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EU Delays Release of Russia Energy Roadmap as It Weighs US Pivot

The European Union delayed the release of a plan to phase out Russian fossil fuels as it weighs the impact of the US administration’s pivot on Ukraine, struggles to lower energy prices and strives to maintain unity among member states. The European Commission dropped the publication of a roadmap – which was meant to outline the […]

The European Union delayed the release of a plan to phase out Russian fossil fuels as it weighs the impact of the US administration’s pivot on Ukraine, struggles to lower energy prices and strives to maintain unity among member states.

The European Commission dropped the publication of a roadmap – which was meant to outline the EU’s path for replacing Russian energy – from its schedule on Wednesday, after previously planning to release it on March 26. While it said work on the phaseout plan continued, no new date was specified for its publication.

The postponement comes a day before an emergency summit of EU heads of government, who will discuss mobilizing hundreds of billions of euros in additional financing as they seek to help Ukraine after President Donald Trump pulled back US commitment to European security.

Hungary and Slovakia – whose leaders are friendly with Russia’s Vladimir Putin – have expressed diverging views on how the EU should respond to the geopolitical shift, and the move to delay plans on Russian energy imports may help to avoid antagonizing member states at a critical moment.

“The roadmap is not in the indicative planning, but it is still on and being prepared; only the timing changed in light of latest geopolitical developments,” said Anna Kaisa Itkonen, the commission’s energy spokesperson.

The delay added to a drop in European gas prices on Wednesday.

Futures have been prone to sharp swings recently as economic and geopolitical uncertainties mount. Higher fuel consumption this winter and the end of a transit deal between Russia and Ukraine have intensified the challenge of replenishing Europe’s gas stockpiles, which dropped to the lowest since 2022. 

While pipeline gas supplies from Russia cover less than 5 percent of Europe’s fuel needs after most major routes have been closed since 2022, the nation remains the second largest supplier of LNG to the EU. Purchases hit a record last year, with Spain, France and Belgium being the biggest buyers. 

While sanctioning Russian gas would in theory be the strongest legal tool to end imports, the EU has stopped short of proposing such a measure because it wouldn’t get the required unanimous support amid opposition from Hungary and Slovakia. The plan was to rely on trade measures instead, as those can be adopted by a qualified majority. 

But even that approach could face pushback amid concerns over potential price increases and the continued reliance of some nations on Russian fuel, according to EU diplomats with knowledge of the discussions. There are also doubts whether trade measures other than sanctions would legally act as a force majeure to extricate companies from long-term contracts with Russia.

A main avenue for reducing reliance on Russia would be to increase imports from the US, which European officials have also expressed concerns about.

“The delay to the publishing of the roadmap is no surprise as Europe is still heavily reliant on Russian LNG and getting rid of any gas in the middle of tariff negotiations with the US puts Europe in a difficult position,” said Florence Schmit, an energy strategist at Rabobank. 

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AI power efficiency the target of Lotus Microsystems energy advances

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Energy Department Releases Finalized Fusion Science and Technology Roadmap to Accelerate Commercial Fusion Power

WASHINGTON—The U.S. Department of Energy (DOE) today released the finalized Fusion Science and Technology (FS&T) Roadmap, a national strategy to accelerate the development and commercialization of fusion energy on the most rapid, responsible timeline in history. Building on earlier roadmap efforts, the finalized roadmap brings together fusion science, technology, infrastructure, workforce development, and commercialization priorities into a single national strategy to support fusion pilot plants and commercial fusion power in the mid-2030s. Fusion is the process that powers the sun and stars. For decades, scientists and engineers have worked to bring that same process to Earth as a source of abundant, reliable energy. The finalized roadmap outlines how DOE, industry, universities, and national laboratories will work together to accelerate the path toward commercial fusion energy in the United States. This effort advances President Trump’s energy dominance agenda and reinforces the Administration’s commitment to expanding reliable American energy production, strengthening domestic supply chains, and maintaining U.S. leadership in critical technologies. By accelerating progress toward commercial fusion power, DOE is helping secure a future of abundant and reliable energy. “Fusion energy has entered a new era defined by extraordinary scientific progress and public-private momentum,” said DOE Under Secretary for Science Dr. Darío Gil. “With this roadmap, we now have the clarity, coordination, and sustained commitment needed to turn the promise of fusion into a reality for the American people.” Developed with input from more than 800 scientists and engineers across the public and private sectors, the finalized FS&T Roadmap reflects contributions from more than 15 private companies, over 10 National Laboratories, and more than 70 universities. The roadmap identifies the critical science and technology gaps that must be closed to realize fusion pilot plants and strengthen U.S. leadership in the global fusion industry. The FS&T Roadmap establishes a unified strategy for the U.S.

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Aramco to divest Malaysian refining assets

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Delfin Midstream takes $5-billion FID for first FLNG vessel

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Chevron files $13.8-billion Argentina oil development proposal

Chevron Corp. applied June 2 to join Argentina’s Large Investment Incentive Regime (RIGI) for a $13.8-billion unconventional oil development at its 100% operated El Trapial-Este block in northern Neuquén province. Until recently, RIGI had attracted about $93 billion across 36 projects. Chevron’s application, which remains subject to government approval, is equivalent to almost one seventh of that total. The filing, which does not consitute a final investment decision, is Chevron’s largest individual investment proposal in Argentina since it entered the country in 1999 and the second-largest project submitted under RIGI, behind YPF SA’s $25-billion LLL Oil development.  Chevron said it is targeting production of about 30,000 b/d from El Trapial-Este, subject to the availability of takeaway infrastructure. The block currently produces about 7,000 b/d. Chevron tested the block with a 7-well pilot in 2021 and has been carrying out development since late 2022, using laterals of more than 3,000 m and techniques transferred from the US Permian basin. In 2023, Chevron committed $500 million to that phase. During the company’s first-quarter earnings call on May 1, chief executive officer Mike Wirth anchored Chevron’s 2030 targets in “assets that are operating today.” El Trapial-Este was not explicitly identified among assets described as the main base for those targets. Wirth also said Chevron would not accelerate Permian production even with Brent above $100/bbl, preferring to manage that asset for free cash flow rather than volume. In the same presentation, Wirth named Argentina among the sources of equity crude that feed Chevron’s global refining system, along with Tengiz, Guyana, the Permian, and Venezuela. The earnings call came weeks before the El proposal filing.  Vaca Muerta costs, takeaway capacity  Breakeven costs in Vaca Muerta’s best blocks are about $40/bbl at the wellhead, according to Rystad Energy, while normalized well productivity—adjusted for lateral length and fracture

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Santos lets rig contract for Bedout subbasin exploration campaign

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US underground natural gas storage capacity edges higher in 2025

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From the data center to the edge: How to build secure, effective enterprise AI infrastructure

While hyperscalers and neo-cloud providers may get the lion’s share of attention for providing AI infrastructure, many enterprises are taking a build-it-themselves approach to meet their specific AI requirements. The success of such projects is crucial to achieving business objectives, yet companies face significant challenges as they try to scale pilots to production. Organizations must keep up with the dynamic, ever-changing demands that AI applications place on compute and network infrastructure, from the data center to the edge. That means architecting systems to grow as demand warrants and to avoid performance bottlenecks. The architecture must also account for AI-driven security vulnerabilities and ensure appropriate defenses are in place. Yes, it’s a tall order. But here, in simplified form, is a three-step plan for meeting those objectives. Step one: Go modular Integrating all the required components in piecemeal fashion for an AI factory is complex, costly, and fraught with integration risk. Start with a modular design, based on proven NVIDIA reference architectures. A modular approach combines pre-validated accelerated computing hardware, AI software, and orchestration platforms, as well as networking and storage capabilities. A modular strategy speeds implementation and creates a faster time to value for your AI infrastructure. Using modules that combine compute, networking, and storage makes it easier to scale capacity as needed, whether in the data center or at edge facilities. In addition, the modular approach simplifies the job of addressing varying requirements, from inferencing engines at the edge to massive-scale model training in the data center, while staying within the same solution family. The same applies to easing integration processes, as modular platforms offer pre-validated software. The Cisco Secure AI Factory with NVIDIA approach, for example, includes hardware (Cisco AI PODS) that is pre-validated to work with NVIDIA AI Enterprise software; Cisco Security and Splunk Observability software; orchestration

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OpenAI weighs Nvidia-backed lease for 10 GW Ohio data center campus

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Arista unveils 1.6T rack-scale switch family for AI infrastructure

The new Arista family joins a growing ecosystem of vendors looking to tap into the 1.6T Ethernet world, which includes Cisco, Nvidia, Celestica and others. “Arista Network’s new 7060XE7 Series is a strong signal of where large-scale AI fabrics are heading: higher bandwidth, better power efficiency, and tighter integration between compute, optics, silicon, cooling, and network operating software,” wrote Sameh Boujelbene, vice president, data center switch and AI networks market research for Dell Oro, in a LinkedIn post. Among the features that stand out to her are “strong customer and ecosystem validation from Microsoft Azure, Oracle Cloud Infrastructure, Meta, AMD, and Broadcom.”

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Water Emerges as a Critical Constraint for AI Data Centers

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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  Mechanical Applications Engineer Pittsburgh, PA This position is also available in: Denver, CO; Richmond, VA and Georgetown, SC (live by the beach!). Relo available. Our client is a leading provider and manufacturer of industrial HVAC mechanical equipment used in industrial cooling 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 mechanical 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 New Albany, OH This traveling position is also available in: New York, NY; White Plains, NY; Dallas, TX; Richmond, VA; Ashburn, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Kansas City, MO; Omaha, NE; Chesterton, IN; Indianapolis, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs ***  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

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Fiber’s Next Act: How AI Is Driving Connectivity Closer to the Edge

ORLANDO, Fla. — Much of the conversation surrounding AI infrastructure has focused on GPUs, power generation, cooling systems, and the unprecedented scale of next-generation data center development. But at Fiber Connect 2026, another reality became increasingly clear: none of those investments matter without the network infrastructure required to connect them. That theme emerged repeatedly during a conversation between Data Center Frontier Editor-in-Chief Matt Vincent and Clearfield Chief Commercial Officer Anis Khemakhem, whose perspective sits at the intersection of broadband infrastructure, fiber deployment, and emerging AI connectivity requirements. While Clearfield is best known throughout the broadband industry for its fiber management and connectivity solutions, Khemakhem argued that AI’s rapid expansion is creating new opportunities, and new challenges, that extend well beyond traditional fiber-to-the-home deployments. “AI is driving that connectivity closer and closer to the edge,” Khemakhem said, noting that growing compute requirements and increasingly latency-sensitive workloads are fundamentally changing assumptions about where infrastructure must reside and how it must be connected. For Data Center Frontier readers, the significance lies in a growing realization that AI infrastructure is becoming as much a networking challenge as a compute challenge. Beyond the Traditional Data Center One of the more notable themes of the discussion was Khemakhem’s view that the term “data center” has become too broad to be useful. The industry often speaks of data centers as a single category, but Clearfield increasingly differentiates between hyperscale campuses, colocation facilities, central office environments, and a rapidly emerging class of edge deployments. “There is no one-size-fits-all data center,” Khemakhem said, describing a continuum that extends from hyperscale facilities all the way to edge locations positioned near users and applications. That distinction matters because many AI applications are introducing latency requirements that cannot always be addressed by centralized facilities alone. As AI inference moves closer to users,

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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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