The field of artificial intelligence is shifting towards a more sustainable and environmentally conscious direction. Researchers are focusing on developing energy-efficient models, reducing carbon footprint, and promoting green AI principles. This trend is driven by the growing concern about the environmental impact of large language models and other AI systems. Studies have shown that small language models can be a more sustainable alternative, and that careful design of prompts can guide these models towards greener software development. Additionally, there is a need for energy-aware scheduling, cost optimization, and physical host-independent energy estimates in virtualized environments. Noteworthy papers include: Generating Energy-Efficient Code via Large-Language Models, which empirically assesses the energy efficiency of Python code generated by LLMs against human-written code. Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization, which provides guidelines for researchers and practitioners on how to minimize the environmental footprint of their work and implement green recommender systems.