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Podcast: On the Frontier of Modular Edge AI Data Centers with Flexnode’s Andrew Lindsey

The modular data center industry is undergoing a seismic shift in the age of AI, and few are as deeply embedded in this transformation as Andrew Lindsey, Co-Founder and CEO of Flexnode. In a recent episode of the Data Center Frontier Show podcast, Lindsey joined Editor-in-Chief Matt Vincent and Senior Editor David Chernicoff to discuss […]

The modular data center industry is undergoing a seismic shift in the age of AI, and few are as deeply embedded in this transformation as Andrew Lindsey, Co-Founder and CEO of Flexnode. In a recent episode of the Data Center Frontier Show podcast, Lindsey joined Editor-in-Chief Matt Vincent and Senior Editor David Chernicoff to discuss the evolution of modular data centers, the growing demand for high-density liquid-cooled solutions, and the industry factors driving this momentum.

A Background Rooted in Innovation

Lindsey’s career has been defined by the intersection of technology and the built environment. Prior to launching Flexnode, he worked at Alpha Corporation, a top 100 engineering and construction management firm founded by his father in 1979. His early career involved spearheading technology adoption within the firm, with a focus on high-security infrastructure for both government and private clients.

Recognizing a massive opportunity in the data center space, Lindsey saw a need for an innovative approach to infrastructure deployment. “The construction industry is relatively uninnovative,” he explained, citing a McKinsey study that ranked construction as the second least-digitized industry—just above fishing and wildlife, which remains deliberately undigitized. Given the billions of square feet of data center infrastructure required in a relatively short timeframe, Lindsey set out to streamline and modernize the process.

Founded four years ago, Flexnode delivers modular data centers with a fully integrated approach, handling everything from site selection to design, engineering, manufacturing, deployment, operations, and even end-of-life decommissioning. Their core mission is to provide an “easy button” for high-density computing solutions, including cloud and dedicated GPU infrastructure, allowing faster and more efficient deployment of modular data centers.

The Rising Momentum for Modular Data Centers

As Vincent noted, Data Center Frontier has closely tracked the increasing traction of modular infrastructure. Lindsey has been at the forefront of this shift, witnessing the market evolve significantly over the last five years.

“Five years ago, we were looking at a graveyard of modular data center companies that leaned heavily on the edge,” Lindsey recalled. Many early modular providers focused on latency-sensitive, interconnected solutions—such as base stations at 5G tower sites. However, the market proved premature, hindered by high costs and the scale of deployment within the telecommunications industry.

Now, macroeconomic and technological factors have driven a fundamental shift toward modular data centers. One of the most significant drivers is the rapid evolution of chip design. “A traditional data center design cycle can take a year or 18 months,” Lindsey explained. “But if we see radical Nvidia chip advancements every 12 months, your design could be obsolete before you even break ground.” The need for embedded flexibility within data center design has made modular solutions an ideal fit.

Labor Scarcity and the Need for Efficiency

Another factor accelerating the adoption of modular infrastructure is the labor shortage in construction. “There just aren’t enough people today to build the scale of infrastructure needed for data centers,” Lindsey noted. Compounding the issue is an aging workforce, with many skilled professionals nearing retirement. “When they leave, they take decades of institutional knowledge with them.”

Modular construction mitigates this problem by shifting labor-intensive processes to manufacturing environments where technical expertise is concentrated. By centralizing production, modular providers can reduce reliance on dispersed construction labor while maintaining high precision and efficiency.

Liquid Cooling and the Future of High-Density Deployments

Flexnode is also a leader in the adoption of high-density liquid-cooled infrastructure. Lindsey attended the CoolerChips event last year and has been vocal about the advantages of liquid cooling for modern workloads. “More recently, modular is everywhere,” he said. “We at Flexnode are seeing demand hand over fist for high-density liquid-cooled systems that integrate seamlessly with broader building designs.”

This demand underscores the shift from the speculative modular edge deployments of five years ago to today’s high-performance, flexible data center solutions. “Modular is no longer just a niche,” Lindsey concluded. “It’s a critical strategy for meeting the growing demand for scalable, high-efficiency data center capacity.”

The realization that liquid cooling would become a building-wide challenge, rather than just an IT challenge, was a pivotal moment for Flexnode. “Four years ago, we recognized that liquid cooling, which had been around for 10 to 15 years in government and research, was now commercially viable. But very few data centers were truly equipped to deploy it to its full potential,” Lindsey explained. Flexnode identified an opportunity to deliver integrated liquid-cooled modules that connect IT systems to building infrastructure through a fully embedded design.

Rather than developing proprietary liquid cooling technology, Flexnode focuses on being “liquid neutral.” “The liquid cooling market is advancing well on its own,” Lindsey said. “We want to enable OEM-driven solutions like JetCool, Motivair, Isotope, and ZutaCore, ensuring they perform optimally in an environment designed to support them.”

Flexnode operates at the building scale, working on innovative heat management strategies that eliminate the need for external heat rejection. “We integrate heat rejection into the panelized construction of our modular data centers,” Lindsey explained. This approach pushes forward a broader, integrated building design suited for liquid cooling.

The Shift Toward Hybrid and Two-Phase Liquid Cooling

David Chernicoff asked Lindsey whether Flexnode leans toward specific liquid cooling methodologies, such as waterless, multi-phase, or single-phase solutions. Lindsey responded that their focus aligns with OEM and ODM preferences.

“Right now, we’re primarily working with direct-to-chip water-based single-phase cooling,” Lindsey said. “But as part of our work with the CoolerChips program, we’re also developing a hybrid immersion approach with Isotope.” This hybrid method integrates both direct-to-chip and immersion cooling.

The industry is currently debating whether to move to a single-phase hybrid approach or leapfrog directly to two-phase cooling. “The big challenge with two-phase is the environmental impact of certain chemicals used in the process,” Lindsey noted. While companies are actively working to address these concerns, two-phase cooling remains a complex consideration.

Even Nvidia is leaning toward a two-phase future. “From what we’ve heard at CoolerChips, Nvidia sees the next generation as being two-phase oriented,” Lindsey said. “But they can speak better to that.”

With liquid cooling now firmly part of the mainstream conversation, the challenge is not just about advancing the technology but also ensuring that the surrounding infrastructure evolves to support it. Flexnode’s approach—integrating liquid cooling at the building level—positions them at the leading edge of this shift.

Customer Demands Drive Cooling Technology Choices

As the industry evolves, cooling technology decisions are increasingly shaped by customer preferences. “Right now, it’s very much customer-driven for us,” Lindsey explained. “We’re working with sophisticated customers—hyperscalers and GPU-as-a-service providers—who already know what they want to deploy.”

While some enterprises may still be evaluating their liquid cooling options, hyperscalers are looking beyond traditional single-phase approaches, including both dielectric and water-based cooling. However, Lindsey emphasized that many of these developments remain in the R&D phase. “We don’t typically recommend one technology over another unless there’s a clear drawback,” he said.

One challenge with direct-to-chip cooling, for example, is achieving full heat absorption into the liquid. “That’s where hybrid approaches come in,” Lindsey noted. He described hybrid designs that integrate both two-phase direct-to-chip cooling and immersion cooling, as seen in the CoolerChips program. “In some cases, direct-to-chip is single-phase, in others, it’s two-phase. We’re working as a category B provider, helping integrate these technologies at the building level.”

Lindsey also touched on sustainability concerns, particularly around immersion cooling. “Immersion is seen as the most sustainable in terms of energy efficiency, but there are still questions about how immersion fluids impact server longevity over time,” he said. Factors like glue degradation and cable insulation breakdown raise questions about immersion cooling’s long-term sustainability profile.

Two-phase cooling also presents challenges. “There’s an ongoing discussion about PFAS and finding non-toxic, non-carcinogenic alternatives,” Lindsey explained. “Beyond that, two-phase cooling can create cavitational forces that affect motherboard and chip integrity over time. That’s why many in the industry—including Nvidia—are still weighing the trade-offs.”

With liquid cooling now firmly in the mainstream, the industry’s next challenge is integrating these technologies seamlessly into modular data centers. “It’s not just about cooling IT gear anymore; it’s about designing buildings that fully support liquid cooling at scale,” Lindsey concluded. Flexnode’s modular approach positions them at the forefront of this transformation.

Modular Configurations and Integrated Power Solutions

Finally, Flexnode’s modular approach offers extreme configurability. “Our modules can be standalone data centers or integrated into powered shell facilities,” Lindsey explained. “We configure everything from 2 MW to 20 MW standalone deployments, and we can scale up to 200 MW campuses.”

Beyond footprint flexibility, power integration is a growing focus. “On-prem generation is gaining traction, particularly with fuel-agnostic generators that can switch between natural gas, hydrogen, methane, and propane,” Lindsey noted. Collaborating with partners like Hyliion, Flexnode is exploring adaptable power solutions, including fuel cells.

Being behind the meter is another key driver. “Utilities are getting smarter about power allocation,” Lindsey said. “In Europe, data centers are facing use-it-or-lose-it policies, and in the U.S., regions like Ohio are imposing tariffs on unused capacity.” On-site power generation provides greater flexibility, helping data centers scale more efficiently and participate in curtailment programs that balance grid demand.

Looking Ahead

As modular data centers become a core part of the industry landscape, Flexnode is pushing the boundaries of what’s possible. “We see modular as a natural extension of utilities—a distributed solution that enhances flexibility,” Lindsey concluded. “And we’re just getting started.”

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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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Introducing Gemma 3

For a deeper dive into the technical details behind these capabilities, as well as a comprehensive overview of our approach to responsible development, refer to

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