DOI: 10.11118/978-80-7701-042-9-0126

QUALITY SIGNALS IN EARLY-STAGE VENTURE CAPITAL DECISIONMAKING IN THE AGE OF AI

István Gábor Zsarnay¹, Mihály Hegedűs², Anna Dunay³

¹ Universiteit Van Amsterdam, Amsterdam, The Netherlands (Alumnus)

² Tomori Pál College, Budapest, Hungary

³ John Von Neumann University, Kecskemét, Hungary

This study will be grounded in the philosophy of positivism, aiming to build upon and contribute to the existing literature in the fields of venture capital (VC) decision-making and quality signaling theory. It analyzes observable and quantifiable variables, identified and evaluated using artificial intelligence (AI), to investigate potential correlations between early-stage signals and venture success. In doing so, the research seeks to either propose a framework or methodology for leveraging AI in VC evaluation processes or to demonstrate that current AI capabilities remain insufficient for reliably distinguishing high-potential startups from less successful ones, particularly when human behavioral traits are involved. While challenges such as model transparency, evolving outputs, and bias remain, the transformative potential of AI to enhance efficiency, objectivity, and predictive power in venture capital decision-making – particularly in the early stages – warrants further investigation into its long-term impact, limitations, and ethical implications.

Keywords: venture capital, artificial intelligence, startup, investment, quality signal

pages: 126-127, online: 2025