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Nvidia revenues hit $35.1B, up 94% in FYQ3 — with no sign of slowdown

Nvidia reported that its revenue for the fourth fiscal quarter ended January 26 was $39.3 billion, up 12% from the previous quarter and up 78% from a year ago. For the quarter, GAAP earnings per diluted share was 89 cents, up 14% from the previous quarter and up 82% from a year ago. Non-GAAP earnings per diluted share was 89 cents, up 10% from the previous quarter and up 71% from a year ago. (Updated with corrected numbers). The company’s stock is trading up to $132.87, up 1.2%, in after-hours trading, above where the analysts estimated that Nvidia’s quarterly results would be: $38.2 billion in revenue and 85 cents per share in earnings. Full-year revenue was $130.5 billion, up 114%. GAAP earnings per diluted share was $2.94, up 147% from a year ago. Non-GAAP earnings per diluted share was $2.99, up 130% from a year ago. There’s always a lot at stake with Nvidia’s earnings these days. Thanks to AI growth, Nvidia’s market value is $3.16 trillion, making it second compared to fellow tech firm Apple, valued at $3.66 trillion. That stock price has meant that investors view Nvidia as invincible. But the company got a shock to its systems in the past month as China’s DeepSeek AI announced it was able to deploy an AI model that performed well even though it was trained at a fraction of the cost of other heavy-duty AI models. That suggested to investors that such companies might not need a ton of Nvidia’s AI processors, and it led to a selloff in the company’s stock. More recently, Microsoft appeared to back off on its decision to invest heavily into AI data centers. This earnings call is the first chance to talk extensively about why Nvidia still has big opportunities ahead of it. Nvidia CEO Jensen Huang shows off Thor. “Demand for Blackwell is amazing as reasoning AI adds another scaling law — increasing compute for training makes models smarter and increasing compute for long thinking makes the answer smarter,” said Jensen Huang, founder and CEO of Nvidia, in a statement. “We’ve successfully ramped up the massive-scale production of Blackwell AI supercomputers, achieving billions of dollars in sales in its first quarter. AI is advancing at light speed as agentic AI and physical AI set the stage for the next wave of AI to revolutionize the largest industries.” During CES 2025, the big tech trade show in Las Vegas in January, Huang gave a keynote speech where he predicted a boom in physical AI, such as industrial robots, would happen because of heavy use of synthetic data, where computer simulations can fully test scenarios for things like robots and self-driving cars, reducing the time it takes to test such products in the real world. Nvidia’s Outlook For the first fiscal quarter ending in April, the company said it expects revenue to be $43.0 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 70.6% and 71.0%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion, respectively. GAAP and non-GAAP other income and expense are expected to be an income of approximately $400 million, excluding gains and losses from non-marketable and publicly-held equity securities. Data Center There are 72 Blackwell chips on this wafer. Fourth quarter Data Center revenue was $35.6 billion, up 16% from Q3 and up 93% from a year ago. Fourth-quarter revenue was a record $35.6 billion, up 16% from the previous quarter and up 93% from a year ago. Full-year revenue rose 142% to a record $115.2 billion. During the quarter, Nvidia announced that Nvidia will serve as a key technology partner for the $500 billion Stargate Project. It revealed that cloud service providers AWS, CoreWeave, Google Cloud Platform (GCP), Microsoft Azure and Oracle Cloud Infrastructure (OCI) are bringing Nvidia GB200 systems to cloud regions around the world to meet surging customer demand for AI. The company partnered with AWS to make the Nvidia DGX Cloud AI computing platform and Nvidia NIM microservices available through AWS Marketplace. And it revealed that Cisco will integrate Nvidia Spectrum-X into its networking portfolio to help enterprises build AI infrastructure. Nvidia revealed that more than 75% of the systems on the TOP500 list of the world’s most powerful supercomputers are powered by Nvidia technologies. It announced a collaboration with Verizon to integrate Nvidia AI Enterprise, NIM and accelerated computing with Verizon’s private 5G network to power a range of edge enterprise AI applications and services. It also unveiled partnerships with industry leaders including IQVIA, Illumina, Mayo Clinic and Arc Institute to advance genomics, drug discovery and healthcare. Nvidia launched Nvidia AI Blueprints and Llama Nemotron model families for building AI agents and released Nvidia NIM microservices to safeguard applications for agentic AI. And it announced the opening of Nvidia’s first R&D center in Vietnam. The company also revealed that Siemens Healthineers has adopted MONAI Deploy for medical imaging AI. Gaming and AI PC Nvidia Blackwell GeForce RTX 50 series graphics card Fourth-quarter Gaming revenue was $2.5 billion, down 22% from the previous quarter and down 11% from a year ago. Full-year revenue rose 9% to $11.4 billion. In the quarter, Nvidia announced new GeForce RTX 50 Series graphics cards and laptops powered by the Nvidia Blackwell architecture, delivering breakthroughs in AI-driven rendering to gamers, creators and developers. It launched GeForce RTX 5090 and 5080 graphics cards, delivering up to a 2x performance improvement over the prior generation. Nvidia introduced Nvidia DLSS 4 with Multi Frame Generation and image quality enhancements, with 75 games and apps supporting it at launch, and unveiled Nvidia Reflex 2 technology, which can reduce PC latency by up to 75%. And it unveiled Nvidia NIM microservices, AI Blueprints and the Llama Nemotron family of open models for RTX AI PCs to help developers and enthusiasts build AI agents and creative workflows. Professional Visualization Fourth-quarter revenue was $511 million, up 5% from the previous quarter and up 10% from a year ago. Full-year revenue rose 21% to $1.9 billion. The company said it unveiled Nvidia Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the Nvidia Grace Blackwell platform. It announced generative AI models and blueprints that expand Nvidia Omniverse integration further into physical AI applications, including robotics, autonomous vehicles and vision AI. And it introduced Nvidia Media2, an AI-powered initiative transforming content creation, streaming and live media experiences, built on NIM and AI Blueprints. Automotive and Robotics Nvidia Isaac robotics platform in action. Fourth-quarter Automotive revenue was $570 million, up 27% from the previous quarter and up 103% from a year ago. Full-year revenue rose 55% to $1.7 billion. The company announced that Toyota, the world’s largest automaker, will build its next-generation vehicles on Nvidia DRIVE AGX Orin running the safety-certified Nvidia DriveOS operating system.   It partnered with Hyundai Motor Group to create safer, smarter vehicles, supercharge manufacturing and deploy cutting-edge robotics with Nvidia AI and Nvidia Omniverse. And it announced that the Nvidia DriveOS safe autonomous driving operating system received ASIL-D functional safety certification and launched the Nvidia Drive AI Systems Inspection Lab. Nvidia launched Nvidia Cosmos, a platform comprising state-of-the-art generative world foundation models, to accelerate physical AI development, with adoption by leading robotics and automotive companies 1X, Agile Robots, Waabi, Uber and others. And it unveiled the Nvidia Jetson Orin Nano Super, which delivers up to a 1.7x gain in generative AI performance.

Nvidia reported that its revenue for the fourth fiscal quarter ended January 26 was $39.3 billion, up 12% from the previous quarter and up 78% from a year ago.

For the quarter, GAAP earnings per diluted share was 89 cents, up 14% from the previous quarter and up 82% from a year ago. Non-GAAP earnings per diluted share was 89 cents, up 10% from the previous quarter and up 71% from a year ago. (Updated with corrected numbers).

The company’s stock is trading up to $132.87, up 1.2%, in after-hours trading, above where the analysts estimated that Nvidia’s quarterly results would be: $38.2 billion in revenue and 85 cents per share in earnings. Full-year revenue was $130.5 billion, up 114%. GAAP earnings per diluted share was $2.94, up 147% from a year ago. Non-GAAP earnings per diluted share was $2.99, up 130% from a year ago.

There’s always a lot at stake with Nvidia’s earnings these days. Thanks to AI growth, Nvidia’s market value is $3.16 trillion, making it second compared to fellow tech firm Apple, valued at $3.66 trillion.

That stock price has meant that investors view Nvidia as invincible. But the company got a shock to its systems in the past month as China’s DeepSeek AI announced it was able to deploy an AI model that performed well even though it was trained at a fraction of the cost of other heavy-duty AI models. That suggested to investors that such companies might not need a ton of Nvidia’s AI processors, and it led to a selloff in the company’s stock.

More recently, Microsoft appeared to back off on its decision to invest heavily into AI data centers. This earnings call is the first chance to talk extensively about why Nvidia still has big opportunities ahead of it.

Nvidia CEO Jensen Huang shows off Thor.
Nvidia CEO Jensen Huang shows off Thor.

“Demand for Blackwell is amazing as reasoning AI adds another scaling law — increasing compute for training makes models smarter and increasing compute for long thinking makes the answer smarter,” said Jensen Huang, founder and CEO of Nvidia, in a statement.

“We’ve successfully ramped up the massive-scale production of Blackwell AI supercomputers, achieving billions of dollars in sales in its first quarter. AI is advancing at light speed as agentic AI and physical AI set the stage for the next wave of AI to revolutionize the largest industries.”

During CES 2025, the big tech trade show in Las Vegas in January, Huang gave a keynote speech where he predicted a boom in physical AI, such as industrial robots, would happen because of heavy use of synthetic data, where computer simulations can fully test scenarios for things like robots and self-driving cars, reducing the time it takes to test such products in the real world.

Nvidia’s Outlook

For the first fiscal quarter ending in April, the company said it expects revenue to be $43.0 billion, plus or minus 2%.

GAAP and non-GAAP gross margins are expected to be 70.6% and 71.0%, respectively, plus or minus 50 basis points.

GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion, respectively.

GAAP and non-GAAP other income and expense are expected to be an income of approximately $400 million, excluding gains and losses from non-marketable and publicly-held equity securities.

Data Center

There are 72 Blackwell chips on this wafer.
There are 72 Blackwell chips on this wafer.

Fourth quarter Data Center revenue was $35.6 billion, up 16% from Q3 and up 93% from a year ago.

Fourth-quarter revenue was a record $35.6 billion, up 16% from the previous quarter and up 93% from a year ago. Full-year revenue rose 142% to a record $115.2 billion.

During the quarter, Nvidia announced that Nvidia will serve as a key technology partner for the $500 billion Stargate Project.

It revealed that cloud service providers AWS, CoreWeave, Google Cloud Platform (GCP), Microsoft Azure and Oracle Cloud Infrastructure (OCI) are bringing Nvidia GB200 systems to cloud regions around the world to meet surging customer demand for AI.

The company partnered with AWS to make the Nvidia DGX Cloud AI computing platform and Nvidia NIM microservices available through AWS Marketplace.

And it revealed that Cisco will integrate Nvidia Spectrum-X into its networking portfolio to help enterprises build AI infrastructure.

Nvidia revealed that more than 75% of the systems on the TOP500 list of the world’s most powerful supercomputers are powered by Nvidia technologies.

It announced a collaboration with Verizon to integrate Nvidia AI Enterprise, NIM and accelerated computing with Verizon’s private 5G network to power a range of edge enterprise AI applications and services.

It also unveiled partnerships with industry leaders including IQVIA, Illumina, Mayo Clinic and Arc Institute to advance genomics, drug discovery and healthcare.

Nvidia launched Nvidia AI Blueprints and Llama Nemotron model families for building AI agents and released Nvidia NIM microservices to safeguard applications for agentic AI. And it announced the opening of Nvidia’s first R&D center in Vietnam.

The company also revealed that Siemens Healthineers has adopted MONAI Deploy for medical imaging AI.

Gaming and AI PC

Nvidia Blackwell GeForce RTX 50 series graphics card
Nvidia Blackwell GeForce RTX 50 series graphics card

Fourth-quarter Gaming revenue was $2.5 billion, down 22% from the previous quarter and down 11% from a year ago. Full-year revenue rose 9% to $11.4 billion.

In the quarter, Nvidia announced new GeForce RTX 50 Series graphics cards and laptops powered by the Nvidia Blackwell architecture, delivering breakthroughs in AI-driven rendering to gamers, creators and developers.

It launched GeForce RTX 5090 and 5080 graphics cards, delivering up to a 2x performance improvement over the prior generation.

Nvidia introduced Nvidia DLSS 4 with Multi Frame Generation and image quality enhancements, with 75 games and apps supporting it at launch, and unveiled Nvidia Reflex 2 technology, which can reduce PC latency by up to 75%.

And it unveiled Nvidia NIM microservices, AI Blueprints and the Llama Nemotron family of open models for RTX AI PCs to help developers and enthusiasts build AI agents and creative workflows.

Professional Visualization

Fourth-quarter revenue was $511 million, up 5% from the previous quarter and up 10% from a year ago. Full-year revenue rose 21% to $1.9 billion.

The company said it unveiled Nvidia Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the Nvidia Grace Blackwell platform.

It announced generative AI models and blueprints that expand Nvidia Omniverse integration further into physical AI applications, including robotics, autonomous vehicles and vision AI.

And it introduced Nvidia Media2, an AI-powered initiative transforming content creation, streaming and live media experiences, built on NIM and AI Blueprints.

Automotive and Robotics

Nvidia Isaac robotics platform in action.
Nvidia Isaac robotics platform in action.

Fourth-quarter Automotive revenue was $570 million, up 27% from the previous quarter and up 103% from a year ago. Full-year revenue rose 55% to $1.7 billion.

The company announced that Toyota, the world’s largest automaker, will build its next-generation vehicles on Nvidia DRIVE AGX Orin running the safety-certified Nvidia DriveOS operating system.  

It partnered with Hyundai Motor Group to create safer, smarter vehicles, supercharge manufacturing and deploy cutting-edge robotics with Nvidia AI and Nvidia Omniverse.

And it announced that the Nvidia DriveOS safe autonomous driving operating system received ASIL-D functional safety certification and launched the Nvidia Drive AI Systems Inspection Lab.

Nvidia launched Nvidia Cosmos, a platform comprising state-of-the-art generative world foundation models, to accelerate physical AI development, with adoption by leading robotics and automotive companies 1X, Agile Robots, Waabi, Uber and others.

And it unveiled the Nvidia Jetson Orin Nano Super, which delivers up to a 1.7x gain in generative AI performance.

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IBM introduces new generation of LinuxOne AI mainframe

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Hornsea 4 cancellation puts pressure on AR7

The UK government has proposed changes to the way it procures offshore wind as it now needs to claw back capacity after the massive Hornsea 4 project ground to a halt. The Department for Energy Security and Net Zero (DESNZ) confirmed changes to the way it will run its contracts for difference (CfD) auctions, starting with the upcoming Allocation Round 7 (AR7), expected this year. Under the reforms, the government would no longer set a monetary budget for the various technologies across the auction, such as the £1.5 billion allocated for offshore wind in AR6, at the start of the auction. Instead, the government would publish a “capacity ambition,” stating instead the amount of power it aims to procure. However, it would still publish a budget for the auction after the process has run. In addition, the reforms envision allowing the secretary of state to see the anonymous bids, including price and capacity. They would use this information to determine how much capacity to procure and to set the final budget. AR7 The amendments will also end flexible bidding for fixed-bottom offshore wind applications. According to the proposals, flexible bids are no longer useful if the auction sets the budget after seeing the bids in advance. Finally, the proposed reforms also considered accelerating the offshore wind part of the auction if developers get their bids in on time and there are no appeals. However, the government said that legislation needed to make change could not be delivered before AR7 – though it did not rule it out for subsequent auctions. © Supplied by OrstedOrsted’s Hornsea One wind farm. It added that the government is exploring non-legislative routes to accelerate a fixed-bottom offshore wind auction in time for AR7. In comments to Energy Voice, Aegir Insights market analyst Signe Tellier Christensen

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Business leaders and SNP call on Starmer to visit Aberdeen amid North Sea job losses

Aberdeen business leaders and the SNP are calling on the Prime Minister to visit the north-east of Scotland as they blamed Labour policies for yet more job losses in the oil and gas sector. On Wednesday, Harbour Energy announced that it would cut 250 jobs from its onshore operations, accounting for a 25% reduction in headcount. The UK’s largest producer of oil and gas has claimed that the hostile fiscal policy facing oil and gas businesses prompted the decision as it slows investment in the country, opting to allocate funds overseas. On the day of this announcement, Aberdeen South MP and SNP Westminster leader Stephen Flynn brought the news to the attention of prime minister Sir Keir Starmer. © BloombergEmissions from chimneys at the British Steel Ltd. plant in Scunthorpe, UK. He asked Starmer to “explain to my constituents why he is willing to move heaven and earth to save jobs in Scunthorpe while destroying jobs in Scotland.” The SNP leader was referring to the government’s recent move to nationalise British Steel. The UK government took control of the British steel company from its Chinese owner, Jingye Group, after losses from its steelmaking operations forced it to the brink. Now the SNP MP, alongside his colleagues in Westminster and Holyrood, has written to the Labour Party leader, inviting him to see the impacts his government’s energy policy is having on Aberdeen and its people. “We are writing to you as the local MPs and MSPs for Aberdeen, to invite you to urgently visit Aberdeen to meet with local representatives, businesses, trade unions and workers to hear about the damaging impact that Labour government policies are having on Scottish energy jobs – and to discuss the urgent investment needed to protect jobs and deliver prosperity,” the letter reads. ‘Haemorrhaging investment in

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Oil Gains 3% as Trade Hopes Rise

Oil rose as President Donald Trump announced a trade framework with the UK, spurring some optimism about deals to come. West Texas Intermediate climbed 3.2% to approach $60 a barrel. Trump said the UK would fast-track US items through its customs process and reduce barriers on billions of dollars of agricultural, chemical, energy and industrial exports, including ethanol. Notably, the terms are limited in scope and a 10% baseline tariff remains. The British deal is raising investors’ confidence that agreements can be reached in the more complicated trade talks that lie ahead, specifically negotiations between US and Chinese officials kicking off this weekend. Trump said that the 145% levy against China, the world’s largest crude-importer, could be lowered if talks go well. “The real driver of risk assets today appears to be renewed optimism around progress in the US–China trade talks,” said Rebecca Babin, a senior energy trader at CIBC Private Wealth Group. “It’s also worth noting that sentiment toward crude remains overwhelmingly bearish.” Crude has slid since Trump took office on concerns that his global trade war will dent economic growth and slow energy demand. Adding to the bearishness, OPEC+ has decided to revive idled output faster than expected. Already, the drop in oil prices is spurring American shale producers to cut spending in the Permian Basin. Still, small pockets of bullishness are visible in the options market. There was active trading of Brent $95 September call options, which profit when futures rise. The US on Thursday sanctioned a third Chinese “teapot” oil refinery and various other entities associated with Iran, days ahead of a fourth round of nuclear talks between Washington and Tehran. The failure of the negotiations could push Brent up toward $70 a barrel, Citigroup analysts including Eric Lee said in a note. In the US,

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Indian LNG Buyers Embrace USA Benchmark to Balance Volatility

Indian liquefied natural gas importers have signed a flurry of long-term purchase agreements linked to the US price benchmark, the latest effort by the nation’s buyers to protect themselves from volatile markets. State-owned companies have signed at least four contracts since December, totaling nearly 11 million tons per year, priced to the Henry Hub index, according to the executives familiar with the deals. Until now, most of India’s long-term contracts have been linked to crude oil, the traditional way to price LNG deals. Pricing the fuel to the Henry Hub index doesn’t necessarily mean that the fuel will come from the US, rather it is a move to hedge risk.  India’s consumers — from power plants to petrochemical facilities — are highly price-sensitive as gas competes head-to-head with cheaper and dirtier alternatives. Companies that relied on the spot market or oil-linked contracts have periodically been forced to cut back purchases due to price spikes. US gas futures have also been relatively less volatile and more liquid than the Asian spot benchmark, the Japan-Korea Marker. “The last ten year average shows that there have been periods during winter months JKM benchmark surged beyond imagination, while Henry Hub prices saw proportionally smaller growth,” Bharat Petroelum Corp Ltd’s Director Finance V.R.K. Gupta said. BPCL in February signed a deal with ADNOC Trading for 2.5 million tons of LNG for five years. The Mumbai-based refiner will evaluate the performance of the deal and may sign more such contracts, Gupta said.  Indian Oil Corp. last week signed a deal with Trafigura for 2.5 million tons, or 27 cargoes, spread over five years, with supplies starting the middle of this year. The recent deals have been signed at a 115% link to Henry Hub plus $5 to $6 per million British thermal units. The supply is

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PJM, utilities urge FERC to dismiss call for colocation settlement talks

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Tech CEOs warn Senate: Outdated US power grid threatens AI ambitions

The implications are clear: without dramatic improvements to the US energy infrastructure, the nation’s AI ambitions could be significantly constrained by simple physical limitations – the inability to power the massive computing clusters necessary for advanced AI development and deployment. Streamlining permitting processes The tech executives have offered specific recommendations to address these challenges, with several focusing on the need to dramatically accelerate permitting processes for both energy generation and the transmission infrastructure needed to deliver that power to AI facilities, the report added. Intrator specifically called for efforts “to streamline the permitting process to enable the addition of new sources of generation and the transmission infrastructure to deliver it,” noting that current regulatory frameworks were not designed with the urgent timelines of the AI race in mind. This acceleration would help technology companies build and power the massive data centers needed for AI training and inference, which require enormous amounts of electricity delivered reliably and consistently. Beyond the cloud: bringing AI to everyday devices While much of the testimony focused on large-scale infrastructure needs, AMD CEO Lisa Su emphasized that true AI leadership requires “rapidly building data centers at scale and powering them with reliable, affordable, and clean energy sources.” Su also highlighted the importance of democratizing access to AI technologies: “Moving faster also means moving AI beyond the cloud. To ensure every American benefits, AI must be built into the devices we use every day and made as accessible and dependable as electricity.”

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Networking errors pose threat to data center reliability

Still, IT and networking issues increased in 2024, according to Uptime Institute. The analysis attributed the rise in outages due to increased IT and network complexity, specifically, change management and misconfigurations. “Particularly with distributed services, cloud services, we find that cascading failures often occur when networking equipment is replicated across an entire network,” Lawrence explained. “Sometimes the failure of one forces traffic to move in one direction, overloading capacity at another data center.” The most common causes of major network-related outages were cited as: Configuration/change management failure: 50% Third-party network provider failure: 34% Hardware failure: 31% Firmware/software error: 26% Line breakages: 17% Malicious cyberattack: 17% Network overload/congestion failure: 13% Corrupted firewall/routing tables issues: 8% Weather-related incident: 7% Configuration/change management issues also attributed for 62% of the most common causes of major IT system-/software-related outages. Change-related disruptions consistently are responsible for software-related outages. Human error continues to be one of the “most persistent challenges in data center operations,” according to Uptime’s analysis. The report found that the biggest cause of these failures is data center staff failing to follow established procedures, which has increased by about 10 percentage points compared to 2023. “These are things that were 100% under our control. I mean, we can’t control when the UPS module fails because it was either poorly manufactured, it had a flaw, or something else. This is 100% under our control,” Brown said. The most common causes of major human error-related outages were reported as:

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Liquid cooling technologies: reducing data center environmental impact

“Highly optimized cold-plate or one-phase immersion cooling technologies can perform on par with two-phase immersion, making all three liquid-cooling technologies desirable options,” the researchers wrote. Factors to consider There are numerous factors to consider when adopting liquid cooling technologies, according to Microsoft’s researchers. First, they advise performing a full environmental, health, and safety analysis, and end-to-end life cycle impact analysis. “Analyzing the full data center ecosystem to include systems interactions across software, chip, server, rack, tank, and cooling fluids allows decision makers to understand where savings in environmental impacts can be made,” they wrote. It is also important to engage with fluid vendors and regulators early, to understand chemical composition, disposal methods, and compliance risks. And associated socioeconomic, community, and business impacts are equally critical to assess. More specific environmental considerations include ozone depletion and global warming potential; the researchers emphasized that operators should only use fluids with low to zero ozone depletion potential (ODP) values, and not hydrofluorocarbons or carbon dioxide. It is also critical to analyze a fluid’s viscosity (thickness or stickiness), flammability, and overall volatility. And operators should only use fluids with minimal bioaccumulation (the buildup of chemicals in lifeforms, typically in fish) and terrestrial and aquatic toxicity. Finally, once up and running, data center operators should monitor server lifespan and failure rates, tracking performance uptime and adjusting IT refresh rates accordingly.

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Cisco unveils prototype quantum networking chip

Clock synchronization allows for coordinated time-dependent communications between end points that might be cloud databases or in large global databases that could be sitting across the country or across the world, he said. “We saw recently when we were visiting Lawrence Berkeley Labs where they have all of these data sources such as radio telescopes, optical telescopes, satellites, the James Webb platform. All of these end points are taking snapshots of a piece of space, and they need to synchronize those snapshots to the picosecond level, because you want to detect things like meteorites, something that is moving faster than the rotational speed of planet Earth. So the only way you can detect that quickly is if you synchronize these snapshots at the picosecond level,” Pandey said. For security use cases, the chip can ensure that if an eavesdropper tries to intercept the quantum signals carrying the key, they will likely disturb the state of the qubits, and this disturbance can be detected by the legitimate communicating parties and the link will be dropped, protecting the sender’s data. This feature is typically implemented in a Quantum Key Distribution system. Location information can serve as a critical credential for systems to authenticate control access, Pandey said. The prototype quantum entanglement chip is just part of the research Cisco is doing to accelerate practical quantum computing and the development of future quantum data centers.  The quantum data center that Cisco envisions would have the capability to execute numerous quantum circuits, feature dynamic network interconnection, and utilize various entanglement generation protocols. The idea is to build a network connecting a large number of smaller processors in a controlled environment, the data center warehouse, and provide them as a service to a larger user base, according to Cisco.  The challenges for quantum data center network fabric

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Zyxel launches 100GbE switch for enterprise networks

Port specifications include: 48 SFP28 ports supporting dual-rate 10GbE/25GbE connectivity 8 QSFP28 ports supporting 100GbE connections Console port for direct management access Layer 3 routing capabilities include static routing with support for access control lists (ACLs) and VLAN segmentation. The switch implements IEEE 802.1Q VLAN tagging, port isolation, and port mirroring for traffic analysis. For link aggregation, the switch supports IEEE 802.3ad for increased throughput and redundancy between switches or servers. Target applications and use cases The CX4800-56F targets multiple deployment scenarios where high-capacity backbone connectivity and flexible port configurations are required. “This will be for service providers initially or large deployments where they need a high capacity backbone to deliver a primarily 10G access layer to the end point,” explains Nguyen. “Now with Wi-Fi 7, more 10G/25G capable POE switches are being powered up and need interconnectivity without the bottleneck. We see this for data centers, campus, MDU (Multi-Dwelling Unit) buildings or community deployments.” Management is handled through Zyxel’s NebulaFlex Pro technology, which supports both standalone configuration and cloud management via the Nebula Control Center (NCC). The switch includes a one-year professional pack license providing IGMP technology and network analytics features. The SFP28 ports maintain backward compatibility between 10G and 25G standards, enabling phased migration paths for organizations transitioning between these speeds.

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Engineers rush to master new skills for AI-driven data centers

According to the Uptime Institute survey, 57% of data centers are increasing salary spending. Data center job roles that saw the highest increases were in operations management – 49% of data center operators said they saw highest increases in this category – followed by junior and mid-level operations staff at 45%, and senior management and strategy at 35%. Other job categories that saw salary growth were electrical, at 32% and mechanical, at 23%. Organizations are also paying premiums on top of salaries for particular skills and certifications. Foote Partners tracks pay premiums for more than 1,300 certified and non-certified skills for IT jobs in general. The company doesn’t segment the data based on whether the jobs themselves are data center jobs, but it does track 60 skills and certifications related to data center management, including skills such as storage area networking, LAN, and AIOps, and 24 data center-related certificates from Cisco, Juniper, VMware and other organizations. “Five of the eight data center-related skills recording market value gains in cash pay premiums in the last twelve months are all AI-related skills,” says David Foote, chief analyst at Foote Partners. “In fact, they are all among the highest-paying skills for all 723 non-certified skills we report.” These skills bring in 16% to 22% of base salary, he says. AIOps, for example, saw an 11% increase in market value over the past year, now bringing in a premium of 20% over base salary, according to Foote data. MLOps now brings in a 22% premium. “Again, these AI skills have many uses of which the data center is only one,” Foote adds. The percentage increase in the specific subset of these skills in data centers jobs may vary. The Uptime Institute survey suggests that the higher pay is motivating workers to stay in the

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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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