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Nvidia’s first exascale system is the 4th fastest supercomputer in the world

The world’s fourth exascale supercomputer has arrived, pitting Nvidia’s proprietary chip technologies against the x86 systems that have dominated supercomputing for decades. For the 66th edition of the TOP500, El Capitan holds steady at No. 1 while JUPITER Booster becomes the fourth exascale system on the list. The JUPITER Booster supercomputer, installed in Germany, uses […]

The world’s fourth exascale supercomputer has arrived, pitting Nvidia’s proprietary chip technologies against the x86 systems that have dominated supercomputing for decades. For the 66th edition of the TOP500, El Capitan holds steady at No. 1 while JUPITER Booster becomes the fourth exascale system on the list.

The JUPITER Booster supercomputer, installed in Germany, uses Nvidia CPUs and GPUs and delivers a peak performance of exactly 1 exaflop, according to the November TOP500 list of supercomputers, released on Monday. The exaflop measurement is considered a major milestone in pushing computing performance to the limits. Today’s computers are typically measured in gigaflops and teraflops—and an exaflop translates to 1 billion gigaflops. Nvidia’s GPUs dominate AI servers installed in data centers as computing shifts to AI. As part of this shift, AI servers with Nvidia’s ARM-based Grace CPUs are emerging as a high-performance alternative to x86 chips.

JUPITER is the fourth-fastest supercomputer in the world, behind three systems with x86 chips from AMD and Intel, according to TOP500. The top three supercomputers on the TOP500 list are in the U.S. and owned by the U.S. Department of Energy.

The top two supercomputers—the 1.8-exaflop El Capitan at Lawrence Livermore National Laboratory and the 1.35-exaflop Frontier at Oak Ridge National Laboratory—use AMD CPUs and GPUs. The third-ranked 1.01-exaflop Aurora at Argonne National Laboratory uses Intel CPUs and GPUs. Intel scrapped its GPU roadmap after the release of Aurora and is now restructuring operations.

The JUPITER Booster, which was assembled by France-based Eviden, has Nvidia’s GH200 superchip, which links two Nvidia Hopper GPUs with CPUs based on ARM designs. The CPU and GPU are connected via Nvidia’s proprietary NVLink interconnect, which is based on InfiniBand and provides bandwidth of up to 900 gigabytes per second.

JUPITER first entered the Top500 list at 793 petaflops, but its construction wasn’t complete. A new HPL benchmark of the completed system barely squeezed past the 1 exaflop mark.

Seven of the top 10 supercomputers have x86 CPUs, while three have ARM. Two supercomputers have Nvidia CPUs and GPUs, including the fifth-ranked Eagle, which is a non-exascale system owned by Microsoft and the fastest supercomputer owned by a private company.

The TOP500 metrics are rooted in measuring conventional computing performance based on the FP64 floating point data type, not AI performance, which is based on low-precision data types such as FP32, FP16, and FP8.

That has resulted in experts questioning the relevance of the TOP500 metrics, especially with the growing clout of AI in servers and applications. For example, Microsoft’s Eagle, which drives its AI infrastructure, isn’t designed for conventional computing.

With that in mind, the TOP500 editors in a press release noted the growing importance of its emerging benchmark called HPL-MxP, which is tuned to measure AI performance based on mixed precision data types. The systems typically have a higher concentration of AI accelerators.

Under HPL-MxP, El Capitan took the top spot with a recorded 16.7 exaflops, with Aurora taking second place at 11.6 exaflops. Frontier—which took second place in the mainline TOP500 benchmarks—placed third.

Also, the TOP500 list does not include high-performance systems from China, which doesn’t submit its supercomputing installations, researcher Alex Haag said in a blog entry on the U.S. Federal Reserve website.

“The data suffers from self-reporting issues, particularly in the context of China, which stopped reporting its supercomputers in 2023, leading to potential overestimation for other countries on this list,” the researcher wrote. The blog entry is related to a research paper on the state of AI in advanced economies published by Haag.

China has become increasingly secretive about its supercomputing resources following trade wars and bans on certain AI chip and high-performance CPU exports imposed by the U.S.

El Capitan took the top spot from Frontier exactly a year ago and is expected to hold the top spot for many years with no new exascale systems on the horizon. But some new systems being developed could challenge El Capitan’s dominance. RIKEN in Japan is developing the FugakuNEXT supercomputer, which will include Fujitsu’s ARM-based Monaka CPU and Nvidia GPUs. It will come online in 2030, according to a RIKEN press release.

The FugakuNEXT flagship system is designed for AI. It will succeed the Fugaku supercomputer, which is currently ranked seventh on the TOP500 list and isn’t designed for AI.

The U.S. Department of Energy is also prioritizing AI and energy efficiency in successors to its current crop of exascale computers.

Eight supercomputers with Nvidia’s Hopper GPUs took the top spots on the TOP500’s Green500 list, which measures energy efficiency relative to performance. Two supercomputers with AMD’s Instinct MI300A GPU rounded out the top 10.

Here’s a breakdown of specific details for the 10 fastest supercomputer systems on the TOP500 list for November 2025:

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Microsoft’s largest quantum site to be built in Denmark

With this strategic move, Denmark will become Microsoft’s global quantum hub. According to the company, the expansion of the Lyngby laboratory will enable the complete core components of the Majorana chip to be manufactured directly on site. This research is based on years of cooperation with leading Danish research institutions,

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The quiet revolution in energy: How private innovation is reshaping the grid

Over the past five years, the energy transition has been loud in some places.  We have seen data centers booming, capacity prices climbing, renewables surging and virtual power plants gaining traction. But beneath those headlines, a revolution message has been unfolding, sector by sector, driven not by policy or utilities but by private businesses stepping into roles once reserved for monopolies. Take Caterpillar, for example. Long known for its heavy machinery, the company now offers a Distributed Energy Resource Management System (DERMS) platform, an unexpected pivot that underscores how deeply energy intelligence is being woven into commercial and industrial operations. From retail and manufacturing to real estate and tech, companies are learning that energy is no longer a fixed cost. It’s a controllable variable, one that can be optimized, monetized and aligned with long-term business strategy. Commercial Customers: The Grid’s Unsung Stabilizers At the center of this shift are commercial customers, who are increasingly the steady force in an uncertain grid. Unlike residential users, whose Peak Load Contribution (PLC) is largely uncontrollable and often meaningless for planning or cost-reduction purposes, commercial PLCs can be actively managed. And that’s where the real innovation is happening. Multifamily communities are increasingly operating like commercial customers, and that shift is transforming their role on the grid. By pairing the right supply contracts with DERMS platforms, multifamily developers and operators can now manage usage, flexibility and capacity just like office parks or manufacturing sites have done for years. This isn’t theoretical; it’s already underway. Properties are optimizing load, reducing costs and contributing to grid stability by applying commercial-grade energy intelligence to what was once a passive residential segment. The result: a fast-growing, data-driven force for grid support emerging from the multifamily sector. From Energy Management to Energy Mastery Nationwide Energy Partners (NEP) has seen this

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Power outages getting longer as extreme weather takes larger toll, report says

Listen to the article 4 min This audio is auto-generated. Please let us know if you have feedback. Dive Brief: Power outages across the United States are getting longer, according to a recent survey by JD Power, which cites “increased frequency and severity of extreme weather events.” The average length of the longest power outage has increased in all regions since 2022, from 8.1 hours to 12.8 by the midpoint of 2025. Customers in the South reported the longest outages, averaging out at 18.2 hours, followed by the West at 12.4 hours, it said.  Mark Spalinger, director of utilities intelligence at J.D. Power, said in an interview that while the duration of outages is increasing, the number of customers experiencing them is not. In fact, over time, the percentage of people who experience “perfect power” without any interruptions is gradually rising. However, disasters like storms and fires “are becoming so much more extreme that it creates these longer outage events that utilities are now having to deal with,” he said.  Dive Insight: Based on a survey, 45% of utility customers nationwide experienced a power outage in the first half of 2025, according to JD Power’s U.S. Electric Utility Residential Customer Satisfaction Study, which the company has been doing for over 20 years.  Of those outages, nearly half were due to extreme weather such as a hurricane, snowstorm, tornado or fire, and 17% of customers who were affected by a natural disaster said it was so severe they had to evacuate their homes. JD Power’s U.S. Electric Utility Residential Customer Satisfaction Study shows power outages getting longer over time.  Permission granted by JD Power Extreme weather isn’t the only problem, however. Spalinger said the subset of customers who experience outages are also reporting more frequent, shorter blackouts.  Part of that could be the rise of

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NRG aims for 2026 next step in 5.4-GW gas deal with GE Vernova, Kiewit

5.4 GW NRG Energy’s planned capacity pipeline to supply data centers through 2032 under its GE Vernova and Kiewit partnership. +32% NRG Q3 earnings per share, relative to Q3 2024. $152M NRG’s GAAP net income for Q3, compared with a loss of $767 million in Q3 2024.  19 GW The capacity NRG expects to gain from its LS Power portfolio acquisition, set to close in Q1 next year. Big bets on building, acquiring gas NRG Energy expects to announce a data center agreement in 2026 associated with new natural gas development in its partnership with GE Vernova and Kiewit, the company’s CEO, Lawrence Coben, said in a Nov. 6 earnings call. The first step of that joint venture is to start work on four gas projects totaling around 5.4 GW for the ERCOT and PJM markets. During the call, Coben said a data center agreement is set to be announced sometime next year, but he declined to pin down an exact month or quarter for the announcement. “As far as [GE Vernova] goes, in this Kiewit partnership, did you have a certain time frame that you need to move some of this equipment — use it or lose it, if you will?” asked Jefferies analyst Julien Dumoulin-Smith.  Coben said the company hasn’t disclosed a time frame yet, but added, “I’m very confident that we’re going to meet all of the timelines that are required under that agreement.” NRG, GE Vernova and Kiewit plan to have 1.2 GW of combined-cycle gas turbines in service by 2029, another 1.2 GW in service by 2030, and an additional 3 GW brought online from 2030 to 2032. Development activities are in progress across all sites, the company said. NRG is also anticipating the closure of its deal to acquire 19 GW of assets from

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Trump Backs Bill to Sanction Russia Trade Partners

President Donald Trump said proposed Senate legislation to sanction countries conducting business with Russia would be “okay with me,” his strongest indication yet that he would support a monthslong push to strangle Moscow’s funding. “The Republicans are putting in legislation that is very tough sanctioning, etcetera, on any country doing business with Russia,” Trump told reporters before leaving Florida on Sunday to return to the White House.  Senate Majority Leader John Thune said in October that he was ready to bring legislation long championed by Senator Lindsey Graham of South Carolina that sanctions Russia to a vote, but didn’t “want to commit to a hard deadline.” The bill would allow Trump to impose tariffs of up to 500 percent on imports from countries that buy Russian energy products and are not actively supporting Ukraine. This specifically targets major consumers of Russian energy, such as China and India. “We may add Iran to that,” Trump said Sunday, without elaborating. Democrats and some Republicans in Congress have pushed for legislation to punish Russia for its continued war on Ukraine. Trump had been reluctant to support it as he tried to bring Russian President Vladimir Putin to peace talks with Ukrainian President Volodymyr Zelenskiy.  Putin is showing no sign of letting up in his military campaign after almost four years of war in Ukraine, with Trump failing to sway Putin even after hosting the Russian leader for a summit in Alaska. While Ukraine is increasingly striking Russian oil targets, Russia has intensified its air strikes on Ukraine and is pushing to capture the rail hub of Pokrovsk. What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is a new social experience created for you and all energy professionals to Speak Up about our industry,

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Petroperu Revamps Board

Peruvian President Jose Jerí appointed a new chairman and board at Petroleos del Peru SA in a bid to shore up the state-owned oil company. Luis Canales Galvez was named chairman and president and the board of directors was overhauled, the company said in a statement Saturday. Canales replaces interim chair Fidel Moreno, who served for less than a month. Finance Minister Denisse Miralles had signaled the overhaul in a Gestion newspaper interview on Oct. 31, describing Petroperu as a strategic state asset while saying it can’t continue generating costs for the government.  She said the new board will be asked to present a fast plan with short-term measures to improve the company. Jeri, who took office after lawmakers voted on Oct. 10 to oust former President Dina Boluarte, has a significantly higher popularity rating than his predecessor. His interim administration is scheduled to end in July. What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is a new social experience created for you and all energy professionals to Speak Up about our industry, share knowledge, connect with peers and industry insiders and engage in a professional community that will empower your career in energy.

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Enbridge Approves $1.4B Mainline Upgrade

Enbridge Inc has reached a positive final investment decision on the Mainline Optimization Phase 1 project (MLO1), earmarking $1.4 billion. “MLO1 will add capacity to the company’s Mainline network and Flanagan South Pipeline (FSP) to meet customer demand for incremental egress, increasing deliveries of Canadian heavy oil to key refining markets in the U.S. Midwest (PADD II) and Gulf Coast (PADD III)”, Calgary, Canada-based Enbridge said in an online statement. The project aims to add 150,000 barrels per day (bpd) of capacity to Mainline and up to 100,000 bpd to FSP. “MLO1 will increase capacity on the Mainline through a combination of upstream optimizations and terminal enhancements”, Enbridge said. “In addition, Enbridge plans to add pump stations and terminal enhancements for FSP to increase capacity and will utilize existing capacity on Seaway Pipeline”. The Seaway Pipeline is a 50-50 venture between Enbridge and Enterprise Products Partners LP. “The FSP expansion is underpinned by long-term take-or-pay contracts for full-path service from Edmonton, Alberta to Houston, Texas, which support attractive returns for MLO1”, Enbridge said. “As part of the open season process on FSP earlier this year, the majority of existing customers elected to extend their existing full-path contracts through the next decade”. The announcement did not say when Enbridge expects to complete the project. In Enbridge’s quarterly report November 7, chief executive Greg Ebel said Enbridge was “advancing” phase 2 to add a further 250,000 bpd of incremental full-path capacity to Mainline “before the end of the decade”. Currently the Mainline pipeline, stretching over 13,800 kilometers (more than 8,500 miles), carries up to about three million bpd of light, medium and heavy crude from the Canadian province of Alberta to Eastern Canada and the U.S. Midwest, according to Enbridge. It started service seven decades ago. Earlier this year Enbridge announced a Mainline investment of up

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Nvidia’s first exascale system is the 4th fastest supercomputer in the world

The world’s fourth exascale supercomputer has arrived, pitting Nvidia’s proprietary chip technologies against the x86 systems that have dominated supercomputing for decades. For the 66th edition of the TOP500, El Capitan holds steady at No. 1 while JUPITER Booster becomes the fourth exascale system on the list. The JUPITER Booster supercomputer, installed in Germany, uses Nvidia CPUs and GPUs and delivers a peak performance of exactly 1 exaflop, according to the November TOP500 list of supercomputers, released on Monday. The exaflop measurement is considered a major milestone in pushing computing performance to the limits. Today’s computers are typically measured in gigaflops and teraflops—and an exaflop translates to 1 billion gigaflops. Nvidia’s GPUs dominate AI servers installed in data centers as computing shifts to AI. As part of this shift, AI servers with Nvidia’s ARM-based Grace CPUs are emerging as a high-performance alternative to x86 chips. JUPITER is the fourth-fastest supercomputer in the world, behind three systems with x86 chips from AMD and Intel, according to TOP500. The top three supercomputers on the TOP500 list are in the U.S. and owned by the U.S. Department of Energy. The top two supercomputers—the 1.8-exaflop El Capitan at Lawrence Livermore National Laboratory and the 1.35-exaflop Frontier at Oak Ridge National Laboratory—use AMD CPUs and GPUs. The third-ranked 1.01-exaflop Aurora at Argonne National Laboratory uses Intel CPUs and GPUs. Intel scrapped its GPU roadmap after the release of Aurora and is now restructuring operations. The JUPITER Booster, which was assembled by France-based Eviden, has Nvidia’s GH200 superchip, which links two Nvidia Hopper GPUs with CPUs based on ARM designs. The CPU and GPU are connected via Nvidia’s proprietary NVLink interconnect, which is based on InfiniBand and provides bandwidth of up to 900 gigabytes per second. JUPITER first entered the Top500 list at 793 petaflops, but

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Samsung’s 60% memory price hike signals higher data center costs for enterprises

Industry-wide price surge driven by AI Samsung is not alone in raising prices. In October, TrendForce reported that Samsung and SK Hynix raised DRAM and NAND flash prices by up to 30% for Q4. Similarly, SK Hynix said during its October earnings call that its HBM, DRAM, and NAND capacity is “essentially sold out” for 2026, with the company posting record quarterly operating profit exceeding $8 billion, driven by surging AI demand. Industry analysts attributed the price increases to manufacturers redirecting production capacity. HBM production for AI accelerators consumes three times the wafer capacity of standard DRAM, according to a TrendForce report, citing remarks from Micron’s Chief Business Officer. After two years of oversupply, memory inventories have dropped to approximately eight weeks from over 30 weeks in early 2023. “The memory industry is tightening faster than expected as AI server demand for HBM, DDR5, and enterprise SSDs far outpaces supply growth,” said Manish Rawat, semiconductor analyst at TechInsights. “Even with new fab capacity coming online, much of it is dedicated to HBM, leaving conventional DRAM and NAND undersupplied. Memory is shifting from a cyclical commodity to a strategic bottleneck where suppliers can confidently enforce price discipline.” This newfound pricing power was evident in Samsung’s approach to contract negotiations. “Samsung’s delayed pricing announcement signals tough behind-the-scenes negotiations, with Samsung ultimately securing the aggressive hike it wanted,” Rawat said. “The move reflects a clear power shift toward chipmakers: inventories are normalized, supply is tight, and AI demand is unavoidable, leaving buyers with little room to negotiate.” Charlie Dai, VP and principal analyst at Forrester, said the 60% increase “signals confidence in sustained AI infrastructure growth and underscores memory’s strategic role as the bottleneck in accelerated computing.” Servers to cost 10-25% more For enterprises building AI infrastructure, these supply dynamics translate directly into

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Arista, Palo Alto bolster AI data center security

“Based on this inspection, the NGFW creates a comprehensive, application-aware security policy. It then instructs the Arista fabric to enforce that policy at wire speed for all subsequent, similar flows,” Kotamraju wrote. “This ‘inspect-once, enforce-many’ model delivers granular zero trust security without the performance bottlenecks of hairpinning all traffic through a firewall or forcing a costly, disruptive network redesign.” The second capability is a dynamic quarantine feature that enables the Palo Alto NGFWs to identify evasive threats using Cloud-Delivered Security Services (CDSS). “These services, such as Advanced WildFire for zero-day malware and Advanced Threat Prevention for unknown exploits, leverage global threat intelligence to detect and block attacks that traditional security misses,” Kotamraju wrote. The Arista fabric can intelligently offload trusted, high-bandwidth “elephant flows” from the firewall after inspection, freeing it to focus on high-risk traffic. When a threat is detected, the NGFW signals Arista CloudVision, which programs the network switches to automatically quarantine the compromised workload at hardware line-rate, according to Kotamraju: “This immediate response halts the lateral spread of a threat without creating a performance bottleneck or requiring manual intervention.” The third feature is unified policy orchestration, where Palo Alto Networks’ management plane centralizes zone-based and microperimeter policies, and CloudVision MSS responds with the offload and enforcement of Arista switches. “This treats the entire geo-distributed network as a single logical switch, allowing workloads to be migrated freely across cloud networks and security domains,” Srikanta and Barbieri wrote. Lastly, the Arista Validated Design (AVD) data models enable network-as-a-code, integrating with CI/CD pipelines. AVDs can also be generated by Arista’s AVA (Autonomous Virtual Assist) AI agents that incorporate best practices, testing, guardrails, and generated configurations. “Our integration directly resolves this conflict by creating a clean architectural separation that decouples the network fabric from security policy. This allows the NetOps team (managing the Arista

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AMD outlines ambitious plan for AI-driven data centers

“There are very beefy workloads that you must have that performance for to run the enterprise,” he said. “The Fortune 500 mainstream enterprise customers are now … adopting Epyc faster than anyone. We’ve seen a 3x adoption this year. And what that does is drives back to the on-prem enterprise adoption, so that the hybrid multi-cloud is end-to-end on Epyc.” One of the key focus areas for AMD’s Epyc strategy has been our ecosystem build out. It has almost 180 platforms, from racks to blades to towers to edge devices, and 3,000 solutions in the market on top of those platforms. One of the areas where AMD pushes into the enterprise is what it calls industry or vertical workloads. “These are the workloads that drive the end business. So in semiconductors, that’s telco, it’s the network, and the goal there is to accelerate those workloads and either driving more throughput or drive faster time to market or faster time to results. And we almost double our competition in terms of faster time to results,” said McNamara. And it’s paying off. McNamara noted that over 60% of the Fortune 100 are using AMD, and that’s growing quarterly. “We track that very, very closely,” he said. The other question is are they getting new customer acquisitions, customers with Epyc for the first time? “We’ve doubled that year on year.” AMD didn’t just brag, it laid out a road map for the next two years, and 2026 is going to be a very busy year. That will be the year that new CPUs, both client and server, built on the Zen 6 architecture begin to appear. On the server side, that means the Venice generation of Epyc server processors. Zen 6 processors will be built on 2 nanometer design generated by (you guessed

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Building the Regional Edge: DartPoints CEO Scott Willis on High-Density AI Workloads in Non-Tier-One Markets

When DartPoints CEO Scott Willis took the stage on “the Distributed Edge” panel at the 2025 Data Center Frontier Trends Summit, his message resonated across a room full of developers, operators, and hyperscale strategists: the future of AI infrastructure will be built far beyond the nation’s tier-one metros. On the latest episode of the Data Center Frontier Show, Willis expands on that thesis, mapping out how DartPoints has positioned itself for a moment when digital infrastructure inevitably becomes more distributed, and why that moment has now arrived. DartPoints’ strategy centers on what Willis calls the “regional edge”—markets in the Midwest, Southeast, and South Central regions that sit outside traditional cloud hubs but are increasingly essential to the evolving AI economy. These are not tower-edge micro-nodes, nor hyperscale mega-campuses. Instead, they are regional data centers designed to serve enterprises with colocation, cloud, hybrid cloud, multi-tenant cloud, DRaaS, and backup workloads, while increasingly accommodating the AI-driven use cases shaping the next phase of digital infrastructure. As inference expands and latency-sensitive applications proliferate, Willis sees the industry’s momentum bending toward the very markets DartPoints has spent years cultivating. Interconnection as Foundation for Regional AI Growth A key part of the company’s differentiation is its interconnection strategy. Every DartPoints facility is built to operate as a deeply interconnected environment, drawing in all available carriers within a market and stitching sites together through a regional fiber fabric. Willis describes fiber as the “nervous system” of the modern data center, and for DartPoints that means creating an interconnection model robust enough to support a mix of enterprise cloud, multi-site disaster recovery, and emerging AI inference workloads. The company is already hosting latency-sensitive deployments in select facilities—particularly inference AI and specialized healthcare applications—and Willis expects such deployments to expand significantly as regional AI architectures become more widely

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Key takeaways from Cisco Partner Summit

Brian Ortbals, senior vice president from World Wide Technology, which is one of Cisco’s biggest and most important partners stated: “Cisco engaged partners early in the process and took our feedback along the way. We believe now is the right time for these changes as it will enable us to capitalize on the changes in the market.” The reality is, the more successful its more-than-half-a-million partners are, the more successful Cisco will be. Platform approach is coming together When Jeetu Patel took the reigns as chief product officer, one of his goals was to make the Cisco portfolio a “force multiple.” Patel has stated repeatedly that, historically, Cisco acted more as a technology holding company with good products in networking, security, collaboration, data center and other areas. In this case, product breadth was not an advantage, as everything must be sold as “best of breed,” which is a tough ask of the salesforce and partner community. Since then, there have been many examples of the coming together of the portfolio to create products that leverage the breadth of the platform. The latest is the Unified Edge appliance, an all-in-one solution that brings together compute, networking, storage and security. Cisco has been aggressive with AI products in the data center, and Cisco Unified Edge compliments that work with a device designed to bring AI to edge locations. This is ideally suited for retail, manufacturing, healthcare, factories and other industries where it’s more cost effecting and performative to run AI where the data lives.

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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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