required to support AI workloads. A Different Kind of Conductor High-temperature superconducting (HTS) wire offers a radically different path forward. Developed from research originating at the University of Houston and now manufactured through advanced thin-film processes, HTS replaces bulk conductive material with a highly efficient layered structure capable of carrying dramatically higher current densities. Vos describes the manufacturing approach in familiar terms for a data center audience: “You can think of it as a semiconductor process. We’re creating thin film depositions on

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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