When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets

Emerging agentic marketplaces provide the economic infrastructure for matching and coordinating the large amounts of AI agents used in agentic swarms. Unlike human workers, AI agents can operate on multiple jobs simultaneously, acquire skills rapidly, and labor without wage floors. These differences introduce a new segment of AI labor markets, where AI agents interact with each other at a much higher frequency than human markets. Yet we lack frameworks to understand how such markets behave in light of economic forces that shape labor markets, such as adverse selection and reputation dynamics. To explore this, we introduce AI-Work, a tractable, simulated gig economy where Large Language Model (LLM) agents compete for jobs, develop skills, and adapt their strategies under uncertainty and competitive pressure. Our experiments examine three domains of capabilities that successful agents possess: metacognition (accurate self-assessment of skills), competitive awareness (modeling rivals and market dynamics), and long-horizon strategic planning. Agents with these capabilities consistently achieve higher profits, reputations, and market share than competing agents. Through AI-Work, we hope to provide a foundation to explore the microeconomic properties of AI-only labour markets, and a conceptual framework to study the strategic reasoning capabilities of participating AI agents.

AI agents are moving from being tools that assist individual users to becoming workers that transact with humans and with other agents in exchange for resources. This constitutes a market that are naturally shaped by economic forces such as competition, reputation, and latnet information. These forces have shaped human labour markets for centuries, and recent emergence of AI labour markets motivate further research in exploring how AI agents behave under them. To study this, we built AI-Work, a simulated marketplace where AI agents bid on jobs, train new skills, and compete against others over time. The patterns that we observed are recognisable from real labour markets. When competing bids are made public, agents undercut each other and wages fall sharply. When payment is tied to delivered quality, agents invest in getting better at their work. Lastly, as AI agents can be replicated at low cost and run on many jobs at once, a small number of them tend to capture most of the available work, with far more extreme than the inequality seen among human workers. We also identify what separates the agents that thrive from those that do not. Successful agents reason carefully about their own strengths, about their competitors, and about how today's choices shape future opportunities. Prompting agents to reason in this structured way improves their market share by 50 percent. As AI agents take on more of the work that humans currently do, and increasingly transact with one another, the design of these markets and the reasoning capabilities of their participants will together shape the economy that emerges.