Executive summary
Artificial intelligence remains the defining narrative of this market cycle. It is transforming how companies allocate capital and how investors price future growth. Yet the scale and concentration of investment around a handful of U.S. technology firms have reached levels that now test both financial and physical limits.
Global AI infrastructure spending is projected to exceed US $5–7 trillion by 2030, approaching the scale of nineteenth-century rail expansions. Yet as Capital Economics highlights, current AI-related capital expenditure among the largest U.S. hyperscalers amounts to roughly 0.6 percent of U.S. GDP (about US $140 billion in real terms). While AI investment has risen by nearly 30 percent this year and is set to expand further in 2026, its direct macroeconomic footprint remains modest, more visible in valuations than in output.
To make this investment profitable, the world would need roughly US $2 trillion in new annual revenue, leaving an US $800 billion shortfall even after accounting for expected productivity gains. This asymmetry—massive investment ahead of monetisation—is the hallmark of a fragile boom.
Edward Chancellor’s capital-cycle framework reminds us that high returns attract capital, competition erodes them, and falling returns eventually restore discipline. The AI build-out is following that same script: a genuine technological revolution financed at a pace and scale that may outstrip near-term cash-flow realities.
As Carlota Perez noted in Technological Revolutions and Financial Capital, every technological surge follows a familiar rhythm of installation, frenzy, and eventual renewal. Each phase reshapes the balance between finance and production, setting the conditions for either excess or sustainable progress.
Notes and references
- Capital Economics (31 October 2025). Capital Daily – “What to make of the mixed reaction to this week’s big-tech results.”
Observations on U.S. hyperscalers’ Q3 2025 earnings: rising capital expenditure despite strong profit growth; increasing reliance on leasing, special-purpose vehicles, and debt financing. - McKinsey & Company (2025). The Cost of Compute: A $7 Trillion Race to Scale Data Centres.
Projects global AI-related data-centre investment between US $5.2 trillion (base case) and US $7.9 trillion (accelerated case) by 2030; roughly 60% hardware, 25% energy infrastructure, 15% construction and land. - Bain & Company (2025). 6th Annual Global Technology Report – “$2 Trillion in New Revenue Needed to Fund AI’s Scaling Trend.”
Estimates 200 GW of global AI compute demand by 2030, US $500 billion annual capex, and US $2 trillion annual revenue required for profitable scaling; identifies an US $800 billion revenue gap under current projections. - J.P. Morgan Asset Management – Michael Cembalest (September 2025). The Blob.
AI-related equities contributed approximately 75% of S&P 500 returns, 80% of earnings growth, and 90% of capex growth since late 2022; highlights increasing debt issuance and financing strain among hyperscalers. - Edward Chancellor (2016). Capital Returns: Investing Through the Capital Cycle. Palgrave Macmillan.
Demonstrates that industries with the fastest asset growth subsequently underperform those with slower growth; outlines the “capital-cycle trap” where abundant investment precedes falling returns. - William Quinn and John Turner (2020). Boom and Bust: A Global History of Financial Bubbles. Cambridge University Press.
Historical parallels across railway, radio, and internet bubbles; documents telecom and internet overinvestment exceeding US $500 billion during the 1990s. - Owen A. Lamont (2024). “No, We Are Not in a Bubble (Yet),” Acadian Asset Management.
Defines a bubble as “a self-sustaining rise in prices over time resulting in the speculative trading of an obviously overvalued asset,” requiring three ingredients: overvaluation, feedback loops, and speculative trading. - Owen A. Lamont (October 2025). “Is the U.S. Stock Market in a Bubble?” Acadian Asset Management.
Introduces “positive dumb alpha” — periods when retail investors outperform professionals as a sign of speculative excess; cites strong performance of retail-heavy ETFs such as the SoFi Social 50 (SFYF) and Samsung KODEX Investor’s Choice (473460).
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