operator stringclasses 4
values | location stringclasses 10
values | W_d_ML_d float64 0.09 14.6 | r float64 0.58 0.83 | PF float64 2.21 30 | K_ML_d float64 7.6 52 | stated_WCI float64 0.18 1.86 | grid_region stringclasses 10
values | reservoir stringclasses 2
values | dam stringclasses 2
values | double_coupled_candidate stringclasses 2
values | source_tag stringclasses 8
values | motive_tier stringclasses 3
values | cooling stringclasses 2
values | WUE_L_per_kWh float64 0.2 1.8 | EWIF_lo float64 0.8 5 | EWIF_hi float64 1.5 15 | W_d_2025_ML_d float64 2.73 18.1 ⌀ | r_2025 float64 0.6 0.84 ⌀ | vintage_note stringclasses 6
values | range_flag stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Meta | Lebanon, IN | 4.81 | 0.77 | 6.3 | 30.3 | 0.77 | MISO-Indiana | null | null | null | ren_seed_NOT_operational_closedloop | planning_not_measured | closed_loop | 0.2 | 1.8 | 3 | null | null | null | null |
Google | Council Bluffs, IA | 14.62 | 0.716 | 6.5 | 36.6 | 1.86 | MISO-Iowa | null | null | null | google_fy2024_verified | primary | evaporative | 1.8 | 1.5 | 3 | 18.112 | 0.7707 | Google 2026 Env Report, FY2025: 1,746.4 M gal withdrawn / 1,346.0 consumed | null |
Google | Mayes Co., OK | 11.49 | 0.752 | 2.5 | 52 | 0.44 | SPP-Oklahoma | null | null | null | utility_openrecords_2024_verified | primary | evaporative | 1.8 | 0.8 | 1.5 | 14.485 | 0.7743 | Google 2026 Env Report, FY2025: 1,396.7 / 1,081.4 | null |
Google | The Dalles, OR | 4.78 | 0.784 | 2.21 | 45.4 | 0.18 | Columbia-River | Columbia-River | The-Dalles-Dam | Y_primary | google_fy2024_verified | primary | evaporative | 1.8 | 5 | 15 | 6.786 | 0.7168 | Google 2026 Env Report, FY2025: 654.3 / 469.0 | null |
Google | Douglas Co., GA | 4.6 | 0.826 | 2.5 | 22.7 | 0.41 | Southern-Co-GA | null | null | null | google_fy2024_reclaimed_verified | primary | evaporative | 1.8 | 1.8 | 3.5 | 4.569 | 0.8332 | Google 2026 Env Report, FY2025: 440.6 / 367.1 | null |
Microsoft | Wisconsin | 0.087 | 0.77 | 30 | 7.6 | 0.34 | MISO-Wisconsin | null | null | null | ren_table6 | planning_not_measured | closed_loop | 0.2 | 1.8 | 3 | null | null | null | null |
Google | Botetourt Co., VA | 7.57 | 0.77 | 2.5 | 10.2 | 1.45 | PJM-Dominion-VA | null | null | null | contracted_2MGD_NOT_operational | planning_not_measured | evaporative | 1.8 | 1.8 | 3 | null | null | null | null |
xAI | Memphis, TN | 3.79 | 0.77 | 4.5 | 22.7 | 0.57 | TVA-Tennessee | null | null | null | ren_seed_W_in_primary_range_multisite | framework_secondary | evaporative | 1.8 | 2 | 3.5 | null | null | null | 0.81-7.0 MGD across Colossus 1 and 2; the static row is Ren's single-site value and is indicative only |
Google | Midlothian, TX | 2.29 | 0.825 | 2.5 | 15.1 | 0.31 | ERCOT-Texas | null | null | null | google_fy2024_verified | primary | evaporative | 1.8 | 0.8 | 1.5 | 2.734 | 0.8354 | Google 2026 Env Report, FY2025: 263.6 / 220.2 | null |
Google | Henderson, NV | 3.73 | 0.576 | 2.5 | 15.2 | 0.36 | Colorado-River | Lake-Mead | Hoover-Dam | Y_source_confirmed | google_fy2024_verified_city_corroborated | primary | evaporative | 1.8 | 2 | 6 | 4.166 | 0.596 | Google 2026 Env Report, FY2025: 401.7 / 239.4 | null |
AI Data-Centre Water Tracker
AI disclosure: parts of this text were artificially generated with AI assistance and reviewed by the author. The models, and the conflicts they create, are named in the Conflict of interest and scope section.
An open, reproducible referee for the water burden of AI data centres. It rebuilds the Water Consumption Impact index for ten sites from public data, self-checks each value against its source, and adds the two channels the original framework leaves open: the hydropower coupling, and the off-site relocation of water that closed-loop cooling produces. Figures are bands and decompositions, every input named and tagged by the standing of its source.
- Author: NM AI Research (independent analyst)
- ORCID: 0009-0003-4213-7769
- DOI (concept, always resolves to the latest version): https://doi.org/10.5281/zenodo.21318960
- This snapshot: v0.5, 8 August 2026, https://doi.org/10.5281/zenodo.21852860
- Source and code: https://github.com/NMAIResearch/ai-water-tracker
Files
sites.csv(10 rows): the per-site inputs and rebuilt index, FY2024 and FY2025. Columns includeoperator,location,W_d_ML_d(daily water, ML/day),r,PF,K_ML_d,stated_WCI,grid_region,reservoir,dam,double_coupled_candidate,source_tag,motive_tier,cooling,WUE_L_per_kWh,EWIF_lo,EWIF_hi,W_d_2025_ML_d,r_2025.reproduce.py: standard-library reproducer that rebuilds the index fromsites.csv. Exit code is 1 whenever a row is flagged, which on the seed as shipped is always: two of the ten rows do not reproduce within tolerance, and those flags are part of the finding.SOURCES.md: the source ledger. Per-row provenance, the inputs that are estimates rather than site measurements, the Layer 2 and Layer 3 sources, and the known limits.LICENSE: Creative Commons Attribution 4.0 International.
Findings
Closed-loop cooling does not remove water, it relocates the great majority of the footprint to the grid, roughly 92 to 95 per cent on the modelled inputs used here. The direction holds and the band does not: raising the closed-loop WUE from 0.2 to 0.3 L/kWh gives 88 to 92 per cent, though even at five times the assumed WUE the share is still 68 to 78 per cent.
Three of the ten seed sites, across three different operators, are non-operational planning figures presented as measured, and are excluded from the measured set.
Recomputing the index on the operators' FY2025 reports, holding capacity and peaking constant, moves five of the six primary-verified sites up by 15.6 to 33.3 per cent in a single reporting year. The sixth is flat on total withdrawal while its draw on potable municipal supply rose more than sixfold.
Method and standing of evidence
Each site's index value is rebuilt from public data and checked against its source, then decomposed into on-site use and the off-site relocation that closed-loop cooling moves to the grid. The six Google rows are traced to the operator's own EY-assured environmental reports. The remaining four rest on records releases, supply contracts and reporting, and are excluded from the measured set for that reason. The relocation ledger is modelled from a published grid-average intensity factor, not metered per facility. SOURCES.md grades every row and names every estimate.
Conflict of interest and scope
The research question, method, sourcing decisions and analytical judgements are the author's. Anthropic Opus 4.8-5.0 assisted with data retrieval, calculation, literature search and drafting across v0 through v0.4. OpenAI GPT-5.6 Sol assisted with the v0.5 disclosure and bundle revision. Parts of this text were artificially generated and were reviewed by the author before publication. Each assisting model is not always a neutral party to the subject matter.
Anthropic states that it signed an agreement to use all of the compute capacity at SpaceX's Colossus 1 data centre (Anthropic, "Higher usage limits for Claude and a compute deal with SpaceX", 6 May 2026, anthropic.com/news/higher-limits-spacex). OpenAI and Oracle state that they entered an agreement to develop an additional 4.5 GW of Stargate data-centre capacity in the United States (OpenAI, "Stargate advances with 4.5 GW partnership with Oracle", 22 July 2025, openai.com/index/stargate-advances-with-partnership-with-oracle). The author therefore treats neither lab as neutral on the buildout. All operators are scored the same way.
Every measured claim is traced to the primary document named in the Verification note, and reproduce.py regenerates both tables from sites.csv. Modelled or estimated inputs are identified as such rather than presented as measurements. A language model's output can be wrong in ways that survive review; in code it simply runs, and a wrong constant or a mis-set filter still returns a clean number. The author does not claim the review is exhaustive, and corrections are logged against the DOI when surfaced.
No warranty is offered beyond the terms of the CC BY 4.0 licence. Independent analysis and open-science documentation only, not investment advice.
Citation
NM AI Research (2026). AI Data-Centre Water Tracker. Zenodo. https://doi.org/10.5281/zenodo.21318960 . Licensed CC BY 4.0.
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