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Why 2025 will redefine data infrastructure: 11 expert insights on sovereign clouds, exploding data, PaaS and more

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More If 2023 was all about generative AI-powered chatbots and search, 2024 introduced agentic AI — tools capable of planning and executing multi-step actions across digital environments. From Devin’s engineering breakthroughs to Microsoft’s early trials with Copilot […]

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If 2023 was all about generative AI-powered chatbots and search, 2024 introduced agentic AI — tools capable of planning and executing multi-step actions across digital environments. From Devin’s engineering breakthroughs to Microsoft’s early trials with Copilot Vision, the innovations were diverse, but one constant remained: the need to keep data infrastructure organized and reliable.

As enterprises leaned into advanced AI initiatives, several trends reshaped how data is managed, secured and used. Businesses increasingly adopted multicloud, open data, and open governance strategies to avoid vendor lock-in and gain more flexibility. They also focused on unstructured data, transforming data marketplaces into hubs providing pre-trained AI models with proprietary datasets and apps. Simultaneously, progress in vector and graph databases added new possibilities, setting the foundation for what’s next.

Now, as the AI story continues to unfold, industry leaders share their predictions for how the data infrastructure underpinning it will evolve in 2025.

1. Real-time multimodal data will fuel intelligent data flywheel

“In 2025, enterprises will fully embrace multimodal data and AI, transforming how they operate and deliver[ing] value. At the core of this shift is the ‘Intelligent Data Flywheel’ — a dynamic cycle where real-time data powers AI-driven insights, fueling continuous innovation and improvement. Today’s dark data — images, videos, audio, and sensor outputs — will become central to unlocking sharper predictions, smarter automations and real-time adaptability, ultimately leading to a richer and more nuanced understanding of the business reality.

“With the real-time data flywheel in place, AI will autonomously diagnose problems, optimize processes and generate innovative solutions. Enterprises will rely on AI agents to ensure data quality, uncover insights and shape strategies, enabling human talent to focus on higher-level tasks. This will redefine efficiency, accelerate innovation and transform businesses into more dynamic and intelligent organizations.”

– Yasmeen Ahmad, MD of strategy and outbound product management for data, analytics and AI at Google Cloud

2. Chill factor: Liquid-cooled data centers

“As AI workloads continue to drive growth, pioneering organizations will transition to liquid cooling to maximize performance and energy efficiency. Hyperscale cloud providers and large enterprises will lead the way, using liquid cooling in new AI data centers that house hundreds of thousands of AI accelerators, networking and software.

“Enterprises will increasingly choose to deploy AI infrastructure in colocation facilities rather than build their own — in part to ease the financial burden of designing, deploying and operating intelligence manufacturing at scale. Or, they will rent capacity as needed. These deployments will help enterprises harness the latest infrastructure without needing to install and operate it themselves. This shift will accelerate broader industry adoption of liquid cooling as a mainstream solution for AI data centers.”

– Charlie Boyle, VP of DGX platforms at Nvidia

3. Global data explosion to create storage shortage 

“The world is creating data at unprecedented volumes. In 2028, as many as 400 zettabytes will be generated, with a compound annual growth rate (CAGR) of 24%. However, the storage install base is forecasted to have a 17% CAGR — therefore [growing] at a significantly slower pace than the growth in data generated. And it takes a whole year to build a hard drive. This disparity in growth rates will disrupt the global storage supply-and-demand equilibrium. As organizations become less experimental and more strategic in the use of AI, they will need to build greater physical data center space and capacity plans to ensure storage supply, and fully monetize investments in AI and data infrastructure — while balancing financial, regulatory and environmental concerns.”

– B.S. Teh, EVP and chief commercial officer at Seagate Technology  

4. AI factories will evolve to PaaS

“In 2025, AI factories will evolve beyond their initial phase of providing infrastructure-as-a-service, offering compute, networking, and storage services, to delivering platform-as-a-service capabilities. While the foundational services have been essential to jumpstart AI adoption, the next wave of AI factories will prioritize platforms that drive data affinity and provide lasting value. This shift will be key to making AI factories sustainable and competitive in the long term.”

– Rajan Goyal, cofounder and CEO at DataPelago 

5. Companies will use their massive datasets but demand reliability

“For the most part, early applications of AI have just used foundation models trained on massive amounts of public data. With sophisticated RAG applications becoming mainstream and the rapid maturity of products to produce structured data, applications that leverage the massive troves of private enterprise data will begin to create true value. But the bar for these applications will be high: Enterprises will demand reliability from AI applications, not just the whiz-bang demo.

“Further, AI companies providing these models will have to play nice with publishers and content providers to safeguard the future of AI development. They will need to enter licensing agreements with content providers to ensure they’re being compensated for the extremely valuable data they offer. This must happen soon, before it’s all a tangle of lawsuits and blocking AI crawlers.”

– Sridhar Ramaswamy, CEO at Snowflake

6. Enterprise agents will devour communications data

“In 2025, enterprises will mine terabytes of communication data, such as emails, Slack messages, and Zoom transcripts, using agents that deliver analytics insights, dashboards, and actionable decision support tools.

“This will drive significant productivity improvements across industries.”

– Nikolaos Vasiloglou, VP of research and ML at RelationalAI

7. Data governance and quality will be biggest barriers to successful and ethical AI adoption

“In 2025, data governance, accuracy and privacy will emerge as the most significant barriers to effective AI adoption. As organizations look to scale AI, the realization will occur that successful AI outcomes are entirely dependent on trustworthy data. Managing and preparing massive amounts of data, ensuring compliance and maintaining accuracy will provide complex challenges. Enterprises will need to overcome these hurdles by investing in foundational data platforms that enable unified management across diverse data sources. 

“As a result, we’ll see a stronger emphasis on data stewardship roles and governance frameworks that align with AI initiatives, as businesses recognize that unreliable data directly impacts AI effectiveness.”

Jeremy Kelway, VP of engineering for analytics, data and AI at EDB

“In 2025, unified data observability platforms will emerge as essential tools for large enterprises, enabling comprehensive visibility into data infrastructure performance, quality, pipeline health, cost management and user behavior to address complex governance and integration challenges. By automating anomaly detection and enabling real-time insights, these platforms will support data reliability and streamline compliance efforts across industries.”

– Ashwin Rajeeva, cofounder and CTO at Acceldata

9. All hail the sovereign cloud

“In 2025, we’re going to see a real push towards sovereign and private clouds. We’re already seeing the largest hyperscalers pouring billions of dollars into constructing data centers around the world to offer these capabilities. This…capacity will take a while to come online; in the meantime, demand will skyrocket fueled by a wave of legislation coming predominantly from the EU. Those with flexible, scalable and elastic cloud infrastructure will be able to adopt sovereign or private approaches quickly. Those with monolithic, rigid infrastructure will be putting themselves behind the curve.”

Kevin Cochrane, CMO of Vultr

10. Rise of data processing at the edge

“I’m keeping an eye on the potential expansion of edge computing, driven by the proliferation of 5G, which brings data processing closer to the source and reduces latency. This could help democratize AI. The question is, can we build efficient AI apps that run on mobile devices, possibly without relying on cloud resources? 

“If 5G is available to field technicians, they could leverage AI to assist in their work — whether it’s medical professionals providing diagnosis and treatment in disaster areas where 5G is available but Wi-Fi isn’t, or engineers and scientists making on-site decisions with AI-assisted research and real-time calculations.”

– Jerod Johnson, Sr. technology evangelist at CData

11. Protection of unstructured data will become more urgent

“Traditionally, data protection has focused on mission-critical data because this is the data that needs faster restores. Yet the landscape has changed, with unstructured data growing to encompass 90% of all data generated in the last 10 years. The large surface area of petabytes of unstructured data coupled with its widespread use and rapid growth make it highly vulnerable to ransomware attacks. Cyber-criminals can use the unstructured data as a Trojan horse to infect the enterprise. Cost-effectively protecting unstructured data from ransomware will become a critical defense tactic, starting with moving the cold, inactive data to immutable object storage where it cannot be modified.

“To this end, IT and storage directors will look for unstructured data management solutions that offer automated capabilities to protect, segment and audit sensitive and internal data use in AI — a use case that is bound to expand as AI matures. Further, they will need to create systematic ways for users to search across corporate data stores, curate the right data, check for sensitive data and move data to AI with audit reporting.”

– Krishna Subramanian, cofounder of Komprise

To sum up, 2025 promises significant advancements in enterprise data infrastructure, ranging from multimodal data flywheels to sovereign clouds. However, challenges such as data governance and storage shortages will persist. Success in this dynamic space will depend on balancing innovation with trust and sustainability, turning data into a lasting competitive advantage.

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NuEnergy Completes Drilling for ‘Early Gas Sales’ Project in Indonesia

NuEnergy Gas Ltd said it had completed drilling for the fourth and final well in its “Early Gas Sales” project under the initial development plan for the Tanjung Enim coalbed methane (CBM) production sharing contract (PSC) in Indonesia. “Gas shows were observed at surface via surface logging equipment, confirming the presence of methane across multiple seams”, the Australian company said in a stock filing. The TE-B01-003 well, drilled 451 meters (1,479.66 feet) deep, intersected five coal seams at depths ranging between 299 and 419 meters, according to NuEnergy. “NuEnergy has installed a progressive cavity pump system for the TE-B01-003 well and preparations are now underway to commence dewatering – a key step toward establishing stable gas flow and optimizing well performance”, the company said. “Gas will be gathered at the surface facility and delivered to the gas processing facility upon reaching target production levels”. It added, “Pursuant to the signed heads of agreement with PT Perusahaan Gas Negara Tbk (PGN), gas produced from the drilled wells, TE-B06-001, TE-B06-002, TE-B06-003 well and the TE-B01-003 well, will be delivered via an infield pipeline to PGN’s processing and distribution facility”. The Early Gas Sales project will sell one million standard cubic feet a day (MMscfd) to Indonesian state-owned gas distributor PGN, toward the 25-MMscfd initial plan for the Tanjung Enim license, according to NuEnergy. On September 8, it announced approval from the Energy and Mineral Resources Ministry for the one-MMscfd sale through its subsidiary Dart Energy (Tanjung Enim) Pte Ltd (DETE). “With the gas allocation approval now secured, DETE will proceed with finalizing the Gas Sale and Purchase Agreement with PGN”, NuEnergy said then. Meanwhile the bigger Tanjung Enim Plan of Development (POD) 1 was approved June 2021 “under a gross split scheme which will allow the PSC to proceed field development, surface facility

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Hammerfest LNG Workers Got Ill from MEG Tanks Venting, Equinor Finds

Dozens of workers at the Hammerfest LNG export terminal on the Norwegian island of Melkoya got ill from exposure to vented gas from tanks storing monoethylene glycol (MEG) during the one year to summer 2025, a probe by operator Equinor ASA has revealed. “We must acknowledge that we should have gone more in-depth to identify the causes when the first incidents of exposure occurred at Melkoya last summer”, Christina Dreetz, Equinor senior vice president for onshore plants, said in an online statement by the majority state-owned company. “Through measures implemented both during and after the investigation, we now have routines that enable us to manage risk more effectively”. The statement reported, “During a period of high activity at Hammerfest LNG, from summer 2024 to summer 2025, 37 people sought medical attention on four different occasions and nine people were absent from work following the exposure incidents. Some experienced health issues such as headaches, nausea and dizziness, while others noticed nothing. “Reactions to vented gas and the associated odor is a cause of the various health issues experienced by personnel, but it is unlikely that the exposure has led to long-term health issues”. Equinor’s investigation “points to insufficient risk assessment before the project start-up and follow-up as the reason why several incidents occurred during the one-year period”, the statement said. Equinor found that venting from the tanks housing MEG, a chemical used to prevent hydrate formation in pipelines from the Snohvit field to the liquefaction facility, had been the main cause of the exposure incidents. The tanks are designed so that vented gas consists of nitrogen and water vapor, according to Equinor. “Changes in the well stream in the MEG tanks or temperature fluctuations have contributed to changes in the composition of the vented gas. This has resulted in odors and,

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Oil Falls on Rising Fuel Stocks

Crude retreated after a US government report showed rising inventories of fuel and other refined products, easing supply concerns while investors tracked stalled diplomatic attempts to end Russia’s war on Ukraine. West Texas Intermediate dropped 2.1% to trade above $59 a barrel, the biggest loss in a week. Ukrainian President Volodymyr Zelenskiy arrived in Turkey to “reinvigorate negotiations,” raising eyebrows among investors that had all but written off a deescalation of a conflict that has spurred restrictions on Russia’s energy sector. An Axios report that Washington has been working in consultation with the Kremlin to draft a new plan also eased supply concerns, though Moscow denied any talks. US envoy Steve Witkoff was expected to meet Ukrainian leaders in Turkey on Wednesday but postponed his trip, Axios reported. The developments may help cushion the impact of US sanctions against Russia’s two biggest oil producers, Rosneft PJSC and Lukoil PJSC, which are set to kick in within days. The US Treasury claimed the restrictions are already undermining Russia’s funding capacity. That’s particularly visible in surging diesel-market tightness, in which Russia is a significant player, raising concerns about shortages of heating fuel just ahead of winter. Some of those fears were allayed after the US Energy Information Administration reported on Wednesday that gasoline and distillate inventories in the US expanded for the first time in more than a month. Heating oil futures dropped as much as 5.2% after touching the highest since April 2024 on Tuesday, leading the energy complex lower. “Higher refining activity and lower implied demand for both helped gasoline and distillate inventories rise, albeit modestly for distillates,” said Matt Smith, Americas lead oil analyst at Kpler. The 3.4 million-barrel decline in US crude inventories last week was smaller than the American Petroleum Institute’s 4.4 million estimate, helping temper some

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Some load forecasts using ‘unrealistically high load factors’: Grid Strategies VP

Dive Brief: Significant load growth is likely to arrive as forecast, but uncertainties associated with data centers are complicating load growth estimation, as are “unrealistically high load factors for the new large loads” in some load forecasts, said John Wilson, a vice president at Grid Strategies. Wilson is one of the lead authors of a November report which found the five-year forecast of U.S. utility peak load growth has increased from 24 GW to 166 GW over the past three years — by more than a factor of six. The report concluded that the “data center portion of utility load forecasts is likely overstated by roughly 25 GW,” based on reports from market analysts. Dive Insight: Despite projected load growth, many utility third-quarter earnings reports have shown relatively flat deliveries of electricity. Wilson said he thinks a definitive answer as to whether or not load growth is materializing will come next year. “If [large loads] start to get put off or canceled, and the load doesn’t come in, then we could see a lot of revisions to forecasts that are really large,” he said. The utility forecast for added data center load by 2030 is 90 GW, “nearly 10% of forecast peak load,” the report said, but “data center market analysts indicate that data center growth is unlikely to require much more than 65 GW through 2030.” Wilson said he thinks the overestimation could be due “simply to the challenge that utilities have in understanding whether a potential customer is pursuing just the site in their service area, or whether they’re pursuing multiple sites and they’re not planning on building out all of them.” This is information that utilities haven’t typically gathered, he said, although he’s seeing a trend toward utilities making those questions part of their application process. Wilson said another factor

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Winter peak demand is rising faster than resource additions: NERC

Listen to the article 4 min This audio is auto-generated. Please let us know if you have feedback. Dive Brief: Peak demand on the bulk power system will be 20 GW higher this winter than last, but total resources to meet the peak have only increased 9.4 GW, according to a report released Tuesday by the North American Electric Reliability Corp. Despite the mismatch, all regions of the bulk power system should have sufficient resources for expected peak demand this winter, NERC said in its 2025-2026 Winter Reliability Assessment. However, several regions could face challenges in the event of extreme weather. There have been 11 GW of batteries and 8 GW of demand response resources added to the bulk power system since last winter, NERC said. Solar, thermal and hydro have also seen small additions, but contributions from wind resources are 14 GW lower following capacity accounting changes in some markets.  Dive Insight: NERC officials described a mixed bag heading into the winter season. “The bulk power system is entering another winter with pockets of elevated risk, and the drivers are becoming more structural than seasonal,” said John Moura, NERC’s director of reliability assessments and performance analysis. “We’re seeing steady demand growth, faster than previous years, landing on a system that’s still racing to build new resources, navigating supply chain constraints and integrating large amounts of variable, inverter-based generation.” Aggregate peak demand across NERC’s footprint will be 20 GW, or 2.5%, higher than last winter. “Essentially, you have a doubling between the last several successive [winter reliability assessments],” said Mark Olson, NERC’s manager of reliability assessment. Nearly all of NERC’s assessment areas “are reporting year-on-year demand growth with some forecasting increases near 10%,” the reliability watchdog said. The U.S. West, Southeast and Mid-Atlantic — areas with significant data center development — have

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Energy Secretary Strengthens Midwest Grid Reliability Heading into Winter Months

WASHINGTON—U.S. Secretary of Energy Chris Wright issued an emergency order to address critical grid reliability issues facing the Midwestern region of the United States heading into the cold winter months. The emergency order directs the Midcontinent Independent System Operator (MISO), in coordination with Consumers Energy, to ensure that the J.H. Campbell coal-fired power plant in West Olive, Michigan remains available for operation and to take every step to minimize costs for the American people. The Campbell Plant was scheduled to shut down on May 31, 2025 — 15 years before the end of its scheduled design life. “Because of the last administration’s dangerous energy subtraction policies targeting reliable and affordable energy sources, the United States continues to face an energy emergency,” said Energy Secretary Wright. “The Trump administration will keep taking action to reverse these energy subtraction policies, lowering energy costs and minimizing the risks of blackouts. Americans deserve access to affordable, reliable and secure energy regardless of whether the wind is blowing or the sun is shining, especially in dangerously cold weather.”  Since the Department of Energy’s (DOE) original order issued on May 23, the Campbell plant has proven critical to MISO’s operations, operating regularly during periods of high energy demand and low levels of intermittent energy production. A subsequent order was issued on August 20, 2025. As outlined in DOE’s Resource Adequacy Report, power outages could increase by 100 times in 2030 if the U.S. continues to take reliable power offline. The emergency conditions that led to the issuance of the original orders persist.MISO’s service area will continue to face emergency conditions both in the near and long term. Two recent winter studies (2024 – 2025 NERC Winter Reliability Assessment and the 2023 – 2024 NERC Winter Reliability Assessment) have assessed the MISO assessment area as an elevated risk, with the “potential

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AWS boosts its long-distance cloud connections with custom DWDM transponder

By controlling the entire hardware stack, AWS can implement comprehensive security measures that would be challenging with third-party solutions, Rehder stated. “This initial long-haul deployment represents just the first implementation of the in-house technology across our extensive long-haul network. We have already extended deployment to Europe, with plans to use the AWS DWDM transponder for all new long-haul connections throughout our global infrastructure,” Rehder wrote. Cloud vendors are some of the largest optical users in the world, though not all develop their own DWDM or other optical systems, according to a variety of papers on the subject. Google develops its own DWDM, for example, but others like Microsoft Azure develop only parts and buy optical gear from third parties. Others such as IBM, Oracle and Alibaba have optical backbones but also utilize third-party equipment. “We are anticipating that the time has come to interconnect all those new AI data centers being built,” wrote Jimmy Yu, vice president at Dell’Oro Group, in a recent optical report. “We are forecasting data center interconnect to grow at twice the rate of the overall market, driven by increased spending from cloud providers. The direct purchases of equipment for DCI will encompass ZR/ZR+ optics for IPoDWDM, optical line systems for transport, and DWDM systems for high-performance, long-distance terrestrial and subsea transmission.”

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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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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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