In recent years, attention has centered on bitcoin mining, particularly its energy usage in securing the network and validating transactions through proof-of-work (PoW). As generative artificial intelligence (AI) emerges as a major force in technology, it’s encountering similar environmental concerns. Moreover, advocates of bitcoin are drawing parallels between the criticisms of AI’s energy demands and those directed at cryptocurrency mining.
Eco-Impact of AI: Facing the Environmental Challenges of Innovation
Although AI has generally received acclaim in the press, its consumption of electricity and water has recently sparked considerable debate. Numerous online articles decry the significant resources AI utilizes, including a Rolling Stone piece alleging that “Microsoft’s global water consumption spiked in a year to nearly 1.7 billion gallons.” The article oversimplifies the intricate issue by comparing AI’s water usage to that of 2,500 Olympic-sized swimming pools.
The digital outlet The Standard employs a similar argument by equating water consumption to the volume of swimming pools. This approach echoes the one used by critics of bitcoin mining, who liken its energy use to a nation’s consumption levels. On the social media platform X, Bitcoin advocate Nic Carter observed in response to The Standard’s headline that they are “literally just copy [&] pasting their anti-bitcoin mining takes into anti-AI articles.”
Equating AI’s consumption to the total usage of a particular human resource is arguably misleading due to various factors, often overlooked in these exaggerated AI narratives, including context, nature, and utility differences. A nuanced, informed discussion should reflect on the quality, type, and repercussions of energy use in each scenario.
The energy source powering AI is significant. If generative AI is fueled by renewable or excess energy that might otherwise remain unused, this is distinct from using non-renewable resources critical for essential human activities, rendering direct comparisons futile. Moreover, as technology progresses, AI infrastructure is becoming more energy-efficient, potentially diminishing its relative energy impact. These same points have been made and can be applied to the resource utilization involved in crypto mining.
Developer and Casa CTO, Jameson Lopp, commented on on an X post that discusses training an AI model or a large language model (LLM). The post claims with a cited article from earth.org that “training an AI model is nearly 7x worse for the environment than U.S. car manufacturing and fuel consumption.” Lopp said it was the “dumbest decel outrage yet.” “I guess the morons don’t realize that a trained LLM can be queried an unlimited number of times by an unlimited number of people,” Lopp added.
Furthermore, in the same X post thread another commenter explained that when considering total emissions rather than emissions per unit, it becomes apparent that the overall impact of AI models on emissions is relatively small compared to the vast number of people, cars, and plane travel. This observation points to a smaller environmental footprint for AI models due to their lesser quantity.
“Even if we assume 10,000 models get trained every year, which is definitely an overestimation, the total emissions from that still pale in comparison,” the individual wrote.
The fervent discourse surrounding the environmental impact of bitcoin mining and generative AI reflects a broader societal concern. However, the rush to sensational headlines often obscures the complex, multifaceted nature of these technologies. Beyond clickbait narratives, there lies a deeper need for comprehensive understanding and nuanced debate about the true implications of our advancing digital era, one that considers all perspectives and seeks to inform rather than inflame public opinion.
What do you think about the recent debates concerning AI and its energy and water consumption? Share your thoughts and opinions about this subject in the comments section below.
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