Agentic Inequality
cs.CY, cs.AI
Submitted: 2025-10-19
Updated: 2026-09-05
Project page: https://www.semrush.com/trending-websites/ng/all
License: http://creativecommons.org/licenses/by/4.0/
The gist: Autonomous AI agents capable of complex planning and action mark a shift beyond today's generative tools.
Terminology
Abstract
Autonomous AI agents capable of complex planning and action mark a shift beyond today's generative tools. As these systems enter political and economic life, who can access them, how capable they are, and how many can be deployed will shape distributions of power and opportunity. We define this emerging challenge as "agentic inequality": disparities in power, opportunity, and outcomes arising from unequal access to, and capabilities of, AI agents. We show that agents could either deepen existing divides or, under the right conditions, mitigate them. The paper makes three contributions. First, it develops a framework for analysing agentic inequality across three dimensions: availability, quality, and quantity. Second, it argues that agentic inequality differs from earlier technological divides because agents function as autonomous delegates rather than tools, generating new asymmetries through scalable goal delegation and direct agent-to-agent competition. Third, it analyses the technical and socioeconomic drivers likely to shape the distribution of agentic power, from model release strategies to market incentives, and concludes with a research agenda for governance.
Sources
- The Ethics of Advanced AI Assistants
- The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
- On the Opportunities and Risks of Foundation Models
- Characterizing AI Agents for Alignment and Governance
- Governing AI Agents
- AI and the Future of Digital Public Squares
- A Vision for Auto Research with LLM Agents
- Computing Power and the Governance of Artificial Intelligence
- The Impact of AI on Developer Productivity: Evidence from GitHub Copilot
- Virtual Agent Economies
- The Agentic Economy
- A Survey on Large Language Model based Autonomous Agents
- Ethical and social risks of harm from Language Models
- Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline
- Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity
- Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents
- The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets
- Can Small Agents Collaborate to Beat a Single Large Language Model?
Related papers
- Reasoning Enhances Robustness to Prompt Injection in LLM-Based Consensus
- Generative AI Purpose-built for Social and Mental Health: A Real-World Pilot
- PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
- What is an intelligent system?
- AI University: An LLM-Powered Learning Assistant for Engineering---A Finite Element Method Case Study
- Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment-Entrapment Framework