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AI Infrastructure Brief: Power, Capital, and Silicon Collide in the Next Phase of the Data Center Buildout

The global race to build infrastructure for artificial intelligence is accelerating across North America, reshaping the economics, politics, and physical footprint of the data center industry. New hyperscale campuses, multi-gigawatt development pipelines, power generation partnerships, and escalating policy debates illustrate how quickly the digital infrastructure landscape is evolving. At the same time, the industry is […]

The global race to build infrastructure for artificial intelligence is accelerating across North America, reshaping the economics, politics, and physical footprint of the data center industry.

New hyperscale campuses, multi-gigawatt development pipelines, power generation partnerships, and escalating policy debates illustrate how quickly the digital infrastructure landscape is evolving. At the same time, the industry is confronting rising scrutiny from communities and policymakers concerned about energy consumption and grid capacity.

From Memphis and Texas to Wisconsin and Northern Virginia, recent developments highlight a central reality: AI infrastructure is now scaling at industrial magnitude.

AI Cloud Providers Race to Deploy Blackwell Infrastructure

Several announcements this week underscore the enormous scale of compute infrastructure now being deployed to support next-generation AI workloads.

AI cloud provider CoreWeave said it plans to add roughly 5 gigawatts of additional data center capacity by 2030, while projecting that capital expenditures in 2026 could double as the company deploys NVIDIA’s next generation of GPU systems.

Much of that spending is expected to center on NVIDIA’s Blackwell architecture, particularly the GB200 NVL72 platform, which is designed to support massive AI training clusters. Industry analysts note that CoreWeave’s GPU capacity for 2026 is already largely sold out, reflecting the extraordinary demand for AI compute from hyperscalers, model developers, and enterprise customers.

Elsewhere, xAI, the artificial intelligence company founded by Elon Musk, filed permits totaling $659 million tied to expansion of its Memphis “Colossus” campus. The original facility, built in partnership with infrastructure providers including Supermicro, houses one of the largest GPU clusters in the world supporting the company’s Grok models.

At the infrastructure layer, hardware vendors are also adapting their architectures to AI scale.

NVIDIA and optical component manufacturer Coherent announced a strategic partnership to develop next-generation optical interconnect technology, addressing one of the biggest bottlenecks in AI cluster design: moving massive volumes of data between GPUs at ultra-low latency.

Meanwhile, Super Micro Computer continues pushing modular data center architectures designed to accelerate deployment of AI clusters by enabling faster assembly of compute infrastructure at scale.

AI Factories and the Rise of Gigawatt Campuses

The next generation of data center campuses is increasingly measured in hundreds of megawatts and, as you might’ve heard, even gigawatts.

A prominent example is Nebius, which refers to its facilities as “AI Factories.” The company’s planned campus in Independence, Missouri will serve as its flagship U.S. deployment and recently secured local approval for an extraordinary $150.6 billion Chapter 100 bond issuance, believed to be one of the largest economic development incentives ever approved at the municipal level.

Other projects across the country highlight the same trend toward hyperscale development.

In West Virginia, developer Penzance is targeting a 600-megawatt campus, reflecting growing interest in regions with available land and power infrastructure.

Prometheus Hyperscale has detailed plans for a major Dallas-area campus, positioning the site as a large-scale AI infrastructure hub within the rapidly expanding Texas data center market.

Texas itself is emerging as one of the epicenters of AI infrastructure growth. Google recently filed plans for a fifth data center building at its Midlothian campus, part of a much broader $40 billion investment in Texas through 2027 that includes additional campuses planned in Armstrong and Haskell Counties.

Hyperscale development continues across the Midwest as well. Meta Platforms recently signed a pre-development agreement for a campus in Beloit, Wisconsin, even as another Meta project in Beaver Dam has become a focal point for debate over power consumption. Regulatory filings indicate that the Beaver Dam site could require roughly 220 megawatts of electricity, a load large enough to rival that of many small cities.

Real Estate Capital Floods Into AI Infrastructure

The rapid expansion of AI infrastructure has attracted enormous interest from institutional investors.

Private equity giant Blackstone is reportedly exploring the creation of a publicly traded vehicle focused on acquiring AI data center assets. The proposed entity could raise tens of billions of dollars from retail investors, extending digital infrastructure investment beyond traditional pension funds and sovereign wealth investors.

In Northern Virginia, HighBrook Investors recently closed an inaugural data center fund targeting 300 megawatts of development capacity, reinforcing the region’s continuing status as the largest data center market in the world.

Other recent transactions include GI Partners acquiring two Baltimore-area facilities and Harrison Street selling two powered-shell sites in Maryland, reflecting the ongoing flow of capital into the sector.

Meanwhile, Canadian institutional investor La Caisse committed C$240 million to support construction of Cologix’s MTL8 facility in Montréal, an AI-ready data center designed to leverage Quebec’s hydroelectric power resources.

The Power Question Becomes Central

Behind almost every data center announcement today lies a deeper question: where will the electricity come from?

That challenge was highlighted by a major power infrastructure project involving Babcock & Wilcox, which received approval to develop a $2.4 billion natural-gas generation facility that will supply power to Applied Digital’s expanding AI campuses.

B&W has received full notice to proceed on a $2.4 billion design-build agreement with Base Electron, an independent power producer (IPP) backed by Applied Digital (NASDAQ: APLD) to deliver 1.2 GW of new generation capacity. The generation is intended to supply power to Applied Digital AI Factory campuses under separate power supply agreements. The project includes four 300-megawatt natural gas-fired boilers and steam turbine generator systems.

Base Electron, the IPP backed by Applied Digital, is focused on developing and owning generation assets that deliver new, dispatchable capacity to the grid and to contracted customers, including power supply agreements supporting Applied Digital’s high-density AI data center campuses.

Under its agreement with Base Electron, B&W will engineer, procure and construct the facility, with engineering and manufacturing activities already underway. Siemens Energy has been formally released to design and supply the steam turbine generator sets.

The project reflects the growing industry shift toward dedicated energy infrastructure.

Elsewhere, Atlantic Energy was selected to provide power for a 380,000-square-foot data center in Akron developed by Viking Data Centers, with plans for expansion to 150 megawatts.

In Canada, an agreement among TransAlta, Brookfield, and the Canada Pension Plan Investment Board outlines plans for a 230-megawatt data center project in Alberta, leveraging the province’s abundant energy resources.

These projects illustrate what many industry executives now describe as the “power island” trend: large data center campuses increasingly pairing with dedicated behind-the-meter generation rather than relying entirely on grid capacity.

In effect, the industry is beginning to shift from grid-dependent infrastructure toward hybrid or partially grid-independent models, a structural change that could reshape how AI campuses are designed and financed.

Hyperscale Expansion Continues

Major operators continue expanding their U.S. footprints.

NTT Global Data Centers recently secured four U.S. leases totaling approximately 115 megawatts, reflecting continued demand from hyperscale cloud providers and AI platform companies.

Amazon, meanwhile, has acquired a university campus in Virginia that could eventually be redeveloped into a data center site, another example of hyperscalers securing land for future expansion.

Industrial real estate giant Prologis has also revealed plans for a hyperscale development in Butler County near Cincinnati, Ohio, highlighting the growing role of logistics real estate developers in the digital infrastructure ecosystem. The San Francisco-based real estate development firm is planning the data center campus, called “Project Mila,” on 141 acres in the city of Trenton’s industrial park.

Prologis is recognized as the world’s largest industrial real estate investment trust (REIT) and one of the largest, most valuable real estate companies globally, with over $200 billion in assets under management and 1.3 billion square feet of logistics space.

Construction Firms Ride the Data Center Boom

The surge in hyperscale development is also transforming the construction industry.

Turner Construction reported record revenue of $29.2 billion in 2025, with data center projects representing a significant driver of that growth.

The company is one of several engineering and construction firms benefiting from the industrialization of data center development, where projects increasingly resemble large energy or manufacturing facilities in scale.

Turner said its project backlog rose 34% year over year to $44.3 billion, the largest year-end backlog in the company’s history. Data center construction now accounts for roughly 37% of that pipeline, reflecting sustained investment in artificial intelligence infrastructure and digital capacity.

Political Scrutiny and Community Resistance Grow

As the industry expands, political scrutiny is intensifying.

In Birmingham, Alabama, the City Council approved a 180-day moratorium on new data center applications on March 3. The measure exempts facilities under 20 megawatts as well as projects with existing permits, including the Nebius site on Lakeshore Parkway.

In Oregon, lawmakers are considering revisions to House Bill 4084, which would temporarily suspend the ability of data centers to qualify for extended 10-year property tax exemptions in enterprise zones. While not a ban on data centers, the proposal reflects growing debate about the public incentives used to attract large projects.

Community opposition has also halted some projects. A proposed campus in Apex, North Carolina, was withdrawn after local resistance, while a West Louisville data center was approved despite significant community concerns.

Even the political arena is beginning to reflect the industry’s growing influence. Data center development has surfaced as an issue in North Carolina primary elections, where candidates have debated the role of digital infrastructure in local economic development.

The Bottom Line

Look past the individual announcements and a clearer pattern emerges: the architecture of the AI data center economy is changing.

Compute is scaling faster than traditional infrastructure models were designed to support. The rapid deployment of Blackwell-era GPU clusters is pushing facilities toward unprecedented density and power demand. At the same time, developers are pursuing hundreds-of-megawatts campuses as the new baseline, with gigawatt-scale planning increasingly common.

The capital markets are responding in kind. Institutional investors and infrastructure funds continue pouring billions into data center development, while new investment vehicles, such as Blackstone’s proposed public data center platform, suggest that AI infrastructure is beginning to look less like niche real estate and more like a new asset class.

But the real pivot point is energy. For decades, the data center industry largely followed the power grid. Today, the relationship is beginning to invert. Faced with transmission bottlenecks and multi-year interconnection queues, developers are increasingly designing projects around dedicated generation, hybrid grid connections, and behind-the-meter energy systems.

The emerging gigawatt era of digital infrastructure is a phase defined not simply by larger buildings, but by a fundamental rethinking of how compute, capital, and energy systems converge. In effect, the modern AI campus is evolving into something closer to a digital industrial complex: part data center, part power plant.

That shift is already visible in projects pairing hyperscale facilities with gas generation, renewable portfolios, or emerging nuclear technologies. It is also why political debates around energy policy, tax incentives, and land use are becoming inseparable from the data center conversation.

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@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: var(–color-primary-main); } .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; font-family: Inter; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #c19a06 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #c19a06 !important; border-color: #c19a06 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #c19a06 !important; border-color: #c19a06 !important; } <!–> –> <!–> ]–> <!–> –> You’ll need free site-access membership to view certain articles below. If you are not already registered with Oil & Gas Journal, sign up now for free. For Offshore articles, sign up here for free. New content will be added as it becomes available.  Oil & Gas Journal content <!–> Economics & Markets –> 26184925 © Robert Hale | Dreamstime.com <!–> ]–> <!–> When the market opened after the initial strike on Iran, oil prices traded $75/bbl on the Open, a $7/bbl jump from Friday’s High, indicating a higher risk premium as the market… –> March 6, 2026 96633437 © Titoonz | Dreamstime.com <!–> ]–> <!–> Broader infrastructure risks are emerging as regional attacks threaten production in Qatar, Saudi Arabia, and Iraq, while Europe and Asia face heightened vulnerability due to … –> March 3, 2026 387409148 © Clare Jackson | Dreamstime.com <!–> ]–> <!–> Despite initial market volatility, oil storage levels and pre-positioned supplies have mitigated immediate price shocks. However, ongoing tensions and insurance issues continue… –> March 2, 2026 220736519 © Pavel Muravev | Dreamstime.com <!–> ]–> <!–> About 20 million b/d of

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Powering AI When the Grid Can’t: Inside the New Behind-the-Meter Playbook

The AI infrastructure boom is forcing a hard reset in how the data center industry thinks about power. What was once a relatively straightforward utility procurement exercise is rapidly evolving into a complex, multi-disciplinary strategy problem spanning generation, fuel logistics, finance, and system architecture. That reality framed a recent special edition of The Data Center Frontier Show Podcast, which recast and updated one of the most consequential sessions from the DCF Trends Summit 2025: From Grid to Onsite Powering: Optimizing Energy Behind the Meter for Data Centers. Moderating the discussion was Fengrong Li, Senior Managing Director at FTI Consulting, whose questions and analytical framing shaped the conversation’s direction. With more than 20 years of experience across energy and infrastructure—including expert testimony before the Federal Energy Regulatory Commission (FERC), the Federal Communications Commission (FCC), and multiple state bodies—Li brought a systems-level perspective that pushed the panel well beyond a simple technology tour. Her premise was clear from the outset: the rise of AI is not just increasing data center demand. It is restructuring the entire power delivery paradigm. A Moderator Focused on the System-Level Shift Li’s role went well beyond traditional moderation. Drawing on a career that includes 13 years at Siemens focused on grid issues and eight years at Mitsui in commodity trading and infrastructure investment, she constructed the discussion around what she described as “one of the most urgent topics shaping digital infrastructure deployment.” “Onsite power and the rise of co-located, integrated power and AI campuses,” Li told the panel, “are accelerating data centers beyond traditional hubs and changing how they interact with the grid.” Throughout the session, Li repeatedly pushed panelists to connect near-term deployment realities with longer-term structural implications particularly around redundancy, financing, and regulatory exposure. The result was a grounded look at an industry that is

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Electrical Applications Engineer Pittsburgh, PA This position is also available in: Denver, CO and Andrews, SC Our client is a leading provider and manufacturer of industrial electrical power equipment used in industrial applications for mission critical operations. They help their customers save money by reducing energy and operating costs and provide solutions for modernizing their customer’s existing electrical infrastructure. This company provides cooling solutions to many of the world’s largest organizations and government facilities and enterprise clients, colocation providers and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Dallas TXThis traveling position is also available in: New York, NY; White Plains, NY;  Ashburn, VA; Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs and a Director of CxA Colos in Dallas, TX *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge

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

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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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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