As Artificial Intelligence (AI) becomes deeply embedded in the software supply chain, traditional governance and management practices are insufficient for addressing complex AI-related dependencies, such as pre-trained models, specialized datasets, and configurations. The Artificial Intelligence Bill of Materials (AIBOM) has emerged as a promising framework for providing the structured inventories needed to improve transparency in AI-powered systems and their underlying models. This paper presents a preliminary empirical investigation into the acceptance and perception of AIBOMs within the Hugging Face community. To this end, we conducted a field study by submitting 278 pull requests containing AIBOMs to a statistically significant sample of popular AI repositories hosted in Hugging Face. Our data analysis reveals a complex landscape: the acceptance of AIBOM-related pull requests was low, but contributions were more likely to be accepted in user-owned repositories than in organization-owned ones. Engagement with the Hugging Face community was limited, and perceptions of AIBOMs were mixed. Overall, these findings suggest that AIBOM adoption may initially emerge in a bottom-up manner and that further efforts, such as improved tooling and targeted adoption initiatives, are needed to support broader uptake.