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How you are getting F*****; AI edition: Difference between revisions

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{{IncidentCargo}}'''AI model quality degradation and consumer transparency''' refers to a set of systemic practices in the artificial intelligence industry that affect consumers using AI-powered products. These issues include the silent degradation of AI output quality over time due to self-referential training loops, increasing electricity costs passed to residential consumers from AI data center expansion, the deployment of AI systems in high-stakes consumer decisions without the ability to explain those decisions, and industry infrastructure narratives that limit independent oversight.
{{Cleanup|Issue 1 = Citations need to follow proper formatting. See [[Template:Cite web]] for info.}}
 
'''AI model quality degradation and consumer transparency''' refers to a set of systemic practices in the artificial intelligence industry that affect consumers using AI-powered products. These issues include the silent degradation of AI output quality over time due to self-referential training loops, increasing electricity costs passed to residential consumers from AI data center expansion, the deployment of AI systems in high-stakes consumer decisions without the ability to explain those decisions, and industry infrastructure narratives that limit independent oversight.


These issues are systemic and have been documented by the International Energy Agency, peer-reviewed research published in ''Nature'', IBM Security, the European Union's AI Act enforcement body, and the U.S. Consumer Financial Protection Bureau.
These issues are systemic and have been documented by the International Energy Agency, peer-reviewed research published in ''Nature'', IBM Security, the European Union's AI Act enforcement body, and the U.S. Consumer Financial Protection Bureau.
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==References==
==References==
<references />
{{Reflist}}


==External links==
==External links==
[https://zenodo.org/communities/pi_origin_architecture Dragolich Research Labs LLC research archive (Zenodo)]
*[https://zenodo.org/communities/pi_origin_architecture Dragolich Research Labs LLC research archive (Zenodo)]
 
[https://www.nature.com/articles/s41586-024-07566-y Shumailov et al. — AI models collapse when trained on recursively generated data (Nature, 2024)]
 
[https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai International Energy Agency — Energy and AI (2025)]


[https://www.bloomberg.com/graphics/2025-ai-data-centers-electricity-prices/ Bloomberg — How AI Data Centers Are Sending Your Power Bill Soaring]
*[https://www.nature.com/articles/s41586-024-07566-y Shumailov et al. — AI models collapse when trained on recursively generated data (Nature, 2024)]


[https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/ MIT Technology Review — AI energy footprint analysis]
*[https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai International Energy Agency — Energy and AI (2025)]


[https://raconteur.net/technology/beyond-the-black-box-the-new-explainability-rule-for-enterprise-ai Raconteur — EU AI Act explained]{{Ph-I-ConR}}
*[https://www.bloomberg.com/graphics/2025-ai-data-centers-electricity-prices/ Bloomberg — How AI Data Centers Are Sending Your Power Bill Soaring]


*[https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/ MIT Technology Review — AI energy footprint analysis]


==References==
*[https://raconteur.net/technology/beyond-the-black-box-the-new-explainability-rule-for-enterprise-ai Raconteur — EU AI Act explained]
{{reflist}}


{{Ph-I-C}}
{{Ph-I-C}}
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