The Data Center Frontier Show

AI is no longer simply another fast-growing demand segment for the data center industry. It has become the “organizing principle” around which development strategy, infrastructure design and market selection are increasingly structured.

On this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent speaks with Colby Cox, Managing Director for the Americas at DC Byte, about the forces determining where the next generation of AI infrastructure can actually get built.

According to Cox, the market is no longer constrained primarily by demand or access to capital. The decisive constraint is executable power: whether capacity has truly been secured, when it can be energized, and whether the grid or an onsite generation strategy can support the intended phases of development.

That gap between announced and deployable capacity is becoming increasingly visible in DC Byte’s data. Cox says the firm has recorded a roughly 20% increase in projects remaining in the committed or early-stage categories, with power availability responsible for much of the delay.

At the same time, the scale of AI development continues to expand. At the beginning of 2023, DC Byte tracked three committed projects of at least 900 MW. Today, it tracks 17, along with 49 additional projects of that size in early-stage development. Fifteen of those early-stage projects exceed 2 GW, with several approaching 10 GW.

Inside the data hall, rack-density assumptions are changing just as quickly. Designs are moving from traditional averages near 6 kW per rack toward serious planning around 100-kW racks, with some AI environments pushing beyond 300 kW.

That shift affects nearly every layer of the facility, including electrical distribution, busway, breaker coordination, floor loading, mechanical plant design, piping, commissioning and heat rejection. Liquid cooling is consequently becoming a primary design system rather than a future retrofit.

Cox also examines the emerging geography of AI infrastructure. Development is spreading beyond established markets toward locations where power, policy and community support can be aligned. Texas activity is extending beyond Dallas-Fort Worth into markets including Pecos and Abilene, while Louisiana has moved from roughly 9 MW of data center capacity several years ago to a pipeline measured in gigawatts. Indiana, West Virginia, Pennsylvania and outer portions of the Atlanta market are also attracting attention.

But power is not the only variable redrawing the map. Local resistance, permitting risk and community trust are now material elements of project underwriting. As Cox puts it, the AI campus map is being redrawn “by power first and politics second.”

The conversation concludes with a look at behind-the-meter generation. Although most data center operators would prefer not to become power companies, onsite systems—particularly those built around natural gas—are becoming necessary in some markets as either a bridge to utility service or a longer-term solution.

Listen to the full episode for a data-driven examination of power availability, gigawatt-scale development, rack density, emerging markets and the new execution realities shaping the AI data center sector.

In this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent sits down with Data Center Frontier Contributing Editor Bill Kleyman, CEO and co-founder of Apolo, to preview the Data Center Frontier Trends Summit 2026, taking place August 4–6 in Reston, Virginia.

This year’s Summit arrives at a defining moment for the data center industry. After two years of massive AI infrastructure announcements, the conversation has shifted from projection to execution. The question is no longer whether AI will reshape digital infrastructure. It is whether the industry can actually build, power, cool, finance, commission, and operate the capacity now being promised.

Kleyman frames the moment around one of the key realities shaping the market: announced megawatts are not the same as energized megawatts. As power constraints, utility delays, supply chain friction, capital risk, rack density, liquid cooling, permitting, and community opposition converge, the winners will be the companies that convert intent into operational capacity.

The conversation previews major themes across the 2026 Trends Summit agenda, including the new geography of AI development, power-first site selection, the rise of the AI factory, high-density design, liquid cooling, behind-the-meter generation, supply chain execution, investment discipline, and the growing importance of earning social license with communities.

Highlights include a look ahead to the opening keynote fireside chat featuring Data Center Frontier founder Rich Miller and EdgeCore Digital Infrastructure CEO Lee Kestler; the Day Two keynote on scaling the AI factory with leaders from NVIDIA, Meta, and the Open Compute Project; sessions on AI power architecture, density, cooling, site selection, supply chain risk, and the final bottlenecks before go-live; and the closing keynote on stewardship, sustainability, and community acceptance.

As Kleyman notes, the AI infrastructure race is moving from announcements to accountability. The industry does not need more theoretical capacity. It needs energized, commissioned, operational capacity.

Listen now for a preview of the conversations shaping Data Center Frontier Trends Summit 2026—and join us in Reston this August for the full discussion.

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.