THE AI INVESTMENT CYCLE: STRUCTURAL ANALOGIES WITH THE DOT-COM BUBBLE AND EVIDENCE FOR A MATURE TECHNOLOGICAL EXPANSION
DOI:
https://doi.org/10.66104/x4ve3t91Keywords:
artificial intelligence; dot-com bubble; speculative investment; technology cycles; scaling laws; infrastructure economics; energy transition; Jevons paradox.Abstract
The rapid expansion of Artificial Intelligence (AI) investment since 2022 has prompted widespread comparisons with the dot-com bubble of the late 1990s. This article critically examines whether the current AI investment cycle shares the structural characteristics that defined the dot-com collapse, focusing on six analytical dimensions: the retrospective anatomy of the dot-com bubble and its infrastructure failures; the theoretical frameworks used to understand speculative technology cycles; the empirical evaluation of the three central pillars supporting current AI investment; the structural differences between the AI cycle and the dot-com era; the emerging energy infrastructure constraint that may represent the principal bottleneck to future growth; and the investment implications arising from these developments. Drawing upon peer-reviewed literature, Federal Reserve analyses, International Energy Agency reports, Brookings Institution research, and contemporary financial market data, the article argues that the AI cycle differs fundamentally from the dot-com era in terms of infrastructure maturity, investor composition, revenue generation, and user adoption. At the same time, it identifies energy availability, grid expansion, and infrastructure financing as the principal unresolved risks facing the sector. The evidence suggests that while localized speculation and valuation excesses may exist, the underlying economic foundations of the AI cycle differ substantially from those that characterized the collapse of the internet bubble. The article concludes that the most significant challenge facing the AI ecosystem is not demand creation but the capacity of supporting infrastructure to scale alongside rapidly growing computational requirements.
Keywords: artificial intelligence; dot-com bubble; speculative investment; technology cycles; scaling laws; infrastructure economics; energy transition; Jevons paradox.
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