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Vertical heterogenous networks (VHetNets) and artificial intelligence (AI)play critical roles in 6G and beyond networks. This article presents anAI-native VHetNets architecture to enable the synergy of VHetNets and AI,thereby supporting varieties of AI services while facilitating automatic andintelligent network management. Anomaly detection in Internet of Things (IoT)is a major AI service required by many fields, including intrusion detection,state monitoring, device-activity analysis, security supervision and so on.Conventional anomaly detection technologies mainly consider the anomalydetection as a standalone service that is independent of any other networkmanagement functionalities, which cannot be used directly in ubiquitous IoT dueto the resource constrained end nodes and decentralized data distribution. Inthis article, we develop an AI-native VHetNets-enabled framework to provide theanomaly detection service for ubiquitous IoT, whose implementation is assistedby intelligent network management functionalities. We first discuss thepossibilities of VHetNets used for distributed AI model training to provideanomaly detection service for ubiquitous IoT, i.e., VHetNets for AI. Afterthat, we study the application of AI approaches in helping provide automaticand intelligent network management functionalities for VHetNets, i.e., AI forVHetNets, whose aim is to facilitate the efficient implementation of anomalydetection service. Finally, a case study is presented to demonstrate theefficiency and effectiveness of the proposed AI-native VHetNets-enabled anomalydetection framework.

VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT