theaimeters.com
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?
> CO2 (PPAs/RECs)
- We currently use location-based grid factors (national/regional) and do not net out PPAs/RECs. That is the conservative choice for a public baseline.
- If a workload is known to be contract-matched (hourly/locational), we can apply a market-based view; I plan to expose a toggle (location- vs market-based) so both views are visible.
> Water (WUE & power-generation water)
- DC_water_per_kWh (WUE): when operators publish site/region values we use them. Otherwise we assign a cooling class (evaporative / closed-loop / seawater / air-only) and take a central value from published ranges. That gives order-of-magnitude accuracy without claiming site precision.
- PowerGen_water_intensity: technology-specific consumption factors (not withdrawals) by fuel/tech (gas, coal, nuclear, hydro, etc.), weighted by the grid mix of the region when it’s known; otherwise a conservative aggregate. Hydropower is treated as low consumption, high withdrawal.
> Electricity (IT load, utilization, PUE)
- IT_load / Training: bottom-up from reported compute for frontier runs + known fleet sizes; extrapolated to mid-scale using public training reports.
- IT_load / Inference: top-down from usage volumes (requests/tokens/images) × energy per unit by model class, calibrated from published perf/W measurements and vendor/benchmark data. We don’t simply sum GPU TDP; we use perf/W + utilization.
- utilization: ranges by workload class; we take a conservative central value (higher for sustained training, lower/peaky for inference). These are sensitivity levers and shown in the methodology.
- PUE: operator/region-specific when disclosed; otherwise we apply a conservative default for hyperscale vs. generic DCs (kept distinct). PUE is another sensitivity knob we surface.
> I’ll add a compact table of constants + ranges + citations in the Methodology page
This is a worthwhile project. HN and 'the discourse' needs a reliable, citable source for these metrics. Adding a table of citations is a crucial step towards that.
Your confidence intervals are probably more precise than an order-of-magnitude (LeBron James is the same size as a six story building, within one order-of-magnitude), and I'm excited to see the ranges as your site evolves.
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.
The goal of the project is to inform public discussion with sourced, reviewable numbers. The “live counters” are there to make scale/salience tangible, not to be sensational.
I know it’s still imperfect and I’m actively improving it. I’d really appreciate critiques and better sources/datasets: - I’m adding a compact table of constants + ranges + citations in /methodology. - A toggle for location- vs market-based CO2 (PPAs/RECs). - Clearer WUE by cooling type and water intensity by generation tech/region. - Small API/CSV export.
If you spot mistakes or have data I should incorporate, please tell me - corrections are welcome: contact@theaimeters.com