Despite massive AI-related investment, the net effect on US GDP is minimal. This is because the necessary hardware is largely imported, and accounting rules treat semiconductors as intermediate inputs, not final investment, obscuring their direct contribution.
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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.
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.
While gross spending on AI appears to be a major growth driver, its net contribution to the US economy is significantly smaller. A large portion of AI-related hardware and software is imported, meaning the immediate GDP impact is diluted. AI's more substantial economic benefit is expected to manifest through longer-term productivity gains.
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.
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.
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.
A surge in business technology investment was misinterpreted as an AI-powered economic boom. It more likely reflected companies front-loading purchases of semiconductors and electronics to avoid paying impending 25% tariffs, rather than a fundamental acceleration in AI-related capital expenditure.
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.