Morson Group acquires Manchester engineering firm Orange Solutions

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Morson Group acquires Manchester engineering firm Orange Solutions

Morson Group has expanded its portfolio with the acquisition of Orange Solutions, a Manchester-based safety and control systems engineering firm. The group confirmed that Orange Solution’s founder and managing director, Tony Hynes, will retain his position ” along with the company’s experienced team of engineers.” Morson told Energy Voice that it is looking to create […]

Morson Group has expanded its portfolio with the acquisition of Orange Solutions, a Manchester-based safety and control systems engineering firm.

The group confirmed that Orange Solution’s founder and managing director, Tony Hynes, will retain his position ” along with the company’s experienced team of engineers.”

Morson told Energy Voice that it is looking to create more than 10 jobs at the firm over the next year and that its “intention is to increase the footprint” of Orange Solutions in the UK and overseas. Currently, Orange Solutions has one base in Irlam.

The deal, agreed for an undisclosed sum, brings Oange Solutions under the group’s engineering division which includes Morson Projects, Waldeck and Ematics.

Morson claims that the work of Orange Solutions will complement fellow Manchester firm Ematics.

Orange Solutions is already well acquainted with its new owners, having worked with Ematics and Morson Projects on “a number of past and current projects,” Morson Group wrote.

Morson Group chief executive, Ged Mason commented: “The synergy between Orange Solutions’ track record and client base, and our Ematics business will enable us to offer clients even more expertise from within the Morson Group, with additional capacity and capabilities to resource projects effectively.”

Morson Group Acquired Orange Solutions for an undisclosed figure as it looks to expand the firm’s UK and international footprint.

Orange Solutions has worked across oil and gas operations, renewables projects and transmission line rollout.

Hynes added: “Orange Solutions has grown by providing the knowledge and experience needed to deliver complex projects on time.

“With so much potential in our sectors, the time was right for us to seek a strategic partner that can support our continued growth. The synergy and existing relationship with Morson, which is based locally to our existing HQ, made them an ideal fit.”

The firm has operated in the oil and gas sector for 20 years offering services in fire, gas and emergency shutdown systems.

Hynes continued: “As part of the Morson Group, we will continue to build on our reputation, while enabling our clients to tap into the 1,200 engineers employed across Morson’s engineering division.”

In addition to his firm’s familiarity with Morson, Hynes has worked with associate director of the group’s power and control division, Paul Ward in the past.

The pair worked together at Tyco Control Systems with a two year overlap between 2002 and 2004.

Ward commented: “The knowledge, skills and experience offered by Orange Solutions are in high demand, thanks to increasing use of automation across a wide range of industries, along with the need to control extremely complex systems and manage safety.”

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Golden Pass LNG Amends Train 2 and 3 Contract

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India Seeks Mideast Oil Shippers amid Russia Sanctions

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NSTA Announces ‘Giant Leap Forward for North Sea Data Sharing’

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NuEnergy Completes Drilling for ‘Early Gas Sales’ Project in Indonesia

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Server memory prices could double by 2026 as AI demand strains supply

Limited options for enterprise buyers As supply tightens, most enterprises face limited leverage in selecting suppliers. “Enterprise will have less control over what memory supplier they can choose unless you are a hyperscaler or tier-2 AI datacenter scale enterprise,” Neil Shah, VP for research and partner at Counterpoint Research, told NetworkWorld. “For most enterprises investing in AI infrastructure, they will rely on vendors such as Dell, Lenovo, HPE, Supermicro, and others on their judgment to select the best memory supplier.” Shah advised enterprises with control over their bill of materials to negotiate and lock in supply and costs in advance. “In most cases for long-tail enterprises, smaller buyers without volume leverage, they will have little control as demand outstrips supply, so the prudent thing would be to spread out the rollout over time to average out the cost spikes,” he said. Legacy shortage opens door for Chinese suppliers The current pricing pressure has its roots in production decisions made months ago. According to Counterpoint, the supply crunch originated at the low end of the market as Samsung, SK Hynix, and Micron redirected production toward high-bandwidth memory for AI accelerators, which commands higher margins but consumes three times the wafer capacity of standard DRAM. That shift created an unusual price inversion: DDR4 used in budget devices now trades at approximately $2.10 per gigabit, while server-grade DDR5 sells for around $1.50 per gigabit, according to the firm. This tightness is creating an opportunity for China’s CXMT, noted Shah. “DDR4 is being used in low- to mid-tier smart devices and considering bigger vendors such as Samsung and SK Hynix planned to ramp down DDR4 capacity, CXMT could gain advantage and balance the supply versus demand dynamics moving into the second half of next year,” Shah said.

Cobalt 200: Microsoft’s next-gen Arm CPU targets lower TCO for cloud workloads

These architectural improvements underpin Cobalt 200’s claimed increase in performance, which, according to Stephen Sopko, analyst at HyperFRAME Research, will lead to a reduction in total cost of ownership (TCO) compared to its predecessor. As a result, enterprise customers can benefit from consolidating workloads onto fewer machines. “For example, a 1k-instance cluster can see up to 30-40% TCO gains,” Sopko said, adding that this also helps enterprises free up resources to allocate to other workloads or projects. Moor Strategy and Insights principal analyst Matt Kimball noted that the claimed improvements in throughput-per-watt could be beneficial for compute-intensive workloads such as AI inferencing, microservices, and large-scale data processing. Some of Microsoft’s customers are already using Cobalt 100 virtual machines (VMs) for large-scale data processing workloads, and the chips are deployed across 32 Azure data centers, the company said. With Cobalt 200, the company will directly compete with AWS’s Graviton series and Google’s recently announced Axion processors, both of which leverage Arm architecture to deliver better price-performance for cloud workloads. Microsoft and other hyperscalers have been forced to design their own chips for data centers due to the skyrocketing costs for AI and cloud infrastructure, supply constraints around GPUs, and the need for energy-efficient yet customizable architectures to optimize workloads.

AWS boosts its long-distance cloud connections with custom DWDM transponder

By controlling the entire hardware stack, AWS can implement comprehensive security measures that would be challenging with third-party solutions, Rehder stated. “This initial long-haul deployment represents just the first implementation of the in-house technology across our extensive long-haul network. We have already extended deployment to Europe, with plans to use the AWS DWDM transponder for all new long-haul connections throughout our global infrastructure,” Rehder wrote. Cloud vendors are some of the largest optical users in the world, though not all develop their own DWDM or other optical systems, according to a variety of papers on the subject. Google develops its own DWDM, for example, but others like Microsoft Azure develop only parts and buy optical gear from third parties. Others such as IBM, Oracle and Alibaba have optical backbones but also utilize third-party equipment. “We are anticipating that the time has come to interconnect all those new AI data centers being built,” wrote Jimmy Yu, vice president at Dell’Oro Group, in a recent optical report. “We are forecasting data center interconnect to grow at twice the rate of the overall market, driven by increased spending from cloud providers. The direct purchases of equipment for DCI will encompass ZR/ZR+ optics for IPoDWDM, optical line systems for transport, and DWDM systems for high-performance, long-distance terrestrial and subsea transmission.”

Nvidia’s first exascale system is the 4th fastest supercomputer in the world

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Samsung’s 60% memory price hike signals higher data center costs for enterprises

Industry-wide price surge driven by AI Samsung is not alone in raising prices. In October, TrendForce reported that Samsung and SK Hynix raised DRAM and NAND flash prices by up to 30% for Q4. Similarly, SK Hynix said during its October earnings call that its HBM, DRAM, and NAND capacity is “essentially sold out” for 2026, with the company posting record quarterly operating profit exceeding $8 billion, driven by surging AI demand. Industry analysts attributed the price increases to manufacturers redirecting production capacity. HBM production for AI accelerators consumes three times the wafer capacity of standard DRAM, according to a TrendForce report, citing remarks from Micron’s Chief Business Officer. After two years of oversupply, memory inventories have dropped to approximately eight weeks from over 30 weeks in early 2023. “The memory industry is tightening faster than expected as AI server demand for HBM, DDR5, and enterprise SSDs far outpaces supply growth,” said Manish Rawat, semiconductor analyst at TechInsights. “Even with new fab capacity coming online, much of it is dedicated to HBM, leaving conventional DRAM and NAND undersupplied. Memory is shifting from a cyclical commodity to a strategic bottleneck where suppliers can confidently enforce price discipline.” This newfound pricing power was evident in Samsung’s approach to contract negotiations. “Samsung’s delayed pricing announcement signals tough behind-the-scenes negotiations, with Samsung ultimately securing the aggressive hike it wanted,” Rawat said. “The move reflects a clear power shift toward chipmakers: inventories are normalized, supply is tight, and AI demand is unavoidable, leaving buyers with little room to negotiate.” Charlie Dai, VP and principal analyst at Forrester, said the 60% increase “signals confidence in sustained AI infrastructure growth and underscores memory’s strategic role as the bottleneck in accelerated computing.” Servers to cost 10-25% more For enterprises building AI infrastructure, these supply dynamics translate directly into

Arista, Palo Alto bolster AI data center security

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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.

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

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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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