# Agentic Inequality

arXiv: https://arxiv.org/abs/2510.16853 · HTML: https://mlsanity.com/paper/2510.16853/

## Key facts

- Cited by 3 papers in the map (data through 14 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.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 22 of 22)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [More Agents Is All You Need](https://mlsanity.com/paper/2402.05120/) (2024)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [Infrastructure for AI Agents](https://mlsanity.com/paper/2501.10114/) (2025)
- [Computing Power and the Governance of Artificial Intelligence](https://mlsanity.com/paper/2402.08797/) (2024)
- [Characterizing AI Agents for Alignment and Governance](https://mlsanity.com/paper/2504.21848/) (2025)
- [Virtual Agent Economies](https://mlsanity.com/paper/2509.10147/) (2025)
- [The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?](https://mlsanity.com/paper/2401.02452/) (2024)
- [A Vision for Auto Research with LLM Agents](https://mlsanity.com/paper/2504.18765/) (2025)
- [The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets](https://mlsanity.com/paper/2506.00073/) (2025)
- [Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence](https://mlsanity.com/paper/2503.03800/) (2025)
- [Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents](https://mlsanity.com/paper/2507.08944/) (2025)
- [The Agentic Economy](https://mlsanity.com/paper/2505.15799/) (2025)
- [LLM Agent Swarm for Hypothesis-Driven Drug Discovery](https://mlsanity.com/paper/2504.17967/) (2025)
- [Build Agent Advocates, Not Platform Agents](https://mlsanity.com/paper/2505.04345/) (2025)
- [Toward a Theory of Justice for Artificial Intelligence](https://mlsanity.com/paper/2110.14419/) (2021)
- [AI and the Future of Digital Public Squares](https://mlsanity.com/paper/2412.09988/) (2024)
- [The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence](https://mlsanity.com/paper/2503.05473/) (2025)

## Built on by (most cited first, 3 of 3)

- [The Agentic Web Requires New Normative Infrastructure](https://mlsanity.com/paper/2606.10711/) (2026)
- [Towards Agentic AI Governance: A Preliminary Assessment](https://mlsanity.com/paper/2607.07612/) (2026)
- [The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality](https://mlsanity.com/paper/2607.08495/) (2026)

## 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.