The field of social simulation and demographic modeling is witnessing significant advancements, driven by the increasing capabilities of Large Language Models (LLMs) and the development of innovative methodologies. Researchers are exploring the potential of LLMs to simulate human behavior, generate synthetic survey respondents, and predict demographic trends. The use of dynamic microsimulation models is also becoming more prevalent, enabling the examination of complex demographic processes and individual life-course transitions. These advancements have the potential to revolutionize the field, enabling more accurate predictions, and informing evidence-based policy development. Noteworthy papers include: Ireland in 2057: Projections using a Geographically Diverse Dynamic Microsimulation, which presents a dynamic microsimulation model for Ireland, and Finetuning LLMs for Human Behavior Prediction in Social Science Experiments, which demonstrates the effectiveness of finetuning LLMs for simulating human behavior. Synthetic Founders: AI-Generated Social Simulations for Startup Validation Research and Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation also present innovative approaches to social simulation, highlighting the potential of LLM-driven personas and the importance of evaluating the internal consistency of LLM agents.