Technology companies are facing immense pressure to meet the rapidly increasing demand for artificial intelligence. Consequently, many are committing billions of dollars to the development of AI data centers, with projections suggesting that the largest firms could collectively invest up to $700 billion in capital expenditures.
This $700 billion figure is substantial, exceeding the Gross Domestic Product of countries like Sweden, Israel, and Argentina. It also surpasses the combined market value of major companies such as Disney, Nike, and Target. Furthermore, this amount is greater than the inflation-adjusted cost of the U.S. Apollo program, which successfully sent astronauts to the moon on multiple occasions.
While this represents a significant investment, it is merely the initial phase of the AI infrastructure buildout, according to Nvidia CEO Jensen Huang. In a blog post published on Tuesday, Huang, whose personal net worth is considerably less at $154 billion, indicated that infrastructure expenditures could escalate into the trillions of dollars.
“We have only just begun this buildout,” Huang stated. “We are a few hundred billion dollars into it. Trillions of dollars of infrastructure still need to be built.”
This perspective is shared by others. McKinsey & Company forecasts that global data center investments could reach a cumulative $6.7 trillion by 2030 to accommodate the surging demand for AI. This robust capital expenditure forecast is a significant contributor to the current U.S. economy. Harvard economist Jason Furman calculated in October that without data centers, U.S. GDP growth in the first half of 2025 would have been a mere 0.1%. Stephanie Aliaga, a global market strategist at JPMorgan Chase, estimated that AI-related capital expenditure contributed 1.1% to GDP growth, “outpacing the U.S. consumer as an engine of expansion.” This trend is expected to continue.
Nvidia is a key player in the ongoing data center construction. Its graphics processing units (GPUs) and other products are fundamental to the operation of large-scale AI facilities. Other major technology firms, including Alphabet, Amazon, Meta, and Microsoft, are also heavily involved, allocating up to $700 billion combined this year for infrastructure development across the United States. Much of this construction is concentrated in Virginia, with substantial projects also planned for Georgia and Pennsylvania.
AI capex driving demand for skilled trades
Huang’s analysis extends beyond the financial scale of AI infrastructure development. He highlights that this investment is beneficial for the labor market, creating demand for a variety of skilled professionals. “The labor required to support this buildout is enormous,” he wrote. “AI factories need electricians, plumbers, pipefitters, steelworkers, network technicians, installers, and operators”—professions that were previously considered relatively safe from AI automation, according to recent pessimistic forecasts.
These roles necessitate specialized training in the trades, but there is a notable scarcity of qualified individuals to fill them, leading to significant shortages of skilled workers, such as electricians. The Bureau of Labor Statistics projects a 9% increase in demand for electricians through 2034, a rate considerably faster than for all occupations, with an average of approximately 81,000 openings annually. This demand extends beyond electricians; the construction and extraction industry is also expected to experience growth faster than the average for all occupations over the next eight years, with an average of about 649,000 job openings each year.
However, experts caution that the jobs created by the data center buildout are often temporary. Research from the Brookings Institution suggests that these short-term positions offer limited long-term or large-scale employment opportunities.
This surge in demand for skilled trades occurs as AI development poses a threat to white-collar jobs, particularly entry-level positions. Recent research from AI company Anthropic indicates that the technology is already theoretically capable of performing most tasks associated with coding, law, and business and finance. Some business leaders, like Microsoft AI chief Mustafa Suleyman, anticipate that white-collar work could be automated by AI within the next 18 months.
Despite these concerning predictions, Huang presents an optimistic outlook on AI’s impact on the workforce, viewing it as a tool that enhances human capabilities rather than a threat to employment.
“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.”