AI is not one thing. Neither is its environmental impact.
We need to keep our eyes, our debates, and our policymaking focused on the problems we’re trying to solve. On the outcomes we’re trying to achieve and the society we want to build.
AI can be machine learning, Grok, or basic (mislabelled) data analytics and simple process automation software.
Its impact on sustainability can of course be environmental – its emissions, energy use, water consumption, effect on the local environment, or through the physical materials and supply chains that build datacentres and all manner of other infrastructure.
AI’s impact can also be social and economic – on employment, education, policing, for creatives, or on local community cohesion.
When considering governance, often the third pillar of sustainability (and the acronym ESG) AI opens up new questions of accountability from self-driving cars to corporate management.
I’m not going to rank these impacts. Or pretend that in one blog post I can fully account for their breadth and complexity. But as a starting point for our collective future conversations, here are some numbers, questions, and counterarguments to consider…
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