Is Generative Ai the Algorithmic Consumer We are Waiting for?

Abstract

Most studies on the competitive effects of Generative AI focus on the supply side. Interest in consumers is generally restricted to their roles as users of the technology, as well as indirect trainers of Generative AI models through their prompt engineering. In this short article, we focus instead on the potential effects of Generative AI on competition that arise from an active use of Generative AI on the demand side, by consumers seeking goods and services. In particular, we explore the possibility that Generative AI Large Language Models (LLMs) can act as truncated algorithmic consumers, assisting consumers in deciding which products and services to purchase, thereby potentially reducing consumers' information costs and increasing competition. We explore how LLMs' unique characteristics – mainly their conversational use and the provision of an authoritative single answer, as well as spillover trust effects from their other uses – might motivate consumers to use them to search for products and services. We then analyze some of the limitations and competition concerns that might result from the use of Generative AI by consumers. In particular, we show how LLMs' modus operandi - trained to seek the most plausible next word - lead to outcome homogenization and increase entry barriers for new competitors in product markets. We also explore the potential of manipulation and gaming of LLM models. As elaborated, a combination of an LLM model with a dataset on consumers' digital profiles might potentially create a strong nudging mechanism, recreating consumer choice architecture to optimize commercial goals and exploiting consumer’s behavioral biases in novel ways not envisioned.

Keywords: Generative AI, LLMs, Large Language Models, Competition, Antitrust, Competition Law, Algorithms, Algorithmic Consumers, Consumers,

JEL Classification: k21, k20, L12, L15, L51, L81