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Podcast: Nomads at the Frontier – AI, Infrastructure, and Data Center Workforce Evolution at DCD Connect New York

The 25th anniversary of the latest Data Center Dynamics event in New York City last month (DCD Connect NY 2025) brought record-breaking attendance, underscoring the accelerating pace of change in the digital infrastructure sector. At the heart of the discussions were evolving AI workloads, power and cooling challenges, and the crucial role of workforce development. […]

The 25th anniversary of the latest Data Center Dynamics event in New York City last month (DCD Connect NY 2025) brought record-breaking attendance, underscoring the accelerating pace of change in the digital infrastructure sector. At the heart of the discussions were evolving AI workloads, power and cooling challenges, and the crucial role of workforce development.

Welcoming Data Center Frontier at their show booth were Phill Lawson-Shanks of Aligned Data Centers and Phillip Koblence of NYI, who are respectively managing director and co-founder of the Nomad Futurist Foundation. Our conversation spanned the pressing issues shaping the industry, from the feasibility of AI factories to the importance of community-driven talent pipelines.

AI Factories: Power, Cooling, and the Road Ahead

One of the hottest topics in the industry is how to support the staggering energy demands of AI workloads. Reflecting on NVIDIA’s latest announcements at GTC, including the potential of a 600-kilowatt rack, Lawson-Shanks described the challenges of accommodating such density. While 120-130 kW racks are manageable today, scaling beyond 300 kW will require rethinking power distribution methods—perhaps moving power sleds outside of cabinets or shifting to medium-voltage delivery.

Cooling is another major concern. Beyond direct-to-chip liquid cooling, air cooling still plays a role, particularly for DIMMs, NICs, and interconnects. However, advances in photonics, such as shared laser fiber interconnects, could reduce switch power consumption, marking a potential turning point in energy efficiency.

“From our perspective, AI factories are highly conceivable,” said Lawson-Shanks. “But we’re going to see hybridization for a while—clients will want to run cloud infrastructure alongside inference workloads. The market needs flexibility.”

Connectivity and the Role of Tier-1 Cities

Koblence emphasized the continuing relevance of major connectivity hubs like New York City in an AI-driven world. While some speculate that dense urban markets may struggle to accommodate hyperscale AI workloads, Koblence argued that they remain vital for data generation and transmission.

“With all the talk about 600 kW racks, let’s not forget: these mature connectivity ecosystems are the engines generating the data that fuels AI,” he said. “The only way to get that data to large language models is through robust networks like those in New York.”

Workforce Development: The Talent Crisis

While technical innovations dominate discussions, the data center industry is also grappling with a growing workforce challenge. The need for skilled professionals is expanding rapidly, yet traditional education systems are not keeping pace. The Nomad Futurist Foundation, in partnership with iMasons, is working to bridge this gap by creating mentorship programs and introducing digital infrastructure concepts into STEAM curricula.

“What we’re selling is ‘cute kittens’—creating a structure to share institutional knowledge with the younger generation,” Koblence said. “There are not enough people globally to develop these assets. As an industry, we have to step up.”

The industry is evolving at an exponential rate, and new perspectives are essential. “Diversity of thought is critical,” Lawson-Shanks added. “Maybe a barista today or someone driving an Amazon truck doesn’t know digital infrastructure exists—but when they hear about it, they realize there’s a career path for them.”

Looking Ahead

As the conversation wrapped up, Koblence reflected on the enduring strength of the data center community. “It’s a fitting 25th anniversary for DCD New York. Engagement is stronger than ever, and the breadth of expertise at these conferences continues to grow.”

With AI reshaping infrastructure demands, power and cooling technologies advancing, and workforce challenges mounting, one thing is clear—the data center industry isn’t slowing down. And as the Nomad Futurist team reminded us, investing in people is just as critical as investing in technology.

Stay tuned for more discussions at the frontier of digital infrastructure, where innovation and community continue to drive the industry forward.

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CoolIT and Accelsius Push Data Center Liquid Cooling Limits Amid Soaring Rack Densities

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Podcast: Nomads at the Frontier – AI, Infrastructure, and Data Center Workforce Evolution at DCD Connect New York

The 25th anniversary of the latest Data Center Dynamics event in New York City last month (DCD Connect NY 2025) brought record-breaking attendance, underscoring the accelerating pace of change in the digital infrastructure sector. At the heart of the discussions were evolving AI workloads, power and cooling challenges, and the crucial role of workforce development. Welcoming Data Center Frontier at their show booth were Phill Lawson-Shanks of Aligned Data Centers and Phillip Koblence of NYI, who are respectively managing director and co-founder of the Nomad Futurist Foundation. Our conversation spanned the pressing issues shaping the industry, from the feasibility of AI factories to the importance of community-driven talent pipelines. AI Factories: Power, Cooling, and the Road Ahead One of the hottest topics in the industry is how to support the staggering energy demands of AI workloads. Reflecting on NVIDIA’s latest announcements at GTC, including the potential of a 600-kilowatt rack, Lawson-Shanks described the challenges of accommodating such density. While 120-130 kW racks are manageable today, scaling beyond 300 kW will require rethinking power distribution methods—perhaps moving power sleds outside of cabinets or shifting to medium-voltage delivery. Cooling is another major concern. Beyond direct-to-chip liquid cooling, air cooling still plays a role, particularly for DIMMs, NICs, and interconnects. However, advances in photonics, such as shared laser fiber interconnects, could reduce switch power consumption, marking a potential turning point in energy efficiency. “From our perspective, AI factories are highly conceivable,” said Lawson-Shanks. “But we’re going to see hybridization for a while—clients will want to run cloud infrastructure alongside inference workloads. The market needs flexibility.” Connectivity and the Role of Tier-1 Cities Koblence emphasized the continuing relevance of major connectivity hubs like New York City in an AI-driven world. While some speculate that dense urban markets may struggle to accommodate hyperscale AI workloads,

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2025 Data Center Power Poll

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How Microgrids and DERs Could Solve the Data Center Power Crisis

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John Deere unveils more autonomous farm machines to address skill labor shortage

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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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