Living report · refreshed from daily source data

AI environmental impact report: energy, water and carbon

A daily evidence report on AI energy use, water consumption, carbon impacts and data-center accountability. Coverage counts are signals of attention—not measures of importance, harm or consensus.

Prepared by the ethics.ai evidence desk · automatically refreshed · editorial scope reviewed against the methodology and corrections policy

Daily source coverage90 days
2026-05-18 2026-08-15
1,792records in archive
780latest 30 days
+117%versus prior 30 days

By record type

Research 1,127
News 498
Policy 98
Field notes 60
Incidents 9

Leading sources in this record

arXiv 513
OpenAlex 225
arXiv cs.AI 90
HuggingFace Daily Papers 68
arXiv cs.HC 45
arXiv cs.CY 42
arXiv cs.LG 30
Fortune AI 23
UK Contracts Finder — AI procurement 22
The Hill Technology 21

What this report tracks

Tracks measured and estimated energy, water, emissions and infrastructure impacts associated with AI. It keeps estimates tied to their source because boundaries and accounting methods differ widely.

Questions to take into the evidence

  • Which environmental costs are measured rather than modeled?
  • How do system and data-center boundaries change estimates?
  • What disclosure, efficiency and mitigation measures are emerging?

Latest evidence

Full topic record →
The Decoder

Investor pressure forces Nvidia to shrink its OpenAI bet just as Anthropic's numbers defy bubble warnings — open the original publisher

Nvidia has cut its guarantee for OpenAI's planned data center in Ohio nearly in half, from $250 billion to just under $120 billion, after investors pushed back on the risk. Meanwhile, Anthropic is complicating the AI bubble debate with revenue that jumped from $4.7 billion to $11.5 billion in a single quarter. The article Investor pressure forces Nvidia to shrink its OpenAI bet just as Anthropic's numbers defy bubble warnings appeared first on The Decoder .

News EnvironmentFinance, VC & PE
The Decoder

World Labs turns one real-world robot task into thousands of simulated variations for training — open the original publisher

World Labs, the startup founded by AI pioneer Fei-Fei Li, has unveiled a simulation engine that trains robot controllers entirely in virtual environments. From a single real-world task, the system generates thousands of controlled variations. The trained models then ran for one hour each on five different robot platforms without human intervention. How well the results hold up in more complex everyday situations remains to be seen. The article World Labs turns one real-world robot task into thou

News Agents & autonomyEnvironment

Method and limits

This report is assembled automatically from source metadata and keyword classifications. It summarizes what the tracked source fleet published; it does not independently validate every linked claim. Source-fleet growth can inflate historical comparisons. Cite the individual evidence record and original publisher for substantive claims.