3 ms·

Show HN: TheAIMeters – Live AI impact (water, electricity, CO2, etc.)

2 points by rboug 1y ago

(Disclosure: I built TheAIMeters) It tracks global AI activity in real time: electricity, water, CO2e, and GPU-hours, etc. We combine operator disclosures, research, and grid factors. Server-side snapshots with smooth client updates. Happy to answer technical/method questions; corrections welcome.

rboug 1y ago
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AftHurrahWinch 1y ago
> "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?
- rboug 1y ago
  Thank you for your msg. Short answers below; happy to go deeper. > 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.
  - AftHurrahWinch 1y ago
    > That gives order-of-magnitude accuracy without claiming site precision. > 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.
    - rboug 1y ago
      Thank you! Really appreciate this. I’ll prioritize the citations/ranges table in /methodology to make this properly citable and easier to audit.
- rboug 1y ago
  > On “sources” 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.
rboug 1y ago
Another quick note - this is a work-in-progress. 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

2 points by rboug 1y ago

rboug 1y ago

[dead]

AftHurrahWinch 1y ago

> "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?

rboug 1y ago
Thank you for your msg. Short answers below; happy to go deeper. > 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.
- AftHurrahWinch 1y ago
  > That gives order-of-magnitude accuracy without claiming site precision. > 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.
  - rboug 1y ago
    Thank you! Really appreciate this. I’ll prioritize the citations/ranges table in /methodology to make this properly citable and easier to audit.
rboug 1y ago
> On “sources” 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.

rboug 1y ago

Another quick note - this is a work-in-progress. 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