An argument can be made that artificial intelligence operations will consume vast quantities of electricity and water, as well as create lots of new e-waste. But higher volume of cloud computing operations--for conventional or AI purposes--is going to increase in any event.
And higher volume necessarily means more power and water consumption, and use of more servers.
Some portion of the AI-specific investment would have been made in any case to support the growth of demand for cloud computing.
So there is a “gross” versus “net” assessment to be made, for data center power, water and e-waste purposes resulting from AI operations.
By some estimates, AI will increase all those metrics by 10 percent to 12 percent. It matters, but not as much as some might claim.
By definition, all computing hardware will eventually become “e-waste.” So use of more computing hardware implies more e-waste, no matter whether the use case is “AI” or just “cloud computing.” And we will certainly see more of both.
Also, “circular economy” measures will certainly be employed to reduce the gross amount of e-waste for all servers. So we face a dynamic problem: more servers, perhaps faster server replacement cycles, more data centers and capacity, offset by circular economy efficiencies and hardware and software improvements.
Study Name | Date | Publishing Venue | Key Conclusions |
The E-waste Challenges of Generative Artificial Intelligence | 2023 | ResearchGate | Quantifies server requirements and e-waste generation of generative AI. Finds that GAI will grow rapidly, with potential for 16 million tons of cumulative waste by 2030. Calls for early adoption of circular economy measures. |
Circular Economy Could Tackle Big Tech Gen-AI E-Waste | 2023 | EM360 | Introduces a computational framework to quantify and explore ways of managing e-waste generated by large language models (LLMs). Estimates annual e-waste production could increase from 2.6 thousand metric tons in 2023 to 2.5 million metric tons per year by 2030. Suggests circular economy strategies could reduce e-waste generation by 16-86%. |
AI has a looming e-waste problem | 2023 | The Echo | Estimates generative AI technology could produce 1.2-5.0 million tonnes of e-waste by 2030 without changes to regulation. Suggests circular economy practices could reduce this waste by 16-86%. |
E-waste from generative artificial intelligence" | 2024 | Nature Computational Science | Predicts AI could generate 1.2-5.0 million metric tons of e-waste by 2030; suggests circular economy strategies could reduce this by up to 86%1 2 |
"AI and Compute" | 2023 | OpenAI (blog) | Discusses exponential growth in computing power used for AI training, implying potential e-waste increase, but doesn't quantify net impact |
"The carbon footprint of machine learning training will plateau, then shrink" | 2024 | MIT Technology Review | Focuses on energy use rather than e-waste, but suggests efficiency improvements may offset some hardware demand growth |
The point is that the specific impact of AI on energy consumption, water and e-waste is significant. But the total data center operations footprint is not caused solely by AI operations. Computing cycles would have grown in any case.
So we cannot simply point to higher energy, water and e-waste impact of data centers, and attribute all of that to AI operations.
Measure | Total Data Center Impact | AI Workload Contribution | AI as % of Total Impact |
Energy Consumption | ~200-250 TWh/year globally | ~20-30 TWh/year | ~10-12% |
Water Consumption | ~600-700 billion liters/year | ~60-90 billion liters/year | ~10-13% |
E-Waste Contribution | ~3.5-4 million metric tons/year | ~350-500 thousand tons/year | ~10-12% |
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