The Economics of AI Governance: Evaluating Data Sovereignty, Platform Monopolies, and the Digital Personal Data Protection Act (DPDPA)
This paper evaluates the economic and strategic implications of generative artificial intelligence architectures on market competition and digital asset ownership in 2026. Focusing on the intersection of platform economics and data governance, the study examines how modern foundation models create market asymmetries by training on vast human-generated datasets. Through an economic analysis of regulatory interventions like India's Digital Personal Data Protection Act (DPDPA), we expose the market limitations of corporate terms-of-service in addressing systemic data scraping. The paper exposes how current paradigms allow data monopolies to centralize digital capital. Finally, we propose a decentralized, private-by-default economic model that treats personal data as a high-value asset tied directly to individual digital identity, thereby shifting structural compliance costs back onto corporate developers.