For decades, wireless communication has been treated as a mere bit pipe, aiming to faithfully reconstruct transmitted signals without regard to the meaning or effect of the source messages. While such classical designs achieve high rates and low bit-level errors, they may not be able to meet the quality-of-experience requirements of emerging applications like connected intelligence or holographic communications, where message intent, resilience, and latency are critical. Semantic communication has recently emerged as a promising solution to address this challenge by embedding the meaning of source messages into the communication design. In this talk, we present a forward-looking vision for semantic communications that moves beyond current limited constructs by enabling network nodes to understand data semantics, build knowledge bases, reason over information, and communicate in a machine language capable of deducing meaning akin to human reasoning. We show how this framework, grounded in advanced artificial intelligence (AI) approaches such as neuro-symbolic AI and causal reasoning, reduces network data volumes while improving reliability, laying the foundation for knowledge-driven, resilient, and AI-native wireless systems. Building on this foundation, we show how semantic communications will be a natural stepping stone toward a bold, pioneering concept that we dub artificial general intelligence (AGI)-native wireless systems. We discuss how designing AGI-native wireless networks, with semantic communication functions, must go beyond current AI-native approaches that remain incremental, relying on opaque models and large datasets, and lack the generalization and reasoning capabilities needed for next-generation networks. We then demonstrate how the fusion of wireless systems, semantic communications, digital twins, and AI can catalyze a transformative paradigm shift in both wireless and AI technologies by conceptualizing a next-generation AGI architecture imbued with "common sense" capabilities, akin to human cognition and founded on three components: a) perception, b) world model, and c) action-planning. This architecture will empower networks with reasoning, planning, and other human-like cognitive faculties such as imagination and deep thinking. We define the technical tenets of common sense and, subsequently, we demonstrate how the proposed AGI architecture can instill a new level of generalizability, explainability, and reasoning into tomorrow’s wireless networks with a focus on the concept of world models. We discuss our recent key results on world models from both the AI and wireless perspectives, laying the groundwork for the realization of AGI-native wireless systems. We conclude with a discussion on the exciting opportunities in this field that can help redefine the intersection of wireless communications and AI.

Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo, Norway in 2010. He is currently the Rolls Royce Commonwealth Professor in Digital Twin Technology, a Professor at the Department of Electrical and Computer Engineering, and a founding faculty of the Institute for Advanced Computing at Virginia Tech, where he leads the Network intelligEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks (5G/6G/beyond), machine learning, game theory, quantum communications/learning, security, UAVs, semantic communications, and cyber-physical systems. Dr. Saad is a Fellow of the IEEE. Dr. Saad was the (co-)author of twelve conference best paper awards. He is the recipient of the 2015 and 2022 Fred W. Ellersick Prize from the IEEE Communications Society, of the IEEE Communications Society Marconi Prize Award in 2023, and of the IEEE Communications Society Award for Advances in Communication in 2023. He was also a co-author of the papers that received the IEEE Communications Society Young Author Best Paper award in 2019, 2021, and 2023. He has been annually listed in the Clarivate Web of Science Highly Cited Researcher List since 2019. He is the Editor-in-Chief for the IEEE Transactions on Machine Learning in Communications and Networking.