The field of Artificial Intelligence (AI) is moving towards in-network computation, where AI computations are performed directly within the network fabric, reducing latency and enhancing throughput. This approach is enabled by the convergence of programmable network architectures, such as Software-Defined Networking (SDN) and Programmable Data Planes (PDPs), with AI. Distributed learning, including in-network aggregation and federated learning, is also gaining traction, allowing for more privacy and scalability. Additionally, edge computing is being explored in various applications, including aerospace, earth observation, and remote sensing, where real-time data processing and low latency are critical. Noteworthy papers in this area include: INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization, which provides a comprehensive analysis of optimizing in-network computation for AI. Airborne Neural Network, which proposes a novel concept of a distributed architecture where multiple airborne devices host a subset of neural network neurons, enabling real-time learning and inference during flight.