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INEOS Completes Acquisition of CNOOC Assets in US Gulf

The INEOS Group has completed its purchase of China National Offshore Oil Corp.’s (CNOOC) stakes on the United States side of the Gulf of Mexico. The acquisition consisted of non-operating stakes in deepwater early-production projects Appomattox and Stampede, as well as “several mature assets and supporting business”, according to INEOS. CNOOC held a 25 percent […]

The INEOS Group has completed its purchase of China National Offshore Oil Corp.’s (CNOOC) stakes on the United States side of the Gulf of Mexico.

The acquisition consisted of non-operating stakes in deepwater early-production projects Appomattox and Stampede, as well as “several mature assets and supporting business”, according to INEOS. CNOOC held a 25 percent stake in Stampede, operated by Hess Corp., and 21 percent in Appomattox, operated by INEOS’ fellow British company Shell PLC.

The new assets raise the global production of INEOS’ energy arm to over 90,000 barrels of oil equivalent a day (boed), according to diversified company INEOS.

“The USA is a very attractive place for INEOS Energy to invest”, INEOS Energy chief executive David Bucknall said in a statement. “This is our third deal in three years following the 1.4 mtpa LNG deal with Sempra and the acquisition of Chesapeake Energy’s oil and gas assets in South Texas.

“Total capital spend on energy assets in the USA now exceeds $3 billion, providing a strong platform for future growth”.

INEOS Energy chair Brian Gilvary commented, “This is a major step for us into the deepwater US Gulf, which builds on our growing energy business”.

“INEOS Energy is all about competing in the energy transition to provide reliable, affordable energy to meet world demand as the population continues to grow – and progressing carbon storage projects”, Gilvary added.

For CNOOC, the divestments will help it “optimize” its global portfolio, CNOOC International Ltd. chair Liu Yongjie said in a statement December 14, 2024, announcing the transaction agreement.

Early last year Shell put into production a subsea tie-back to the Appomattox floating production hub, adding an estimated peak production of 16,000 boed.

Located in the Mississippi Canyon, the Rydberg project has estimated proven and probable reserves of 38 million boe, according to Shell.

“Rydberg will further boost production in the Norphlet Corridor at Appomattox, which is consistently one of our highest producing assets”, Rich Howe, deepwater executive vice president at Shell, said in a statement February 22, 2024.

Appomattox started up 2019, becoming the first commercial discovery brought into production in the deepwater Norphlet formation, according to Shell. It sits about 80 miles southeast of Louisiana’s Gulf Coast in around 7,400 feet of water, according to Shell.

In 2023 Appomattox contributed 19,000 boe to CNOOC’s net production, according to information on CNOOC’s website.

Stampede started production 2018. It has an average water depth of 3,500 feet, according to CNOOC.

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Trump targets state climate laws in latest executive order

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EIA reduces global oil demand projections on tariff uncertainties

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EVOL: Neo Next speculation, Kistos’ island plans, and HAR2 just has to be good

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The power industry must oppose Trump’s coal bailout … again

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Natural Gas Searching for ‘Near Term Equilibrium’

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Mubadala Secures Its First Gas Production, Export Assets in US

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Podcast: Nomads at the Frontier – AI, Infrastructure, and Data Center Workforce Evolution at DCD Connect New York

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2025 Data Center Power Poll

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How Microgrids and DERs Could Solve the Data Center Power Crisis

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

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How Tariffs Could Impact Data Centers, AI, and Energy Amid Supply Chain Shifts

The present imposition of sweeping tariffs by the U.S. government has sent ripples through various sectors of the economy, with the data center industry poised to experience significant ramifications. These tariffs, encompassing a baseline 10% duty on all imports and escalating to higher percentages for specific countries—such as 54% on Chinese imports—are set to influence data center construction, hardware manufacturing, software development, supply chains, user demand, and energy consumption.​ Impact on Data Center Construction, Energy Access and Site Seletion Data center construction has long been dependent on key materials such as steel and aluminum, which are essential for everything from structural frameworks to power infrastructure. The newly enacted 25% tariff on steel imports represents a significant escalation in material costs, with analysts predicting a ripple effect throughout the entire data center ecosystem. For the construction industry, this price hike means an immediate increase in the cost per square foot of building new facilities—costs that are likely to be passed on to developers and ultimately to end users. More concerning, however, is the potential for delayed project timelines. The data center industry, already operating under tight deadlines to meet surging demand for digital infrastructure, could see construction timelines stretched as a result of both rising material costs and the limited availability of key components. Steel and aluminum are used in not just the physical building, but in critical power systems—transformers, switchgear, and cooling equipment. A shortage of these materials could, therefore, exacerbate ongoing supply chain bottlenecks, pushing back go-live dates for new facilities and forcing operators to reevaluate their development strategies. Furthermore, analysts are particularly worried about the compounded impact of these tariffs on already-strained energy access. In regions like Northern Virginia, Silicon Valley, and parts of Texas, data center growth has been stifled by grid congestion, making it difficult to

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With Ampere Deal, SoftBank Tightens Its Grip on AI Data Centers

From Silicon to Stargate: Aligning with OpenAI, Oracle, and the Future of AI Infrastructure The Ampere acquisition doesn’t stand alone. It is the latest and perhaps most strategic move in a broader chess game SoftBank is playing across the AI and data infrastructure landscape. To understand its full impact, the deal must be seen in context with SoftBank’s recent alignment with two other heavyweight players: OpenAI and Oracle. As you might’ve heard, earlier this year, OpenAI unveiled plans for its Stargate project—a massive, multi-billion-dollar supercomputing campus set to come online by 2028. Stargate is expected to be one of the largest AI infrastructure builds in history, and Oracle will be the primary cloud provider for the project. Behind the scenes, SoftBank is playing a key financial and strategic role, helping OpenAI secure capital and compute resources for the long-term training and deployment of advanced AI models. Oracle, in turn, is both an investor in Ampere and a major customer—one of the first hyperscale operators to go all-in on Ampere’s Arm-based CPUs for powering cloud services. With SoftBank now controlling Ampere outright, it gains a stronger seat at the table with both Oracle and OpenAI—positioning itself as an essential enabler of the AI supply chain from silicon to software. The Ampere deal gives SoftBank direct access to a custom silicon pipeline purpose-built for the kind of high-efficiency, high-throughput compute that AI inference and model serving demand at scale. Combine this with SoftBank’s ownership of Arm, the bedrock of energy-efficient chip design, and its portfolio now spans everything from the instruction set to the cloud instance. More importantly, it gives SoftBank leverage. In a world where NVIDIA dominates AI training workloads, there’s growing appetite for alternatives in inference, especially at scale where power, cost, and flexibility become deciding factors. Ampere’s CPU roadmap,

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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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