While AI is often viewed abstractly through software and models, its most significant current contribution to GDP growth is physical. The boom in data center construction—involving steel, power infrastructure, and labor—is a tangible economic driver that is often underestimated.
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Morgan Stanley frames AI-related capital expenditure as one of the largest investment waves ever recorded. This is not just a sector trend but a primary economic driver, projected to be larger than the shale boom of the 2010s and the telecommunications spending of the late 1990s.
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.
The capital expenditure for AI infrastructure mirrors massive industrial projects like LNG terminals, not typical tech spending. This involves the same industrial suppliers who benefited from previous government initiatives and were later sold off by investors, creating a fresh opportunity as they are now central to the AI buildout.
For 2026, massive capital expenditure on AI infrastructure like data centers and semiconductors will fuel economic demand and inflation. The widely expected productivity gains that lower inflation are a supply-side effect that will take several years to materialize.
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.
The tangible economic effect of the AI boom is currently concentrated in physical capital investment, such as data centers and software, rather than widespread changes in labor productivity or employment. A potential market correction would thus directly threaten this investment-led growth.
Instead of relying on hyped benchmarks, the truest measure of the AI industry's progress is the physical build-out of data centers. Tracking permits, power consumption, and satellite imagery reveals the concrete, multi-billion dollar bets being placed, offering a grounded view that challenges both extreme skeptics and believers.
VC Joe Lonsdale argues investors are overly focused on software 'infinity stories' that could be worth trillions. Meanwhile, the 'real economy' (construction, quarrying, manufacturing) represents 85% of capital and is ripe for AI-driven transformation. These less-hyped applications represent a massive, misunderstood, and less competitive investment area.
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.
