PJM agrees to lower price cap for upcoming capacity auctions

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PJM agrees to lower price cap for upcoming capacity auctions

The PJM Interconnection will move to set a price cap and price floor for its next two capacity auctions under an agreement reached Tuesday with Pennsylvania Gov. Josh Shapiro, D. PJM plans to set a $325/MW-day price cap and a $175/MW-day floor for its 2026/27 and 2027/28 delivery year capacity auctions, according to a notice […]

The PJM Interconnection will move to set a price cap and price floor for its next two capacity auctions under an agreement reached Tuesday with Pennsylvania Gov. Josh Shapiro, D.

PJM plans to set a $325/MW-day price cap and a $175/MW-day floor for its 2026/27 and 2027/28 delivery year capacity auctions, according to a notice PJM sent to its members. The next base capacity auction is set to be held in July.

“The governor worked with PJM to significantly lower the capacity auction price cap — from over $500/Megawatt-day to $325/MW-day — averting a runaway auction price that would have unnecessarily increased energy bills,” Shapiro said in a press release. The price cap could save PJM consumers $21 billion over two years, according to Shapiro.

If approved, the planned price cap/floor would settle a complaint Shapiro filed in December at the Federal Energy Regulatory Commission on behalf of Pennsylvania. In part, Shapiro argued there isn’t enough time for generators to respond to high capacity prices, so consumers wouldn’t benefit from the high prices.

The complaint was supported by governors from Delaware, Illinois, Maryland and New Jersey, nine state utility commissions, ratepayer advocates and others, according to filings at FERC. Constellation Energy Generation, the Electric Power Supply Association and the PJM Power Providers Group opposed it.

PJM’s last capacity auction, held in July, produced record high capacity prices that will cost ratepayers across the grid operator’s footprint $14.7 billion for the delivery year that begins in June, up from $2.2 billion in the previous auction.

Capacity prices for most of PJM soared to $269.92/MW-day, up from $28.92/MW-day in the previous auction. Prices hit zonal caps of $466.35/MW-day for the Baltimore Gas and Electric zone in Maryland, and $444.26/MW-day for the Dominion zone in Virginia and North Carolina.

Those results triggered a series of actions, including a complaint by ratepayer advocates and proposals from PJM to change elements of its capacity auction and measures to help bring power supplies online.

The price cap/floor agreement is subject to review by PJM stakeholders and its board. PJM plans to hold a Special Members Committee meeting on Feb. 7, where the grid operator’s staff can give guidance on how the proposed cap/floor mechanism could be implemented, according to the notice.

PJM said that in order to keep its “approximate” capacity auction schedule, the grid operator would propose the cap and floor mechanism through a Federal Power Act section 205 filing at FERC.

Evergreen Action, an advocacy group focused on climate policy, said the price cap/floor agreement was a boon for consumers, but PJM needs to do more.

“PJM needs to undertake major reforms to address its current interconnection crisis and get on with the important business of rapidly connecting cheaper, affordable clean energy to the grid so we actively address the energy cost and demand crisis,” Evergreen Action Deputy State Policy Director Julia Kortrey said in a press release.

PJM runs the grid and wholesale power markets in 13 Mid-Atlantic and Midwest states and the District of Columbia.

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Energy Department Announces Partnership to Ensure Affordable Energy and Power America’s AI Future

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Energy Department Announces $500 Million to Strengthen Domestic Critical Materials Processing and Manufacturing

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Trump Administration Keeps Coal Plant Open to Ensure Affordable, Reliable and Secure Power in the Northwest

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Nvidia overhauls the data center for OpenClaw era

Nvidia’s products for data centers now encompass a full stack with all the pieces, said Sandeep Gupta, executive managing director and head of global strategic alliances at NTT Data. “From a customer perspective, if they believe in an integrated stack, it makes things simple,” Gupta said. The integrated data center cuts complexity and improves efficiency across cooling, networking and storage. “It is driven by the sentiment of an enterprise on how dependent they want to be on one provider versus mix and match,” Gupta said. AI complexity has gone up manifold with multi-agent systems and technologies like OpenClaw, which Huang said is as big a deal as HTML and Linux. Those technologies will generate tokens at an unprecedented pace and strain network, memory and storage simultaneously. AI data also has context, and moving it inefficiently wastes power and cost. A new networking and storage layer is needed to move data intelligently and efficiently. A technology called KV Cache holds the contextual memory necessary for processing agentic AI systems. “It’s going to pound on memory really hard… It’s going to be pounding on the storage system really really hard, which is the reason why we reinvented the storage system,” Huang said. Nvidia’s blueprint turns data centers into one giant AI GPU. It is spearheaded by the GPU known as Rubin and CPU called Vera, which were announced at GTC. Nvidia also slipped in a new inference chip; the Groq LPU has significantly more memory bandwidth than GPUs and is designed for low-latency token generation.

Nvidia joins push for data centers in space

For example, instead of sending down raw image data, which can take hours, or even days, a satellite can transmit the information that, say, a particular bridge is down, or that a certain road is having issues—actionable information of immediate business value. “AI can also help satellites navigate low earth orbit much more confidently, avoid other satellites, and operate much more autonomously,” says Su. And it can be used for other heavy workloads as well. For example, Kepler Communications is using Jetson Orin in its satellite communication network. That helps the company make its satellites smarter, CEO Mina Mitry said in a statement, “allowing us to intelligently manage and route data across our constellation.” The Jetson Orin is already bringing data center-level compute capability to space, Su says, and, with the new chips, there will be even more real-time capability for the next generation of satellites. According to Gartner analyst Bill Ray, orbital data centers are a waste of time and money. “The rush to develop orbital data centers has reached a period of peak insanity,” he wrote in a recent report. “For all the hype around them, these space-based data centers will not be able to deliver on the promise of useful analysis of terrestrial data for terrestrial applications for decades, and may not ever be able to do so.” But that’s not where today’s use cases are, Su points out. “It is edge computing workloads,” he says. “It’s AI inference for multi-dimensional data for disaster recovery and weather forecasting.”

Microsoft’s laser-free cable tech promises to slash AI data center power bills in half

The power problem, Microsoft argues, starts with the cables themselves. How MOSAIC works Copper interconnects top out at roughly two meters at high data rates, limiting them to within a single rack. Laser-based fiber optic cables go further but consume more power and are sensitive to temperature and dust, Microsoft said in the post. MOSAIC reaches up to 50 meters while drawing less power than either, the company added. “Imaging fiber looks like a standard fiber, but inside it has thousands of cores,” Paolo Costa, a Microsoft partner research manager and the project’s lead researcher, wrote in the post. “That was the missing piece. We finally had a way to carry thousands of parallel channels in one cable.” MOSAIC is not Microsoft’s only optical networking bet, and it is not the one furthest along. HCF is already in production across Azure regions MOSAIC arrives alongside Hollow Core Fiber (HCF), a complementary technology Microsoft is already deploying globally. HCF carries optical signals through air rather than glass, delivering up to 47% faster data transmission and 33% lower latency than conventional single-mode fiber, according to published research from the University of Southampton cited by Microsoft. Frank Rey, Microsoft’s general manager of Azure Hyperscale Networking, said in the post that the two technologies are complementary — HCF for long-distance inter-datacenter links, MOSAIC for in-facility GPU and server connectivity.

Beyond the fan: Crossing the liquid cooling rubicon

At 20 kW per rack, the airflow velocity required to maintain safe operating temperatures triggers two failure modes. First, the acoustic vibration becomes severe enough to damage equipment. Organizations learn this lesson the hard way — high-frequency vibration from upgraded CRAC units causing bit errors in high-density Non-Volatile Memory Express (NVMe) storage arrays. The signature is mechanical resonance in drive enclosures. Fans shake storage infrastructure to death. Second, the power required for that airflow becomes self-defeating. At 100 kW densities, nearly 30 percent of the total facility power goes to fans alone — before accounting for compressors and chillers working overtime to cool the air. According to Uptime Institute research, data centers spend an estimated $1.9 to $2.8 million per MW annually on operations, with cooling-related costs consuming nearly $500,000 of that figure. The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) TC 9.9 guidelines governing data center thermal management were written for a 15 kW world. Many organizations now operate so far outside those parameters that the guidelines have become irrelevant. One moment crystallized this reality. A single CRAC unit failed in a training cluster. Within eight minutes, hot-aisle temperatures exceeded 120°F. Monitoring systems triggered automatic throttling on millions of dollars of compute infrastructure. A multi-day processing run crashed and restarted from a checkpoint. Standing in that sweltering aisle watching temperature readouts climb, the conclusion was inescapable: air had carried the industry as far as it could go. Crossing the Rubicon: Cold plates versus rear-door heat exchangers Bringing liquid into a data center is terrifying. Water — or water-adjacent fluids — enters rooms filled with equipment worth tens of millions of dollars. Equipment that fails catastrophically when wet. “Crossing the Rubicon” captures the commitment: once started down this path, there is no returning to the comfortable certainty of

System-level ‘coopetition’: Why Nvidia’s DGX Rubin NVL8 runs on Intel Xeon 6

Not a strategic alliance Despite working together at the system level, the relationship between the two companies does not amount to a formal strategic alliance. “The Intel–Nvidia dynamic is best understood as system-level coopetition. Long-standing collaboration persists across data center and PC ecosystems, with Intel CPUs paired alongside Nvidia GPUs forming standardized AI server architectures and enabling deeper integration,” said Manish Rawat, semiconductor analyst at TechInsights. However, competition is accelerating structurally. Even though Nvidia dominates the GPU space, the company is also expanding its presence across more layers of the data-center stack. It has been developing its own CPUs, such as the Grace CPU, aimed at tighter integration between compute, memory, and interconnect. The company has also launched Vera CPU, purpose-built for agentic AI at GTC 2026. This reflects Nvidia’s broader approach of building more of the system in-house, spanning both hardware and software, even as it continues to incorporate external components where required. “Nvidia’s push into CPUs (Grace, Vera) and tightly integrated, NVLink-based systems signals a shift toward full-stack ownership spanning compute, networking, and software. This challenges Intel’s traditional dominance in CPUs and system control. In essence, Nvidia is partnering tactically to sustain ecosystem adoption while strategically positioning to displace incumbents and capture greater control of next-generation AI infrastructure,” added Rawat.

Nvidia announces Vera Rubin platform, signaling a shift to full-stack AI infrastructure

The transition reflects a deeper move from optimizing individual components to engineering entire systems for scalability and efficiency, said Sanchit Vir Gogia, chief analyst at Greyhound Research. “Compute, memory behavior, interconnect bandwidth, and workload orchestration are being engineered together,” Gogia said. “Even physical design choices such as rack modularity, serviceability, and assembly efficiency are now part of performance engineering. Infrastructure is beginning to resemble an appliance at scale, but one that operates at extreme density and complexity.” Industry observers said rack-scale systems, including Nvidia’s NVL72 and open standards such as OCP Open Rack, are enabling more flexible pooling and orchestration of infrastructure resources for AI and machine learning workloads. “I am also seeing other operators are increasingly adopting chip-to-grid strategies, integrating onsite power generation (microgrids, batteries), advanced cooling technologies, and co-packaged optics to effectively manage power spikes, reduce conversion losses, and support rack densities exceeding 100kW,” said Franco Chiam, VP of Cloud, Datacenter, Telecommunication, and Infrastructure Research Group at IDC Asia Pacific. “This collective industry response to adapt to the needs for higher power and thermal demands is further reinforced by leading vendors and hyperscalers aligning around open standards, facilitating scalable, gigawatt-class datacenter deployments,” Chiam added. Networking takes center stage Networking is emerging as a central component of AI infrastructure, as platforms such as Vera Rubin place greater emphasis on how data moves across systems rather than treating connectivity as a supporting layer.

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.

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

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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

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

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