The Future of Enterprise Transformation Starts Now Harnessing Agentic AI and Model Context Protocol (MCP) for Sustainable Growth Businesses are entering a new era driven by autonomy, security, and intelligent adaptability. Agentic AI and MCP are redefining how
SUSTAINABILITY
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Efficiency improvements keeping pace with model demand and quality
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Yes, I think the efficiency improvements are doing a pretty good job of keeping up with demand (and what people care about is not necessarily bigger models, but better models).
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Meta Restructures AI; Musk Claims Grok 5 Approaches AGI
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Top stories in AI today: – Meta’s massive AI restructure
– Google analyzes Gemini’s environmental footprint
– Automate performance reviews and PIPs
– Musk: Grok 5 has ‘a shot at being true AGI’ – 4 new AI tools, community workflows, and more Read more: https://
therundown.ai/p/metas-major-
ai-restructure
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Make Your AI More Sustainable: Energy Efficiency Challenge
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𝐋𝐞ç𝐨𝐧 𝟳 – 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 𝟯 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 : 𝗥𝗲𝗻𝗱𝗲𝘇 𝘃𝗼𝘁𝗿𝗲 𝗜𝗔 𝗽𝗹𝘂𝘀 𝘃𝗲𝗿𝘁𝗲 Les coûts environnementaux de l’IA explose. Il est grand temps de challenger votre IA sur le terrain de la sobriété. Réduire l’empreinte énergétique de l’IA, cela n'est pas
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Google Gemini vs Mistral: Environmental Impact Analysis
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The Verge publishes a piece criticizing some of these measures: https://
theverge.com/report/763080/
google-ai-gemini-water-energy-emissions-study
… And Mistral's audited report for their older model gave larger amounts of water (50 mL) and carbon emissions (1.14g) per average query. https://
mistral.ai/news/our-contr
ibution-to-a-global-environmental-standard-for-ai
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Environmental Impact of Large-Scale AI Models and Energy Consumption
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Google paper on Gemini: https://
services.google.com/fh/files/misc/
measuring_the_environmental_impact_of_delivering_ai_at_google_scale.pdf
… Sam Altman post: https://
blog.samaltman.com/the-gentle-sin
gularity
… Google search energy in 2008: https://
googleblog.blogspot.com/2009/01/poweri
ng-google-search.html
… Llama 3.3 power usage: -
Energy consumption metrics for Llama 3.3 and GPT-4 training
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These numbers match independent direct measures: 0.00004 kWh for 400 tokens on Llama 3.3 70B on a H100 node. We do not know the amount of energy required to train these models, which was estimated at a little above 500,000 kWh for GPT-4, about 18 hours of a Boeing 737 in flight.
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AI Model Energy Efficiency Improves 33x Year-over-Year
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We have data on the environmental impact per AI prompt:
Gemini: 0.00024 kWh & 0.26 mL water
ChatGPT: 0.0003 kWh & 0.38 mL
…the same energy as one Google search in 2008 & 6 drops of water. Seems to be improving, too: Google reports a 33x drop in energy use per prompt in a year. -
Google releases 2025 environmental impact report covering 2024
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We produce an annual report that covers Google's overall environmental impact (of which Gemini models inference is a portion). See our 2025 report (covering 2024):
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Prompts per kWh as a Performance Metric for Energy Efficiency
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Think of it as a performance measure, like queries per second, transactions per second or miles per gallon or something, and then prompts per kWh makes more sense, perhaps. The energy on the X axis is a constant unit of 1 kWh.