We present MetaAI, a novel wireless computing paradigm that integrates neural network computation directly into wireless signal propagation. Unlike traditional approaches that treat wireless channels as mere data conduits, MetaAI transforms them into active computing elements through programmable metasurfaces, enabling concurrent data transmission and neural network processing. By leveraging the inherent linearity of both wireless propagation and neural networks, our design resolves the fundamental mismatch between sequential wireless transmission and parallel neural computation, while supporting efficient multi-sensor late-stage data fusion. We implemented MetaAI using metasurfaces at both dual-band (2.4/5 GHz) and single-band (3.5 GHz) frequencies. Extensive experiments demonstrate robust performance across diverse classification tasks, achieving 82.8% average accuracy (up to 89.8%) even with a simple linear architecture. Multi-sensor fusion further improves accuracy by up to 27.06%. MetaAI represents a fundamental shift in Edge AI architecture, where wireless infrastructure becomes an integral part of the computing pipeline.

The complexity and volatility of real-world radio environments often hamper wireless networks from achieving optimal performance. Recently, intelligent metasurfaces have been explored to dynamically reshape the radio propagation environment. However, existing systems are limited to standalone metasurfaces, only enabling one-time signal redirection/reshaping effects within their direct line-of-sight. They cannot effectively scale to cover larger areas. In this paper, we propose RFMagus, which employs a network of metasurfaces to overcome the limitation. We carefully optimize the configurations of the networked metasurfaces so that they can cooperatively and coherently propagate the analog signals towards the target regions. We have implemented the networked metasurfaces and deployed them in a variety of real-world environments. Experimental results demonstrate that RFMagus can effectively expand the coverage, improve the throughput, and operate transparently to different wireless standards.