Algorithmic Monopsony: Reasoning-Mode Wage Suppression in LLM Agents

We ask how changing the runtime configuration of an LLM agent affects people beyond its operator when the agent sets wages or prices. We build the wage-setting mirror of a standard algorithmic-pricing game. We measure wage suppression against static Nash and joint monopsony, which maximizes employers' combined payoff. Across eight open models, initial behavior and responses to optimization vary. With weights fixed, turning the reasoning-mode configuration on moves wages toward joint monopsony at all three Qwen3 sizes tested. Total wages paid fall by about half. Employment and the combined surplus of workers and employers also fall, even as employers gain. In the separate mirrored product market, the same switch lowers mean prices and increases consumer surplus. These findings concern the runtime configuration as a whole; they do not isolate deliberation or establish coordination. With reasoning on, small samples and fixed action bounds leave the fair-competition prompt comparison inconclusive. Evaluation should therefore account for runtime settings and measure outcomes for affected third parties, including workers outside the direct interaction with the agent.