Since the release of ChatGPT, there is an ongoing debate about the consequences of generative AI models on the production of scientific knowledge. Currently, genAI is either discussed as a promise for enhancing productivity and efficiency or viewed as a potential threat to human expertise. Beyond such very general discussions, it becomes clear that the scientific field comprises highly diverse application contexts due to various disciplines which means that genAI can be integrated into the research process in completely different ways ranging from being used as a mere tool to being treated as a “critical friend”. However, it also becomes clear that there are increasingly gray areas where the use of genAI is possible but raises important questions about what truly constitutes science and where the boundaries of applicability lie. This applies to questions about the necessity of human creativity in scientific knowledge production or with regard to authenticity, i.e., the traceability and accountability of scientific results. Connected to this is also the question of the corresponding skills that researchers must possess to use genAI adequately. These include not only skills in applying the tools but also knowledge of how these tools actually function in order to gain insights into possible sources of error. In our contribution, we explore these three aspects of scientific knowledge production--creativity, authenticity, and skills--and discuss the extent to which these aspects are influenced by genAI.