The huge scale of AI data center construction, requiring thousands of skilled laborers in one location, creates a 'crowding out' effect. Local businesses in places like Abilene, Texas, cannot compete for labor like HVAC technicians, leading to shortages and potential inflationary pressures on regional economies.
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Contrary to the post-COVID trend of tech decentralization, the intense talent and capital requirements of AI have caused a rapid re-centralization. Silicon Valley has 'snapped back' into a hyper-concentrated hub, with nearly all significant Western AI companies originating within a small geographic radius.
While there's a popular narrative about a US manufacturing resurgence, the massive capital spending on AI contradicts it. By consuming a huge portion of available capital and accounting for half of GDP growth, the AI boom drives up the cost of capital for all non-AI sectors, making it harder for manufacturing and other startups to get funded.
A recent Harvard study reveals the staggering scale of the AI infrastructure build-out, concluding that if data center investments were removed, current U.S. economic growth would effectively be zero. This highlights that the AI boom is not just a sector-specific trend but a primary driver of macroeconomic activity in the United States.
For 2026, AI's primary economic effect is fueling demand through massive investment in infrastructure like data centers. The widely expected productivity gains that would lower inflation (the supply-side effect) won't materialize for a few years, creating a short-term inflationary pressure from heightened business spending.
AI will primarily threaten purely cognitive jobs, but roles combining thought with physical dexterity—like master electricians or plumbers—will thrive. The AI-driven infrastructure boom is increasing demand and pushing their salaries above even those of some Silicon Valley engineers.
Developed nations are building massive infrastructure projects like data centers, yet the construction workforce is aging and shrinking. This creates a critical bottleneck, as every project fundamentally relies on excavator operators—a role younger generations are avoiding.
The massive capital rush into AI infrastructure mirrors past tech cycles where excess capacity was built, leading to unprofitable projects. While large tech firms can absorb losses, the standalone projects and their supplier ecosystems (power, materials) are at risk if anticipated demand doesn't materialize.
The enormous market caps of leading AI companies can only be justified by finding trillions of dollars in efficiencies. This translates directly into a required labor destruction of roughly 10 million jobs, or 12.5% of the vulnerable workforce, suggesting market turmoil or mass unemployment is inevitable.
The infrastructure demands of AI have caused an exponential increase in data center scale. Two years ago, a 1-megawatt facility was considered a good size. Today, a large AI data center is a 1-gigawatt facility—a 1000-fold increase. This rapid escalation underscores the immense and expensive capital investment required to power AI.
The massive capex spending on AI data centers is less about clear ROI and more about propping up the economy. Similar to how China built empty cities to fuel its GDP, tech giants are building vast digital infrastructure. This creates a bubble that keeps economic indicators positive and aligns incentives, even if the underlying business case is unproven.