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

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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 26^th 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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BW Energy granted 25-year extension of license offshore Gabon

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Santos plans development of North Slope’s Quokka Unit

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EIA: US crude inventories up 3.1 million bbl

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Oil prices plunge as Iran war tensions ease amid tentative Hormuz reopening

Crude oil prices plunged sharply on Apr. 7 after US President Donald Trump announced a conditional 2-week ceasefire agreement with Iran, contingent on reopening the Strait of Hormuz and restoring safe passage for energy shipments. Both Brent and WTI crude oil fell towards $95/bbl, marking their largest single-day decline since 2020. Under the agreement, Iran signaled willingness to halt attacks on shipping and allow transit through Hormuz while broader negotiations continue. The US also indicated it would assist in clearing a backlog of tankers and stabilizing maritime traffic. Benchmark crude prices initially surged above $110/bbl in early April amid fears of prolonged supply disruption after Iran effectively restricted traffic through the strait—a corridor responsible for roughly 20% of global oil flows. The blockade, triggered by escalating US-Iran hostilities, caused tanker traffic to collapse and stranded millions of barrels of crude and refined products in the region. Despite the price correction, analysts caution that supply disruptions and infrastructure damage will continue to constrain markets. The conflict has already impaired regional energy assets, including LNG infrastructure in Qatar, and forced producers across the Middle East to curtail output or delay exports. The US Energy Information Administration (EIA) warned that fuel prices may remain elevated for months even if flows normalize, citing logistical bottlenecks, depleted inventories, and continued geopolitical uncertainty. “In theory, the 10–13 million b/d of crude oil and product supply stranded behind the Strait should now be gradually released. Whether the pre-March status quo will be re-established depends entirely on whether the truce can be turned into a permanent peace during the negotiations in Pakistan,” said Tamas Varga, analyst, PVM Oil Associates. “What appears evident, at least for now, is that the current quarter, the April–June period, will be the tightest, as the scarcity of available oil, both crude and refined

EIA: Brent crude to reach $115/bbl in second-quarter 2026

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OpenAI puts part of Stargate project on hold over runaway power costs

OpenAI has postponed plans to open one of the data centers central to its Stargate project. It announced its plan to open the data center in the UK with great fanfare last September, when it was regarded as a major boost for the country’s nascent AI industry, as well as proving a step up for OpenAI’s international credentials. At the time, Sam Altman, CEO of OpenAI, said, “The UK has been a longstanding pioneer of AI, and is now home to world-class researchers, millions of ChatGPT users, and a government that quickly recognized the potential of this technology.” All of that has been quietly forgotten. The plans for the data center in Northumberland, in the Northeast of England, have been put on hold, with the project ready to be revived when the conditions are ripe for major infrastructure investment, according to a report by the BBC.

Neoclouds gain momentum in a supply-constrained world

And since they used the same hardware, both neoclouds and traditional cloud providers are subject to the same shortage problem. Component suppliers are reporting significant shortages due to demand for AI data centers and Synergy sees neoclouds also experiencing delays just like traditional cloud providers. “Demand is currently outstripping supply,” said Dinsmore. “It will take a while before that starts to come into more balance.” Among neoclouds, CoreWeave stands out as the most direct challenger to traditional hyperscale cloud providers. Meanwhile, OpenAI and Anthropic represent a distinct but increasingly important category, and that is platform-centric providers offering cloud-like access to foundational models and AI development environments. Synergy says that as demand for AI infrastructure accelerates, neoclouds are positioning themselves as focused alternatives to traditional hyperscale providers such as Amazon, Microsoft and Google.

What is AI networking? How it adds intelligence to your infrastructure

The end goal is to make networks more reliable, efficient and performant. Enterprises are already seeing notable results when AI is applied to IT operations, including shorter deployment times, a decrease in trouble tickets, and faster time to resolution. With the help of AI, networks will become more autonomous and self-healing (that is, able to address issues without the need for human intervention). In fact, Tier 1 and Tier 2 infrastructure is moving toward ‘no human in the loop,’ Nick Lippis, co-founder and co-chair of enterprise user community ONUG, recently told Network World. In time, humans will only need to step in for policy exceptions and high-risk decisions. “Layering in AI capabilities makes LAN management applications easier to use and more accessible across an organization,” Dell’Oro Group analyst Sian Morgan said. Gartner predicts that, by 2030, AI agents will drive most network activities, up from “minimal adoption” in 2025. The firm emphasizes that leaders who overlook the AI networking shift “risk higher MTTR [meantime to repair], rising costs, and growing security exposure.” The core components of AI networking It’s important to note that the use of AI and machine learning (ML) in network management is not new. AI for IT operations (AIOps), for instance, is a common practice that uses automation to improve broader IT operations. AI networking is specific to the network itself, covering domains including multi-cloud software, wired and wireless LAN, data center switching, SD-WAN and managed network services (MNS). The incorporation of generative AI, in particular, has brought AI networking to the fore, as enterprise leaders are rethinking every single aspect of their business, networking included.

Aria Networks raises $125M and debuts its approach for AI-optimized networks

That embedded telemetry feeds adaptive tuning of Dynamic Load Balancing parameters, Data Center Quantized Congestion Notification (DCQCN) and failover logic without waiting for a threshold breach or a manual intervention. The platform architecture is layered. At the lowest levels, agents react in microseconds to link-level events such as transceiver flaps, rerouting leaf-spine traffic in milliseconds. At higher layers, agents make more strategic decisions about flow placement across the cluster. At the cloud layer, a large language model-based agent surfaces correlated insights to operators in natural language, allowing them to ask questions about specific jobs or alert conditions and receive context-aware responses. Karam argued that simply bolting an LLM onto an existing architecture does not deliver the same result. “If you ask it to do anything, it could hallucinate and bring down the network,” he said. “It doesn’t have any of the context or the data that’s required for this approach to be made safe.” Aria also exposes an MCP server, allowing external systems such as job schedulers and LLM routers to query network state directly and integrate it into their own decision-making. MFU and token efficiency as the target metrics Traditional networking is often evaluated in terms of bandwidth and latency. Aria is centering its platform around two metrics: Model FLOPS Utilization (MFU) and token efficiency. MFU is defined as the ratio of achieved FLOPS per accelerator to the theoretical peak. In practice, Karam said, MFU for training workloads typically runs between 33% and 45%, and inference often comes in below 30%. “The network has a major impact on the MFU, and therefore the token efficiency, because the network touches every aspect, every other component in your cluster,” Karam said.

New v2 UALink specification aims to catch up to NVLink

But given there are no products currently available using UALink 1.0, UALink 2.0 might be viewed as a premature launch Need to play catch up David Harold, senior analyst with Jon Peddie Research, was guarded in his reaction. “While 2.0 is a significant step forward from 1.0, we need to bear in mind that even 1.0 solutions aren’t shipping yet – they aren’t due until later this year. So, Nvidia is way ahead of the open alternatives on connectivity, indeed ahead of the proprietary or Ethernet based solutions too,” he said. What this means, he added, is that non-Nvidia alternatives are currently lagging in the market. “They need to play catch up on several fronts, not just networking. … I can’t think of a single shipping product that meaningfully has advantages over a Nvidia solution,” he said. “Ultimately UALink remains desirable since it will enable heterogeneous, multi-vendor environments but it’s quite a way behind NVLink today. ” There are plenty of signs that organizations will find it hard to break free of the Nvidia dominance, however. A couple of months ago, RISC-V pioneer SiFive signed a deal with Nvidia to incorporate Nvidia NVLink Fusion into its data center products, a departure for RISC companies. According to Harold, other companies could be joining it. “Custom ASIC company MediaTek is an NVLink partner, and they told me last week that they are planning to integrate it directly into next-generation custom silicon for AI applications,” he said. “This will enable a wider range of companies to use NVLink as their high-speed interconnect.” Other options And, Harold noted, Nvidia is already looking at other options. “Nvidia is now shifting to look at the copper limit for networking speed, with an interest in using optical connectivity instead,” said Harold.

Nvidia’s SchedMD acquisition puts open-source AI scheduling under scrutiny

Is the concern valid? Dr. Danish Faruqui, CEO of Fab Economics, a US-based AI hardware and datacenter advisory, said the risk was real. “The skepticism that Nvidia may prioritize its own hardware in future software updates, potentially delaying or under-optimizing support for rivals, is a feasible outcome,” he said. As the primary developer, Nvidia now controls Slurm’s official development roadmap and code review process, Faruqui said, “which could influence how quickly competing chips are integrated on new development or continuous improvement elements.” Owning the control plane alongside GPUs and networking infrastructure such as InfiniBand, he added, allows Nvidia to create a tightly vertically integrated stack that can lead to what he described as “shallow moats, where advanced features are only available or performant on Nvidia hardware.” One concrete test of that, industry observers say, will be how quickly Nvidia integrates support for AMD’s next-generation chips into Slurm’s codebase compared with how quickly it integrates its own forthcoming hardware and networking technologies, such as InfiniBand. Does the Bright Computing precedent hold? Analysts point to Nvidia’s 2022 acquisition of Bright Computing as a reference point, saying the software became optimized for Nvidia chips in ways that disadvantaged users of competing hardware. Nvidia disputed that characterization, saying Bright Computing supports “nearly any CPU or GPU-accelerated cluster.” Rawat said the comparison was instructive but imperfect. “Nvidia’s acquisition of Bright Computing highlights its preference for vertical integration, embedding Bright tightly into DGX and AI Factory stacks rather than maintaining a neutral, multi-vendor orchestration role,” he said. “This reflects a broader strategic pattern — Nvidia seeks to control the full-stack AI infrastructure experience.”

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.

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

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

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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