Power & Sites · United States

AWS plans 36-building data center campus in Indiana County, Pennsylvania

Credited to Data Center Dynamics · datacenterdynamics.com · 22h ago

Photograph · Data Center Dynamics

Amazon Web Services (AWS) has filed a Preliminary Land Development Plan (PLDP) for a 36-building data center campus in Homer City, Pennsylvania.

The company first shared an interest in developing in the area in early August, noting that it was in discussions with Homer City Redevelopment.

It has since filed a PLDP for the project, which will be located at the Homer City Energy Campus, which lies approximately 50 miles east of Pittsburgh.

The campus will span 1,100 acres of land in Center and Blacklick Townships, Indiana County, and will cover six sites dubbed Bruner, Helen, Green, West, Harvey/Gaston, and Rura.

AWS plans to build 36 data centers on the site, as well as ancillary office and storage space, electrical substation infrastructure, occupied and unoccupied security features, and redundant electrical generation. Each data center building will have two stories, reaching heights of 70 feet (21 meters), and 225,000 sq ft (20,903 sqm) of floor space.

Once operational, an estimated 1,500-2,000 jobs are expected to be created with salaries averaging between $80,000 and $100,000.

Construction will proceed over four phases, beginning in December 2026, with the last phase set to commence in January 2032. The campus aims to be fully completed by June 2036.

Knighthead Capital Management is helping finance the project.

The site will be powered by a 4.5GW natural gas power generation facility being constructed by Homer City Redevelopment.

It previously hosted the Homer City Generating Station, a 2GW coal power plant in operation from 1969 to 2023 and was the largest coal-burning power plant in Pennsylvania. Plans for the new gas plant were announced last year, with data centers named as a targeted customer.

Most of the energy generated from the gas plant will be used to power the data center campus, but more than 1GW will be injected back into the grid.

AWS has also committed to establishing a 'community benefits agreement' to "mitigate community impacts and ensure the Project delivers tangible local benefits."

Amazon has pledged to invest some $20 billion in Pennsylvania across multiple campuses.

AWS has been looking to develop in Salem Township since early 2024. The company acquired an existing data center from Talen Energy and around 1,200 acres of land adjacent to the latter’s nuclear power plant, with the aim of developing up to 15 buildings there.

The company has also been looking to develop in Falls and Kline Townships.

Earlier this month, AWS announced it was dropping non-disclosure agreements for its data center projects, and committed $1bn in additional community investments across the US.

Power & SitesUnited States

Fit Energy Will Use Gas-Powered Fuel Cells For Pennsylvania Data Center Project

Credited to Power Magazine · powermag.com21h ago

Useful

Fit Energy, a developer of energy solutions for data center projects, has announced plans for a fuel cell power generation and data center project in Newport Township, Luzerne County, Pennsylvania. The project received township zoning approval for both uses in August of this year. Fit Energy on October 6 said engineering and the remaining permitting work are now underway ahead of construction.

The project is planned on land that has already been mined and quarried, putting previously used industrial property back into productive use rather than developing untouched forests or farmland. The on-site power will be generated by natural gas fuel cells supplied by FuelCell Energy, Inc., a Connecticut-based manufacturer of utility-scale fuel cell technology, under its strategic agreement with Fit Energy.

The fuel cells produce electricity through an electrochemical process rather than combustion, which helps deliver cleaner energy with minimal impact on the surrounding community. On-site power generation will reduce the project’s reliance on the regional power grid.

The site plan includes setbacks, landscaping, and screening designed to limit views of the site from nearby homes and local roads, demonstrating a focus on balancing energy infrastructure needs with community character.

The project includes investment in power, natural gas, water, and sewer infrastructure that will serve the site and add to local utility capacity and stability for the future. Construction is expected to create significant employment and business opportunities for contractors, skilled trades, and suppliers over a multi-year buildout. Once operational, the project is expected to support permanent skilled positions in operations, maintenance, security, facility management, and technology. The project is also expected to generate new tax revenue for Newport Township, the local school district, and Luzerne County.

Fit Energy is working with Newport Township on a Community Benefits Agreement that would create a formal framework for directing additional investment toward priorities identified by the community.

“The Newport Township Commissioners have been proactive in their response to promote development in appropriately zoned sections of the Township that respects community input and promotes investments that create new jobs and new local tax revenue that enhance the quality of life for all Township residents,” said Joe Hillan, Newport Township Manager. “We recognize Fit Energy has made significant commitment to this development, and we welcome the company’s investment in our community and appreciate Fit Energy’s willingness to work with the Township to address any and all community concerns.”

Fit Energy said it will work closely with township officials and residents through permitting, construction, and operations. As part of that commitment, Fit Energy will host a public information session where residents can review the plans, ask questions, and hear directly from the project team. The date, time, and location will be announced at www.newportdatacenter.com, where residents can also submit questions, read answers to frequently asked questions, and follow project updates.

“A project like this is most successful when the community has the opportunity to hear directly from the people building it,” said Joel Leonoff, CEO of Fit Energy. “We’re committed to being transparent, answering questions and building a long-term relationship with Newport Township as we move through the next stages of this project.”

—This content was contributed by the communications team for Fit Energy.

Few moments have placed more pressure on the U.S. power sector than the one we’re in now—surging load growth, an evolving generation mix, mounting reliability concerns, rising costs, and a regulatory environment in flux. But those pressures look very different depending on where you sit on the grid.

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Attendees will leave with a clearer picture of how different segments of the power sector are approaching today’s defining issues, and where their priorities align, diverge, and intersect.

This executive panel brings together leaders from across the power sector for an in-depth discussion of the issues shaping the industry today. The discussion may explore current trends in generation, transmission, and distribution, along with the regulatory environment, supply chain pressures, demand growth, and workforce considerations influencing strategic decisions. The session will offer attendees a direct view into how utilities are adapting their planning, operations, and investment priorities to meet a rapidly evolving landscape.

Women are driving innovation and transformation across the power sector. This panel highlights how diverse leadership strengthens organizational resilience, fosters inclusive practices, and fuels progress in a rapidly evolving industry. Hear from trailblazers as they share strategies for recruiting women into energy roles, advancing careers, and breaking barriers. Whether you're new to the field or a seasoned professional, this session offers insights to empower your journey and build a more inclusive energy future.

Across the country, state policymakers and regulators are taking proactive steps to meet the challenge of load growth. What are the policies, regulatory strategies, and state programs that have worked for responding to growth while keeping costs low for ratepayers?

The U.S power sector is going through profound change. The new administration slowed the pace of regulatory initiatives, courts placed key rules on hold, and multiple environmental standards affecting generation and transmission entered periods of reconsideration. At the same time, demand pressures from data centers, electrification, and broader economic growth continued to mount, leaving utilities and power producers facing critical choices about where to invest in new generation, how quickly to retire aging assets, and how to plan for compliance in a policy environment defined by uncertainty.

Against this backdrop, the session will provide a comprehensive update on the environmental regulations shaping the industry and examine how ongoing litigation could alter the trajectory of rulemaking and permitting timelines into 2026 and beyond. It will also challenge the traditional view of permitting as a purely technical and regulatory exercise governed by statutes, studies, and agency schedules. In practice, permitting is deeply political: community sentiment, local leadership dynamics and organized advocacy cam all determine whether a project advances smoothly or becomes mired in controversy. Building on this dual regulatory and political context, attendees will learn practical approaches for treating permitting as both a compliance requirement and a political pathway.

This panel will focus on AI’s role and application across the utility lifecycle. From generation, control systems, and emission monitoring, AI is playing an ever increasingly important role on security and predictive maintenance.

Creating Value from Data: Maximizing Plant Performance Through Real-Time Analytics

Modern process industries are experiencing fluctuating market conditions and tight operational margins, leading chemical engineers to rely on real-time data to boost efficiency and reduce costs. Yet, many organizations are at different stages in their digital transformation journey. Some are just starting, while others are looking to optimize existing solutions. This webinar explores practical ways […]

Smart MRO: Using Technology and Assessment to Enhance Power Plant Performance

How One Electric Utility Is Using AI and Extreme Weather Modeling to Make Critical Grid Decisions

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Chips & Supply · United States

The AI Boom Has a Gigawatt Accounting Problem

Credited to EE Times · eetimes.com · 16h ago

Photograph · EE Times

Track energized compute, not gigawatts, as AI data centers face delays in memory, networking, cooling, and power.

Broadcom can lend Anthropic up to $42 billion to help finance computing infrastructure. Nvidia says it intends to invest as much as $100 billion in OpenAI as Nvidia systems are deployed. AMD has issued warrants to major customers whose vesting is tied partly to gigawatt-scale GPU purchases.

Capital is clearly available for AI infrastructure. The harder question is what those commitments will produce in the physical world.

The industry increasingly describes AI infrastructure in gigawatts. OpenAI and Nvidia have announced plans for at least 10 GW of Nvidia systems. OpenAI has a separate definitive agreement with AMD covering 6 GW of GPUs and another collaboration with Broadcom covering 10 GW of OpenAI-designed accelerators and networking systems. Those figures look comparable, but they describe different things.

A gigawatt of contracted GPUs is not necessarily a gigawatt of data center capacity. Contracted cloud compute is not the same as installed hardware, and infrastructure described as secured is not necessarily infrastructure that has memory, networking, cooling, and power available to operate.

That distinction matters because AI infrastructure is becoming a systems problem, not simply a semiconductor capacity problem.

I have followed semiconductor capacity cycles for more than two decades, including during my years at Intel. Purchase orders are only one part of the capacity equation. Manufacturing, packaging, memory, and the infrastructure around the chip determine how quickly demand turns into usable systems.

Nvidia’s Vera Rubin reference design illustrates the scale involved. Its published configuration for a 100 MW AI factory includes 40,000 Rubin GPUs and 12 petabytes of HBM4. A simple linear scaling of that reference design would imply roughly 400,000 GPUs and 120 petabytes of HBM4 per gigawatt. That is an illustration, not a universal conversion factor for AI capacity.

The memory requirement alone is significant. Samsung expects HBM to account for nearly 30% of industry DRAM wafer capacity in 2027, up from about 20% currently. Samsung also notes that HBM and standard DRAM share wafer production capacity, so growth in one affects the capacity available for the other.

The next challenge is connecting the processors. Large AI clusters require switches, network-interface cards and large volumes of high-speed optical links. At rack level, rising power density adds liquid cooling, coolant distribution, large power systems, and more complex commissioning.

The GPU is therefore only one component in a much larger industrial system. The supply chain extends from semiconductor manufacturers and memory producers to cooling suppliers, electrical contractors, utilities, and power generation companies.

That becomes clear when construction starts.

JLL estimates that a 50 MW data center takes about 18 months to build globally. Developers are ordering some equipment as much as two years in advance, yet 57% of projects experienced construction delays of at least three months in 2025. In the U.S., JLL puts average equipment lead times at roughly 42 weeks, 83% above 2019 levels. Transformers and switchgear average about 43 weeks, while generators average roughly 51 weeks. A contracted gigawatt can therefore exist long before it becomes usable compute.

The difference also becomes more important closer to the power grid. Semiconductor capacity is relatively fungible. Wafers can be reallocated between customers, and some production steps can be moved between suppliers. A substation, transmission connection, or gas pipeline belongs to a specific location.

That makes power increasingly central to AI deployment. Enverus forecasts that 22.5 GW of new U.S. data center demand between 2026 and 2030 will be served by behind-the-meter generation, equivalent to about 36% of projected U.S. data center capacity additions during that period. Its forecast includes 29.6 GW of gas-fired generation.

Developers are effectively considering power generation as part of the data center project rather than waiting for conventional grid expansion. That can shorten some timelines, but it creates another set of constraints around turbines, engines, transformers, pipelines, and permits. Enverus itself cautions that access to a theoretical power source does not automatically make a site ready for construction.

The financing structures now emerging around AI make these physical constraints more relevant.

OpenAI and Nvidia’s announced partnership calls for at least 10 GW of Nvidia systems, representing millions of GPUs. Nvidia says it intends to invest up to $100 billion in OpenAI as the systems are deployed. The first gigawatt is targeted for the second half of 2026 using Vera Rubin. Importantly, the arrangement was announced as a letter of intent rather than a completed purchase obligation.

OpenAI separately entered a definitive agreement with AMD covering 6 GW of GPUs across multiple generations. The first 1 GW deployment using MI450-series products is scheduled to begin in the second half of 2026. AMD also issued OpenAI a warrant for up to 160 million AMD shares, with vesting linked in part to GPU-purchase milestones.

A separate OpenAI collaboration with Broadcom covers 10 GW of OpenAI-designed accelerators and networking systems, with deployment targeted to begin in the second half of 2026 and finish by the end of 2029.

Those announcements cannot simply be added together and treated as separate data center capacity. They describe different layers of the same infrastructure buildout.

The Meta-AMD agreement shows why that distinction matters. AMD describes the broader relationship as covering up to 6 GW, but its SEC filing says Meta made a binding commitment for the initial 1 GW. Full vesting of Meta’s warrant for up to 160 million AMD shares depends on purchases eventually reaching 6 GW. The headline number and the current binding obligation are not identical.

Anthropic provides another example of how financing and hardware are becoming intertwined.

According to Anthropic’s IPO prospectus, as reported by Reuters, Broadcom agreed to lend the company up to $42 billion to help finance infrastructure. Reuters reported that the facility could cover roughly one-third of a five-year, $125.2 billion commitment for TPU computing capacity. Anthropic is also expected to become Broadcom’s largest chip design customer in 2027.

This does not mean the underlying demand is artificial. It does mean that the roles of supplier, customer, investor, and financier are becoming increasingly difficult to separate.

The same future unit of compute can now appear in several places: as a semiconductor order, a cloud compute commitment, a data center project, a financing obligation, and eventually as capacity described by an AI company as secured.

Each disclosure may be accurate on its own. Problems arise when they are added together as though each represents separate physical capacity. That is the accounting issue behind the industry’s growing use of gigawatts. A gigawatt is a precise unit of electrical power. It is much less precise as a measure of AI capacity.

None of this means the AI infrastructure boom cannot be built. Semiconductor manufacturers are expanding production, memory suppliers are adding HBM capacity, and data center developers are ordering equipment earlier and looking for alternatives to conventional grid connections.

The more important constraint may be timing. Capital can be committed quickly. A GPU agreement can be signed years before delivery, and a cloud customer can contract for compute before the underlying facility exists. Usable compute becomes available only when the semiconductor, memory, network, rack, cooling, and power layers are ready in the same place.

A one-year delay does more than move a construction schedule. It affects utilization, financing costs, depreciation assumptions, and the timing of revenue throughout the infrastructure supply chain.

For that reason, announced gigawatts may become less useful as the AI buildout matures. A better measure may be time to energized compute: How long it takes a financial commitment to become a functioning system that can actually run workloads.

The AI industry has shown that it can finance enormous amounts of future capacity. The next test is how quickly the physical infrastructure can catch up.

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The Energy Barrier Reshaping AI Hardware

OpenAI and Nvidia Systems Partnership Supports the announced 10 GW framework, millions of GPUs, intended investment of up to $100 billion and the initial Vera Rubin deployment. https://openai.com/index/openai-nvidia-systems-partnership/

OpenAI and AMD Strategic Partnership Supports the 6 GW definitive agreement, first 1 GW MI450-series deployment and warrant for up to 160 million AMD shares. https://openai.com/index/openai-amd-strategic-partnership/

OpenAI and Broadcom Strategic Collaboration Supports the 10 GW accelerator and networking collaboration and targeted 2026-2029 deployment period. https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/

Nvidia Vera Rubin NVL72 Supports the 100 MW reference configuration with 40,000 Rubin GPUs and 12 PB of HBM4. The 1 GW figures in the article are derived by simple linear scaling for illustration only. https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/

AMD SEC filing, Meta Agreement Supports the binding initial 1 GW commitment, broader arrangement up to 6 GW and warrant for up to 160 million AMD shares. https://www.sec.gov/Archives/edgar/data/2488/000000248826000045/amd-20260223.htm

Reuters, Broadcom, and Anthropic Supports the financing facility of up to $42 billion, the $125.2 billion five-year TPU compute commitment and expected 2027 customer relationship. Reuters

Reuters, Samsung HBM capacity Supports Samsung’s expectation that HBM will approach 30% of industry DRAM wafer capacity in 2027, versus about 20% currently. Reuters

JLL, Global Data Center Outlook 2026 Supports construction timelines, 2025 project delays and U.S. equipment lead-time estimates. https://www.jll.com/content/dam/jllcom/en/global/documents/reports/research-reports/26-research-global-data-center-outlook-new.pdf

Enverus, behind-the-meter generation forecast Supports the forecast for 22.5 GW of U.S. data center demand served behind the meter, the 36% share of capacity additions and the modeled gas-generation requirement. https://www.enverus.com/newsroom/behind-the-meter-generation-forecast-skipping-the-queue/

Enverus, site readiness Supports the discussion of pipeline, permitting and infrastructure constraints on data center sites. https://www.enverus.com/blog/data-center-sites-powered-by-gas-isnt-the-same-as-ready-to-build/

RELATED TOPICS: AI AND BIG DATA, POWER MANAGEMENT

Ron Honig is co-CEO of From Honig Family Office. He previously spent more than two decades in the technology industry, including at Intel, with a long-standing focus on the semiconductor sector.