The AI infrastructure market has become very good at announcing gigawatts. Delivering them is another matter. That distinction framed one of the closing sessions of Day 1 at the Data Center Frontier Trends Summit 2026 (Aug. 4-6), where Sean Farney, vice president of data center strategy at JLL and a member of the Data Center Frontier Editorial Advisory Board, moderated a discussion on why some AI data center projects advance from concept to construction while others remain little more than ambitious site plans. Farney was joined by Lawrence Vo, vice president of M&A and capex at Csquare; John Day, chief commercial officer at CleanArc Data Centers; Justin Loth, executive director of power development at Provident Data Centers; and Roshan Shah, co-founder and CEO of Decimal Digital. The question Farney put before the group was straightforward: amid a market moving at what he called “the speed of light,” what separates the developers that actually get projects done from those that do not? The answers repeatedly came back to the same point. In the current market, land, capital and an announcement are no longer enough. Developers have to prove that power is deliverable, infrastructure is ready, regulatory processes are moving, communities are receptive, talent is available and the commercial model can withstand changing conditions. A Gigawatt on Paper Is Not a Gigawatt of Capacity For Loth, who spent roughly 15 years on the utility side before joining Provident, the scale of current data center proposals alone should force the industry to think differently about what constitutes a credible project. Before the hyperscale and AI expansion, he noted, gigawatts were a measure more commonly associated with cities than individual loads. “A 3.5 gigawatt campus,” Loth said, is roughly equivalent to the native load of Austin or San Antonio. That scale makes the distinction

As a result, Microsoft has said six additional data centers planned in the region are being designed around biomimicry principles rather than treating landscaping as something added after the engineering work is finished. The change, from landscaping as decoration to ecology as a design input, is now being applied elsewhere. There is already a significant US example, set in Mecklenburg County, Virginia, where Microsoft originally announced the Chase City Conservancy in 2022, as part of a data center development south of Chase City. The completed project, which opened in April 2025, protects more than 230 acres from development. It includes more than eight acres of wetlands, over 16,300 linear feet of restored streams, 185 acres of native pollinator habitat, more than 25,000 planted trees and over three miles of publicly accessible walking trails. Local environmental organizations helped shift the design away from what the company describes as a more conventional recreational area toward biodiversity and habitat conservation illustrating the community-engagement side of Microsoft’s model, which, given the current temperature of such relationships, can’t be understated. For data center developers, that may be as important as the ecological results. Community impact is no longer being evaluated on just tax revenue and jobs. Turning portions of a site into protected wetlands, forests, trails or habitat potentially creates a visible local benefit in ways that renewable-energy contracts hundreds of miles away cannot. Microsoft’s commitment to the local community has been led by their Community First AI Infrastructure Plan announced in January 2026. Wetlands in Wisconsin, Screening in Georgia At Microsoft’s massive Mount Pleasant, Wisconsin, AI data center development, the company is working with the Root-Pike Watershed Initiative Network on restoration projects involving wetlands, native prairie and forested riparian buffers. One element involves returning previously straightened streams to more natural, winding channels, improving aquatic

Software is still the gatekeeper Axelera In terms of software enablement, Axelera’s Voyager SDK spans its existing Metis products and the new Europa architecture, providing a common environment across embedded, edge and server deployments, with support for a multitude of computer vision models, LLMs, VLMs, diffusion models, speech and other AI workloads. To automate setup, Axelera’s Voyager Wingman uses natural-language prompts to help developers create or port inference pipelines, while AxeleraScript, or AxScript, provides a Python-enabled domain-specific language with lower-level AIPU control for custom operators and transformer models. This could prove every bit as important as Europa’s performance and efficiency. Enterprises already have models, development environments and application stacks. Extensive rewriting or specialized expertise adds development and operational costs that can quickly undermine savings on hardware and power.

For years, fiber was one of the more forgiving elements of data center site selection. Developers could secure land, line up power, begin planning the facility and then work with carriers to establish the connectivity required by tenants. In a traditional multi-tenant data center, that model generally worked. At AI scale, Scott Bergs says it increasingly does not. “The architecture of those original communications service provider networks just don’t meet the latency and/or capacity needs” of today’s high-density compute environments, said Bergs, CEO of Kirkwood Infrastructure Group, during a recent episode of the Data Center Frontier Show. The result is a significant change in the data center development stack: network infrastructure can no longer be treated as something that gets solved after the site is chosen. For hyperscalers and neo-cloud providers, fiber route diversity, latency, physical security and future capacity increasingly need to enter the conversation alongside power and land. And as data center campuses follow available power farther from established digital infrastructure hubs, the scale of the network challenge is expanding with them. A connection between data center campuses that might once have extended two or 30 miles can now stretch 250 miles or more, Bergs said. What would traditionally have been considered a long-haul fiber route is increasingly becoming another piece of inter-campus infrastructure. That change is helping drive Kirkwood’s own expansion. From DF&I to Kirkwood Bergs previously led DF&I, a dark-fiber infrastructure platform concentrated in Northern Virginia and Maryland. Kirkwood Infrastructure Group is not simply DF&I under a new name, he said. Rather, it represents what Bergs described as a second phase in a broader infrastructure investment strategy developed originally through IPI Partners. IPI, an investment platform focused on digital infrastructure, backed DF&I after identifying communications infrastructure serving dense compute environments as an area requiring greater direct

Cisco executives said during a press briefing that AI agents are operating across data centers, campuses, and branches, and interacting with enterprise resources and other agents. For example, if an agent deletes thousands of files, IT teams need to determine whether it malfunctioned or was compromised. On-premises option: Cisco AI POD for Splunk Aimed at enterprises that need to keep sensitive machine data within their own environments, Cisco AI POD for Splunk “brings Splunk AI to on-premises customers with new AI runtime software, Cisco infrastructure, Nvidia accelerated computing, and Kubernetes-based architecture, pre-validated and optimized for Splunk AI workloads,” according to Cisco. “One of the biggest roadblocks to enterprise AI today is that it’s too hard to deploy,” said Jeetu Patel, Cisco’s president and chief product officer, in a statement. “Customers want to know: Can I trust it to do the job? Can I afford it? And, most importantly, can I secure it? By running Splunk AI on the infrastructure customers already trust, they can move faster to put AI to work in their business with confidence and control.”

For data center developers facing multiyear utility interconnection queues and tightening power markets, nuclear energy is entering a different phase of its AI infrastructure story. The near-term opportunity still rests largely with the existing reactor fleet. Holtec International has moved the Palisades Nuclear Plant in Michigan into fuel loading, one of the final major stages before reactor startup activities. Constellation Energy, meanwhile, continues to work toward a 2027 restart of the former Three Mile Island Unit 1, now the Christopher M. Crane Clean Energy Center, under its long-term power agreement with Microsoft. Together, Palisades and Crane represent roughly 1.64 GW of existing nuclear capacity that could return to service without waiting for entirely new plants to be licensed, financed and constructed. That makes reactor restarts one of the few ways nuclear generation can materially intersect with data center power demand before the end of the decade. But the more consequential change may be taking place further upstream. A burst of activity from advanced nuclear developers at the end of August pointed increasingly toward the industrial systems required to move new reactor designs from demonstrations to repeatable infrastructure. X-energy, TerraPower, GE Vernova Hitachi, Oklo, Westinghouse, Kairos Power and others reported progress involving fuel supply, reactor testing, manufacturing, licensing and commercial deployment. None of these advanced reactor projects will solve the industry’s 2027 or 2028 power shortage. That is no longer the most useful test. The more important question is whether advanced nuclear can begin acquiring the characteristics of an industrial supply chain: dependable fuel, standardized manufacturing, repeatable construction, tested reactor systems and enough commercial certainty for large power customers to plan around deployment schedules measured in years rather than speculation. For data center infrastructure, that is the transition worth watching. Palisades Moves From Restoration to Startup The clearest near-term proof point