Amazon reports that its 2025 carbon footprint rose 16% to 80.85 million metric tons CO₂e, with carbon intensity up 3%; it attributes part of the increase to supply-chain emissions tied to building and data-center construction, and says purchased-electricity emissions rose 34% driven partly by data centers. The same report says AWS added more than 1.2 GW of data-center capacity in Q4 2025 alone and expects AI/cloud demand to keep growing, while emphasizing Trainium efficiency, liquid-to-chip cooling, and a 1.14 PUE.
Amazon reports that its 2025 carbon footprint rose 16% to 80.85 million metric tons CO₂e, with carbon intensity up 3%; it attributes part of the increase to supply-chain emissions tied to building and data-center construction, and says purchased-electricity emissions rose 34% driven partly by data centers.
This is one of the clearest current industry disclosures showing that AI infrastructure growth is materially visible in corporate emissions accounting.
Read the evidence behind this analysis. External links open in a new tab.
Amazon Sustainability
A review of evidence across cognitive science, education, medicine, and human-factors research finds that fully offloading mental work to AI can weaken the acquisition and retention of the specific skills people stop practicing. The authors distinguish that evidence from broader claims about declining intelligence: effects on foundational abilities such as attention and working memory remain uncertain, while AI used as a collaborator, tutor, or source of feedback can preserve or improve learning.
The NUS/Harbin-led team identifies a domain-specific form of generative-AI hallucination: molecular candidates can receive strong predicted binding scores while violating basic chemistry or producing physically impossible atomic arrangements. Its DrugRPG framework incorporates chemical-foundation-model priors and differentiable physical constraints during molecule generation, reducing severe steric clashes by 65.4% relative to the reported state-of-the-art baseline and increasing by 28.6% the share of generated candidates meeting combined potency, stability, and synthetic-feasibility criteria.
Reviewing 72 studies of reinforcement-learning systems for sepsis treatment, the authors found that every study was retrospective, 58 studies—80.6%—relied on the same MIMIC critical-care database, and only 10 used private datasets. Although many papers claimed that AI-derived treatment policies outperformed clinicians, variation in how patient states, treatment actions, rewards, and counterfactual outcomes were defined made those comparisons difficult to validate.