Digital tools and services powered by artificial intelligence (AI) have rapidly evolved from the realm of experimental technologies to readily available consumer products. Chatbots, digital assistants, and data analysis tools have all become interlaced with AI and its unique suite of features. This swift and coordinated effort to integrate AI into the digital ecosystem has raised concerns regarding the extent of its energy and human costs. As our global society attempts to address the present burdens associated with climate change, the emergence of AI presents a significant curveball in the sustainability equation. With the presence of AI generally expected to expand in future years, it becomes increasingly important for those concerned with sustainability to understand the nature of its costs and benefits, along with where it stands amongst other pressing sustainability concerns.<\/p>\n
One of the more prominent concerns with the proliferation of AI is the costs it will place unto energy grids, the environment, and communities where the industry is most present. The United Nations Environment Programme notes increasing electronic waste, use of exceedingly scarce water resources, mineral demand for computer components, and electricity to power growing AI infrastructure as some of their foremost concerns on the matter. The cost of increased electricity consumption is corroborated by professionals at the Massachusetts Institute of Technology, who write that the collective electricity demand of AI data centers outpaces entire nations and will soon approach the usage of some of the globe\u2019s economic powerhouses.<\/p>\n
The energy usage of generative AI, while staggering when viewed in its totality, consumes at a variable rate and is worth recognizing in all its complexity. Large fluctuations in energy use occur during the training period of generative AI models, which can often stress power grids to the point of activating diesel-powered generators to meet these electricity demands. The frequent iterations of updated AI models, which can occur in mere months or even weeks, compound these costs by rendering previous models, and the energy required to train them, obsolete. So while the present energy demand for AI queries bears an energy cost five times greater than that of a standard web search, current forecasts indicate that this is set to increase with the introduction of new models. As the use of AI for data processing and inference tasks becomes increasingly commonplace, concerns amongst research scientists and professionals continue to mount as these models grow more sophisticated and require additional energy supplies to satiate them.<\/p>\n
The environmental concerns with generative AI include:<\/p>\n
Proponents of artificial intelligence note that there exist several benefits presented by AI which may assist in addressing present and future environmental concerns. The Columbia Climate School states that the current task-based AI we possess can sometimes perform better than humans in certain pattern recognition tasks, such as weather forecasting, so long as it is trained with a sufficient level of information. This, in the eyes of the United Nations Environment Programme, indicates that AI can play a crucial role in assisting the development of more advanced emissions models. This, in theory, could also extend to AI being deployed to measure environmental footprints of various industries and climate metrics such as changes in glacier mass and sea level. By furthering the extent to which climate data can be collected and accelerating the rate at data analysis, AI tools have the potential to present clearer data that informs future research efforts and global decision making.<\/p>\n
The use of these tools for enhancing climate data, while beneficial in many regards, does not come without its own associated costs and growing pains. The relative novelty of AI use in data analytics and research means there exists something of a disconnect between the expertise of environmental scientists and actual AI professionals. To better utilize AI in mitigating environmental challenges, further collaboration between the two must be explored first. The environmental costs of sprawling AI data centers will also continue to be of concern, leading to inevitable discussions on the environmental cost to benefit ratio of AI usage in its entirety. Discussions on how AI may be used in a limited and ethical capacity may be on the horizon, but the market for these technologies are presently in a relatively unregulated situation which makes future predictions challenging.<\/p>\n
Generative AI can potentially be used to support sustainability goals in the following ways:<\/p>\n
Generative AI experts agree on\u00a0<\/span>a number of<\/span>\u00a0policy recommendations<\/span>\u00a0and research opportunities<\/span> to mitigate the environmental impacts of generative AI.<\/span><\/span><\/p>\n The meteoric speed at which AI has integrated into digital ecosystems and internet services speaks to its staying power in a world still adjusting to its presence. Inevitably, this has raised concerns amongst the public regarding how to use these tools in a sustainable fashion. There are many instances in which one can forego AI altogether, though many digital tasks have become so thoroughly entangled with this technology that avoiding it can prove tedious in even the best of circumstances. Many of the most prolific consumer AI tools such as Google Gemini, Microsoft Copilot, and Apple Intelligence are encouraged for casual use and thus do not have clear instructions for how to moderate or turn them off. While some workarounds do exist that allow users to circumvent the use of these otherwise omnipresent AI features, the processes can often be convoluted, and such methods are often quickly patched in updates to devices and internet browsers.<\/p>\n For an in-depth guide and how to disable certain AI tools from your digital space, visit this Consumer Reports article<\/a>.<\/p>\n Less frequent use of these AI features often comes with the added benefit of increasing device storage space while decreasing power usage and computational strain. Though as previously mentioned, mainstream digital platforms have continued to solidify the presence of AI in their infrastructure, thus making it more difficult to curate your experience on their platforms. In the case of Google\u2019s search engine, it is possible to switch to \u201cWeb Mode\u201d in the quick settings beneath the search bar, though this must be done manually and seemingly does not mitigate the energy cost of AI search queries. This remains true of other services, which offer more classic experiences, but often only cloaks the presence of AI which continues to run in the background. A more sustainable option would be to utilize alternative internet browsers that have more customizability in terms of AI experience and stronger sustainability commitments.<\/p>\n There are also many instances in which AI tools cannot be ignored, especially when it comes to completing certain work tasks. The professional applications of AI have existed for longer than the consumer models that recently debuted to the public, and can be efficiently used for pattern recognition tasks, writing code, and the interpretation of large datasets that can otherwise prove difficult for researchers to process on their own. Given this history of use and the growing scope of AI assistants and tools, determining whether the utility of AI exceeds traditional task management can be challenging to do. These decisions should account for the efficiency of using AI in accordance with the scope of the task at hand. General writing tasks, whether they be work emails, essays or articles, can each be accomplished independently in lieu of defaulting AI. Image generation can also be easily avoided and is often a poor use of energy when considering the already impressive library of art and stock images on the internet. Determining whether AI is providing mere convenience or genuine assistance in a task can often guide the decision to utilize it or to forego it entirely.<\/p>\n <\/p>\n\n Read more about Artificial Intelligence at NAU<\/a><\/p>\n<\/div><\/div>\n Learn about NAU’s Institute for Advancing Applications of Artificial Intelligence (IAAAI<\/a>)<\/p>\n<\/div><\/div>\n Did you know?<\/strong><\/p>\n If you prefer not to use AI for an NAU class, you can ask your professor for accommodations.<\/p>\n\n
Sustainable Personal Use of AI<\/h2>\n
Sustainable Internet Browser Alternatives<\/h4>\n
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