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Government must put in hard yards to anchor the underwater industry in the UK

It was a year of change for much of the world. Elections held in more than 60 countries – many of which yielded seismic results – have irreversibly shifted the political dial and no sector, subsea or otherwise, is free from the shocks. Fiscal and economic uncertainty have been part and parcel of the UK […]

It was a year of change for much of the world. Elections held in more than 60 countries – many of which yielded seismic results – have irreversibly shifted the political dial and no sector, subsea or otherwise, is free from the shocks.

Fiscal and economic uncertainty have been part and parcel of the UK in recent years and, sadly, that appears to be one of the few things that isn’t changing.

There is a dark cloud over much of the country, but even within that there are more than a few rays of light.

Among the headline findings to emerge from Global Underwater Hub’s recently released 2024 Business Survey was that the UK’s underwater industry has grown by 15% since 2019, and is now worth £9.2bn and supports 51,000 jobs.

The mood in the subsea supply chain is relatively buoyant, with over half (57%) of respondents to our survey confident that roadblocks to success can be overcome, while overall market prospects remain high, spurred on by the wide range of opportunities across the subsea sector, most notably offshore energy.

Everyone should be enthused by the fact that we have a sector ripe with potential and capable of steering the energy transition right on our doorstep. But things are rarely that simple.

Without firm government action to ensure project certainty and attract sustained investment, these unmatched potential risks are being squandered.

Optimism about supply chain capacity is being overshadowed by growing unease over project timelines.

Industry stakeholders believe that 62% of projects will face delays, eroding the confidence needed to align investment and business strategies.

With only 11% of surveyed companies seeing sufficient policy and financial commitment, many UK-based firms are weighing up the prospect of relocating overseas.

Decisive action at the highest level is needed, or else the UK risks exporting not just technology and expertise, but the high-paying jobs and economic benefits they generate.

GUH’s ask of government is simple: deliver an industrial strategy that leverages the UK’s underwater industry’s excellence, maximises its competitive advantage, and ensures it maintains its edge in global markets.

This strategy must include clear policies, timely approvals, and robust financing mechanisms to translate concepts into completed projects.

The stakes are high. The sector’s ability to service a mix of underwater markets is a unique advantage that could make the UK a hub for clean energy and industrial innovation. But this requires an investment-friendly environment, something that is sorely lacking.

Without this, our world-leading position will erode, along with the share of the jobs and wealth the industry creates.

Looking ahead, February is a pivotal month in the subsea calendar when our annual, flagship exhibition and conference – Subsea Expo – takes place at P&J Live.

This is when we unite the underwater industry to showcase innovation and foster collaboration.

The key themes from our survey will be discussed in our sessions, offering an invaluable chance to dive into the issues affecting our supply chain against the backdrop of massive opportunity.

GUH will be encouraging policy-makers to visit Aberdeen for the conference, where they will be able to see first-hand how vibrant and innovative the UK’s underwater sector is.

The UK government is committed to growing the economy and the country’s underwater industry – be that across energy, defence, aquaculture or telecommunications – can play a major role in achieving further significant growth. But only if we have positive market signals that allow subsea companies to make investment decisions.

Neil Gordon is the chief executive of trade body, Global Underwater Hub

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DTECH 2026: 5 observations ahead of the biggest grid event of the year

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Solving the AI power puzzle: Taming data center demand with flexible grid-scale storage

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How flexible loads are revolutionizing grid capacity

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At the Crossroads of AI and the Edge: Inside 1623 Farnam’s Rising Role as a Midwest Interconnection Powerhouse

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Schneider Electric’s $2.3 Billion in AI Power and Cooling Deals Sends Message to Data Center Sector

When Schneider Electric emerged from its 2025 North American Innovation Summit in Las Vegas last week with nearly $2.3 billion in fresh U.S. data center commitments, it didn’t just notch a big sales win. It arguably put a stake in the ground about who controls the AI power-and-cooling stack over the rest of this decade. Within a single news cycle, Schneider announced: Together, the deals total about $2.27 billion in U.S. data center infrastructure, a number Schneider confirmed in background with multiple outlets and which Reuters highlighted as a bellwether for AI-driven demand.  For the AI data center ecosystem, these contracts function like early-stage fuel supply deals for the power and cooling systems that underpin the “AI factory.” Supply Capacity Agreements: Locking in the AI Supply Chain Significantly, both deals are structured as supply capacity agreements, not traditional one-off equipment purchase orders. Under the SCA model, Schneider is committing dedicated manufacturing lines and inventory to these customers, guaranteeing output of power and cooling systems over a multi-year horizon. In return, Switch and Digital Realty are providing Schneider with forecastable volume and visibility at the scale of gigawatt-class campus build-outs.  A Schneider spokesperson told Reuters that the two contracts are phased across 2025 and 2026, underscoring that this arrangement is about pipeline, as opposed to a one-time backlog spike.  That structure does three important things for the market: Signals confidence that AI demand is durable.You don’t ring-fence billions of dollars of factory output for two customers unless you’re highly confident the AI load curve runs beyond the current GPU cycle. Pre-allocates power & cooling the way the industry pre-allocated GPUs.Hyperscalers and neoclouds have already spent two years locking up Nvidia and AMD capacity. These SCAs suggest power trains and thermal systems are joining chips on the list of constrained strategic resources.

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The Data Center Power Squeeze: Mapping the Real Limits of AI-Scale Growth

As we all know, the data center industry is at a crossroads. As artificial intelligence reshapes the already insatiable digital landscape, the demand for computing power is surging at a pace that outstrips the growth of the US electric grid. As engines of the AI economy, an estimated 1,000 new data centers1 are needed to process, store, and analyze the vast datasets that run everything from generative models to autonomous systems. But this transformation comes with a steep price and the new defining criteria for real estate: power. Our appetite for electricity is now the single greatest constraint on our expansion, threatening to stall the very innovation we enable. In 2024, US data centers consumed roughly 4% of the nation’s total electricity, a figure that is projected to triple by 2030, reaching 12% or more.2 For AI-driven hyperscale facilities, the numbers are even more staggering. With the largest planned data centers requiring gigawatts of power, enough to supply entire cities, the cumulative demand from all data centers is expected to reach 134 gigawatts by 2030, nearly three times the current load.​3 This presents a systemic challenge. The U.S. power grid, built for a different era, is struggling to keep pace. Utilities are reporting record interconnection requests, with some regions seeing demand projections that exceed their total system capacity by fivefold.4 In Virginia and Texas, the epicenters of data center expansion, grid operators are warning of tight supply-demand balances and the risk of blackouts during peak periods.5 The problem is not just the sheer volume of power needed, but the speed at which it must be delivered. Data center operators are racing to secure power for projects that could be online in as little as 18 months, but grid upgrades and new generation can take years, if not decades. The result

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The Future of Hyperscale: Neoverse Joins NVLink Fusion as SC25 Accelerates Rack-Scale AI Architectures

Neoverse’s Expanding Footprint and the Power-Efficiency Imperative With Neoverse deployments now approaching roughly 50% of all compute shipped into top hyperscalers in 2025 (representing more than a billion Arm cores) and with nation-scale AI campuses such as the Stargate project already anchored on Arm compute, the addition of NVLink Fusion becomes a pivotal extension of the Neoverse roadmap. Partners can now connect custom Arm CPUs to their preferred NVIDIA accelerators across a coherent, high-bandwidth, rack-scale fabric. Arm characterized the shift as a generational inflection point in data-center architecture, noting that “power—not FLOPs—is the bottleneck,” and that future design priorities hinge on maximizing “intelligence per watt.” Ian Buck, vice president and general manager of accelerated computing at NVIDIA, underscored the practical impact: “Folks building their own Arm CPU, or using an Arm IP, can actually have access to NVLink Fusion—be able to connect that Arm CPU to an NVIDIA GPU or to the rest of the NVLink ecosystem—and that’s happening at the racks and scale-up infrastructure.” Despite the expanded design flexibility, this is not being positioned as an open interconnect ecosystem. NVIDIA continues to control the NVLink Fusion fabric, and all connections ultimately run through NVIDIA’s architecture. For data-center planners, the SC25 announcement translates into several concrete implications: 1.   NVIDIA “Grace-style” Racks Without Buying Grace With NVLink Fusion now baked into Neoverse, hyperscalers and sovereign operators can design their own Arm-based control-plane or pre-processing CPUs that attach coherently to NVIDIA GPU domains—such as NVL72 racks or HGX B200/B300 systems—without relying on Grace CPUs. A rack-level architecture might now resemble: Custom Neoverse SoC for ingest, orchestration, agent logic, and pre/post-processing NVLink Fusion fabric Blackwell GPU islands and/or NVLink-attached custom accelerators (Marvell, MediaTek, others) This decouples CPU choice from NVIDIA’s GPU roadmap while retaining the full NVLink fabric. In practice, it also opens

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Flex’s Integrated Data Center Bet: How a Manufacturing Giant Plans to Reshape AI-Scale Infrastructure

At this year’s OCP Global Summit, Flex made a declaration that resonated across the industry: the era of slow, bespoke data center construction is over. AI isn’t just stressing the grid or forcing new cooling techniques—it’s overwhelming the entire design-build process. To meet this moment, Flex introduced a globally manufactured, fully integrated data center platform aimed directly at multi-gigawatt AI campuses. The company claims it can cut deployment timelines by as much as 30 percent by shifting integration upstream into the factory and unifying power, cooling, compute, and lifecycle services into pre-engineered modules. This is not a repositioning on the margins. Flex is effectively asserting that the future hyperscale data center will be manufactured like a complex industrial system, not built like a construction project. On the latest episode of The Data Center Frontier Show, we spoke with Rob Campbell, President of Flex Communications, Enterprise & Cloud, and Chris Butler, President of Flex Power, about why Flex believes this new approach is not only viable but necessary in the age of AI. The discussion revealed a company leaning heavily on its global manufacturing footprint, its cross-industry experience, and its expanding cooling and power technology stack to redefine what deployment speed and integration can look like at scale. AI Has Broken the Old Data Center Model From the outset, Campbell and Butler made clear that Flex’s strategy is a response to a structural shift. AI workloads no longer allow power, cooling, and compute to evolve independently. Densities have jumped so quickly—and thermals have risen so sharply—that the white space, gray space, and power yard are now interdependent engineering challenges. Higher chip TDPs, liquid-cooled racks approaching one to two megawatts, and the need to assemble entire campuses in record time have revealed deep fragility in traditional workflows. As Butler put it, AI

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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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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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