Navigating the funding landscape: how later-stage investments propel generative AI startups

Purpose This study aims to examine the funding dynamics of generative artificial intelligence (GAI) startups, focusing on how different investment stages influence financial success. Grounded in Signaling Theory, it seeks to identify the critical funding phases that drive total funding amounts (TFA) and assess the heterogeneous nature of funding dynamics in GAI startups. Design/methodology/approach A data set of 561 GAI startups from 2010 to 2024 is analyzed using two-stage least squares (2SLS) regression to address potential endogeneity. The method of moments quantile regression (MMQR) is used to capture heterogeneous effects across different performance levels, while sub-sample analyses further explore the influence of team size, investor diversity and LinkedIn presence on funding outcomes. Findings The results show that although Angel and Seed funding are essential for early growth, they are associated with lower TFA. In contrast, Series A, B and C investments significantly increase TFA, highlighting the importance of later-stage funding. Team size and investor diversity positively affect funding success, and startups with strong LinkedIn engagement gain greater benefits from later-stage investments. Practical implications The findings imply that entrepreneurs should build diverse teams and strategically leverage professional networks, particularly LinkedIn, to improve funding outcomes. Investors are encouraged to prioritize later-stage funding to maximize scalability, while policymakers may support GAI startups by creating co-investment schemes and reducing regulatory barriers to facilitate growth across funding stages. Originality/value This study contributes to the literature by providing novel empirical evidence on the stage-specific effects of funding in the GAI sector; highlighting the role of team and investor characteristics in shaping funding outcomes; and demonstrating the importance of professional networking in enhancing funding success. By applying Signaling Theory, the study extends understanding of how different funding stages serve as signals of startup quality and scalability, offering both theoretical and practical guidance for entrepreneurs, investors and policymakers.