Exposing the hidden water footprint of AI data centers
AI data centers consume massive amounts of water for cooling, depleting local supplies and triggering widespread community backlash against new builds.
UsageUnveil automatically tracks and visualizes water consumption data from AI data centers using public records, utility reports, and voluntary disclosures. It enables residents and watchdog groups to create compelling visualizations for town halls and generates automated public records requests to fill data gaps.
Residents and local governments in US regions targeted for AI data center construction
Crowdsourced and automated FOIA/PRR filing system that turns raw documents into structured, comparable water usage metrics over time.
transparent and factual
Searchable database of operating and proposed facilities with known water metrics
Beautiful charts showing monthly water consumption trends and comparisons
Creates and tracks public records requests to utilities and operators
Email and web alerts when facilities exceed usage thresholds
One-click creation of town hall presentations and fact sheets
Allow residents to submit leaked documents and utility bills
Maps showing cumulative impact of multiple facilities on local water sources
CSV and API access to structured water usage data for researchers
Forecast future consumption based on expansion plans
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| operator | text | No |
| location | text | No |
| lat | text | Yes |
| lng | text | Yes |
| status | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| facility_id | uuid | No |
| month | timestamp | No |
| water_gallons | int | No |
| source | text | No |
| confidence | int | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| facility_id | uuid | Yes |
| user_id | uuid | No |
| status | text | No |
| request_text | text | No |
| response_summary | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| role | text | No |
| zip_code | text | Yes |
| created_at | timestamp | No |
/api/facilitiesSearch facilities and retrieve usage data
/api/requestsCreate new public records request
/api/readingsSubmit crowdsourced water usage data
/api/reports/generateGenerate advocacy report from selected facilities
/api/alerts/subscribeSubscribe to usage alerts for specific facilities
2 reports per month, no automated requests
Individual use
Up to 8 team members
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 180 | 6% | $378 | $4,536 |
| Month 6 | 1,250 | 11% | $4,812 | $57,744 |
Automated transparency tools, compelling visualizations, and FOIA generators to hold Big Tech accountable.
Post in anti-data-center Facebook groups in Northern Virginia, Arizona, and Georgia offering free Organization accounts to the most active organizers. Reach out to local environmental nonprofits (Sierra Club chapters, local riverkeeper groups) with personalized demos. Scrape recent news articles about data center opposition and cold email the quoted residents offering free access.
Strong industry news coverage
Limited public transparency focus and no citizen advocacy tools
Built specifically for residents and activists rather than industry insiders
Official government data
Extremely difficult to use and not specific to data centers
Curated, visualized, and actionable for non-technical users with FOIA automation
Growing structured dataset of water usage readings extracted from public records combined with network effects of users contributing new FOIA responses and leaked documents.
2024-2025 has seen an explosion of both data center proposals and community backlash, paired with utilities beginning to publicly report hyperscale water usage for the first time.
Data centers or utilities may challenge published numbers
Only publish data from official records with clear sourcing and disclaimers
Scraping and parsing public records is brittle
Combine automation with manual review queue and crowdsourced verification
Users may lose interest if data remains sparse in early days
Seed with all publicly known data points and focus launch in the 5 most active regions
Success: 80% indicate they would use a tool that automates FOIA and creates visualizations
Success: At least 12 users generate and share reports, 4 become paid subscribers
Success: 500 total users and $800 MRR within 30 days
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