mlsanityData to 7 Oct 2026

Agentic Inequality

arXiv 2510.16853 ↗First posted 19 Oct 2025cs.CY cs.AIcited by 3

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 22 of its 85 references that are in the map. First posted on arXiv 19 Oct 2025.

Builds on

22 of 85 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents11 Jul
  2. 2025Build Agent Advocates, Not Platform Agents7 May
  3. 2025The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence7 Mar
  4. 2025Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence5 Mar
  5. 2024More Agents Is All You Need3 Feb
  6. 2023A Survey on Large Language Model based Autonomous Agents22 Aug

Other references · 16

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  2. 2023A Survey of Large Language Modelscited by 2,537
  3. 2021Ethical and social risks of harm from Language Modelscited by 957
  4. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  5. 2024The Ethics of Advanced AI Assistantscited by 103
  6. 2025Infrastructure for AI Agentscited by 40
  7. 2024Computing Power and the Governance of Artificial Intelligencecited by 38
  8. 2025Characterizing AI Agents for Alignment and Governancecited by 30
  9. 2024The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?cited by 20
  10. 2025A Vision for Auto Research with LLM Agentscited by 17
  11. 2025Virtual Agent Economiescited by 17
  12. 2025The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Marketscited by 12
4 more
  1. 2025The Agentic Economycited by 9
  2. 2025LLM Agent Swarm for Hypothesis-Driven Drug Discoverycited by 8
  3. 2021Toward a Theory of Justice for Artificial Intelligencecited by 7
  4. 2024AI and the Future of Digital Public Squarescited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Increasing intelligence in AI agents can worsen collective outcomes
  2. 2025Agentic AI: Autonomy, Accountability, and the Algorithmic Society
  3. 2026Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
  4. 2025Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System
  5. 2026The CASE Framework: A Multi-Disciplinary Control Architecture for Governing Enterprise Agentic AI
  6. 2026No One to Blame: A Framework of Constitutive AI Unaccountability
  7. 2026Agentic Digital Twins: A Taxonomy of Capabilities for Understanding Possible Futures
  8. 2026Active Inference: A method for Phenotyping Agency in AI systems?