AI Factories Redefine Infrastructure The architecture of AI factories reflects a paradigm shift that mirrors the evolution of the industrial age itself—from manual processes to automation, and now to autonomous intelligence. Nvidia’s framing of these systems as “factories” isn’t just branding; it’s a conceptual leap that positions AI infrastructure as the new production line. GPUs are the engines, data is the raw material, and the output isn’t a physical product, but predictive power at unprecedented scale. In this vision, compute capacity becomes a strategic asset, and the ability to iterate faster on AI models becomes a competitive differentiator, not just a technical milestone. This evolution also introduces a new calculus for data center investment. The cost-per-token of inference—how efficiently a system can produce usable AI output—emerges as a critical KPI, replacing traditional metrics like PUE or rack density as primary indicators of performance. That changes the game for developers, operators, and regulators alike. Just as cloud computing shifted the industry’s center of gravity over the past decade, the rise of AI factories is likely to redraw the map again—favoring locations with not only robust power and cooling, but with access to clean energy, proximity to data-rich ecosystems, and incentives that align with national digital strategies. The Economics of AI: Scaling Laws and Compute Demand At the heart of the AI factory model is a requirement for a deep understanding of the scaling laws that govern AI economics. Initially, the emphasis in AI revolved around pretraining large models, requiring massive amounts of compute, expert labor, and curated data. Over five years, pretraining compute needs have increased by a factor of 50 million. However, once a foundational model is trained, the downstream potential multiplies exponentially, while the compute required to utilize a fully trained model for standard inference is significantly less than

Google Unveils Groundbreaking AI Partnership with PJM and Tapestry to Reinvent the U.S. Power Grid In a move that underscores the growing intersection between digital infrastructure and energy resilience, Google has announced a major new initiative to modernize the U.S. electric grid using artificial intelligence. The company is partnering with PJM Interconnection—the largest grid operator in North America—and Tapestry, an Alphabet moonshot backed by Google Cloud and DeepMind, to develop AI tools aimed at transforming how new power sources are brought online. The initiative, detailed in a blog post by Alphabet and Google President Ruth Porat, represents one of Google’s most ambitious energy collaborations to date. It seeks to address mounting challenges facing grid operators, particularly the explosive backlog of energy generation projects that await interconnection in a power system unprepared for 21st-century demands. “This is our biggest step yet to use AI for building a stronger, more resilient electricity system,” Porat wrote. Tapping AI to Tackle an Interconnection Crisis The timing is critical. The U.S. energy grid is facing a historic inflection point. According to the Lawrence Berkeley National Laboratory, more than 2,600 gigawatts (GW) of generation and storage projects were waiting in interconnection queues at the end of 2023—more than double the total installed capacity of the entire U.S. grid. Meanwhile, the Federal Energy Regulatory Commission (FERC) has revised its five-year demand forecast, now projecting U.S. peak load to rise by 128 GW before 2030—more than triple the previous estimate. Grid operators like PJM are straining to process a surge in interconnection requests, which have skyrocketed from a few dozen to thousands annually. This wave of applications has exposed the limits of legacy systems and planning tools. Enter AI. Tapestry’s role is to develop and deploy AI models that can intelligently manage and streamline the complex process of

The AI revolution is charging ahead—but powering it shouldn’t cost us the planet. That tension lies at the heart of Vaire Computing’s bold proposition: rethinking the very logic that underpins silicon to make chips radically more energy efficient. Speaking on the Data Center Frontier Show podcast, Vaire CEO Rodolfo Rossini laid out a compelling case for why the next era of compute won’t just be about scaling transistors—but reinventing the way they work. “Moore’s Law is coming to an end, at least for classical CMOS,” Rossini said. “There are a number of potential architectures out there—quantum and photonics are the most well known. Our bet is that the future will look a lot like existing CMOS, but the logic will look very, very, very different.” That bet is reversible computing—a largely untapped architecture that promises major gains in energy efficiency by recovering energy lost during computation. A Forgotten Frontier Unlike conventional chips that discard energy with each logic operation, reversible chips can theoretically recycle that energy. The concept, Rossini explained, isn’t new—but it’s long been overlooked. “The tech is really old. I mean really old,” Rossini said. “The seeds of this technology were actually at the very beginning of the industrial revolution.” Drawing on the work of 19th-century mechanical engineers like Sadi Carnot and later insights from John von Neumann, the theoretical underpinnings of reversible computing stretch back decades. A pivotal 1961 paper formally connected reversibility to energy efficiency in computing. But progress stalled—until now. “Nothing really happened until a team of MIT students built the first chip in the 1990s,” Rossini noted. “But they were trying to build a CPU, which is a world of pain. There’s a reason why I don’t think there’s been a startup trying to build CPUs for a very, very long time.” AI, the

The redevelopment of the Homer City Generating Station in Pennsylvania represents an important transformation from a decommissioned coal-fired power plant to a state-of-the-art natural gas-powered data center campus, showing the creative reuse of a large brownfield site and the creation of what can be a significant location in power generation and the digital future. The redevelopment will address the growing energy demands of artificial intelligence and high-performance computing technologies, while also contributing to Pennsylvania’s digital advancement, in an area not known as a hotbed of technical prowess. Brownfield Development Established in 1969, the original generating station was a 2-gigawatt coal-fired power plant located near Homer City, Indiana County, Pennsylvania. The site was formerly the largest coal-burning power plant in the state, and known for its 1,217-foot chimney, the tallest in the United States. In April 2023, the owners announced its closure due to competition from cheaper natural gas and the rising costs of environmental compliance. The plant was officially decommissioned on July 1, 2023, and its demolition, including the iconic chimney, was completed by March 22, 2025. ​ The redevelopment project, led by Homer City Redevelopment (HCR) in partnership with Kiewit Power Constructors Co., plans to transform the 3,200-acre site into the Homer City Energy Campus, via construction of a 4.5-gigawatt natural gas-fired power plant, making it the largest of its kind in the United States. Gas Turbines This plant will utilize seven high-efficiency, hydrogen-enabled 7HA.02 gas turbines supplied by GE Vernova, with deliveries expected to begin in 2026. ​The GE Vernova gas turbine has been seeing significant interest in the power generation market as new power plants have been moving to the planning stage. The GE Vernova 7HA.02 is a high-efficiency, hydrogen-enabled gas turbine designed for advanced power generation applications. As part of GE Vernova’s HA product line, it

The update starts with new PowerEdge R470, R570, R670 and R770 servers featuring Intel Xeon 6 with P-cores processors in single- and dual-socket configurations designed to handle high-performance computing, virtualization, analytics and artificial intelligence inferencing. Dell said they save up to half of the energy costs of previous server generations while supporting up to 50% more cores per processors and 67% better performance. With the R770, up to 80% of space can be saved and a 42U rack. They feature the Dell Modular Hardware System architecture, which is based on Open Compute Project standards. The controller system also received a significant update, with improvements to Dell OpenManage and Integrated Dell Remote Access Controller providing real-time monitoring, while the Dell PowerEdge RAID Controller for PCIe Gen 5 hardware reduces write latency up to 33-fold.

Altera will continue offering field-programmable gate array (FPGA) products across a wide range of use cases, including automotive, communications, data centers, embedded systems, industrial, and aerospace.  “People were a bit surprised at Intel’s sale of the majority stake in Altera, but they shouldn’t have been. Lip-Bu indicated that shoring up Intel’s balance sheet was important,” said Jim McGregor, chief analyst with Tirias Research. The Altera has been in the works for a while and is a relic of past mistakes by Intel to try to acquire its way into AI, whether it was through FPGAs or other accelerators like Habana or Nervana, note Anshel Sag, principal analyst with Moor Insight and Research. “Ultimately, the 50% haircut on the valuation of Altera is unfortunate, but again is a demonstration of Intel’s past mistakes. I do believe that finishing the process of spinning it out does give Intel back some capital and narrows the company’s focus,” he said. So where did it go wrong? It wasn’t with FPGAs because AMD is making a good run of it with its Xilinx acquisition. The fault, analysts say, lies with Intel, which has a terrible track record when it comes to acquisitions. “Altera could have been a great asset to Intel, just as Xilinx has become a valuable asset to AMD. However, like most of its acquisitions, Intel did not manage Altera well,” said McGregor.