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Balancing AI Growth and Environmental Impact: An Imperative for Sustainability

I am a big fan of AI, particularly generative AI. It has the power to transform our world it so many amazing ways, like granting me the ability to create an awesome image for this post which captures a thought.


An AI robot overlooking a damaged environment
Image generated by Leonardo.ai

But we're living in interesting times. As we relish the advancements of Artificial Intelligence , the sheer magnitude of its growth paints a complex picture. As our reliance on AI tools, such as ChatGPT and machine learning, continues to grow, it's critical we're mindful of the trade-offs. The expected compound annual growth rate of AI is a staggering 37.3% between 2023 and 2030 (Grand View Research), a testament to its transformative impact on a multitude of industries.


With such immense growth, there's something that continues to come to my mind – environmental impact. As we ramp up our use of AI, are we simultaneously increasing our carbon footprint? Well, yes, according to an MIT study, the cloud now has a larger carbon footprint than the entire airline industry and training a single AI model can emit more than 280 tonnes (626,000 pounds) of carbon dioxide equivalent [1].


Simultaneously, AI emerges as a crucial player in addressing intricate environmental challenges. Its capacity for rapid assimilation of data from diverse sources has been harnessed to enhance real-time analysis and surveillance of emissions, air quality, meteorological disasters, and ecological consequences. A recent study by BCG revealed a striking consensus amongst climate and AI leaders - 87% regard AI as an instrumental ally in combatting climate change [2]. They identified its ability to mitigate and quantify emissions as key factors in generating business value.


As we think about our environmental future and the adverse environmental impacts of AI, are there any other positives that may balance the scales. There is one thing, that while on its own is not entirely impactful, at least demonstrates options to work towards an equilibrium. The increased use of ARM processors in the cloud, these efficient systems might just be an example of the balancing act we need to offset the environmental impact without stifling the digital revolution.


In my view, while the AI growth-emissions trade-off might seem daunting, trends like an accelerated shift towards ARM architectures could help us strike a delicate balance between innovation and sustainability. The first step is getting an understanding of the impact we are having, as it is impossible to know if our efforts for sustainability are getting better or worse if we don't have the data to track it. That is why we work hard at Cloud Ctrl to extend the information available to our customers beyond monetary cost, because for a growing number of organisations there are other values we want to use to judge our success.


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