The article "IoT in the Era of Generative AI: Vision and Challenges" by Xin Wang, Zhongwei Wan, Arvin Hekmati, Mingyu Zong, Samiul Alam, Mi Zhang, and Bhaskar Krishnamachari explores the potential and challenges of integrating Generative AI with the Internet of Things (IoT). The authors highlight the transformative impact of Generative AI on various IoT applications, including mobile networks, autonomous vehicles, the metaverse, robotics, healthcare, and cybersecurity. They discuss how Generative AI can enhance data generation, processing, and system development, but also identify critical challenges such as the lack of generative models for IoT data, deploying large-scale models under limited memory, generating content with low latency, model adaptation and personalization, the need for IoT-based AI agents, privacy and security threats, and the lack of development tools and benchmarks. To address these challenges, the authors propose several opportunities, including building generative models for IoT data, model compression, runtime optimization, edge-cloud collaboration, efficient fine-tuning, designing IoT-based AI agents, leveraging federated learning and trusted execution environments, and developing IoT-oriented development tools and benchmarks. The article aims to inspire further research and innovation in the field of Generative AI for IoT.The article "IoT in the Era of Generative AI: Vision and Challenges" by Xin Wang, Zhongwei Wan, Arvin Hekmati, Mingyu Zong, Samiul Alam, Mi Zhang, and Bhaskar Krishnamachari explores the potential and challenges of integrating Generative AI with the Internet of Things (IoT). The authors highlight the transformative impact of Generative AI on various IoT applications, including mobile networks, autonomous vehicles, the metaverse, robotics, healthcare, and cybersecurity. They discuss how Generative AI can enhance data generation, processing, and system development, but also identify critical challenges such as the lack of generative models for IoT data, deploying large-scale models under limited memory, generating content with low latency, model adaptation and personalization, the need for IoT-based AI agents, privacy and security threats, and the lack of development tools and benchmarks. To address these challenges, the authors propose several opportunities, including building generative models for IoT data, model compression, runtime optimization, edge-cloud collaboration, efficient fine-tuning, designing IoT-based AI agents, leveraging federated learning and trusted execution environments, and developing IoT-oriented development tools and benchmarks. The article aims to inspire further research and innovation in the field of Generative AI for IoT.