#67 Can we be optimistic about AI sustainability? With Iuna Tsyrulneva

Update: 2025-10-21

Description

The potential impact of Artificial Intelligence on our worsening climate crisis is certainly a hot topic, pardon the pun.

Fortunately, we have an expert on today’s show to help us make sense of it all. Iuna Tsyrulneva is one of the leading voices in AI and sustainability. Well versed on both topics, Iuna holds a PhD in materials science, and works at Earth Observatory of Singapore with an interdisciplinary approach. She’s full of interesting insights, and brings a dynamic approach to thinking about the larger issue.

For example, did you know that saying “thank you” after every prompt actually contributes to AI energy usage? And they say kindness costs nothing…

As you’ll hear from Iuna, optimisation is the key to affecting sustainability in Artificial Intelligence. And that goes beyond ‘clean code’. Throughout our conversation, Iuna unpacks the end-to-end scope of optimisation, or what she calls her ‘four pillars’ of sustainable AI, that being algorithmic, hardware, data center and usage optimisation.

In this episode, Gaël and Iuna also discuss:

Sustainable AI vs AI for Sustainability
How (if at all) can we mitigate Jevons paradox?
Tackling the financial incentive vs sustainability in AI
Why she’s optimistic about the future of sustainable AI
Using more energy efficient languages to build next-gen AI
Leveraging open-source frameworks to lower resource costs
Practical tips on how to make AI models more energy efficient

And much more!

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Connect with Iuna Tsyrulneva

https://linkedin.com/in/iuna-tsyrulneva

Connect with Gaël and Green IO

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Referenced in this episode:

Research: https://institute.global/insights/climate-and-energy/greening-ai-a-policy-agenda-for-the-artificial-intelligence-and-energy-revolutions
Research: https://institute.global/insights/climate-and-energy/responsible-progress-sustainable-ai-the-benefits-of-greening-our-digital
https://www.iea.org/reports/energy-and-ai/ai-and-climate-change
Greening AI: A Policy Agenda for the Artificial Intelligence and Energy Revolutions. Tony Blair Institute (2024) https://institute.global/insights/climate-and-energy/greening-ai-a-policy-agenda-for-the-artificial-intelligence-and-energy-revolutions
Bashir, Noman, Priya Donti, James Cuff, Sydney Sroka, Marija Ilic, Vivienne Sze, Christina Delimitrou, and Elsa Olivetti. 2024. “The Climate and Sustainability Implications of Generative AI.” An MIT Exploration of Generative AI, March. https://doi.org/10.21428/e4baedd9.9070dfe7.
Computational efficiency of AI models: https://openai.com/index/ai-and-compute/
Computational efficiency of AI models: arXiv:2104.10350 [cs.LG]. https://doi.org/10.48550/arXiv.2104.10350
Techniques and approaches for developing Sustainable AI: https://institute.global/insights/climate-and-energy/greening-ai-a-policy-agenda-for-the-artificial-intelligence-and-energy-revolutions
Trade between accuracy and efficiency of AI models: Dhar, P. The carbon impact of artificial intelligence. Nat Mach Intell 2, 423–425 (2020). https://doi.org/10.1038/s42256-020-0219-9
AI for Sustainability: Vinuesa, R., Azizpour, H., Leite, I. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat Commun 11, 233 (2020). https://doi.org/10.1038/s41467-019-14108-y
Examples of AI for Sustainability: https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/; https://www.iea.org/reports/energy-and-ai/ai-and-climate-change

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