Corporate investment in artificial intelligence (AI) has reached unprecedented levels, with expected spending by major technology companies increasing nearly eightfold since 2020. As the leading technology companies began reporting weaker free cash flows—a consequence of this investment—investors grew increasingly skeptical about whether the scale of AI spending will generate adequate shareholder returns. We argue that such skepticism is not only justified, but also predictable—and therefore avoidable.

What history tells us

History shows that some of the greatest technological revolutions created enormous economic value while destroying shareholder wealth: Canal Mania of the 1700s, the Railway Boom of the 1800s, Automobile Revolution of the 1900s, and the Dot-Com Boom of the 2000s. When industries invest aggressively at the same time, future competition, excess capacity and lower returns on capital follow.

Does history still apply? Academic research has long documented that the firms investing most aggressively tend to deliver weaker subsequent stock returns. Inspired by Titman et al (2004), Bradshaw et al. (2006), and Cooper et al. (2008), we examine three indicators of over-investment: asset growth, abnormal capital expenditure, and external finance. Consistent with their findings, we observe that overinvestment has been associated with weaker subsequent stock returns.

The asymmetry is also noteworthy. Returns do not continue improving monotonically as companies invest less. Avoiding investment altogether is not the answer either.
 

Is this time different?

AI may indeed prove different. Demand may continue growing for many years, and today’s investment may eventually be justified by future earnings.

But there are several ways that relationship could disappoint. Improvements in AI model and hardware efficiency could reduce the computing capacity required for a given level of AI output. Monetization could develop more slowly than investment, particularly if customers prove unwilling to pay enough for AI services to support the infrastructure behind them.

There is also a competitive dimension. Amazon, Microsoft, Alphabet, and Meta are not investing in isolation. Each may have a rational reason to expand capacity, but simultaneous expansion can produce excess capacity at the industry level even when the individual investment decisions initially appear reasonable.

What do we watch?

Periods of rapid technological change often increase the gap between winners and losers, creating a richer opportunity set for disciplined stock selection. The question is not whether AI succeeds, but whether it succeeds by more than investors already expect.

No single metric can determine whether today's AI investments will generate attractive shareholder returns. Our research therefore combines information from company fundamentals, valuation, capital allocation decisions, market expectations and investor behavior. By integrating multiple independent signals, we seek to distinguish between companies generating durable improvements in profitability and those benefiting primarily from optimistic expectations.















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