Five Sustainable AI Trends: Q1 2025

by Jeremy Tamanini, Dual Citizen LLC founder

I absorbed a lot of sustainable AI news and information during Q1 2025. Here are five highlights:

1. While compute has never been carbon free, the resource intensity of AI systems can be large, and has propelled the issue into the mainstream. We have academia to thank for this: A 2023 paper by Dutch analyst Alex de Vries used AI hardware and software specs published by Nvidia on their graphics processing units (GPUs) to estimate AI-associated global energy usage. His finding: that by 2027, global AI could consume the same amount of energy as his home country of the Netherlands. University of California (Riverside) researcher Shaolei Ren observed a similar trend with water usage linked to data processing centers. His research illuminates global AI’s scope 1 (onsite server cooling) & scope 2 (offsite electricity generation) water withdrawals. His conclusion: that by 2027, global AI’s scope 1 & 2 water withdrawals will be 4-6x that of Denmark.
2. As the resource intensity of AI systems becomes a concern, measurement tools are emerging to quantify and compare AI’s energy-linked emissions and water usage. Hugging Face’s AI Energy Score project offers a clear and standardized benchmark for measuring AI energy consumption, ensuring that the AI community can make informed, sustainable choices. AllAI Consulting, LLC is releasing the world’s first public Data Center Water Consumption Calculator to help municipal officials estimate water withdrawals or consumption of proposed data centers. This tool puts municipal leaders in a position to more fully negotiate data center contracts and ensure water access for their constituents.
3. While large-language models (LLMs) like ChatGPT etc. are quite resource intensive, many smaller, targeted applications linked to sustainable AI are not. Small and medium-sized enterprises (SMEs) can improve team productivity, reduce energy expenses, optimize product development & supply chains, and streamline reporting and stakeholder communication by thinking strategically about sustainable AI. Put differently: most use cases for AI offer clear sustainability benefits. Learn more here.
4. For most SMEs, AI is top of mind but their leaders are still in the learning phase, undecided on when and where to explore deeper integration. A recent survey confirmed this: the majority of respondents indicated that they had limited integration of AI today but that it was a priority to find ways it can advance sustainable practices. Further, a gap persists between forward-looking claims about how AI will advance sustainability and real-world case studies.
5. SMEs must consider the extent to which AI integration will impact their scope 3 emissions. AI systems integration with large environmental impacts need to be evaluating against broader sustainability goals. At the same time, there can be costs (some environmental) to not integrating AI: energy consumption isn’t optimized leading to higher utility bills; supply chains are inefficient or produce unnecessary waste; stakeholder communications aren’t automated, leaving employees less time for other tasks.

Contact Jeremy Tamanini to continue the conversation on this topic, as well as reading these related insights from the practice:

AI Everywhere: Tangible Applications for Sustainability Teams (link here)

The AI Elephant in the Room (link here)

AI x Sustainability in Trump 2.0 (link here)

Remarks to the National Sustainability Society (link here)

AI in Building & Construction: Tangible Applications for Sustainability Teams (link here)

How to Work with Satellite-Based Sustainability Data (link here)