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Energy Secretary Wright Testifies Before House Energy Subcommittee on FY2026 Budget Request

WASHINGTON— U.S. Secretary of Energy Chris Wright testified today before the U.S. House Energy Subcommittee on the Department of Energy’s Fiscal Year 2026 budget request. Last month, Secretary Wright testified before the U.S. Senate Appropriations Subcommittee on Energy and Water Development and the U.S. House Appropriations Subcommittee on Energy and Water Development to outline the […]

WASHINGTON— U.S. Secretary of Energy Chris Wright testified today before the U.S. House Energy Subcommittee on the Department of Energy’s Fiscal Year 2026 budget request.

Last month, Secretary Wright testified before the U.S. Senate Appropriations Subcommittee on Energy and Water Development and the U.S. House Appropriations Subcommittee on Energy and Water Development to outline the Department’s priorities and provide an overview of the FY2026 request.

The FY2026 Budget aligns with President Trump’s directive to restore American energy dominance and rein in bloated federal spending. It brings non-defense discretionary spending to the most disciplined level since 2017 and redirects more than $15 billion away from Green New Scam programs that drive up costs and weaken the U.S. energy system. For more details, view the budget toplines here.

Secretary Wright’s opening remarks:

Thank you Chairman Latta, Chairman Guthrie, Ranking Member Castor, and Ranking Member Pallone, and Members of the Committee. It is an honor to appear before you today as Secretary of Energy to discuss the President’s Fiscal Year 2026 Budget request for the Department of Energy.   

Under President Trump’s leadership, our priorities for the Department are clear – to unleash a golden era of American energy dominance, strengthen our national security, and lead the world in innovation. A reliable and abundant energy supply is the foundation of a strong and prosperous nation. When America leads in energy, we lead in prosperity, security and human flourishing.  

America has a historic opportunity to secure our energy systems, propel scientific and technological innovation, including AI; maintain and strengthen our weapons stockpiles; and meet Cold War legacy waste commitments. The Department of Energy will advance this critical mission while cutting red tape, increasing efficiency, and ensuring we are better stewards of taxpayer dollars.   

The President’s Fiscal Year 26 budget will ensure taxpayer resources are allocated appropriately and cost-effectively. We will invest DOE’s resources in sources and technologies that support affordable, reliable, and secure energy and provide a return on investment for the American taxpayers. We will return the Department to its core mission and eliminate spending on projects that fail to provide such a return, fail to advance our energy needs, and fail the test of economic viability.  

It is deeply concerning how many billions of dollars were rushed out the door without proper due diligence in the final days of the Biden administration. DOE is undertaking a thorough review of financial assistance that identifies waste of taxpayer dollars, protects America’s national security and advances President Trump’s commitment to unleash American energy dominance.  As a result, we recently announced the termination of 24 projects totaling over $3.7 billion in taxpayer-funded financial assistance. These projects failed to meet the economic, national security or energy security standards necessary to sustain DOE’s investment, and the taxpayers should not be forced to subsidize them. 

Instead, we are advancing a policy of energy addition – fully leveraging affordable, reliable and secure resources that have powered our country for generations. The United States is blessed with an abundance of coal, oil, and natural gas, and our Administration is committed to using them to meet growing energy needs of the American people.  

Every one of these resources was unleashed through the world-changing power of American innovation. Our National Labs are the engine that drives research and development to expand our energy dominance. We will prioritize research that supports true technological breakthroughs and maintains America’s global competitiveness. 

America must play a leading role commercializing of reliable, safe and secure nuclear energy, and we are taking steps to accelerate innovation in this sector. DOE is working to advance the rapid deployment of next-generation nuclear technology, including small modular reactors.  

I am proud to report that we have officially ended the previous administration’s reckless pause on LNG export permits and have returned to regular order for reviewing and approving new permits. DOE will also work to replenish the Strategic Petroleum Reserve – a national asset that protects our security in times of crisis. I want to thank this committee for prioritizing funding to refill the SPR in the One Big Beautiful Bill as well. 

We are advancing President Trump’s pledge to lower the cost of living and expand choice by rightsizing DOE’s approach to home efficiency standards and regulations. Under the President’s direction, we’ve begun slashing more than 47 regulations as part of the largest deregulatory effort in history. These actions are projected to save the American people approximately $11 billion while restoring consumer freedom and lowering costs. 

The responsible stewardship and modernization of the nation’s nuclear weapons system is paramount for this Administration. DOE is focused on addressing critical upgrades for the U.S. nuclear stockpile and maintaining our engine powerhouses for submarines and aircraft carriers.  Both tasks will be even more crucial in the next few years. 

Our nuclear innovation as a nation began with the Manhattan Project, and the next Manhattan Project is clearly AI. DOE has a significant role to play in driving AI innovation for scientific discovery and national security. Our agency has world-class high-performance computing capabilities, including four of the world’s top ten supercomputers.  

Harnessing our energy potential to power global AI leadership while meeting growing energy demand will be the challenge of our time. But America doesn’t back down from big challenges or big builds.  

As Secretary of Energy, I am honored by the responsibility to help meet the American people’s growing energy needs and lead the world in energy development. I appreciated the opportunity to work with many of you on this committee to unlock America’s full energy potential and drive down costs for families with the One Big Beautiful Bill, and I look forward to continuing to work together to achieve President Trump’s energy dominance agenda. 

Thank you for the opportunity to testify before this committee.  

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Egypt to Buy LNG from Aramco, Trafigura, Shell

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Secretaries Wright, Burgum Join JERA and U.S. LNG Producers to Finalize Agreements Expected to Add over $200 Billion to U.S. GDP

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ADNOC Is Evaluating Possibility of Buying Some BP Assets

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Anaergia Tapped to Build Biogas Plant in South Korea

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ScottishPower Shows Charging Windfarm Service Vessels Offshore Is Feasible

Two studies commissioned by ScottishPower Renewable Energy Limited, an Iberdrola company, say that offshore charging for battery-powered crew transfer and service operation vessels is on the horizon for future wind farms. The studies – conducted by MJR Power & Automation and Oasis Marine – are the last of three commissioned by ScottishPower Renewables to investigate methods for lowering greenhouse gas (GHG) emissions from offshore wind farm operations. The studies reinforced initial conclusions that utilizing battery-powered Service Operation Vessels (E-SOVs) for offshore operations is technically viable, allowing them to remain at sea for prolonged durations, ScottishPower Renewables said in a media release. The two studies also looked at the potential of decarbonizing offshore operations using electric crew transfer vessels (CTVs). Studies found that this option is technically and operationally feasible for windfarms close to shore. “These latest studies have the potential to help the industry take a step closer to a new era for offshore windfarm operations – not just here in the UK, but right across the globe”, Ross Ovens, ScottishPower Renewables’ Managing Director for Offshore, said. “The valuable depth and insight this research offers – regardless of whether you’re considering an SOV or CTV operating model – could help inform future windfarm operations as the country continues to build the green generation we need to meet the expected doubling of electricity demand”. In both scenarios, windfarms would also benefit environmentally and economically, with a significant reduction in both GHG emissions as well as fuel costs. The MJR study found that electrical solutions are well-suited for offshore windfarm operations, allowing for regular charging at production sites and shore-based quays. It also noted that O&M electric vessels will soon be cheaper than Marine Gas Oil (MGO) alternatives, with operating expenses already competitive for SOVs and fully competitive for CTVs in a few years. The Oasis Marine study

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Oil Surges as USA Orders Partial Evacuation of Iraqi Embassy

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Micron joins HBM4 race with 36GB 12-high stack, eyes AI and data center dominance

Race to power the next generation of AI By shipping samples of the HMB4 to the key customers, Micron has joined SK hynix in the HBM4 race. In March this year, SK hynix shipped the 12-Layer HBM4 samples to customers. SK hynix’s HBM4 has implemented bandwidth capable of processing more than 2TB of data per second, processing data equivalent to more than 400 full-HD movies (5GB each) in a second, said the company. “HBM competitive landscape, SK hynix has already sampled and secured approval of HBM4 12-high stack memory early Q1’2025 to NVIDIA for its next generation Rubin product line and plans to mass produce HBM4 in 2H 2025,” said Danish Faruqui, CEO, Fab Economics. “Closely following, Micron is pending Nvidia’s tests for its latest HBM4 samples, and Micron plans to mass produce HBM4 in 1H 2026. On the other hand, the last contender, Samsung is struggling with Yield Ramp on HBM4 Technology Development stage, and so has to delay the customer samples milestones to Nvidia and other players while it earlier shared an end of 2025 milestone for mass producing HBM4.” Faruqui noted another key differentiator among SK hynix, Micron, and Samsung: the base die that anchors the 12-high DRAM stack. For the first time, both SK hynix and Samsung have introduced a logic-enabled base die on 3nm and 4nm process technology to enable HBM4 product for efficient and faster product performance via base logic-driven memory management. Both Samsung and SK hynix rely on TSMC for the production of their logic-enabled base die. However, it remains unclear whether Micron is using a logic base die, as the company lacks in-house capability to fabricate at 3nm.

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Cisco reinvigorates data center, campus, branch networking with AI demands in mind

“We have a number of … enterprise data center customers that have been using bi-directional optics for many generations, and this is the next generation of that feature,” said Bill Gartner, senior vice president and general manager of Cisco’s optical systems and optics business. “The 400G lets customer use their existing fiber infrastructure and reduces fiber count for them so they can use one fiber instead of two, for example,” Gartner said. “What’s really changed in the last year or so is that with AI buildouts, there’s much, much more optics that are part of 400G and 800G, too. For AI infrastructure, the 400G and 800G optics are really the dominant optics going forward,” Gartner said. New AI Pods Taking aim at next-generation interconnected compute infrastructures, Cisco expanded its AI Pod offering with the Nvidia RTX 6000 Pro and Cisco UCS C845A M8 server package. Cisco AI Pods are preconfigured, validated, and optimized infrastructure packages that customers can plug into their data center or edge environments as needed. The Pods include Nvidia AI Enterprise, which features pretrained models and development tools for production-ready AI, and are managed through Cisco Intersight. The Pods are based on Cisco Validated Design principals, which offer customers pre-tested and validated network designs that provide a blueprint for building reliable, scalable, and secure network infrastructures, according to Cisco. Building out the kind of full-scale AI infrastructure compute systems that hyperscalers and enterprises will utilize is a huge opportunity for Cisco, said Daniel Newman, CEO of The Futurum Group. “These are full-scale, full-stack systems that could land in a variety of enterprise and enterprise service application scenarios, which will be a big story for Cisco,” Newman said. Campus networking For the campus, Cisco has added two new programable SiliconOne-based Smart Switches: the C9350 Fixed Access Smart Switches and C9610

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Qualcomm’s $2.4B Alphawave deal signals bold data center ambitions

Qualcomm says its Oryon CPU and Hexagon NPU processors are “well positioned” to meet growing demand for high-performance, low-power compute as AI inferencing accelerates and more enterprises move to custom CPUs housed in data centers. “Qualcomm’s advanced custom processors are a natural fit for data center workloads,” Qualcomm president and CEO Cristiano Amon said in the press release. Alphawave’s connectivity and compute technologies can work well with the company’s CPU and NPU cores, he noted. The deal is expected to close in the first quarter of 2026. Complementing the ‘great CPU architecture’ Qualcomm has been amassing Client CPUs have been a “big play” for Qualcomm, Moor’s Kimball noted; the company acquired chip design company Nuvia in 2021 for $1.4 billion and has also announced that it will be designing data center CPUs with Saudi AI company Humain. “But there was a lot of data center IP that was equally valuable,” he said. This acquisition of Alphawave will help Qualcomm complement the “great CPU architecture” it acquired from Nuvia with the latest in connectivity tools that link a compute complex with other devices, as well as with chip-to-chip communications, and all of the “very low level architectural goodness” that allows compute cores to deliver “absolute best performance.” “When trying to move data from, say, high bandwidth memory to the CPU, Alphawave provides the IP that helps chip companies like Qualcomm,” Kimball explained. “So you can see why this is such a good complement.”

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LiquidStack launches cooling system for high density, high-powered data centers

The CDU is serviceable from the front of the unit, with no rear or end access required, allowing the system to be placed against the wall. The skid-mounted system can come with rail and overhead piping pre-installed or shipped as separate cabinets for on-site assembly. The single-phase system has high-efficiency dual pumps designed to protect critical components from leaks and a centralized design with separate pump and control modules reduce both the number of components and complexity. “AI will keep pushing thermal output to new extremes, and data centers need cooling systems that can be easily deployed, managed, and scaled to match heat rejection demands as they rise,” said Joe Capes, CEO of LiquidStack in a statement. “With up to 10MW of cooling capacity at N, N+1, or N+2, the GigaModular is a platform like no other—we designed it to be the only CDU our customers will ever need. It future-proofs design selections for direct-to-chip liquid cooling without traditional limits or boundaries.”

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Enterprises face data center power design challenges

” Now, with AI, GPUs need data to do a lot of compute and send that back to another GPU. That connection needs to be close together, and that is what’s pushing the density, the chips are more powerful and so on, but the necessity of everything being close together is what’s driving this big revolution,” he said. That revolution in new architecture is new data center designs. Cordovil said that instead of putting the power shelves within the rack, system administrators are putting a sidecar next to those racks and loading the sidecar with the power system, which serves two to four racks. This allows for more compute per rack and lower latency since the data doesn’t have to travel as far. The problem is that 1 mW racks are uncharted territory and no one knows how to manage the power, which is considerable now. ”There’s no user manual that says, hey, just follow this and everything’s going to be all right. You really need to push the boundaries of understanding how to work. You need to start designing something somehow, so that is a challenge to data center designers,” he said. And this brings up another issue: many corporate data centers have power plugs that are like the ones that you have at home, more or less, so they didn’t need to have an advanced electrician certification. “We’re not playing with that power anymore. You need to be very aware of how to connect something. Some of the technicians are going to need to be certified electricians, which is a skills gap in the market that we see in most markets out there,” said Cordovil. A CompTIA A+ certification will teach you the basics of power, but not the advanced skills needed for these increasingly dense racks. Cordovil

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HPE Nonstop servers target data center, high-throughput applications

HPE has bumped up the size and speed of its fault-tolerant Nonstop Compute servers. There are two new servers – the 8TB, Intel Xeon-based Nonstop Compute NS9 X5 and Nonstop Compute NS5 X5 – aimed at enterprise customers looking to upgrade their transaction processing network infrastructure or support larger application workloads. Like other HPE Nonstop systems, the two new boxes include compute, software, storage, networking and database resources as well as full-system clustering and HPE’s specialized Nonstop operating system. The flagship NS9 X5 features support for dual-fabric HDR200 InfiniBand interconnect, which effectively doubles the interconnect bandwidth between it and other servers compared to the current NS8 X4, according to an HPE blog detailing the new servers. It supports up to 270 networking ports per NS9 X system, can be clustered with up to 16 other NS9 X5s, and can support 25 GbE network connectivity for modern data center integration and high-throughput applications, according to HPE.

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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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