ARTIFICIAL INTELLIGENCE, INEQUALITIES OF KNOWLEDGE AND RESOURCES, AND SOCIO-ECONOMIC SECURITY: GLOBAL EVIDENCE WITH IMPLICATIONS FOR INDIA'S LABOR AND IT WORKFORCE

Shrikant Dattatraya Wagh, Dr. Shyam Jivan Salunkhe · Zenodo (CERN European Organization for Nuclear Research) · 22 May 2026

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Where this sits in the debate

Will AI widen inequality? · its finding supports the claim (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

Wagh and Salunkhe synthesize secondary evidence from OECD, ILO, UNDP, WTO, and Indian official statistics (NSO/MoSPI, PLFS, HCES) to argue that AI widens "capability inequality" — unequal access to knowledge, compute, digital infrastructure, and organizational adoption — with acute risks for India's segmented labor market and IT services workforce facing AI-driven efficiency pressures. The paper adds to warnings (echoing UNDP's "Next Great Divergence" framing) that AI's distributional effects run through institutions and complementary resources rather than technology per se, and that task transformation with accelerated skill change, not mass job destruction, is the central risk for vulnerable workers in emerging economies.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

Could AI widen inequality in access to knowledge, digital infrastructure and computing, and what would that mean for low-income groups and India's IT workforce?

The approach

A political-economy framework applied to secondary sources from the OECD, ILO, UNDP, WTO and WEF, Indian official statistics, and sector reporting, organized across past, present and future phases.

The findings

The sources point to faster AI adoption in advanced economies, persistent adoption gaps, and task change rather than uniform job loss; in India, efficiency pressure in IT services may compress billable work and change hiring and wages. The paper proposes policies for shared gains.

How to read it: review. A synthesis of existing studies — breadth over novelty; quality depends on what it includes and excludes.

Limitations and counterpoints AI-written

The paper synthesizes secondary sources and news reports rather than estimating effects; it does not present new data or causal identification.

Abstract

Abstract: Artificial intelligence (AI) is increasingly embedded in production, services, and workforce management. Although AI can raise productivity and output, its distributional effects are uncertain and mediated by institutions and access to complementary resources. This paper investigates how AI may widen capability inequality—inequalities in access to knowledge, digital infrastructure, computational resources, and organizational adoption—thereby shaping income opportunities and socio-economic security for low-income groups. Using an integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) alongside official Indian statistics (NSO/MoSPI GDP estimates, PLFS, HCES) and high-reliability sector evidence (Reuters; Nasscom), the analysis is structured across past, present, and future phases. Evidence indicates accelerating AI adoption among firms in advanced economies and persistent adoption gaps among groups, suggesting unequal access to AI-enabled productivity. OECD (2026) reported, Global frameworks warn that uneven readiness may produce a “Next Great Divergence” between countries. (UNDP, 2025), (WTO, 2025), For labor markets, refined exposure measures imply widespread task transformation rather than uniform job destruction, with accelerated skill change as a central risk for vulnerable workers. (ILO, 2025) India’s macro growth remains robust, yet labor-market segmentation and digital capability gaps create distributional vulnerabilities. (MoSPI–NSO, 2025) In addition, AI-driven efficiency pressures in IT services—an important mobility channel for Indian households—may compress billable work and alter hiring and wage structures, raising transition risks even for technical workers. (Reuters, 2026a) The paper proposes a policy architecture for “shared gains” centered on learning equity, transition protections, accountable algorithmic management, and distribution-sensitive metrics beyond GDP. Keywords: Artifi

About this publication

Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.

Published: 2026 (5 months ago)
Citations: 0 (OpenAlex)