Sustainability Becomes an Infrastructure Requirement Sustainability has long been central to the Nordic data center proposition. Hydropower, nuclear generation, wind resources and relatively cool ambient temperatures provide advantages for operators pursuing lower-carbon operations and efficient facility design. However, the growing concentration of AI infrastructure investment is changing the terms of that discussion. Rather than evaluating sustainability primarily through electricity sourcing or annualized power usage effectiveness, developers increasingly face questions about their contribution to regional energy systems, electricity supply, environmental protection and local economic development. One area where the Nordics offer a particularly developed model is waste-heat reuse. District heating networks are widespread across northern Europe, creating opportunities to recover thermal energy from data centers and distribute it to homes, businesses and other facilities. The approach has a long history in the region, including the Danish and Swedish projects explored by Rich Miller in Data Center Frontier’s earlier coverage of using server heat to warm nearby homes. In Espoo, Finland, atNorth has begun supplying recovered heat from its FIN02 data center to a neighboring retail property operated by Kesko. The system became operational in November 2025, providing a tangible example of data center thermal energy being put to productive use. In Helsinki, AI infrastructure developer OnZero has reached an agreement with municipal energy company Helen to connect a planned AI data center to the district heating network. The project is designed to supply as much as 525,000 MWh of recovered heat annually once operational, with service targeted for 2027. Denmark offers another variation on the approach. atNorth and Selected Group are advancing a project in Ølgod that would use surplus heat from atNorth’s DEN02 campus to support greenhouse food production. In October, Selected Group reported approval of the local development plan. The first phase involves two 10-hectare greenhouses, with the initial

Investors are pouring more into AI data center infrastructure than they can hope to recoup by 2030, according to Gartner. “Enterprise and consumer AI revenue will be insufficient to support profitable returns on all planned AI capital expenditures during this period,” said Gartner Senior Director Analyst Will Sommer. AI data center owners would need to generate $8.5 trillion in revenue through 2030 to cover announced capital spending, financing costs and expected investment returns, he said. “However, total enterprise and consumer AI revenue that can be attributed to AI data centers during this period is forecast to reach no more than $3.6 trillion.”

Google: Using AI to Engineer the AI Factory Google Vice President and Engineering Fellow Parthasarathy Ranganathan closed the keynote program by extending the discussion beyond infrastructure designed for AI toward infrastructure increasingly designed and operated with AI. The distinction was central to his presentation. For several years, hyperscalers have pursued progressively more sophisticated custom systems to meet the demands of artificial intelligence. Google’s own approach has included TPUs, custom CPUs, optical networking, power distribution, liquid cooling, and extensive hardware-software co-design. But Ranganathan argued that the next opportunity involves applying AI directly to the processes of systems engineering. Google has already been pursuing the concept of agile and fungible data centers, capable of accommodating changing generations of computing hardware without requiring fundamental redesigns of every supporting system. Its OCP contributions include Project Deschutes, an open cooling distribution unit architecture; Project Brazos, intended to bring liquid cooling capabilities into existing air-cooled facilities; and power distribution specifications supporting higher-voltage DC architectures. Ranganathan also highlighted collaborative work involving Google, Meta, Microsoft, and NVIDIA on specifications intended to improve coordination between AI data centers and utility systems. Those efforts address the physical infrastructure required to support AI. The next stage, he suggested, is to apply AI to the engineering processes themselves. Google is exploring AI-assisted hardware architecture, automated software vulnerability detection and repair, data center climate modeling, and infrastructure capacity planning. Among the examples Ranganathan discussed were agentic systems capable of evaluating computer architecture choices and accelerating engineering workflows, alongside AI-assisted tools for software maintenance and operational optimization. In data center design, the company is applying AI and climate modeling to decisions involving cooling capacity, extreme weather risk, and long-term infrastructure investment. The approach could enable more site-specific designs, reducing the need to rely on broad assumptions about operating conditions throughout a facility’s useful life.

Arm announces methodology for evaluating CPU performance Arm released a white paper, developed with partners, that proposes a use-case-driven methodology for measuring the CPU’s contribution to agentic AI systems. It defines five representative workload types—software engineering, enterprise operations, financial and scientific research, personal assistants, and robotics—and pairs component-level benchmarking with end-to-end task measurement built on re-playable agent trajectories. Arm also highlighted independent testing by Signal65 that found its AGI CPU could deliver up to twice the rack-level performance of AMD’s Epyc Turin generation server processors across select agentic AI workloads. The comparison emphasizes completed work under rack-level constraints rather than conventional CPU benchmarks alone. In the testing, an Arm AGI CPU averaged 1.7x better performance for tooling, 2.0x for agentic workflows and orchestration, and 2.2x for reasoning and planning in modeled per-rack performance versus an AMD Epyc Turin processor. Arm’s AGI CPU reduces sandbox creation latency by up to 45% versus AMD Epyc 9965, helping agents spin up isolated execution environments faster, according to Arm. Kioxia introduces SSDs for AI Kioxia unveiled its LD4 Series enterprise solid-state drives (SSD), designed to increase flash storage density in hyperscale data centers and high density 1U servers. The drives target read-intensive workloads, including data storage applications supporting artificial intelligence infrastructure.

What Took Effect—and What Still Requires Legislation Spanberger laid out the administration’s rationale in announcing the framework: “In the years before I took office, data centers came to Virginia and the Commonwealth did not have a clear or coordinated plan to address their impacts on Virginians — on their electric bills, their water, their land, their air, or their quality of life. That changes today. I have said many times that Virginia should be leading the nation in setting the highest standards for this industry. And now, I believe we are doing just that.” The distinction between what Virginia has already implemented and what the administration intends to pursue is consequential. Executive Order 22 establishes immediate directives for state agencies, while significant portions of the broader Data Center Accountability Framework remain proposals for the 2027 General Assembly or future regulatory action. Among the immediate changes, executive-branch agencies are prohibited from entering into or enforcing new nondisclosure agreements that conceal material information about proposed commercial data centers, public incentives, resource demands or community impacts. Existing contractual commitments are preserved, with limited exceptions contemplated for extraordinary circumstances, including national security. The prohibition does not yet extend automatically to local governments. The order also excludes new data center projects with anticipated peak electrical demand of 25 MW or greater from the Virginia Business Ready Sites Program and similar discretionary state site-readiness and expedited-review assistance. A separate, potentially more consequential proposal would eliminate by-right development approval for data centers using more than 25 MW, requiring local approval even for projects with prior authorizations. That provision has not been enacted statewide and could introduce significant legal questions concerning established development rights. The administration is also accelerating environmental oversight. Within 120 days of the order, the Office of the Chief Energy Officer must publish an initial

NVIDIA Opens More of the AI Factory Architecture NVIDIA’s Ian Buck closed the opening keynote program with an extensive presentation covering Vera Rubin systems, open rack designs, networking, power delivery and AI factory software. The company announced additional contributions to OCP based on its third-generation MGX 1.2 reference architecture. The specification includes high-density liquid-cooled computing systems, improved cooling connections, standardized power interfaces and modular mechanical designs. Buck emphasized that these developments are as much about manufacturing and serviceability as they are about computational performance. He described how NVIDIA reduced the assembly time for a compute tray from approximately 90 minutes in the Grace Blackwell generation to roughly one minute for Vera Rubin through simplified mechanical construction and more automated assembly. At the scale of thousands of racks, those manufacturing improvements can materially influence deployment schedules. NVIDIA also previewed its next-generation Vera Rubin Ultra architecture, designed to interconnect 576 GPUs using an advanced NVLink fabric with co-packaged optics. Open Data Center and Grid Resilience Specifications For data center developers and operators, however, NVIDIA’s most consequential OCP contributions may lie outside the computing rack. NVIDIA, AMD, Google and Meta have published an Open Data Center Specification, currently at version 0.7, intended to establish common design principles for AI factory facilities. The specification addresses power architecture, cooling infrastructure and physical layout, helping equipment suppliers and developers coordinate around shared infrastructure requirements. That shared facilities work unfolds alongside vendor-specific programs such as NVIDIA’s DSX Ready, which establishes qualification requirements for battery energy storage and liquid cooling equipment supporting its AI factory designs. NVIDIA also joined Google, Meta and Microsoft in publishing an OCP Grid Resilience Specification addressing the behavior of large AI installations during grid disturbances. The specification establishes expectations for facilities to remain connected, respond automatically to disturbances and recover toward normal operating