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The growing energy footprint of artificial intelligence

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de Vries, Alex·2023·Joule

DOI 10.1016/j.joule.2023.09.004

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Research works printed but not linked: 8

Heaven, 2023, Google just launched Bard, its answer to ChatGPT—and it wants you to make it better, MIT Technology Review
Dastin, 2023, Focus: For tech giants, AI like Bing and Bard poses billion-dollar search problem, Reuters
Mehta, 2023, Nvidia adds jet fuel to AI optimism with record results, $25 billion buyback, Reuters
Leswing, 2023, Meet the $10,000 Nvidia chip powering the race for A.I., CNBC
Bary, 2023, Nvidia is ‘dominating’ and could unlock $300 billion in AI revenue by 2027, analyst says, MarketWatch
Chen, 2023, TSMC new CoWoS packaging plant to start volume production in mid-2027, DigiTimes
Syed, 2023, The Demand Elasticity Paradox: More than Meets the AI, Medium
Moses, 2023, Mining 2.0 Trends as Defunct Crypto Mining Rigs Tap into the AI Boom, Cryptopolitan

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Authors: de Vries, A.
Venue: Joule
Publisher: Elsevier BV
Published: 2023-10
Volume: 7
Issue: 10
Pages: 2191-2194
ISSN: 2542-4351 (ISSN-L)
DOI: 10.1016/j.joule.2023.09.004
OpenAlex: W4387494819
Cited by: 597
References: 6
Citations by year: 2026: 275 · 2025: 199 · 2024: 117 · 2023: 6
FWCI: 44.35
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 383 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Joule (publishedVersion)
VU Research Portal (submittedVersion, other-oa)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
Keywords: artificial intelligence, energy consumption, environmental impact, carbon footprint, sustainability, digital technologies

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