In this paper, we analyze the causes and discuss potential consequences of perceived privatization of AI research, particularly the transition of AI researchers from academia to industry. We explore the scale of the phenomenon by quantifying transition flows between industry and academia, and providing a descriptive account and exploratory analysis of characteristics of industry transition. Here we find that industry researchers and those transitioning into industry produce more impactful research as measured by citations. Using a survival regression approach we identify mechanisms that trigger these university-industry transitions focusing on researcher characteristics, performance, and research field as documented in bibliographic data. We find that researchers working within the field of deep learning as well as those with higher average impact tend to transition into industry. These findings highlight the importance of strengthening academic research in public organizations within AI to balance a potential dominance of private companies and to maintain public supervision of the development and application of this technology.