THE ECONOMICS OF AI-DRIVEN PRODUCTIVITY: ARE TRADITIONAL GROWTH MODELS OBSOLETE?
Keywords:
Artificial Intelligence, productivity, economic growth model, algorithmic capitalAbstract
This study aims to analyze the relevance of traditional economic growth models in the context of artificial intelligence-driven productivity gains. The rapid development of AI has triggered significant changes in the structure of production, distribution, and consumption, raising questions about whether classical theoretical frameworks such as the Solow growth model, endogenous theory, and human capital-based models are still capable of explaining modern growth dynamics. Using a literature review, this study examines recent empirical and theoretical findings related to AI's contribution to productivity, its impact on labor markets, and its implications for income distribution. The analysis shows that AI introduces a new factor of production, "algorithmic capital," characterized by high scalability and low marginal costs, potentially shifting the fundamental assumptions of conventional growth models. Furthermore, the disruptive nature of AI has the potential to create wider productivity gaps between countries and industries, not fully captured by traditional models. The study concludes that while classical growth models remain relevant as a foundation for analysis, adaptations to the theoretical framework are needed to integrate the role of AI technology as a key determinant of 21st-century productivity. The study also recommends the development of a hybrid growth model capable of capturing the dynamics of exponential technology and the more asymmetric distribution of benefits.
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