Abstract

The integration of Artificial Intelligence (AI) into venture capital (VC) is reshaping how startup risk is analyzed and investment decisions are made. This paper explores the application of AI as a decision-support system within VC, focusing on how it enhances startup risk evaluation, improves decision-making efficiency, and redefines investor–founder dynamics. Traditional VC decision processes have long been influenced by biases, intuition, and reliance on personal networks—factors that can limit objectivity and accessibility. By contrast, AI introduces data-driven methods that increase consistency, scalability, and speed. The paper builds its foundation on a structured literature review of recent academic contributions covering AI adoption in VC, startup risk modeling, and the evolving relationship between human judgment and algorithmic evaluation. The review is complemented by insights gathered from the Capital Networking Virtual Conference (April 2025), where leading investors discussed the role of AI in investment workflows. The study also includes a qualitative case analysis of NoCap, the first AI-based angel investor. Through direct engagement with its founders, we examine how NoCap leverages natural language processing and machine learning to evaluate early-stage startups, identify risks, and enable faster, bias-aware funding decisions. Our findings show that AI systems like NoCap can compress deal cycles, democratize capital access, and support more consistent risk assessment. At the same time, they raise new questions around model transparency, bias mitigation, and the evolving role of human oversight. This work offers both theoretical and practical insights into how AI is redefining VC investment practices in an increasingly data-centric era.

Download

Share

COinS