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Google adds Gemini to its on-prem cloud for increased data protection

Google has announced the general availability of its Gemini artificial intelligence models on Google Distributed Cloud (GDC), making its generative AI product available on enterprise and government data centers. GDC is an on-premises implementation of Google Cloud, aimed at heavily regulated industries like medical and financial services to bring Google Cloud services within company firewalls […]

Google has announced the general availability of its Gemini artificial intelligence models on Google Distributed Cloud (GDC), making its generative AI product available on enterprise and government data centers.

GDC is an on-premises implementation of Google Cloud, aimed at heavily regulated industries like medical and financial services to bring Google Cloud services within company firewalls rather than the public cloud. The launch of Gemini on GDC allows organizations with strict data residency and compliance requirements to deploy generative AI without compromising control over sensitive information.

GDC uses Nvidia Hopper and Blackwell 0era GPU accelerators with automated load balancing and zero-touch updates for high availability. Security features include audit logging and access control capabilities that provide full transparency for customers. The platform also features Confidential Computing support for both CPUs (with Intel TDX) and GPUs (with Nvidia’s confidential computing) to secure sensitive data and prevent tampering or exfiltration.

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JF Acquires 4 Corners

JF Petroleum Group (JF) is continuing its expansion with the acquisition of 4 Corners Petroleum Equipment, a service contractor based in Texarkana, Texas. 4 Corners was founded in 2015 by industry veteran Kenny Allen and it has since served Northeast Texas and Southwest Arkansas, JF noted in a media release.

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Oil Slips Ahead of OPEC+ Supply Talks

Oil declined on concerns that OPEC+ will once again bolster supply at a meeting on Sunday, compounding fears of higher volumes later in the year. West Texas Intermediate futures fell 0.8% to settle near $64.50 a barrel, building on an earlier decline after softer-than-expected US jobs data added to concerns demand may wane. Prices slumped Wednesday following a report that OPEC and its allies would consider fresh increases at a weekend policy meeting. Russian Deputy Prime Minister Alexander Novak subsequently said OPEC+ will “look at the current situation as a whole” before making a decision. Several delegates from the group said it has yet to decide on how to proceed. Algorithmic traders also may have contributed to oil’s slide on Thursday. Trend-following commodity trading advisers have been steadily selling crude since reaching “buying exhaustion” at the $65-a-barrel level, “creating just a few ripples and weighing on prices,” according to Daniel Ghali, a commodity strategist at TD Securities. “We think a tidal wave is coming next,” Ghali said. “We expect that algos are now set to imminently sell a massive 40% of their maximum size.” Oil stockpile data also weighed on prices. In the US, crude inventories rose by 2.4 million barrels, more than expected, while stockpiles at the storage hub at Cushing, Oklahoma, increased to the highest since early May. Diesel inventories also swelled, eroding some bullishness surrounding the fuel that has been supporting oil market prices. Gasoline reserves fell. US oil has retreated about 12% this year as OPEC+ has boosted production targets at a rapid clip to reclaim market share from rival drillers. At the same time, producers from outside the alliance have ramped up output, while concerns about demand have intensified as the Trump administration imposed a wave of tariffs. The combination has spawned widespread predictions for

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Angola Seeks Onshore Oil Investment

Angola’s president urged energy companies to boost investment in onshore oil fields to counter a sustained decline in crude production.  “Onshore exploration must be stimulated and promoted,” João Lourenço said Wednesday at the opening of an oil and gas conference in Luanda, the capital. Adding that the country is offering investors “contractual stability, legal security, predictability and due return in accordance with the best international practices.”  Angola’s crude output slipped below one million barrels a day in July, threatening state revenues that fund more than 90% of exports.  Africa’s third-biggest crude producer is finalizing a new licensing plan covering 2026 to 2030, which will include both offshore blocks and inland basins, said Paulino Jeronimo, head of Angolan Agency for Oil, Gas and Biofuels, said at same event. The government also plans to shorten the time it takes to award concessions to attract more exploration drilling. Angola has drilled only a handful of wells compared with the number of blocks awarded, making it important to study new ways to provide incentives for exploration, Jeronimo said. BP Plc Chief Executive Officer Murray Auchincloss told the conference the company’s offshore West Hub project could reach 175,000 barrels a day at peak, helping Angola maintain production near the 1 million-barrel mark. 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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Hitachi unveils $1B grid manufacturing investment, including Virginia transformer factory

Dive Brief: Hitachi Energy will build a $457 million transformer production facility in South Boston, Virginia, and make more than $500 million in other investments to meet the growing need for U.S. grid equipment, the company, state officials and the Trump administration announced Thursday. The investment comes as lead times for utilities to procure transformers have stretched from weeks to years, as grid expansion and a shortage of manufacturing capacity have created a bottleneck. In particular, large transformers like the ones Hitachi will make in Virginia can now take up to three years from order to delivery, experts say. “Bringing production of large power transformers to the U.S. is critical to building a strong domestic supply chain for the U.S. economy and reducing production bottlenecks, which is essential as demand for these transformers across the economy is surging,” Andreas Schierenbeck, CEO of Hitachi Energy, said in a statement. Dive Insight: “Power transformers are a linchpin technology for a robust and reliable electric grid and winning the AI race,” Schierenbeck said. The U.S. grid now faces rapid load increases following decades of stagnant growth. Data centers could be the source of 44% of U.S. electricity load growth from 2023 to 2028, Bain & Co. estimated in October. They could potentially consume 9% of the United States’ electricity generation by 2030 — double the amount consumed today, according to the Electric Power Research Institute. Electrification of transportation and buildings, and an industrial expansion, are also creating demand for electricity and a need to expand the power grid. “Particularly at this critical moment for our growing energy demands, I’m excited to see Hitachi Energy expand their Virginia footprint,” Sen. Mark Warner, D-Va., said in a statement. The company already owns a transformer production facility in South Boston. And in March, it announced a $250 million investment to

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Energy meets urgency: Solving the data center power problem with solar

Jared Burden, a partner in the law firm GreeneHurlocker, is a Virginia-based renewable energy lawyer with a practice encompassing corporate transactions, real estate, and land use law. In Virginia and across the country, the explosive growth of data centers is forcing a fundamental shift in how we think about electricity. For years, data center developers could build with confidence that the grid — and the utilities behind it — would keep up. That’s no longer the case. In May, the PJM Interconnection, which manages the grid serving Virginia and much of the Mid-Atlantic, issued a warning for the first time in its history: Under extreme summer conditions, it may not have enough power to go around. At a Federal Energy Regulatory Commission technical conference the following month, as reported by Utility Dive in an article titled “Does PJM Have a Data Center Problem,” PJM’s independent market monitor, Joseph Bowring, was blunt: “Data centers could overwhelm the grids if they chose to.” The solution suggested by Browning, according to the article, “is to require data center owners to procure new generation for their projects.” Virginia already imports more electricity than any other state (50 million MWh in 2023), according to the U.S. Energy Information Administration. Demand is surging, driven mostly by data centers and the explosive energy needs of AI, crypto, and high-density computing. A 2024 report from the General Assembly’s Joint Legislative Audit and Review Commission projected that Virginia’s electric demand could triple by 2040 if data center growth continues on its current path. If there’s one emerging consensus, it’s this: the grid can’t grow fast enough to keep up. The only way to meet this demand reliably is for large power users, especially data centers, to build their own. But that raises another critical question: What kind of power

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Global solar installations rise 64% to 380 GW in first half of 2025

Listen to the article 3 min This audio is auto-generated. Please let us know if you have feedback. Worldwide, 380 GW of new solar capacity was installed in the first half of this year, marking a 64% increase from 2024, when 232 GW was installed in the same timeframe, according to a report from the clean energy think tank Ember. “In 2024, it took until September for global solar capacity additions to surpass 350 GW, while in 2025, the milestone was reached in June,” Ember said in a Tuesday release. China led the pack in solar installations, adding 256 GW — more than twice as much solar capacity as the rest of the world combined — and making up 67% of the global total, Ember said. In comparison, in the first half of 2024, 54% of global installations were in China. India came in second behind China, installing 24 GW, while the U.S. installed 21 GW, “up 4% year-on-year, despite recent moves by the US government to restrict clean power deployment,” Ember said. “The rapid expansion of solar capacity in recent years has made it the fastest growing source of new electricity generation,” Ember said. “In 2024, global solar output rose by 28% (+469 TWh) compared to 2023, more than any other source.” An Aug. 20 report from the U.S. Energy Information Administration said developers the agency surveyed plan to add another 21 GW of solar capacity in the U.S. over the second half of 2025, after adding 12 GW in the first half of the year. Renewables made up 91% of the 15 GW of generation the US added in first five months of 2025, according to the Federal Energy Regulatory Commission. “If those plans are realized, solar would account for more than half of the 64 GW that developers

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ConocoPhillips Plans to Cut Up to 25 Pct of Workforce

Oil and natural gas producer ConocoPhillips plans to cut 20% to 25% of its workforce. The majority of the reductions will occur this year, a company spokesman said. ConocoPhillips has informed employees of the cuts, he said.  “We are always looking at how we can be more efficient with the resources we have,” ConocoPhillips spokesman Dennis Nuss said in an email. The job cuts include both employees and contractors. They were reported earlier by Reuters. The company plans to hold a town hall meeting Thursday, according to Reuters.  Houston-based ConocoPhillips acquired Marathon Oil Corp. in November for about $17 billion. The previous month, Marathon warned of layoffs involving more than 500 employees. It was not immediately clear Wednesday whether the layoffs are related to the Marathon acquisition.  ConocoPhillips expected to make $500 million in cost and capital savings in the first year of the takeover. ConocoPhillips shares fell 4.6% Wednesday as crude prices and other energy stocks declined.   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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Google adds Gemini to its on-prem cloud for increased data protection

Google has announced the general availability of its Gemini artificial intelligence models on Google Distributed Cloud (GDC), making its generative AI product available on enterprise and government data centers. GDC is an on-premises implementation of Google Cloud, aimed at heavily regulated industries like medical and financial services to bring Google Cloud services within company firewalls rather than the public cloud. The launch of Gemini on GDC allows organizations with strict data residency and compliance requirements to deploy generative AI without compromising control over sensitive information. GDC uses Nvidia Hopper and Blackwell 0era GPU accelerators with automated load balancing and zero-touch updates for high availability. Security features include audit logging and access control capabilities that provide full transparency for customers. The platform also features Confidential Computing support for both CPUs (with Intel TDX) and GPUs (with Nvidia’s confidential computing) to secure sensitive data and prevent tampering or exfiltration.

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Nvidia networking roadmap: Ethernet, InfiniBand, co-packaged optics will shape data center of the future

Nvidia is baking into its Spectrum-X Ethernet platform a suite of algorithms that can implement networking protocols to allow Spectrum-X switches, ConnectX-8 SuperNICs, and systems with Blackwell GPUs to connect over wider distances without requiring hardware changes. These Spectrum-XGS algorithms use real-time telemetry—tracking traffic patterns, latency, congestion levels, and inter-site distances—to adjust controls dynamically. Ethernet and InfiniBand Developing and building Ethernet technology is a key part of Nvidia’s roadmap. Since it first introduced Spectrum-X in 2023, the vendor has rapidly made Ethernet a core development effort. This is in addition to InfiniBand development, which is still Nvidia’s bread-and-butter connectivity offering. “InfiniBand was designed from the ground up for synchronous, high-performance computing — with features like RDMA to bypass CPU jitter, adaptive routing, and congestion control,” Shainer said. “It’s the gold standard for AI training at scale, connecting more than 270 of the world’s top supercomputers. Ethernet is catching up, but traditional Ethernet designs — built for telco, enterprise, or hyperscale cloud — aren’t optimized for AI’s unique demands,” Shainer said. Most industry analysts predict Ethernet deployment for AI networking in enterprise and hyperscale deployments will increase in the next year; that makes Ethernet advancements a core direction for Nvidia and any vendor looking to offer AI connectivity options to customers. “When we first initiated our coverage of AI back-end Networks in late 2023, the market was dominated by InfiniBand, holding over 80% share,” wrote Sameh Boujelbene, vice president of Dell ’Oro Group, in a recent report. “Despite its dominance, we have consistently predicted that Ethernet would ultimately prevail at scale. What is notable, however, is the rapid pace at which Ethernet gained ground in AI back-end networks. As the industry moves to 800 Gbps and beyond, we believe Ethernet is now firmly positioned to overtake InfiniBand in these high-performance deployments.”

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Inside the AI-optimized data center: Why next-gen infrastructure is non-negotiable

How are AI data centers different from traditional data centers? AI data centers and traditional data centers can be physically similar, as they contain hardware, servers, networking equipment, and storage systems. The difference lies in their capabilities: Traditional data centers were built to support general computing tasks, while AI data centers are specifically designed for more sophisticated, time and resource-intensive workloads. Conventional data centers are simply not optimized for AI’s advanced tasks and necessary high-speed data transfer. Here’s a closer look at their differences: AI-optimized vs. traditional data centers Traditional data centers: Handle everyday computing needs such as web browsing, cloud services, email and enterprise app hosting, data storage and retrieval, and a variety of other relatively low-resource tasks. They can also support simpler AI applications, such as chatbots, that do not require intensive processing power or speed. AI data centers: Built to compute significant volumes of data and run complex algorithms, ML and AI tasks, including agentic AI workflows. They feature high-speed networking and low-latency interconnects for rapid scaling and data transfer to support AI apps and edge and internet of things (IoT) use cases. Physical infrastructure Traditional data centers: Typically composed of standard networking architectures such as CPUs suitable for handling networking, apps, and storage. AI data centers: Feature more advanced graphics processing units (GPU) (popularized by chip manufacturer Nvidia), tensor processing units (TPUs) (developed by Google), and other specialized accelerators and equipment. Storage and data management Traditional data centers: Generally, store data in more static cloud storage systems, databases, data lakes, and data lakehouses. AI data centers: Handle huge amounts of unstructured data including text, images, video, audio, and other files. They also incorporate high-performance tools including parallel file systems, multiple network servers, and NVMe solid state drives (SSDs). Power consumption Traditional data centers: Require robust cooling

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From Cloud to Concrete: How Explosive Data Center Demand is Redefining Commercial Real Estate

The world will generate 181 ZB of data in 2025, an increase of 23.13% year over year and 2.5 quintillion bytes (a quintillion byte is also called an exabyte, EB) created daily, according to a report from Demandsage. To put that in perspective: One exabyte is equal to 1 quintillion bytes, which is 1,000,000,000,000,000,000 bytes. That’s 29 TB every second, or 2.5 million TB per day. It’s no wonder data centers have become so crucial for creating, consuming, and storing data — and no wonder investor interest has skyrocketed.  The surging demand for secure, scalable, high-performance retail and wholesale colocation and hyperscale data centers is spurred by the relentless, global expansion of cloud computing and demand for AI as data generation from businesses, governments, and consumers continues to surge. Power access, sustainable infrastructure, and land acquisition have become critical factors shaping where and how data center facilities are built.  As a result, investors increasingly view these facilities not just as technology assets, but as a unique convergence of real estate, utility infrastructure, and mission-critical systems. Capitalizing on this momentum, private equity and real estate investment firms are rapidly expanding into the sector through acquisitions, joint ventures, and new funds—targeting opportunities to build and operate facilities with a focus on energy efficiency and scalability.

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Ai4 2025 Navigates Rapid Change in AI Policy, Education

The pace of innovation in artificial intelligence is fundamentally reshaping the landscape of education, and the changes are happening rapidly. At the forefront of this movement stand developers, policy makers, educational practitioners, and associated experts at the recent Ai4 2025 conference (Aug. 11-13) in Las Vegas, where leading voices such as Geoffrey Hinton “The Godfather of AI,” top executives from Google and U.S. Bank, and representatives from multiple government agencies gathered to chart the future of AI development. Importantly, educators and academic institutions played a central role, ensuring that the approach to AI in schools is informed by those closest to the classroom. Key discussions at Ai4 and recent educator symposia underscored both the promise and peril of swift technological change. Generative AI, with its lightning-fast adoption since the advent of tools like ChatGPT, is opening new possibilities for personalized learning, skills development, and operational efficiency. But participants were quick to note that acceleration brings good and bad consequences. On one hand, there’s excitement about practical classroom implementations and the potential for students to engage with cutting-edge technology. On the other, concerns about governance, ethics, safety, and the depth of genuine learning remain at the forefront. This urgency to “do this right” is echoed by teachers, unions, and developers who are united by the challenges and opportunities on the ground. Their voices highlight the need for agreement on education policy and associated regulations to keep pace with technological progress, create frameworks for ethical and responsible use, and ensure that human agency remains central in shaping the future of childhood and learning. In this rapidly evolving environment, bringing all stakeholders to the table is no longer optional; it is essential for steering AI in education toward outcomes that benefit both students and society. Global Context: America, China, and the AI Race

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Two Lenses on One Market: JLL and CBRE Show Data Centers in a Pinch

The two dominant real estate research houses, JLL and CBRE, have released midyear snapshots of the North American data center market, and both paint the same picture in broad strokes: demand remains insatiable, vacancy has plunged to record lows, and the growth of AI and hyperscale deployments is reshaping every aspect of the business. But their lenses capture different angles of the same story: one emphasizing preleasing and capital flows, the other highlighting hyperscale requirements and regional shifts. Vacancy Falls Through the Floor JLL sets the stage with a stark headline: colocation vacancy is nearing 0%. The JLL Midyear 2025 North America Data Center report warns that this scarcity “is constraining economic growth and undermining national security,” underscoring the role of data centers as critical infrastructure. CBRE’s North American Data Center Trends H1 2025 numbers back this up, recording an all-time low North America vacancy rate of 1.6%, the tightest in more than a decade. Both agree that market loosening is years away — JLL projecting vacancy hovering around 2% through 2027, CBRE noting 74.3% of new capacity already spoken for. The takeaway seems clear: without preleasing, operators and tenants alike are effectively shut out of core markets. Absorption and Preleasing Drive Growth JLL drills down into the mechanics. With virtually all absorption tied to preleasing, the firm points to Northern Virginia (647 MW) and Dallas (575 MW) as the twin engines of growth in H1, joined by Chicago, Austin/San Antonio, and Atlanta. CBRE’s absorption math is slightly different, but the conclusion aligns: Northern Virginia again leads the nation, with 538.6 MW net absorption and a remarkable 80% surge in under-construction capacity. CBRE sharpens the view by noting that the fiercest competition is at the top end: single-tenant requirements of 10 MW or more are setting pricing records as hyperscalers

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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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