Spirit Energy forced to close Morecambe Bay platform over fire safety concerns

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Spirit Energy forced to close Morecambe Bay platform over fire safety concerns

Spirit Energy has been forced to curtail natural gas production at a platform in Morecambe Bay in order to conduct a fire safety inspection, a spokesperson has confirmed. The company’s majority owner Centrica is seeking to redevelop the Morecambe Bay facility into a carbon capture and storage hub that would cater to industrial businesses seeking […]

Spirit Energy has been forced to curtail natural gas production at a platform in Morecambe Bay in order to conduct a fire safety inspection, a spokesperson has confirmed.

The company’s majority owner Centrica is seeking to redevelop the Morecambe Bay facility into a carbon capture and storage hub that would cater to industrial businesses seeking to reduce their carbon footprint.

A spokesperson confirmed that the fire protection sprinkler network known as a deluge system on the CPC-1 platform is in the process of being tested, and that production has been “paused” to enable any necessary remedial measures.

The Morecambe central processing complex in the Irish Sea will be shuttered for four weeks while the company conducts fire safety inspections at the behest of the health and safety regulator.

Morecambe Bay hub comprises three gas fields in North Morecambe, South Morecambe and Rhyl that have produced natural gas for more than 30 years.

The closure follows talks between the health and safety executive (HSE) and Spirit Energy in advance of a routine inspection conducted on 26 March. The platform shutdown was initiated on 12 March.

The operator subsequently agreed to a more wide-ranging inspection of deluge systems on the platform in the East Irish Sea, which is usually occupied by about 130 workers.

HSE has urged North Sea operators to improve their culture of safety and issued new guidelines on deluge systems maintenance, which are designed to extinguish fires on offshore platforms.

In December, it served Repsol with a prohibition notice after an oil platform, the Fulmar platform in the central North Sea, failed a safety inspection due to concerns about its deluge system.

Production of natural gas at the Morecambe Bay hub has been curtailed by about 7,500 barrels of oil equivalent per day, down from usual production of about 18,000 barrels of oil equivalent each day.

It remains unclear when the plant will resume full production.

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Fracker Liberty’s Profit Falls to 3-Year Low as Oil Slumps

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Intel sells off majority stake in its FPGA business

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Intelligence at the edge opens up more risks: how unified SASE can solve it

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Keysight tools tackle data center deployment efficiency

Test and performance measurement vendor Keysight Technologies has developed Keysight Artificial Intelligence (KAI) to identify performance inhibitors affecting large GPU deployments. It emulates workload profiles, rather than using actual resources, to pinpoint performance bottlenecks. Scaling AI data centers requires testing throughout the design and build process – every chip, cable, interconnect, switch, server, and GPU needs to be validated, Keysight says. From the physical layer through the application layer, KAI is designed to identify weak links that degrade the performance of AI data centers, and it validates and optimizes system-level performance for optimal scaling and throughput. AI providers, semiconductor fabricators, and network equipment manufacturers can use KAI to accelerate design, development, deployment, and operations by pinpointing performance issues before deploying in production.

U.S. Advances AI Data Center Push with RFI for Infrastructure on DOE Lands

ORNL is also the home of the Center for Artificial Intelligence Security Research (CAISER), which Edmon Begoli, CAISER founding director, described as being in place to build the security necessary by defining a new field of AI research targeted at fighting future AI security risks. Also, at the end of 2024, Google partner Kairos Power started construction of their Hermes demonstration SMR in Oak Ridge. Hermes is a high-temperature gas-cooled reactor (HTGR) that uses triso-fueled pebbles and a molten fluoride salt coolant (specifically Flibe, a mix of lithium fluoride and beryllium fluoride). This demonstration reactor is expected to be online by 2027, with a production level system becoming available in the 2030 timeframe. Also located in a remote area of Oak Ridge is the Tennessee Valley Clinch River project, where the TVA announced a signed agreement with GE-Hitachi to plan and license a BWRX-300 small modular reactor (SMR). On Integrating AI and Energy Production The foregoing are just examples of ongoing projects at the sites named by the DOE’s RFI. Presuming that additional industry power, utility, and data center providers get on board with these locations, any of the 16 could be the future home of AI data centers and on-site power generation. The RFI marks a pivotal step in the U.S. government’s strategy to solidify its global dominance in AI development and energy innovation. By leveraging the vast resources and infrastructure of its national labs and research sites, the DOE is positioning the country to meet the enormous power and security demands of next-generation AI technologies. The selected locations, already home to critical energy research and cutting-edge supercomputing, present a compelling opportunity for industry stakeholders to collaborate on building integrated, sustainable AI data centers with dedicated energy production capabilities. With projects like Oak Ridge’s pioneering SMRs and advanced AI security

Generac Sharpens Focus on Data Center Power with Scalable Diesel and Natural Gas Generators

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CoolIT and Accelsius Push Data Center Liquid Cooling Limits Amid Soaring Rack Densities

The CHx1500’s construction reflects CoolIT’s 24 years of DLC experience, using stainless-steel piping and high-grade wetted materials to meet the rigors of enterprise and hyperscale data centers. It’s also designed to scale: not just for today’s most power-hungry processors, but for future platforms expected to surpass today’s limits. Now available for global orders, CoolIT is offering full lifecycle support in over 75 countries, including system design, installation, CDU-to-server certification, and maintenance services—critical ingredients as liquid cooling shifts from high-performance niche to a requirement for AI infrastructure at scale. Capex Follows Thermals: Dell’Oro Forecast Signals Surge In Cooling and Rack Power Infrastructure Between Accelsius and CoolIT, the message is clear: direct liquid cooling is stepping into its maturity phase, with products engineered not just for performance, but for mass deployment. Still, technology alone doesn’t determine the pace of adoption. The surge in thermal innovation from Accelsius and CoolIT isn’t happening in a vacuum. As the capital demands of AI infrastructure rise, the industry is turning a sharper eye toward how data center operators account for, prioritize, and report their AI-driven investments. To wit: According to new market data from Dell’Oro Group, the transition toward high-power, high-density AI racks is now translating into long-term investment shifts across the data center physical layer. Dell’Oro has raised its forecast for the Data Center Physical Infrastructure (DCPI) market, predicting a 14% CAGR through 2029, with total revenue reaching $61 billion. That revision stems from stronger-than-expected 2024 results, particularly in the adoption of accelerated computing by both Tier 1 and Tier 2 cloud service providers. The research firm cited three catalysts for the upward adjustment: Accelerated server shipments outpaced expectations. Demand for high-power infrastructure is spreading to smaller hyperscalers and regional clouds. Governments and Tier 1 telecoms are joining the buildout effort, reinforcing AI as a

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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