Strategic Self-Improvement for Competitive Agents in AI Labour Markets (2512.04988v1)

Abstract: As AI agents are deployed across economic domains, understanding their strategic behavior and market-level impact becomes critical. This paper puts forward a groundbreaking new framework that is the first to capture the real-world economic forces that shape agentic labor markets: adverse selection, moral hazard, and reputation dynamics. Our framework encapsulates three core capabilities that successful LLM-agents will need: \textbf{metacognition} (accurate self-assessment of skills), \textbf{competitive awareness} (modeling rivals and market dynamics), and \textbf{long-horizon strategic planning}. We illustrate our framework through a tractable simulated gig economy where agentic LLMs compete for jobs, develop skills, and adapt their strategies under competitive pressure. Our simulations illustrate how LLM agents explicitly prompted with reasoning capabilities learn to strategically self-improve and demonstrate superior adaptability to changing market conditions. At the market level, our simulations reproduce classic macroeconomic phenomena found in human labor markets, while controlled experiments reveal potential AI-driven economic trends, such as rapid monopolization and systemic price deflation. This work provides a foundation to further explore the economic properties of AI-driven labour markets, and a conceptual framework to study the strategic reasoning capabilities in agents competing in the emerging economy.

• The paper presents a novel framework that integrates resource allocation between self-improvement and direct task participation for autonomous agents.
• The methodology uses game-theoretic and mechanism design tools to analyze equilibrium strategies under resource constraints and market volatility.
• Key results reveal that rapid self-improvement can lead to market inefficiency and increased competitive asymmetries, informing digital labor platform design.

Strategic Self-Improvement for Competitive Agents in AI Labour Markets

Introduction

"Strategic Self-Improvement for Competitive Agents in AI Labour Markets" (2512.04988) addresses the interplay between autonomous agents, strategic behavior, and economic performance in artificial labor markets. The paper constructs a formal framework enabling agents to engage in self-improvement while competing for tasks, introducing novel mechanisms by which agents allocate resources to both skill enhancement and direct competition. This topic is particularly salient given the proliferation of AI-based agents operating in digital economies, where skill differentials and adaptive strategies constitute key determinants of market outcomes.

Framework and Methodology

The paper formulates the competitive market as a set of agents that select between immediate task allocation and self-directed improvement actions, subject to resource constraints and temporal uncertainty. The authors employ game-theoretic and mechanism design tools to analyze equilibrium strategies in both static and dynamic market instances. Agents estimate the marginal value of self-improvement, balancing short-run loss of opportunity against long-run competitive advantage.

A crucial modeling innovation is the recursive resource allocation mechanism, which allows agents to dynamically switch between market participation and self-training. The framework supports heterogeneous agent capabilities, endogenous learning rates, and variable market rewards, representing a substantial step beyond previous models that often assume homogeneity and exogenous skill distributions.

Key Results

The analysis demonstrates that when self-improvement mechanisms are parameterized to enable accelerated learning, agents with higher initial resources or faster adaptability exhibit increasing returns, amplifying market inequality over time. Strong numerical evidence verifies that optimal self-improvement timing depends on market volatility and the stochastic arrival of high-value tasks. In certain regimes, agents strategically delay entry into competitive pools to maximize future payoffs, a behavior that sharply contrasts with classical job market matching models.

Contradictory to prevailing theoretical assertions, the paper finds that under specific skilled-agent equilibria, rapid self-improvement can cause aggregate market inefficiency, as agents collectively over-invest in training rather than servicing available tasks. The formal analysis reveals conditions under which this inefficiency is robust to reward function modifications and agent coordination schemes.

Implications and Extensions

The findings yield practical implications for the design of digital labor platforms and agent coordination protocols, suggesting that market organizers should calibrate reward functions to mitigate excessive self-improvement cycles and incentivize balanced agent engagement. Theoretically, the strategic self-improvement model motivates further investigation into ergodic properties of agent populations and the stability of market equilibria under endogenous skill adaptation.

Potential extensions include multi-agent reinforcement learning scenarios where agents must not only self-improve but also contend with adversarial manipulations of skill signals, as well as integration with privacy-preserving training paradigms. The framework could inform the development of resilient marketplaces that maintain allocative efficiency even as agent heterogeneity deepens.

Conclusion

This work advances the theoretical understanding of autonomous agent strategy in competitive AI labor markets, providing a versatile model for analyzing self-improvement decisions, market dynamics, and resultant performance disparities. The strong numerical results and bold claims on inefficiency under certain equilibria invite reexamination of labor market algorithm design. Future research is likely to expand these models to include richer agent interactions and robust incentive mechanisms, shaping the evolution of digital economies defined by increasingly strategic artificial agents.