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A critical reflection on the AI industry's environmental costs and overhyped claims about chatbot capabilities, arguing that many harms are ignored and that not using AI remains an easy choice.
Irish datacenters consumed 23% of the country's electricity in 2025, up 10% from 2024, despite a moratorium on new grid connections. New regulations require large datacenter operators to provide backup generation and feed power back to the grid.
The article discusses the growing local opposition to AI data centers due to environmental and energy concerns, highlighting a historic case in Ireland and current widespread resistance.
Google's electricity consumption surged 12 TWh from 2024 to 2025, reaching 43 TWh, driven by generative AI infrastructure, causing exponential growth in emissions and undermining climate goals.
The author argues that current AI scaling methods, despite being the pinnacle of engineering, are woefully inefficient and will be viewed as primitive in hindsight, similar to how we now see 1960s mainframes.
Henrico County, Virginia, home to 37 data centers, is asking schools and government employees to conserve electricity after a 25% rate increase driven by data center power demands, highlighting the growing conflict between tech infrastructure and local communities.
An exploration of the water consumption associated with training and running AI models, often overlooked in discussions of AI's environmental footprint.
A UN report reveals that by 2030, AI data centers could consume water equivalent to the basic annual needs of 1.3 billion people, with 80-90% of energy used for daily operations rather than training. Generating a single AI image uses over 1000 times more energy than a basic text task.
Presents a systematic study of parameter-efficient fine-tuning using LoRA on Qwen2.5-3B for telecommunications customer support, comparing 16 LoRA configurations with both traditional metrics and energy consumption analysis. Finds divergence between quantitative and qualitative performance.
Kevin O'Leary agreed to reduce his planned 40,000-acre Project Stratos data center in Utah by roughly half, following pressure from residents, activists, and state officials. The downsized project will still cover approximately 20,000 acres, larger than Manhattan.
The rise of artificial intelligence is driving a data centre boom that may require 40% more energy than previously estimated, potentially increasing household energy bills, according to campaigners.
An academic study presented at the Americas Conference on Information Systems maps five systemic tensions from AI's data-centre boom, including energy paradox, water strain, hyperscaler dominance, sovereignty erosion and urban displacement, highlighting the growing environmental and social costs.
This paper proposes using the Ensemble Score Filter (EnSF), a score-based diffusion data assimilation method, to correct forecasts from a pretrained spatio-temporal energy consumption model using noisy partial observations. Numerical experiments show EnSF significantly improves state estimation over open-loop propagation and outperforms the Ensemble Kalman Filter under nonlinear observations.
The article argues that the physical power requirements for AGI are so immense that it may never be feasible, comparing the energy needs of a potential AGI system to the entire electricity consumption of a country like Japan.
This article compares the electricity, water, and noise consumption of AI data centers to other American industries and everyday activities, using data from various sources to contextualize the resource use of data centers.
A Gallup poll finds 70% of Americans oppose AI data center construction in their local area, citing concerns over resource usage, utility costs, and environmental impact, with opposition rising sharply since late 2025.