The Evolution of Pricing Power: A Paradigm Shift in Virtual Asset Valuation Driven by Algorithms and AI?

Abstract

This study constructs a theoretical framework for algorithm-driven pricing, revealing how algorithms fundamentally reshape pricing logic across three dimensions: market structure, equilibrium mechanisms, and value discovery. Automated market makers algorithmize pricing mechanisms through mathematical functions, arbitrage algorithms become micro-level mechanisms for market equilibrium, and AI prediction models pioneer a new data-driven paradigm for value discovery. Simultaneously, algorithmic pricing has triggered deepseated contradictions, including the restructuring of systemic risk, shifts in power dynamics, and regulatory efficacy dilemmas. The homogenization of algorithmic consensus, positive feedback loops, and oracle dependency constitute novel sources of vulnerability. Meanwhile, the democratic paradox of code governance and the implicit concentration of algorithmic power pose fundamental challenges to traditional financial governance frameworks. Consequently, establishing a new governance framework tailored to algorithmic finance is urgently needed. This research breaks through the explanatory boundaries of traditional financial theory, offering a fresh analytical perspective for studying price formation mechanisms in the digital finance era.