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Blaming data centers for PJM supply challenges misses the bigger picture

Todd Snitchler is president and CEO of the Electric Power Supply Association, which represents competitive power suppliers that own and operate about 200,000 MW of capacity throughout the U.S. The independent market monitor for the nation’s largest grid operator made waves in early June when it released a report warning that data centers’ power consumption could […]

Todd Snitchler is president and CEO of the Electric Power Supply Association, which represents competitive power suppliers that own and operate about 200,000 MW of capacity throughout the U.S.

The independent market monitor for the nation’s largest grid operator made waves in early June when it released a report warning that data centers’ power consumption could trigger regional energy shortages as early as next year. While data center growth is a significant factor in rising electricity demand, focusing narrowly on this sector risks distracting from the broader and more persistent challenges facing the grid.

In a brief analysis of PJM Interconnection’s capacity auction held in July 2024 for the 2025/2026 delivery year — the mechanism by which the regional transmission organization procures resources to satisfy demand years in advance — Monitoring Analytics lays the blame for last July’s capacity prices squarely at the feet of data centers. The paper’s brevity can be attributed to the fact that this assessment is technically structured as an excerpt (“Part G”) of a forthcoming comprehensive report that ostensibly will take into consideration the myriad supply and demand pressures confronting PJM.

An observer could question whether dedicating a standalone report to a single, isolated variable that happens to be an emerging focus for regulators inappropriately creates alarm in policy circles. Such posturing ignores other documented demand stressors in the regional transmission organization’s service territory, including the electrification of the nation’s second-largest port (and the broader economy) and the proliferation of electric vehicles in the region, which S&P Global estimates will grow significantly over the next 15 years.

Unfortunately, this suspicion is confirmed by the market monitor’s emphasis on certain key data points throughout the report. One example is a passage lamenting this year’s projection for summer peak load in 2026, which the report explains is a “substantial upward revision (6,678 MW or 4.4%)” from the projected 2026 summer peak load of 152,259 MW in the 2022 load forecast report. The 4.4% upward revision sounds like a lot in the carefully balanced world of wholesale power markets. However, the analysis glosses over the 2026 summer peak load projections contained in the 2024 forecast. When compared with the 2025 figures, the difference narrows to just over a 1% increase. This emphasis diverts attention away from longstanding issues that PJM has been slow to address.

One persistent challenge has been the rash of power plant retirements across PJM’s footprint and the specter of more to come. The RTO petitioned the Federal Energy Regulatory Commission in winter 2014 to keep uneconomic generators online amid the “historic” loss of approximately 11,700 MW of power while adding only 3,800 MW to the system. Fast forward to June 2023, and PJM President and CEO Manu Asthana testified before the Senate that dispatchable resources were retiring at a faster rate than new capacity could be built. Last year, the IMM released a report stating that up to 30% of the RTO’s installed capacity could face retirement by 2030 without a clear path for replacement resources.

Making this underlying supply deficit worse is the gridlock that previously defined PJM’s interconnection queue, the process for energy projects to be approved and plugged into the grid. PJM has undertaken meaningful reforms to process 140 GW of projects since 2023, including 46 GW with signed agreements. It also has taken measures like the Reliability Resource Initiative, which selected more than 9,300 MW of additional power generation to meet the region’s power needs. And PJM recently began harnessing AI tools — powered by the data centers the IMM pinpoints — to accelerate the grid operator’s interconnection regime and get projects through the pipeline faster.

Supply additions are also hampered by regulatory uncertainty, such as PJM’s evolving resource accreditation requirements and whipsawing price floor rules that delayed the organization’s capacity auction for years. The scenarios in the IMM report assume a functional market that responds to price signals, but PJM’s framework for the last auction was plagued by flaws that blunt those signals and deter entry of new resources. What is needed is for the market to be allowed to function and deliver appropriate signals to all stakeholders. PJM’s quadrennial review, which informs the market inputs, is currently underway and may result in better data to deliver sound outcomes.

With these significant challenges in mind, we encourage PJM to lay out an objective assessment of the supply and demand dynamics impacting the 67 million people across its service territory and continue doing the hard work to reform the generation interconnection queue, establish and maintain a predictable regulatory framework and introduce discipline to the load forecast to better reflect the pace and scale of large energy users.

Data centers are not only major energy users — they are also critical to the modern economy. Their growth should be seen in this broader context: as an economic driver that policymakers and grid operators must plan for rather than simply fault.

Data center growth will continue to have a sizable impact on our energy resources. But reliability will not be secured by focusing on a single industry. Instead, PJM and policymakers must take a comprehensive, solutions-oriented approach that acknowledges the essential role data centers play in society while addressing the full range of supply and demand challenges shaping the region’s energy future.

As the voice for competitive power suppliers, we know this is critical to finding solutions that allow our members to continue the investments needed in reliable power generation to support this vital industry.

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Energy Secretary Keeps Critical Generation Available in Mid-Atlantic

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Energy Department Announces $500 Million to Secure America’s Critical Mineral and Battery Supply Chains

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bp lets Shah Deniz compression automation contract

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Federal court voids Texas GulfLink license over agency’s ‘serious procedural errors’

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IBM unveils dual-architecture processor to run Arm-native apps on Z mainframes

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PJM’s New Data Center Power Equation

PJM Interconnection has now filed one of the most consequential proposed changes yet in the relationship between data centers and the electric grid. Rather than simply treating a new hyperscale or AI facility like any other customer whose demand will be backed through regional capacity procurement, PJM is proposing a framework under which the largest new loads would need to be supported by new capacity, have their needs covered through the Reliability Backstop Procurement, or face potential curtailment when the regional power system is short of supply. The approach has been developing since PJM launched its Critical Issue Fast Path process for large loads in 2025, but it became substantially more concrete in late July and August 2026. PJM filed its proposed Reliability Backstop Procurement with FERC on July 31 and began accepting applications that day for its FERC-approved Expedited Interconnection Track. On Aug. 13, PJM filed its proposed Interim Resource Adequacy Service, or IRAS, along with the Large Load Registry that would support it. The immediate numbers explain the urgency. PJM’s July 2026 capacity auction for the 2028/2029 delivery year procured 138,318 MW of unforced capacity through the centralized auction. Even after including Fixed Resource Requirement resources, however, PJM came up 6,831 MW short of its reliability requirement. The auction cleared at the FERC-approved $325/MW-day price cap. It was the second consecutive auction in which the PJM region failed to procure its full reliability requirement, something that had not happened before these two auctions. That gap is occurring while demand continues to accelerate. PJM’s 2026 long-term forecast projects summer peak demand growing at an average 3.6% annually over the next decade, compared with just 0.3% in the comparable forecast issued in 2021. Summer peak demand is projected to rise by nearly 66 GW over 10 years. Data centers are

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Zayo, NVIDIA Build the Long-Haul Backbone for Distributed AI

The data center industry’s increasingly power-first approach to site selection has created a follow-on question: Once the megawatts are found, is there enough network infrastructure to make the site useful at AI scale? Zayo and NVIDIA are putting real infrastructure behind that question. Zayo said it is working with NVIDIA to expand network capacity supporting AI factories across North America, including an 8,000-route-mile program targeting some of the fastest-growing AI corridors in the United States. The project encompasses six new long-haul routes along with overbuilds of existing network across 10 high-demand corridors. The announcement arrives as AI data center development moves beyond the largest established hubs toward markets where power and land may be more readily available, but fiber capacity cannot necessarily be taken for granted. That geography is increasingly important. NVIDIA has separately developed “scale-across” networking technology designed to allow AI infrastructure distributed among different buildings — or even data centers separated by hundreds of kilometers — to operate as a more unified computing environment. Put together, the developments suggest that networking is becoming inseparable from the AI factory buildout itself. Power may determine where the next generation of AI infrastructure can be built. Fiber will increasingly determine how effectively those sites can participate in the larger AI ecosystem. Fiber Follows the Power Zayo CEO Steve Smith said AI demand is changing both where network infrastructure is needed and how aggressively capacity must be deployed ahead of development. “AI is fundamentally reshaping where and how network infrastructure needs to be built across the U.S.,” Smith said. The company’s 8,000-mile program is more nuanced than that top-line number might suggest. Zayo disclosed in April that the expansion includes approximately 3,000 route miles across six new long-haul routes, plus more than 5,000 route miles of overbuilds across 10 existing corridors. Zayo

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Southern’s 17 GW Pipeline Puts AI Power Demand Into Utility Math

The headline number from Southern Company’s latest earnings report is hard to miss: electricity use by data centers across the utility’s system increased 55% in the second quarter compared with a year earlier. But the more consequential numbers may be the ones sitting behind it. Southern now has more than 1.2 GW of operating data center load, up by more than 500 MW from a year ago. At the same time, its electric utilities have signed contracts and large-load agreements totaling more than 17 GW by the mid-2030s, with another 8 GW in late-stage development and a prospective pipeline of large industrial and data center projects exceeding 75 GW. That leaves an enormous gap between the data center megawatts consuming electricity today and the load Southern has contractually positioned itself to serve during the next decade. For the data center industry, that gap may be the most important part of Southern’s second-quarter story. It offers a look at how utilities are beginning to convert the AI infrastructure boom from forecasts and campus announcements into contracts, generation procurement, transmission investment and eventually energized capacity. From Contracts to Megawatts Southern added roughly 6 GW of contracted large load during the quarter alone. Alabama Power signed three projects representing about 3 GW, while Georgia Power reached a 25-year agreement to serve OpenAI’s planned project in Effingham County near Savannah. That facility is expected to require approximately 3.2 GW and begin taking electric service in phases in 2028. The numbers nevertheless require an important distinction. Seventeen gigawatts contracted does not mean 17 GW will suddenly appear on Southern’s grid. Large data center campuses ramp gradually, often over several years, and Southern executives acknowledged that actual customer ramp schedules do not always match the assumptions made when projects are first approved. CEO Chris Womack said

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PORTS-Pike Takes Shape as an 8-GW AI Infrastructure Model

Back on March 31, 2026, we discussed we discussed SoftBank and SB Energy’s plans to redevelop the former Portsmouth Gaseous Diffusion Plant site near Piketon as a 10-GW artificial intelligence data center campus supported by almost an equal amount of new power generation. At the time, the plan called for as much as 10 GW of new generation, including 9.2 GW of natural gas capacity, along with approximately $4.2 billion of high-voltage transmission infrastructure developed with AEP Ohio. An initial 800-MW data center phase was targeted for service in 2028. The March story was notable because Pike County appeared to offer a preview of a new model for building hyperscale infrastructure: develop the generation, transmission and data center simultaneously rather than wait for an increasingly congested regional grid to deliver multiple gigawatts of capacity. Not to mention the reuse of a brownfield site with the encouragement of the federal government. Since then, almost every important part of the project has moved forward, and on August 17, the most consequential missing pieces fell into place. NVIDIA announced that it will become the exclusive AI compute infrastructure provider for the PORTS-Pike Technology Campus. OpenAI will be the data center customer, signing a 20-year lease with SB Energy for approximately 8 GW of IT capacity. NVIDIA will invest another $1.5 billion in SB Energy and provide credit support for the land, power and shell infrastructure behind an initial 4.25 GW of IT load, with an option covering approximately another 3.75 GW. The Securities and Exchange Commission filing accompanying the announcement makes the financial commitment even more significant. NVIDIA disclosed that its aggregate payment obligation associated with its initial commitment is capped at $105 billion. That is not a conventional capital commitment to spend $105 billion building the campus, nor is it simply a

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Nvidia scales back financing guarantee for OpenAI data center

Nvidia is scaling back a proposed financial guarantee tied to a massive OpenAI data center project in Ohio, reducing its initial commitment from as much as $250 billion to less than $120 billion, according to report in the Wall Street Journal. Earlier this month, Nvidia announced partnerships with major financial firms including Apollo Global Management, BlackRock, Blackstone, Brookfield Asset Management, Goldman Sachs and KKR, aimed at mobilizing more than $500 billion in capital for AI computing infrastructure. The change represents a significant restructuring of Nvidia’s role in financing the planned facility, which is being developed by SB Energy, a subsidiary of SoftBank. Under the revised arrangement, Nvidia would guarantee financing for the project’s first phase, representing roughly 5 gigawatts of capacity, or half of the total proposed capacity. Financing for the remaining capacity would be considered separately at a later stage.

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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When my oldest child was born, I immediately set up Gmail and Twitter accounts in her name. I broadly announced her birth online and proceeded

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