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The Data Center Power Squeeze: Mapping the Real Limits of AI-Scale Growth

As we all know, the data center industry is at a crossroads. As artificial intelligence reshapes the already insatiable digital landscape, the demand for computing power is surging at a pace that outstrips the growth of the US electric grid. As engines of the AI economy, an estimated 1,000 new data centers1 are needed to […]

As we all know, the data center industry is at a crossroads. As artificial intelligence reshapes the already insatiable digital landscape, the demand for computing power is surging at a pace that outstrips the growth of the US electric grid. As engines of the AI economy, an estimated 1,000 new data centers1 are needed to process, store, and analyze the vast datasets that run everything from generative models to autonomous systems.

But this transformation comes with a steep price and the new defining criteria for real estate: power. Our appetite for electricity is now the single greatest constraint on our expansion, threatening to stall the very innovation we enable. In 2024, US data centers consumed roughly 4% of the nation’s total electricity, a figure that is projected to triple by 2030, reaching 12% or more.2 For AI-driven hyperscale facilities, the numbers are even more staggering. With the largest planned data centers requiring gigawatts of power, enough to supply entire cities, the cumulative demand from all data centers is expected to reach 134 gigawatts by 2030, nearly three times the current load.​3

This presents a systemic challenge. The U.S. power grid, built for a different era, is struggling to keep pace.

Utilities are reporting record interconnection requests, with some regions seeing demand projections that exceed their total system capacity by fivefold.4 In Virginia and Texas, the epicenters of data center expansion, grid operators are warning of tight supply-demand balances and the risk of blackouts during peak periods.5 The problem is not just the sheer volume of power needed, but the speed at which it must be delivered. Data center operators are racing to secure power for projects that could be online in as little as 18 months, but grid upgrades and new generation can take years, if not decades. The result is a bottleneck that is forcing the industry to rethink our approach to energy sourcing, grid integration, and infrastructure planning.​

The stakes could not be higher. If the power constraint is not resolved, the AI revolution could stall, with ripple effects across the economy. Companies may be forced to delay or scale back projects, and regions that fail to attract data centers could fall behind in the race for digital leadership. The solution will require a combination of policy innovation, technological advancement, and collaboration between the public and private sectors. Our industry is at a turning point, where the question is not just how much power is needed, but where it will come from.

The Scale of Demand: AI’s Insatiable Appetite

There is no denying it: the rise of artificial intelligence has fundamentally altered the energy landscape for data centers. In the past, data centers were primarily tasked with storing and serving digital content; this role required significant, but manageable, amounts of electricity. The advent of AI has changed that equation. AI workloads, particularly those involving large language models and deep learning, are orders of magnitude more energy-intensive than traditional computing tasks. We hear it; we know it.

Recent studies estimate that AI-specific servers in US data centers consumed 53 terawatt-hours of electricity in 2024, enough to power over 7 million homes for a year. Now consider the previous estimates that this figure could triple by 2030. Our facilities rank among the largest power consumers in the world.

The impact of this demand is already being felt. In Virginia, the nation’s data center capital, utility power demand from data centers is expected to reach 12.1 gigawatts in 2025, up from 9.3 gigawatts in 2024.5 In Texas, the figure is projected to hit 9.7 gigawatts, driven by both hyperscale and crypto-mining projects.4 These statistics reveal a fundamental shift in the way electricity is consumed. Because power is also needed for residential and industrial users, limited grid capacity is driving up prices and straining infrastructure. In a market where power availability is the critical gatekeeper of growth, many operators are willing to pay a premium for access to reliable, scalable energy.​

With all operators facing similar challenges, there are many comprehensive guides, maps, and strategies available. With these tools, we can conduct our own advance power study to hopefully shorten the due diligence timeline.

National and Regional Maps of Power Capacity

Mapping the US power infrastructure has become fundamental to site selection, risk assessment, and portfolio planning. This enables our industry to visualize broad market capacity for new load, sketching supply pockets at the regional and state level. Key siting decisions often evaluate the proximity of new projects to high-capacity, reliable generation and substations, or, where redundancy is paramount, to a mix of sources (nuclear, hydro, renewables, gas). Taken together, this also allows early teams to scan for legacy sites (retired or retiring plants) that may have available transmission interconnects and cooling resources, creating brownfield opportunities to accelerate deployment timelines.

For a macro view, EIA’s U.S. Power Plant Map provides a searchable inventory of thousands of power plants nationwide, including each facility’s nameplate capacity, fuel mix, status (operational, retired, planned), and ownership overlays. The Synapse Energy Interactive Map further supplements these records with owner and emissions data, drawing from EPA datasets to create a lens into the carbon profile of every major generator from 2018 to 2023.

Beyond their direct use in site selection, these maps are essential for long-term risk management. The trending overlays for “planned” and “standby” status provide a leading indicator for regions where capacity could swing abruptly as plants retire or convert, or where market signals are prompting investment in new generation. Emission overlays and ownership data also help anticipate political and community acceptance for new large loads—critical as our industry becomes more visible to local stakeholders.

Yet these resources have clear limits: they do not show live system operating constraints, price volatility, or grid flexibility under real-world conditions. As more data centers migrate to AI and HPC deployments, national generation maps serve as a starting point, but not the finish line, for finding available power.

Distribution-Level Hosting Capacity Maps

A decade ago, most projects could assume easy grid access at the distribution level. Today, as hyperscale nodes seek loads orders of magnitude above historic norms, local bottlenecks often dictate project feasibility more than market fundamentals. Hosting capacity maps are also increasingly being integrated into RFP processes, ensuring that new sites are competitively vetted on grid readiness, not just cost or fiber.

For granular siting, the most powerful tool now available is the distribution-level hosting capacity map. Utilities increasingly publish these as GIS portals, color-coding feeders and substations by how much new load they can connect without requiring major upgrades. Green typically means available headroom; red means grid constraints, costly reinforcements, or multi-year approvals.

The DOE U.S. Atlas of Hosting Capacity Map offers an aggregated index of these tools, linking to hundreds of live utility maps across the country, updated as of July 2025. They are designed for use by developers, municipalities, and site selectors, rapidly surfacing neighborhoods, substations, or service territories where distribution circuits are either open for business or already tapped out.

However, caveats remain. No amount of research can guarantee approval, just a “first-pass” filter. Local markets cannot always anticipate and accommodate the collective impacts if multiple projects land in their geography, as we have seen when Atlanta became a sudden hotbed of data center activity. It is also much more challenging to and slower solve the underlying reality of upstream transmission congestion and backlog queue. Despite these limitations, hosting capacity maps are fast becoming table stakes for early site planning and for engagement with utilities on real-time system flexibility.

Real-Time and Congestion Insights

Granular, real-time awareness of grid conditions is no longer optional for our industry’s planners, especially when financial commitments are six- and seven-figure monthly energy bills. Traditional models relied on historical curtailments, seasonal forecasts, or average LMPs; today, actionable intelligence depends on live dashboards and congestion sensors.

The EIA Real-Time Electricity Dashboard provides public, near-real-time data on system frequency, demand curves, regional interchange, and market pricing. This tool, combined with richer commercial and ISO dashboards, enables teams to track supply-demand imbalances, outage risks, and peak load events as they happen. The Ember US Electricity Data Explorer further breaks down generation, fuel mix, and emissions by state and ISO, with monthly, albeit lagged, detail to monitor market shifts and decarbonization trends.

These resources are not just for energy procurement teams: siting professionals, risk officers, and even marketing teams monitor real-time congestion to anticipate permitting narratives and local political risk. During acute grid scarcity or after major transmission outages, demand spikes and curtailments can rapidly upend long-term cost models. Many operators have begun overlaying congestion maps from regional ISOs (such as ISO-NE’s system maps) to triangulate lowest-risk interconnection points.

It should be noted that these dashboards do not capture “latent” or untapped potential at the substation or feeder level; their principal value is as warning systems for stress, not as market growth blueprints. Still, as grid risks become more dynamic and are often weather-driven, near-real-time mapping is now integral to keeping projects both bankable and reliable across increasingly volatile markets.

Untapped Generation Potential

Alongside known sources, the US grid harbors substantial underutilized power that could be unlocked for new demand, including non-powered dams, retired or retiring industrial infrastructure, and grid corridors with falling load. For example, only 3% of the nation’s 80,000 dams generate electricity; the National Hydropower Association and DOE estimate that retrofitting non-powered sites could add 10–12 GW of low-carbon capacity to the national grid.6 Projects like the Red Rock Hydroelectric Project and the Ohio River dam retrofits serve as prime case studies in tapping this overlooked resource, with thousands of megawatts of capacity potentially available using existing water infrastructure.

Hydropower presents one potential generation source, but we can easily think beyond it. Regions with legacy industrial assets, from decommissioned steel mills to former chemical facilities, sometimes possess transmission and land suitable for advanced data center development, as shown in brownfield overlays provided by DOE’s Clean Energy Resources toolkit. Many of these locations enjoy proximity to grid backbones and are already zoned for heavy electricity use, though timelines for securing new generation or upgrading connections remain a challenge.

For forward-thinking operators, untapped grid assets offer a hedge against oversubscribed regions, aligning economic growth with sustainability goals while diversifying risk across regions and fuel types.

Where Will the Power Head To?

No single tool or map can answer, “Where will the power come from?” at the scale and granularity our industry now requires. But by integrating national capacity maps, distribution-level hosting data, real-time grid congestion dashboards, and overlays of untapped generation, industry leaders can build a multi-layered picture of risk and opportunity. Closing the power gap for digital infrastructure will demand complex insights and tools to map not only power potential, but local acceptance as we grow into new markets.

When you layer the national generation maps, utility hosting-capacity tools, DOE clean‑energy siting work, and current development pipelines, a consistent picture emerges of a handful of markets and corridors that look structurally advantaged for power‑hungry AI and HPC builds. Five, in particular, show up repeatedly as “next‑wave” destinations for capacity, backup, and infrastructure readiness—even if each comes with its own strengths and caveats. By overlaying with JLL’s  latest data center market report, the same broad set of power‑advantaged regions keeps resurfacing as most likely to push the next wave of AI and HPC growth, because they combine power potential, industrial land, and infrastructure readiness. These five markets reveal themselves:

1. Pennsylvania / Mid‑Atlantic interior

Pennsylvania increasingly shows up as the pressure valve between Northern Virginia and the Midwest, with both power and industrial land positioning it as a natural corridor market. JLL’s U.S. Industrial Market Dynamics, Q3 2025 points to an active national pipeline and a long list of Pennsylvania and Mid‑Atlantic industrial markets—Eastern and Central Pennsylvania, Pittsburgh, and Richmond among them—where large‑scale sites and logistics infrastructure remain available, even as vacancy stabilizes. Noteworthy highlights include the state’s ample power, strong transmission position between NY/NJ and NOVA, and growing interest from both owner‑users and developers seeking 200 MW‑plus sites with 18–36 month power timelines. Regional maps of PJM’s grid show robust backbone transmission and legacy industrial corridors that can be repurposed, while state‑level land and power costs still undercut coastal metros. From our industry’s perspective, Pennsylvania scores well across all four map types: solid underlying generation, promising hosting and brownfield potential, and strategic proximity to the country’s largest existing hub.

In parallel, JLL’s mid‑year North America data center reporting highlights the continued dominance of Northern Virginia and the emergence of Pennsylvania‑adjacent sites marketed specifically for AI and hyperscale growth. When you overlay these signals with grid and clean‑energy maps that show strong transmission backbones and legacy industrial corridors, Pennsylvania and the broader interior Mid‑Atlantic begin to look like a logical next‑wave power corridor—close to existing demand, but not yet fully saturated.​

2. Dallas–Fort Worth and the Texas triangle

Texas already ranks as one of the two largest state‑level demand centers, with utility power to data centers projected at roughly 9.7 GW in 2025, up from under 8 GW a year earlier. Growth projections for Dallas–Fort Worth alone call for more than 4,300 MW of future data center power needs, making it one of the fastest‑expanding hubs in the country. What the maps show is a state with abundant generation (including gas, wind, and solar), extensive transmission corridors, and multiple utilities experimenting with tariffs and on‑site “bridge” solutions such as fuel cells to cover near‑term gaps. Hosting‑capacity style intelligence is not as consistently public as in some coastal states, and ERCOT volatility is a real risk, but from a pure power‑access and scale perspective, the Texas triangle remains high on every shortlist.

JLL’s North America Data Center Report identifies Dallas as one of the two dominant absorption engines in the continent, recording 575 MW of demand in the first half of 2025 and more than 1,000 MW of cumulative capacity growth in recent years. The same analysis notes a substantial pipeline under construction and planned, with most of it already pre‑leased—clear evidence that power‑served land in the Dallas–Fort Worth region is being locked up for AI and hyperscale users. JLL’s broader industrial work confirms that the Texas triangle (Dallas–Fort Worth, Houston, Austin/San Antonio) has an expansive industrial inventory and an active development pipeline, supporting power‑intensive uses that can plug into existing logistics, workforce, and grid infrastructure. Against the backdrop of state‑level maps showing abundant generation and ongoing grid initiatives to accelerate large‑load interconnections, Dallas and its neighboring metros stand out as a core “power‑first” growth cluster, even as utilities flag that timelines for new capacity are tightening.​

3. Atlanta and the broader Southeast

Georgia shows up repeatedly in both load‑growth forecasts and development trackers, with analysts pointing to “ample land, reasonable power costs, dense fiber, and demand from hyperscalers” as the mix driving rapid expansion around Atlanta. Regional utility maps and DOE clean‑energy siting work highlight strong transmission corridors, growing solar capacity, and a regulatory environment that has been relatively receptive to large loads. Neighboring Carolinas and Tennessee Valley territories add nuclear‑heavy baseload, which many in our industry view as attractive for AI‑class uptime and carbon narratives. While formal hosting‑capacity maps are patchier than on the West Coast or in the Northeast, the directional signal across sources is clear: the Southeast is consolidating its role as a power‑ready growth belt.

Atlanta’s data center footprint has grown from a secondary hub to a genuine powerhouse, and JLL’s mid‑year report frames it as one of the top five markets for absorption, with the market size having doubled since 2023 and on pace to double again by 2026. Fundamentals in JLL’s snapshot—low vacancy, hundreds of megawatts under construction, and over 200 MW planned—underscore how much of the city’s future grid headroom is being dedicated to our industry. At the same time, JLL’s industrial reports highlight Atlanta and a series of Southeastern markets (Charlotte, Nashville, Savannah, Jacksonville, and others) as active logistics and industrial corridors, where large tracts of industrial‑zoned land, transportation nodes, and utility‑served sites are already in play. Layer this on top of DOE clean‑energy siting work that points to growing solar, nuclear baseload nearby, and strengthening transmission in the region, and the picture that emerges is a Southeast arc anchored by Atlanta: a belt where both the grid and industrial real estate ecosystems are being tuned for very large, very power‑dense deployments.​

4. Ohio / Midwest datacenter corridor

Columbus and the surrounding Ohio corridor are now firmly on the industry’s radar as an emerging “power plus land” play. The market particularly around Columbus has quietly become one of the most interesting power‑and‑land plays on the map, with American Electric Power reporting interconnection requests for 36 sites totaling 13 GW of load in its Ohio service territory alone, down from over 30 GW after queue pruning, and roughly 18 GW of new demand from data centers across its multi‑state footprint. Analysts now flag “significant growth in Ohio” as operators cluster near existing fiber, interstate transmission, and legacy industrial infrastructure. From a mapping perspective, DOE’s clean‑energy resources work and Midwestern advocacy groups point to competitively priced power, access to renewables and storage, and brownfield opportunities at former heavy‑industry sites, such as steel, auto, and chemical sites. This combination—grid backbone, stranded or underused capacity, and supportive state‑level engagement—makes Ohio and adjacent Midwest states a recurring “up‑and‑to‑the‑right” region in long‑range plans.

Wisconsin is now joining that corridor in a visible way. Microsoft has announced multi‑billion‑dollar plans for large data center campuses in Mount Pleasant and other southeastern Wisconsin locations, leveraging the high‑capacity infrastructure originally developed for the Foxconn project, including substantial transmission build‑out and industrial‑zoned land near Lake Michigan. Regional grid maps and ISO data show that this corner of Wisconsin sits on strong high‑voltage corridors connecting into both Midcontinent Independent System Operator (MISO) and neighboring PJM interfaces, while state and local leaders are positioning these investments as anchors for broader clean‑energy and advanced‑manufacturing strategies. In practical terms, the same attributes that defined Ohio’s rise—available power, legacy industrial sites, and proximity to major load centers like Chicago—are now being replicated just across the state line, turning southeastern Wisconsin into a new node on the Midwest data‑center spine.​

While JLL’s North America Data Center Report focuses on Chicago as the Midwest’s incumbent core, with substantial inventory and ongoing expansion, it also notes significant capacity growth and hyperscale interest in other Central U.S. markets tied into major transmission and fiber routes. JLL’s industrial local reports for Columbus, Cleveland, Milwaukee, and other Midwest metros show robust pipelines and healthy absorption, signaling that large, infrastructure‑ready parcels remain available even as demand from manufacturing, logistics, and data centers accelerates. When you combine that with grid analyses showing heavy transmission corridors, legacy industrial substations, and DOE‑identified clean‑energy and brownfield opportunities across the region, the Midwest looks less like a peripheral option and more like a central growth spine for AI‑class infrastructure over the next decade.​

5. Emerging “stranded‑power” markets: Phoenix, Las Vegas/Reno, and the interior/landlocked West

Finally, several western metros and sub‑regions repeatedly appear in power and data center mapping as candidates for large‑scale AI growth: Phoenix, parts of Nevada like Las Vegas/Reno, and pockets in states like Idaho, Oklahoma, and Louisiana. Visualizations of future capacity requirements suggest that Phoenix could ultimately support over 5,000 MW of data center demand, with Las Vegas/Reno and other desert or interior hubs not far behind. The through‑line here is a search for “stranded” or under‑utilized power: regions with strong high‑voltage infrastructure, growing renewables, relatively low land costs, and, in some cases, nearby non‑powered dams or other upgradeable assets. Utilities and developers are testing more integrated models, such as combining new generation, storage, and large on‑site backup, to turn these maps from theoretical potential into power‑ready campuses.

JLL’s view of the “Landlocked” West points to a set of interior markets that pair strong high‑voltage networks and industrial land with rising data center interest. Phoenix, for example, is highlighted in JLL’s data center report with nearly 900+ MW of inventory, low vacancy, more than 1.3 GW under construction, and over 4 GW planned, an extraordinary signal of how much power‑enabled capacity developers expect to bring online there in the next few years. JLL’s industrial market coverage for Phoenix, Las Vegas, Salt Lake City, and Denver adds another layer, showing active development and logistics ecosystems that make it easier to stand up and support very large campuses.

When these markets are cross‑referenced with DOE clean‑energy and resource maps, which highlight nearby renewables, storage projects, and in some cases non‑powered or underutilized infrastructure, they form a loose “stranded‑power” arc: places where our industry is betting that today’s relative headroom can be converted into tomorrow’s AI‑scale footprints before they, too, become crowded.​

Industrial real estate and data center reporting validates what the power and hosting‑capacity maps already imply: our industry’s future is coalescing around a series of corridors—Pennsylvania and the Mid‑Atlantic interior, the Texas triangle centered on Dallas–Fort Worth, an Atlanta‑anchored Southeast, the Ohio/Midwest belt, and select interior‑West hubs—where power availability, industrial land, and infrastructure readiness are aligning fastest.

References:

1.       https://itif.org/publications/2025/10/27/data-center-capacity-will-need-increase-130-percent-by-2030-meet-demand-for-ai/

2.       https://www.pewresearch.org/short-reads/2025/10/24/what-we-know-about-energy-use-at-us-data-centers-amid-the-ai-boom/

3.       https://www.spglobal.com/commodity-insights/en/news-research/latest-news/electric-power/101425-data-center-grid-power-demand-to-rise-22-in-2025-nearly-triple-by-2030-1068451

4.       https://gridstrategiesllc.com/wp-content/uploads/National-Load-Growth-Report-2024.pdf

5.       https://www.brownadvisory.com/intl/insights/data-center-balancing-act-powering-sustainable-ai-growth

6.       https://www.hydro.org/waterpower/converting-non-powered-dams/

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Amazon claims its data centers are 7x more water-efficient than the industry average

“Amazon is on the leading edge, but it’s not a secret recipe,” he said. What sets the company apart is scale, execution, facility design, geographic mix, and its aggressive pursuit of energy goals. Others are doing the similar things, if through different avenues: Microsoft is investing in closed-loop cooling systems that dramatically reduce evaporative water loss. Google is heavily focused on reclaimed water and using AI to optimize data centers. Meta has long relied on outside-air cooling. And overall, the industry is moving toward liquid cooling for dense AI deployments, “which changes the water equation again,” said Kimball. One of the big variables is location: Climate influences water efficiency, so where a company builds its infrastructure is as important as its cooling methods. Further, power-consumptive AI changes the discussion, he emphasized; traditional enterprise workloads and dense AI training clusters create very different thermal profiles.

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Marvell announces 102.4 Tbps switch silicon built for AI

Data movement has become an important concern in modern AI data centers. In the past, a cluster of a few servers could adequately handle back-office applications and databases. But with AI’s gigantic models, all sections of the data center need to move and receive data at high speeds. That requires a lot more power use than in the past. GPU- and XPU-based systems are approaching 120KW per rack, and switching and networking components consume approximately 15-25% of total rack power, making low-power switch silicon a strategic requirement. The Teralynx T100 delivers up to 25% lower power consumption than competitive solutions at a higher data rate. This enables AI infrastructures to deploy more accelerators within existing power envelopes without requiring additional power infrastructure. “As AI workloads evolve and scale exponentially, hyperscalers require network architectures that optimize latency, power and scalability simultaneously,” said Rishi Chugh, vice president and general manager of the data center switch business unit at Marvell, in a statement.

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From the data center to the edge: How to build secure, effective enterprise AI infrastructure

While hyperscalers and neo-cloud providers may get the lion’s share of attention for providing AI infrastructure, many enterprises are taking a build-it-themselves approach to meet their specific AI requirements. The success of such projects is crucial to achieving business objectives, yet companies face significant challenges as they try to scale pilots to production. Organizations must keep up with the dynamic, ever-changing demands that AI applications place on compute and network infrastructure, from the data center to the edge. That means architecting systems to grow as demand warrants and to avoid performance bottlenecks. The architecture must also account for AI-driven security vulnerabilities and ensure appropriate defenses are in place. Yes, it’s a tall order. But here, in simplified form, is a three-step plan for meeting those objectives. Step one: Go modular Integrating all the required components in piecemeal fashion for an AI factory is complex, costly, and fraught with integration risk. Start with a modular design, based on proven NVIDIA reference architectures. A modular approach combines pre-validated accelerated computing hardware, AI software, and orchestration platforms, as well as networking and storage capabilities. A modular strategy speeds implementation and creates a faster time to value for your AI infrastructure. Using modules that combine compute, networking, and storage makes it easier to scale capacity as needed, whether in the data center or at edge facilities. In addition, the modular approach simplifies the job of addressing varying requirements, from inferencing engines at the edge to massive-scale model training in the data center, while staying within the same solution family. The same applies to easing integration processes, as modular platforms offer pre-validated software. The Cisco Secure AI Factory with NVIDIA approach, for example, includes hardware (Cisco AI PODS) that is pre-validated to work with NVIDIA AI Enterprise software; Cisco Security and Splunk Observability software; orchestration

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OpenAI weighs Nvidia-backed lease for 10 GW Ohio data center campus

OpenAI would control the computing equipment under a 20-year lease and begin payments once the site starts operating, with the first phase expected in 2028. Nvidia is expected to supply the hardware and guarantee both OpenAI’s lease obligations and the developer’s financing, the report added. The reported structure highlights a broader shift in AI infrastructure strategy, where model developers, chip suppliers, and energy providers are forging increasingly long-term partnerships to secure compute capacity amid surging demand. “These types of symbiotic deals are becoming the norm as AI infrastructure rolls out,” said Neil Shah, vice president for research and partner at Counterpoint Research. “If a CIO picks OpenAI to be the base layer, they shouldn’t just accept whatever infrastructure comes with it. CIOs need to negotiate and demand that OpenAI uses a mix of capacity so all your eggs are not in one premium basket like Nvidia.” OpenAI and Nvidia did not immediately respond to requests for comment.

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Arista unveils 1.6T rack-scale switch family for AI infrastructure

The new Arista family joins a growing ecosystem of vendors looking to tap into the 1.6T Ethernet world, which includes Cisco, Nvidia, Celestica and others. “Arista Network’s new 7060XE7 Series is a strong signal of where large-scale AI fabrics are heading: higher bandwidth, better power efficiency, and tighter integration between compute, optics, silicon, cooling, and network operating software,” wrote Sameh Boujelbene, vice president, data center switch and AI networks market research for Dell Oro, in a LinkedIn post. Among the features that stand out to her are “strong customer and ecosystem validation from Microsoft Azure, Oracle Cloud Infrastructure, Meta, AMD, and Broadcom.”

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Water Emerges as a Critical Constraint for AI Data Centers

“There really has been a major shift within the last couple of years,” Bajpayee said. “I would even say within the last 12 months is where we have seen suddenly a rapid increase in the data center operators’ desire to control their water destiny.” For Gradiant, the MIT-born water technology company that built its reputation serving semiconductor manufacturers, pharmaceutical companies, and industrial customers worldwide, that shift has translated into a rapidly expanding pipeline of data center opportunities. More importantly, Bajpayee believes it signals a fundamental change in how the industry thinks about water itself. The conversation is no longer centered primarily on sustainability metrics or corporate environmental goals. Instead, operators increasingly view water as a business continuity issue. “We’re seeing operators themselves come to us and tell us that these are issues they are facing,” Bajpayee said. “They want to make sure they don’t get stalled, their permits don’t get pulled, their business doesn’t get stopped, and communities don’t push them out because they didn’t figure out a way to control their water.” From Water Treatment to Water Strategy That shift is occurring as Gradiant expands deployments of its recently announced HyperSolved platform, an end-to-end cooling water management system purpose-built for AI data centers. The company says HyperSolved is now being deployed with several of the world’s largest hyperscale operators across North America, Europe, and Asia, reflecting growing industry demand for integrated approaches to water infrastructure. While compute, networking, and power systems have evolved rapidly during the AI era, water management often remains fragmented, requiring operators to coordinate multiple vendors responsible for sourcing, treatment, cooling, wastewater management, reuse, discharge, and regulatory compliance. Gradiant’s approach seeks to consolidate those functions into a single integrated platform and operating model. The timing reflects the growing scale of the challenge. New AI data center

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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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