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Virginia’s Loudoun County Grapples with Future of Data Centers as New Developments Continue Statewide

Where Economic Growth Meets Sustainability The data center industry’s contributions to local economies are undeniable. From job creation to substantial tax revenues, these facilities have reshaped Virginia’s economic landscape. Loudoun County alone hosts 199 operational data centers, with another 148 applications under review. The economic growth in ongoing data center development has spread out across […]

Where Economic Growth Meets Sustainability

The data center industry’s contributions to local economies are undeniable. From job creation to substantial tax revenues, these facilities have reshaped Virginia’s economic landscape. Loudoun County alone hosts 199 operational data centers, with another 148 applications under review.

The economic growth in ongoing data center development has spread out across the state even as it’s remained close to Data Center Alley. The ongoing growth underscores the demand for the infrastructure to support the digital economy, while highlighting ways forward and also challenges in sustainability and community compatibility. A pair of recent examples in the news exemplify this trend.

TECfusions Grows In Clarksville

In Clarksville, Virginia in far southern Mecklenburg County, TECfusions is spearheading innovative AI data center development. Backed by a $300 million loan agreement to be used for the development and expansion of its site there, the company’s flagship facility aims to meet the increasing demands of AI workloads.

Not simply growth to meet projected demand, the TECfusion expansion is a result of an urgent capacity requirement from one of their key tenants. 

According to a November press release by TECfusions:

In response to urgent capacity needs from a key tenant, gradual funding began in January 2024 and has now been solidified in a formal loan agreement, which includes the cumulative monies invested earlier. To date, $160 million has been allocated towards construction, with the remaining funds earmarked for completing Phase I of the Clarksville facility.

According to the company’s development timline, the site’s phased build-out is projected to reach 37.5 megawatts (MW) of capacity upon completion of the Clarksville data center’s Hall D. 

With the funding spanning a 15-year term, Mike Picchi, CFO of TECfusions, commented on its alignment with the company’s long term goals and meeting the needs of tenants:

“This agreement fully funds our Clarksville Phase I buildout and aligns perfectly with our long-term growth strategy, demonstrating the economic vitality of our approach and opening the doors for future expansion projects. With tenants that require immediate, scalable data center capacity, this funding enables us to meet that demand efficiently to ensure rapid deployment of capacity for today’s digital world.”

The company said that funds will be strategically allocated across several key initiatives, including AI-ready infrastructure deployment, on-site sustainable power generation solutions, and site infrastructure development. The investment is also expected to have a significant positive impact on the local community, creating numerous jobs in construction and operations while substantially expanding the region’s digital infrastructure and tax base.

Significantly, in being designed to house one of the world’s largest GPU clusters, TECfusions emphasizes that its adaptive reuse model exemplifies a sustainable approach, converting existing facilities into state-of-the-art data centers. This strategy not only accelerates deployment timelines but also minimizes environmental impact.

Iron Mountain Adds 350 MW of Data Center Capacity Across VA

Similarly, Iron Mountain’s recent expansion in Richmond and Manassas, with the company acquiring two new data center sites, highlights the Virginia data center industry’s rapid and ongoing scaling.

Iron Mountain is adding over 350 MW of planned capacity across the two new sites, which bring more than 100 acres to the company’s data center development portfolio.

The 66-acre site in Richmond is planned to become a 200 MW data center campus. With over 200 MW of expected capacity, Iron Mountain says its new Richmond campus “will be perfectly suited for highly regulated customers, thanks to its rigorous compliance program,” which will encompass: HIPAA, FISMA High, PCI-DSS, ISO 27001, ISO 50001, SOC2/3, among other codes.

The new Iron Mountain Richmond campus will be situated at the White Oak Technology Park in Henrico County, a unique business park with more than 2,200 acres for technology and data center campuses. Richmond has a robust power and network infrastructure, positioned along the I64 and I95 corridors, connecting to Northern Virginia and the subsea fiber cable landings in Virginia Beach.

For its part, the 40 acre site in Manassas will allow expansion of the existing 142-acre Iron Mountain campus there, which offers over 2 million square feet of energy-efficient space, with two new buildings, and potentially an additional 150 MW of capacity.

This acquisition includes the planned development of an electricity substation to ensure continued uninterrupted power supply across the Manassas campus.

Leveraging investment by energy providers to modernize transmission and distribution infrastructure, and a close partnership with local economic development authorities, Iron Mountain says its commitment to Virginia offers its customers secure, sustainable data centers that meet strict government regulations, all while benefiting from smart property tax savings.

The new developments promise significant economic benefits, including job creation and enhanced tax bases, while adhering to stringent sustainability standards, such as 100% renewable energy usage.

Mark Kidd, Executive Vice President and General Manager, Asset Lifecycle Management and Data Centers, Iron Mountain, said:

“The Commonwealth of Virginia has abundant infrastructure, a highly skilled workforce, strong fiber connectivity, and is a pro-business community – making it an ideal location to support our commitment to investing in high-growth markets that help drive our expansion strategy. As a leading data center provider, we’re excited to offer further critical capacity to our retail and hyperscale customers where and when they need it most.”

Points of Contention

While data centers bring economic advantages, their environmental and spatial footprints cannot be ignored, and have been continual points of contention with local government. These facilities consume vast amounts of electricity and require substantial land, often sparking debates over resource allocation.

In Loudoun County, for instance, Commission Chair Michelle Frank highlighted concerns about losing thriving businesses to data center developments, noting that skyrocketing land costs driven by data center demand could squeeze out other industries.

Proponents of data centers, however, argue that technological advancements and strategic planning can mitigate these issues.

Companies like TECfusions and Iron Mountain are pioneering energy-efficient designs and sustainable power generation solutions. These measures not only reduce carbon footprints but also align with broader environmental goals, ensuring that data centers remain viable in the long term.

The Path Forward: Zoning and Strategic Development

The future of data center development in Virginia hinges on thoughtful planning and regulatory clarity. As highlighted by Rizer and other stakeholders, identifying zones suitable for data centers is a critical step.

This approach would provide business owners with stability while safeguarding community interests. It would also prevent data centers from encroaching on residential areas or displacing other industries, as seen in recent debates over developments near Goose Creek and the Arcola area.

Moreover, collaboration between government bodies and industry leaders is essential. A letter sent to the commission on November 26th from County Chair Phyllis Randall and Transportation and Land Use Committee Chair Michael Turner underscores the urgency of reaching a consensus on zoning amendments.

Their proposed joint meetings between the Board of Supervisors and the Planning Commission aim to expedite decision-making, ensuring that regulatory changes reflect the county’s broader goals.

Innovative Models for Growth

Beyond zoning and regulation, the data center industry’s growth in Virginia offers an opportunity to embrace innovative development models. Adaptive reuse, as demonstrated by TECfusions, can serve as a blueprint for future projects. By repurposing existing structures, this approach not only accelerates deployment but also reduces the environmental impact of new construction.

Additionally, leveraging renewable energy and sustainable practices can address concerns about electricity consumption. Iron Mountain’s commitment to renewable energy and efficient cooling techniques exemplifies how data centers can align with environmental objectives. These innovations not only benefit the planet but also enhance the industry’s reputation, fostering goodwill among local communities and policymakers.

The evolution of data centers in Virginia reflects broader trends shaping the digital economy. As local governments navigate the challenges of zoning, regulation, and community impact, the need for collaboration and forward-thinking strategies becomes increasingly evident.

By balancing economic growth with sustainability and community well-being, Virginia can continue to lead in data center development, setting an example for regions worldwide.

JLARC Report

The Joint Legislative Audit & Review Commission of the Virginia legislature recently released a report on the impact of data centers on the state. This detailed review of data center impact (over 150 pages) covers everything from land use issues, to sustainability, water, and power impact.

The legislative report concludes that development of data centers in Virginia could triple the state’s energy demands if unconstrained. As succinctly reported by Virginia Mercury‘s Charlie Paullin:

“The report is in line with a recent regulatory filing from Dominion Energy stating annual increases in electric power demand would be relatively flat, if it weren’t for data centers […] Modelling from E3, a third-party consultant, showed that energy demand for the state would increase from just over 10,000 gigawatt hours in 2023 to just over 30,000 gigawatt hours by 2040, if data center development didn’t have to deal with constraints, including needing energy requirements like transmission lines to be available prior to coming online. 

Without data center development, the demand increased to about 12,500 gigawatt hours […] To meet those demands, more renewable energy facilities like solar and offshore will be needed, but so will natural gas, JLARC’s report stated, which would amount to a new plant being built every one and a half years, approximately […] ither meeting the full unconstrained demand, or half of it, relies on offshore wind and nuclear technology, which JLARC stated could come from the “unproven” small modular reactor technology.”

While we will be further covering the content of the JLARC report in an upcoming story, the nutshell is this: the report found that data centers provide a positive impact to Virginia’s economy, though it is mostly during the initial construction. And in the end, the success of this endeavor will depend on the ability of stakeholders to find common ground.

Whether through zoning reforms, innovative development models, or enhanced sustainability measures, the future of data centers lies in their capacity to adapt to changing demands while remaining rooted in the communities they serve.

Softening NIMBY in VA

And while NIMBY issues often dominate local politics, the development of data centers, with their global footprint, is slowly changing the perspective some Virginia communities have on their development.

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Russian Crude Output Rose Last Month

Russia’s crude oil production edged up in October, but remained below its OPEC+ quota as international pressure mounted on the country’s energy sector. Russia pumped an average 9.411 million barrels a day last month, people with knowledge of the data said, asking not to be identified discussing confidential information. While that’s 43,000 barrels a day higher than in September, it’s 70,000 a day below a quota that includes compensation cuts for previous overproduction, Bloomberg calculations show. Oil watchers are closely following Russian production data to assess the impact of sanctions — and Ukrainian drone strikes — against the country’s energy industry. The latest US penalties on the sector, which hit oil giants Rosneft PJSC and Lukoil PJSC, have already eroded crude exports as some refiners in India, China and Turkey prove less willing to take sanctioned barrels. Meanwhile, Ukrainian attacks have intensified, putting pressure on Russia’s crude-processing sector even as refinery owners rush to repair infrastructure.  If Moscow eventually finds itself unable to find buyers for oil from its sanctioned producers, and struggles to restore refining, it’ll be forced to halt output at some fields, risking damage to wells. The Energy Ministry didn’t immediately respond to a request for comment on the production data. Deputy Prime Minister Alexander Novak said last month that the nation has capacity to raise oil production further, but will do it gradually, according to Tass news service. Compensation Cuts Russia, historically one of the biggest laggards in complying with OPEC+ output agreements, has agreed to make additional cuts to compensate for previous overproduction. The monthly schedule for those curbs has been regularly revised, with the latest plan published earlier this month.  It shows that October was the last month when Russia had to make such cuts. Moscow’s pledge to reduce daily output by 10,000 barrels below a quota of 9.491

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Oil Rises but Logs Second Weekly Loss

Oil rose on Friday but still notched a second weekly loss as the market continued to weigh the threat to output from sanctions on Russia against a looming oversupply. West Texas Intermediate futures rose around 0.5% to settle below $60 a barrel, but were still down for the week. Adding to fears of a glut, oil prices have also been buffeted by swings in equity markets this week. Meanwhile, the White House’s move to clamp down on the buying of Russian crude led oil trading giant Gunvor Group to withdraw an offer for the international assets of Lukoil PJSC. The fate of the assets, which include stakes in oil fields, refineries and gas stations, remains unclear. One possible exception to that crackdown could emerge soon: President Donald Trump signaled an openness to exempting Hungary from sanctions on Russian energy purchases as he hosted Prime Minister Viktor Orban, briefly pushing futures to intraday lows. The development appeared to allay shortage fears, given that Budapest imports over 90% of its crude from Moscow. Senior industry figures have warned the latest US curbs on Russia’s two largest oil companies are beginning to have an impact on the market, particularly in diesel, where prices have been surging in recent days, with time spreads for the fuel signaling supply pressure. At the same time, the US measures have come against a backdrop of oversupply that has weighed on key crude oil metrics. The spread between the nearest West Texas Intermediate futures closed at the weakest level since February on Thursday. “If the market flips to contango, we may see more bearish funds enter the crude space,” said Dennis Kissler, senior vice president for trading at BOK Financial said of the potential that longer-dated contracts trade at a premium to nearer-term ones. “Most traders remain surprised

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Gunvor Scraps Lukoil Deal

Commodity trader Gunvor Group has withdrawn its offer for the international assets of sanctioned Russian oil producer Lukoil PJSC after the US Treasury Department called it “the Kremlin’s puppet” and said the oil and gas trader would never get a license. Gunvor pushed back on the Treasury comment on social media, calling it “fundamentally misinformed and false.” The Geneva-based company said it would seek to correct a “clear misunderstanding” but that it would withdraw its bid for now. President Trump has been clear that the war must end immediately. As long as Putin continues the senseless killings, the Kremlin’s puppet, Gunvor, will never get a license to operate and profit. — Treasury Department (@USTreasury) November 6, 2025 The comment is a remarkable volte-face after a week in which Gunvor has been in talks with the US Office of Foreign Assets Control, part of the Treasury Department, and other bodies in charge of sanctions to help press its case for a deal that would have transformed it into an integrated oil producing and processing colossus. Gunvor swooped on the assets at the end of last month following the US blacklisting of Lukoil and fellow Russian oil giant Rosneft PJSC, and its exit may leave the door open to other suitors. Gunvor on Thursday also announced it had raised $2.81 billion in a credit facility financed by US arms of global banks. Like other major commodity traders, the firm funds the bulk of its trades of oil, gas and metals around the world with bank financing. For the trader, the comments are likely to revive questions about its connections in Moscow at a time when many oil industry participants are wary of any links to Russia.  The trader’s co-founder, Gennady Timchenko, is a friend of Russian President Vladimir Putin, and when the US imposed sanctions

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Ship With Russia Oil Makes Rare Move Offshore India

A tanker carrying crude from recently-sanctioned Rosneft PJSC has made a rare cargo transfer off Mumbai, as the Trump administration ramps up its scrutiny of India’s oil trade with Russia. But the unusual move has puzzled traders. The cargo was transferred from one blacklisted tanker to another sanctioned ship, meaning there’s been no attempt to hide its origin — typical of such a move — and the crude is still heading for an Indian port: Kochi in the south, rather than Mumbai on the west coast. India’s purchases of Russian oil have drawn the ire of President Donald Trump, and the US penalties on Rosneft along with Lukoil PJSC are expected to severely impact the trade. The market is keenly watching for disruptions to established flows before a grace period related to the sanctions ends later this month. “What we’re seeing now is this uncertainty in the market about what the sanctions risks are,” said Rachel Ziemba, an analyst at the Center for a New American Security in Washington. “The net result is more ship-to-ship transfers, more subterfuge, longer routes, more complicated transactions.” The Fortis took around 720,000 barrels of Russian Urals from Ailana on Tuesday near Mumbai, according to ship-tracking data compiled by Bloomberg, Kpler and Vortexa. The cargo was collected from the Baltic port of Ust-Luga before the US sanctioned Rosneft, and Ailana had idled in the area for nearly two weeks with no clear reason.  Ailana is on its way back to Russia, while Fortis is expected to arrive at Kochi early next week with the cargo, ship-tracking data shows. Both vessels have been sanctioned by the European Union and the UK. Fortis’ owner and manager — Vietnam-based Pacific Logistic & Maritime and North Star Ship Management — didn’t respond to emailed requests for comment. There are no contact details on maritime database

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Southwest Power Pool to develop 765-kV regional transmission ‘backbone’

Listen to the article 4 min This audio is auto-generated. Please let us know if you have feedback. Dive Brief: The Southwest Power Pool board of directors on Wednesday approved an $8.6 billion slate of 50 transmission projects across its 14-state footprint. The projects are intended to help the grid operator meet peak demand, which it expects will double, to reach 109 GW, in the next 10 years. Key to the 2025 Integrated Transmission Plan is development of a 765-kV regional transmission “backbone” that can carry four times the power SPP’s existing 345-kV lines do, and do so more efficiently. The grid operator’s transmission system “is at capacity and forecasted load growth will only exacerbate the existing strain,” it said. “Simply adding new generation will not resolve the challenges.” 765-kV transmission lines are the highest operating voltages in the U.S. but are new in both SPP and in the neighboring Electric Reliability Council of Texas market. Texas regulators approved the higher voltage lines for the first time in April. Dive Insight: Transmission developers in SPP and ERCOT are turning to 765-kV projects to mitigate line losses and move greater volumes of power into demand centers at a time when electricity demand is expected to rise significantly. “With the new load being integrated into the system, SPP could see an increase in the footprint’s annual energy consumption by as much as 136%,” the grid operator said in its ITP. “Investments in transmission are the key to keep costs low, maintain reliability, and power economic growth.” Even under conservative assumptions, SPP forecasts a 35% increase in demand, “making timely transmission investment essential,” the grid operator said. SPP selected Xcel Energy in February to construct the first 765-kV lines in its footprint. Those lines were identified in its 2024 plan. AEP Texas will build

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The week in 5 numbers: Gas valuations soar but solar leads new capacity

The price gas power merger and acquisitions have reached in some markets, according to energy analytics firm Enverus. The artificial intelligence boom, along with expectations of increased manufacturing and electrification, is driving a surge in natural gas investment, but thermal generation remains risky, some analysts say, drawing parallels to the dot com bubble at the turn of the century. 

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Designing the AI Century: 7×24 Exchange Fall ’25 Charts the New Data Center Industrial Stack

SMRs and the AI Power Gap: Steve Fairfax Separates Promise from Physics If NVIDIA’s Sean Young made the case for AI factories, Steve Fairfax offered a sobering counterweight: even the smartest factories can’t run without power—and not just any power, but constant, high-availability, clean generation at a scale utilities are increasingly struggling to deliver. In his keynote “Small Modular Reactors for Data Centers,” Fairfax, president of Oresme and one of the data center industry’s most seasoned voices on reliability, walked through the long arc from nuclear fusion research to today’s resurgent interest in fission at modular scale. His presentation blended nuclear engineering history with pragmatic counsel for AI-era infrastructure leaders: SMRs are promising, but their road to reality is paved with physics, fuel, and policy—not PowerPoint. From Fusion Research to Data Center Reliability Fairfax began with his own story—a career that bridges nuclear reliability and data center engineering. As a young physicist and electrical engineer at MIT, he helped build the Alcator C-MOD fusion reactor, a 400-megawatt research facility that heated plasma to 100 million degrees with 3 million amps of current. The magnet system alone drew 265,000 amps at 1,400 volts, producing forces measured in millions of pounds. It was an extreme experiment in controlled power, and one that shaped his later philosophy: design for failure, test for truth, and assume nothing lasts forever. When the U.S. cooled on fusion power in the 1990s, Fairfax applied nuclear reliability methods to data center systems—quantifying uptime and redundancy with the same math used for reactor safety. By 1994, he was consulting for hyperscale pioneers still calling 10 MW “monstrous.” Today’s 400 MW campuses, he noted, are beginning to look a lot more like reactors in their energy intensity—and increasingly, in their regulatory scrutiny. Defining the Small Modular Reactor Fairfax defined SMRs

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Top network and data center events 2025 & 2026

Denise Dubie is a senior editor at Network World with nearly 30 years of experience writing about the tech industry. Her coverage areas include AIOps, cybersecurity, networking careers, network management, observability, SASE, SD-WAN, and how AI transforms enterprise IT. A seasoned journalist and content creator, Denise writes breaking news and in-depth features, and she delivers practical advice for IT professionals while making complex technology accessible to all. Before returning to journalism, she held senior content marketing roles at CA Technologies, Berkshire Grey, and Cisco. Denise is a trusted voice in the world of enterprise IT and networking.

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Google’s cheaper, faster TPUs are here, while users of other AI processors face a supply crunch

Opportunities for the AI industry LLM vendors such as OpenAI and Anthropic, which still have relatively young code bases and are continuously evolving them, also have much to gain from the arrival of Ironwood for training their models, said Forrester vice president and principal analyst Charlie Dai. In fact, Anthropic has already agreed to procure 1 million TPUs for training and its models and using them for inferencing. Other, smaller vendors using Google’s TPUs for training models include Lightricks and Essential AI. Google has seen a steady increase in demand for its TPUs (which it also uses to run interna services), and is expected to buy $9.8 billion worth of TPUs from Broadcom this year, compared to $6.2 billion and $2.04 billion in 2024 and 2023 respectively, according to Harrowell. “This makes them the second-biggest AI chip program for cloud and enterprise data centers, just tailing Nvidia, with approximately 5% of the market. Nvidia owns about 78% of the market,” Harrowell said. The legacy problem While some analysts were optimistic about the prospects for TPUs in the enterprise, IDC research director Brandon Hoff said enterprises will most likely to stay away from Ironwood or TPUs in general because of their existing code base written for other platforms. “For enterprise customers who are writing their own inferencing, they will be tied into Nvidia’s software platform,” Hoff said, referring to CUDA, the software platform that runs on Nvidia GPUs. CUDA was released to the public in 2007, while the first version of TensorFlow has only been around since 2015.

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Cisco launches AI infrastructure, AI practitioner certifications

“This new certification focuses on artificial intelligence and machine learning workloads, helping technical professionals become AI-ready and successfully embed AI into their workflows,” said Pat Merat, vice president at Learn with Cisco, in a blog detailing the new AI Infrastructure Specialist certification. “The certification validates a candidate’s comprehensive knowledge in designing, implementing, operating, and troubleshooting AI solutions across Cisco infrastructure.” Separately, the AITECH certification is part of the Cisco AI Infrastructure track, which complements its existing networking, data center, and security certifications. Cisco says the AITECH cert training is intended for network engineers, system administrators, solution architects, and other IT professionals who want to learn how AI impacts enterprise infrastructure. The training curriculum covers topics such as: Utilizing AI for code generation, refactoring, and using modern AI-assisted coding workflows. Using generative AI for exploratory data analysis, data cleaning, transformation, and generating actionable insights. Designing and implementing multi-step AI-assisted workflows and understanding complex agentic systems for automation. Learning AI-powered requirements, evaluating customization approaches, considering deployment strategies, and designing robust AI workflows. Evaluating, fine-tuning, and deploying pre-trained AI models, and implementing Retrieval Augmented Generation (RAG) systems. Monitoring, maintaining, and optimizing AI-powered workflows, ensuring data integrity and security. AITECH certification candidates will learn how to use AI to enhance productivity, automate routine tasks, and support the development of new applications. The training program includes hands-on labs and simulations to demonstrate practical use cases for AI within Cisco and multi-vendor environments.

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Chip-to-Grid Gets Bought: Eaton, Vertiv, and Daikin Deals Imply a New Thermal Capital Cycle

This week delivered three telling acquisitions that mark a turning point for the global data center supply chain; and more specifically, for the high-density liquid cooling mega-play now unfolding across the power-thermal continuum. Eaton is acquiring Boyd Thermal for $9.5 billion from Goldman Sachs Asset Management. Vertiv is buying PurgeRite for about $1 billion from Milton Street Capital. And Daikin Applied has moved to acquire Chilldyne, one of the most proven negative-pressure direct-to-chip pioneers. On paper, they’re three distinct transactions. In reality, they’re chapters in the same story: the acceleration of strategic vertical integration around thermal infrastructure for AI-class compute. The Equity Layer: Private Capital Builds, Strategics Buy From an equity standpoint, these are classic handoff moments between private-equity construction and corporate consolidation. Goldman Sachs built Boyd Thermal into a global platform spanning cold plates, CDUs, and high-density liquid loop design, now sold to Eaton at an enterprise multiple north of 5× 2026E revenue. Milton Street Capital took PurgeRite from a specialist contractor in fluid flushing and commissioning into a nationwide services platform. And Daikin, long synonymous with chillers and air-side thermal, is crossing the liquid Rubicon by buying its way into the D2C ecosystem. Each deal crystallizes a simple fact: liquid cooling is no longer an adjunct; it’s core infrastructure. Private equity did its job scaling the parts. Strategic players are now paying up for the system. Eaton’s Bid: The Chip-to-Grid Thesis For Eaton, Boyd Thermal is the final missing piece in its “chip-to-grid” thesis. The company already owns the electrical side of the data center: UPS, busway, switchgear, and monitoring. Boyd plugs the thermal gap, allowing Eaton to market full rack-to-substation solutions for AI loads in the 50–100 kW+ range. It’s a statement acquisition that places Eaton squarely against Schneider Electric, Vertiv and ABB in the race to

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Space: The final frontier for data processing

There are, however, a couple of reasons why data centers in space are being considered. There are plenty of reports about how the increased amount of AI processing is affecting power consumption within data centers; the World Economic Forum has estimated that the power required to handle AI is increasing at a rate of between 26% and 36% annually. Therefore, it is not surprising that organizations are looking at other options. But an even more pressing reason for orbiting data centers is to handle the amount of data that is being produced by existing satellites, Judge said. “Essentially, satellites are gathering a lot more data than can be sent to earth, because downlinks are a bottleneck,” he noted. “With AI capacity in orbit, they could potentially analyze more of this data, extract more useful information, and send insights back to earth. My overall feeling is that any more data processing in space is going to be driven by space processing needs.” And China may already be ahead of the game. Last year, Guoxing Aerospace  launched 12 satellites, forming a space-based computing network dubbed the Three-Body Computing Constellation. When completed, it will contain 2,800 satellites, all handling the orchestration and processing of data, taking edge computing to a new dimension.

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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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