TXO Identifies Potential 3 Tcfe of Gas in San Juan Basin

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TXO Identifies Potential 3 Tcfe of Gas in San Juan Basin

TXO Partners LP said Wednesday it has identified potential natural gas of nearly three trillion cubic feet equivalent in the Mancos Shale of the San Juan Basin in the Southwestern United States. “On an oil equivalent basis, we believe this could represent as much as five times our current total reserve base”, chair and chief […]

TXO Partners LP said Wednesday it has identified potential natural gas of nearly three trillion cubic feet equivalent in the Mancos Shale of the San Juan Basin in the Southwestern United States.

“On an oil equivalent basis, we believe this could represent as much as five times our current total reserve base”, chair and chief executive Bob Simpson said in an online statement.

“The catalyst for action in developing this project is commodity price, and we anticipate strong natural gas economics ahead”.

The Fort Worth, Texas-based company holds production rights in a contiguous area of 58,500 acres in the Mancos field. It also holds water rights and an option for “key” gas gathering systems in the basin, according to TXO.

“We believe the Mancos Shale development will be a game-changer for our reserve holdings and production potential”, added Gary Simpson, president for production and development. “TXO acreage and operations reside in prime position. Offset drilling on adjoining acreage has confirmed well results.

“Given all the important criteria—reservoir characteristics, acreage location, productivity data, and infrastructure access—we have identified a tactical 3,520-acre block as phase I for developing and monetizing reserves, representing about six percent of our current Mancos position.

“Specifically, our internal engineers estimate that this single position holds about 200 to 300 Bcf [billion cubic feet] of natural gas with 25 Bcfe estimated per drill well and has the potential to almost double our existing natural gas reserves.

“Importantly, the company’s acres for exploitation are held by production with no leasehold expiration dates.

“We expect to drill, develop, and monetize at an economically opportune time and pace”.

TXO closed higher at $17.93 on the New York Stock Exchange on Wednesday.

Last year TXO expanded with the acquisition of Williston Basin assets in Montana and North Dakota through separate transactions with Eagle Mountain Energy Partners, a portfolio company of Pearl Energy Investments, and Vendera.

With a total price of $243 million in cash plus 2.5 million common shares of TXO, the acquisitions are expected to add about 4,500 barrels of oil equivalent a day to TXO’s production. The assets are the Elm Coulee field in Montana and the Russian Creek field in North Dakota.

“This acquisition in the Elm Coulee field represents the return to a region where our team previously had success”, Simpson said in a company statement June 25, 2024, announcing the agreements. “We expect the significant oil-in-place targets, with the application of our technology, to create equity value while delivering high returns.”

“These transactions provide the right blend of low-decline rate, high margin and growth potential for TXO”, Brent Clum, president of business operations and chief financial officer, said then.

The transactions were completed August 2024, TXO confirmed in its third-quarter report published November 5, 2024.

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Microsoft launches its second generation AI inference chip, Maia 200

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3D Energi Runs Out of Cash for Victoria Drill Campaign, Suspends Trading

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EIA Sees NatGas Price Dropping in 2026 and Rising in 2027

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Aramco Raises $4B in 1st Bond Sale of Year

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New Pact Signed to Advance North Seas Wind Development

North Sea countries and transmission system operators on Monday signed an agreement committing to advancing investment in offshore wind projects and expanding cleaner power interconnection. The Joint Offshore Wind Investment Pact for the North Seas supports a goal set by the neighbors to reach 300 gigawatts (GW) of installed wind generation capacity in the North Seas by 2050, including 100 GW from cooperation projects. The pact was signed by the governments of Belgium, Denmark, France, Germany, Ireland, Luxembourg, the Netherlands, Norway and the United Kingdom, as well as electricity and transmission system operators, during the North Sea Summit in Hamburg, Germany. Under the pact, the governments pledged to ensure “a more evenly distributed offshore wind tender pipeline between 2031 and 2040 across the North Seas contributing to a European installation capacity of up to 15 GW per year”, according to the official text published online by Germany’s Economic Affairs Ministry. They also committed to crafting an Offshore Financing Framework for North Sea wind cooperation projects. The framework would “build on and strengthen existing instruments and processes such as the framework for trans-European networks for energy and the identification of projects of common interest and projects of mutual interest”. The Offshore TSO Collaboration and project developers have agreed to implement cooperation projects with a combined capacity of 20 GW in the 2030s. The first of these would be identified by 2026, according to the pact. The participating governments also pledged to improve their national regulatory frameworks “to increase investor certainty in cooperation projects, including cross-border liability schemes, connection and balancing schemes”. The pact provides for an investigation by the participating European Union member states into “market arrangements for offshore hybrid projects to address hybrid-specific risks for generators”, read the text. “The UK is also committed to the development of the most

North America Adds More Rigs Week on Week

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Photonic chip vendor snags Gates investment

“Moore’s Law is slowing, but AI can’t afford to wait. Our breakthrough in photonics unlocks an entirely new dimension of scaling, by packing massive optical parallelism on a single chip,” said Patrick Bowen, CEO of Neurophos. “This physics-level shift means both efficiency and raw speed improve as we scale up, breaking free from the power walls that constrain traditional GPUs.” The new funding includes investments from Microsoft’s investment fund M12 that will help speed up delivery of Neurophos’ first integrated photonic compute system, including datacenter-ready OPU modules. Neurophos is not the only company exploring this field. Last April, Lightmatter announced the launch of photonic chips to tackle data center bottlenecks, And in 2024, IBM said its researchers were exploring optical chips and developing a prototype in this area.

Intel wrestling with CPU supply shortage

“We have important customers in the data center side. We have important OEM customers on both data center and client and that needs to be our priority to get the limited supply we have to those customers,” he added. CEO Lip-Bu Tan added that the continuing proliferation and diversification of AI workloads is placing significant capacity constraints on traditional and new hardware infrastructure, reinforcing the growing and essential role CPUs play in the AI era. Because of this, Intel decided to simplify its server road map, focusing resources on the 16-channel Diamond Rapids product and accelerate the introduction of Coral Rapids. Intel had removed multithreading from diamond Rapids, presumably to get rid of the performance bottlenecks. With each core running two threads, they often competed for resources. That’s why, for example, Ampere does not use threading but instead applies many more cores per CPU. With Coral Rapids, Intel is not only reintroducing multi-threading back into our data center road map but working closely with Nvidia to build a custom Xeon fully integrated with their NVLink technology to Build the tighter connection between Intel Xeon processors and Nvidia GPUs. Another aspect impacting supply has been yields or the new 18A process node. Tan said he was disappointed that the company could not fully meet the demand of the markets, and that while yields are in line with internal plans, “they’re still below where I want them to be,” Tan said. Tan said yields for 18A are improving month-over-month and Intel is targeting a 7% to 8% improvement each month.

Intel’s AI pivot could make lower-end PCs scarce in 2026

However, he noted, “CPUs are not being cannibalized by GPUs. Instead, they have become ‘chokepoints’ in AI infrastructure.” For instance, CPUs such as Granite Rapids are essential in GPU clusters, and for handling agentic AI workloads and orchestrating distributed inference. How pricing might increase for enterprises Ultimately, rapid demand for higher-end offerings resulted in foundry shortages of Intel 10/7 nodes, Bickley noted, which represent the bulk of the company’s production volume. He pointed out that it can take up to three quarters for new server wafers to move through the fab process, so Intel will be “under the gun” until at least Q2 2026, when it projects an increase in chip production. Meanwhile, manufacturing capacity for Xeon is currently sold out for 2026, with varying lead times by distributor, while custom silicon programs are seeing lead times of 6 to 8 months, with some orders rolling into 2027, Bickley said. In the data center, memory is the key bottleneck, with expected price increases of more than 65% year over year in 2026 and up to 25% for NAND Flash, he noted. Some specific products have already seen price inflation of over 1,000% since 2025, and new greenfield capacity for memory is not expected until 2027 or 2028. Moor’s Sag was a little more optimistic, forecasting that, on the client side, “memory prices will probably stabilize this year until more capacity comes online in 2027.” How enterprises can prepare Supplier diversification is the best solution for enterprises right now, Sag noted. While it might make things more complex, it also allows data center operators to better absorb price shocks because they can rebalance against suppliers who have either planned better or have more resilient supply chains.

Reports of SATA’s demise are overblown, but the technology is aging fast

The SATA 1.0 interface made its debut in 2003. It was developed by a consortium consisting of Intel, Dell, and storage vendors like Seagate and Maxtor. It quickly advanced to SATA III in 2009, but there never was a SATA IV. There was just nibbling around the edges with incremental updates as momentum and emphasis shifted to PCI Express and NVMe. So is there any life to be had in the venerable SATA interface? Surprisingly, yes, say the analysts. “At a high level, yes, SATA for consumer is pretty much a dead end, although if you’re storing TB of photos and videos, it is still the least expensive option,” said Bob O’Donnell, president and chief analyst with TECHnalysis Research. Similarly for enterprise, for massive storage demands, the 20 and 30 TB SATA drives from companies like Seagate and WD are apparently still in wide use in cloud data centers for things like cold storage. “In fact, both of those companies are seeing recording revenues based, in part, on the demand for these huge, high-capacity low-cost drives,” he said. “SATA doesn’t make much sense anymore. It underperforms NVMe significantly,” said Rob Enderle, principal analyst with The Enderle Group. “It really doesn’t make much sense to continue make it given Samsung allegedly makes three to four times more margin on NVMe.” And like O’Donnell, Enderle sees continued life for SATA-based high-capacity hard drives. “There will likely be legacy makers doing SATA for some time. IT doesn’t flip technology quickly and SATA drives do wear out, so there will likely be those producing legacy SATA products for some time,” he said.

DCN becoming the new WAN for AI-era applications

“DCN is increasingly treated as an end-to-end operating model that standardizes connectivity, security policy enforcement, and telemetry across users, the middle mile, and cloud/application edges,” Sanchez said. Dell’Oro defines DCN as platforms and services that deliver consistent connectivity, policy enforcement, and telemetry from users, across the WAN, to distributed cloud and application edges spanning branch sites, data centers and public clouds. The category is gaining relevance as hybrid architectures and AI-era traffic patterns increase the operational penalty for fragmented control planes. DCN buyers are moving beyond isolated upgrades and are prioritizing architectures that reduce operational seams across connectivity, security and telemetry so that incident response and change control can follow a single thread, according to Dell’Oro’s research. What makes DCN distinct is that it links user-to-application experience with where policy and visibility are enforced. This matters as application delivery paths become more dynamic and workloads shift between on-premises data centers, public cloud, and edge locations. The architectural requirement is eliminating handoffs between networking and security teams rather than optimizing individual network segments. Where DCN is growing the fastest Cloud/application edge is the fastest-growing DCN pillar. This segment deploys policy enforcement and telemetry collection points adjacent to workloads rather than backhauling traffic to centralized security stacks. “Multi-cloud remains a reality, but it is no longer the durable driver by itself,” Sanchez said. “Cloud/application edge is accelerating because enterprises are trying to make application paths predictable and secure across hybrid environments, and that requires pushing application-aware steering, policy enforcement, and unified telemetry closer to workloads.”

Edged US Builds Waterless, High-Density AI Data Center Campuses at Scale

Edged US is targeting a narrow but increasingly valuable lane of the hyperscale AI infrastructure market: high-density compute delivered at speed, paired with a sustainability posture centered on waterless, closed-loop cooling and a portfolio-wide design PUE target of roughly 1.15. Two recent announcements illustrate the model. In Aurora, Illinois, Edged is developing a 72-MW facility purpose-built for AI training and inference, with liquid-to-chip cooling designed to support rack densities exceeding 200 kW. In Irving, Texas, a 24-MW campus expansion combines air-cooled densities above 120 kW per rack with liquid-to-chip capability reaching 400 kW. Taken together, the projects point to a consistent strategy: standardized, multi-building campuses in major markets; a vertically integrated technical stack with cooling at its core; and an operating model built around repeatable designs, modular systems, and readiness for rapidly escalating AI densities. A Campus-First Platform Strategy Edged US’s platform strategy is built around campus-scale expansion rather than one-off facilities. The company positions itself as a gigawatt-scale, AI-ready portfolio expanding across major U.S. metros through repeatable design targets and multi-building campuses: an emphasis that is deliberate and increasingly consequential. In Chicago/Aurora, Edged is developing a multi-building campus with an initial facility already online and a second 72-MW building under construction. Dallas/Irving follows the same playbook: the first facility opened in January 2025, with a second 24-MW building approved unanimously by the city. Taken together with developments in Atlanta, Chicago, Columbus, Dallas, Des Moines, Kansas City, and Phoenix, the footprint reflects a portfolio-first mindset rather than a collection of bespoke sites. This focus on campus-based expansion matters because the AI factory era increasingly rewards developers that can execute three things at once: Lock down power and land at scale. Standardize delivery across markets. Operate efficiently while staying aligned with community and regulatory expectations. Edged is explicitly selling the second

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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