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From Real Estate to AI Factories: 7×24 Exchange’s Michael Siteman on Power, Politics, and the New Logic of Data Center Development

The data center industry’s explosive growth in the AI era is transforming how projects are conceived, financed, and built. What was once a real estate-driven business has become something far more complex: an engineering and infrastructure challenge defined by power availability, network topology, and local politics. That was one of the key themes in this […]

The data center industry’s explosive growth in the AI era is transforming how projects are conceived, financed, and built. What was once a real estate-driven business has become something far more complex: an engineering and infrastructure challenge defined by power availability, network topology, and local politics.

That was one of the key themes in this recent episode of the Data Center Frontier Show podcast, where Editor-in-Chief Matt Vincent spoke with Michael Siteman, President of Prodigious Proclivities and a longtime leader and board member within 7×24 Exchange International.

Drawing on decades of experience spanning brokerage, development, connectivity strategy, and infrastructure advisory, Siteman offered a field-level view of how the industry is adapting to the demands of AI-driven infrastructure.

“The business used to be a pure real estate play,” Siteman said. “Now it’s a systems engineering problem. It’s power, network topology, the real estate itself, and political risk—all of these factors that have to work together.”

Site Selection Becomes Systems Engineering

For much of the early data center era, location decisions revolved around traditional real estate considerations: available buildings, proximity to customers, and nearby fiber connectivity.

That logic has fundamentally changed.

“Years ago, the question was: Is there a building? Are there carriers nearby?” Siteman recalled. “Now it’s completely different. Power availability, network topology, community acceptance—these are the variables that define whether a site works.”

Utilities themselves have become gatekeepers in the process.

“You go to a utility and ask if there’s power,” he explained. “They might say, ‘We might have power, but you have to pay us to study whether we actually have power.’”

In many regions experiencing rapid digital infrastructure expansion, the answer increasingly comes back the same: there simply isn’t enough grid capacity available.

Power Becomes the Project

In the gigawatt-scale era of AI infrastructure, power strategy has moved from a supporting detail to the defining factor in development.

When asked what data center companies might be getting wrong about power, Siteman argues that developers’ varying approaches aren’t mistakes so much as reflections of different business strategies.

“I wouldn’t characterize it as mistakes,” he said. “Each developer has their own business algorithm that drives their power strategy.”

Still, the constraints are real. Many utilities are unable to provide large amounts of new capacity on the timelines hyperscalers and AI developers require.

That reality is accelerating interest in behind-the-meter generation, particularly natural gas.

“What I’m seeing most often are projects that might have some grid connectivity, but not enough to power the entire data center,” Siteman said. “So they supplement it with onsite generation—usually gas.”

He has also observed a rapid shift in market acceptance.

“Six months ago, partners and customers would say, If you don’t have grid interconnection, we’re not interested.” he said. “In the last 30 days, it’s completely different. Now they say, We’d like grid power—but if you don’t have it, we’ll take onsite generation.

The driver behind this shift is simple: the urgency of AI deployment combined with historically low vacancy rates in the data center market.

Power Gravity vs. Network Gravity

Historically, fiber connectivity determined where data centers were built. Today, Siteman believes the equation has flipped.

“Network access has taken a back seat to the availability of power,” he said. “Because without power, the network means nothing.”

If power is available, networks can usually be extended to the site, even if the cost is significant.

“With power, you can drive the network to the location,” Siteman said. “It might be expensive. It might be really expensive. But at least you’ve got a path.”

He cited a project he is currently advising where an island-powered data center will rely entirely on onsite generation due to a lack of grid capacity.

“The fiber is about a thousand feet away,” he said. “So we’re doing a fiber study to understand how much additional capacity we can pull in and create parity with competitors.”

Selling Capacity Before It Exists

Another major shift in the industry is the increasing prevalence of pre-leasing data center capacity years before facilities are completed.

During Siteman’s tenure working with Digital Realty in the early 2010s, convincing customers to sign contracts for unfinished buildings was extremely difficult.

“People in the IT world are incredibly risk-averse,” he said. “Signing a colo deal felt like giving away their firstborn.”

Today, the situation is reversed.

“Now the model is on its head,” he said. “Companies are contracting for space before it exists.”

The risk, however, has not disappeared; it has simply been redistributed.

“The risk is shared,” Siteman explained. “Developers may have bankable contracts with large customers, but those contracts often include termination rights. Lenders don’t like that.”

At the same time, customers must protect themselves from delivery delays.

“If you’re an end user, you need the right to cancel if milestones aren’t met,” he said. “Otherwise you could get locked into a deal that doesn’t deliver when you need it.”

In practice, he believes hyperscale customers typically retain the upper hand.

“The end user is pretty much in the driver’s seat,” he said.

The Politics of Place

Even when power and network conditions align, developers face another growing challenge: community opposition.

Local politics, zoning battles, and regulatory friction are increasingly shaping data center project timelines across the United States.

Siteman believes the key to navigating this environment is early engagement with local stakeholders.

“It comes down to relationships,” he said. “You have to meet with every department in the city—mayor, city manager, zoning, fire, economic development.”

But he also acknowledges that the political climate has changed significantly.

“Communities are pushing back,” he said. “They don’t want the noise. They don’t see much revenue benefit. They like the fast internet, but they don’t want the data center in their backyard.”

As a result, government relations is becoming a core competency for developers.

“I think this is becoming a full-time job for some teams,” he said.

Hyperscale vs. AI Factories

Perhaps the most dramatic transformation in the industry lies in the infrastructure itself.

Siteman describes modern AI facilities as fundamentally different from traditional hyperscale or colocation data centers.

“They’re worlds apart,” he said.

Conventional data centers typically operate at 10 to 20 kilowatts per rack, with cooling systems designed around air-based architectures.

AI workloads, by contrast, demand radically higher densities and new thermal management strategies.

“In an AI environment you might have 120 or 150 kilowatts per rack,” Siteman explained. “It’s almost entirely liquid cooled—direct liquid to chip.”

These environments also create hybrid cooling ecosystems.

“You’ve got liquid-cooled AI racks but network racks that still require airflow,” he said. “So you end up with mixed topologies that completely change the data center environment.”

Despite the excitement around AI, Siteman believes traditional colocation facilities will remain essential for enterprise workloads.

“Most enterprises aren’t running their own AI workloads,” he noted. “They’re still using traditional infrastructure.”

Is AI a Bubble?

The extraordinary scale of investment in AI infrastructure has led some observers to speculate about the possibility of a technology bubble.

Siteman believes the question is worth asking—but says it is too early to draw conclusions.

“AI infrastructure is extremely expensive,” he said. “One high-performance AI server with GPUs and storage can cost over half a million dollars.”

The economic challenge will ultimately come down to whether companies can generate sufficient revenue from those investments.

“There’s an arms race in AI,” he said. “Everyone wants the biggest, fastest models and the newest hardware.”

That competition could fuel speculative investment, but for now Siteman believes the industry still has a long runway.

“I don’t think we’re seeing a bubble yet,” he said. “It’s still too new.”

The Workforce Challenge

While power shortages dominate industry headlines, Siteman believes the sector faces another critical constraint: talent.

“Data centers don’t run themselves,” he said.

The industry’s workforce is aging, and demand for skilled professionals continues to accelerate.

We simply don’t have enough people to build and operate the infrastructure that’s coming,” he said.

Organizations like 7×24 Exchange International are attempting to address the issue through initiatives such as International Data Center Day, mentorship programs, and workforce outreach.

“We’re trying to educate people about the opportunities in this industry,” Siteman said. “Because there are meaningful careers here.”

The Digital Industrial Revolution

Ultimately, Siteman sees the current moment as part of a much larger transformation.

“We are in a digital industrial revolution,” he said. “Digital infrastructure is the foundation of everything.”

And at the center of that revolution sits the data center.

“All that digital information comes from a data center.”

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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.

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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.”

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Two New England states say no to new data centers

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Microsoft will invest $80B in AI data centers in fiscal 2025

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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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