Les dessous de l'IA 30. Peut-on réconcilier IA et limites planétaires ? - Théo Alves Da Costa
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Jun 1, 2025 
Théo Alves D'Acosta, an engineer in machine learning and environmental responsibility, and president of Data for Good, dives into the intersection of AI and environmental sustainability. He discusses the hidden ecological costs of generative AI, from energy consumption to social implications. The conversation shifts to the necessity of democratic planning for sustainable transitions in tech, emphasizing ethical approaches. Théo highlights AI's transformative power in climate solutions, particularly in forest fire prevention and emissions monitoring, all while navigating the complexities of socio-political factors.
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AI's Broad Environmental Impact
- AI's environmental impact involves energy, water use, raw materials, and e-waste, not just carbon emissions.
- The opaque industry lacks transparency on AI's full ecological footprint and local adverse effects.
AI's Resource Conflicts
- Growth in AI demand strains electricity and water resources, causing conflicts with local communities.
- Data centers' preferential energy access can disproportionately impact residents' utilities and costs.
Rebound Effects in AI Usage
- Efficiency gains in AI often trigger rebound effects that increase overall energy consumption.
- New AI uses like reasoning models massively increase computational demand despite optimization.