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This dataset contains firm-level, social media, founder, and funding information for 75 Generative AI startups founded and funded between 2020 and 2025. The dataset integrates X (formerly Twitter) engagement metrics, including average likes, replies, and reposts per post, with venture funding and founder characteristics collected from publicly available sources such as Crunchbase, X, and LinkedIn. Engagement metrics were standardized and combined using Principal Component Analysis (PCA) to construct a Weighted Engagement Score (WES), which captures overall digital engagement intensity. Funding outcomes are measured as the log-transformed total funding amount. Structural variables are measured as the log-transformed that include the number of founders, number of investors, and number of funding rounds. Founder demographic variables (gender, highest degree attained, field of study, and geographic region) are included as categorical variables. The dataset includes both raw variables and processed variables (log-transformed and Z-score standardized) used in the regression analyses. All transformations, PCA calculations, and regression analyses were performed using Python and Excel. This dataset supports empirical analysis of the relationship between social media engagement, founder characteristics, and startup funding outcomes in the Generative AI sector and is suitable for replication and extension studies in entrepreneurship, innovation, and digital marketing research.