The Data Center Frontier Show

Optics is no longer a supporting accessory in the data center network. As AI infrastructure advances from 400G and 800G toward 1.6-terabit connectivity, optical components are consuming a larger share of network cost, power and operational risk.

In this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent speaks with Bill Gartner, vice president and general manager of Cisco’s Optical Systems and Optics business, about how AI is changing the strategic role of optics.

Gartner explains that optics represented roughly 10% of a network port’s bill of materials at 10G. At 400G and above, the optics can cost more than the switch port itself. Reliability has also become critical: A single unstable link can force GPUs operating in parallel to stop, return to a checkpoint and restart. According to data Cisco has seen from hyperscale customers, link flaps can reduce GPU infrastructure efficiency by as much as 40%.

The conversation maps the AI network across three distinct tiers:

Scale-up: Connections within the rack, carrying approximately 500 times the bandwidth of a traditional WAN environment.

Scale-out: Connections between racks, commonly using 400G and 800G pluggable optics.

Scale-across: Coherent optical connections between data centers as AI clusters expand beyond the power limits of a single facility.

Gartner also discusses Cisco’s 1.6T roadmap, routed optical networking, coherent pluggable optics and the emerging debate around co-packaged and near-packaged optics. These architectures promise lower power consumption and greater density, but introduce new questions involving interoperability, replacement and operational resilience.

Looking ahead, Gartner emphasizes that optics is not constraining AI network growth. It is enabling clusters to scale across racks, campuses and geographically distributed data centers, while the coming inference wave shifts the industry’s focus toward cost and power efficiency.

AI infrastructure is rewriting the energy playbook.

In this episode of the Data Center Frontier Show, Sage Geosystems CEO Cindy Taff joins DCF Editor-in-Chief Matt Vincent to discuss how the explosive growth of AI has shifted data center priorities from long-term decarbonization goals toward the immediate challenge of securing enough power, quickly and in the right location.

Taff explains why aggregate generation capacity means little if electricity cannot reach a data center when it is needed. With transmission constraints and grid interconnection timelines stretching for years, hyperscalers and developers are increasingly exploring behind-the-meter generation and participating directly in energy infrastructure development.

The conversation examines Sage Geosystems’ next-generation geothermal technology, which targets hot dry rock rather than the naturally occurring underground water resources required by conventional geothermal projects. By engineering subsurface reservoirs, Sage aims to make firm, dispatchable geothermal power available across a much wider geographic footprint.

Taff also discusses how Sage combines geothermal generation with energy storage and pressure management, while addressing the water losses and high operating energy requirements associated with traditional enhanced geothermal systems.

The episode explores geothermal’s evolution from a primarily “green” energy resource into strategic infrastructure for AI. Unlike intermittent renewables, geothermal can provide firm, 24/7 power and potentially be developed close to major loads.

Taff argues that the oil and gas industry’s drilling expertise and existing infrastructure could provide a powerful scaling advantage. A hypothetical 5-gigawatt geothermal buildout over five years would require roughly 500 wells annually—a small fraction of current U.S. oil and gas drilling activity.

The central takeaway: gigawatt-scale AI campuses may dominate the headlines, but they will still be built 50 to 100 megawatts at a time.

For the next generation of data center development, Taff says the industry’s most important metric is becoming clear: time to power now matters more than cost or total capacity.

This conversation is about how the demands on data centers are changing and what that means for the systems that support them. As AI and high performance computing continue to scale, cooling is no longer a background function. It is central to whether these environments operate efficiently and reliably.

Liquid cooling is becoming more common because it can handle the heat loads that air cooling cannot. But as systems move in that direction, the margin for error becomes much smaller. These are precision environments. Everything has to work as expected, and small issues can have larger consequences than people anticipate.

Valves are a good example of something that is often overlooked but plays a critical role. They control flow, manage pressure, and help protect the integrity of the system. If they are not selected correctly, they can introduce problems that are difficult to detect early but show up later as inefficiencies or risk.

One of the biggest points Eddie will make is that these systems depend on exact specifications. Engineers are not looking for something that is close. They need a valve that matches the system requirements exactly, whether that is flow performance, pressure characteristics, materials, connections, or physical dimensions. If something does not match, it can create integration issues, reduce efficiency, or delay the project.

At the same time, the pace of data center construction is accelerating. Projects are moving quickly, and delays are not easily absorbed. That means availability and lead time are part of the technical decision, not just an operational detail. If the right solution is not available when it is needed, it creates risk for the entire build.

This creates a real challenge for engineers, buyers, and OEMs. They need highly specific solutions, but they also need them delivered quickly and consistently. It is not enough to have a product that performs. The supplier has to be able to meet the spec, support the application, and deliver on time.

Another important part of the conversation is how performance is evaluated. Published specifications do not always reflect real operating conditions. Systems do not run at a single point. They run across a range of flows and conditions. That is where the idea of usable Cv becomes important. It reflects how the valve actually performs in the system, not just how it performs in an ideal scenario.

There is also growing awareness around hidden inefficiencies. Pressure drop, turbulence, and potential leak paths can all impact system performance. In high-density environments, these factors can reduce cooling effectiveness, increase energy usage, and introduce long-term reliability concerns.

What this all points to is a shift in how components are selected. Valve selection is not a secondary decision. It is part of the overall system strategy. Getting it right helps protect uptime, maintain efficiency, and keep projects on track. Getting it wrong can introduce risks that are difficult and expensive to correct later.

The goal of the conversation is to give people a clearer understanding of what matters most as they design and support modern cooling systems. It is about making better decisions upfront so systems perform the way they are intended to over time.

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.