Channels are one of the five critical components of a communication system, and their ergodic capacity is based on all realizations of a statistical channel model. This statistical paradigm has successfully guided the design of mobile communication systems from first generation (1G) to fifth generation (5G). However, this approach relies on offline channel measurements in specific environments, and thus, the system passively adapts to new environments, resulting in deviation from the optimal performance. As sixth generation (6G) expands into ubiquitous environments and pursues higher capacity, numerous sensing and artificial intelligence (AI)-based methods have emerged to combat random channel fading. However, there remains an urgent need for a proactive and online system design paradigm. From a system perspective, we propose an environment intelligence communication (EIC) based on wireless environmental information theory (WEIT) for 6G. The proposed EIC architecture operates in three steps. First, wireless environmental information (WEI) is acquired using sensing techniques. Then, leveraging WEI and channel data, AI techniques are employed to predict channel fading, thereby mitigating channel uncertainty. Finally, the communication system autonomously determines the optimal air–interface transmission strategy based on real-time channel predictions, enabling intelligent interaction with the physical environment. To make this attractive paradigm shift from theory to practice, we establish WEIT for the first time by answering three key problems: How should WEI be defined? Can it be quantified? Does it hold the same properties as statistical communication information? Subsequently, EIC aided by WEI (EIC-WEI) is validated across multiple air–interface tasks, including channel state information prediction, beam prediction, and radio resource management. Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization. Finally, several open problems and challenges, including regarding its accuracy, complexity, and generalization, are discussed. This work explores a novel and promising way for integrating communication, sensing, and AI capability in 6G.