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Gunvor Financed Gabon Deal for Tullow Oil Assets

Commodity trader Gunvor Group has made another significant oil deal in Gabon, its second since the country sought more control of national assets following a coup d’etat in 2023. State-owned Gabon Oil Company financed $220 million of a $300 million deal for Tullow Oil Plc’s assets in the country through an underwritten prepayment facility from […]

Commodity trader Gunvor Group has made another significant oil deal in Gabon, its second since the country sought more control of national assets following a coup d’etat in 2023.

State-owned Gabon Oil Company financed $220 million of a $300 million deal for Tullow Oil Plc’s assets in the country through an underwritten prepayment facility from Gunvor’s Middle East subsidiary, Tullow said in a filing Tuesday.

Gunvor last year provided around $800 million to help Gabon finance another nationalization deal, the acquisition of Carlyle Group’s Assala Energy. A spokesperson for Gunvor declined to comment on the deal.

Coming out of the most profitable period in their histories, trading firms are funneling cash into asset deals and prepayments to lock in more lucrative oil supply contracts as more competition from national oil companies and hedge funds lowers trading margins in oil markets.

The Tullow assets are set to produce around 10,000 barrels of oil a day this year, the company said, adding that it will use the funds to reduce its debt profile. The company has been setting up deals to sell assets and bring down debt ahead of $1.39 billion in maturities next year. 

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Key takeaways from IBM Think partner event

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LandBridge Posts Higher Revenue

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Thousands of energy experts are leaving federal government. Here’s where they should go.

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Tallgrass Secures Shipping Deals for Planned Permian Gas Pipeline

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Sweden’s CorPower to build UK’s largest wave energy array in Orkney

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Electrification and net zero coming ‘no matter your politics’, ScottishPower chief says

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Tenaz Closes Acquisition of NOBV, Widens Q1 Net Loss

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EIA Expects USA Gasoline Price to Drop in 2025 and 2026

The U.S. Energy Information Administration (EIA) expects the U.S. regular gasoline retail price to drop this year and next year, according to its latest short term energy outlook, which was released on May 6. According to that STEO, the EIA sees the U.S. regular gasoline retail price averaging $3.09 per gallon in 2025 and $3.07 per gallon in 2026. The EIA highlighted in the STEO that the U.S. regular gasoline retail price averaged $3.31 per gallon in 2024. In its latest STEO the EIA projected that the U.S. regular gasoline retail price will come in at $3.16 per gallon in the second quarter of this year, $3.12 per gallon in the third quarter, $2.99 per gallon in the fourth quarter, $2.96 per gallon in the first quarter of 2026, $3.16 per gallon in the second quarter, $3.17 per gallon in the third quarter, and $2.96 per gallon in the fourth quarter of next year. In its previous STEO, which was released back in April, the EIA projected that the U.S. regular gasoline retail price would average $3.09 per gallon in 2025 and $3.11 per gallon in 2026. That STEO also pointed out that the U.S. regular gasoline retail price averaged $3.31 per gallon last year. The EIA’s April STEO forecast that the U.S. regular gasoline retail price would come in at $3.10 per gallon in the second quarter of 2025, $3.14 per gallon in the third quarter, $3.01 per gallon in the fourth quarter, $2.99 per gallon in the first quarter of 2026, $3.21 per gallon in the second quarter, $3.22 per gallon in the third quarter, and $3.00 per gallon in the fourth quarter of next year. “We expect gasoline prices across the United States will average $3.14 per gallon over 2Q25 and 3Q25, down nine percent from the

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HPE ‘morphs’ private cloud portfolio with improved virtualization, storage and data protection

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AMD targets hosting providers with affordable EPYC 4005 processors

According to Pinkesh Kotecha, chairman and MD of Ishan Technologies, AMD’s 4th Gen EPYC processors stood out because they offer the right combination of high performance, energy efficiency, and security. “Their high core density and ability to optimize performance per watt made them ideal for managing data-intensive operations like real-time analytics and high-frequency transactions. Additionally, AMD’s strong AI roadmap and growing portfolio of AI-optimised solutions position them as a forward-looking partner, ready to support our customers’ evolving AI and data needs. This alignment made AMD a clear choice over alternatives,” Kotecha said. By integrating AMD EPYC processors, Ishan Technologies’ Ishan Cloud plans to empower enterprises across BFSI, ITeS, and manufacturing industries, as well as global capability centers and government organizations, to meet India’s data localization requirements and drive AI-led digital transformation. “The AMD EPYC 4005 series’ price-to-performance ratio makes it an attractive option for cloud hosting and web services, where cost-efficient, always-on performance is essential,” said Manish Rawat, analyst, TechInsights. Prabhu Ram, VP for the industry research group at CMR, said EPYC 4005 processors deliver a compelling mix of performance-per-watt, higher core counts, and modern I/O support, positioning it as a strong alternative to Intel’s Xeon E-2400 and 6300P, particularly for edge deployments. Shah of Counterpoint added, “While ARM-based Ampere Altra promises higher power efficiencies and is ideally adopted in more cloud and hyperscale data centers, though performance is something where x86-based Zen 5 architecture excels and nicely balances the efficiencies with lower TDPs, better software compatibilities supported by a more mature ecosystem.”

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Shell’s immersive cooling liquids the first to receive official certification from Intel

Along with the certification, Intel is offering a Xeon processor single-phase immersion warranty rider. This indicates Intel’s confidence in the durability and effectiveness of Shell’s fluids. Yates explained that the rider augments Intel’s standard warranty terms and is available to data center operators deploying 4th and 5th generation Xeon processors in Shell immersion fluids. The rider is intended to provide data center operators confidence that their investment is guaranteed when deployed correctly. Shell’s fluids are available globally and can be employed in retrofitted existing infrastructure or used in new builds. Cuts resource use, increases performance Data centers consume anywhere from 10 to 50 times more energy per square foot than traditional office buildings, and they are projected to drive more than 20% of the growth in electricity demand between now and 2030. Largely due to the explosion of AI, data center energy consumption is expected to double from 415 terawatt-hours in 2024 to around 945 TWh by 2030. There are several other technologies used for data center cooling, including air cooling, cold plate (direct-to-chip), and precision cooling (targeted to specific areas), but the use of immersion cooling has been growing, and is expected to account for 36% of data center thermal management revenue by 2028. With this method, servers and networking equipment are placed in cooling fluids that absorb and dissipate heat generated by the electronic equipment. These specialized fluids are thermally conductive but not electrically conductive (dielectric) thus making them safe for submerging electrical equipment.

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Cisco joins AI infrastructure alliance

“The addition of Cisco reinforces AIP’s commitment to an open-architecture platform and fostering a broad ecosystem that supports a diverse range of partners on a non-exclusive basis, all working together to build a new kind of AI infrastructure,” the group said in a statement.  Separately, Cisco announced AI initiatives centered in the Middle East region. Last week, Cisco CEO Chuck Robbins visited Saudi Arabia, UAE, Qatar, and Bahrain. This week, Jeetu Patel, executive vice president and chief product officer, is in Saudi Arabia, where he is participating in President Trump’s state visit to the region, according to Cisco. Related new projects include:  An initiative with HUMAIN, Saudi Arabia’s new AI enterprise to help build an open, scalable, resilient and cost-efficient AI infrastructure: “This landmark collaboration will set a new standard for how AI infrastructure is designed, secured and delivered – combining Cisco’s global expertise with the Kingdom’s bold AI ambitions. The multi-year initiative aims to position the country as a global leader in digital innovation,” Cisco stated. A collaboration with the UAE-basedG42 to co-develop a secure AI portfolio and AI-native services: Cisco and G42 will work together to assess the potential to co-develop and jointly deploy AI-powered cybersecurity packages, as well as a reference architecture that integrates Cisco’s networking, security, and infrastructure solutions specifically designed for high-performance computing. This collaboration aims to help customers build and secure AI-ready data centers and develop AI workloads effectively, according to the companies. Interest in Qatar’s digital transformation: Qatar’s Ministry of Interior and Cisco signed a letter of intent to collaborate on Qatar’s digital transformation, AI, infrastructure development and cybersecurity.

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Cato Networks introduces AI-powered policy analysis engine

Cato Networks this week announced a new policy analysis engine for its cloud-based secure access service edge platform that the company says will optimize and improve SASE policies, reduce risk, simplify compliance, and reduce manual maintenance efforts. Cato Autonomous Policies is built into the Cato SASE Cloud Platform and can provide enterprises with AI-driven recommendations to eliminate security exposure, tighten access controls, and improve network performance. The first use case of the policy engine is designed for firewall as a service (FWaaS) environments in which “firewall rule bloat” is present, Cato explained in a statement. The bloat comes from organizations accumulating thousands of rules that were designed to protect the environment, but after becoming outdated or misconfigured, actually lead to increased risk. “Most enterprises rely on a mix of firewalls deployed in data centers, branch offices, and cloud environments. Over time, rule sets grow, become inconsistent, and are filled with redundant, outdated, or conflicting entries,” wrote Demetris Booth, product marketing director at Cato Networks, in a blog post on the product news. “As a result, security policies become hard to manage, even harder to audit, and often misaligned with zero-trust principles. AI-driven firewall policy management is necessary for modern enterprises to streamline and optimize security operations.”

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Riverbed bolsters network acceleration for AI’s performance bottlenecks

“Enterprises are worried about bad actors capturing encrypted traffic and saving copies for when quantum computing advances can break the encryption, providing the bad actors with free access to data. It’s a real concern,” Frey explains. “Post-quantum cryptography is a way to get ahead of that now.” Riverbed also introduced the SteelHead 90 series of network acceleration appliances, which the company says will provide resilient network performance to customers. The series includes: SteelHead 8090, which delivers up to 60 Gbps of data movement over a WAN. It supports multiple 100 Gigabyte network interfaces to pull data from the LAN. SteelHead 6090, which delivers up to 20 Gbps of data movement over a WAN, targeted for mid-scale data centers. SteelHead 4090 and 2090, which support mid-sized data center and edge use cases, with 500 Mbps and 200 Mbps of accelerated traffic, as well as up to 10 Gbps of total traffic processing for quality of service (QoS) and application classification use cases. Riverbed SteelHead Virtual, is a software-only version designed for virtualization environments and private cloud deployments, which is compatible with VMWare ESXI, KVM, and Microsoft Hyper-V. “For customers that are familiar with Riverbed, this is a big change in performance. We’ve gone from moving one appliance at 30 Gbps to 60 Gbps. We want to make sure that whether it’s new AI projects or existing data projects, we have ubiquitous availability across clouds,” says Chalan Aras, senior vice president and general manager of Acceleration at Riverbed. “We’re making it less expensive to move data—we are about half the price of traditional data movement methods.” With this announcement, Riverbed also unveiled its Flex licensing subscription offering. According to Riverbed, Flex makes it possible for enterprises to transfer licenses from hardware to virtual to cloud devices at no cost. Enterprises can reassign

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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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