datacenters

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AI in Wyoming may soon use more electricity than state’s human residents

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Wyoming’s data center boom

Cheyenne is no stranger to data centers, having attracted facilities from Microsoft and Meta since 2012 due to its cool climate and energy access. However, the new project pushes the state into uncharted territory. While Wyoming is the nation’s third-biggest net energy supplier, producing 12 times more total energy than it consumes (dominated by fossil fuels), its electricity supply is finite.

While Tallgrass and Crusoe have announced the partnership, they haven’t revealed who will ultimately use all this computing power—leading to speculation about potential tenants.

A potential connection to OpenAI’s Stargate AI infrastructure project, announced in January, remains a subject of speculation. When asked by The Associated Press if the Cheyenne project was part of this effort, Crusoe spokesperson Andrew Schmitt was noncommittal. “We are not at a stage that we are ready to announce our tenant there,” Schmitt said. “I can’t confirm or deny that it’s going to be one of the Stargate.”

OpenAI recently activated the first phase of a Crusoe-built data center complex in Abilene, Texas, in partnership with Oracle. Chris Lehane, OpenAI’s chief global affairs officer, told The Associated Press last week that the Texas facility generates “roughly and depending how you count, about a gigawatt of energy” and represents “the largest data center—we think of it as a campus—in the world.”

OpenAI has committed to developing an additional 4.5 gigawatts of data center capacity through an agreement with Oracle. “We’re now in a position where we have, in a really concrete way, identified over five gigawatts of energy that we’re going to be able to build around,” Lehane told the AP. The company has not disclosed locations for these expansions, and Wyoming was not among the 16 states where OpenAI said it was searching for data center sites earlier this year.

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OpenAI and partners are building a massive AI data center in Texas

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Stargate moves forward despite early skepticism

When OpenAI announced Stargate in January, critics questioned whether the company could deliver on its ambitious $500 billion funding promise. Trump ally and frequent Altman foe Elon Musk wrote on X that “They don’t actually have the money,” claiming that “SoftBank has well under $10B secured.”

Tech writer and frequent OpenAI critic Ed Zitron raised concerns about OpenAI’s financial position, noting the company’s $5 billion in losses in 2024. “This company loses $5bn+ a year! So what, they raise $19bn for Stargate, then what, another $10bn just to be able to survive?” Zitron wrote on Bluesky at the time.

Six months later, OpenAI’s Abilene data center has moved from construction to partial operation. Oracle began delivering Nvidia GB200 racks to the facility last month, and OpenAI reports it has started running early training and inference workloads to support what it calls “next-generation frontier research.”

Despite the White House announcement with President Trump in January, the Stargate concept dates back to March 2024, when Microsoft and OpenAI partnered on a $100 billion supercomputer as part of a five-phase plan. Over time, the plan evolved into its current form as a partnership with Oracle, SoftBank, and CoreWeave.

“Stargate is an ambitious undertaking designed to meet the historic opportunity in front of us,” writes OpenAI in the press release announcing the latest deal. “That opportunity is now coming to life through strong support from partners, governments, and investors worldwide—including important leadership from the White House, which has recognized the critical role AI infrastructure will play in driving innovation, economic growth, and national competitiveness.”

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Nvidia announces “Rubin Ultra” and “Feynman” AI chips for 2027 and 2028

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On Tuesday at Nvidia’s GTC 2025 conference in San Jose, California, CEO Jensen Huang revealed several new AI-accelerating GPUs the company plans to release over the coming months and years. He also revealed more specifications about previously announced chips.

The centerpiece announcement was Vera Rubin, first teased at Computex 2024 and now scheduled for release in the second half of 2026. This GPU, named after a famous astronomer, will feature tens of terabytes of memory and comes with a custom Nvidia-designed CPU called Vera.

According to Nvidia, Vera Rubin will deliver significant performance improvements over its predecessor, Grace Blackwell, particularly for AI training and inference.

Specifications for Vera Rubin, presented by Jensen Huang during his GTC 2025 keynote.

Specifications for Vera Rubin, presented by Jensen Huang during his GTC 2025 keynote.

Vera Rubin features two GPUs together on one die that deliver 50 petaflops of FP4 inference performance per chip. When configured in a full NVL144 rack, the system delivers 3.6 exaflops of FP4 inference compute—3.3 times more than Blackwell Ultra’s 1.1 exaflops in a similar rack configuration.

The Vera CPU features 88 custom ARM cores with 176 threads connected to Rubin GPUs via a high-speed 1.8 TB/s NVLink interface.

Huang also announced Rubin Ultra, which will follow in the second half of 2027. Rubin Ultra will use the NVL576 rack configuration and feature individual GPUs with four reticle-sized dies, delivering 100 petaflops of FP4 precision (a 4-bit floating-point format used for representing and processing numbers within AI models) per chip.

At the rack level, Rubin Ultra will provide 15 exaflops of FP4 inference compute and 5 exaflops of FP8 training performance—about four times more powerful than the Rubin NVL144 configuration. Each Rubin Ultra GPU will include 1TB of HBM4e memory, with the complete rack containing 365TB of fast memory.

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Google and Kairos sign nuclear reactor deal with aim to power AI

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Google isn’t alone in eyeballing nuclear power as an energy source for massive datacenters. In September, Ars reported on a plan from Microsoft that would re-open the Three Mile Island nuclear power plant in Pennsylvania to fulfill some of its power needs. And the US administration is getting into the nuclear act as well, signing a bipartisan ADVANCE act in July with the aim of jump-starting new nuclear power technology.

AI is driving demand for nuclear

In some ways, it would be an interesting twist if demand for training and running power-hungry AI models, which are often criticized as wasteful, ends up kick-starting a nuclear power renaissance that helps wean the US off fossil fuels and eventually reduces the impact of global climate change. These days, almost every Big Tech corporate position could be seen as an optics play designed to increase shareholder value, but this may be one of the rare times when the needs of giant corporations accidentally align with the needs of the planet.

Even from a cynical angle, the partnership between Google and Kairos Power represents a step toward the development of next-generation nuclear power as an ostensibly clean energy source (especially when compared to coal-fired power plants). As the world sees increasing energy demands, collaborations like this one, along with adopting solutions like solar and wind power, may play a key role in reducing greenhouse gas emissions.

Despite that potential upside, some experts are deeply skeptical of the Google-Kairos deal, suggesting that this recent rush to nuclear may result in Big Tech ownership of clean power generation. Dr. Sasha Luccioni, Climate and AI Lead at Hugging Face, wrote on X, “One step closer to a world of private nuclear power plants controlled by Big Tech to power the generative AI boom. Instead of rethinking the way we build and deploy these systems in the first place.”

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