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India Is Now UAE’s Largest Customer of LNG

India is now the United Arab Emirates’ (UAE) largest customer of LNG, ADNOC Gas said in a release sent to Rigzone by the ADNOC Gas team on Monday. The company noted in the release that 20 percent of LNG operated by ADNOC Gas will be supplied to India by 2029, revealing that $20 billion worth […]

India is now the United Arab Emirates’ (UAE) largest customer of LNG, ADNOC Gas said in a release sent to Rigzone by the ADNOC Gas team on Monday.

The company noted in the release that 20 percent of LNG operated by ADNOC Gas will be supplied to India by 2029, revealing that $20 billion worth of LNG contracts were signed in last 24 months between ADNOC Gas and Indian companies.

In Monday’s release, ADNOC Gas announced the signing of a sales and purchase agreement valued at between $2.5 billion and $3 billion for a period of ten years with Hindustan Petroleum Corporation Limited (HPCL).

The latest agreement was announced during a visit to India by President Sheikh Mohamed bin Zayed Al Nahyan, where he met with the Indian Prime Minister, Narendra Modi, the release noted. It added that, during the visit, Ahmed Al Jaber, UAE Minister of Industry and Advanced Technology and ADNOC Managing Director and Group CEO, and Vikas Kaushal, Chairman and Managing Director of Hindustan Petroleum Corporation Limited, exchanged the signed contract, “reiterating the importance of the growing relationship between ADNOC, its partners, and customers in India”.

The deal converted a previously signed heads of agreement between the two companies into a long-term SPA, the release highlighted, pointing out that the agreement is for the export of 0.5 million tons per annum of LNG.

“The milestone agreement represents a further step in strengthening the strategic partnership between the UAE and India, while reinforcing ADNOC Gas’ role as a reliable and trusted supplier of LNG to Asia’s fast-growing markets,” ADNOC Gas said in the release.

“India is now the UAE’s largest customer and a very important part of ADNOC Gas’ LNG strategy. The company’s growth is tied to the continued success of India,” it added.  

By 2029 ADNOC Gas will be the operator for 15.6 million tons per annum of LNG, ADNOC Gas noted in its release, adding that, of that, 3.2 million tons per annum is contracted to Indian energy companies including HPCL.

“This agreement will be supplied from ADNOC Gas’ Das Island liquefaction facility, which has a production capacity of up to six million tons per annum and ranks among the world’s longest-operating LNG plants,” ADNOC Gas said in the release.

“Since commencing operations, Das Island has delivered more than 3,500 LNG cargoes globally, demonstrating its strong operational performance and long-standing reliability,” it added.

“The Hindustan Petroleum agreement aligns with ADNOC Gas’ strategy to broaden its customer base and expand its presence in India and in key growth markets across Asia,” it continued.

“Over the past three years, the company has secured a series of long-term LNG agreements ranging from 0.4 to 1.2 million tons per annum, with contract durations of up to 14 years,” it said.

“These contracts further reinforce ADNOC Gas’ position as a leading supplier of reliable, lower-carbon LNG to Asia’s rapidly growing energy markets,” ADNOC Gas went on to state.

In the release, Fatema Al Nuaimi, Chief Executive Officer of ADNOC Gas, said, “we are pleased to sign this long-term LNG supply agreement with Hindustan Petroleum Corporation which reflects the strong and growing energy partnership between the UAE and India”.

“This agreement underscores ADNOC Gas’ commitment to delivering reliable LNG to meet global demand, while supporting India’s ambition to increase natural gas to 15 percent of its energy mix by 2030,” Al Nuaimi added.

In a release posted on its website on Monday, HPCL confirmed that it had signed a long-term LNG sale and purchase agreement with ADNOC Gas.

“Under the terms of this SPA, HPCL will receive LNG at its five million ton per annum LNG Regasification Terminal at Chhara, Gujarat, which was dedicated to the nation by Hon’ble Prime Minister in September 2025,” HPCL said in its release.

“The supplies under this agreement will support HPCL in meeting the requirements of its refineries, City Gas Distribution (CGD) network and gas demand across key sectors, such as, fertilizers, power and petrochemicals etc.,” it added.

“This partnership will further strengthen HPCL’s position as a reliable supplier of natural gas, complementing its portfolio of other petroleum products, to meet the nation’s growing and evolving energy needs,” it continued.

“This strategic partnership aligns HPCL with India’s aspiration of increasing the share of gas in its energy basket. Such long-term contracts play a vital role in ensuring reliability, affordability and supply security amidst a highly volatile geo-political and evolving global energy landscape,” HPCL noted.

HPCL went on to state in its release that “this agreement also signifies a deepening relationship between India and UAE, with India being the largest LNG customer of UAE”.

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Murphy Oil Makes Noncommercial Find in Ivorian Frontier Campaign

Murphy Oil Corp said Monday it had encountered noncommercial quantities of hydrocarbons in the first of its three-well exploration campaign offshore Côte d’Ivoire. “A key outcome at Civette is that we confirmed the presence of hydrocarbons in this frontier play – a meaningful success in early-stage exploration”, president and chief executive Eric Hambly said in an online statement. “While Civette did not meet commercial thresholds, the well provided insights that strengthen our subsurface understanding for the potential of the [Tano] basin and inform the remaining prospectivity on the CI-502 Block”. The Houston, Texas-based oil and gas explorer and developer had placed a gross resource potential of 440-1,000 million barrels of oil equivalent (MMboe) on Civette, according to Murphy Oil’s investor presentation on January 7, 2026. The Civette well is in Block CI-502. The company said Monday it would continue with the Bubale prospect in Block CI-709 and the Caracal prospect in CI-102, “both targeting independent plays with significant resource potential”. According to the investor presentation, the gross resource potential for Bubale and Caracal is 340-850 MMboe and 150-360 MMboe respectively. They are scheduled to be spudded this year. The three blocks are among five held by Murphy Oil in the West African country. The five, all in deep waters, are co-owned with Société Nationale d’Opérations Pétrolières de la Côte d’Ivoire, the American company holding 85-90 percent operating interests. The licenses, acquired 2023, cover about 1.5 million gross acres in the Tano basin, according to Murphy Oil. Murphy Oil was scheduled to submit a development plan for the Paon discovery in Block CI-103 to Ivorian authorities by the end of 2025, according to the company’s annual report for 2024. Elsewhere, Murphy Oil has scheduled two more spuds in Vietnam this year, both appraisal wells for last year’s Hai Su Vang oil discovery.

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TotalEnergies, Bapco Launch Trading JV for Bahraini Fuels

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India Is Now UAE’s Largest Customer of LNG

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EnBW Gives Up 2 Irish Sea Wind Projects with BP-JERA JV

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RISC-V chip designer SiFive integrates Nvidia NVLink Fusion to power AI data centers

RISC-V pioneer SiFive has signed a deal with Nvidia to incorporate Nvidia NVLink Fusion into its data center products. The agreement means that SiFive will be able to connect its RISC-V CPUs to Nvidia GPUs and accelerators over a high bandwidth interconnect that lets multiple GPUs share compute and memory resources, offering more options to operators of AI data centers. Historically, RISC-V technology has not had access to these types of high-level interconnects and pathways. In a statement, Patrick Little, president and CEO of SiFive, said, “AI infrastructure is no longer built from generic components, it is co-designed from the ground up. By integrating NVLink Fusion with SiFive’s high-performance compute subsystems, we’re enabling customers with an open and customizable CPU platform that pairs seamlessly with Nvidia’s AI Infrastructure to deliver exceptional efficiency at data center scale.”

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NVIDIA’s Rubin Redefines the AI Factory

The Architecture Shift: From “GPU Server” to “Rack-Scale Supercomputer” NVIDIA’s Rubin architecture is built around a single design thesis: “extreme co-design.” In practice, that means GPUs, CPUs, networking, security, software, power delivery, and cooling are architected together; treating the data center as the compute unit, not the individual server. That logic shows up most clearly in the NVL72 system. NVLink 6 serves as the scale-up spine, designed to let 72 GPUs communicate all-to-all with predictable latency, something NVIDIA argues is essential for mixture-of-experts routing and synchronization-heavy inference paths. NVIDIA is not vague about what this requires. Its technical materials describe the Rubin GPU as delivering 50 PFLOPS of NVFP4 inference and 35 PFLOPS of NVFP4 training, with 22 TB/s of HBM4 bandwidth and 3.6 TB/s of NVLink bandwidth per GPU. The point of that bandwidth is not headline-chasing. It is to prevent a rack from behaving like 72 loosely connected accelerators that stall on communication. NVIDIA wants the rack to function as a single engine because that is what it will take to drive down cost per token at scale. The New Idea NVIDIA Is Elevating: Inference Context Memory as Infrastructure If there is one genuinely new concept in the Rubin announcements, it is the elevation of context memory, and the admission that GPU memory alone will not carry the next wave of inference. NVIDIA describes a new tier called NVIDIA Inference Context Memory Storage, powered by BlueField-4, designed to persist and share inference state (such as KV caches) across requests and nodes for long-context and agentic workloads. NVIDIA says this AI-native context tier can boost tokens per second by up to 5× and improve power efficiency by up to 5× compared with traditional storage approaches. The implication is clear: the path to cheaper inference is not just faster GPUs.

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Power shortages, carbon capture, and AI automation: What’s ahead for data centers in 2026

“Despite a broader use of AI tools in enterprises and by consumers, that does not mean that AI compute, AI infrastructure in general, will be more evenly spread out,” said Daniel Bizo, research director at Uptime Institute, during the webinar. “The concentration of AI compute infrastructure is only increasing in the coming years.” For enterprises, the infrastructure investment remains relatively modest, Uptime Institute found. Enterprises will limit investment to inference and only some training, and inference workloads don’t require dramatic capacity increases. “Our prediction, our observation, was that the concentration of AI compute infrastructure is only increasing in the coming years by a couple of points. By the end of this year, 2026, we are projecting that around 10 gigawatts of new IT load will have been added to the global data center world, specifically to run generative AI workloads and adjacent workloads, but definitely centered on generative AI,” Bizo said. “This means these 10 gigawatts or so load, we are talking about anywhere between 13 to 15 million GPUs and accelerators deployed globally. We are anticipating that a majority of these are and will be deployed in supercomputing style.” 2. Developers will not outrun the power shortage The most pressing challenge facing the industry, according to Uptime, is that data centers can be built in less than three years, but power generation takes much longer. “It takes three to six years to deploy a solar or wind farm, around six years for a combined-cycle gas turbine plant, and even optimistically, it probably takes more than 10 years to deploy a conventional nuclear power plant,” said Max Smolaks, research analyst at Uptime Institute. This mismatch was manageable when data centers were smaller and growth was predictable, the report notes. But with projects now measured in tens and sometimes hundreds of

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Google warns transmission delays are now the biggest threat to data center expansion

The delays stem from aging transmission infrastructure unable to handle concentrated power demands. Building regional transmission lines currently takes seven to eleven years just for permitting, Hanna told the gathering. Southwest Power Pool has projected 115 days of potential loss of load if transmission infrastructure isn’t built to match demand growth, he added. These systemic delays are forcing enterprises to reconsider fundamental assumptions about cloud capacity. Regions including Northern Virginia and Santa Clara that were prime locations for hyperscale builds are running out of power capacity. The infrastructure constraints are also reshaping cloud competition around power access rather than technical capabilities. “This is no longer about who gets to market with the most GPU instances,” Gogia said. “It’s about who gets to the grid first.” Co-location emerges as a faster alternative to grid delays Unable to wait years for traditional grid connections, hyperscalers are pursuing co-location arrangements that place data centers directly adjacent to power plants, bypassing the transmission system entirely. Pricing for these arrangements has jumped 20% in power-constrained markets as demand outstrips availability, with costs flowing through to cloud customers via regional pricing differences, Gogia said. Google is exploring such arrangements, though Hanna said the company’s “strong preference is grid-connected load.” “This is a speed to power play for us,” he said, noting Google wants facilities to remain “front of the meter” to serve the broader grid rather than operating as isolated power sources. Other hyperscalers are negotiating directly with utilities, acquiring land near power plants, and exploring ownership stakes in power infrastructure from batteries to small modular nuclear reactors, Hanna said.

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OpenAI turns to Cerebras in a mega deal to scale AI inference infrastructure

Analysts expect AI workloads to grow more varied and more demanding in the coming years, driving the need for architectures tuned for inference performance and putting added pressure on data center networks. “This is prompting hyperscalers to diversify their computing systems, using Nvidia GPUs for general-purpose AI workloads, in-house AI accelerators for highly optimized tasks, and systems such as Cerebras for specialized low-latency workloads,” said Neil Shah, vice president for research at Counterpoint Research. As a result, AI platforms operating at hyperscale are pushing infrastructure providers away from monolithic, general-purpose clusters toward more tiered and heterogeneous infrastructure strategies. “OpenAI’s move toward Cerebras inference capacity reflects a broader shift in how AI data centers are being designed,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “This move is less about replacing Nvidia and more about diversification as inference scales.” At this level, infrastructure begins to resemble an AI factory, where city-scale power delivery, dense east–west networking, and low-latency interconnects matter more than peak FLOPS, Ram added. “At this magnitude, conventional rack density, cooling models, and hierarchical networks become impractical,” said Manish Rawat, semiconductor analyst at TechInsights. “Inference workloads generate continuous, latency-sensitive traffic rather than episodic training bursts, pushing architectures toward flatter network topologies, higher-radix switching, and tighter integration of compute, memory, and interconnect.”

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

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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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