LNG Cargoes Land at Wider Discounts in Europe

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LNG Cargoes Land at Wider Discounts in Europe

It’s getting cheaper to bring a shipment of liquefied natural gas into Europe because of heightening competition between terminals to accommodate extra cargoes. The delivered price of LNG for northwest Europe widened its discount to the continental benchmark Title Transfer Facility in recent weeks, according to data from Spark Commodities Pte Ltd. The price difference […]

It’s getting cheaper to bring a shipment of liquefied natural gas into Europe because of heightening competition between terminals to accommodate extra cargoes.

The delivered price of LNG for northwest Europe widened its discount to the continental benchmark Title Transfer Facility in recent weeks, according to data from Spark Commodities Pte Ltd. The price difference was as much as minus 71.5 cents last week, according to the data.

Imports in western Europe reached their highest level for March in records going back to 2017, according to ship-tracking data compiled by Bloomberg. That’s happening as demand in Asia weakens, most noticeably in China, and Europe prepares to refill depleted inventories during the summer.

Greece’s Public Power Corp. SA last week bought an LNG cargo on a DES basis for May delivery at roughly a 70-cent discount to the TTF benchmark.

The widening difference demonstrates an increase in demand for delivery slots at European terminals, said Qasim Afghan, a commercial analyst at Spark.

As a result, all those facilities are now in the money, he said.

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Malaysia, Vietnam to Extend Agreements for Border Fields

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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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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,

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

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2025 playbook for enterprise AI success, from agents to evals

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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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