The rapid expansion of AI-generated content (AIGC) reflects the iteration from assistive AI towards generative AI (GAI) with creativity. Meanwhile, the 6G networks will also evolve from the Internet-of-everything to the Internet-of-intelligence with hybrid heterogeneous network architectures. In the future, the interplay between GAI and the 6G will lead to new opportunities, where GAI can learn the knowledge of personalized data from the massive connected 6G end devices, while GAI's powerful generation ability can provide advanced network solutions for 6G network and provide 6G end devices with various AIGC services. However, they seem to be an odd couple, due to the contradiction of data and resources. To achieve a better-coordinated interplay between GAI and 6G, the GAI-native networks (GainNet), a GAI-oriented collaborative cloud-edge-end intelligence framework, is proposed in this paper. By deeply integrating GAI with 6G network design, GainNet realizes the positive closed-loop knowledge flow and sustainable-evolution GAI model optimization. On this basis, the GAI-oriented generic resource orchestration mechanism with integrated sensing, communication, and computing (GaiRom-ISCC) is proposed to guarantee the efficient operation of GainNet. Two simple case studies demonstrate the effectiveness and robustness of the proposed schemes. Finally, we envision the key challenges and future directions concerning the interplay between GAI models and 6G networks.

翻译：人工智能生成内容（AIGC）的快速扩展反映了从辅助型AI向具有创造力的生成式AI（GAI）的迭代演进。与此同时，6G网络也将从万物互联向具有混合异构网络架构的智能互联演进。未来，GAI与6G的交互将催生新机遇：GAI可从海量连接的6G终端设备中学习个性化数据知识，而其强大的生成能力则能为6G网络提供先进网络解决方案，并为6G终端设备提供多样化的AIGC服务。然而，由于数据与资源之间的矛盾，两者看似“矛盾组合”。为实现GAI与6G更优的协同交互，本文提出面向GAI的云边端协同智能框架——GAI原生网络（GainNet）。通过将GAI深度融入6G网络设计，GainNet实现了知识流的正向闭环与GAI模型的可持续进化优化。在此基础上，提出面向GAI的通用资源编排机制——通感算一体化（GaiRom-ISCC），以保障GainNet的高效运行。两个简单案例研究验证了所提方案的有效性与鲁棒性。最后，我们展望了GAI模型与6G网络交互中的关键挑战与未来方向。