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EIA Cuts 2025 and 2026 Brent Oil Price Forecast

The U.S. Energy Information Administration (EIA) cut its average Brent oil spot price forecast for 2025 and 2026 in its latest short term energy outlook (STEO), which was released on May 6. According to that STEO, the EIA sees the Brent spot price averaging $65.85 per barrel this year and $59.24 per barrel next year. […]

The U.S. Energy Information Administration (EIA) cut its average Brent oil spot price forecast for 2025 and 2026 in its latest short term energy outlook (STEO), which was released on May 6.

According to that STEO, the EIA sees the Brent spot price averaging $65.85 per barrel this year and $59.24 per barrel next year. In its previous STEO, which was released in April, the EIA projected that the Brent spot price would average $67.87 per barrel in 2025 and $61.48 per barrel in 2026.

The EIA’s latest STEO sees the commodity averaging $65.04 per barrel in the second quarter of this year, $62 per barrel in the third quarter, $61 per barrel in the fourth quarter, $60 per barrel across the first and second quarters of next year, $59 per barrel in the third quarter of 2026, and $58 per barrel in the fourth quarter.

In its April STEO, the EIA forecast that the Brent spot price would come in at $66.33 per barrel in the second quarter of 2025, $65.67 per barrel in the third quarter, $64 per barrel in the fourth quarter, $63 per barrel in the first quarter of next year, $62 per barrel in the second quarter, $61 per barrel in the third quarter, and $60 per barrel in the fourth quarter of 2026.

“The Brent crude oil spot price averaged $68 per barrel in April, $5 per barrel lower than in March,” the EIA highlighted in its latest STEO.

“Crude oil prices fell for the third consecutive month, driven primarily by expectations of lower global oil demand growth following the implementation of new tariffs from the United States and its largest trading partners,” it added.

“In April, OPEC+ members also reaffirmed and accelerated their planned production increases, adding to expectations that global oil inventories will grow and put additional downward pressure on oil prices,” it continued.

In its May STEO, the EIA noted that crude oil prices have been lower this year than it expected in its January 2025 STEO release, “which largely reflects lower expectations for global oil demand growth both among market participants and in our forecast”.

“Since the January release, we have reduced our expectations for global oil demand by a total of 0.5 million barrels per day throughout the forecast period, while we have lowered the Brent crude oil price forecast by $8 per barrel on average,” the EIA said in its latest STEO.

“With less oil being consumed this year, oil inventories have risen. We estimate about 0.3 million barrels per day of oil was put into inventory during the first four months of 2025,” it added.

“In our January outlook, we had expected inventories to fall by more than 0.2 million barrels per day over this period. Perceptions of oversupply among oil market participants also reflect production growth from non-OPEC producers, along with announced production increases from OPEC+,” it continued.

“In addition to our lower forecast for global oil demand compared with January, uncertainty about tariff rates and the degree to which those tariffs will affect economic growth and, in turn, oil demand growth has also led to an increase in short-term price volatility,” the EIA went on to state.

The EIA also stated in the STEO that high levels of implied volatility suggest considerable market uncertainty. It highlighted that implied volatility is “a measure of market participants’ expectations for the range of crude oil futures price changes”.

“Since early April, crude oil implied volatility has averaged more than 35 percent, based on futures and options contract data from the CME Group, with daily Brent crude oil implied volatility reaching as high as 39 percent on April 8,” the EIA noted in the May STEO.

“With the exception of periods of heightened risks of supply disruptions from geopolitical events – such as concerns over the potential for widening conflict in the Middle East that occurred last October – implied volatility has generally been less than 30 percent since the beginning of 2024,” it added.

“The recent increase in implied volatility has been mostly driven by concerns of an economic slowdown or recession, rather than any risk of supply disruption. As a result, the increased volatility has been reflected primarily in downward oil price movements over recent weeks,” it continued.

In its May STEO, the EIA said it anticipates that global oil inventories will start to increase in 2025, “growing 0.5 million barrels per day on average in the second quarter of 2025 before increasing by 0.7 million barrels per day in 4Q25”. The EIA highlighted in the report that it expects global oil inventories “to grow on average by 0.4 million barrels per day in 2025 and accelerate to 0.8 million barrels per day on average in 2026”.

“Given our expectation that oil inventories will accumulate over the next several quarters, we forecast that Brent crude oil prices will generally decline throughout the forecast period,” the EIA said in its May STEO.

“As global oil inventories begin to grow, we expect Brent crude oil prices will fall from an average of $76 per barrel in 1Q25 to an average of $61 per barrel by 4Q25 and will average $59 per barrel overall next year,” it added.

The EIA went on to warn in the STEO that significant uncertainty remains in its price forecast.

“The effect that new or additional tariffs will have on global economic activity and associated oil demand is still highly uncertain and could weigh heavily on oil prices going forward,” the EIA said.

Rigzone has contacted the White House and OPEC for comment on the EIA’s May STEO. The White House and OPEC have not responded to Rigzone at the time of writing.

To contact the author, email [email protected]

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BP, Chevron Top US Gulf Lease Sale

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Oil Price Did Not Shift on Fed Cut

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Aramco, ExxonMobil Mull Petrochemical Complex at Samref

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Naftogaz Seals New PrivatBank Loan for Winter Gas

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CNOOC Ltd Announces 12th Startup in Chinese Waters in 2025

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New Nvidia software gives data centers deeper visibility into GPU thermals and reliability

Addressing the challenge Modern AI accelerators now draw more than 700W per GPU, and multi-GPU nodes can reach 6kW, creating concentrated heat zones, rapid power swings, and a higher risk of interconnect degradation in dense racks, according to Manish Rawat, semiconductor analyst at TechInsights. Traditional cooling methods and static power planning increasingly struggle to keep pace with these loads. “Rich vendor telemetry covering real-time power draw, bandwidth behavior, interconnect health, and airflow patterns shifts operators from reactive monitoring to proactive design,” Rawat said. “It enables thermally aware workload placement, faster adoption of liquid or hybrid cooling, and smarter network layouts that reduce heat-dense traffic clusters.” Rawat added that the software’s fleet-level configuration insights can also help operators catch silent errors caused by mismatched firmware or driver versions. This can improve training reproducibility and strengthen overall fleet stability. “Real-time error and interconnect health data also significantly accelerates root-cause analysis, reducing MTTR and minimizing cluster fragmentation,” Rawat said. These operational pressures can shape budget decisions and infrastructure strategy at the enterprise level.

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Arista goes big with campus wireless tech

In a white paper describing how VESPA works, Arista wrote: The first component of VESPA involves Arista access points creating VXLAN tunnels to Arista switches serving as WLAN Gateways…. Second, as device packets arrive via the AP, it dynamically creates an Ethernet Segment Identifier (Type 6 ESI) based on the AP’s VTEP IP address. These dynamically created tunnels can scale to 30K ESI’s spread across paired switches in the cluster which provide active/active load sharing (performance+HA) to the APs. Third, the gateway switches use Type 2 EVPN NLRI (Network Layer Reachability Information) to learn and exchange end point MAC addresses across the cluster. … With this architecture, adding more EVPN WLAN gateways scales both AP and user connections, to tens of thousands of end points. To manage the forwarding information for hundreds of thousands of clients (e.g: FIB next hop and rewrite) would prove very complex and expensive if using conventional networking solutions. Arista’s innovation is to distribute this function across the WiFi access points with a unique MAC Rewrite Offload feature (MRO). With MRO, the access point is responsible for servicing mobile client ARP requests (using its own mac address), building a localized MAC-IP binding table, and forwarding client IP addresses to the WLAN gateways with the APs MAC address. The WLAN Gateways therefore only learns one (MAC) address for all the clients associated with the AP. This improves the gateway’s scaling from 10X to 100X, allowing these cost effective gateways to support hundreds of thousands of clients attached to the APs. AVA system gets a boost In addition to the new wireless technology, Arista is also bolstering the capabilities of its natural-language, generative AI-based Autonomous Virtual Assist (AVA) system for delivering network insights and AIOps.  AVA is aimed at providing an intelligent assistant that’s not there to replace

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Most significant networking acquisitions of 2025

Cisco makes two AI deals: EzDubs and NeuralFabric Last month Cisco completed its acquisition of EzDubs, a privately held AI software company with speech-to-speech translation technology. EzDubs translates conversations across 31 languages and will accelerate Cisco’s delivery of next-generation features, such as live voice translation that preserves the characteristics of speech, the vendor stated. Cisco plans to incorporate EzDubs’ technology in its Cisco Collaboration portfolio. Also in November, Cisco bought AI platform company NeuralFabric, which offers a generative AI platform that lets organizations develop domain-specific small language models using their own proprietary data. Coreweave buys Core Scientific Nvidia-backed AI cloud provider CoreWeave acquired crypto miner Core Scientific for about $9 billion, giving it access to 1.3 gigawatts of contracted power to support growing demand for AI and high-performance computing workloads. CoreWeave said the deal augments its vertical integration by expanding its owned and operated data center footprint, allowing it to scale GPU-powered services for enterprise and research customers. F5 picks up three: CalypsoAI, Fletch and MantisNet F5 acquired Dublin, Ireland-based CalypsoAI for $180 million. CalypsoAI’s platform creates what the company calls an Inference Perimeter that protects across models, vendors, and environments. F5 says it will integrate CalypsoAI’s adaptive AI security capabilities into its F5 Application Delivery and Security Platform (ADSP). F5’s ADSP also stands to gain from F5’s acquisition of agentic AI and threat management startup Fletch. Fletch’s technology turns external threat intelligence and internal logs into real-time, prioritized insights; its agentic AI capabilities will be integrated into ADSP, according to F5. Lastly, F5 grabbed startup MantisNet to enhance cloud-native observability in F5’s ADSP. MantisNet leverages extended Berkeley Packet Filer (eBPF)-powered, kernel-level telemetry to provide real-time insights into encrypted protocol activity and allow organizations “to gain visibility into even the most elusive traffic, all without performance overhead,” according to an F5 blog

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Aviz Networks launches enterprise-grade community SONiC distribution

First, the company enabled FRR (Free Range Routing) features that exist in the community code but aren’t consistently implemented across different ASICs. VRRP (Virtual Router Redudancy Protocol) provides router redundancy for high availability. Spanning tree variants prevent network loops in layer 2 topologies. MLAG allows two switches to act as a single logical device for link aggregation. EVPN enhancements support layer 2 and layer 3 VPN services over VXLAN overlays. These protocols work differently depending on the underlying silicon, so Aviz normalized their implementation across Broadcom, Nvidia, Cisco and Marvell chips. Second, Aviz fixed bugs discovered in production deployments. One customer deployed community SONiC with OpenStack and started migrating virtual machines between hosts. The network fabric couldn’t handle the workload and broke. Aviz identified the failure modes and patched them.  Third, Aviz built a software component that normalizes monitoring data across vendors. Broadcom’s Tomahawk ASIC generates different telemetry formats than Nvidia’s Spectrum or Cisco’s Silicon One. Network operators need consistent data for troubleshooting and capacity planning. The software collects ASIC-specific logs and network operating system telemetry, then translates them into a standardized format that works the same way regardless of which silicon vendor’s chips are running in the switches. Validated for enterprise deployment scenarios The distribution supports common enterprise network architectures.  IP CLOS provides the leaf-spine topology used in modern data centers for predictable latency and scalability. EVPN/VXLAN creates layer 2 and layer 3 overlay networks that span physical network boundaries. MLAG configurations provide link redundancy without spanning tree limitations. Aviz provides validated runbooks for these deployments across data center, edge and AI fabric use cases. 

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US approves Nvidia H200 exports to China, raising questions about enterprise GPU supply

Shifting demand scenarios What remains unclear is how much demand Chinese firms will actually generate, given Beijing’s recent efforts to steer its tech companies away from US chips. Charlie Dai, VP and principal analyst at Forrester, said renewed H200 access is likely to have only a modest impact on global supply, as China is prioritizing domestic AI chips and the H200 remains below Nvidia’s latest Blackwell-class systems in performance and appeal. “While some allocation pressure may emerge, most enterprise customers outside China will see minimal disruption in pricing or lead times over the next few quarters,” Dai added. Neil Shah, VP for research and partner at Counterpoint Research, agreed that demand may not surge, citing structural shifts in China’s AI ecosystem. “The Chinese ecosystem is catching up fast, from semi to stack, with models optimized on the silicon and software,” Shah said. Chinese enterprises might think twice before adopting a US AI server stack, he said. Others caution that even selective demand from China could tighten global allocation at a time when supply of high-end accelerators remains stretched, and data center deployments continue to rise.

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What does Arm need to do to gain enterprise acceptance?

But in 2017, AMD released the Zen architecture, which was equal if not superior to the Intel architecture. Zen made AMD competitive, and it fueled an explosive rebirth for a company that was near death a few years prior. AMD now has about 30% market share, while Intel suffers from a loss of technology as well as corporate leadership. Now, customers have a choice of Intel or AMD, and they don’t have to worry about porting their applications to a new platform like they would have to do if they switched to Arm. Analysts weigh in on Arm Tim Crawford sees no demand for Arm in the data center. Crawford is president of AVOA, a CIO consultancy. In his role, he talks to IT professionals all the time, but he’s not hearing much interest in Arm. “I don’t see Arm really making a dent, ever, into the general-purpose processor space,” Crawford said. “I think the opportunity for Arm is special applications and special silicon. If you look at the major cloud providers, their custom silicon is specifically built to do training or optimized to do inference. Arm is kind of in the same situation in the sense that it has to be optimized.” “The problem [for Arm] is that there’s not necessarily a need to fulfill at this point in time,” said Rob Enderle, principal analyst with The Enderle Group. “Obviously, there’s always room for other solutions, but Arm is still going to face the challenge of software compatibility.” And therein lies what may be Arm’s greatest challenge: software compatibility. Software doesn’t care (usually) if it’s on Intel or AMD, because both use the x86 architecture, with some differences in extensions. But Arm is a whole new platform, and that requires porting and testing. Enterprises generally don’t like disruption —

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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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