Dynamic Shifts: Understanding the network position as social capital to enhancing the innovation performance of frontier AI labs

Artificial intelligence (AI) is rapidly transforming industries, with pioneering labs emerging as key players in driving innovation. This paper explores the evolving dynamics of frontier AI labs, such as OpenAI and DeepMind, within this rapidly evolving field. Drawing on Social Capital Theory (SCT), we argue that key network positions in innovation collaborations constitute highly valued social capital, which drives the rapid growth and success of frontier AI labs. Our study combines network analysis with fuzzy-set Qualitative Comparative Analysis (fsQCA) to quantitatively assess the factors influencing research impact. We address two key questions: first, we portray the evolutionary patterns of how frontier AI labs have occupied different “network positions” over time; second, we conduct an in-depth investigation into the causal effects of network positions and other critical factors on their innovation capabilities. Analyzing a dataset of 41,055 AI research papers and 40,033 AI-related patents published from 2013 to 2024, we find that these labs have consistently improved their network positions across three stages—early stage (2013–2016), growth stage (2017–2021), and mature stage (2022–2024)—shifting from follower roles to leadership positions in institutional cooperation networks. In the second study, we find that various factors, such as network positions and computing power, are key drivers of the rapid growth of frontier AI labs in the current landscape. Our findings highlight the importance of collaborative networks in fostering research innovation, emphasizing the strategic role of frontier AI labs in shaping the future of AI technology.