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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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How Cisco’s platform mindset is meeting the AI era

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EIA Raises 2026 WTI Forecast, Lowers 2027 Projection

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Some OPEC+ Members See Scope to Resume Hikes in April

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ADNOC Drilling Delivers ‘Best Year on Record’

In a release sent to Rigzone on Thursday, ADNOC Drilling said it had delivered its “best year on record with 2025 net profit of $1.45 billion”. “ADNOC Drilling Company PJSC announced today record fourth quarter and full year 2025 results, marking a step change in scale, technology-enabled performance and excellence in execution,” the company noted in the release. “This performance represents the strongest in the company’s history, reflecting high asset utilization and continued growth across integrated drilling and oilfield services, and driven by strong operational execution across the fleet,” it added. ADNOC Drilling highlighted in the release that its 2025 net profit marked an 11 percent year on year increase. In 2025, the company reported revenue of $4.9 billion, which it pointed out was a 22 percent increase year on year, and EBITDA of $2.2 billion, which it highlighted was an increase of nine percent year on year. “2025 was a defining year for ADNOC Drilling,” ADNOC Drilling CEO Abdulla Ateya Al Messabi said in the release. “Our record breaking results were delivered by our people, whose discipline, innovation and commitment to operational excellence and safety underpin every milestone we achieve,” Messabi added. “Our resilience as a business, built on strong systems, disciplined operations and the ability to adapt at pace, continues to reinforce our competitive strength,” the CEO continued. “Through execution excellence, technology‑led efficiency and a disciplined approach to capital allocation and operations, we continue our transformation into the region’s most advanced energy services company,” Messabi said. “By expanding across the GCC, pioneering AI‑driven operations and setting new benchmarks in sustainability, we are unlocking value and helping power the UAE’s energy future. This is just the beginning of a new era of growth, innovation and impact,” Messabi went on to note. In a release posted on its site in November last year, ADNOC Drilling announced that

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Oil Trading Giants Say Sanctions Driving Up Prices

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Market Positioning Itself for Period of Higher Geopolitical Risk

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Secretary Wright Delivers Remarks Alongside Interim Venezuelan President Delcy Rodriguez

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Starcloud prepares to launch AWS Outpost into space

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Data center capex to hit $1.7 trillion by 2030 due to AI boom

Asked on Thursday about the fact that capex is expected to approach the $1 trillion mark in 2026, he said it is somewhat surprising. “Last year, I thought it would take at least three years to get to that trillion dollar mark,” he said. “It seems increases are supported by the result of larger models needed for training infrastructure, and in turn, you need inference as well. You also need a supporting infrastructure in storage, networking, power, and cooling.” AI, he said, has become “the tide that lift all boats, meaning that in addition to the core accelerated compute, AI also positively impacts complementary infrastructure, such as storage, networking, and physical infrastructure.” Fung added that while much of the achievement of projected spending estimates will depend on whether or not this growth is sustainable, he pointed out, “it seems like the large hyperscalers have a lot of weight in optimizing cash flow and cost structures. They’re trying to get as creative as possible, generally moving towards a more vertical, integrated stack with their own custom networking and external financing, which would help  [create] more sustainable deployments and operations.” Enterprises thinking of expanding their own infrastructure can learn from this growth. In a recent article on the hyper spending of hyperscalers, Greyhound Research chief analyst Sanchit Vir Gogia said their capex spending  levels can help pinpoint where the hyperscalers are expecting bottlenecks, which is useful information for enterprises planning their own cloud strategy across multiple geographies. These and other factors can help enterprises plan their own execution timelines, he said. This article originally appeared on CIO.com.

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Cisco highlights memory costs, Silicon One growth in Q2 recap

“AI infrastructure orders taken from hyperscalers totaled $2.1 billion in Q2 compared to $1.3 billion just last quarter and equal to the total orders taken in all of fiscal year ’25, marking another significant acceleration in growth across our silicon, systems and optics,” Robbins said. “Given the strong demand for our Silicon One systems and optics, we now expect to take AI orders in excess of $5 billion and to recognize over $3 billion in AI infrastructure revenue from hyperscalers in FY ’26.” Regarding enterprise uptake, Robbins said Cisco took in $350 million in AI orders from enterprise customers in Q2 and has a pipeline in excess of $2.5 billion for its high-performance AI infrastructure portfolio. Cisco is seeing early enterprise use cases for AI around fraud detection and video analytics in sectors such as financial, manufacturing and pharmaceuticals, for example. “I also see examples in retail, where customers are leveraging agents on mobile devices in retail to help their staff do a better job engaging with their customers. We’re seeing a combination of both investment in cloud-based architectures as well as on prem,” Robbins said. Networking rules Cisco is experiencing a faster-than-historical ramp-up of next-generation platforms, including its Catalyst 9K, Wi-Fi 7, and smart switches, stated Sebastien Naji, a research analyst with William Blair, in a report after the call. He attributed it to three factors: an accelerated refresh cycle in the data center; early AI-readiness efforts in the enterprise; and end-of-support for legacy Catalyst and Nexus switches.  “We are seeing strong demand for our next-generation switching, routing and wireless products, which continue to ramp faster than prior product launches. We’re delivering AI-native capabilities across these products, including weaving security into the fabric of the network and modernizing the operational stack of campus networks,” Robbins said. Co-packaged optics? When asked

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Energy providers seek flexible load strategies for data center operations

“In theory, yes, they’d have to wait a little bit longer while their queries are routed to a data center that has capacity,” said Lawrence. The one thing the industry cannot do is operate like it has in the past, where data center power was tuned and then forgotten for six months. Previously, data centers would test their power sources once or twice a year. They don’t have that luxury anymore. They need to check their power sources and loads far more regularly, according to Lawrence. “I think that for that for the data center industry to continue to survive like we all need it, there’s going to have to be some realignment on the incentives to why somebody would become flexible,” said Lawrence. The survey suggests that utilities and load operators expect to expand their demand response activities and budgets in the near term. Sixty-three percent of respondents anticipate DR program funding to grow by 50% or more over the next three years. While they remain a major source of load growth and system strain, 57% of respondents indicate that onsite power generation from data centers will be most important to improving grid stability over the next five years. One of the proposed fixes to the power shortage has been small modular nuclear reactors. These have gained a lot of traction in the marketplace even if they have nothing to sell yet. But Lawrence said that that’s not an ideal solution for existing power generators, ironically enough.

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Nokia predicts huge WAN traffic growth, but experts question assumptions

Consumer, which includes both mobile access and fixed access, including fixed wireless access. Enterprise and industrial, which covers wide-area connectivity that supports knowledge work, automation, machine vision, robotics coordination, field support, and industrial IoT. AI, including applications that people directly invoke, such as assistants, copilots, and media generation, as well as autonomous use cases in which AI systems trigger other AI systems to perform functions and move data across networks. The report outlines three scenarios: conservative, moderate, and aggressive. “Our goal is to present scenarios that fall within a realistic range of possible outcomes, encouraging stakeholders to plan across the full spectrum of high-impact demand possibilities,” the report says. Nokia’s prediction for global WAN traffic growth ranges from a 13% CAGR for the conservative scenario to 16% CAGR for moderate and 22% CAGR for aggressive. Looking more closely at the moderate scenario, it’s clear that consumer traffic dominates. Enterprise and industrial traffic make up only about 14% to 17% of overall WAN traffic, although their share is expected to grow during the 10-year forecast period. “On the consumer side, the vast majority of traffic by volume is video,” says William Webb, CEO of the consulting firm Commcisive. Asked whether any of that consumer traffic is at some point served up by enterprises, the answer is a decisive “no.” It’s mostly YouTube and streaming services like Netflix, he says. In short, that doesn’t raise enterprise concerns. Nokia predicts AI traffic boom AI is a different story. “Consumer- and enterprise-generated AI traffic imposes a substantial impact on the wide-area network (WAN) by adding AI workloads processed by data centers across the WAN. AI traffic does not stay inside one data center; it moves across edge, metro, core, and cloud infrastructure, driving dense lateral flows and new capacity demands,” the report says. An

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Cisco amps up Silicon One line, delivers new systems and optics for AI networking

Those building blocks include the new G300 as well as the G200 51.2 Tbps chip, which is aimed at spine and aggregation applications, and the G100 25.6 Tbps chip, which is aimed at leaf operations. Expanded portfolio of Silicon One P200-powered systems Cisco in October rolled out the P200 Silicon One chip and the high-end, 51.2 Tbps 8223 router aimed at distributed AI workloads. That system supports Octal Small Form-Factor Pluggable (OSFP) and Quad Small Form-Factor Pluggable Double Density (QSFP-DD) optical form factors that help the box support geographically dispersed AI clusters. Cisco grew the G200 family this week with the addition of the 8122X-64EF-O, a 64x800G switch that will run the SONiC OS and includes support for Cisco 800G Linear Pluggable Optics (LPO) connectivity. LPO components typically set up direct links between fiber optic modules, eliminating the need for traditional components such as a digital signal processor. Cisco said its P200 systems running IOS XR software now better support core routing services to allow data-center-to-data-center links and data center interconnect applications. In addition, Cisco introduced a P200-powered 88-LC2-36EF-M line card, which delivers 28.8T of capacity. “Available for both our 8-slot and 18-slot modular systems, this line card enables up to an unprecedented 518.4T of total system bandwidth, the highest in the industry,” wrote Guru Shenoy, senior vice president of the Cisco provider connectivity group, in a blog post about the news. “When paired with Cisco 800G ZR/ZR+ coherent pluggable optics, these systems can easily connect sites over 1,000 kilometers apart, providing the high-density performance needed for modern data center interconnects and core routing.”

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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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