  Artificial intelligence (AI) plays an important role in the dynamic landscape
of wireless communications, solving challenges unattainable by traditional
approaches. This paper discusses the evolution of wireless AI, emphasizing the
transition from isolated task-specific models to more generalizable and
adaptable AI models inspired by recent successes in large language models
(LLMs) and computer vision. To overcome task-specific AI strategies in wireless
networks, we propose a unified wireless physical-layer foundation model (WPFM).
Challenges include the design of effective pre-training tasks, support for
embedding heterogeneous time series and human-understandable interaction. The
paper presents a strategic framework, focusing on embedding wireless time
series, self-supervised pre-training, and semantic representation learning. The
proposed WPFM aims to understand and describe diverse wireless signals,
allowing human interactivity with wireless networks. The paper concludes by
outlining next research steps for WPFMs, including the integration with LLMs.
