Economic Model Links LLM Scaling to Profit Optimization

A new paper on arXiv develops an economic model to analyze whether scaling large language models (LLMs) can be profit-optimal. The authors combine scaling laws with microeconomic theory to characterize a firm's rational behavior. They model that increasing parameters and training tokens improves LLM quality, which drives adoption by consumers with varying quality thresholds. However, these improvements also raise training and inference costs. The work addresses the gap between known quality gains from scaling and the uncertain revenue implications. The model formalizes the trade-off between quality-driven adoption and cost escalation, aiming to determine optimal scale for profit maximization. The paper is a theoretical contribution at the intersection of AI and economics.