Beyond Price: A Technical Quality Framework for AI Antitrust

Abstract

Disruptive and nascent competitors in the artificial intelligence (AI) market are uniquely positioned to exploit the wide margin for innovation and explore multiple innovation trajectories. However, incumbent tech firms are increasingly dominating the AI landscape to protect their platforms. These firms can aggressively compete for market share while offering AI models and tools at subsidized prices to maintain users on their platforms, making antitrust frameworks focused on price effects inadequate for detecting anticompetitive behavior. To address this gap, we propose a technically informed framework for antitrust analysis that leverages established benchmarks and quality metrics: the Technical Qualities and Metrics Vector (TQMV). This framework offers a pathway to operationalize quality rather than price effects, allowing for more nuanced definitions of relevant markets and better characterization of the competitive effects of transactions such as “acquihires.” We illustrate through case studies how this framework can inform antitrust analysis. More broadly, our paper lays the foundation for interdisciplinary work at the intersection of computer science, AI, and law that examines competitive dynamics and the role of antitrust enforcement in this new market.