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The Next Data Center Constraint: Trust

When Facts Aren’t Enough Few places offer a more revealing test case than Loudoun County, Virginia. Data Center Alley has spent decades living with data center development at a scale most emerging markets will never approach. Rizer said Loudoun’s experience gives the county an unusually deep record with which to answer questions about environmental impacts, […]

When Facts Aren’t Enough

Few places offer a more revealing test case than Loudoun County, Virginia.

Data Center Alley has spent decades living with data center development at a scale most emerging markets will never approach. Rizer said Loudoun’s experience gives the county an unusually deep record with which to answer questions about environmental impacts, infrastructure and economic benefits.

But those facts increasingly struggle to penetrate the broader public debate.

Rizer said Loudoun today has more than 250 data centers, while the entire sector uses less than 10% of the county water system. He also pointed to improved air quality over the past decade and approximately $1.2 billion in tax revenue from the industry.

Yet he acknowledged that simply producing another data point does little good when residents no longer trust the people presenting it.

“I call it community concern whack-a-mole, because every time you address one thing, there are three others that pop up,” Rizer said.

The problem, in his view, has become partly emotional rather than informational.

“You can’t change how people think until you change how they feel,” he said. “And right now they feel angry, they feel confused, they are fearful, they are mistrustful, both of government and the big tech industry.”

That distinction matters.

The industry’s instinct has often been to counter criticism with facts: tax receipts, job numbers, water-use calculations, emissions data or explanations of how a particular cooling system works.

Those facts remain important. But Rizer’s argument is that the industry must first rebuild enough credibility for communities to hear them.

The Industry’s Unforced Errors

Not all of the distrust has arrived from outside the industry.

Rizer and Waitkunas were equally pointed about mistakes by developers and operators that have given opponents powerful examples to use against data center projects elsewhere.

“The industry has made some unforced errors along the way,” Rizer said.

He pointed to isolated cases involving noise, water and prolonged use of onsite generation. These may be exceptions across a huge and varied industry, he said, but once they happen they can quickly be presented as representative of the entire sector.

That dynamic is particularly difficult in an environment where organized opposition can move information across communities almost instantly.

Waitkunas said national interest groups increasingly provide local opponents with a common set of arguments and tactics, even though the appropriate response to data center development can vary dramatically from one locality to another.

At the same time, some developers have made the job easier for their critics.

Waitkunas cited a recent controversy surrounding a major proposed development in Utah as an example of how dismissing opposition rather than engaging it can escalate a local dispute into a much larger story.

“A lot of it is behavioral too,” he said.

Even the language surrounding a project can matter.

“Size and scale matters,” Rizer said. “When you call your project Colossus … those kinds of things automatically people go, ‘Whoa, I’m not sure I want a Colossus.’”

The point was partly humorous, but the underlying problem is serious. The AI infrastructure cycle is producing projects measured in hundreds of megawatts and, increasingly, gigawatts. The industry understands why that scale is arriving.

For a resident encountering a data center proposal for the first time, however, the numbers can sound less like an engineering specification than a warning.

Secrecy Has Become Its Own Risk

Waitkunas identified another recurring source of distrust: communities finding out about major projects indirectly.

A resident may first learn about a proposed data center through a news article based on property records or regulatory filings. The purchaser may be an unfamiliar LLC. Nondisclosure agreements may limit what local officials can say. The eventual operator or customer may remain unnamed.

Even when there are legitimate commercial reasons for confidentiality, the experience from the community side can look very different.

“There’s been a perception of secrecy with a lot of developments throughout the country,” Waitkunas said.

That perception can poison the relationship before a developer has formally introduced itself.

By the time the company begins explaining the project, residents may believe important decisions have already been made without them.

For Waitkunas, that is precisely why community engagement cannot begin at the end of the entitlement process. Developers need to understand the community before they need something from it.

Rethinking the Community Benefit Agreement

Community Benefit Agreements, or CBAs, are emerging as one potential framework for changing that relationship.

The concept itself is not new. Developers have long supported schools, parks, workforce programs, nonprofit organizations and other local priorities.

What Waitkunas wants to change is who helps determine those priorities — and what counts as a benefit.

Historically, he said, negotiations have often occurred primarily between developers and government officials. A new model would bring residents into the process much earlier, potentially through resident task forces or committees that can identify priorities and concerns before a benefit package is assembled.

“When developers go into an area, they need to really engage the public at the beginning and get their buy-in for what a community benefit framework should look like,” Waitkunas said.

That also means moving beyond the checkbook.

A community benefit could include money for a school or park. But it could also include operational commitments covering noise, traffic or other impacts that matter directly to neighboring residents.

If a jurisdiction establishes one allowable noise level, for example, a developer might voluntarily commit to operating materially below it.

Rizer sees that distinction as fundamental.

“Community benefits should not buy acceptance, but they should demonstrate real partnership and transparency,” he said.

Later in the discussion, he offered an even sharper test.

“It’s not okay to say we want to be good partners and we meet all the current regulations,” Rizer said. “When the noise ordinance says 55 and they come in at 54.5, that to me, that’s not partnership.”

Compliance establishes the legal floor. Partnership asks what happens above it.

That is an increasingly important distinction for developers accustomed to treating zoning approval and regulatory compliance as the principal tests of whether a project can proceed.

A project can satisfy those tests and still lose its social license.

Two Balance Sheets

For years, the industry’s community case has leaned heavily on economic development.

Data centers can expand a tax base, finance public services, generate construction activity and attract enormous capital investment. In Loudoun County, the fiscal consequences of the data center economy have been transformative.

Rizer does not discount any of that.

He argues that it is no longer sufficient.

“People look at it and they want to just sell economic success, but economic success and community trust are two different balance sheets,” he said. “And industry has to invest in both.”

That may be one of the most consequential changes confronting data center development in 2026.

A developer can arrive with a compelling financial package and still face intense opposition if residents believe the project is being done to them rather than with them.

Rizer said the larger benefits need to become tangible.

“This means new schools, this means new roads, this means ambulances so that you can cut response time down by six minutes and save lives,” he said.

But even those benefits will be difficult to discuss if the relationship begins in conflict.

“We’re not getting to that conversation because all we’re doing is responding to the things that are being thrown at us,” Rizer said.

Local Governments Need a Playbook Too

The responsibility does not rest entirely with developers.

Rizer, who has spent much of his career in government, said public agencies also need to reconsider how they communicate.

Government is generally good at making information available, he said. That does not necessarily make the information understandable.

For experienced data center markets, the distinction can be manageable. Loudoun County has a large economic development staff and decades of institutional knowledge surrounding data centers.

That is far removed from the situation confronting many emerging markets.

Rizer said some communities encountering their first major data center proposal may have a single economic development official and an administrative assistant. Planning staff may never have reviewed anything approaching the project’s scale or complexity.

The industry therefore cannot assume that every locality already understands data center design, cooling systems, electrical infrastructure, construction schedules or the economics of hyperscale development.

Communities that establish expectations before projects arrive are in a stronger position, Rizer said.

He pointed to Culpeper County, Virginia, and its technology-zone approach as an example of the larger principle: identify where development is appropriate, establish the conditions and infrastructure, and create ground rules before a developer has already acquired land and begun moving a project through the system.

“The communities that are … finding their opportunity up front, taking the time to have infrastructure in place and saying, ‘This is where we want them and these are the conditions,’ those are the ones that are winning and will win,” Rizer said.

The alternative is policymaking under pressure.

Once land has changed hands and opposition has organized, local officials often find themselves trying to write rules while simultaneously adjudicating a live project.

That is when moratoriums become attractive.

Show People the Infrastructure

Waitkunas sees another relatively simple tool that remains underused: site tours.

Bring officials, residents and other stakeholders into an operating data center. Let them hear it. Let them see the cooling systems, generators, setbacks and landscaping. Let them talk to the community already living around it.

But the choice of facility matters.

Rizer said Loudoun County hosts two or three data center tours in a typical week and could host far more. Yet Data Center Alley can itself be an intimidating comparison for a small community considering a much smaller development.

“They’re kind of overwhelmed because this is not what they’re going to be getting,” Rizer said. “They’re not going to be getting Loudoun County in their community.”

Waitkunas said his firm has worked on arranging tours of operating facilities that more closely resemble what is actually being proposed — similar footprint, setting and scale.

“You don’t want it to be a completely opposite experience of what’s being proposed and then have it potentially backfire,” he said.

The idea is straightforward: education works better when the example feels real.

The Moratorium Is Already Late

Perhaps the clearest warning from Waitkunas concerns timing.

Developers often seek community-relations help only after a project is in trouble.

A company may discuss an engagement strategy, wait several months and then return when a moratorium is pending or has already passed.

At that point, the work becomes much harder.

“They need to understand that they have to do the work ahead of time,” Waitkunas said. “Go in and meet with those folks and build relationships. Go in and take the pulse of the community, see what they want.”

Even a moratorium, however, does not necessarily have to become dead time.

Waitkunas said developers can use the period to establish resident committees, listen to concerns and demonstrate that the company is willing to change its approach.

The larger lesson is that community engagement cannot be treated as crisis communications.

“Until there are behavioral changes in the industry, we’re going to continue to see this pushback,” he said.

Does Community Engagement Need a Standard?

Waitkunas also raised a provocative question for an industry built around standards.

Data centers have mature frameworks for measuring reliability, redundancy and operating performance. Could the industry eventually develop something similar for community engagement?

Should developers be evaluated against a defined framework covering transparency, resident participation, operational commitments and community benefits?

“We have a standard with uptime and power in different tiers and everything else,” Waitkunas said. “Should the industry come up with some type of framework for a community engagement standard that they get graded on?”

The idea would represent a significant evolution.

Community relations has traditionally been highly local and difficult to standardize. A rural project in the Midwest, a brownfield redevelopment in an industrial city and a hyperscale campus in Northern Virginia do not present identical issues.

But a standard would not necessarily have to prescribe identical outcomes.

It could instead establish expectations around process: how early a developer engages, how information is disclosed, how residents participate, how concerns are documented, and whether commitments extend beyond minimum regulatory requirements.

At a moment when social-license risk increasingly affects schedules, capital and site viability, those practices are moving closer to the core development process anyway.

Worse Before Better

Neither Rizer nor Waitkunas expects the political temperature to drop quickly.

Waitkunas anticipates additional moratoriums as communities try to determine how — or whether — they want to accommodate large-scale data center development.

Rizer sees data centers becoming an increasingly potent campaign issue.

His worst-case scenario is not an election between pro-data center and anti-data center candidates.

“It’s going to be between who can be the most anti-data center,” he said.

Elements of that dynamic are already visible in Northern Virginia, he added.

Waitkunas emphasized that the opposition does not fit neatly into conventional partisan lines.

“It’s bipartisan pushback,” he said, pointing to populist factions across the political spectrum.

AI did not create that tension. But the scale, speed and visibility of the AI infrastructure buildout have accelerated it.

“AI hasn’t really created this conversation, but it definitely has put it on fast forward,” Rizer said.

And there is a human dimension behind the political rhetoric.

Rizer said the intensity of the debate has already included objects being thrown at him and death threats.

“I think it is going to get worse before it gets better,” he said. “I’m scared what getting worse looks like. Frankly, it’s tough out there right now.”

That should remove any temptation to dismiss community opposition as a communications inconvenience.

For developers, political durability is becoming a project-development requirement.

Social License as an Operating Result

Near the end of the DCF Show discussion, Rizer reduced the issue to a basic truth that can sometimes disappear amid competing claims about taxes, water, power and economic growth.

“Data centers are not impact-free,” he said. “No major infrastructure is.”

The meaningful question is not whether impacts exist.

“The question is whether we understand them, can mitigate them, communicate them honestly, and determine whether the long-term benefits justify whatever trade-offs there are.”

That is a more demanding proposition than winning a zoning vote.

It means developers will need to understand communities with some of the same seriousness with which they study transmission capacity, fiber routes, water availability and land.

It means governments need to decide where development belongs and establish expectations before projects force those decisions.

And it means community benefits will increasingly need to be demonstrated through operations and behavior, not simply described through tax projections and corporate giving.

The data center industry has spent years learning how to engineer around physical constraints.

Its next challenge may be proving that it can build the same discipline around trust.

Because social license is increasingly an outcome of execution, not messaging.

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Reports: Data Center Expansion Finds Its Contours

AI Density Is Arriving Unevenly Inside the data center, the AI transition remains equally uneven. Uptime’s 2026 survey found the average modal, or most common, rack density across respondents exceeding 11 kW for the first time, up from 9 kW in 2025. But that number requires context. A relatively small group of very high-density facilities pulls the average upward. Without those facilities, Uptime puts average modal rack density at 7.8 kW, only modestly higher than 7.5 kW in 2025. The industry therefore continues to operate two realities at once: a vast installed base running conventional rack densities and a rapidly emerging class of AI facilities pushing far beyond them. The latter is becoming more visible. Some 24% of Uptime respondents now report racks at 30 kW or higher, up from 19% last year. Much of the increase occurred above 50 kW, and some operators reported deployments exceeding 100 kW. Still, most surveyed facilities have no racks at 30 kW or above. AI inference is also moving up the density curve. For the first time in Uptime’s survey, generative AI inference matched AI training as a driver of respondents’ highest-density deployments, with 21% citing each workload. That matters because inference potentially pushes AI infrastructure requirements beyond a relatively concentrated population of model-training campuses and into a broader set of facilities and markets. Power Is Both Constraint and Risk No issue connects the three reports more consistently than power. It limits new site availability. It redirects development toward emerging markets. It shapes community debates. It affects density and cooling architecture. And once a facility is operating, it remains the largest source of outage risk. Uptime says 56% of operators who experienced an impactful outage identified power as the primary cause of their most recent incident. The institute cautions against treating the increase

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DCF Poll: What Will Constrain Data Center Growth Next?

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

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Is your networking built for AI’s traffic patterns and data volumes?

As data centers evolve into AI factories, compute has shifted from a cost center to a revenue driver. “Compute is revenue,” said Jensen Huang, co-founder and CEO of NVIDIA. “Without compute, there is no way to generate tokens. Without tokens, there’s no way to generate revenue. So, in this new world of AI, compute equals revenue.” This reframe changes an organizations’ calculus. If compute is revenue, what do you optimize for? Here are 5 questions to consider: Are you measuring what actually drives AI factory revenue? Most AI factories are power-constrained, so tokens per watt dictate how much revenue you can generate and the cost per token impacts the AI factory profit margin. But neither of these metrics should be evaluated at a single operating point. Batch jobs, real-time chat, and agentic workloads demand different points on the throughput-latency curve. AI chips that perform well at only a few points will underserve the full range of workloads. Additional key operational metrics like time to first token (TTFT), mean time between interruptions (MTBI), and platform useful life are the bedrock of AI factory efficiency. They dictate how quickly an AI factory comes online to generate tokens, the reliability of its revenue streams, and its long-term ability to remain productive as AI workloads evolve. How does agentic AI change what your CPU needs to deliver? Data center CPUs have historically been optimized for parallel throughput, where more cores improve aggregate capacity.  Agentic workloads run in loops and make different demands. The model reasons on the GPU, the CPU executes tool calls such as code compilation and data retrieval, and the result returns to the GPU so the model can reason again. Every step runs in sequence, gated by the one before it. Per-core performance and memory latency determine how fast each step

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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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