Tech companies are scrambling to keep up with skyrocketing AI demand. And many are investing billions in the buildout of AI data centers, with some estimates placing the combined capital expenditures of the largest firms at up to $700 billion.
$700 billion. That’s larger than the GDP of Sweden, Israel, or Argentina. $700 billion is roughly more than the value of Disney, Nike, and Target combined. $700 billion is even more than the total inflation-adjusted cost of the U.S. Apollo program, which sent humans to the moon—twiceover.
It’s a lot, to say the least. But that sky-high expenditure is just the beginning of the AI infrastructure buildout, according to Nvidia CEO Jensen Huang. In a blog post released on Tuesday, the billionaire, himself worth a paltry $154 billion in comparison, said the infrastructure expenditures could easily reach trillions of dollars.
“We have only just begun this buildout,” Huang wrote. “We are a few hundred billion dollars into it. Trillions of dollars of infrastructure still need to be built.”
He’s not alone in his thinking. McKinsey estimates data center investment could reach a cumulative $6.7 trillion globally by 2030 to meet booming AI demand. That soaring capital expenditure forecast is one of the key forces driving the U.S. economy today. Harvard economist Jason Furman crunched the numbers last October and found that without data centers, U.S. GDP growth in the first half of 2025 would have been a paltry 0.1%. JPMorgan Chase global market strategist Stephanie Aliaga estimated AI-related capital expenditure contributed 1.1% to GDP growth, “outpacing the U.S. consumer as an engine of expansion.” And that’s not stopping anytime soon.
Nvidia is currently one of the central drivers of the data center buildout. Its graphics processing units (GPUs) and other products serve as the backbone of hyperscale AI facilities. Other tech companies like Alphabet, Amazon, Meta, and Microsoft are fueling much of the buildout, dedicating up to $700 billion combined this year to the building of infrastructure across the U.S., with much of the construction concentrated in Virginia, and significant buildouts planned in Georgia and Pennsylvania.
AI capex driving demand for skilled trades
Yet Huang’s analysis extends beyond observing the high sums of cash fueling the AI infrastructure buildout. He says that investment is a boon for the labor market, fueling demand for an array of skilled workers. “The labor required to support this buildout is enormous,” he wrote. “AI factories need electricians, plumbers, pipefitters, steelworkers, network technicians, installers, and operators,” jobs long considered safe from AI, according to recent doomsday estimations.
These roles require specialized training in the trades, but the talent to fill them is in short supply,leading to dire shortages of skilled workers such as electricians. The Bureau of Labor Statistics estimates demand for electricians will increase 9% through 2034, a rate much faster than for all occupations and averaging around 81,000 openings for the position each year. And it’s not just electricians: demand for the construction and extraction industry will also grow faster than the average for all occupations over the next eight years, with an average of about 649,000 openings each year.
However, experts warn the jobs produced by the data center buildout are typically short-term. According to Brookings Institution research, the temporary jobs offer little long-term or large-scale employment opportunities.
That demand comes as AI development threatens white-collar jobs, especially entry-level roles. New research from the AI company Anthropic finds the technology is already theoretically capable of performing most tasks associated with coding, law, and business and finance. Some business leaders, such as Microsoft AI chief Mustafa Suleyman, think white-collar work will be automated by AI within 18 months.
Despite those dismal predictions, Huang paints an optimistic picture of AI’s role in the workforce, framing it as a tool that enhances human capability rather than a threat to someone’s 9-to-5.
“A radiologist’s purpose is to care for patients,” he wrote. “When AI takes on more of the routine work, radiologists can focus on judgment, communication, and care. Hospitals become more productive. They serve more patients. They hire more people.”
