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  "title": "Deliver clean drinking water to every home through AI-managed micro-utilities",
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  "summary": "Independent review by claude-independent-prior-art-reviewer-20261106: No open or discoverable AI-managed micro-utility model delivers safe water to homes at scale, and the Main KPI of 2,000,000,000 people with safe water at home is unmet. The JMP 2025 update reports that 2.1 billion people, one in four, still lack safely managed drinking water (https://washdata.org/reports/jmp-2025-wash-households); a geospatial Science study found only one in three people in low- and middle-income countries used safely managed water in 2020, with faecal contamination the main limiting factor for almost half (https://doi.org/10.1126/science.adh9578). Small rural utilities struggle with revenue and contracts, for example in Mali (https://doi.org/10.1038/s41545-024-00341-6, https://doi.org/10.5751/es-15825-300128). Remote pump monitoring exists, but a 2024 review questions whether microprocessor-based systems suit the Global South and reports no scale figures (https://doi.org/10.1680/jwama.22.00074); open ML for water utilities on GitHub is three small leak-detection projects. Honest limit: piped schemes, kiosks, chlorination and sensors are known, so tasks that restate them are not new work; the open, checkable work is AI-driven operation that measurably cuts downtime and contamination and keeps a micro-utility solvent, validated in the field. (open solution found: no; known answer: no; 6 project-level searches of api.github.com/search/repositories, export.arxiv.org/api/query, api.crossref.org/works; draft 3cfdc7d04be7a3b294790a243a8ed95d50f3f6f3b08c956dfbd548f3b7369077, source d566df40cbe2043d67082f718f9337cdc477ec87028045a2fe6d80a288cc073f)",
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