The amount Solar For All funding plaintiffs say the federal government is illegally withholding in a lawsuit filed this week in the U.S. District Court for the District of Rhode Island. The plaintiffs argue that the executive branch did not have the statutory authority to rescind the funding, even after Congress repealed large parts of the Inflation Reduction […]
The amount Solar For All funding plaintiffs say the federal government is illegally withholding in a lawsuit filed this week in the U.S. District Court for the District of Rhode Island. The plaintiffs argue that the executive branch did not have the statutory authority to rescind the funding, even after Congress repealed large parts of the Inflation Reduction Act, as Congress “did not repeal the Solar for All program retroactively.”
Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.
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The new chips will be able to support up to 12-channel DDR5 memory with speeds of up to 8000 MT/s, a substantial increase over the 8 channels of 6400MT/s in the prior generation. In addition to that, the platform will support up to 6 UPI 2.0 links with up to
The solution and challenges For AI to work for us and alert us in advance, it should have good quality, reliable data over time, and this data can be retrieved from our classic logs when any event is triggered. Ping and SNMP would only provide data in polling time intervals
What’s more, I’m sure many of the employees of these companies have studied mathematics. That’s right, many are former mathematicians or professionals with a mathematical background. And, with respect to those trained in computer science, well, you have to remember that, at the beginning, computer scientists were part of the
The amount Solar For All funding plaintiffs say the federal government is illegally withholding in a lawsuit filed this week in the U.S. District Court for the District of Rhode Island. The plaintiffs argue that the executive branch did not have the statutory authority to rescind the funding, even after Congress repealed large parts of the Inflation Reduction Act, as Congress “did not repeal the Solar for All program retroactively.”
Colombian companies are bracing for gas rationing as the nation’s sole import terminal closes for scheduled maintenance, casting a spotlight on the Andean country’s widening supply deficit. In a bid to avoid blackouts on the populous Atlantic coast, the Energy Ministry has said it will prioritize supply to local gas-fired power plants, households and small businesses during the Oct. 10-14 outage of the SPEC liquefied natural gas terminal outside Cartagena. While the LNG import facility pauses operations, around one-third of industrial gas demand will be unmet, according to industry trade group Asoenergía. Although the maintenance is brief, some factories have furnaces that require several days to completely shut off and days more to restart. The scarcity is pitting Colombia’s biggest industrial and commercial energy users against power generators. The government prefers to allocate gas supply cuts to industries even though power generators should have timely backups in place that they are paid to maintain, said Sandra Fonseca, head of Asoenergía. “This hurts industrial output,” she said in a written response to questions, adding that the companies won’t be compensated for the shortfall. The trade group representing power generators, known as Andeg, declined to comment. Colombia began importing LNG in 2016 to occasionally fuel thermal power plants as recurring droughts restricted the country’s mainstay hydroelectric units. Then at the end of 2024, Colombia resorted to LNG to meet demand from households and businesses as domestic gas production declined. The Andean nation, which is now facing a structural gas shortfall of around 17% of total demand, will see that gap widen to as much as 20% next year, according to trade group Naturgas. If by 2029 no new domestic sources of gas are added, the shortfall could reach as high as 56%, the group says. While Colombia’s oil and gas reserves
In its latest short term energy outlook (STEO), which was released on October 7, the U.S. Energy Information Administration (EIA) raised its average West Texas Intermediate (WTI) spot price forecasts for 2025 and 2026. According to its latest STEO, the EIA now sees the commodity averaging $65.00 per barrel this year and $48.50 per barrel next year. In its previous STEO, which was released in September, the EIA projected that the WTI spot price would average $64.16 per barrel in 2025 and $47.77 per barrel in 2026. A quarterly breakdown included in the EIA’s October STEO showed that the EIA sees the WTI spot price coming in at $58.05 per barrel in the fourth quarter of this year, $47.97 per barrel in the first quarter of next year, $48.33 per barrel in the second quarter, $48.68 per barrel in the third quarter, and $49.00 per barrel in the fourth quarter of 2026. In its previous September STEO, the EIA forecast that the commodity would average $65.14 per barrel in the third quarter, $55.41 per barrel in the fourth quarter, $45.97 per barrel in the first quarter of next year, $46.33 per barrel in the second quarter, $48.68 per barrel in the third quarter, and $50.00 per barrel in the fourth quarter. The EIA’s October STEO showed that the WTI spot price averaged $65.79 per barrel in the third quarter of this year. Both STEOs highlighted that the commodity averaged $71.85 per barrel in the first quarter of this year, $64.63 per barrel in the second quarter of 2025, and $76.60 per barrel overall in 2024. In a J.P. Morgan report sent to Rigzone on Friday by Natasha Kaneva, head of global commodities strategy at the company, J.P. Morgan projected that the WTI crude oil price will average $62 per barrel
Ari Matusiak is the founder and CEO of Rewiring America. Around the country, American families are finding their electricity bills harder and harder to afford. Electricity prices have risen twice as fast as inflation — and they’re still going up, as utilities requested $29 billion in rate hikes so far this year alone. Low-income families already pay nearly 18% of their income on energy. It’s not adding up for households. Soaring energy demand from hyperscalers, the tech companies building AI data centers, has shattered two decades of relatively flat electricity use. U.S. electricity demand is projected to grow by 128 GW by 2029 — 93 GW of which is to support data centers. State officials and utilities feel they have to choose between attracting prospective AI business and enduring long interconnection waiting periods and a volatile national landscape for renewables. Many are opting to resurrect coal plants that had been planned for retirement, or greenlight construction on new gas-fired power plants. And many decision makers view increasingly electric households as passive energy consumers that are in direct competition for a limited power supply. It doesn’t have to be this way. There is actually a near-term solution that won’t require forgoing climate pledges or waiting years for new power plants: Data center operators should pay for residential upgrades to heat pumps, rooftop solar and storage — to secure the power they need right now, and establish the grid stability we all need for the future. It’s time to recognize that households — far from being passive energy consumers — are the foundation of our energy infrastructure, and the hero of our energy story. The roadmap This is not just a nice idea. It’s a data-backed solution. In our new report, Homegrown Energy: How household upgrades can meet 100 percent of data center demand growth, we demonstrate how upgrading households is
ARM Energy Holdings LLC on Thursday announced a FID (final investment decision) to proceed with the Mustang Express Pipeline project, designed to add 2.5 billion cubic feet a day of natural gas transport capacity on the Texas Gulf Coast. The pipeline system represents an investment of $2.3 billion, ARM Energy and Pacific Investment Management Co (PIMCO) said in a joint statement. “Mustang Express Pipeline will be constructed and operated by ARM Energy, along with its financial partners, PIMCO and associated co-investors”, they said. Expected to be completed late 2028 or early 2029, the project will have a 178-mile mainline from the Katy Hub to Port Arthur. The conveyor, 42 inches in diameter, will total 236 miles in length, the companies said. Mustang Express is supported by an anchor shipper commitment from Sempra Infrastructure for the provision of feed gas for its Port Arthur LNG Phase II project, the statement said. Houston, Texas-based ARM Energy plans an open season this month for the project’s remaining capacity. ARM Energy contracted Jindal Tubular USA for the mill capacity and Solar Turbines Inc for the turbines for the project’s three gas-driven compressor stations, which will have a total capacity of 300,000 horsepower, the statement said. Mustang Express will “significantly enhance the efficiency of the U.S. natural gas supply chain”, ARM Energy chief executive Zach Lee said. “By linking two of the most prolific natural gas-producing regions in the U.S. directly to LNG export facilities in Texas, we are helping ensure a reliable supply of natural gas for liquefaction and export with a route that crosses four storage facilities – delivering positive impacts for global energy needs and Texas communities”. PIMCO managing director and portfolio manager Adam Gubner said the project “reflects our commitment to invest in infrastructure to support LNG expansion in the U.S., while
We need more energy. That’s what NVIDIA Co-Founder, President, and CEO Jensen Huang said in a CNBC interview on CNBC’s Squawk Box on October 8. “AI [Artificial Intelligence] is several things,” Huang stated in the CNBC interview. “AI is energy, AI is chips, the models, and the applications … And we need more energy, we need more chips, we need better models and more models, and we need a lot more applications,” he added. Also in the CNBC interview, Huang said he was “so happy that President Trump leaned into … pro energy growth, so that an entire industry above it could grow”. “If you could just imagine, without President Trump’s pro energy policy, that entire layer above … energy would have been constrained,” he added in the interview. Huang also highlighted in the CNBC interview that “China is well ahead of … [the U.S.] on energy” but said the U.S. is “way ahead on chips”. NVIDIA is the world’s biggest company by market capitalization as of Friday, with a market cap of $4.6 trillion, according to a list of the largest companies by market cap on the companiesmarketcap website. As of Friday, the second highest ranked company on the list is Microsoft, with a market cap of $3.8 trillion, and the highest ranked oil and gas company on the list is Saudi Aramco, in eighth, with a market cap of $1.6 trillion. The companiesmarketcap site notes that its largest companies by market cap list ranks the most valuable public companies. It adds that private companies are not included in its lists “as it is difficult to calculate their market value and know their financials”. According to the Energy Institute’s (EI) latest statistical review of world energy, total global energy supply came in at 592.22 exajoules in 2024. This marked
Some say deep buffers shouldn’t be used to handle this type of traffic; the contention is that these buffers fill and drain, creating jitter in the workloads, and that slows things down, Chopra told Network World. “But the real source of that challenge is not the buffers. It’s a poor congestion management scheme and poor load balancing with AI workloads, which are completely deterministic and predictable. You can actually proactively figure out how to place flows across the network and avoid the congestion,” he said. The 8223’s deep-buffer design provides ample memory to temporarily store packets during congestion or traffic bursts, an essential feature for AI networks where inter-GPU communication can create unpredictable, high-volume data flows, according to Gurudatt Shenoy, vice president of Cisco Provider Connectivity. “Combined with its high-radix architecture, the 8223 allows more devices to connect directly, reducing latency, saving rack space, and further lowering power consumption. The result is a flatter, more efficient network topology supporting high-bandwidth, low-latency communication that is critical for AI workloads,” Shenoy wrote in a blog post. NOS options Notably, the first operating systems that the 8223 supports are the Linux Foundation’s Software for Open Networking in the Cloud (SONiC) and Facebook open switching system (FBOSS) – not Cisco’s own IOS XR. IXR will be supported, too, but at a later date, according to Cisco. SONiC decouples network software from the underlying hardware and lets it run on hundreds of switches and ASICs from multiple vendors while supporting a full suite of network features such as Border Gateway Protocol (BGP), remote direct memory access (RDMA), QoS, and Ethernet/IP. One of the keys to SONiC is its switch-abstraction interface, which defines an API to provide a vendor-independent way of controlling forwarding elements such as a switching ASIC, an NPU, or a software switch in a uniform
Over the last 18 months or so, the energy generation industry and its public utilities have been significantly impacted by the AI data center boom. It has been demonstrated across North America that the increase in demand for power, as driven by the demand for hyperscale and AI data centers, greatly exceeds the ability of the industry to actually generate and deliver power to meet the demand. We have covered many of the efforts being made to control the availability of power. In response, utilities and regulators have begun rethinking how to manage power availability through means such as: temporary moratoriums on new data center interconnections; the creation of new rate classes; cogeneration and load-sharing agreements; renewable integration; and power-driven site selection strategies. But the bottom line is that in many locations utilities will need to change the way they work and how and where they spend their CAPEX budgets. The industry has already realized that their demand forecast models are hugely out of date, and that has had a ripple effect on much of the planning done by public utilities to meet the next generation of power demand. Most utilities now acknowledge that their demand forecasting models have fallen behind reality, triggering revisions to Integrated Resource Plans (IRPs) and transmission buildouts nationwide. This mismatch between forecast and actual demand is forcing a fundamental rethink of capital expenditure priorities and long-term grid planning. Spend More, Build Faster Utilities are sharply increasing CAPEX and rebalancing their resource portfolios—not just for decarbonization, but to keep pace with multi-hundred-megawatt data center interconnects. This trend is spreading across the industry, not confined to a few isolated utilities. Notable examples include: Duke Energy raised its five-year CAPEX plan to $83 billion (a 13.7% increase) and plans to add roughly 5 GW of natural gas capacity
Duos Technology Group has launched the fifth of its AI edge data centers, part of a plan to deploy 15 units by the end of 2025. The projects are executed through Duos Edge AI, a subsidiary focused on modular, rapidly installed edge data centers (EDCs) in underserved markets, beginning with school districts and regional carrier hubs across Texas. The newest site is being deployed on-premises with the Dumas Independent School District in Dumas, Texas. High-Density Edge Design Duos’ EDCs emphasize very high rack densities (100 kW+ per rack), SOC 2 Type II compliance, N+1 power with dual generators, and a 90-day build/turn-up cycle. Each site is positioned approximately 12 miles from end users, cutting latency for real-time workloads. To meet the power demands of these edge deployments, Duos formed Duos Energy and partnered with Fortress/APR Energy to deliver behind-the-meter mobile gas turbines. This approach allows compute to go live in 90 days without waiting years for utility interconnection upgrades. The goal is straightforward: move power and compute close to demand, with rapid deployment. Duos’ modular pods are designed for exurban and rural locations as localized compute hubs for carriers, schools, healthcare systems, and municipal users. The rugged design pairs high-density racks with the short deployment cycle and proximity targeting, enabling a wide range of applications. With Dumas ISD now live, Duos has five sites in Texas, including Amarillo/Region 16, Victoria/Region 3, Dumas ISD, and multiple Corpus Christi locations. Mobile Power vs. Modular Compute While Duos doesn’t consistently describe its data center units as “mobile,” they are modular and containerized, engineered for rapid, site-agnostic deployment. The “mobile” label more explicitly applies to Duos’ power strategy—a turbine fleet that can be fielded or re-fielded to match demand. From an operator’s perspective, the combined proposition functions like a mobile platform: pre-integrated compute pods
[ Related: More Intel news and insights ] AMD has lagged behind Nvidia in the AI business but has done well in the federal supercomputing business, holding numerous top spots with supercomputers like El Capitan and Frontier. Manufacturing its chips in the United States would be a good way to get the Trump administration off its back given its push for domestic manufacturing of semiconductors. The Trump administration is pushing for 50% of chips sold in America to be manufactured domestically, and tariffs on chips that are not. It also faces outbound restrictions. Earlier this year, AMD faced export restrictions GPUs meant for China as part of U.S. export controls against China’s AI business. “I believe this is a smart move by AMD to secure capacity in the local market without fighting against Nvidia and Apple and their deeper pockets for the limited capacity at TSMC,” said Alvi Nguyen, senior analyst with Forrester Research.” With the US investment in Intel, followed by Nvidia, this is can be seen as diversifying their supply chain and providing cheaper, locally sourced parts.” For Intel, this will continue a streak of good news it has enjoyed recently. “Having customers take up capacity at their foundries will go a long way in legitimizing their semiconductor processes and hopefully create the snowball effect of getting even more US-based customers,” said Nguyen. In recent weeks, Intel has partnered with Nvidia to jointly make PC and data center chips. Nvidia also took a $5B stake in Intel. Earlier the Trump administration made a $11.1B, or 10%, stake in Intel.
NVIDIA’s AI Factory: Engineering the Future of Compute NVIDIA’s keynote illuminated the invisible but indispensable heart of the AI revolution—the AI factory. This factory blends hardware and software innovation to achieve performance breakthroughs that transcend traditional limits. Technologies such as disaggregated rack-scale GPUs and the novel 4-bit floating point numerical representation move beyond incremental improvements; they redefine what is achievable in energy efficiency and cost-effectiveness. The software ecosystem NVIDIA fosters, including open-source frameworks like Dynamo, enables unprecedented flexibility in managing inference workloads across thousands of GPUs. This adaptability is crucial given the diverse, dynamic demands of modern AI, where workloads can fluctuate dramatically in scale and complexity. The continuous leap in benchmark performance, often quadrupling hardware capabilities through software alone, continues to reinforce their accelerated innovation cycle. NVIDIA’s framing of AI factories as both technology platforms and business enablers highlights an important shift. The value computed is not merely in processing raw data but in generating economic streams through optimizing speed, reducing costs, and creating new AI services. This paradigm is central to understanding how the new industrial revolution will operate through highly efficient AI factories uniting production and innovation. AWS and the Cloud’s Role in Democratizing AI Power Amazon Web Services (AWS) represents a key pillar in making AI capabilities accessible across the innovation spectrum. AWS’ focus on security and fault tolerance reflects maturity in cloud design, ensuring trust and resilience are priorities alongside raw compute power. The evolution towards AI agents capable of specification-driven operations signifies a move beyond traditional computing paradigms towards contextual, autonomous AI services embedded deeply in workflows. Their launch of EC2 P6-B200 instances with next-generation Blackwell GPUs and specialized Trainium chips represents a continual drive to optimize AI training and inference at scale and out-of-box improvements in performance of 85% reduction in training time;
Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Business Development Manager – Mechanical Data Center Solutions Remote / traveler This position can be located anywhere in the U.S. as long as candidate can travel and will need to be located near a major airport in the U.S. Key for this role is data center mechanical experience selling engineered products and solutions with a rolodex of either colo or hyperscale contacts. This opportunity is working directly with a successful global OEM looking to expand market share in the critical facilities industry. They help data centers reduce energy and operating costs by providing mechanical solutions that modernize their infrastructure. By ensuring high-reliability mission-critical facilities for some of the world’s largest hyperscale, colocation, and enterprise customers, this company offers a career-growth-minded opportunity with exciting projects, cutting-edge technology, competitive salaries, and benefits. Engineering Design Director – Data CenterDenver, CO / Dallas, TX / Remote This position is a remote position in the Midwest supporting HPC / AI colo design projects being built in TX. Previous A/E experience with data center hyperscale design a must! We are seeking an experienced director of data center engineering design who will lead the development and oversight of critical power and mechanical infrastructure design for new data center builds and existing data center facilities. The ideal candidate will bring strong technical acumen, leadership skills, and extensive experience in mission-critical environments. You will oversee all aspects of engineering design, guide project teams, interface with stakeholders, and ensure best practices are upheld in quality,
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.
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
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
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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Attentive readers might have noticed my absence over the last couple of weeks. I’ve been trying to recover from a bout of illness. It got
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How
