According to @JeffDean, Google has published a technical paper outlining a comprehensive methodology for measuring the environmental impact of Gemini AI inference. The analysis reveals that a median text prompt in Gemini Apps consumes only 0.24 watt-hours of energy, comparable to the energy used for watching a brief online video. This benchmark sets a new standard for AI model efficiency and provides businesses with actionable data to assess the sustainability of AI-powered applications. The detailed reporting on Gemini's energy use highlights growing industry emphasis on sustainable AI development and offers enterprises key insights for optimizing operational costs and meeting environmental goals (source: Jeff Dean on Twitter, August 21, 2025).

According to DeepLearning.AI, researchers evaluated the energy consumption and resulting greenhouse gas emissions of 14 open-weights language models by having each model answer 100 questions across five subjects in the MMLU (Massive Multitask Language Understanding) benchmark and generate extended, open-ended responses. The study provides concrete data for AI developers and enterprise users to assess the environmental impact of deploying large language models, highlighting the need for greener AI solutions and optimization strategies in high-volume AI applications (source: DeepLearning.AI, August 13, 2025).

According to @timnitGebru, there are increasing concerns about AI companies' environmental impact, labor exploitation, and data privacy practices, specifically referencing leaders like Dario Amodei. These issues highlight the urgent need for transparent reporting and ethical standards in AI development to address resource consumption, fair compensation for data labelers, and responsible data use (source: @timnitGebru, June 5, 2025). The AI industry faces mounting pressure to adopt sustainable practices and improve working conditions, creating business opportunities for companies prioritizing green AI, ethical sourcing, and privacy-compliant data solutions.