Recent work has suggested substituting human participation and labor in research and development--e.g., in surveys, experiments, and interviews--with synthetic research data generated by Large Language Models (LLMs). I will provide an overview of our work in which we conducted interviews with 19 qualitative researchers to understand how they define and interact with LLM personas as proxies for research participants, and their perspectives on this paradigm shift. I will contextualize this work within the broader set of initiatives to study the use of GenAI on the ground by end users---an important layer of risk evaluation referred to as "field-testing" or "human uplift studies" in AI policy documents. I will conclude with an agenda for future work in this space.