Wired: Generative AI and Climate Change Are on a Collision Course

Dec 18, 2024

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Source URL: https://www.wired.com/story/true-cost-generative-ai-data-centers-energy/
Source: Wired
Title: Generative AI and Climate Change Are on a Collision Course

Feedly Summary: From energy to resources, data centers have grown too greedy.

AI Summary and Description: Yes

Summary: The text highlights the environmental impact of AI, particularly the energy consumption and resource use associated with large language models (LLMs) and data centers. It emphasizes the need for greater transparency and regulation regarding AI’s ecological footprint, predicting that by 2025, legislative actions will emerge to enforce sustainability initiatives.

Detailed Description:

– **AI’s Environmental Impact**: The text discusses the intersection of AI and climate change, stressing that the widespread use of AI, particularly through powerful language models, incurs significant environmental costs.

– **Energy Consumption**:
– Data centers currently account for 2% of global electricity use, with this number spiking to 20% in specific regions like Ireland.
– The transition from older standard AI models to generative models can increase energy usage by up to 30 times, raising concerns over sustainability.

– **Water Resources**:
– Data centers consume vast amounts of freshwater for cooling, leading to conflicts over these resources between local communities and tech providers.

– **Government Actions**:
– In response to high energy consumption, countries like Ireland are imposing moratoriums on new data centers.
– The text mentions Taiwan’s prioritization of water resources for chip manufacturing over agricultural needs amid severe droughts.

– **Industry Accountability**:
– There’s a notable lack of transparency regarding the actual carbon footprint and energy costs of AI technologies, particularly generative models. Key industry figures have differing views on how to address these environmental concerns, ranging from denial to optimism about future technological breakthroughs.

– **Needed Initiatives**:
– The author advocates for voluntary transparency initiatives, such as the proposed AI Energy Star project, to help users assess the energy efficiency of various AI models.
– The prediction for 2025 includes not only heightened public awareness but also regulatory frameworks to mitigate AI’s ecological footprint, reflecting a shift towards sustainability in tech operations.

This analysis underscores the crucial role that security, compliance, and governance professionals must play in addressing environmental concerns tied to AI and cloud computing. The implications could extend to modifying compliance frameworks to include sustainability metrics, thus ensuring that emerging technologies align with global environmental goals.

2 3 a accountability Act AI AI models AI technologies analysis art as awareness by C chip chip manufacturing climate change Cloud cloud computing compliance compliance framework compliance frameworks Computing concerns core cost Costs Current D data data center data centers denial e efficiency Emerging Technologies end energy energy consumption energy costs energy efficiency environment environmental environmental concerns environmental impact fact for framework future g Gen generative Generative AI generative model Generative Models GIS Go governance government government actions high Highlight http HTTPS implications in industry inter k l language language model language models large large language model large language models led legislative legislative action llm llms lm logic manufacturing metrics model models ModI no o of on operation ory over Power pre prioritization professionals public public awareness R raising RCE Region Regulation regulatory regulatory framework regulatory frameworks resources response Role s sec security Sig SoC source SSE sustainability sustainability initiatives T Taiwan tech technologies text the to Tor transition transparency trie up usage user uth Wi x