Large Language Models ignore the actual diversity of the market and recommend a tiny, narrow group of brands every time.
April 25, 2026
Original Paper
AI Meets Antitrust: How Large Language Models Reshape Market Concentration
SSRN · 6627078
The Takeaway
AI systems are flattening the competitive landscape by focusing on a handful of dominant names. Even when a market is highly fragmented with many small players, the model only points users toward the biggest ones. This creates a new kind of market distortion that could kill small businesses. The AI does not reflect reality. it reshapes it by narrowing consumer choice. Antitrust regulators must now look at algorithms as a primary source of illegal market concentration.
From the abstract
Large language models are rapidly becoming a primary interface for consumer product discovery, yet little is known about how their recommendations relate to real-world market structure. This paper provides the first empirical benchmark of LLM brand recommendations against actual market shares. Using 1,429 responses from three frontier models (GPT-4o, Claude Sonnet 4, Gemini 2.5 Flash Lite) across sixteen U.S. consumer categories, I compare the implied concentration of LLM recommendations to reta