For years, AI infrastructure conversations have focused primarily on securing enough power to support increasingly dense compute environments. But as hyperscale AI campuses scale toward gigawatt deployments, another resource is rapidly becoming just as consequential: water.

On this episode of the Data Center Frontier Show podcast, DCF Editor in Chief Matt Vincent is joined by Leif Percifield, Chief Product Officer at Emergence Water, and Vamsi Mokkapati, Technical Director at Nimbus Advanced Process Cooling Systems, to examine why water is evolving from a sustainability metric into a strategic infrastructure consideration.

The discussion explores how water availability is increasingly influencing data center site selection, cooling architectures, regulatory approvals, and long-term operational planning. The guests explain why communities are placing greater scrutiny on water use, why developers must now evaluate water availability 10 to 15 years into the future, and how water has effectively joined power and fiber as a foundational element of AI infrastructure planning.

The conversation also examines the industry's growing focus on balancing water and energy efficiency rather than treating them as competing priorities. Percifield and Mokkapati discuss the importance of smarter water sourcing, construction-phase water requirements that are often overlooked, and why there is unlikely to be a single cooling solution capable of serving every AI deployment.

The episode also explores the partnership between Emergence Water and Nimbus, which combines atmospheric water generation with highly water-efficient adiabatic cooling to reduce dependence on municipal water supplies while improving overall cooling efficiency. Looking ahead, the guests discuss how predictive controls, adaptive cooling strategies, and integrated water management will become increasingly important as AI infrastructure continues to scale.

The result is a timely conversation about one of the industry's fastest-emerging challenges—and why water is becoming every bit as strategic to the future of AI data centers as the power that drives them.

Artificial intelligence will continue to transform how data centers are designed, built, and operated, placing new demands on energy systems, infrastructure, and reliability. As AI workloads grow more intensive and always‑on, meeting these challenges will require a coordinated, systems‑level approach. 

In this episode, Patrick Hughes, SVP of Technical and Industry Affairs at the National Electrical Manufacturers Association (NEMA) will explore the AI Data Center Energy Performance Framework, developed in collaboration with ASHRAE and Pacific Northwest National Laboratory. The Framework provides practical, expert‑driven guidance to help owners, operators, engineers, and policymakers navigate the evolving AI landscape. 

He will provide an overview of why the Framework was created and how it is intended to be used. With thousands of data centers already operating—and many more planned—AI will drive higher load densities and increase pressure on both facilities and the grid. The Framework offers a shared foundation to align energy performance, reliability, and resilience across the full lifecycle of a data center. 

The conversation will also highlight NEMA’s role in ensuring electrical systems are fully integrated into data center design. Power distribution, safety, and infrastructure will need to work seamlessly with cooling and thermal management to avoid operational risks and support long‑term performance. 

A key theme of this Framework is collaboration. By bringing together NEMA’s leadership in electrical infrastructure, ASHRAE’s expertise in building systems, and PNNL’s energy research capabilities, the Framework will bridge traditional silos and promote a more integrated approach. 

We will also discuss how the Framework supports both new builds and existing facilities, helping organizations modernize infrastructure to meet AI demands. As a living, evolving resource, it will adapt alongside rapid changes in technology and energy needs. He’ll also explore what it means for communities and policymakers as data center growth accelerates—offering a path to balance innovation with reliability, efficiency, and long‑term infrastructure planning. 

Hyperscale AI campuses command the headlines, but the next major wave of AI adoption may play out across enterprise data centers measured in megawatts rather than hundreds of megawatts.

In this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent sits down with Kirk Killian, President of Partners National Mission Critical Facilities, to examine how Fortune 1000 and Global 2000 organizations are preparing for AI—and why their infrastructure priorities differ sharply from those of hyperscalers.

Killian argues that while enterprises are comfortable outsourcing AI training, the rise of AI inference could drive sensitive workloads back toward on-premises environments and private colocation deployments, where latency, security, compliance, and operational control become paramount. He also explains why enterprise customers continue to prioritize reliability and flexibility over sheer scale, how cabinet densities are evolving, why liquid cooling optionality matters even when it's not immediately needed, and what developers can do to better serve this often-overlooked market.

The conversation also explores the future of hybrid cloud, the economics of AI infrastructure, emerging enterprise site selection trends, and why “cloud plus controlled” may become the dominant architecture for enterprise AI.

For anyone focused on the next phase of AI infrastructure—not just the largest campuses, but the environments where AI will be embedded into everyday business operations—this discussion offers an important and frequently overlooked perspective.

As AI infrastructure scales from megawatts to gigawatts, liquid cooling is rapidly becoming a foundational technology rather than a specialized option.

In this episode of the Data Center Frontier Show podcast, recorded at Motivair's headquarters and manufacturing facility in Buffalo, New York, DCF Editor in Chief Matt Vincent sits down with Motivair CEO Rich Whitmore to discuss the evolution of liquid cooling from its roots in high-performance computing to its central role in today's AI data centers.

Whitmore explains how Motivair's decade-plus experience supporting supercomputing environments helped position the company for the current AI boom, which he describes as the commercialization of traditional HPC at unprecedented scale. The conversation explores how liquid cooling products are developed years ahead of silicon roadmaps, why manufacturing discipline and testing standards have become competitive differentiators, and how global production capacity is increasingly essential as AI deployments accelerate worldwide.

The discussion also examines one of the industry's emerging technical debates: whether ever-larger "facility-scale" coolant distribution units are the best answer for AI infrastructure. Whitmore offers a unique perspective on the realities of thermal management, noting that while AI workloads can change almost instantaneously, mechanical cooling systems must still operate within the physical constraints of pumps, valves, and fluid dynamics.

The interview was recorded during a Schneider Electric global media event that included a tour of Motivair's Buffalo manufacturing operations and the nearby 750 MW TeraWulf Lake Mariner AI campus. There, Motivair liquid cooling technologies—including CDUs, in-rack manifolds, and ChilledDoor rear-door heat exchangers—are helping support one of North America's most ambitious AI infrastructure developments.

As Whitmore explains, the question facing the industry is no longer whether liquid cooling will become mainstream. That transition is already underway. The challenge now is executing at scale—and building the manufacturing, supply chain, and engineering capabilities required to support the next generation of AI infrastructure.

Water has long been an overlooked piece of data center infrastructure, but that is rapidly changing as AI development accelerates across the industry.

In this episode of the Data Center Frontier Show podcast, DCF Editor in Chief Matt Vincent sits down with Anurag Bajpayee, co-founder and executive chairman of Gradiant, to discuss why water is increasingly emerging alongside power as one of the most important constraints facing future data center development.

Bajpayee explains how hyperscale operators are beginning to view water availability, reuse, discharge management, and community acceptance as strategic business issues rather than simply sustainability concerns.

He also discusses Gradiant's end-to-end approach to industrial water treatment, including advanced recycling technologies, AI-driven operational optimization, and the company's vision for helping data centers become less dependent on municipal water supplies.

Among the topics touched on:

• Why operator interest in water strategy has surged over the past 12 to 24 months

• How water availability is becoming a siting, permitting, and business continuity issue for AI campuses

• The concept of "controlling your water destiny"

• Turning wastewater into a resource through recycling and reuse

• How AI can optimize water treatment operations in real time

• What data centers can learn from the semiconductor industry's evolution in water management

• The water implications of direct liquid cooling and next-generation AI infrastructure

• Why water stewardship is increasingly becoming a business strategy rather than solely an environmental initiative

As AI infrastructure scales to unprecedented levels, the industry's resource challenges are expanding beyond power alone. This conversation offers a timely look at why water is becoming a critical component of data center planning, operations, and long-term growth.

Listen now to hear how Gradiant views the future of water infrastructure in the AI era and why operators are increasingly seeking greater control over one of their most essential resources.

Recorded live at Data Center World 2026, Data Center Frontier Editor in Chief Matt Vincent sits down with Phillip Koblence, COO of NYI and co-founder of Nomad Futurist, for the latest installment of Nomads at the Frontier.

The conversation explores the accelerating realities of AI infrastructure buildouts, the industry’s growing focus on community engagement, workforce shortages, and the shift toward inference-driven deployments following NVIDIA GTC 2026.

Koblence discusses why major interconnection hubs and edge-adjacent urban facilities may become increasingly important in the inference era, the operational realities of deploying AI infrastructure in legacy carrier hotels like 60 Hudson Street, and why the industry can no longer remain invisible to the communities where it builds.

Additional topics include:

“Every element of everything within the data center has a full sub-vertical industry associated with it,” Koblence says during the discussion. “People would be surprised how large of an ecosystem is involved in creating the digital economy that exists today.”

Listen now for a candid, fast-moving conversation on the state of AI infrastructure and the future of digital infrastructure development.

On the latest episode of the DCF Show Podcast, Data Center Frontier Editor in Chief Matt Vincent sits down with Kelly Gray, Senior Director at Delta Electronics, for an in-depth conversation about how AI is fundamentally reshaping data center power, cooling, and systems architecture.

Gray explains how Delta’s “chip-to-grid” strategy positions the company at the intersection of server design, thermal management, high-voltage DC power distribution, and next-generation AI infrastructure deployment.

As GPU densities climb and liquid cooling becomes mandatory for advanced AI systems, Gray argues that power and thermal design are no longer secondary considerations. They are now driving the entire facility architecture.

The discussion explores Delta’s leadership role in emerging 800 VDC architectures, including rack-level and facility-wide DC distribution systems, along with the company’s recently introduced 2.4 MW CDU designed for 800 VDC environments.

Gray describes the transition to high-voltage DC as “very real” and already underway with hyperscale and AI infrastructure customers.

The conversation also dives into microgrids, solid-state transformers (SSTs), solid oxide fuel cells, and the growing importance of on-site power generation as utilities struggle to keep pace with AI demand growth.

Gray outlines Delta’s vision for AI data centers that operate as “good neighbors” through cleaner generation, energy storage integration, and grid support capabilities.

Additional topics include Nvidia Omniverse-driven digital twins, modular infrastructure deployment, prefabrication strategies, and how AI itself may help solve the operational and architectural challenges AI creates.

The episode provides a detailed look at how one of the industry’s major power and thermal players sees the future of AI infrastructure evolving, from the rack all the way to the grid.

The AI infrastructure buildout has a gating problem, and it isn't megawatts. It's certainty of delivery.

In this episode, Data Center Frontier Editor-in-Chief Matt Vincent sits down with Jim Summers, CEO of GPC Infrastructure, to examine what large-scale power delivery actually requires in today's market.

Summers argues that hyperscalers are no longer shopping for energy. They're buying speed to market, guaranteed timelines, and risk transfer.

Utilities, hamstrung by interconnection queues and uncertain delivery dates, increasingly can't provide those things.

The conversation covers the full picture: why on-site natural gas has moved from bridge solution to permanent architectural layer, how battery systems have become essential infrastructure for managing AI's volatile load profiles, and what the supply chain — not energy policy — now governs project timelines.

Summers also walks through GPC's mobile PPA structure, designed to give operators long-term cost amortization without locking equipment in place, and makes the case that waste heat capture will eventually become standard practice.

The broader theme is risk. On-site generation shifts capital and operational responsibility to the developer. But it also hands them something utilities can't offer: direct control over their cost exposure, in a commodity market that is liquid and hedgeable.

Power in the AI era, Summers concludes, is no longer a utility assumption. It is a negotiated outcome.

On this episode of the Data Center Frontier Show, DCF Editor-in-Chief Matt Vincent speaks with Steve Altizer, CEO of Compu Dynamics, about how AI is fundamentally reshaping data center infrastructure.

Altizer explains why traditional facilities—designed for 300–400 watts per square foot—are being pushed aside by AI environments demanding up to 10x greater density.

The conversation explores what “AI-ready” really means today, from liquid cooling at the rack to evolving power topologies and the need for flexible white space that can keep pace with rapidly changing GPU architectures.

A central theme is modularity, but not the containerized version the industry has long associated with the term.

Altizer outlines a shift toward factory-built IT modules and scalable 5 MW building blocks, pointing to a future where data centers are assembled as systems rather than constructed as buildings.

The discussion also digs into the industry’s biggest execution challenges.

Liquid cooling remains a key risk area, with inconsistent installation practices and limited field experience raising concerns about long-term reliability.

At the same time, power constraints continue to sit outside the facility, with utilities and generation strategies shaping what can actually be built.

Looking ahead, Altizer offers a clear prediction: data centers will evolve into purpose-built industrial plants—“token factories”—designed for output, not occupancy.

This episode is a grounded look at how AI is moving data centers from adaptable real estate to highly specialized infrastructure systems.

As AI workloads continue to scale, data centers are facing a new class of electrical challenges—ones driven not by total energy demand alone, but by how quickly that demand can change. AI training environments, particularly those built around dense GPU clusters, can cause rapid and unpredictable swings in power consumption. These fast load changes place stress on power systems that were originally designed for steadier, more predictable behavior. 

In the podcast, we explore why traditional approaches to power stabilization may not fully address the demand of AI-driven variability. While these approaches can absorb momentary spikes, they may fall short when it comes to sustained smoothing or supporting broader system stability. This becomes even more complex as many data centers are powered by on-site generation before transitioning to utility grid connections later in their lifecycle. 

The conversation highlights how newer energy storage strategies are evolving to meet these demands. Advanced battery-based systems, when paired with more adaptive control strategies, are designed to respond rapidly to load changes while operating effectively across different grid conditions. Rather than reacting only after voltage or frequency disturbances occur, these systems can proactively manage fluctuations at the point of interconnection, helping protect generation assets, improve power quality, and facilitate faster project timelines. 

As AI continues to push infrastructure into unfamiliar territory, the industry will need flexible, high-speed solutions that work across both islanded and grid-connected environments. Technologies designed with this adaptability in mind are quickly becoming a key enabler for the next generation of AI-ready data centers. 

These capabilities described herein reflect general technology characteristics and may vary based on system configuration, site conditions, and grid environment.