According to @DeepLearningAI, Mistral published an 18-month life-cycle analysis of Mistral Large 2 that measures greenhouse-gas emissions, energy use, and consumption of water and other materials across data-center construction, hardware manufacturing, training, and inference (source: @DeepLearningAI post on Sep 1, 2025). According to @DeepLearningAI, the analysis covers total training impacts and includes inference-stage measurement, but the post does not provide numeric results, requiring traders to review the full report for metrics (source: @DeepLearningAI). According to @DeepLearningAI, the scope explicitly extends beyond model operations to include upstream infrastructure like data-center buildout and hardware production, clarifying where environmental costs accrue in the AI stack (source: @DeepLearningAI). According to @DeepLearningAI, the post does not mention cryptocurrencies or related tokens, and no crypto market impacts are specified (source: @DeepLearningAI).

According to Google DeepMind, a median Gemini text prompt now uses less than 9 seconds of TV-equivalent energy, about 5 drops of water, and emits 0.03 gCO2e, with energy per prompt reduced 33x and carbon footprint reduced 44x over a recent 12-month period (source: Google DeepMind on X, Aug 22, 2025, https://twitter.com/GoogleDeepMind/status/1958855876116455894). For trading, these reported per-inference ESG metrics provide a concrete benchmark to model AI workload resource intensity and to compare sustainability disclosures across AI-exposed assets (source: Google DeepMind on X, Aug 22, 2025). Crypto market participants focused on AI narratives can reference these figures when assessing ESG alignment for AI-integrated blockchain projects and AI-related tokens (source: Google DeepMind on X, Aug 22, 2025).