Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents ``AI-Generated Everything'' (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks.

翻译：生成式人工智能（GAI）作为人工智能领域的重要进展，以其语言与图像生成能力而闻名。本文提出“AI生成一切”（AIGX）概念，将GAI的应用从单纯的内容创作拓展至跨技术领域的实时适配与控制。在网络领域，AIGX与物理层、数据链路层、网络层及应用层紧密协作，以增强实时网络管理能力，使其能够响应各类系统与服务配置以及应用与用户需求。与此同时，网络通过AIGX生命周期（即数据收集、分布式预训练与快速决策）成为进一步优化AIGX能力的关键组成部分，从而形成相互促进的协同效应。此外，我们以功率分配为典型案例展开深度研究，阐释AIGX与网络系统间的相互依存关系。通过这一探索，本文分析了GAI对网络领域的重要作用，阐明了网络增强AIGX功能的具体路径，并强调了二者形成的良性互动循环。本研究为后续充分释放GAI与网络潜能的未来工作奠定了基础。