Technological Polarization and Unequal Growth in the Era of Generative Artificial Intelligence

Abstract

The research paper analyzes the socioeconomic impact Generative Artificial Intelligence (GenAI) and Agentic AI have on labor markets, concentrating on wage polarization, employment disruption due to automation, and fair distribution of productivity gains caused by AI. A mixed-method research design was utilized in this study. For Research Question 1, the team conducted a PRISMA-based literature review using 94 articles sourced from Scopus, Web of Science, Google Scholar, SSRN, and IEEE Xplore, which included a quantitative analysis of 43 empirical papers. To solve Research Questions 2 and 3, the researchers used qualitative case studies based on Universal Basic Income (UBI), data dividend laws, and human-centric AI implementations in Industry 5.0. The results show that more widespread use of AI leads to increased wage inequality, affects cognitive work more, and facilitates the polarization of the labor market; meanwhile, employees skilled in AI benefit from the rise in productivity and wages. The case studies help reveal that redistribution policies, human-centric design, and responsible governance can lessen these negative effects. In light of the findings, the authors develop the EQUATE framework, based on Rogers' Innovation Diffusion Theory, with the components of Equity, Quality Augmentation, UBI Policy Calibration, Agentic Alignment, Technology Access, Ethical Governance. The framework provides a comprehensive roadmap for balancing technological innovation with equitable and sustainable economic development.