Energy Voice Live – Where are the green jobs?

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Energy Voice Live – Where are the green jobs?

You are invited to join us for Energy Voice Live, our first podcast recording in front of a live studio audience. Join our news editor Erikka Askeland and special guest speakers as they tackle the most pressing issues facing the energy sector today, all through a relaxed and insightful journalistic lens. We’re kicking things off […]

You are invited to join us for Energy Voice Live, our first podcast recording in front of a live studio audience.

Join our news editor Erikka Askeland and special guest speakers as they tackle the most pressing issues facing the energy sector today, all through a relaxed and insightful journalistic lens.

We’re kicking things off in Aberdeen – experience a candid discussion with two trailblazers in the energy industry: Nick Dunn, chief executive of Score, and Professor Paul de Leeuw, director of the Energy Transition Institute at Robert Gordon University.

This live discussion will dive deep into the ‘just transition’ and its impact on offshore energy workers – and YOU can ask the tough questions.

If you are an Energy Voice subscriber, tickets are absolutely free.

Simply apply this offer code at check-out: SubscriberAdmissionMarch2025

Get your tickets here

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EthosEnergy CEO boasts ‘healthy pipeline of global opportunities’

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Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

Intel bets on Terafab to help it reassert itself in the AI chip race

Tesla, SpaceX, and xAI say that Terafab will be the largest chip manufacturing facility ever, outputting 1TW a year of compute power and “combining logic, memory and advanced packaging under one roof.” Intel’s ability to “design, fabricate, and package ultra-high-performance chips at scale” will help accelerate those 1TW/year ambitions, the

Cisco joins Anthropic’s multivendor effort to secure AI software

In addition to model usage credits, Anthropic donated $2.5 million to Alpha-Omega and OpenSSF through the Linux Foundation, and $1.5 million to the Apache Software Foundation “to enable the maintainers of open-source software to respond to this changing landscape.” “Partners will, to the extent they’re able, share information and best

Cloud-first vs. sovereign-first: Navigating the trade-off

Encryption is often suggested as a way to address data sovereignty because the customer holds the key to protect data in motion, in use, and at rest. However, Buest noted, most regulators have not explicitly approved the use of encryption or other security measures or deemed them sufficient for compliance.

Cisco: AI simplifies wireless operations but also taxes legacy Wi-Fi networks

Additionally, 28% of organizations surveyed have already deployed AI workloads, and that percentage is estimated to climb to 79% by 2027. Some 29% will be in pilot stage, and 22% are planning deployment over the next 12 months. “This rapid trajectory further highlights that AI deployment is indeed the defining

Energy Department Issues Funding Opportunity to Strengthen American Critical Minerals and Materials Supply Chain

WASHINGTON—The U.S. Department of Energy’s (DOE) Office of Critical Minerals and Energy Innovation (CMEI) and Hydrocarbons and Geothermal Energy Office (HGEO) today announced a funding opportunity of up to $69 million for technologies or processes that advance the domestic production and refining of critical materials. Projects selected through this Notice of Funding Opportunity (NOFO) will address the greatest technical obstacles to a stronger critical materials supply chain. “This funding will help establish a more secure and affordable supply of the critical minerals and materials that are foundational to American energy dominance, national security, and industrial competitiveness,” said Assistant Secretary of Energy (EERE) Audrey Robertson. DOE is seeking projects that bridge the gap between bench-scale innovations and commercially viable technologies. Selected project teams will form industry-led partnerships and conduct research and development with support from the U.S. national laboratories. The NOFO, which is part of DOE’s Critical Minerals and Materials Accelerator Program and jointly funded by CMEI’s Advanced Materials and Manufacturing Technologies Office and HGEO’s Office of Geothermal, has three primary topic areas: Production and material efficiency for critical materials including rare earth elements Processes to refine and alloy gallium, gallium nitride, germanium, and silicon carbide Cost-competitive direct lithium extraction, separation, and processing CMEI will host an informational webinar on April 16, 2026, to discuss the NOFO and application requirements. Letters of intent are due on April 21, 2026, by 5 p.m. ET. Deadlines for full applications will be staggered based on topic area, starting in May 2026. For more details on sub-topics and deadlines, visit the NOFO landing page. The Critical Minerals and Materials Accelerator is one of several programs developed through DOE’s Critical Materials Collaborative. This NOFO is part of $1 billion in critical materials funding announced by DOE in August 2025, and follows the Manufacturing Deployment Office’s announcement

Latin America returns to the energy security conversation at CERAWeek

With geopolitical risk central to conversations about energy, and with long-cycle supply once again in focus, Latin America’s mix of hydrocarbons and export potential drew renewed attention at CERAWeek by S&P Global in Houston. Argentina, resource story to export platform Among the regional stories, Argentina stood out as Vaca Muerta was no longer discussed simply as a large unconventional resource, but whether the country could turn resource quality into sustained export capacity. Country officials talked about scale: more operators, more services, more infrastructure, and a larger industrial base around the unconventional play. Daniel González, Vice Minister of Energy and Mining for Argentina, put it plainly: “The time has come to expand the Vaca Muerta ecosystem.” What is at stake now is not whether the basin works, but whether the country can build enough above-ground capacity and regulatory consistency to keep development moving. Horacio Marín, chairman and chief executive officer of YPF, offered an expansive version of that argument. He said Argentina’s energy exports could reach $50 billion/year by 2031, backed by roughly $130 billion in cumulative investment in oil, LNG, and transportation infrastructure. He said Argentine crude output could reach 1 million b/d by end-2026. He said Argentina wants to be seen less as a recurrent frontier story and more as a future supplier with scale. “The time to invest in Vaca Muerta is now,” Marín said. The LNG piece is starting to take shape. Eni, YPF, and XRG signed a joint development agreement in February to move Argentina LNG forward, with a first phase planned at 12 million tonnes/year. Southern Energy—backed by PAE, YPF, Pampa Energía, Harbour Energy, and Golar LNG—holds a long-term agreement with SEFE for 2 million tonnes/year over 8 years. The movement by global standards is early-stage and relatively modest, but it adds to Argentina’s export

Market Focus: LNG supply shocks expose limited market flexibility

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Libya’s NOC, Chevron sign MoU for technical study for offshore Block NC146

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Finder Energy advances KTJ Project with development area approval

Finder Energy Holdings Ltd. received regulatory approval for a development area covering the Kuda Tasi and Jahal oil fields offshore Timor‑Leste, enabling progression toward field development. Autoridade Nacional do Petróleo (ANP) approved an 88‑sq km development area over the Kuda Tasi and Jahal oil fields (KTJ Project) within PSC 19‑11 offshore Timor‑Leste, representing the first stage of the regulatory approvals process for the project. The declaration of the development area is a precursor to the field development plan (FDP), which Finder is currently preparing for submission to ANP in second‑quarter 2026. Upon approval of the FDP, the development area would secure tenure for up to 25 years or until production ceases, allowing Finder to conduct development and production operations within the area, subject to applicable regulatory approvals and conditions. The company said its upside strategy centers on the potential for the Petrojarl I FPSO to serve as a central processing and export hub for future tiebacks of surrounding discoveries, contingent on successful appraisal and/or exploration activities within PSC 19‑11. Alternatively, longer tie‑back distances could be accommodated through a secondary standalone development in the southern portion of the PSC. Finder is continuing technical evaluation of appraisal and exploration opportunities to generate drilling targets. PSC 19‑11 lies within the Laminaria High oil province of Timor‑Leste. The KTJ Project contains an estimated 25 million bbl of gross 2C contingent resources, with identified upside of an additional 23 million bbl gross 2C contingent resources and 116 million bbl gross 2U prospective resources. Finder operates PSC 19‑11 with a 66% working interest.

Newly formed Polar LNG aims to develop nearshore LNG project on Alaska’s North Slope

Polar Train LNG LLC, a newly launched company aiming to build an LNG plant (Polar LNG) on Alaska’s North Slope, has appointed Joel Riddle as president and chief executive officer. “Alaska’s North Slope holds one of the most significant undeveloped natural gas resources in the world,” said Riddle, adding “Polar LNG is uniquely positioned to bring this resource online—delivering reliable energy for Alaska and a strategic supply for the United States… and provides trusted energy to our allies.” In a release Mar. 31, the company said it is advancing a nearshore project at Prudhoe Bay, citing “one of the shortest LNG shipping routes from North America to key Asian markets, approximately 3,600 miles to Japan compared to over 10,000 miles from the US Gulf Coast.” The company is aiming for first LNG from the 7-million tonnes/year plant—to be developed nearshore with modular infrastructure—in 2029-2030 at a cost of $8–9 billion. According to Polar LNG, natural gas would be sourced from existing infrastructure at Prudhoe Bay and transported via a short pipeline to a nearshore plant. There, a modular gravity-based structure would process and liquefy the gas. LNG would then be loaded onto specialized ice-class carriers for year-round export. The company is exploring potential repurposing of sanctioned equipment built for Russia’s Arctic LNG 2 project and is seeking permission from the US govenment to acquire parts impacted by the sanctions, according to reports. Before joining Polar LNG, Riddle served as managing director and chief executive officer of Tamboran Resources Ltd.

New v2 UALink specification aims to catch up to NVLink

But given there are no products currently available using UALink 1.0, UALink 2.0 might be viewed as a premature launch Need to play catch up David Harold, senior analyst with Jon Peddie Research, was guarded in his reaction. “While 2.0 is a significant step forward from 1.0, we need to bear in mind that even 1.0 solutions aren’t shipping yet – they aren’t due until later this year. So, Nvidia is way ahead of the open alternatives on connectivity, indeed ahead of the proprietary or Ethernet based solutions too,” he said. What this means, he added, is that non-Nvidia alternatives are currently lagging in the market. “They need to play catch up on several fronts, not just networking. … I can’t think of a single shipping product that meaningfully has advantages over a Nvidia solution,” he said. “Ultimately UALink remains desirable since it will enable heterogeneous, multi-vendor environments but it’s quite a way behind NVLink today. ” There are plenty of signs that organizations will find it hard to break free of the Nvidia dominance, however. A couple of months ago, RISC-V pioneer SiFive signed a deal with Nvidia to incorporate Nvidia NVLink Fusion into its data center products, a departure for RISC companies. According to Harold, other companies could be joining it. “Custom ASIC company MediaTek is an NVLink partner, and they told me last week that they are planning to integrate it directly into next-generation custom silicon for AI applications,” he said. “This will enable a wider range of companies to use NVLink as their high-speed interconnect.” Other options And, Harold noted, Nvidia is already looking at other options. “Nvidia is now shifting to look at the copper limit for networking speed, with an interest in using optical connectivity instead,” said Harold.

Nvidia’s SchedMD acquisition puts open-source AI scheduling under scrutiny

Is the concern valid? Dr. Danish Faruqui, CEO of Fab Economics, a US-based AI hardware and datacenter advisory, said the risk was real. “The skepticism that Nvidia may prioritize its own hardware in future software updates, potentially delaying or under-optimizing support for rivals, is a feasible outcome,” he said. As the primary developer, Nvidia now controls Slurm’s official development roadmap and code review process, Faruqui said, “which could influence how quickly competing chips are integrated on new development or continuous improvement elements.” Owning the control plane alongside GPUs and networking infrastructure such as InfiniBand, he added, allows Nvidia to create a tightly vertically integrated stack that can lead to what he described as “shallow moats, where advanced features are only available or performant on Nvidia hardware.” One concrete test of that, industry observers say, will be how quickly Nvidia integrates support for AMD’s next-generation chips into Slurm’s codebase compared with how quickly it integrates its own forthcoming hardware and networking technologies, such as InfiniBand. Does the Bright Computing precedent hold? Analysts point to Nvidia’s 2022 acquisition of Bright Computing as a reference point, saying the software became optimized for Nvidia chips in ways that disadvantaged users of competing hardware. Nvidia disputed that characterization, saying Bright Computing supports “nearly any CPU or GPU-accelerated cluster.” Rawat said the comparison was instructive but imperfect. “Nvidia’s acquisition of Bright Computing highlights its preference for vertical integration, embedding Bright tightly into DGX and AI Factory stacks rather than maintaining a neutral, multi-vendor orchestration role,” he said. “This reflects a broader strategic pattern — Nvidia seeks to control the full-stack AI infrastructure experience.”

Two New England states say no to new data centers

It’s getting harder and harder for governments to ignore the impact that data centers are having on their communities, consuming vast amounts of water and driving up electricity prices, experts say. According to a Pew Research Center analysis, data centers consumed 183 terawatt-hours of electricity in 2024, more than 4% of total U.S. electricity use. That demand is projected to more than double to 426 terawatt-hours by 2030. The impact is significant. In 2023, data centers consumed about 26% of Virginia’s electricity supply, although Virginia is notable for having an extremely dense collection of data centers. Alan Howard, senior analyst for infrastructure at Omdia, says he is not surprised at all. “The amount of national press coverage regarding what is arguably a limited number of data center ‘horror’ stories has many jurisdictions and states spooked over the potential impacts data center projects might have,” he said. It’s an evolution that’s been coming for some time whereby local legislators have embraced the idea that they don’t want to learn the hard way as others already have, he argues. “All that said, it seems unlikely that there will be broad bans on data center development that would cripple the industry. There’s lots of places to go in the U.S. and developers have warmed up to siting projects in places amenable to their needs, although not ideally convenient,” said Howard.

Nscale Expands AI Factory Strategy With Power, Platform, and Scale

Nscale has moved quickly from startup to serious contender in the race to build infrastructure for the AI era. Founded in 2024, the company has positioned itself as a vertically integrated “neocloud” operator, combining data center development, GPU fleet ownership, and a software stack designed to deliver large-scale AI compute. That model has helped it attract backing from investors including Nvidia, and in early March 2026 the company raised another $2 billion at a reported $14.6 billion valuation. Reuters has described Nscale’s approach as owning and operating its own data centers, GPUs, and software stack to support major customers including Microsoft and OpenAI. What makes Nscale especially relevant now is that it is no longer content to operate as a cloud intermediary or capacity provider. Over the past year, the company has increasingly framed itself as an AI hyperscaler and AI factory builder, seeking to combine land, power, data center shells, GPU procurement, customer offtake, and software services into a single integrated platform. Its acquisition of American Intelligence & Power Corporation, or AIPCorp, is the clearest signal yet of that shift, bringing energy infrastructure directly into the center of Nscale’s business model. The AIPCorp transaction is significant because it gives Nscale more than additional development capacity. The company said the deal includes the Monarch Compute Campus in Mason County, West Virginia, a site of up to 2,250 acres with a state-certified AI microgrid and a power runway it says can scale beyond 8 gigawatts. Nscale also said the acquisition establishes a new division, Nscale Energy & Power, headquartered in Houston, extending its platform further into power development. That positioning reflects a broader shift in the AI infrastructure market. The central bottleneck is no longer simply access to GPUs. It is the ability to assemble power, cooling, land, permits, data center

Google Research touts memory-compression breakthrough for AI processing

The last time the market witnessed a shakeup like this was China’s DeepSeek, but doubts emerged quickly about its efficacy. Developers found DeepSeek’s efficiency gains required deep architectural decisions that had to be built in from the start. TurboQuant requires no retraining or fine-tuning. You just drop it straight into existing inference pipelines, at least in theory. If it works in production systems with no retrofitting, then data center operators will get tremendous performance gains on existing hardware. Data center operators won’t have to throw hardware at the performance problem. However, analysts urge caution before jumping to conclusions. “This is a research breakthrough, not a shipping product,” said Alex Cordovil, research director for physical infrastructure at The Dell’Oro Group. “There’s often a meaningful gap between a published paper and real-world inference workloads.” Also, Dell’Oro notes that efficiency gains in AI compute tend to get consumed by more demand, known as the Jevons paradox. “Any freed-up capacity would likely be absorbed by frontier models expanding their capabilities rather than reducing their hardware footprint.” Jim Handy, president of Objective Analysis, agrees on that second part. “Hyperscalers won’t cut their spending – they’ll just spend the same amount and get more bang for their buck,” he said. “Data centers aren’t looking to reach a certain performance level and subsequently stop spending on AI. They’re looking to out-spend each other to gain market dominance. This won’t change that.” Google plans to present a paper outlining TurboQuant at the ICLR conference in Rio de Janeiro running from April 23 through April 27.

Amazon Middle East datacenter suffers second drone hit as Iran steps up attacks

Amazon was contacted for comment on the latest Bahrain drone incident, but said it had nothing to add beyond the statement in its current advisory. Denial of infrastructure Doing the damage is the Shaheed 136, a small and unsophisticated drone designed to overwhelm defenders with numbers. If only one in twenty reaches its target, the price-performance still exceeds that of more expensive systems. When aimed at critical infrastructure such as datacenters, the effect is also psychological; the threat of an attack on its own can be enough to make it difficult for organizations to continue using an at-risk facility. Iran’s targeting of the Bahrain datacenter is unlikely to be random. Amazon opened its ME-SOUTH-1 AWS presence in 2019, and it is still believed to be the company’s largest site in the Middle East. Earlier this week, the Islamic Revolutionary Guard Corps (IRGC) Telegram channel explicitly threatened to target at least 18 US companies operating in the region, including Microsoft, Google, Nvidia, and Apple. This follows similar threats to an even longer list of US companies made on the IRGC-affiliated Tasnim News Agency in recent weeks. That strategy doesn’t bode well for US companies that have made large investments in Middle Eastern datacenter infrastructure in recent years, drawn by the growing wealth and influence of countries in the region. This includes Amazon, which has announced plans to build a $5.3 billion datacenter in Saudi Arabia, due to become available in 2026. If this is now under threat, whether by warfare or the hypothetical possibility of attack, that will create uncertainty.

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.

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

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

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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