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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Gauging the real impact of AI agents

That creates the primary network issue for AI agents, which is dealing with implicit and creeping data. There’s a singular important difference between an AI agent component and an ordinary software component. Software is explicit in its use of data. The programming includes data identification. AI is implicit in its data use; the model was trained on data, and there may well be some API linkage to databases that aren’t obvious to the user of the model. It’s also often true that when an agentic component is used, it’s determined that additional data resources are needed. Are all these resources in the same place? Probably not. The enterprises with the most experience with AI agents say it would be smart to expect some data center network upgrades to link agents to databases, and if the agents are distributed away from the data center, it may be necessary to improve the agent sites’ connection to the corporate VPN. As agents evolve into real-time applications, this requires they also be proximate to the real-time system they support (a factory or warehouse), so the data center, the users, and any real-time process pieces all pull at the source of hosting to optimize latency. Obviously, they can’t all be moved into one place, so the network has to make a broad and efficient set of connections. That efficiency demands QoS guarantees on latency as well as on availability. It’s in the area of availability, with a secondary focus on QoS attributes like latency, that the most agent-experienced enterprises see potential new service opportunities. Right now, these tend to exist within a fairly small circle—a plant, a campus, perhaps a city or town—but over time, key enterprises say that their new-service interest could span a metro area. They point out that the real-time edge applications

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“Moore’s Law is slowing, but AI can’t afford to wait. Our breakthrough in photonics unlocks an entirely new dimension of scaling, by packing massive optical parallelism on a single chip,” said Patrick Bowen, CEO of Neurophos. “This physics-level shift means both efficiency and raw speed improve as we scale up, breaking free from the power walls that constrain traditional GPUs.” The new funding includes investments from Microsoft’s investment fund M12 that will help speed up delivery of Neurophos’ first integrated photonic compute system, including datacenter-ready OPU modules. Neurophos is not the only company exploring this field. Last April, Lightmatter announced the launch of photonic chips to tackle data center bottlenecks, And in 2024, IBM said its researchers were exploring optical chips and developing a prototype in this area.

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“We have important customers in the data center side. We have important OEM customers on both data center and client and that needs to be our priority to get the limited supply we have to those customers,” he added. CEO Lip-Bu Tan added that the continuing proliferation and diversification of AI workloads is placing significant capacity constraints on traditional and new hardware infrastructure, reinforcing the growing and essential role CPUs play in the AI era. Because of this, Intel decided to simplify its server road map, focusing resources on the 16-channel Diamond Rapids product and accelerate the introduction of Coral Rapids. Intel had removed multithreading from diamond Rapids, presumably to get rid of the performance bottlenecks. With each core running two threads, they often competed for resources. That’s why, for example, Ampere does not use threading but instead applies many more cores per CPU. With Coral Rapids, Intel is not only reintroducing multi-threading back into our data center road map but working closely with Nvidia to build a custom Xeon fully integrated with their NVLink technology to Build the tighter connection between Intel Xeon processors and Nvidia GPUs. Another aspect impacting supply has been yields or the new 18A process node. Tan said he was disappointed that the company could not fully meet the demand of the markets, and that while yields are in line with internal plans, “they’re still below where I want them to be,” Tan said. Tan said yields for 18A are improving month-over-month and Intel is targeting a 7% to 8% improvement each month.

Intel’s AI pivot could make lower-end PCs scarce in 2026

However, he noted, “CPUs are not being cannibalized by GPUs. Instead, they have become ‘chokepoints’ in AI infrastructure.” For instance, CPUs such as Granite Rapids are essential in GPU clusters, and for handling agentic AI workloads and orchestrating distributed inference. How pricing might increase for enterprises Ultimately, rapid demand for higher-end offerings resulted in foundry shortages of Intel 10/7 nodes, Bickley noted, which represent the bulk of the company’s production volume. He pointed out that it can take up to three quarters for new server wafers to move through the fab process, so Intel will be “under the gun” until at least Q2 2026, when it projects an increase in chip production. Meanwhile, manufacturing capacity for Xeon is currently sold out for 2026, with varying lead times by distributor, while custom silicon programs are seeing lead times of 6 to 8 months, with some orders rolling into 2027, Bickley said. In the data center, memory is the key bottleneck, with expected price increases of more than 65% year over year in 2026 and up to 25% for NAND Flash, he noted. Some specific products have already seen price inflation of over 1,000% since 2025, and new greenfield capacity for memory is not expected until 2027 or 2028. Moor’s Sag was a little more optimistic, forecasting that, on the client side, “memory prices will probably stabilize this year until more capacity comes online in 2027.” How enterprises can prepare Supplier diversification is the best solution for enterprises right now, Sag noted. While it might make things more complex, it also allows data center operators to better absorb price shocks because they can rebalance against suppliers who have either planned better or have more resilient supply chains.

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The SATA 1.0 interface made its debut in 2003. It was developed by a consortium consisting of Intel, Dell, and storage vendors like Seagate and Maxtor. It quickly advanced to SATA III in 2009, but there never was a SATA IV. There was just nibbling around the edges with incremental updates as momentum and emphasis shifted to PCI Express and NVMe. So is there any life to be had in the venerable SATA interface? Surprisingly, yes, say the analysts. “At a high level, yes, SATA for consumer is pretty much a dead end, although if you’re storing TB of photos and videos, it is still the least expensive option,” said Bob O’Donnell, president and chief analyst with TECHnalysis Research. Similarly for enterprise, for massive storage demands, the 20 and 30 TB SATA drives from companies like Seagate and WD are apparently still in wide use in cloud data centers for things like cold storage. “In fact, both of those companies are seeing recording revenues based, in part, on the demand for these huge, high-capacity low-cost drives,” he said. “SATA doesn’t make much sense anymore. It underperforms NVMe significantly,” said Rob Enderle, principal analyst with The Enderle Group. “It really doesn’t make much sense to continue make it given Samsung allegedly makes three to four times more margin on NVMe.” And like O’Donnell, Enderle sees continued life for SATA-based high-capacity hard drives. “There will likely be legacy makers doing SATA for some time. IT doesn’t flip technology quickly and SATA drives do wear out, so there will likely be those producing legacy SATA products for some time,” he said.

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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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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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