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.

翻译：主流部署的生成式AI广告系统在商业内容与AI生成回复之间保留了可见边界。然而实证研究表明，直接嵌入大语言模型输出中的广告往往被用户忽略。我们认为，生成式AI从根本上改变了广告形态：它不再将产品置入离散槽位，而是实现对生成过程本身的干预，并通过更隐蔽的渠道施加商业影响。这重新定义了生成式AI广告问题——不再是内容投放，而是可信干预。我们提出按干预层级分类的框架，对应逐步深入潜变量的干预：产品提及、信息框架、行为重定向及长期偏好塑造；并揭示这些层级如何在不同模态与系统架构中实现，包括检索增强生成与智能体流水线——其中上游决策可能急剧限制下游结果。当前主流部署系统与设计机制均聚焦于最可见、最易治理的层级，而对用户自主权影响最重大的商业干预形式仍缺乏理解，且欠缺检测、测量与披露框架。核心挑战在于：生成系统中的商业干预能否实现可信——即可归因、可测量、可质疑且与用户福祉对齐。