Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence

AI-native 6G visions increasingly invoke wireless foundation models, large wireless models, and wireless world models as the natural endpoint of AI-native networking, drawing analogy to current evolutions of large language models (LLMs). In this paper, we argue that this analogy is structurally incomplete. The success of LLMs is based on a broad, reusable, self-contained tokenized data substrate whereas wireless AI models lack an equivalent data perspective. Wireless observations are configuration-dependent, simulator-conditioned, task-disaggregated, and stripped of operational feedback---four structural bottlenecks that undermine the monolithic pretraining recipe. We argue that monolithic wireless world models are not the most realistic near-term path to deployable AI-native networks. Instead, we propose agentic wireless AI: an architecture in which general reasoning models orchestrate specialized signal-processing models, classical algorithms, digital twins, telemetry databases, standards-aware retrieval, and safety checks via explicit and programmable interfaces. We further argue that wireless needs not merely larger datasets, but composable ones annotated with configuration, provenance, task, and deployment metadata.