Patterns, a Cell Press journal, published this analysis by researcher Alex de Vries-Gao in December 2025, and PMC hosts the full text without a paywall, which is useful because most writing about AI's environmental footprint gets cited far more often than it gets actually read.

The piece starts from an admission: while there are established ways to estimate the global power demand of artificial intelligence, the carbon and water footprints of the data centers that host it are far less well characterized. De Vries-Gao works through why that gap exists. Cooling systems draw water directly, but the water and carbon embedded in the electricity a facility buys depends heavily on the local grid mix, and operators rarely publish facility-level numbers. The paper pulls together existing estimation methods and their assumptions rather than presenting new field measurements from inside a data center.

That framing matters for who should read it. This is a literature synthesis and modeling exercise from a single author, not a measurement campaign, so its figures are informed estimates with real uncertainty rather than audited facts. It suits readers who want to understand how footprint numbers for AI infrastructure get produced in the first place, including the assumptions and the gaps where estimates diverge, more than readers hunting for one headline statistic. The trade-off is density: this is a peer-reviewed research article rather than a blog explainer, and it expects some patience with methodology before it gets to a bottom line.