Beyond attention: Advancing AI token valuation through user engagement and market dynamics

Abstract

The valuation of artificial intelligence (AI) tokens representing computational power and access to AI functionalities is critical for stakeholders in the digital economy. This study advances existing research by focusing on AI token valuation through the lens of user engagement and market dynamics, specifically introducing the Akpan AI token valuation scale. Unlike previous models that primarily focused on technical performance or general economic factors, this research integrates monthly active users (MAU) as a key engagement metric and explores the novel relationship between website visits and token valuation. The study’s findings reveal that higher MAU and website visits converted to MAU significantly correlate with increased AI token valuation, providing a deeper understanding of user-driven value creation. Furthermore, the results highlight how pricing per million tokens influences valuation, particularly in relation to cost efficiency, expanding on prior work that overlooked this aspect. The introduction of the Akpan scale offers a new standardized framework for comparing AI token values, addressing gaps in current valuation methods, and providing practical insights for developers, investors, and businesses. These contributions represent a significant advancement over previous research by offering a comprehensive, empirical analysis of AI token valuation factors that have not been explored in detail before.