Still Writing Ads for Humans? The Algorithmic Audience Has Arrived!

The widespread adoption of generative AI assistants, built on Transformer‑based large language models, has shifted the primary audience of advertising and communication from humans to algorithms. Within the framework of gatekeeping theory, these systems can be conceptualized as new algorithmic gatekeepers that search, aggregate, and re‑synthesize information before it reaches human users. A paradigm shift is required from the logic of the attention economy to that of epistemic signals—structured, auditable, and multi‑sourced claims designed for machine readability. A research agenda is outlined that emphasizes the analysis of algorithmic evidence preferences, the exploration of principles of machine‑readable communication, the development of new metrics such as answer share, and the examination of governance and ethical challenges. Case illustrations from healthcare, finance, and education demonstrate how these inquiries can be operationalized. Collectively, these directions highlight the need to redefine audience research and to renew methodologies in communication and marketing scholarship in the era of generative AI.