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Abstract
Artificial Intelligence (AI) is increasingly viewed as a major
driver of productivity growth and economic transformation. This paper
challenges the assumption that AI-related technological expansion necessarily
generates widespread macroeconomic productivity gains. Drawing on endogenous
growth theory and the productivity paradox literature, it introduces the
Relative Economic Growth Illusion (REGI) framework, which argues that
contemporary AI-driven growth may operate through concentrated and intangible-intensive
economic structures rather than through broad productivity diffusion across the
economy. Using cross-country evidence from OECD Productivity, OECD STAN, OECD
Patents, INTAN-Invest, and Functional Urban Areas (FUAs) databases, the study
combines descriptive analysis with panel and robust regression techniques to
examine the relationship between AI innovation, productivity, and intangible
capital accumulation. The results show that AI patent intensity has weak and
statistically insignificant associations with aggregate Total Factor
Productivity (TFP), while intangible capital accumulation remains strongly
linked to localized sectoral productivity gains. Evidence also reveals a marked
geographical concentration of AI-related innovation within a limited number of
technologically advanced economies. These findings suggest that AI-driven
technological progress generates localized efficiency improvements while
diffusing only weakly across the broader economy. As a result, observed
economic expansion may increasingly reflect concentrated growth driven by
intangible capital and technological concentration rather than broad-based
productivity improvements.
JEL classification numbers: O33, O47, D43.
Keywords: Artificial Intelligence, Economic Growth, Productivity Paradox, Intangible Capital, Market Concentration, Technological
Diffusion, Relative Growth.