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Outdated billing systems are holding back the energy transition

Across the US, utilities are making significant advancements in the energy transition, one such being electrification: switching end-use devices like heating systems and vehicles away from fossil fuels and onto electric fuel. Because electrification – and the associated demand increase – could increase system costs if demand is left unmanaged, a subsequent trend has emerged: […]

Across the US, utilities are making significant advancements in the energy transition, one such being electrification: switching end-use devices like heating systems and vehicles away from fossil fuels and onto electric fuel. Because electrification – and the associated demand increase – could increase system costs if demand is left unmanaged, a subsequent trend has emerged: demand management at the grid-edge. Utilities are investing in new solutions to serve customer demand (particularly from new grid-edge devices like EVs and heat pumps) more efficiently. An important tool in a utility’s demand management toolbox is pricing innovation – that is, developing new rate designs that more accurately communicate to customers the costs and benefits of their use of the power system. With new time-of-use rate designs, dynamic pricing models, location-based incentives, utilities have a wealth of solutions at their disposal. But to implement these pricing innovations at scale, utilities will need modern billing technologies that can keep up.

Implementing rate design innovation requires more than just smart meters

Over the last decade, US utilities have invested billions of dollars into advanced metering infrastructure (AMI). The transition away from manually-read meters to communications-enabled smart meters has allowed utilities to collect more granular consumption data from end customers. The value of these AMI investments relates to the outcomes utilities are able to achieve with this new, granular meter data. Utilities across the US are seeking to deliver value from their AMI investments by implementing time-varying rates for customers, something they weren’t able to do without granular meter data. However, many utilities are finding that even with modernized metering equipment, the process of implementing new rate designs proves difficult and expensive if their customer IT systems (specifically, billing) have yet to be modernized. Coding a new rate design or product offering into a legacy billing system can take months to years and cost millions of dollars. This implementation bottleneck posed by legacy billing technology comes at the expense of market innovation and system efficiency – and ultimately borne by ratepayers.

What features should modern utility billing technology include?

Ability to utilize new types of data for billing purposes: utilities are collecting new types and formats of data that could be used for price-setting and billing, from increasingly-granular consumption data (collected by smart meters) to individual preference information (collected through customer surveys). Billing technology must be able to efficiently make use of those new data types as they become available.

Configurability for new rate designs and pricing:  as utilities explore new rate design offerings, they are moving from traditional flat volumetric rates to variable pricing structures that more accurately reflect marginal costs and benefits. Often implementing these has taken months or years to complete, costing millions of dollars in the process. Billing systems — and their necessary upgrades — should not serve as a bottleneck to rate design innovation.

Easily integrated with the full customer IT stack: designing new products and programs will only pay off if customers actually make use of those new offerings. To ensure robust uptake and enrollment, utilities will need to actively engage their customers regarding new products and programs, through tailored marketing and outreach, creative enrollment solutions, rate comparisons, etc. To do it well, this engagement will require a utility’s billing system to work with its other backend customer IT systems and seamlessly pass information between them.

Modern billing technology in action: Octopus Energy’s Fan Club and Agile Octopus Tariff

Octopus Energy, a retail energy supplier serving 7.95 million customers, is known for their innovative rate designs. In 2021, Octopus Energy’s British retail business launched a new product for electric customers called Fan Club. Fan Club is designed to increase popular support for onshore wind development by offering customers who live near certain onshore wind projects a discount off their electric supply rate in hours when those projects generate electricity.

Using Kraken’s billing platform, Octopus Energy cross-references a customer’s account-level zip code against the zip code of participating wind turbines (to check for participation eligibility) and then calculates and assigns bill credits to individual participants based on precise time-varying determinants (e.g. energy market prices, wind speed, etc.). Without Kraken, these billing processes may have otherwise been performed by different software programs, which could have been complicated and expensive to execute. With Kraken, Octopus Energy was able to implement Fan Club all from a single billing platform, in a matter of weeks.

Agile Octopus is another innovative smart rate that exposes customers directly to wholesale electricity market prices, using half-hourly market data. Customers enrolled on Agile Octopus are exposed to “plunge pricing,” which occurs in hours when more electricity is generated than consumed, resulting in negative price events. Customers receive alerts when wholesale prices drop below zero and can use an API from Kraken to program their smart devices (e.g. EVs) to charge when prices are negative. By consuming electricity when prices are negative, customers get paid for the electricity they use. This rate design is incredibly complex to bill because it relies on dynamic wholesale prices as key billing determinants. To implement this rate for customers, Octopus Energy required a modern billing system — which they found in Kraken.

Modern billing technology is foundational to the energy transition

To implement advanced rate designs that accelerate the energy transition and make full use of new data that is being collected at the grid edge, utilities will need to modernize their customer billing technology. Information technology vendors like Kraken are enabling this modernization by developing billing systems that allow for agility, scalability and innovation.

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DKnife targets network gateways in long running AitM campaign

Beyond update hijacking, the framework supports DNS manipulation, binary replacement, and selective traffic forwarding, giving attackers control over how specific requests are handled. Indicators point to China-Nexus development and targeting Several aspects of DKnife’s design and operation suggested ties to China-aligned threat actors. Talos identified configuration data and code comments written in

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Four new vulnerabilities found in Ingress NGINX

NGINX is a reverse proxy/load balancer that generally acts as the front-end web traffic receiver and directs it to the application service for data transformation. Ingress NGINX is a version used in Kubernetes as the controller for traffic coming into the infrastructure. It takes care of mapping traffic to pods

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OEUK Flags ‘Prolonged Bout of Severe Weather’ in North Sea

In a statement posted on its website last Thursday, industry body Offshore Energies UK (OEUK) noted that, “amid a prolonged bout of severe weather” in the UK North Sea, “some companies” were “removing non essential staff from their sites as supplies are running short”. OEUK’s Health and Safety Manager, Graham Skinner, said in the statement, “we’re proud of the resilience of our workforce and we’re proud of the fact our industry keeps the lights on whatever happens”. “Although we get this sort of weather every two or three years or so, it can be quite uncomfortable and there will be people in the workforce who are experiencing it for the first time,” he added. “The waves are up to six meters [19 feet], about the height of an average house, which isn’t that big by North Sea standards. The problem is that the stormy weather has gone on for so long, supply boats can’t deliver,” Skinner continued. “That means fresh water and fresh food start to run short so it’s better to take non essential people off platform so there’s enough to go round the people who are left,” he went on to state. A shipping forecast issued by the Met Office, and hosted on its website, on behalf of the Maritime and Coastguard Agency, at 11:30 UTC on February 9, for the period 12:00 UTC on February 9 to 12:00 UTC on February 10, stated that “there are warnings of gales in FitzRoy and Southeast Iceland”. On the Met Office site, the sea state in FitzRoy is described as “rough or very rough, becoming very rough later” and the sea state in Southeast Iceland is described as “rough or very rough”. The Met Office website is issuing UK weather warnings for Monday, Tuesday, and Wednesday at the time of writing. In a statement posted on its

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Energy Department Launches Genesis Mission Consortium to Accelerate AI-Driven Scientific Discovery and American innovation

 WASHINGTON—The U.S. Department of Energy (DOE) today announced the launch of the Genesis Mission Consortium, a historic public-private partnership advancing the Department’s Genesis Mission to harness the power of artificial intelligence (AI) to accelerate scientific discovery, strengthen national security, and ensure America leads in energy and emerging technologies. Building on President Trump’s Executive Orders Launching The Genesis Mission and Removing Barriers to American Leadership In Artificial Intelligence, the consortium brings together technical capabilities and expertise from the Department of Energy, National Laboratories, private sector leaders, and academic institutions to usher in a new era of science and technology exploration. “The Genesis Mission Consortium represents a bold step toward transforming the way we approach scientific challenges,” said DOE Under Secretary for Science and Genesis Mission Lead Dr. Darío Gil. “Thanks to President Trump’s leadership, we’re uniting government, industry, and academia to create a powerful engine for innovation that will drive breakthroughs across multiple disciplines.” The consortium will help identify high-value partnerships among its members and external stakeholders, strengthening collaborative responses to funding opportunities. It will amplify DOE’s outreach by promoting solicitations, executing agreements, and tracking project successes. Functioning as a collaborative hub, the consortium will serve as a single, coordinated access point for members and their resources. To advance technical priorities, the consortium will facilitate member-driven working groups focused on AI model development and validation, data integration and standards, high-performance computing and cloud infrastructure, and robotics and automation. These working groups will provide an efficient mechanism for engaging industry and academic organizations in co-creation efforts. The Genesis Mission Consortium will also host regular events, including annual member meetings, workshops, and technology showcases, providing members with high-impact networking and collaboration opportunities. The consortium will be administered by TechWerx, a DOE partnership intermediary operated by RTI International. For more information on the Genesis Mission Consortium and how to get involved, visit www.genesismissionconsortium.org.

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Russia Crude Output Shrinks

Russia’s crude output declined for a second straight month in January as the world’s third largest oil producer faces difficulty in marketing its barrels because of US sanctions. The nation pumped an average of 9.28 million barrels a day of crude oil last month, according to people with knowledge of the data, who asked not to be identified discussing classified information. The figure — which doesn’t include output of condensate — is 46,000 barrels a day below an already-reduced level in December, and almost 300,000 barrels a day lower than what Russia is allowed to produce under an agreement with the Organization of the Petroleum Exporting Countries and allies. Russia has classified its data on oil production, exports and refinery operations, making independent assessments difficult. Its Energy Ministry didn’t immediately respond to a Bloomberg request for a comment on the January output level and future production plans. The decline in production comes as the amount of Russian crude held on tankers continues to grow, indicating that some cargoes are taking significant time to find buyers amid growing US pressure on the Kremlin. Earlier this month, US President Donald Trump said he eliminated an extra 25% tariff he had imposed on India in exchange for New Delhi halting oil purchases from Russia.  While India confirmed the trade deal, it has not commented on details including oil. Still, nearly all state-owned and private Indian refiners have paused buying any spot cargoes since Trump first mentioned the deal in a social media post about a week ago. By the start of February, accumulated volumes of Russian crude on water reached 143 million barrels, almost doubling from a year ago and creeping up by more than a quarter compared to late November. As India has pulled back from purchases, some tankers with sanctioned barrels are now heading for

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Vitol Pushes Back Peak Oil Demand Forecast

The world’s largest independent oil trader Vitol Group said oil demand will take longer to peak than it previously estimated as countries prioritize growth and energy security over efforts to curb consumption. “Over the past year, decarbonisation policies have become a less decisive driver of efforts to curb oil consumption and reduce carbon dioxide emissions,” it said in a report on Monday. “Policy priorities have increasingly been reframed around economic competitiveness and geopolitical strategy.” The forecast is the latest signal that the energy industry is shaping up for a longer and bigger future for hydrocarbons. Vitol trades about 7% of the world’s oil every day, in addition to running a network of refineries and filling stations.  Oil demand is now expected to reach a high of around 112 million barrels a day at some point in the mid-2030s, Vitol said in the report. In 2040, it will be about 5 million barrels a day higher than current levels, it said. That’s a marked change from the firm’s forecasts just a year ago, when the trading house said it expected a lower peak in the early 2030s to be followed by a steeper decline. A slower near-term uptake of electric vehicles in the US and parts of Asia “underpins” the firm’s changing outlook. US gasoline demand is expected to drop by 800,000 barrels a day by 2040, it said. The company also expects European gasoline demand to be “broadly similar” in 2040 to where it is today, though demand in China is expected to more than halve because electric vehicles are becoming cheaper and much more widespread. The firm cautioned that oil’s use in transportation could be greater and longer lasting than it previously anticipated. “Our caveat remains that, if EV adoption stalls and policy targets continue to be deferred, road transport

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North America Increases Rig Count

North America added one rig week on week, according to Baker Hughes’ latest North America rotary rig count, which was published on February 6. The total U.S. rig count rose by five week on week and the total Canada rig count dropped by four during the same period, pushing the total North America rig count up to 779, comprising 551 rigs from the U.S. and 228 rigs from Canada, the count outlined. Of the total U.S. rig count of 551, 532 rigs are categorized as land rigs, 16 are categorized as offshore rigs, and three are categorized as inland water rigs. The total U.S. rig count is made up of 412 oil rigs, 130 gas rigs, and nine miscellaneous rigs, according to Baker Hughes’ count, which revealed that the U.S. total comprises 483 horizontal rigs, 55 directional rigs, and 13 vertical rigs. Week on week, the U.S. land rig count rose by three, its offshore rig count rose by two, and its inland water rig count remained unchanged, Baker Hughes highlighted. The U.S. oil rig count increased by one week on week, while its gas rig count increased by five and its miscellaneous rig count dropped by one, the count showed. The U.S. horizontal rig count rose by five week on week, its directional rig count rose by two week on week, and its vertical rig count dropped by two during the same period, the count revealed. A major state variances subcategory included in the rig count showed that, week on week, Texas added six rigs, Louisiana added one rig, and California and New Mexico each dropped one rig. A major basin variances subcategory included in the rig count showed that, week on week, the Haynesville basin added seven rigs and the Permian basin dropped one rig. Canada’s total rig

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More Indian Refiners Take Venezuelan Oil

Indian Oil Corp. and Hindustan Petroleum Corp. jointly bought a cargo of Venezuelan crude, marking a second deal on the trade by the nation’s processors after Reliance Industries Ltd. snapped up a shipment. The country’s largest state-owned refiner and its smaller counterpart purchased 2 million barrels of Merey crude, according to people familiar with the matter, asking not to be identified speaking about confidential information. Oil will be delivered to IOC’s Paradip refinery and HPCL’s Visakhapatnam plant, they said. The Trump administration has tapped trading giants Vitol Group and Trafigura Group to market Venezuelan oil after the US seized President Nicolás Maduro and asserted control over the nation’s energy industry. Indian private refiner Reliance Industries Ltd. recently acquired a cargo, returning to the trade after hitting pause last year following the expiry of US sanctions waivers. IOC and HPCL didn’t immediately respond to emails seeking comment. The deal was first reported by Reuters. India’s oil buying is under the spotlight after President Donald Trump said last week that the country had agreed to stop taking Russian crude as part of trade deal with the US. New Delhi hasn’t directly addressed the Russian oil trade in its public responses. A foreign ministry spokesperson reiterated over the weekend that energy security remained a top priority for India. IOC and a unit of HPCL — HPCL-Mittal Energy Ltd. — last took Venezuelan oil in 2024, according to data compiled by Kpler. The processors also halted purchases after the expiry of sanctions waivers from the US. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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Intel teams with SoftBank to develop new memory type

However, don’t expect anything anytime soon. Intel’s Director of Global Strategic Partnerships Sanam Masroor outlined the plans in a blog post. Operations are expected to begin in Q1 2026, with prototypes due in 2027 and commercial products by 2030. While Intel has not come out and said it, that memory design is almost identical to HBM used in GPU accelerators and AI data centers. HBM sits right on the GPU die for immediate access to the GPU, unlike standard DRAM which resides on memory sticks plugged into the motherboard. HBM is much faster than DDR memory but is also much more expensive to produce. It’s also much more profitable than standard DRAM which is why the big three memory makers – Micron, Samsung, and SK Hynix – are favoring production of it.

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Nvidia’s $100 Billion OpenAI Bet Shrinks and Signals a New Phase in the AI Infrastructure Cycle

One of the most eye-popping figures of the AI boom – a proposed $100 billion Nvidia commitment to OpenAI and as much as 10 gigawatts of compute for the company’s Stargate AI infrastructure buildout – is no longer on the table. And that partial retreat tells the data center industry something important. According to multiple reports surfacing at the end of January, Nvidia has paused and re-scoped its previously discussed, non-binding investment framework with OpenAI, shifting from an unprecedented capital-plus-infrastructure commitment to a much smaller (though still massive) equity investment. What was once framed as a potential $100 billion alignment is now being discussed in the $20-30 billion range, as part of OpenAI’s broader effort to raise as much as $100 billion at a valuation approaching $830 billion. For data center operators, infrastructure developers, and power providers, the recalibration matters less for the headline number and more for what it reveals about risk discipline, competitive dynamics, and the limits of vertical circularity in AI infrastructure finance. From Moonshot to Measured Capital The original September 2025 memorandum reportedly contemplated not just capital, but direct alignment on compute delivery: a structure that would have tightly coupled Nvidia’s balance sheet with OpenAI’s AI-factory roadmap. By late January, however, sources indicated Nvidia executives had grown uneasy with both the scale and the structure of the deal. Speaking in Taipei on January 31, Nvidia CEO Jensen Huang pushed back on reports of friction, calling them “nonsense” and confirming Nvidia would “absolutely” participate in OpenAI’s current fundraising round. But Huang was also explicit on what had changed: the investment would be “nothing like” $100 billion, even if it ultimately becomes the largest single investment Nvidia has ever made. That nuance matters. Nvidia is not walking away from OpenAI. But it is drawing a clearer boundary around

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Onsite Engineer – Critical FacilitiesCharleston, SC This is NOT a traveling position. Having degreed engineers seems to be all the rage these days. I can also use this type of candidate in following cities: Ashburn, VA; Moncks Corner, SC; Binghamton, NY; Dallas, TX or Indianapolis, IN. Our client is an engineering design and commissioning company that is a subject matter expert in the data center space. This role will be onsite at a customer’s data center. They will provide onsite design coordination and construction administration, consulting and management support for the data center / mission critical facilities space with the mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Ashburn, VA This traveling position is also available in: New York, NY; White Plains, NY;  Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Dallas, TX; Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They

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Operationalizing AI at Scale: Google Cloud on Data Infrastructure, Search, and Enterprise AI

The AI conversation has been dominated by model announcements, benchmark races, and the rapid evolution of large language models. But in enterprise environments, the harder problem isn’t building smarter models. It’s making them work reliably with real-world data. On the latest episode of the Data Center Frontier Show Podcast, Sailesh Krishnamurthy, VP of Engineering for Databases at Google Cloud, pulled back the curtain on the infrastructure layer where many ambitious AI initiatives succeed, or quietly fail. Krishnamurthy operates at the intersection of databases, search, and AI systems. His perspective underscores a growing reality across enterprise IT: AI success increasingly depends on how organizations manage, integrate, and govern data across operational systems, not just how powerful their models are. The Disconnect Between LLMs and Reality Enterprises today face a fundamental challenge: connecting LLMs to real-time operational data. Search systems handle documents and unstructured information well. Operational databases manage transactions, customer data, and financial records with precision. But combining the two remains difficult. Krishnamurthy described the problem as universal. “Inside enterprises, knowledge workers are often searching documents while separately querying operational systems,” he said. “But combining unstructured information with operational database data is still hard to do.” Externally, customers encounter the opposite issue. Portals expose personal data but struggle to incorporate broader contextual information. “You get a narrow view of your own data,” he explained, “but combining that with unstructured information that might answer your real question is still challenging.” The result: AI systems often operate with incomplete context. Vector Search Moves Into the Database Vector search has emerged as a bridge between structured and unstructured worlds. But its evolution over the past three years has changed how enterprises deploy it. Early use cases focused on semantic search, i.e. finding meaning rather than exact keyword matches. Bug tracking systems, for example, began

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Transmission at the Breaking Point: Why the Grid Is Becoming the Defining Constraint for AI Data Centers

Regions in a Position to Scale California (A- overall)California continues to lead in long-term, scenario-based transmission planning. CAISO’s most recent transmission plan identifies $4.8 billion in new projects to accommodate approximately 76 gigawatts of additional capacity by 2039, explicitly accounting for data center growth alongside broader electrification. For data center developers, California’s challenge is less about planning quality and more about execution. Permitting timelines, cost allocation debates, and political scrutiny remain significant hurdles. Plains / Southwest Power Pool (B- overall, A in regional planning)SPP stands out nationally for embracing ultra-high-voltage transmission as a backbone strategy. Its recent Integrated Transmission Plans approve more than $16 billion in new projects, including multiple 765-kV lines, with benefit-cost ratios exceeding 10:1. This approach positions the Plains region as one of the most structurally “AI-ready” grids in North America, particularly for multi-gigawatt campuses supported by wind, natural gas, and emerging nuclear resources. Midwest / MISO (B overall)MISO’s Long-Range Transmission Planning framework aligns closely with federal best practices, co-optimizing generation and transmission over long planning horizons. While challenges remain—particularly around interregional coordination—the Midwest is comparatively well positioned for sustained data center growth. Regions Facing Heightened Risk Texas / ERCOT (D- overall)Texas has approved massive new transmission investments, including 765-kV projects tied to explosive load growth in the Permian Basin. However, the report criticizes ERCOT’s planning for remaining largely siloed and reliability-driven, with limited long-term scenario analysis and narrow benefit assessments. For data centers, ERCOT still offers speed to market, but increasingly with risks tied to congestion, price volatility, and political backlash surrounding grid reliability. Southeast (F overall)The Southeast receives failing grades across all categories, with transmission development remaining fragmented, utility-driven, and largely disconnected from durable regional planning frameworks. As AI data centers increasingly target the region for its land availability and tax incentives, the lack of

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From Row-Level CDUs to Facility-Scale Cooling: DCX Ramps Liquid Cooling for the AI Factory Era

Enter the 8MW CDU Era The next evolution arrived just days later. On Jan. 20, DCX announced its second-generation facility-scale unit, the FDU V2AT2, pushing capacity into territory previously unimaginable for single CDU platforms. The system delivers up to 8.15 megawatts of heat transfer capacity with record flow rates designed to support 45°C warm-water cooling, aligning directly with NVIDIA’s roadmap for rack-scale AI systems, including Vera Rubin-class deployments. That temperature target is significant. Warm-water cooling at this level allows many facilities to eliminate traditional chillers for heat rejection, depending on climate and deployment design. Instead of relying on compressor-driven refrigeration, operators can shift toward dry coolers or other simplified heat rejection strategies. The result: • Reduced mechanical complexity• Lower energy consumption• Improved efficiency at scale• New opportunities for heat reuse According to DCX CTO Maciek Szadkowski, the goal is to avoid obsolescence in a single hardware generation: “As the datacenter industry transitions to AI factories, operators need cooling systems that won’t be obsolete in one platform cycle. The FDU V2AT2 replaces multiple legacy CDUs and enables 45°C supply water operation while simplifying cooling topology and significantly reducing both CAPEX and OPEX.” The unit incorporates a high-capacity heat exchanger with a 2°C approach temperature, N+1 redundant pump configuration, integrated water quality control, and diagnostics systems designed for predictive maintenance. In short, this is infrastructure built not for incremental density growth, but for hyperscale AI facilities where megawatts of cooling must scale as predictably as compute capacity. Liquid Cooling Becomes System Architecture The broader industry implication is clear: cooling is no longer an auxiliary mechanical function. It is becoming system architecture. DCX’s broader 2025 performance metrics underscore the speed of this transition. The company reported 600% revenue growth, expanded its workforce fourfold, and shipped or secured contracts covering more than 500 MW

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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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