The Big Tech firms are set to invest $5.2 trillion in AI infrastructure over five years. However, historical analysis shows that such infrastructure booms often lead to overinvestment, excess competition, and poor stock returns. Kai Wu’s research reveals the risks associated with high capital spending and the importance of analyzing past trends.

Wu’s study compares major capital expenditure cycles in US history, including railroad expansion in the 1860s-1890s, the late 1990s telecom fiber optic buildout, and current AI infrastructure spending. By examining the performance of companies during and after these booms, Wu highlights the potential pitfalls of excessive capital spending.

The Magnificent Seven, including Apple, Microsoft, Amazon, Meta Platforms, Google, Nvidia, and Tesla, are transitioning to capital-intensive operations from asset-light models. This shift raises concerns about deteriorating fundamentals, rising debt levels, and circular financing arrangements that could impact their long-term performance.

Companies aggressively growing their balance sheets have historically underperformed conservative peers by 8.4% annually. The AI boom, already exceeding the internet boom’s peak, requires a 100-fold increase in revenues by 2030 to justify costs. The risks associated with high capital spending and asset-heavy business models are highlighted in Wu’s research.

Investors are advised to focus on robust valuation metrics, diversify beyond obvious AI plays, and monitor free cash flow and balance sheets. Wu’s research suggests that incorporating tangible and intangible assets into valuation models can help navigate the risks associated with the AI spending boom.

The winners of past infrastructure booms were often not the infrastructure builders but those who used it wisely. By understanding the risks associated with capital-intensive operations, investors can position themselves to benefit from the AI revolution while managing the challenges posed by excessive spending and competition.

Read more at Morningstar: Why the AI Spending Spree Could Spell Trouble for Investors