• AI, electrification, and more onshore manufacturing in the US will further drive energy demand.
• The Department of Energy estimates that the nuclear sector will create 375,000 new jobs by 2050.
• About 60% of the current nuclear workforce is between the ages of 30 and 54, per DOE.

The nuclear sector is approaching an inflection point where the need for a more reliable energy solution in the next few decades is about to confront an aging workforce that's prime for retirement.

A big driver of nuclear demand — that is, artificial intelligence — may also be part of the solution.

"I think there's sort of this two-way street on AI and nuclear," Craig Piercy, CEO of American Nuclear Society, told Business Insider. "AI needs nuclear because AI needs energy to run the data centers that run AI. And then AI is going to help nuclear be more efficient."

The US has seen a cyclical demand for nuclear power from the 1960s to the present.

A 1987 paper published by the International Atomic Energy Agency said that by 1970, about 90 nuclear units across 15 countries went online. However, by the next decade, growth slowed due to increased public resistance to nuclear energy, tighter government regulations, and the high-profile disaster at the Chernobyl Nuclear Power Plant in Ukraine.

Since then, nuclear power entered a period of inertia, which created a gap between experts with institutional knowledge and the industry's future.

The Department of Energy found that 60% of the nuclear energy workforce was between the ages of 30 to 54. The agency said that a significant portion of the workforce is likely to retire over the next decade, which will lead to increased job opportunities in the sector.

"The nuclear industry has been stagnant for some time now," Massimiliano Fratoni, chair of the nuclear engineering department at the University of California, Berkeley, told BI. "So a lot of the know-how got lost because people retire, so there is a need to catch up."

The energy demand curve

Artificial intelligence — and the power-hungry data centers behind the technology — have put nuclear energy back on the map in just the past three years.

Big Tech companies like Microsoft and Amazon, which are fueling the AI demand, are investing in newer reactor designs such as small modular reactors (SMRs), driving what BI has called "a nuclear renaissance."

Just four SMR companies, for example, have received nearly $3 billion in equity funding, BI previously reported.

It's not only AI that's driving the need for a reliable and constant source of energy.

Piercy, the American Nuclear Society CEO, said the US's push toward electrification and domestic manufacturing, which has been a top priority for the second Trump administration, will also significantly increase energy demand.

"For the last 20 years or so, US electricity demand has essentially been flat. You could draw a straight line from 2007 to 2022," he said, adding that the "demand curve" was at 1% or less. "In the last two years, we're looking now at 2 or 3% energy demand growth over the next 10 years. Everyone's assumptions about how much electricity we're going to need have gone way up."

As nuclear is increasingly considered an alternative energy solution, the Department of Energy estimated in 2023 that 375,000 additional workers with technical and non-technical backgrounds, such as construction and manufacturing, would be added to the sector over the next two decades.

"I think essentially what we're looking at here is a tripling or more of the workforce by 2050," Piercy said.

A digital assistant for nuclear workers

At Argonne National Laboratory, a federally funded R&D center located in the suburbs of Lemont, Illinois, the lab's senior nuclear engineer, Richard Vilim, is finding ways to automate some jobs at nuclear plants, which may help with some of the lifting for the incoming workforce demand.

"Humans will always be there in the loop," Vilim told BI in an interview. But there are what he called more "prosaic" tasks that could be assigned to a computer and help nuclear plants run more efficiently.

"For example, just monitoring if there's anything going wrong. People, humans do that," Vilim said. "Now you can assign that to an algorithm."

One automation tool Vilim works with is PRO-AID, or Parameter-Free Reasoning Operator for Automated Identification and Diagnosis. It can be thought of as a digital assistant for monitoring and diagnosing reactors.

Vilim said the earliest iterations of PRO-AID were developed around the late 1990s. But over the next few decades, and with the arrival of ChatGPT in 2022, PRO-AID has been updated with a few new tricks, including reasoning abilities.

Vilim said that before 2022, PRO-AID would be able to tell an operator or system engineer whether there was a leak or if a particular component went offline.

What operators can do now is ask PRO-AID: Why?

"So we're now using ChatGPT-type algorithms to go into the inner workings of the model, examine the logic that led to the diagnosis, and compose an answer into human-understandable form," Vilim said. "So not only does the operator learn, 'Oh, there's a leak outside containment,' he or she can query the system and say, 'Well, why do you say that?'"

Vilim said that PRO-AID continues to be improved so that the tool churns out answers that are much easier for the human worker to understand.

With the tool, Argonne is also figuring out how to develop remote monitoring systems so that a human worker cannot only monitor away from the site, adding to the safety factor, but also monitor multiple systems at once. Vilim roughly estimates that the number of systems one operator could remotely monitor could increase by a factor of 10.

Fratoni, the UC Berkeley professor, said the current workforce at power plants is quite large. One of the equations to solve is how to reduce the on-site manpower at a single plant.

Could AI meet the gap?

"Potentially," he said. "Potentially."

• AI leaders are preparing to take America into what Sam Altman calls the "Intelligence Age."
• Getting there will depend on building vast amounts of new AI infrastructure on US soil.
• Whether investments in this infrastructure will ever pay off is another matter.

America is ready to reach a new age of intelligence. Getting there —and staying ahead of rival nations in the AI race — depends on a plan to transform the physical world that's becoming more formidable by the day.

Leaders driving the AI boom entered 2025 by getting louder about the radical transformation they say is needed on US soil to deliver an era of AI-led superintelligence: more data centers, more chip plants, and more power infrastructure.

By taking root in the physical world — huge data center facilities depend on complex wiring, hardware, and integration with power infrastructure across vast amounts of landmass — the hope is that AI software could one day transform society the way the Industrial Age did.

Sam Altman, the CEO of OpenAI, calls this next leap the "Intelligence Age." In a September blog post, Altman said its defining characteristic would be "massive prosperity." However, he cautioned that without enough infrastructure, "AI will be a very limited resource that wars get fought over and that becomes mostly a tool for rich people."

Last week, in one of his final executive orders, President Joe Biden signaled intent to build more at home with plans to lease acres of federal land to private sector firms with the know-how to develop complex AI infrastructure. The intent to build is likely to continue after Donald Trump's inauguration on Monday, as tech leaders rally around the incoming president and put AI among the top priorities on his agenda.

Biden's executive order followed the release of a blueprint from OpenAI a day earlier, which claimed "the economic opportunity AI presents is too compelling to forfeit" by not building the infrastructure needed.

Data centers, power plants, and chip manufacturing plants will all cost money — a lot of money. Goldman Sachs estimates that roughly $1 trillion will be spent in the next few years alone to develop the infrastructure needed to bring today's AI models closer to superintelligence.

It's why the big question investors and companies must now grapple with is whether or not they are willing to put up money for a vision of the future that is hardly guaranteed.

The case for building AI infrastructure

Altman has offered no shortage of reasons for spending so much money on achieving superintelligence.

Ensuring technological hegemony over China is one. As his company said last week, "there's an estimated $175 billion sitting in global funds awaiting investment in AI projects" that "will flow to China-backed projects" and strengthen Beijing if not directed to the US.

Another is that superintelligence could unlock unimaginable prosperity for society. Altman recently said that "if we could fast-forward a hundred years," the prosperity from superintelligence would feel just as unimaginable as today's world would to a lamplighter, a person employed to light and maintain street lights until about the 1950s.

The third reason is perhaps more surprising. In a blog published at the start of the year, Altman said his company is now confident that it knows how to build artificial general intelligence, a term often interchanged with superintelligence despite their differences.

It's a combination of factors that will, in some way, have triggered the flood of comments from those who want to play their part in developing the infrastructure needed to deliver superintelligence.

In a blog published this month titled "The Golden Opportunity for American AI," Microsoft president Brad Smith said the company planned to spend $80 billion alone this year on data centers. "Not since the invention of electricity has the United States had the opportunity it has today to harness new technology to invigorate the nation's economy," he said.

Last year, in conjunction with BlackRock and others, the tech giant unveiled a fund focused on AI infrastructure with an investment potential of up to $100 billion.

In an interview with Semafor last month, Google CEO Sundar Pichai said that he was ready to work on a "Manhattan Project" for AI once Donald Trump takes office, underscoring the scale of the development and investment needed by invoking the World War II program that eventually produced the atomic bomb.

Meanwhile, Japanese conglomerate SoftBank committed $100 billion to investing in the US over the next four years, focusing on AI and related infrastructure.

A risky investment

While there is clear intent to develop AI infrastructure, it's not clear if or when the investments will pay off — for two key reasons.

First, much of the infrastructure needed in the US faces an uphill struggle to get built.

Take chip plants. US companies like Nvidia, Google, AMD, and others that specialize in designing chips have developed a significant reliance on Taiwanese firm TSMC to manufacture those chips in the Far East, where a combination of cheap, skilled labor, economies of scale, and a long history of government support for the semiconductor sector has made the incredibly expensive business of manufacturing chips easier to pull-off.

Simply throwing capital at projects aimed at getting chips manufactured in the US won't cut it. Efforts to build chip manufacturing plants at home have been taking shape — the Biden administration's CHIPS Act has provided billions of dollars of grants to semiconductor firms in the US — but there remains a huge gap between the capabilities of manufacturers in the East versus those at home.

The AI boom has been kind to TSMC, with its value roughly doubling last year to $1.1 trillion. US chip manufacturer Intel, meanwhile, more than halved to around $85 billion.

Clean power infrastructure, increasingly focused on nuclear power, also faces challenges. Returns on investment in nuclear power projects meant to provide clean energy to intensive data centers are highly uncertain. These projects also face significant regulatory hurdles.

In December, for instance, the States of Texas, Utah, and Washington D.C.-based company Last Energy sued the Nuclear Regulatory Commission over claims that the government agency was applying the same risk analysis to small modular reactors as it was to large-scale power plants. These SMRs, as they're known, are meant to make access to nuclear power cheaper, given their compactness and greater affordability versus traditional nuclear plants. But even these face roadblocks.

The second big reason that investors may want to approach infrastructure investment with caution is that the emergence of superintelligence remains highly speculative.

Altman's claim that there is now a clear path to AGI is worth taking seriously, as new models like OpenAI's o3 released in December demonstrate increasingly sophisticated reasoning capabilities that do more than just parrot their training data.

That said, there have been rumblings across the industry recently about AI models hitting a wall in terms of performance improvements.

Without really serious advances in capabilities, then, or a clearly defined path forward to superintelligence, it is not clear how or when these colossal bets on AI infrastructure will pay off. But with China and other nations showing no sign of slowing down, it is clear that the cost of not being in the AI race could be far greater.

• Meta is seeking nuclear energy developers to power its AI and sustainability goals.
• The company said in a blog post it's targeting delivery of the project in the early 2030s.
• Meta is not alone in turning to nuclear power, with Microsoft and Google making investments.

Meta is looking for nuclear energy developers to power its AI ambitions.

On Tuesday, Meta said in a blog post that it's targeting 1 to 4 gigawatts of new nuclear generation capacity to be delivered starting in the early 2030s.

The move is the latest push from Big Tech to use nuclear power to meet the rapidly growing energy demands from the AI boom.

Meta said it's releasing a request for proposals to identify nuclear energy developers with skills in permitting and community engagement who could help the company meet its AI and sustainability objectives.

The company added that it was planning for its data center energy needs while "simultaneously contributing to a reliable grid and advancing our sustainability commitments."

"As new innovations bring impactful technological advancements across sectors and support economic growth, we believe that nuclear energy can help provide firm, baseload power to support the growth needs of the electric grids that power both our data centers," the company said in the blog post shared on its website.

Big Tech goes nuclear

Major tech companies, including Microsoft and Google, are investing in nuclear power to provide energy for AI data centers.

Nuclear energy provides clean, constant power to fuel data centers, the infrastructure that supports the training and running of AI models.

In September, energy supplier Constellation Energy struck a deal with Microsoft to provide the tech giant with nuclear power for the next two decades by resurrecting part of the Three Mile Island nuclear plant in Pennsylvania.

Google announced in Ocotber it was purchasing nuclear energy from Kairos Power, a California-based company developing small modular reactors.

Representatives for Meta did not immediately respond to a request for comment from Business Insider, made outside normal working hours.