The Intelligent Monopoly: Why AI Economics Points Toward a Natural Monopoly
The Question
While most conversations about AI focus on capabilities, I wanted to understand the financial realities. In my paper, I analyzed real financial cost data for running Large Language Models — specifically the WINGPT study — through the lens of regulatory economics.
The Cost of Scale
My regression analysis shows that AI infrastructure behaves like a natural monopoly. When you increase the scale of operation (concurrency) by 10%, the unit cost drops by approximately 2.6%. This efficiency advantage makes it nearly impossible for smaller competitors to enter the market without regulation.
The Data Problem
AI models need a constant flow of new human data to stay intelligent. Current frameworks allow companies to extract this data for free, which discourages creators from producing new work. If this data supply dries up, model performance will inevitably degrade.
A Practical Solution
My paper proposes a two-pronged regulatory approach: Price Caps (RPI-X) on infrastructure to motivate major providers to pass savings to consumers, and a Statutory Licensing system to pay creators fairly and keep the data supply flowing. This research uses actual numbers rather than theoretical debates to find a strategy that works.