Quantifying the Mutual Requirements Driving AI and 6G Co-Evolution

This paper studies the mutual requirements of artificial intelligence (AI) and sixth-generation wireless networks (6G) as they evolve together. Unlike previous wireless network generations, which added AI after deployment, 6G aims to be AI-native by embedding intelligence into its core design, interfaces, and operations. We analyze how advanced AI, such as generative AI (GenAI) and large language models (LLMs), create strict requirements for 6G networks, including low latency, high bandwidth, efficient resource usage, scalability, security, and compliance with regulations. Additionally, we identify key features that 6G networks need to support for efficient AI, such as distributed AI training, inference capabilities, dynamic resource management, programmable interfaces, and intelligent orchestration. By examining specific use cases like AI Training as a Service and LLM-based network management, the paper provides measurable insights into important performance indicators. This analysis serves as a practical guide for designing and standardizing future 6G networks optimized for AI-driven services.