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What Keeps Data Centers and Their Utility Partners Up at Night: The Power Problem

AI has the potential to revolutionize how we manage the grid, marking a transformative shift in how utilities optimize operations, enhance reliability, and meet evolving consumer demands. Through the deployment of AI-driven algorithms and predictive analytics, utilities can anticipate grid dynamics, optimize energy flows, and proactively address challenges in real time. The integration of AI […]

AI has the potential to revolutionize how we manage the grid, marking a transformative shift in how utilities optimize operations, enhance reliability, and meet evolving consumer demands. Through the deployment of AI-driven algorithms and predictive analytics, utilities can anticipate grid dynamics, optimize energy flows, and proactively address challenges in real time. The integration of AI with cloud infrastructure further enhances efficiency and performance, enabling utilities to leverage vast amounts of data from diverse sources, including weather data, edge data, and advanced metering systems (AMS). 

By leveraging machine learning and analytics to merge and assess data streams and sensored information, utilities can unlock new levels of efficiency and performance. The challenges of our power needs are so complex that a system will be best utilized to process the various permutations and uncertainties; this will need to be a highly sophisticated predictive tool, but if properly developed it can enhance grid equipment lifespans, apply data-driven decision making, identify issues quickly, and reduce unplanned downtime. 

Utilities are increasingly recognizing the importance of leveraging AI to gain intimate insights into their customers’ energy needs and behaviors, allowing them to prepare for future power demands effectively. From improving customer experiences through innovative applications to reimagining day-to-day operations with self-healing grid technology, utilities are embracing AI to drive digital transformation and move beyond their traditional roles. This data-driven approach not only optimizes grid performance but also enhances customer experiences and drives digital transformation within the industry.

Strategic Grid Planning for Looming Demand

Part of the planning that worries them most is not just how to supply power to more data centers. At least data centers clue our local utilities in on our upcoming needs. Electric vehicles are altogether unpredictable, except for areas that have seen regulatory timelines enforced. They also tend to flock together, with charging stations handling many at a time. More than just consumer use, they have potential fleets being converted in bulk.

The proliferation of electric vehicles as well as data centers presents both challenges and opportunities for grid planners. Word on the street is that electrification of the transportation market will double energy usage in 10 years and lead to an 800% increase over the next 20 years. That’s the load that has them most worried, and calculating how many electric vehicles they can handle. They need to get uncomfortably close to what consumers and businesses are going to want in the future to predict and plan for this demand. 

Strategic grid planning is essential to accommodate the surge in electricity demand while ensuring reliability and stability. Utilities are exploring innovative solutions such as smart charging infrastructure, vehicle-to-grid integration, and energy storage to manage peak demand and optimize resource utilization. With the exponential growth of EVs and data centers, grid planning has never been more critical. We must invest in scalable and resilient infrastructure to support this electrified future.

Embracing the Grid Edge and Prosumer Movement

The emergence of the prosumer movement and the evolution of the grid edge are reshaping the traditional utility-consumer relationship, transforming consumers from passive recipients to active participants in the energy transition. This shift is driven by the proliferation of rooftop solar, home energy storage, and distributed energy resources (DERs), highlighting the importance of grid-edge innovations and community energy initiatives.

Consumers are no longer merely consumers; they are prosumers actively shaping the energy landscape. Utilities must adapt to this transformation and empower consumers to become active stakeholders in the energy transition. At the grid edge, where consumers interact directly with energy systems, better data quality, validity, and granularity are achieved, leading to low latency, high reliability, and scalability. This proximity to data sources enables predictive infrastructure and empowers citizens to be part of the solution.

The path to edge intelligence involves various components, including metrology for energy, demand, and power quality, as well as anomaly detection for outage, temperature, loose neutral, and tampering. Despite existing limitations in edge technology, such as firmware-driven systems and communication bottlenecks, rapid advancements in hardware, communication protocols, and software are driving progress. Software deployed at the edge is customizable, agile, and driven by an application mindset, leveraging more advanced algorithms, especially in machine learning.

Overcoming challenges at the edge requires leveraging technologies that enable robust networks capable of making informed decisions and identifying various devices, such as EVs, solar panels, batteries, and pump controls. This necessitates funneling and utilizing data effectively to empower consumers to make informed energy decisions and optimize energy usage. Despite the complexities introduced by IP addresses and evolving technologies, the focus remains on enabling consumers to actively participate in the energy transition while ensuring the reliability and scalability of grid-edge solutions. 

Renewable Energy Integration

Renewable energy integration is driving a significant transformation in the energy landscape, with solar and wind power playing increasingly prominent roles in the generation mix. Utilities are investing in renewable energy infrastructure, grid-scale energy storage, and innovative grid-edge technologies to maximize the potential of renewables and reduce carbon emissions.

With sustainability at the forefront of efforts, integrating renewable energy sources into the grid and leveraging advanced technologies are seen as crucial steps toward achieving environmental goals while ensuring reliability and affordability for customers. Last year, 84% of new installed capacity was renewables and storage, marking a substantial shift in the generation mix. Demand response, accounting for 60% of capacity, is becoming increasingly significant.

Orchestrating the energy transition requires flexible resources and demand-side capabilities, with virtual power plants (VPPs) emerging as cost-effective solutions. However, managing the transition poses challenges, particularly in forecasting net load, VPP capabilities, and battery capacity at scale. Artificial intelligence and machine learning are key applications that can help the industry navigate these transitions and keep moving forward.

Some companies are exploring off-grid solutions due to frustrations with traditional electricity networks. Off-grid technology, once frowned upon, is now considered a necessity for certain operations. Companies like Microsoft and Google are exploring options such as small nuclear plants and zero-emissions fusion power to power energy-intensive operations, although regulatory and land acquisition challenges remain significant hurdles in this endeavor.

Fostering Innovation and Scalability

In the midst of rapid change, utilities are recognizing the critical importance of innovation and scalability in navigating the evolving energy landscape. By fostering a culture of innovation, establishing strategic partnerships, and prioritizing scalability, utilities can unlock new opportunities for success and drive significant progress towards a smarter, more resilient grid.

To meet the challenges of tomorrow, it is essential to invest in cutting-edge technologies and scalable solutions. This proactive approach enables utilities to pioneer the power grid of the future while delivering tangible value to customers and communities alike.

As electrification continues to grow rapidly and new technologies emerge, such as nuclear energy, utilities are embracing innovative projects to enhance reliability and resiliency. For instance, there are some pretty cool utility-driven projects in my local area I’ve been following: Duke Energy’s floating solar project in South Florida and residential battery installations in neighborhoods like Hunter’s Creek exemplify the shift towards cleaner, more resilient energy solutions. Additionally, initiatives like the 100% green hydrogen project in DeBary, FL highlight the ongoing efforts to integrate renewable energy sources and drive sustainability forward.

Not Your Grandparents’ Power Grid

The pulse of energy shapes our present and affords our future. The job to be done itself has not changed over time: people need light and power. What has changed is the complexities that utility providers must navigate in the modern energy landscape: the convergence of AI, EV integration, grid-edge innovations, renewables, and scalable solutions are reshaping the trajectory of the power grid. By embracing these key themes and driving meaningful progress in each area, utilities can unlock new opportunities for growth, sustainability, and resilience, propelling the power grid into a new era of innovation and prosperity. 

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How Lumen is dismantling decades of network complexity

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An “Unstoppable Web” is a reality that Tether is orchestrating

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Trump Administration Keeps Colorado Coal Plant Open to Ensure Affordable, Reliable and Secure Power in Colorado

WASHINGTON—U.S. Secretary of Energy Chris Wright today issued an emergency order to keep a Colorado coal plant operational to ensure Americans maintain access to affordable, reliable and secure electricity. The order directs Tri-State Generation and Transmission Association (Tri-State), Platte River Power Authority, Salt River Project, PacifiCorp, and Public Service Company of Colorado (Xcel Energy), in coordination with the Western Area Power Administration (WAPA) Rocky Mountain Region and Southwest Power Pool (SPP), to take all measures necessary to ensure that Unit 1 at the Craig Station in Craig, Colorado is available to operate. Unit One of the coal plant was scheduled to shut down at the end of 2025 but on December 30, 2025, Secretary Wright issued an emergency order directing Tri-State and the co-owners to ensure that Unit 1 at the Craig Station remains available to operate. “The last administration’s energy subtraction policies threatened America’s energy security and positioned our nation to likely experience significantly more blackouts in the coming years—thankfully, President Trump won’t let that happen,” said Energy Secretary Wright. “The Trump Administration will continue taking action to ensure we don’t lose critical generation sources. Americans deserve access to affordable, reliable, and secure energy to power their homes all the time, regardless of whether the wind is blowing or the sun is shining.” Thanks to President Trump’s leadership, coal plants across the country are reversing plans to shut down. In 2025, more than 17 gigawatts (GW) of coal-power electricity generation were saved. On April 1, once Tri-State and the WAPA Rocky Mountain Region join the SPP RTO West expansion, SPP is directed to take every step to employ economic dispatch to minimize costs to ratepayers. According to DOE’s Resource Adequacy Report, blackouts were on track to potentially increase 100 times by 2030 if the U.S. continued to take reliable

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NextDecade contractor Bechtel awards ABB more Rio Grande LNG automation work

NextDecade Corp. contractor Bechtel Corp. has awarded ABB Ltd. additional integrated automation and electrical solution orders, extending its scope to Trains 4 and 5 of NextDecade’s 30-million tonne/year (tpy)  Rio Grande LNG (RGLNG) plant in Brownsville, Tex. The orders were booked in third- and fourth-quarters 2025 and build on ABB’s Phase 1 work with Trains 1-3, totaling 17 million tpy.  The scope for RGLNG Trains 4 and 5 includes deployment of an integrated control and safety system consisting of a distributed control system, emergency shutdown, and fire and gas systems. An electrical controls and monitoring system will provide unified visibility of the plant’s electrical infrastructure. These two overarching solutions will provide a common automation platform. ABB will also supply medium-voltage drives, synchronous motors, transformers, motor controllers and switchgear.  The orders also include local equipment buildings—two for Train 4 and one for Train 5— housing critical control and electrical systems in prefabricated modules to streamline installation and commissioning on site. The solutions being delivered to Bechtel use ABB adaptive execution, a methodology for capital projects designed to optimize engineering work and reduce delivery timelines. Phase 1 of RGLNG is under construction and expected to begin operations in 2027. Operations at Train 4 are expected in 2030 and Train 5 in 2031. ABB’s senior vice-president for the Americas, Scott McCay, confirmed to Oil & Gas Journal at CERAWeek by S&P Global in Houston that the company is doing similar work through Tecnimont for Argent LNG’s planned 25-million tpy plant in Port Fourchon, La.; 10-million tpy Phase 1 and 15-million tpy Phase 2. Argent is targeting 2030 completion for its plant.

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Persistent oil flow imbalances drive Enverus to increase crude price forecast

Citing impacts from the Iran war, near-zero flows through the Strait of Hormuz, accelerating global stock draws, and expectations for a muted US production response despite higher prices, Enverus Intelligence Research (EIR) raised its Brent crude oil price forecast. EIR now expects Brent to average $95/bbl for the remainder of 2026 and $100/bbl in 2027, reflecting what it described as a persistent global oil flow imbalance that continues to draw down inventories. “The world has an oil flow problem that is draining stocks,” said Al Salazar, director of research at EIR. “Whenever that oil flow problem is resolved, the world is left with low stocks. That’s what drives our oil price outlook higher for longer.” The outlook assumes the Strait of Hormuz remains largely closed for 3 months. EIR estimates that each month of constrained flows shifts the price outlook by about $10–15/bbl, underscoring the scale of the disruption and uncertainty around its duration. Despite West Texas Intermediate (WTI) prices of $90–100/bbl, EIR does not expect US producers to materially increase output. The firm forecasts US liquids production growth of 370,000 b/d by end-2026 and 580,000 b/d by end-2027, citing drilling-to-production lags, industry consolidation, and continued capital discipline. Global oil demand growth for 2026 has been reduced to about 500,000 b/d from 1.0 million b/d as higher energy prices and anticipated supply disruptions weigh on economic activity. Cumulative global oil stock draws are estimated at roughly 1 billion bbl through 2027, with non-OECD inventories—particularly in Asia—absorbing nearly half of the impact. A 60-day Jones Act waiver may provide limited short-term US shipping flexibility, but EIR said the measure is unlikely to materially affect global oil prices given broader market forces.

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

Equinor has started drilling the Raia natural gas project in the Campos basin presalt offshore Brazil. The $9-billion project is Equinor’s largest international investment, its largest project under execution, and marks the deepest water depth operation in its portfolio. The drilling campaign, which began Mar. 24 with the Valaris DS‑17 drillship, includes six wells in the Raia area 200 km offshore in water depths of around 2,900 m. The area is expected to hold recoverable natural gas and condensate reserves of over 1 billion boe. Raia’s development concept is based on production through wells connected to a 126,000-b/d floating production, storage and offloading unit (FPSO), which will treat produced oil/condensate and gas. Natural gas will be transported through a 200‑km pipeline from the FPSO to Cabiúnas, in the city of Macaé, Rio de Janeiro state. Once in operation, expected in 2028, the project will have the capacity to export up to 16 million cu m/day of natural gas, which could represent 15% of Brazil’s natural gas demand, the company said in a release Mar. 24. “While drilling takes place, integration and commissioning activities on the FPSO are progressing well putting us on track towards a safe start of operations in 2028,” said Geir Tungesvik, executive vice-president, projects, drilling and procurement, Equinor. The Raia project is operated by Equinor (35%), in partnership with Repsol Sinopec Brasil (35%) and Petrobras (30%).

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Woodfibre LNG receives additional modules as construction advances

Woodfibre LNG LP has received two major modules within a week for its under‑construction, 2.1‑million tonne/year (tpy) LNG export plant near Squamish, British Columbia, advancing construction to about 65% complete. The deliveries include the liquefaction module—the project’s heaviest and most critical process unit—and the powerhouse module, which will serve as the plant’s central power and control hub. The liquefaction module, delivered aboard the heavy cargo vessel Red Zed 1, is the 15th of 19 modules scheduled for installation at the site, the company said in a Mar. 24 release. Weighing about 10,847 metric tonnes and occupying a footprint roughly equivalent to a football field, it is among the largest modules fabricated for the project. Once installed and commissioned, the liquefaction module will cool natural gas to about –162°C, converting it into LNG for export. Shortly after the liquefaction module’s arrival, Woodfibre LNG received the powerhouse module, the 16th module delivered to site. Weighing more than 4,200 metric tonnes, the powerhouse module will function as a power and control system, receiving electricity from BC Hydro and managing and distributing power to the plant’s electric‑drive compressors. The Woodfibre LNG project is designed as the first LNG export plant to use electric‑drive motors for liquefaction, replacing conventional gas‑turbine‑driven compressors. The Siemens electric‑drive system will be powered by renewable hydroelectricity from BC Hydro, eliminating the largest operational source of greenhouse gas emissions typically associated with liquefaction, the company said. The project is being built near the community of Squamish on the traditional territory of the Sḵwx̱wú7mesh Úxwumixw (Squamish Nation) and is regulated in part by the Indigenous government.  All 19 modules are expected to arrive on site by spring 2026. Construction is scheduled for completion in 2027. Woodfibre LNG is owned by Woodfibre LNG Ltd. Partnership, which is 70% owned by Pacific Energy Corp.

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

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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

For the fourth installment of DCF’s Executive Roundtable for the First Quarter of 2026, we turn to a question that increasingly sits alongside power and capital as a defining constraint. Credibility. As AI-driven data center development accelerates, public scrutiny is rising in parallel. Communities, regulators, and policymakers are taking a closer look at the industry’s footprintin terms of its energy consumption, its land use, and its broader impact on local infrastructure and ratepayers. What was once a relatively low-profile sector has become a visible and, at times, contested presence in regional economies. This shift reflects the sheer scale of the current build cycle. Multi-hundred-megawatt and gigawatt campuses are no longer theoretical in any sense. They are actively being proposed and constructed across key markets. With that scale comes heightened expectations around transparency, accountability, and tangible community benefit. At the same time, the industry faces a more complex regulatory and political landscape. Questions around grid capacity, rate structures, environmental impact, and economic incentives are increasingly being debated in public forums, from state utility commissions to local zoning boards. In this environment, the ability to secure approvals is no longer assured, even in historically favorable markets. The concept of a “social license to operate” has therefore moved to the forefront. Beyond technical execution, developers and operators must now demonstrate that AI infrastructure can be deployed in a way that aligns with community priorities and delivers shared value. In this roundtable, our panel of industry leaders explores what will define that credibility in the years ahead and what the data center industry must do to sustain its momentum in an era of growing public scrutiny.

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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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DCF Poll: AI Data Center Assumptions

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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A Faster Path to Power: What Natrium’s NRC Approval Means for AI Infrastructure

The race to build AI infrastructure at scale has exposed a deeper constraint than capital or compute: power that can be delivered on predictable timelines. That constraint is now colliding with a system that has historically moved at the pace of decades. But in early March, a key signal emerged that the equation may be starting to change. A Regulatory Breakthrough at the Moment of Peak Power Demand TerraPower’s Natrium reactor cleared a major milestone with the Nuclear Regulatory Commission, which approved a construction permit for Kemmerer Power Station Unit 1 in Wyoming, representing the company’s first commercial-scale plant. It is the first reactor construction approval the NRC has granted in nearly a decade, and the first for a commercial non-light-water reactor in more than 40 years. More significantly, it is the first advanced reactor to reach this stage under the modern U.S. licensing framework. For an industry increasingly defined by gigawatt-scale AI campuses and compressed build cycles, that milestone lands with unusual timing. Construction Approved — But Not Yet ‘Power Delivered’ The distinction between construction approval and operational readiness is critical. TerraPower has not received a license to generate electricity. What the NRC has granted is permission to begin nuclear-related construction at the Kemmerer site, following safety and environmental review. Before the plant can operate, TerraPower’s subsidiary, US SFR Owner, must still secure a separate operating license. But in practical terms, this is the moment when a project transitions from concept to execution. It is a regulatory green light not for power generation, but for steel, concrete, and capital deployment. And in the context of advanced nuclear, that step has historically been the hardest to reach. An 18-Month Signal to the Market The speed of that approval may ultimately matter as much as the approval itself. TerraPower submitted its construction

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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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