AI, digital platforms, and structural convergence through solopreneurship

Purpose Digital platforms and artificial intelligence (AI) are reshaping labor markets across advanced and emerging economies. This paper aims to revisit the classical convergence hypothesis to examine whether platformization and AI diffusion generate convergence in employment structures while producing divergence in income security and welfare outcomes, with particular attention to the rise of solo self-employment (solopreneurship). Design/methodology/approach This research paper develops a theory-driven analytical framework grounded in convergence economics. It systematically synthesizes cross-country evidence and policy reports alongside peer-reviewed studies on technological diffusion, platform economies, and comparative labor market structures to distinguish between structural convergence in work organization and divergence in welfare outcomes. Findings Digital platforms and AI operate as contemporary general-purpose technologies (GPTs) that promote convergence in the organization of work, particularly through task-based, platform-mediated solo self-employment. However, convergence in employment form does not imply convergence in earnings stability, social protection, or income distribution. Welfare outcomes vary systematically with institutional capacity, labor market regulation, digital infrastructure, and social insurance design. Originality/value The paper reconceptualizes convergence as a two-layer process that separates structural labor-market transformation from welfare outcomes. It positions solopreneurship as a central labor form in the platform and AI economy and argues that long-run convergence depends on complementary institutional and policy responses, not technology diffusion alone.