The article explores the potential and limitations of Generative AI in social science research. Generative AI, capable of producing realistic text, images, and other human-like outputs, is transforming various industries. The author argues that it has the potential to enhance survey research, online experiments, automated content analyses, agent-based models, and other techniques used to study human behavior. However, the article also discusses significant limitations and potential dangers, including bias in training data, ethical concerns, replication issues, and environmental impacts. The author suggests that social scientists can address many of these challenges by creating open-source infrastructure for research on human behavior, ensuring broad access to high-quality tools and fostering deeper understanding of social forces. The article concludes by emphasizing the need for open-source infrastructure to manage the risks and maximize the benefits of Generative AI in social science.The article explores the potential and limitations of Generative AI in social science research. Generative AI, capable of producing realistic text, images, and other human-like outputs, is transforming various industries. The author argues that it has the potential to enhance survey research, online experiments, automated content analyses, agent-based models, and other techniques used to study human behavior. However, the article also discusses significant limitations and potential dangers, including bias in training data, ethical concerns, replication issues, and environmental impacts. The author suggests that social scientists can address many of these challenges by creating open-source infrastructure for research on human behavior, ensuring broad access to high-quality tools and fostering deeper understanding of social forces. The article concludes by emphasizing the need for open-source infrastructure to manage the risks and maximize the benefits of Generative AI in social science.