Almost every industry today confronts the potential role of artificial intelligence and machine learning in its future. While many studies examine AI in consumer marketing, less attention addresses AI's role in creating and selecting trademarks that are distinctive, recognizable, and meaningful to consumers. Traditional economic approaches to trademarks focus almost exclusively on consumer-based, demand-side considerations regarding search. However, these approaches are incomplete because they fail to account for substantial costs faced not just by consumers, but by trademark applicants as well. Given AI's rapidly increasing role in trademark search and similarity analysis, lawyers and scholars should understand its dramatic implications. This paper proposes that AI should interest anyone studying trademarks and their role in economic decision-making. We examine how machine learning techniques will transform the application and interpretation of foundational trademark doctrines, producing significant implications for the trademark ecosystem. We run empirical experiments regarding trademark search to assess the efficacy of various trademark search engines, many of which employ machine learning methods. Through comparative analysis, we evaluate how these AI-powered tools function in practice. In an age where artificial intelligence increasingly governs trademark selection, the classic division between consumers and trademark owners deserves an updated, supply-side framework. This insight has transformative potential for encouraging both innovation and efficiency in trademark law and practice.