"#Europe must choose between #AI and #climate goals, #data center lobby says "
Ah.
Well.
Good then.
That will make the decision easier.
https://www.politico.eu/article/europe-choose-ai-climate-goals-data-center-chief-warns/
#datacenters
#thereIsNoAI
#thereIsInParticularNoSustainableAI
I was not aware of this aspect of the #AI madness/crime -
AI companies hording & destroying books after extracting their #content, so that in the future, their portals be the only path to knowledge.
First source [#german] is new, about #ZoomBooks buying 100,000s of #books in #europe, building dedicated warehouses just to collect them -
second source is 2025 about #Anthropic s practice
luckily, noone ever planned to decarbonize them -
one of their main reason for existence is to use fossil fuels. </s>
First ever #AI-#native operating system. From hardware to UI –
#Claude #Code controls everything on your computer
hint:
#thereIsNoAI
#thereIsInParticularNoSustainableAI
#OS #OperatingSystem #vibe
yes, this is the idea of large scale corporate scraping/stealing -
a global monopoly on information/data/content, owned by a handful of power centers.
the web as we knew it is in fact dead - about 90% of all content to be "#AI"-generated in 2026.
we have been naive to think that a decentralized free resource would *not* be taken over by #corporate power.
How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI
Sophia N. Wilson, Sebastian Mair, Mophat Okinyi, Erik B. Dam, Janin Koch, Raghavendra Selvan
Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade. This expansion has relied on sustained efforts by large technology corporations to aggregate and curate internet-scale datasets. In this work, we examine the environmental, social, and economic costs of large-scale data in AI through a sustainability lens. We argue that the field is shifting from building models from data to actively creating data for building models. We characterise this transition as hyper-datafication, which marks a critical juncture for the future of frontier AI and its societal impacts. To quantify and contextualise data-related costs, we analyse approximately 550,000 datasets from the Hugging Face Hub, focusing on dataset growth, storage-related energy consumption and carbon footprint, and societal representation using language data. We complement this analysis with qualitative responses from data workers in Kenya to examine the labour involved, including direct employment by big tech corporations and exposure to graphic content. We further draw on external data sources to substantiate our findings by illustrating the global disparity in data centre infrastructure. Our analyses reveal that hyper-datafication does not merely increase resource consumption but systematically redistributes environmental burdens, labour risks, and representational harms toward the Global South, precarious data workers, and under-represented cultures. Thus, we propose Data PROOFS recommendations spanning provenance, resource awareness, ownership, openness, frugality, and standards to mitigate these costs. Our work aims to make visible the often-overlooked costs of data that underpin frontier AI and to stimulate broader debate within the research community and beyond.
People's resistance against #hyperscale
#datacenters in #Denmark
https://www.information.dk/moti/2026/04/nej-tak-kan-godt-skrive-stort
[paywall]
the line between #vulnerability #disclosure and #AI #advertisement becomes ever more blurry ....
#thereIsNoAI
#thereIsInParticularNoSustainableAI
#alsoNoReponsibleAI
CVE-2026-31431 #copyFail
Always a favorite - FruitML = teaching simple #TinyML with #fruit detection as the task, in our #IoT course
#yolo #mobileNets #InternetOfThings #MachineLearning #EdgeImpulse #TeachableMachines
#thereIsNoAI
#thereIsInParticularNoSustainableAI
but some tiny ML is fun
Sebastian Büttrich @ ITU
