Indian Refiner Boosts Mideast Oil Imports as Russian Flows Slow

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Indian Refiner Boosts Mideast Oil Imports as Russian Flows Slow

One of India’s largest refiners has been forced to seek alternative and more expensive crude cargoes from the Middle East to make up for lower supplies from Russia, putting a spotlight on shifting export patterns as traders focus on the global market’s prospects in 2025. “We are short of three, four Russian cargoes for January-loading […]

“We are short of three, four Russian cargoes for January-loading and February-delivery,” Bharat Petroleum Corp. Finance Director Vetsa Ramakrishna Gupta said. “We issued tenders and have secured alternate grades from Iraq, UAE and others, he said, referring to nations including the United Arab Emirates.

India became a mainstay market for Russian oil flows in the aftermath of Moscow’s 2022 invasion of Ukraine, boosting imports that aid its fast-growing economy. Still, the nation has faced a drop-off in shipments in recent weeks as Western states tightened the web of sanctions against Moscow’s so-called dark fleet of tankers, and Russian refiners boosted run rates. In addition, Moscow has also been under pressure to abide by OPEC+ production goals.

While Middle Eastern supplies were $2 a barrel costlier than Russia’s Urals, there’s no shortage of crude in the wider market, Gupta said in an interview on Tuesday. At present, BPCL has no plans to raise volumes under year-long deals with national oil companies that will be negotiated next month, he said.

India’s imports of oil from Russia slipped to 1.47 million barrels a day this month, the lowest since December last year, according to analytics firm Kpler.

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Kinder Morgan Reports Record Year

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New Equinor Gas Discovery in Norway Eyed to Supply Poland

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Oil Slides on Rising Supply, Peace Hopes

Oil dropped 2.1% to settle near $59 a barrel, weighed down by swelling crude inventories and signs of progress in Russia-Ukraine peace talks. Ukrainian President Volodymyr Zelenskiy will join trilateral meetings with the US and Russia Friday, stoking hopes of a breakthrough to end Moscow’s war in Ukraine. An end to sanctions on Russia would add to an already oversupplied global market. In the US, crude inventories rose 3.6 million barrels while gasoline stocks reached their highest level since 2021 and exports fell by more than a half-million barrels a day. In Kazakhstan, repairs at a key Black Sea oil-loading facility are nearing completion while a backlog of cargoes at the Caspian Pipeline Consortium terminal is easing. Supplies are also returning to the global market from Venezuela, while Indian refiner Reliance Industries Ltd. has once again purchased Russian crude, with deliveries scheduled to February and March. Offering something of a floor to prices, the International Energy Agency, which advises major economies, nudged up its estimate for oil demand growth on Wednesday. That will offer some relief for producers, but the agency still maintained its view for a major glut this year. “The geopolitical temperature has eased a few degrees,” said Ole Sloth Hansen, a strategist at Saxo Bank A/S in Copenhagen. But with a range of supply threats unresolved, and colder weather set to bolster US demand, prices will likely “hold firm.” Oil Prices WTI for March delivery fell 2.1% to settle at $59.36 a barrel in New York. Brent for March settlement fell 1.8% to settle at $64.06. What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is a new social experience created for you and all energy professionals to Speak Up about our industry, share

Energy Secretary Prepares to Unleash Backup Generation Ahead of Winter Storm Fern

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USA Won’t Offer OTG Security to Oil Firms in VEN

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CBRE’s 2026 Data Center Outlook: Demand Surges as Delivery Becomes the Constraint

The U.S. data center market is entering 2026 with fundamentals that remain unmatched across commercial real estate, but the nature of the dominant constraint has shifted. Demand is no longer gated by capital, connectivity, or even land. It is gated by the ability to deliver very large blocks of power, on aggressive timelines, at a predictable cost. According to the CBRE 2026 U.S. Real Estate Market Outlook as overseen by Gordon Dolven and Pat Lynch, the sector is on track to post another record year for leasing activity, even as vacancy remains at historic lows and pricing reaches all-time highs. What has changed is the scale at which demand now presents itself, and the difficulty of meeting it. Large-Block Leasing Rewrites the Economics AI-driven workloads are reshaping leasing dynamics in ways that break from prior hyperscale norms. Where 10-MW-plus deployments once commanded pricing concessions, CBRE now observes the opposite behavior: large, contiguous blocks of capacity are commanding premiums. Neocloud providers, GPU-as-a-service platforms and AI startups, many backed by aggressive capital deployment strategies, are actively competing for full-building and campus-scale capacity. For operators, this is altering development and merchandising strategies. Rather than subdividing shells for flexibility, owners increasingly face a strategic choice: hold buildings intact to preserve optionality for single-tenant, high-density users who are willing to pay for scale. In effect, scale itself has become the scarce asset. Behind-the-Meter Power Moves to the Foreground For data centers, power availability meaning not just access, but certainty of delivery, is now the defining variable in the market. CBRE notes accelerating adoption of behind-the-meter strategies as operators seek to bypass increasingly constrained utility timelines. On-site generation using natural gas, solar, wind, and battery storage is gaining traction, particularly in deregulated electricity markets where operators have more latitude to structure BYOP (bring your own power) solutions.

Blue Origin targets enterprise networks with a multi-terabit satellite connectivity plan

“It’s ideal for remote, sparse, or sensitive regions,” said Manish Rawat, analyst at TechInsights. “Key use cases include cloud-to-cloud links, data center replication, government, defense, and disaster recovery workloads. It supports rapid or temporary deployments and prioritizes fewer customers with high capacity, strict SLAs, and deep carrier integration.” Adoption, however, is expected to largely depend on the sector. For governments and organizations operating highly critical or sensitive infrastructure, where reliability and security outweigh cost considerations, this could be attractive as a redundancy option. “Banks, national security agencies, and other mission-critical operators may consider it as an alternate routing path,” Jain said. “For most enterprises, however, it is unlikely to replace terrestrial connectivity and would instead function as a supplementary layer.” Real-world performance Although satellite connectivity offers potential advantages, analysts note that questions remain around real-world performance. “TeraWave’s 6 Tbps refers to total constellation capacity, not per-user throughput, achieved via multiple optical inter-satellite links and ground gateways,” Rawat said. “Optical crosslinks provide high aggregate bandwidth but not a single terabit-class pipe. Performance lies between fiber and GEO satellites, with lower intercontinental latency than GEO but higher than fiber.” Operational factors could also affect network stability. Jitter is generally low, but handovers, rerouting, and weather conditions can introduce intermittent performance spikes. Packet loss is expected to remain modest but episodic, Rawat added.

CyrusOne Hones AI-Era Data Center Strategy for Power, Pace, and Reliability

In the second half of 2025, CyrusOne was racing to secure buildable power and faster time-to-market capacity for AI-era customers. At the same time, its reputation for mission-critical reliability took a very public hit when a disruption at a CyrusOne facility helped knock CME trading offline. The incident forced the company into an unusually open conversation about redundancy, cooling systems, and operational discipline: systems that are meant to disappear in normal operation, and dominate the story when they malfunction. From Projects to a Playbook Which projects, missteps, and strategic moves from 2025 are now shaping how CyrusOne enters 2026? Nowhere is that view clearer than in Texas. There, CyrusOne has been leaning hard into a “power + land + interconnect” model: treating deliverable power and grid position as part of the product, not just a prerequisite. If you map the company’s announcements since late July, Texas reveals the playbook. Secure power, secure substations and grid position, then build multi-phase campuses designed to scale quickly as demand materializes. The Calpine “Powered Land” Deal: From 190 MW to 400 MW in Three Months On July 30, 2025, CyrusOne and Calpine announced a 190-MW agreement tied to a hyperscale campus (DFW10) adjacent to Calpine’s Thad Hill Energy Center in Bosque County, Texas. The structure bundled power, grid connection, and land into a single development package, with CyrusOne saying the site was already under construction and targeting operation by Q4 2026. Just three months later, on November 3–4, the partners announced a second phase, adding 210 MW and taking the campus to 400 MW. The update emphasized coordination to support grid reliability during scarcity; such curtailment and operational-coordination concepts are becoming table stakes for ERCOT-scale megaprojects. Together, the two announcements show CyrusOne placing a large bet on an emerging model: power-ready campuses, or “powered

Forrester study quantifies benefits of Cisco Intersight

If IT groups are to be the strategic business partners their companies need, they require solutions that can improve infrastructure life cycle management in the age of artificial intelligence (AI) and heightened security threats. To quantify the value of such solutions, Cisco recently commissioned Forrester Consulting to conduct a Total Economic Impact™ analysis of Cisco Intersight. The comprehensive study found that for a composite organization, Intersight delivered 192% return on investment (ROI) and a payback period of less than six months, along with significant tangible benefits to IT and businesses. Cisco Intersight overview Cisco Intersight is a cloud-native IT operations platform for infrastructure life cycle management. It provides IT teams with comprehensive visibility, control, and automation capabilities for Cisco’s portfolio of compute solutions for data centers, colocation facilities, and edge environments based on the Cisco Unified Computing System (Cisco UCS). Intersight also integrates with leading operating systems, storage providers, hypervisors, and third-party IT service management and security tools. Intersight’s unified, policy-driven approach to infrastructure management helps IT groups automate numerous tasks and, as Forrester found, free up time to dedicate to strategic projects. Forrester study quantifies the benefits of Cisco Intersight A composite organization using Cisco Intersight achieved:192% ROI and payback in less than six months$3.3M net present value over three years$2.7M from improved uptime and resilience 50% reduction in mean time to resolution $1.7M from increased IT productivity$267K benefit from decreased time to value due to faster project execution and earlier return on infrastructure investments Forrester Total Economic Impact study findings The analyst firm conducted detailed interviews with IT decision-makers and Intersight users at six organizations, from which it created one composite organization: a multinational technology-driven company with $10 billion in annual revenue, 120 branch locations, and a team of six engineers managing its 1,000 servers deployed in several

SoftBank launches software stack for AI data center operations

Addressing enterprise challenges The software provides two main services, according to SoftBank. The Kubernetes-as-a-Service component automates the stack from BIOS and RAID settings through the OS, GPU drivers, networking, Kubernetes controllers, and storage, the company said. It reconfigures physical connectivity using Nvidia NVLink and memory allocation as users create, update, or delete clusters, according to the announcement. The system allocates nodes based on GPU proximity and NVLink domain configuration to reduce latency, SoftBank said. Enterprises currently face complex GPU cluster provisioning, Kubernetes lifecycle management, inference scaling, and infrastructure tuning challenges that require deep expertise, according to Dai. SoftBank’s automated approach addresses these pain points by handling BIOS-to-Kubernetes configuration, optimizing GPU interconnects, and abstracting inference into API-based services, he said. This allows teams to focus on model development rather than infrastructure maintenance, Dai said. The Inference-as-a-Service component lets users deploy inference services by selecting large language models without configuring Kubernetes or underlying infrastructure, according to the company. It provides OpenAI-compatible APIs and scales across multiple nodes on platforms including the GB200 NVL72, SoftBank said. The software includes tenant isolation through encrypted communications, automated system monitoring and failover, and APIs for connecting to portal, customer management, and billing systems, according to the announcement.

OpenAI shifts AI data center strategy toward power-first design

The shift to ‘energy sovereignty’ Analysts say the move reflects a fundamental shift in data center strategy, moving from “fiber-first” to “power-first” site selection. “Historically, data centers were built near internet exchange points and urban centers to minimize latency,” said Ashish Banerjee, senior principal analyst at Gartner. “However, as AI training requirements reach the gigawatt scale, OpenAI is signaling that they will prioritize regions with ‘energy sovereignty’, places where they can build proprietary generation and transmission, rather than fighting for scraps on an overtaxed public grid.” For network architecture, this means a massive expansion of the “middle mile.” By placing these behemoth data centers in energy-rich but remote locations, the industry will have to invest heavily in long-haul, high-capacity dark fiber to connect these “power islands” back to the edge. “We should expect a bifurcated network: a massive, centralized core for ‘cold’ model training located in the wilderness, and a highly distributed edge for ‘hot’ real-time inference located near the users,” Banerjee added. Manish Rawat, a semiconductor analyst at TechInsights, also noted that the benefits may come at the cost of greater architectural complexity. “On the network side, this pushes architectures toward fewer mega-hubs and more regionally distributed inference and training clusters, connected via high-capacity backbone links,” Rawat said. “The trade-off is higher upfront capex but greater control over scalability timelines, reducing dependence on slow-moving utility upgrades.”

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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