The AI investment race
Summary
Focus
Exuberance about new technologies often brings about investment booms. The race among firms to capture a share of the revenues can result in excessive investment that makes the boom prone to a disruptive end. The boom-bust cycle recurs across history, from the canal and railway manias of the 19th century to the dotcom boom of the 1990s. The current artificial intelligence (AI) build-out ranks among the largest technology-driven investment booms in US history. Could it share the same fate as prior episodes?
Contribution
We model the AI boom as a race between firms in a winner-take-most environment. Contest externalities induce firms to over-commit capital relative to the social returns from the technology. The use of debt and circular stakes to finance investment introduces financial fragility, exposing firms to fire sales of specialised assets in busts. We calibrate the model using data from company accounts and disclosed deals to obtain quantitative insights. Finally, we analyse the risk of systemic contagion arising from circular financial linkages within the sector.
Findings
The AI race generates significant over-investment, exceeding the socially efficient level by around 50% under a conservative baseline. Larger booms end in more disruptive busts. As AI hardware is specialised, fire- sale dynamics induce losses that grow with debt. The rush to commit investment early to gain a head start also increases vulnerability and raises the likelihood of a bust. The boom can only be sustained by a strong realisation of the technology's productivity. Failure of a single firm can propagate, especially in a more concentrated network.
Abstract
The AI build-out ranks among the largest technology-driven investment booms in US history. Its scale, reliance on debt and circular equity ties raise questions about the boom's sustainability and financial stability. We study a dynamic contest in which firms competing for a few dominant positions over-commit resources. The over-investment leaves the sector exposed to revenue disappointment that could turn boom into bust. The larger the boom, the deeper the eventual bust. The race to commit early through debt and circular financing also makes a bust more likely. Calibrated to balance sheet and deal data, the model points to over-investment of around 1.5 times the efficient level, rising to around three times where demand is less elastic. A network analysis shows that stress in one firm could cascade to others through chains of financial exposures.
JEL Codes: G01, G32, L13, O33
Keywords: artificial intelligence, investment, contest theory, circular financing, boom-bust cycle, financial fragility, network
