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ChargeScape’s EV Virtual Power Plant ensures summertime grid reliability

As we enter the summer months, America’s aging power grid faces a double-whammy of unprecedented, 24/7 electricity demand from AI data centers coupled with the prospect of temporary but severe peaks from air conditioning load during heat waves. Without smart energy management, parts of the country could face the rolling blackouts recently seen in California. […]

As we enter the summer months, America’s aging power grid faces a double-whammy of unprecedented, 24/7 electricity demand from AI data centers coupled with the prospect of temporary but severe peaks from air conditioning load during heat waves. Without smart energy management, parts of the country could face the rolling blackouts recently seen in California.

Electric vehicles (EVs) have been painted by some as a liability to the power grid. But at ChargeScape, we are building a Virtual Power Plant of electric vehicles that will bolster the nation’s power grid using a distributed network of batteries. As a joint venture of BMW, Ford, Honda and Nissan, ChargeScape is on a mission to transform EVs from a grid liability into an asset. ChargeScape’s platform does this by:

  • Balancing the complexity of fast-growing distributed energy resources (DERs) alongside intermittent renewable generation;
  • Delivering real-time peak load reduction at the bulk system level;
  • Preventing asset overload from localized demand on specific parts of the distribution network; and
  • Meeting heightened customer expectations for energy management.

Real-world-impact: enhancing grid resilience across the U.S.

Our vision of a more resilient, reliable U.S. power grid isn’t just a dream: it’s a reality that we are actively building today with our utility partners across the country. Below are a few examples of how ChargeScape is leveraging EVs to deliver grid reliability:

1. PSEG Long Island’s System Peak Relief Program

To support summer grid reliability, ChargeScape is participating in PSEG Long Island’s System Peak Relief Program. Through this program, EV charging is automatically paused during high-demand hours in the summer, helping to reduce system stress without disrupting driver routines. Enrolled customers receive $50 upfront and another $50 at the end of the season for staying enrolled and participating in demand response events.

ChargeScape, through its direct partnerships with automakers, was the first to bring residential EVs into this historically commercial-focused program—demonstrating the value of EV telematics in enabling targeted, automated load curtailment. With multiple demand response events planned throughout the summer, ChargeScape is proving how EVs can be reliably and intelligently orchestrated to strengthen grid resilience and reduce peak demand as temperatures rise.

2. Eversource Managed Charging Program Summer Demand Response

In collaboration with Eversource, ChargeScape supports a multi-tiered EV managed charging program across Connecticut and (soon) Massachusetts. This program offers customers flexibility to participate passively—by charging during off-peak hours—or actively, through EV load-shifting automated by ChargeScape. Customers can earn up to $300 per year based on their level of participation.

In previous summers, ChargeScape has supported grid stability by orchestrating demand response events during peak periods, delivering up to 5 kW of load reduction per vehicle per event in 2024. Through our direct partnerships with automakers, ChargeScape enables targeted communications to EV drivers about upcoming grid events using existing OEM apps and channels—boosting participation and trust. Through clear communication via direct OEM channels, EV drivers can make informed choices and confidently support grid reliability without compromising their mobility requirements.

3. DTE Energy’s Vehicle-to-Home Pilot

In Michigan, ChargeScape is working with DTE Energy and Ford to prove how vehicle-to- home (V2H) technology can bolster grid resilience by leveraging EVs as a flexible bidirectional power source. Our pilot enables eligible Ford F-150 Lightning drivers to export energy back to their homes during peak periods and emergency grid events, which reduces load on the system in real-time. A single F-150 Lightning can deliver up to 19.2 kW of power, which is enough to offset most of a home’s peak electricity use during summer demand periods (and enough to power the average American home for three days).

Customers will earn up to $500 annually for participating, with ChargeScape working with Ford to coordinate charging and discharging to ensure drivers save on charging while supporting grid stability when it matters most. By shifting from simple load reduction to bidirectional energy flow, our pilot with DTE and Ford is an important step toward unlocking the full grid potential of EVs— particularly when electricity demand spikes during a heatwave.

4. California Virtual Power Plant

ChargeScape is building America’s largest EV Virtual Power Plant (VPP) together with Leap, aiming to deliver 100 MW of flexibility to the California grid through both smart charging (V1G) – reducing demand during periods of high grid demand – and through bi-directional power export, including vehicle-to-grid (V2G). Initial results have shown the capacity of EVs to deliver ~5 kW each of V1G demand-side reduction, and to export as much as ~20 kW of power from each vehicle through V2G.

Join us in building a resilient energy future

ChargeScape’s direct OEM integrations and sophisticated load management capabilities empower utilities and grid operators to leverage EVs as a grid resilience tool. To discuss partnership opportunities or learn more, contact us at [email protected].

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Essential commands for Linux server management

Any Linux systems administrator needs to be proficient with a wide range of commands for user management, file handling, system monitoring, networking, security and more. This article covers a range of commands that are essential for managing a Linux server. Keep in mind that some commands will depend on the

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Who should pay to keep Michigan coal plant running past retirement date?

The Federal Energy Regulatory Commission should limit who pays for the U.S. Department of Energy’s decision to keep a coal-fired power plant in Michigan operating past its planned retirement, according to comments filed at the agency on Friday. Further, there is no evidence that delaying the retirement of Consumer Energy’s majority-owned, 1,560-MW J.H. Campbell power plant will provide benefits to ratepayers in the Midcontinent Independent System Operator footprint, according to the Citizens Utility Board of Illinois and 15 other public interest groups. Forcing MISO customers to pay for a power plant that does not benefit them would violate the Federal Power Act’s cost causation requirement, the groups said in their comments. On May 21, DOE declared that there is an energy emergency in MISO’s northern and central regions and ordered that the Campbell plant should run until Aug. 21, past its planned May 30 shutdown date. The Michigan attorney general’s office and a coalition of advocacy groups last week asked DOE to reverse its order. Consumers Energy on June 6 filed a complaint at FERC seeking to establish a pathway for it to recover its costs to keep the plant in Michigan running. Currently, MISO lacks a mechanism to pay generators that run under DOE emergency orders, and the grid operator has no way to allocate those costs, according to Consumers. Consumers proposed allocating its costs related to DOE’s decision to ratepayers in MISO zones 1 to 7, which covers parts of Michigan, Indiana, Kentucky, Illinois, Missouri, Iowa, Wisconsin, Minnesota, North Dakota, South Dakota and Montana. MISO supported creating a cost-recovery mechanism for the DOE’s order on the Campbell plant, according to a filing at FERC. MISO said it did not intend to contest DOE’s characterization that a shortage of electricity or power plants has triggered an emergency in its footprint. However, MISO’s capacity

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US DOE Launches New Pathway for Testing Advanced Nuclear Reactors

The United States Department of Energy has opened application for testing advanced nuclear reactors outside of national laboratories using the federal authorization process. “Seeking DOE authorization provided under the Atomic Energy Act will help unlock private funding and provide a fast-tracked approach to enable future commercial licensing activities for potential applicants”, the DOE said in a press release. The call for applications results from an order issued by President Donald Trump May 23 for reforming the national lab process for reactor testing, establishing a pilot program for reactor construction and operation outside of national labs and streamlining environmental reviews for reactor facilities. “With some rare and arguable exceptions, no advanced reactors have yet been deployed in America”, Trump said in the executive order. “I find that design, construction, operation, and disposition of such reactors under the auspices of the Department – and not to produce commercial electric power – would be for research purposes” rather than for demonstration of commercial viability, the president wrote. “The purpose of testing these reactors at this stage in America’s industrial evolution is to establish fundamental technological viability. “Thus, at least for the foreseeable future, advanced reactors over which the Department exercises sufficient control and that do not produce commercial electric power, including those ‘under contract with and for the account of the [Department],” 42 U.S.C. 2140(a)(2), fall within the jurisdiction of the Department, which has authority to foster research and development in nuclear reactors”. The order aims to have at least three advanced reactors achieve “criticality” by July 2026. U.S. law defines an advanced nuclear reactor as a fusion reactor or a radioisotope power system that uses heat from radioactive decay to generate energy. A nuclear fission reactor may also be considered an advanced reactor if it has significant improvements compared to reactors operating as of

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Texas RRC Reveals Latest Preliminary Oil, Gas Production Figures

In a statement posted on its site recently, the Texas Railroad Commission (RRC) revealed its latest preliminary crude oil and natural gas production figures. The preliminary reported total volume of crude oil in Texas in March was 121,361,548 barrels, according to the statement, which showed that the preliminary reported total volume of natural gas in the state during the same month was 918.4 billion cubic feet. The RRC noted in the statement that crude oil and natural gas production for March came from 157,192 oil wells and 83,898 gas wells. In its statement, the RRC highlighted that crude oil production reported by the RRC is limited to oil produced from oil leases and does not include condensate, which the organization said is reported separately by the RRC. The RRC also pointed out in the statement that preliminary figures are based on production volumes reported by operators and said they will be updated as late and corrected production reports are received. The RRC’s statement showed that the updated reported total volume of crude oil in Texas in March 2024 was 143,460,647 barrels. The preliminary reported total volume was 120,766,747 barrels, the statement highlighted. It showed that the updated reported total volume of natural gas in the state came in at 1.07 trillion cubic feet in March last year. The preliminary reported total volume was 931.5 billion cubic feet, the statement outlined. According to the RRC’s statement, Martin was Texas’ top crude oil producing county by preliminary production in March, with 20,235,596 barrels. Midland ranked second, with 17,514,942 barrels, Upton was third, with 7,866,736 barrels, Loving was fourth, with 7,395,973 barrels, Reeves was fifth, with 6,406,708 barrels, Karnes was sixth, with 5,980,867 barrels, Howard was seventh, with 5,760,904 barrels, Reagan was eighth, with 5,597,650 barrels, Andrews was ninth, with 4,590,526 barrels, and Glasscock

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Trump’s NRC firing raises alarms at pro-nuclear and watchdog groups alike

Dive Brief: Nuclear industry watchdogs and advocates alike worry that President Trump’s abrupt firing of Commissioner Christopher Hanson from the U.S. Nuclear Regulatory Commission will hinder ongoing NRC reforms and threaten Trump’s promise of an American nuclear renaissance. Hanson’s dismissal opens a vacancy on the five-member Commission, which requires three members to meet quorum. A second vacancy looms if the U.S. Senate fails to act on Trump’s renomination of Chairman David Wright by July 1.  The move comes less than a month after Trump issued four executive orders aimed at speeding up reactor approvals, expanding the U.S. nuclear supply chain, restructuring the NRC itself and adding 300 GW of new nuclear capacity by 2050. Dive Insight: In a public statement, Hanson described his termination late on June 13 as “without cause, contrary to existing law and longstanding precedent regarding removal of independent agency appointees.” Hanson has not yet taken legal action to challenge his dismissal or indicated whether he plans to do so. The U.S. Supreme Court in a May ruling gave the President broad latitude to dismiss most independent agency appointees without cause. A White House spokesperson told National Public Radio that Trump “reserves the right to remove employees within his own executive branch who exert his executive authority” and that “[a]ll organizations are more effective when leaders are rowing in the same direction.” The spokesperson did not indicate what prompted Hanson’s dismissal. NRC is currently reviewing construction permit applications for grid-connected advanced nuclear reactors in Tennessee, Texas and Wyoming; and respective operating license applications from Holtec International and Constellation Energy to restart the 800-MW Palisades nuclear plant in Michigan and 835-MW Crane Clean Energy Center in Pennsylvania. With more than 30 advanced reactor designs in pre-application engagement, its docket is all but certain to grow. Industry groups and

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U-Turning Oil Supertankers Cross Hormuz After Avoiding It

Two supertankers, each capable of hauling about 2 million barrels of crude, headed through the Hormuz shipping strait at the mouth of the Persian gulf, having performed U-turns in the past 24 hours. On Sunday, the Coswisdom Lake and South Loyalty entered the waterway and abruptly changed course, according to vessel tracking data compiled by Bloomberg. After a pause, both then headed back through Hormuz. One was almost through and the other entering it, signals from each showed on Monday. The two vessels’ movements into the world’s most important oil-producing region come as shipbrokers and others in the oil tanker market report a cautious willingness to enter the stretch of water that’s pivotal to the global oil trade. Separately, Greece’s shipping ministry warned on Sunday that the country’s owners should think twice about Hormuz transit and instead head to a safe port.  While neither tanker is Greek, the communication from Athens underscored an initial wariness about going through an area that handles about a fifth of the world’s oil. Greece is the owner of the world’s largest tanker fleet by transportation capacity. Coswisdom Lake is managed by Cosco Shipping Energy and South Loyalty by Sinokor Merchant Marine, according to industry databases. Both are very large crude carriers, or VLCCs. Neither company immediately responded to emailed requests for comment. There remain examples of wariness. Two large large Japanese shipping companies said they will cut exposure to the strait, an unavoidable searoute any vessel entering the Persian Gulf. 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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Behind-the-meter flexibility is the best response to investment uncertainty

Molly Podolefsky is a managing director at Clarum Advisors. Investment uncertainty is spurring conversations in strategy sessions and boardrooms across our industry. Utility executives find themselves at the nexus of sweeping geopolitical, policy and market change, compounding uncertainty around investments in grid expansion. Inflation, tariffs, shifting global supply chains and extreme weather combined with explosive demand growth are increasing utilities’ risk exposure beyond business as usual. Massive levels of investment are needed to secure clean, affordable, reliable energy to satisfy increased demands due to ongoing electrification, data centers and hyperscalers, and industrial customers onshoring their plants. Utilities must contend with unprecedented levels of uncertainty as they decide which investments to make and are faced with very long lead times to build new generation, transmission and distribution assets. Utilities must consider significant regulatory, political and environmental barriers to asset development which could slow roll-out, negatively impacting budgets. Tariffs and bottlenecks in global supply chains may delay generation build-out, worsening capacity constraints and increasing cost. Insufficient data and imprecise market forecasts make it difficult for utilities to anticipate data center and hyperscaler growth, increasing the risk of over-building generation resources. At the same time, by not acting aggressively, utilities jeopardize their ability to meet customer demand. Today we often hear “all of the above” proposed as the answer to exponential demand growth. While there’s an element of truth in this, a more nuanced approach, given geopolitical and economic uncertainty, entails considering all options but prioritizing and implementing low-risk, high-reward investment strategies first. Against this backdrop, behind-the-meter flexibility tops the list of no-regret investment strategies utilities should consider under uncertainty. In a recent article for Public Utilities Fortnightly, Clarum Advisors proposed a framework for de-risking investment under uncertainty. Three key themes inform the framework: Investing in the systems we need, independence from uncertain

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Can Intel cut its way to profit with factory layoffs?

Matt Kimball, principal analyst at Moor Insights & Strategy, said, “While I’m sure tariffs have some impact on Intel’s layoffs, this is actually pretty simple — these layoffs are largely due to the financial challenges Intel is facing in terms of declining revenues.” The move, he said, “aligns with what the company had announced some time back, to bring expenses in line with revenues. While it is painful, I am confident that Intel will be able to meet these demands, as being able to produce quality chips in a timely fashion is critical to their comeback in the market.”  Intel, said Kimball, “started its turnaround a few years back when ex-CEO Pat Gelsinger announced its five nodes in four years plan. While this was an impressive vision to articulate, its purpose was to rebuild trust with customers, and to rebuild an execution discipline. I think the company has largely succeeded, but of course the results trail a bit.” Asked if a combination of layoffs and the moving around of jobs will affect the cost of importing chips, Kimball predicted it will likely not have an impact: “Intel (like any responsible company) is extremely focused on cost and supply chain management. They have this down to a science and it is so critical to margins. Also, while I don’t have insights, I would expect Intel is employing AI and/or analytics to help drive supply chain and manufacturing optimization.” The company’s number one job, he said, “is to deliver the highest quality chips to its customers — from the client to the data center. I have every confidence it will not put this mandate at risk as it considers where/how to make the appropriate resourcing decisions. I think everybody who has been through corporate restructuring (I’ve been through too many to count)

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Intel appears stuck between ‘a rock and a hard place’

Intel, said Kimball, “started its turnaround a few years back when ex-CEO Pat Gelsinger announced its five nodes in four years plan. While this was an impressive vision to articulate, its purpose was to rebuild trust with customers, and to rebuild an execution discipline. I think the company has largely succeeded, but of course the results trail a bit.” Asked if a combination of layoffs and the moving around of jobs will affect the cost of importing chips, Kimball predicted it will likely not have an impact: “Intel (like any responsible company) is extremely focused on cost and supply chain management. They have this down to a science and it is so critical to margins. Also, while I don’t have insights, I would expect Intel is employing AI and/or analytics to help drive supply chain and manufacturing optimization.” The company’s number one job, he said, “is to deliver the highest quality chips to its customers — from the client to the data center. I have every confidence it will not put this mandate at risk as it considers where/how to make the appropriate resourcing decisions. I think everybody who has been through corporate restructuring (I’ve been through too many to count) realizes that, when planning for these, ensuring the resilience of these mission critical functions is priority one.”  Added Bickley, “trimming the workforce, delaying construction of the US fab plants, and flattening the decision structure of the organization are prudent moves meant to buy time in the hopes that their new chip designs and foundry processes attract new business.”

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Next-gen AI chips will draw 15,000W each, redefining power, cooling, and data center design

“Dublin imposed a 2023 moratorium on new data centers, Frankfurt has no new capacity expected before 2030, and Singapore has just 7.2 MW available,” said Kasthuri Jagadeesan, Research Director at Everest Group, highlighting the dire situation. Electricity: the new bottleneck in AI RoI As AI modules push infrastructure to its limits, electricity is becoming a critical driver of return on investment. “Electricity has shifted from a line item in operational overhead to the defining factor in AI project feasibility,” Gogia noted. “Electricity costs now constitute between 40–60% of total Opex in modern AI infrastructure, both cloud and on-prem.” Enterprises are now forced to rethink deployment strategies—balancing control, compliance, and location-specific power rates. Cloud hyperscalers may gain further advantage due to better PUE, renewable access, and energy procurement models. “A single 15,000-watt module running continuously can cost up to $20,000 annually in electricity alone, excluding cooling,” said Manish Rawat, analyst at TechInsights. “That cost structure forces enterprises to evaluate location, usage models, and platform efficiency like never before.” The silicon arms race meets the power ceiling AI chip innovation is hitting new milestones, but the cost of that performance is no longer just measured in dollars or FLOPS — it’s in kilowatts. The KAIST TeraLab roadmap demonstrates that power and heat are becoming dominant factors in compute system design. The geography of AI, as several experts warn, is shifting. Power-abundant regions such as the Nordics, the Midwest US, and the Gulf states are becoming magnets for data center investments. Regions with limited grid capacity face a growing risk of becoming “AI deserts.”

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Edge reality check: What we’ve learned about scaling secure, smart infrastructure

Enterprises are pushing cloud resources back to the edge after years of centralization. Even as major incumbents such as Google, Microsoft, and AWS pull more enterprise workloads into massive, centralized hyperscalers, use cases at the edge increasingly require nearby infrastructure—not a long hop to a centralized data center—to take advantage of the torrents of real-time data generated by IoT devices, sensor networks, smart vehicles, and a panoply of newly connected hardware. Not long ago, the enterprise edge was a physical one. The central data center was typically located in or very near the organization’s headquarters. When organizations sought to expand their reach, they wanted to establish secure, speedy connections to other office locations, such as branches, providing them with fast and reliable access to centralized computing resources. Vendors initially sold MPLS, WAN optimization, and SD-WAN as “branch office solutions,” after all. Lesson one: Understand your legacy before locking in your future The networking model that connects centralized cloud resources to the edge via some combination of SD-WAN, MPLS, or 4G reflects a legacy HQ-branch design. However, for use cases such as facial recognition, gaming, or video streaming, old problems are new again. Latency, middle-mile congestion, and the high cost of bandwidth all undermine these real-time edge use cases.

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Cisco capitalizes on Isovalent buy, unveils new load balancer

The customer deploys the Isovalent Load Balancer control plane via automation and configures the desired number of virtual load-balancer appliances, Graf said. “The control plane automatically deploys virtual load-balancing appliances via the virtualization or Kubernetes platform. The load-balancing layer is self-healing and supports auto-scaling, which means that I can replace unhealthy instances and scale out as needed. The load balancer supports powerful L3-L7 load balancing with enterprise capabilities,” he said. Depending on the infrastructure the load balancer is deployed into, the operator will deploy the load balancer using familiar deployment methods. In a data center, this will be done using a standard virtualization automation installation such as Terraform or Ansible. In the public cloud, the load balancer is deployed as a public cloud service. In Kubernetes and OpenShift, the load balancer is deployed as a Kubernetes Deployment/Operator, Graf said.  “In the future, the Isovalent Load Balancer will also be able to run on top of Cisco Nexus smart switches,” Graf said. “This means that the Isovalent Load Balancer can run in any environment, from data center, public cloud, to Kubernetes while providing a consistent load-balancing layer with a frictionless cloud-native developer experience.” Cisco has announced a variety of smart switches over the past couple of months on the vendor’s 4.8T capacity Silicon One chip. But the N9300, where Isovalent would run, includes a built-in programmable data processing unit (DPU) from AMD to offload complex data processing work and free up the switches for AI and large workload processing. For customers, the Isovalent Load Balancer provides consistent load balancing across infrastructure while being aligned with Kubernetes as the future for infrastructure. “A single load-balancing solution that can run in the data center, in public cloud, and modern Kubernetes environments. This removes operational complexity, lowers cost, while modernizing the load-balancing infrastructure in preparation

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Oracle’s struggle with capacity meant they made the difficult but responsible decisions

IDC President Crawford Del Prete agreed, and said that Oracle senior management made the right move, despite how difficult the situation is today. “Oracle is being incredibly responsible here. They don’t want to have a lot of idle capacity. That capacity does have a shelf life,” Del Prete said. CEO Katz “is trying to be extremely precise about how much capacity she puts on.” Del Prete said that, for the moment, Oracle’s capacity situation is unique to the company, and has not been a factor with key rivals AWS, Microsoft, and Google. During the investor call, Katz said that her team “made engineering decisions that were much different from the other hyperscalers and that were better suited to the needs of enterprise customers, resulting in lower costs to them and giving them deployment flexibility.” Oracle management certainly anticipated a flurry of orders, but Katz said that she chose to not pay for expanded capacity until she saw finalized “contracted noncancelable bookings.” She pointed to a huge capex line of $9.1 billion and said, “the vast majority of our capex investments are for revenue generating equipment that is going into data centers and not for land or buildings.”

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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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