1. The paper is unclear on what basis the calculations are made - no numbers are provided, as I mentioned in my analysis. It repeatedly mentions time when discussing diesel generators e.g. "The backup generators are assumed to emit air pollutants at 10% of the permitted levels per year." and "This trend may necessitate extended reliance on backup generators, e.g., possibly 15 days per year".
I suggested that this could be clarified by publishing the calculations behind the paper. A sensitivity analysis exploring a range of usage scenarios, particularly using publicly available emissions data from operators, could better contextualize this estimate and address variability across regions or operators.
2. It's fair to point out that the Berkeley study postdates the paper, however this is still in the high end of the range without justification. The reliance on a McKinsey “medium scenario” white paper, without transparent methodology or public access to the underlying 2023 Global Energy Perspective, leaves readers unable to assess its basis. Greater clarity on why 519 TWh was selected would strengthen the analysis.
1. Your clarification that the 10% figure reflects permitted emission levels, not runtime, is helpful. However, the paper’s language on Page 5 remains ambiguous:
"This trend may necessitate extended reliance on backup generators, e.g., possibly 15 days per year. Such prolonged usage of diesel generators could substantially elevate AI’s scope-1 air pollution, creating even higher public health costs. Concretely, if the backup generators in northern Virginia emit air pollutants at the maximum permitted level, the total public health cost could reach $2.2-3.0 billion per year."
The juxtaposition of "15 days per year" and "maximum permitted level" muddles the picture, suggesting a linkage that may mislead readers. This likely contributed to my initial interpretation ("10% annual capacity"), though my broader analysis holds regardless. The issue is significant: this ambiguity could undermine confidence in your health cost estimates, which hinge on accurate Scope 1 emissions.
Comparing your figure to operator published data would be useful. Recent sustainability reports from major operators show minimal Scope 1 contributions.
My critique stems from legitimate concerns about clarity and accuracy that could be addressed. The paper’s language could be sharpened to prevent misinterpretation, and the 10% assumption warrants scrutiny given the potential for overestimation—supported by industry trends and usage patterns.
2. The inaccessibility of the McKinsey report limits scrutiny of the "medium-growth" scenario. Transparency is critical in academic projections, particularly for long-term estimates. My 2022 Joule publication https://www.cell.com/joule/fulltext/S2542-4351(22)00358-0 documented a pattern of overestimation in data center energy studies due to opaque assumptions. This history amplifies the need for justification why the medium-growth scenario chosen over alternatives.
3. As you note, actual power draw as measured by real systems is not the maximum TDP. Using the maximum risks overestimating the results.
I am commenting on the preprint version currently available, which is what was publicized by the media. This is the problem with preprints - preliminary findings often gain traction without peer review. Later revisions rarely correct the public record because the press has moved on.
thanks this made me not nervous to learn more on this topic because i am just a undergrad student with little knowledge please tell me would you've any suggestions for learning more Mr. Ren?
Thanks for your comment.
1. The paper is unclear on what basis the calculations are made - no numbers are provided, as I mentioned in my analysis. It repeatedly mentions time when discussing diesel generators e.g. "The backup generators are assumed to emit air pollutants at 10% of the permitted levels per year." and "This trend may necessitate extended reliance on backup generators, e.g., possibly 15 days per year".
I suggested that this could be clarified by publishing the calculations behind the paper. A sensitivity analysis exploring a range of usage scenarios, particularly using publicly available emissions data from operators, could better contextualize this estimate and address variability across regions or operators.
2. It's fair to point out that the Berkeley study postdates the paper, however this is still in the high end of the range without justification. The reliance on a McKinsey “medium scenario” white paper, without transparent methodology or public access to the underlying 2023 Global Energy Perspective, leaves readers unable to assess its basis. Greater clarity on why 519 TWh was selected would strengthen the analysis.
3. I didn't discuss inference - this was in reference to training. See the link I included about the risks of using TDP, which does not accurately represent training energy consumption: https://www.devsustainability.com/p/how-useful-is-gpu-manufacturer-tdp.
I have written about AI energy consumption, including referencing the literature you mention, in the past e.g. https://www.devsustainability.com/p/expect-more-overestimates-of-ai-energy. One of the articles you reference has since been fully published at https://ieeexplore.ieee.org/document/9810097 and shows that "by 2030, total carbon emissions from training will decline.".
4. Thanks for the updated reference.
1. Your clarification that the 10% figure reflects permitted emission levels, not runtime, is helpful. However, the paper’s language on Page 5 remains ambiguous:
"This trend may necessitate extended reliance on backup generators, e.g., possibly 15 days per year. Such prolonged usage of diesel generators could substantially elevate AI’s scope-1 air pollution, creating even higher public health costs. Concretely, if the backup generators in northern Virginia emit air pollutants at the maximum permitted level, the total public health cost could reach $2.2-3.0 billion per year."
The juxtaposition of "15 days per year" and "maximum permitted level" muddles the picture, suggesting a linkage that may mislead readers. This likely contributed to my initial interpretation ("10% annual capacity"), though my broader analysis holds regardless. The issue is significant: this ambiguity could undermine confidence in your health cost estimates, which hinge on accurate Scope 1 emissions.
Comparing your figure to operator published data would be useful. Recent sustainability reports from major operators show minimal Scope 1 contributions.
My critique stems from legitimate concerns about clarity and accuracy that could be addressed. The paper’s language could be sharpened to prevent misinterpretation, and the 10% assumption warrants scrutiny given the potential for overestimation—supported by industry trends and usage patterns.
2. The inaccessibility of the McKinsey report limits scrutiny of the "medium-growth" scenario. Transparency is critical in academic projections, particularly for long-term estimates. My 2022 Joule publication https://www.cell.com/joule/fulltext/S2542-4351(22)00358-0 documented a pattern of overestimation in data center energy studies due to opaque assumptions. This history amplifies the need for justification why the medium-growth scenario chosen over alternatives.
3. As you note, actual power draw as measured by real systems is not the maximum TDP. Using the maximum risks overestimating the results.
I am commenting on the preprint version currently available, which is what was publicized by the media. This is the problem with preprints - preliminary findings often gain traction without peer review. Later revisions rarely correct the public record because the press has moved on.
thanks this made me not nervous to learn more on this topic because i am just a undergrad student with little knowledge please tell me would you've any suggestions for learning more Mr. Ren?