Beyond the AI Bubble Analogy: Valuation, Productivity, and Infrastructure in the Generative AI Investment Boom

Abstract

The investment boom surrounding generative artificial intelligence has revived comparisons with the dot-com bubble. This article argues that the analogy is useful only when technological diffusion, financial valuation, market structure, and physical infrastructure are analyzed separately. A technology may create substantial social value while investors overpay for particular securities, and a profitable incumbent may still misallocate capital during a competitive investment race. Through a selective, mechanism-oriented integrative narrative review and comparative historical analysis, the study evaluates the AI cycle across adoption, task-level productivity, model economics, semiconductor and cloud concentration, capital expenditure, energy demand, and regulatory risk. The evidence supports a dual diagnosis. Generative AI has achieved rapid adoption and measurable productivity gains in bounded tasks and populations. Current causal evidence does not, however, establish a large economy-wide productivity effect. Monetization remains uneven; model convergence is task- and benchmark-dependent; and infrastructure commitments embed demanding assumptions about utilization, asset life, external customer demand, and willingness to pay. AI displays several characteristics associated with an emerging general-purpose technology, but definitive classification remains premature. The dot-com comparison therefore neither proves an imminent collapse nor validates current prices. The more defensible interpretation is technological transformation accompanied by localized speculative excess and capital-allocation risk. The article proposes an author-developed monitoring framework based on revenue quality, contribution margin, inference economics, economic obsolescence, utilization, enterprise retention, grid execution, and complementary organizational investment.