Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming
Andrey Fradkin
TL;DR
The paper examines how demand for large language models behaves in a differentiated, rapidly evolving market using OpenRouter data. It adopts a descriptive, event-focused approach centered on three 2025 model releases to reveal adoption dynamics, substitution versus market expansion, and multihoming patterns. The main findings show rapid initial adoption that stabilizes within weeks, heterogeneous substitution effects across releases, and widespread multi-homing across apps, implying meaningful horizontal and vertical differentiation. The work suggests that providers may sustain demand and pricing power despite rapid technological progress, while noting limitations from partial market coverage and data restricted to OpenRouter-based interactions.
Abstract
This paper documents three stylized facts about the demand for Large Language Models (LLMs) using data from OpenRouter, a prominent LLM marketplace. First, new models experience rapid initial adoption that stabilizes within weeks. Second, model releases differ substantially in whether they primarily attract new users or substitute demand from competing models. Third, multihoming, using multiple models simultaneously, is common among apps. These findings suggest significant horizontal and vertical differentiation in the LLM market, implying opportunities for providers to maintain demand and pricing power despite rapid technological advances.