Artificial intelligence is rapidly changing the startup landscape, raising new questions about how venture capital investors evaluate early-stage companies. This project investigates how the venture capital due-diligence process has evolved as artificial intelligence becomes more widely integrated into startups. While venture capital has traditionally focused on factors such as market demand, scalability, competition, and growth potential, AI introduces additional considerations including proprietary data, system scalability, and potential risks associated with automated decision-making. This research is conducted in collaboration with Bright Ventures, a New York–based venture capital firm focused on early-stage startups. The project combines analysis of venture capital with direct observation of investor meetings, startup evaluations, and a survey distributed to venture capital professionals. These methods allow for a comparison between evaluation frameworks used before the rise of AI and those used today. The project expects to show that although traditional investment fundamentals—such as customer demand – remain essential, AI expands the due-diligence process by introducing new technical, ethical, and scalability considerations. Understanding how these evaluation frameworks are evolving helps clarify how investors identify startups with long-term potential in an increasingly AI-driven economy.