Beyond Connectivity: Digital Inequality 2.0 and Emerging Gaps in Skills, AI Access, and Data as Capital

Introduction. Research and policy discussions on the digital divide have long been organised around a relatively simple question: who has access to technology, and who does not? Yet in a context shaped by ubiquitous artificial intelligence (AI) and platform capitalism, inequality is increasingly produced elsewhere—through uneven competencies, asymmetric influence over algorithmic systems, and unequal control of digital assets (especially data). Methods. The paper offers a conceptual synthesis of interdisciplinary scholarship (sociology, economics, and data science) published between 2015 and 2025, using a framework analysis approach. To quantify the main gaps, the study draws on meta-analytical evidence from global reports (ITU, Eurostat, NAFI), national statistics (Uzbekistan), and empirical studies and datasets (UNICEF, UNDP, DataReportal). Results. The analysis points to three interrelated layers of “Digital Inequality 2.0”: 1) Skills gap—a widening divide in algorithmic and AI literacy (only 22% of people in developing countries and 32.7% of the EU population have basic competencies for interacting with AI; in Uzbekistan, 38% report no computer skills and 53.9% have never used the internet). 2) Algorithmic access gap —asymmetries in the ability to shape, oversee, and contest AI-driven decisions (only 14% of firms report formal AI governance structures; 95% of organisations indicate they cannot fully audit AI decisions). 3) Data-capitalisation gap—inequality in the capacity to convert information into economic value. Although the global data monetisation market has expanded to US$708.86 billion, more than two-thirds of users obtain no direct benefit from the data they generate. In Uzbekistan, data localisation requirements are associated with an estimated foregone benefit of US$3.2–4.5 billion annually. Conclusions. Policies focused primarily on “connectivity” appear close to exhaustion. What is increasingly needed is a shift toward strengthening user agency—through legal instruments (data trusts, data cooperatives), mandatory algorithmic auditing, and education reform. To make these gaps measurable, the paper proposes a Digital Agency Index (DAI) aligned with the three-layer model.