Abstract: In the era of generative AI, the privacy crisis is intensifying. Current research primarily focuses on new methods of privacy invasion brought about by large models and their countermeasures, yet there is scarce attention to the more fundamental issue of the generative information privacy divide. This refers to the gap between the "ought" and the "is" of individual informational privacy, exacerbated by the predictive analytical capabilities and generative functions of multi-modal large models. These technologies enhance the privacy awareness and manipulation power of TA (human and machine agents), resulting in individuals facing a widening disparity in their enjoyment of information privacy. This concept helps to more intuitively reveal the "entanglement" between AI technology, privacy subjects, and privacy itself. The generative information privacy divide may lead to ontological privacy alienation, epistemological cognitive reification, and praxeological behavior "domestication". The key to addressing these issues lies in two main strategies: Firstly, achieving three major paradigm shifts in regulation; Secondly, a tripartite governance approach that simultaneously employs heteronomy, technonomy, and autonomy.