AI Zero-Cost Multiplier Effect on White-Collar Labor: An Economic Analysis

This paper analyzes how readily-available, low-marginal-cost AI agents (exemplified by systems like Moltbot) affect demand for routine and mid-skilled white-collar labor. We formalize the novel concept of an AI zero-cost multiplier — an agent that replicates and scales productive labor-like output at near-zero marginal cost once integrated into organizational workflows — and show that rational firms optimize by freezing new hires rather than immediately mass-laying-off staff. The model demonstrates a structural contraction in entry-level hiring, a reallocation of human labor toward exception handling and high-judgement tasks, and dynamic effects that differ from classic automation because of the AI agent's low replication cost and rapid scalability. We provide testable propositions, comparative statics, predicted labor-market magnitudes under plausible adoption scenarios, and policy implications. Keywords: AI agents, labor economics, automation, hiring freeze, workforce transitions, human-AI collaboration