This paper combines design, machine learning and social computing to explore generative deep learning as both tool and probe for respiratory care. We first present GANspire, a deep learning tool that generates finegrained breathing waveforms, which we crafted in collaboration with one respiratory physician, attending to joint materialities of human breathing data and deep generative models. We then relate a probe, produced with breathing waveforms generated with GANspire, and led with a group of ten respiratory care experts, responding to its material attributes. Qualitative annotations showed that respiratory care experts interpreted both realistic and ambiguous attributes of breathing waveforms generated with GANspire, according to subjective aspects of physiology, activity and emotion. Semi-structured interviews also revealed experts' broader perceptions, expectations and ethical concerns on AI technology, based on their clinical practice of respiratory care, and reflexive analysis of GANspire. These findings suggest design implications for technological aids in respiratory care, and show how ambiguity of deep generative models can be leveraged as a resource for qualitative inquiry, enabling socio-material research with generative deep learning. Our paper contributes to the CSCW community by broadening how generative deep learning may be approached not only as a tool to design human-computer interactions, but also as a probe to provoke open conversations with communities of practice about their current and speculative uses of AI technology.