According to @_KarenHao, there was an error in the cited water footprint data for a proposed data center in Chile, and updated figures are now being addressed to ensure accuracy in reporting on AI infrastructure's environmental impact (source: @_KarenHao, Twitter). The correction highlights the growing importance of reliable environmental data in AI data center operations, as water usage is a critical sustainability concern for hyperscale AI infrastructure. Recent industry reports show that leading AI companies are increasingly transparent about their water consumption and are adopting water-efficient cooling technologies to mitigate environmental impact, which offers new business opportunities for AI-driven sustainability solutions (source: Uptime Institute, 2024).

According to DeepLearning.AI, Mistral has released an 18-month life-cycle analysis of its Mistral Large 2 AI model, providing detailed metrics on greenhouse-gas emissions, energy consumption, water usage, and material consumption. The report covers the full spectrum of AI deployment, including data center construction, hardware manufacturing, model training, and inference stages. This comprehensive assessment enables businesses to benchmark and optimize the environmental footprint of large language models, highlighting the need for sustainable AI practices and green data infrastructure (source: DeepLearning.AI, September 1, 2025).