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.