| > "We aim to inform debate with clear, sourced numbers while avoiding sensationalism." This is a great aspiration, but it seems to be contradicted by the rest of the page, which provides unclear numbers from unsourced categories. > CO₂: $AI_{CO_2e} \approx (AI_{electricity} \times grid_{emission\_factor})$ How are you accounting for Power Purchase Agreements (PPAs) and Renewable Energy Credits (RECs)? > Water: $AI_{water} \approx (DC_{water\_per\_kWh} \times AI_{electricity}) + (PowerGen_{water\_intensity} \times AI_{electricity})$ Where do the values for $DC_{water\_per\_kWh}$ (the Water Usage Effectiveness, or WUE) and $PowerGen_{water\_intensity}$ come from? These vary wildly by cooling system (evaporative vs. closed-loop) and energy source (hydro vs. nuclear vs. gas). > Electricity: $AI_{electricity} \approx (IT_{load} \times utilization \times hours) \times PUE$ How do you estimate $IT_{load}$? Is this based on TDP of GPUs? A specific list of GPUs? Market share estimates? What is the assumed $utilization$ for inference vs. training? Which $PUE$ is used? A global average? A regional one? A company-specific one? |
Each of the above pulls from operator sustainability reports, industry surveys/benchmarks, grid datasets (national/regional emission factors), and academic studies for water/energy intensities and inference energy per token. Where multiple ranges exist, we pick a conservative central value and call out the range.
I’ll add a compact table of constants + ranges + citations in the Methodology page so it’s easy to audit and nitpick. If you have a favorite dataset for WUE by cooling type or per-region grid water intensity, I’d love pointers—this is exactly the kind of feedback that improves the baseline.