When China joined the World Trade Organization in 2001, that one move set off the largest capital spending boom the world had ever seen. Investments in factories, machinery, housing and other long-term assets rose from roughly $360 billion in 2000 to $3.2 trillion by 2010. Adjusted for inflation, cumulative Chinese fixed investment over that decade totaled about $20 trillion. In the process, China reshaped global trade, commodities, inflation, labor markets and politics for a generation.
Today, another investment surge of comparable magnitude is underway, but most people are still assuming it’s a technology sector story. They shouldn’t. The artificial intelligence buildout now rivals China’s post-WTO investment boom in scale, and its consequences will extend far beyond the tech sector.
The numbers are staggering. U.S. hyperscalers alone — Microsoft, Amazon, Alphabet Meta and Oracle — are projected to shell out roughly $800 billion of capital expenditures in 2026, an 83% increase year over year. Research firm Gartner estimates global AI spending will surpass $2 trillion this year. For hard AI capital investment — chips, data centers, power, cooling and networking — estimates cluster around $10 trillion to $15 trillion globally over the next decade. For broader AI spending, including software, services and AI-enabled products, the 10-year total could approach $30 trillion. AI is already China-scale on a hard-capex basis. Under the broader definition, it could be significantly larger than China’s entire 2000–2010 investment surge.
Of course, there are significant differences between the AI and China booms. China produced a physical investment, manufacturing, trade and labor-supply shock. AI is a compute, power, software and cognitive labor shock. But the scale rhymes, and that is what matters for investors, policymakers and anyone trying to understand the macroeconomic landscape as it develops over the next decade. It’s also important to note that China’s story isn’t over. These days, it has continued to move into higher-value industries, including auto manufacturing and health care, along with the development of cheaper AI models that are, by some estimates, only about 5% behind the cutting-edge U.S. models.
Here’s another interesting parallel: China’s manufacturing buildout lowered the cost of physical goods worldwide, unleashing disinflationary forces that suppressed interest rates and compressed manufacturing margins in developed economies. AI is doing something similar through a different channel: lowering the cost of cognitive work. Every task that involves pattern recognition, language processing or data synthesis is now subject to the same deflationary pressure that Chinese manufacturing brought to physical goods two decades ago.
AI’s physical footprint is real, too. The International Energy Agency projects that global data center electricity use will more than double by 2030, reaching 945 terawatt hours, which is roughly equivalent to Japan’s entire current power consumption.
There are a few other critical differences, and they cut both ways. China’s fixed asset investment reached roughly 50% of GDP at its peak, fueled by state-subsidized capital. AI investment, while enormous in absolute terms, is still perhaps 2% of global GDP and driven overwhelmingly by market forces. That may mean AI diffuses faster, since it does not depend on the physical relocation of hundreds of millions of people. China’s shock involved the urbanization of roughly 15% of the world’s population. AI’s shock is global from day one.
The political economy risks may be even more acute. The China shock accelerated inequality and contributed to the political polarization that many democracies are still living with today. AI could turbocharge that dynamic. If it functions primarily as a substitute for cognitive labor rather than a complement, the distributional consequences could be severe. Historian Will Durant argued that inequality tends to be resolved in one of two ways: enlightened redistribution or capital destruction. Neither path is comfortable.
Early labor market data already hints at differentiation. In the U.S., job categories with high exposure to AI are deteriorating faster than low-exposure categories on the order of a quarter of a percentage point per month. And this trend appears to be accelerating.
The bottom line is, the AI debate should not be framed as a tech-sector cycle or a question about whether Nvidia’s valuation is justified. The numbers suggest AI may constitute a macro investment regime, large enough to matter for GDP growth, power demand, capital allocation, labor markets and asset prices across every sector.
We have seen this before. The China shock gave way to new industries and enormous wealth creation. But it also left behind displaced workers, hollowed-out communities, and a populist backlash that took two decades to materialize. That doesn’t mean all investment booms result in similarly difficult circumstances, but it might mean that their second- and third-order effects can define an era. AI’s investment boom is now large enough to be that kind of story. Whether we manage the transition better this time is the defining economic question of the next decade.
Jared Franz is an economist with Capital Group, a Los Angeles-based investment firm with $3.6 trillion in assets under management. He holds a PhD in economics from the University of Illinois at Chicago and a bachelor’s degree in mathematics from Northwestern University.
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