Capex-Induced Sell-Off? We Are Not Surprised
Capex-Induced Sell-Off? We Are Not Surprised
Corporate investment in artificial intelligence has reached unprecedented levels, with expected spending by major technology companies increasing nearly eightfold since 2020.¹ As the leading technology companies began reporting weaker free cash flows as a consequence of this investment, investors grew increasingly sceptical about whether the scale of AI spending will generate adequate shareholder returns. In this paper, we argue that such scepticism 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 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 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 overinvestment: 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.
Source: SEI QiM, using FactSet data. US equities excluding Financials, December 1988–June 2026. Stocks are ranked quarterly into equal-weighted decile portfolios using asset growth, abnormal capital expenditure and external financing. Decile 1 represents the most aggressive investors and Decile 10 the most conservative. Returns are annualised over the full sample. Past performance is not indicative of future results.
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. Monetisation 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 behaviour. By integrating multiple independent signals, we seek to distinguish between companies generating durable improvements in profitability and those benefiting primarily from optimistic expectations.
Related insights
References
1FactSet Fundamentals and Estimates. Capital expenditure for Amazon, Microsoft, Alphabet and Meta, standardised calendar-year data, 2020–2026E.
Cooper, M. J., Gulen, H. and Schill, M. J. (2008), “Asset Growth and the Cross-Section of Stock Returns,” The Journal of Finance, 63(4), pp. 1609–1651.
Titman, S., Wei, K. C. J. and Xie, F. (2004), “Capital Investments and Stock Returns,” Journal of Financial and Quantitative Analysis, 39(4), pp. 677–700.
Bradshaw, M. T., Richardson, S. A. and Sloan, R. G. (2006), “The Relation Between Corporate Financing Activities, Analysts’ Forecasts and Stock Returns,” Journal of Accounting and Economics, 42(1–2), pp. 53–85.
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