AI infrastructure

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At $250 million, top AI salaries dwarf those of the Manhattan Project and the Space Race


A 24 year-old AI researcher will earn 327x what Oppenheimer made while developing the atomic bomb.

Silicon Valley’s AI talent war just reached a compensation milestone that makes even the most legendary scientific achievements of the past look financially modest. When Meta recently offered AI researcher Matt Deitke $250 million over four years (an average of $62.5 million per year)—with potentially $100 million in the first year alone—it shattered every historical precedent for scientific and technical compensation we can find on record. That includes salaries during the development of major scientific milestones of the 20th century.

The New York Times reported that Deitke had cofounded a startup called Vercept and previously led the development of Molmo, a multimodal AI system, at the Allen Institute for Artificial Intelligence. His expertise in systems that juggle images, sounds, and text—exactly the kind of technology Meta wants to build—made him a prime target for recruitment. But he’s not alone: Meta CEO Mark Zuckerberg reportedly also offered an unnamed AI engineer $1 billion in compensation to be paid out over several years. What’s going on?

These astronomical sums reflect what tech companies believe is at stake: a race to create artificial general intelligence (AGI) or superintelligence—machines capable of performing intellectual tasks at or beyond the human level. Meta, Google, OpenAI, and others are betting that whoever achieves this breakthrough first could dominate markets worth trillions. Whether this vision is realistic or merely Silicon Valley hype, it’s driving compensation to unprecedented levels.

To put these salaries in a historical perspective: J. Robert Oppenheimer, who led the Manhattan Project that ended World War II, earned approximately $10,000 per year in 1943. Adjusted for inflation using the US Government’s CPI Inflation Calculator, that’s about $190,865 in today’s dollars—roughly what a senior software engineer makes today. The 24-year-old Deitke, who recently dropped out of a PhD program, will earn approximately 327 times what Oppenheimer made while developing the atomic bomb.

Many top athletes can’t compete with these numbers. The New York Times noted that Steph Curry’s most recent four-year contract with the Golden State Warriors was $35 million less than Deitke’s Meta deal (although soccer superstar Cristiano Ronaldo will make $275 million this year as the highest-paid professional athlete in the world).  The comparison prompted observers to call this an “NBA-style” talent market—except the AI researchers are making more than NBA stars.

Racing toward “superintelligence”

Mark Zuckerberg recently told investors that Meta plans to continue throwing money at AI talent “because we have conviction that superintelligence is going to improve every aspect of what we do.” In a recent open letter, he described superintelligent AI as technology that would “begin an exciting new era of individual empowerment,” despite declining to define what superintelligence actually is.

This vision explains why companies treat AI researchers like irreplaceable assets rather than well-compensated professionals. If these companies are correct, the first to achieve artificial general intelligence or superintelligence won’t just have a better product—they’ll have technology that could invent endless new products or automate away millions of knowledge-worker jobs and transform the global economy. The company that controls that kind of technology could become the richest company in history by far.

So perhaps it’s not surprising that even the highest salaries of employees from the early tech era pale in comparison to today’s AI researcher salaries. Thomas Watson Sr., IBM’s legendary CEO, received $517,221 in 1941—the third-highest salary in America at the time (about $11.8 million in 2025 dollars). The modern AI researcher’s package represents more than five times Watson’s peak compensation, despite Watson building one of the 20th century’s most dominant technology companies.

The contrast becomes even more stark when considering the collaborative nature of past scientific achievements. During Bell Labs’ golden age of innovation—when researchers developed the transistor, information theory, and other foundational technologies—the lab’s director made about 12 times what the lowest-paid worker earned.  Meanwhile, Claude Shannon, who created information theory at Bell Labs in 1948, worked on a standard professional salary while creating the mathematical foundation for all modern communication.

The “Traitorous Eight” who left William Shockley to found Fairchild Semiconductor—the company that essentially birthed Silicon Valley—split ownership of just 800 shares out of 1,325 total when they started. Their seed funding of $1.38 million (about $16.1 million today) for the entire company is a fraction of what a single AI researcher now commands.

Even Space Race salaries were far cheaper

The Apollo program offers another striking comparison. Neil Armstrong, the first human to walk on the moon, earned about $27,000 annually—roughly $244,639 in today’s money. His crewmates Buzz Aldrin and Michael Collins made even less, earning the equivalent of $168,737 and $155,373, respectively, in today’s dollars. Current NASA astronauts earn between $104,898 and $161,141 per year. Meta’s AI researcher will make more in three days than Armstrong made in a year for taking “one giant leap for mankind.”

The engineers who designed the rockets and mission control systems for the Apollo program also earned modest salaries by modern standards. A 1970 NASA technical report provides a window into these earnings by analyzing salary data for the entire engineering profession. The report, which used data from the Engineering Manpower Commission, noted that these industry-wide salary curves corresponded directly to the government’s General Schedule (GS) pay scale on which NASA’s own employees were paid.

According to a chart in the 1970 report, a newly graduated engineer in 1966 started with an annual salary of between $8,500 and $10,000 (about $84,622 to $99,555 today). A typical engineer with a decade of experience earned around $17,000 annually ($169,244 today). Even the most elite, top-performing engineers with 20 years of experience peaked at a salary of around $278,000 per year in today’s dollars—a sum that a top AI researcher like Deitke can now earn in just a few days.

Why the AI talent market is different

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This isn’t the first time technical talent has commanded premium prices. In 2012, after three University of Toronto academics published AI research, they auctioned themselves to Google for $44 million (about $62.6 million in today’s dollars). By 2014, a Microsoft executive was comparing AI researcher salaries to NFL quarterback contracts. But today’s numbers dwarf even those precedents.

Several factors explain this unprecedented compensation explosion. We’re in a new realm of industrial wealth concentration unseen since the Gilded Age of the late 19th century. Unlike previous scientific endeavors, today’s AI race features multiple companies with trillion-dollar valuations competing for an extremely limited talent pool. Only a small number of researchers have the specific expertise needed to work on the most capable AI systems, particularly in areas like multimodal AI, which Deitke specializes in. And AI hype is currently off the charts as “the next big thing” in technology.

The economics also differ fundamentally from past projects. The Manhattan Project cost $1.9 billion total (about $34.4 billion adjusted for inflation), while Meta alone plans to spend tens of billions annually on AI infrastructure. For a company approaching a $2 trillion market cap, the potential payoff from achieving AGI first dwarfs Deitke’s compensation package.

One executive put it bluntly to The New York Times: “If I’m Zuck and I’m spending $80 billion in one year on capital expenditures alone, is it worth kicking in another $5 billion or more to acquire a truly world-class team to bring the company to the next level? The answer is obviously yes.”

Young researchers maintain private chat groups on Slack and Discord to share offer details and negotiation strategies. Some hire unofficial agents. Companies not only offer massive cash and stock packages but also computing resources—the NYT reported that some potential hires were told they would be allotted 30,000 GPUs, the specialized chips that power AI development.

Also, tech companies believe they’re engaged in an arms race where the winner could reshape civilization. Unlike the Manhattan Project or Apollo program, which had specific, limited goals, the race for artificial general intelligence ostensibly has no ceiling. A machine that can match human intelligence could theoretically improve itself, creating what researchers call an “intelligence explosion” that could potentially offer cascading discoveries—if it actually comes to pass.

Whether these companies are building humanity’s ultimate labor replacement technology or merely chasing hype remains an open question, but we’ve certainly traveled a long way from the $8 per diem that Neil Armstrong received for his moon mission—about $70.51 in today’s dollars—before deductions for the “accommodations” NASA provided on the spacecraft. After Deitke accepted Meta’s offer, Vercept co-founder Kiana Ehsani joked on social media, “We look forward to joining Matt on his private island next year.”

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Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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

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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White House unveils sweeping plan to “win” global AI race through deregulation

Trump’s plan was not welcomed by everyone. J.B. Branch, Big Tech accountability advocate for Public Citizen, in a statement provided to Ars, criticized Trump as giving “sweetheart deals” to tech companies that would cause “electricity bills to rise to subsidize discounted power for massive AI data centers.”

Infrastructure demands and energy requirements

Trump’s new AI plan tackles infrastructure head-on, stating that “AI is the first digital service in modern life that challenges America to build vastly greater energy generation than we have today.” To meet this demand, it proposes streamlining environmental permitting for data centers through new National Environmental Policy Act (NEPA) exemptions, making federal lands available for construction and modernizing the power grid—all while explicitly rejecting “radical climate dogma and bureaucratic red tape.”

The document embraces what it calls a “Build, Baby, Build!” approach—echoing a Trump campaign slogan—and promises to restore semiconductor manufacturing through the CHIPS Program Office, though stripped of “extraneous policy requirements.”

On the technology front, the plan directs Commerce to revise NIST’s AI Risk Management Framework to “eliminate references to misinformation, Diversity, Equity, and Inclusion, and climate change.” Federal procurement would favor AI developers whose systems are “objective and free from top-down ideological bias.” The document strongly backs open source AI models and calls for exporting American AI technology to allies while blocking administration-labeled adversaries like China.

Security proposals include high-security military data centers and warnings that advanced AI systems “may pose novel national security risks” in cyberattacks and weapons development.

Critics respond with “People’s AI Action Plan”

Before the White House unveiled its plan, more than 90 organizations launched a competing “People’s AI Action Plan” on Tuesday, characterizing the Trump administration’s approach as “a massive handout to the tech industry” that prioritizes corporate interests over public welfare. The coalition includes labor unions, environmental justice groups, and consumer protection nonprofits.

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

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

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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Trump announces $500B “Stargate” AI infrastructure project with AGI aims

Video of the Stargate announcement conference at the White House.

Despite optimism from the companies involved, as CNN reports, past presidential investment announcements have yielded mixed results. In 2017, Trump and Foxconn unveiled plans for a $10 billion Wisconsin electronics factory promising 13,000 jobs. The project later scaled back to a $672 million investment with fewer than 1,500 positions. The facility now operates as a Microsoft AI data center.

The Stargate announcement wasn’t Trump’s only major AI move announced this week. It follows the newly inaugurated US president’s reversal of a 2023 Biden executive order on AI risk monitoring and regulation.

Altman speaks, Musk responds

On Tuesday, OpenAI CEO Sam Altman appeared at a White House press conference alongside Present Trump, Oracle CEO Larry Ellison, and SoftBank CEO Masayoshi Son to announce Stargate.

Altman said he thinks Stargate represents “the most important project of this era,” allowing AGI to emerge in the United States. He believes that future AI technology could create hundreds of thousands of jobs. “We wouldn’t be able to do this without you, Mr. President,” Altman added.

Responding to off-camera questions from Trump about AI’s potential to spur scientific development, Altman said he believes AI will accelerate the discoveries for cures of diseases like cancer and heart disease.

Screenshots of Elon Musk challenging the Stargate announcement on X.

Screenshots of Elon Musk challenging the Stargate announcement on X.

Meanwhile on X, Trump ally and frequent Altman foe Elon Musk immediately attacked the Stargate plan, writing, “They don’t actually have the money,” and following up with a claim that we cannot yet substantiate, saying, “SoftBank has well under $10B secured. I have that on good authority.”

Musk’s criticism has complex implications given his very close ties to Trump, his history of litigating against OpenAI (which he co-founded and later left), and his own goals with his xAI company.

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OpenAI asked US to approve energy-guzzling 5GW data centers, report says

Great scott! —

OpenAI stokes China fears to woo US approvals for huge data centers, report says.

OpenAI asked US to approve energy-guzzling 5GW data centers, report says

OpenAI hopes to convince the White House to approve a sprawling plan that would place 5-gigawatt AI data centers in different US cities, Bloomberg reports.

The AI company’s CEO, Sam Altman, supposedly pitched the plan after a recent meeting with the Biden administration where stakeholders discussed AI infrastructure needs. Bloomberg reviewed an OpenAI document outlining the plan, reporting that 5 gigawatts “is roughly the equivalent of five nuclear reactors” and warning that each data center will likely require “more energy than is used to power an entire city or about 3 million homes.”

According to OpenAI, the US needs these massive data centers to expand AI capabilities domestically, protect national security, and effectively compete with China. If approved, the data centers would generate “thousands of new jobs,” OpenAI’s document promised, and help cement the US as an AI leader globally.

But the energy demand is so enormous that OpenAI told officials that the “US needs policies that support greater data center capacity,” or else the US could fall behind other countries in AI development, the document said.

Energy executives told Bloomberg that “powering even a single 5-gigawatt data center would be a challenge,” as power projects nationwide are already “facing delays due to long wait times to connect to grids, permitting delays, supply chain issues, and labor shortages.” Most likely, OpenAI’s data centers wouldn’t rely entirely on the grid, though, instead requiring a “mix of new wind and solar farms, battery storage and a connection to the grid,” John Ketchum, CEO of NextEra Energy Inc, told Bloomberg.

That’s a big problem for OpenAI, since one energy executive, Constellation Energy Corp. CEO Joe Dominguez, told Bloomberg that he’s heard that OpenAI wants to build five to seven data centers. “As an engineer,” Dominguez said he doesn’t think that OpenAI’s plan is “feasible” and would seemingly take more time than needed to address current national security risks as US-China tensions worsen.

OpenAI may be hoping to avoid delays and cut the lines—if the White House approves the company’s ambitious data center plan. For now, a person familiar with OpenAI’s plan told Bloomberg that OpenAI is focused on launching a single data center before expanding the project to “various US cities.”

Bloomberg’s report comes after OpenAI’s chief investor, Microsoft, announced a 20-year deal with Constellation to re-open Pennsylvania’s shuttered Three Mile Island nuclear plant to provide a new energy source for data centers powering AI development and other technologies. But even if that deal is approved by regulators, the resulting energy supply that Microsoft could access—roughly 835 megawatts (0.835 gigawatts) of energy generation, which is enough to power approximately 800,000 homes—is still more than five times less than OpenAI’s 5-gigawatt demand for its data centers.

Ketchum told Bloomberg that it’s easier to find a US site for a 1-gigawatt data center, but locating a site for a 5-gigawatt facility would likely be a bigger challenge. Notably, Amazon recently bought a $650 million nuclear-powered data center in Pennsylvania with a 2.5-gigawatt capacity. At the meeting with the Biden administration, OpenAI suggested opening large-scale data centers in Wisconsin, California, Texas, and Pennsylvania, a source familiar with the matter told CNBC.

During that meeting, the Biden administration confirmed that developing large-scale AI data centers is a priority, announcing “a new Task Force on AI Datacenter Infrastructure to coordinate policy across government.” OpenAI seems to be trying to get the task force’s attention early on, outlining in the document that Bloomberg reviewed the national security and economic benefits its data centers could provide for the US.

In a statement to Bloomberg, OpenAI’s spokesperson said that “OpenAI is actively working to strengthen AI infrastructure in the US, which we believe is critical to keeping America at the forefront of global innovation, boosting reindustrialization across the country, and making AI’s benefits accessible to everyone.”

Big Tech companies and AI startups will likely continue pressuring officials to approve data center expansions, as well as new kinds of nuclear reactors as the AI explosion globally continues. Goldman Sachs estimated that “data center power demand will grow 160 percent by 2030.” To ensure power supplies for its AI, according to the tech news site Freethink, Microsoft has even been training AI to draft all the documents needed for proposals to secure government approvals for nuclear plants to power AI data centers.

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