Generative AI Advertising as a Problem of Trustworthy Commercial Intervention

TL;DR

This paper examines how generative AI advertising influences users through less observable channels, proposing a taxonomy of influence tiers and highlighting challenges in ensuring trustworthy commercial interventions.

Contribution

It introduces a new taxonomy of influence tiers in generative AI advertising and discusses the challenges in making commercial influence trustworthy and detectable.

Findings

Most deployed systems focus on observable influence tiers.

Influence on long-term user preferences remains poorly understood.

Frameworks for detection and disclosure are lacking.

Abstract

Major deployed generative AI advertising systems preserve a visible boundary between commercial content and AI-generated responses. Yet empirical research shows that ads woven directly into large language model (LLM) outputs often go undetected by users. We argue that generative AI fundamentally changes advertising: rather than placing products into discrete slots, it enables interventions on the generative process itself, which induce commercial influence through less observable channels. This reframes generative AI advertising as a problem of trustworthy intervention rather than content placement. We introduce a taxonomy organized by influence tier, corresponding to interventions on progressively more latent variables: product mentions, information framing, behavioral redirection, and long-term preference shaping; and show how these tiers instantiate across modalities and system architectures, including retrieval-augmented generation and agentic pipelines where upstream decisions can sharply constrain downstream outcomes. Both major deployed systems and designed mechanisms concentrate on the most observable and easiest-to-govern tier, while the forms of commercial influence most consequential for user autonomy remain poorly understood and lack frameworks for detection, measurement, or disclosure. The central challenge is whether commercial influence in generative systems can be made trustworthy, i.e., attributable, measurable, contestable, and aligned with user welfare.