For several years, the Prince William Digital Gateway represented data center ambition at its largest scale: a proposed 2,100-acre technology corridor near Gainesville, Virginia, capable of accommodating tens of millions of square feet of digital infrastructure. Its location also made it uniquely contentious. The corridor bordered Manassas National Battlefield Park and other historic, environmental and residential resources, drawing the data center development debate beyond its usual industry and land-use constituencies. Opposition increasingly centered not only on the project’s scale, but on whether development of that magnitude belonged alongside one of the country’s most significant Civil War landscapes. In July 2026, that vision effectively ended. QTS Data Centers terminated its participation in the Digital Gateway and withdrew its remaining petitions before the Supreme Court of Virginia. The decision followed Compass Datacenters’ withdrawal in April, leaving neither of the project’s original developers pursuing the corridor. QTS said it reached the decision after “careful consideration,” while emphasizing that Virginia remains an important market for the company. From Proposed Capacity to Executable Capacity The collapse of the Digital Gateway is more than the cancellation of one unusually large development. It comes as the data center industry confronts a widening gap between announced capacity and executable capacity. Power remains the most visible constraint. But permitting discipline, environmental review, community acceptance and the durability of political support are increasingly determining whether a project can progress from land control and conceptual capacity to construction and operation. A separate development in New York underscored that shift less than two weeks after QTS withdrew. On July 14, Gov. Kathy Hochul issued Executive Order 62, establishing what the state describes as the nation’s first statewide moratorium on new hyperscale data centers. The order temporarily holds in abeyance certain incomplete state environmental permit applications for data centers capable of drawing at

Mark Lohmeyer: We’ve seen the rise of agents and agentic use cases. Years ago, it was the chat phase: Ask a question, get an answer. Now we’re in the agentic era, where you express your intent, agents spin off multiple sub-agents, working in parallel, preserving state. This is a radical shift in what infrastructure needs to do; make them fast, cost effective, secure, reliable. We’re delivering infrastructure optimized for the age of agents. NW: What’s the goal of the infrastructure buildout, and what should customers expect regarding costs? ML: Ultimately, it’s about enabling customers with leading-edge capabilities and models at scale cost-effectively. With agents, inference transactions increase by 50x, 100x versus non-agentic workloads. We’re driving the cost per transaction down exponentially. In our latest platforms, we reduce the cost by almost 2x for the same work. Customers serve twice the number of users at the same cost, directly driving profitability.

Google adjusted the Google Kubernetes Engine into an agent-native environment, where agents could be quickly spun up in sandboxes and containers. “From an infrastructure perspective, you need to spin up a bunch of TPUs or GPUs very rapidly. Then you need to be able to run them and spin them back down,” Lohmeyer said. Google also made drastic improvements to its silicon to support its middleware changes. It recently introduced new AI chips, with the TPU-8t for training, and TPU-8i for inference. The 8t chip has three times more computing power than the previous-generation Ironwood chip. The 8i chip has 384 megabytes of SRAM and 288GB of HBM3e memory, which is 50% more than the previous-generation chip. The platform is optimized for KV cache (key-value cache), which stores important contextual information needed by agents to make decisions, which reduces the round trips to other memory and storage systems. “Being able to store more of the KV cache directly on the chip allows you to respond much more rapidly and cost-effectively,” Lohmeyer said.

The data center industry has no shortage of AI infrastructure ambition. What it lacks is certainty. Power is harder to secure. Designs are advancing faster than facilities can be built. Supply chains remain vulnerable. Liquid cooling is adding operational demands. Projects that look viable on paper can still stall on permitting, commissioning or community opposition. The question in 2026 is no longer how large the AI opportunity may become. It is what can actually be delivered, and who has learned how to deliver it. That question defines the 2026 Data Center Frontier Trends Summit, August 4–6 at the Hyatt Regency Reston. Across three days, the people building, powering, financing and operating next-generation infrastructure will examine what is working, where execution is failing and how the market is responding. This is not a conference about whether AI will create demand. It is about who will be able to meet it. The advantage will belong to those who join the conversation before its conclusions become market consensus. Here are 10 reasons to be in the room. 1. The industry has entered the execution era For several years, the market has been defined by projected demand, capacity, density and investment. The next phase will be defined by execution. AI data center announcements remain abundant. Energized, commissioned and operational capacity is harder to find. DCFTS begins with a live editorial calibration, followed by “The New Geography of AI,” featuring EdgeCore CEO Lee Kestler, Data Center Frontier founder Rich Miller and DCF Editor in Chief Matt Vincent. The focus: how power, entitled land, utility partnerships and execution speed are determining where AI capacity can be built—and who can deliver it. Demand creates opportunity. Execution determines who captures it. 2. Power will be treated as the foundation of AI strategy Power is no longer one workstream

Three years ago, the data center industry’s energy conversation was largely framed around emissions. Hyperscale operators were setting carbon-free energy targets, signing renewable power agreements, and aligning their expanding infrastructure portfolios with corporate sustainability commitments. The arrival of generative AI has not eliminated those priorities. But it has reordered them. “Three years ago, data center energy, they were really focused on low emissions, no emissions,” said Cindy Taff, CEO of Sage Geosystems. “Now the primary challenge is just enough energy.” Speaking on the Data Center Frontier Show podcast, Taff described an energy market being reshaped by the speed and physical scale of AI infrastructure development. After decades of relatively flat U.S. electricity demand, AI has introduced a new class of concentrated, rapidly arriving industrial load. The result is a shift away from thinking only about how much generating capacity exists in aggregate and toward a harder question: Can usable power be delivered at a specific site, on a predictable schedule, in the quantities an AI campus requires? For hyperscalers, neocloud providers, data center developers, utilities, and energy companies, that distinction is becoming central to project execution. “I think time to power is the most precious metric right now versus cost or total capacity,” Taff said. Capacity on Paper Is Not Power at the Site Announcements of new generation can create the appearance of an energy system capable of meeting rising data center demand. But a megawatt located far from a planned campus, trapped behind a transmission constraint, or unavailable until the next decade has limited value to a developer trying to energize an AI facility within several years. “Aggregate capacity is not going to solve the problem if the power really isn’t where and when you need it,” Taff said. Data centers are large physical facilities tied to specific parcels,

Data Center Cooling Glossary The following definitions reflect common terminology used in Department of Energy guidance, ASHRAE TC 9.9 materials, Berkeley Lab resources and Green Grid efficiency metrics. Adiabatic Cooling — A cooling process that uses water evaporation to lower the temperature of air before it reaches a heat exchanger or cooling coil. It can reduce compressor demand but consumes water when evaporative assistance is active. Air-Cooled Data Center — A facility in which heat is removed from IT equipment primarily by moving conditioned air through servers, even if that heat is later transferred to water or refrigerant elsewhere in the cooling system. Air Handler — Equipment that moves, filters and conditions air before delivering it to a data hall or other controlled space. Air-Side Economizer — A system that uses suitable outdoor air, either directly or mixed with return air, to reduce or avoid compressor-based refrigeration. Airflow Management — The practice of delivering conditioned air where it is needed while preventing hot exhaust air from recirculating into server inlets. Approach Temperature — The temperature difference between the two fluids leaving a heat exchanger at their closest thermal point. In a cooling tower, it commonly refers to the difference between leaving-water temperature and entering-air wet-bulb temperature. A smaller approach generally indicates more effective heat transfer. ASHRAE TC 9.9 — The ASHRAE technical committee focused on mission-critical facilities, data centers, technology spaces and electronic equipment. It is a major source of environmental and thermal guidance for data center operators and equipment manufacturers. Blanking Panel — A panel installed in unused rack spaces to prevent hot exhaust air from recirculating to server intakes. British Thermal Unit, or BTU — A unit of heat energy commonly used to express the heating or cooling capacity of equipment. Cabinet — An enclosure, also commonly called