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Secretaries Wright & Burgum to Deliver Remarks at Louisiana LNG Export Facility

WASHINGTON—U.S. Secretary of Energy Chris Wright and Secretary of the Interior Doug Burgum will travel to Plaquemines Parish, Louisiana on Thursday to give remarks and tour Venture Global’s Plaquemines LNG export facility. The visit highlights the Trump administration’s energy dominance agenda and achievements. On day one, President Trump and the Department of Energy ended the […]

WASHINGTON—U.S. Secretary of Energy Chris Wright and Secretary of the Interior Doug Burgum will travel to Plaquemines Parish, Louisiana on Thursday to give remarks and tour Venture Global’s Plaquemines LNG export facility. The visit highlights the Trump administration’s energy dominance agenda and achievements.

On day one, President Trump and the Department of Energy ended the Biden ban on new LNG export approvals, sending a signal to the world that American energy dominance is back. The Plaquemines LNG export facility was approved by President Trump in 2019 and is the newest LNG Export facility to come online in the United States.

During the visit, Secretaries Wright and Burgum will join Venture Global CEO Mike Sabel, Louisiana Governor Jeff Landry, and other officials for a press availability.

Media wishing to attend should RSVP to [email protected] and [email protected].

Who:  Secretary of Energy Chris Wright

            Secretary of the Interior Doug Burgum

            Louisiana Governor Jeff Landry

            Mike Sabel, Venture Global CEO

            Additional stakeholders and officials to be announced 

What: Open-press remarks, tour, and press availability

When: Thursday, March 6, 2025

             11:00 am CT

Where: Plaquemines Parish

RSVP no later than 4 pm CST // 5 pm ET on Wednesday, March 5th for additional details.

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AI-driven network management gains enterprise trust

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South Sudan Oil Exports at Risk

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Exxon CFO to Retire

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Eni Announces ‘Significant’ Find Offshore Indonesia

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Phase 1 of Varco’s Sizing John BESS in Liverpool Reaches Full Operation

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Kinder Morgan Expects to Ride on LNG, Power Demand Growth

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Gas Deals Take Center Stage in Q4

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Arista goes big with campus wireless tech

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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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Intel decides to keep networking business after all

That doesn’t explain why Intel made the decision to pursue spin-off in the first place. In July, NEX chief Sachin Katti issued a memo that outlined plans to establish key elements of the Networking and Communications business as a stand-alone company. It looked like a done deal, experts said. Jim Hines, research director for enabling technologies and semiconductors at IDC, declined to speculate on whether Intel could get a decent offer but noted NEX is losing ground. IDC estimates Intel’s market share in overall semiconductors at 6.8% in Q3 2025, which is down from 7.4% for the full year 2024 and 9.2% for the full year 2023. Intel’s course reversal “is a positive for Intel in the long term, and recent improvements in its financial situation may have contributed to the decision to keep NEX in house,” he said. When Tan took over as CEO earlier this year, prioritized strengthening the balance sheet and bringing a greater focus on execution. Divest NEX was aligned with these priorities, but since then, Intel has secured investments from the US Government, Nvidia and SoftBank that have reduced the need to raise cash through other means, Hines notes. “The NEX business will prove to be a strategic asset for Intel as it looks to protect and expand its position in the AI datacenter market. Success in this market now requires processor suppliers to offer a full-stack solution, not just silicon. Scale-up and scale-out networking solutions are a key piece of the package, and Intel will be able to leverage its NEX technologies and software, including silicon photonics, to develop differentiated product offerings in this space,” Hines said.

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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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