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Is ChatGPT actually harming the environment? 

by Izel Abanilla

Recently updated on April 27, 2023 02:31 pm

THEY say that if in the next few months and years you still don’t utilize the power of ChatGPT or other artificial intelligence chatbots, you would be left behind by people that have earlier harnessed the advantages of this new piece of technology. 

Many also say it is the in thing, a very powerful device that has shaken up many industries. 

But there are risks to using this. And one of the concerns about these is that they pose a danger to the environment. 

What is ChatGPT? 

Launched by Artificial Intelligence company OpenAI last November 30, 2022, ChatGPT is one of several AI systems that have been developed to allow users to ask anything from a chatbot that has access to a large body of text. 

Since its release, ChatGPT has soared in global popularity and has already created unimaginable disruption. 

Described by Twitter CEO Elon Musk as “scary good,” this innovative tool can perform a wide range of actions. 

It can compose poems, do math homework, write essays, and draft emails, among others. It can take a great weight off people’s lives. 

There are ethical concerns that this can be used for deception in various settings, such as in online applications and in schools. 

But another pressing concern coming to light is how much resources ChatGPT and the like consume.  


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Why is it a danger to the environment? 

People might be wondering, how can a technological feature be a potential threat to the environment? 

While AI models have been praised for revolutionizing creative industries, they also put one of the world’s most valuable resources at risk as these have been proven to be guzzling water to function. 

According to the 2023 study Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models, “training GPT-3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater.”

As the study noted, this is enough to produce 370 BMW cars or 320 Tesla electric vehicles.  

The study also pointed out that ChatGPT needs 500 ml of fresh water for every 20 to 50 questions and answers depending on time and location the chatbot was used. 

The total water requirement for the chatbot operations is extremely voluminous considering the millions who use AI models globally. With such an enormous need for water, the study said overall operations of these models are “extremely concerning” especially given the dwindling freshwater supply. 

The water fuels the operations of its data centers to generate electricity and cool the servers. This is because chatbots rely on thousands of servers that are spread across the world. These are basically high functioning machines that need to be cooled down regularly so that they could maintain optimal performance.  

The study also said that through the years, the carbon footprints of various AI models had gone through public scrutiny. However, it said the water footprint problem “has remained under the radar, which, if not properly addressed, can become a major roadblock for socially responsible and environmentally sustainable evolution of future AI.”

It said that to respond to global water challenges, AI models could “take social responsibility and lead by example by addressing their own water footprint.” 

The study was penned by Pengfei Li, Jianyi Yang and Shaolei Ren from the University of California Riverside and Mohammad A. Islam from the University of Texas Arlington. 

Needless to say, ChatGPT’s water consumption is a serious environmental issue. The more complex the chatbot, the more water it needs. 

How can the environmental impact of ChatGPT be reduced? 

While there may be little room for action from a user’s standpoint, providers may look into alternatives to mitigate the environmental impact of AI models like ChatGPT. 

Environmental website sigmaearth.com has come up with a few recommendations to address these concerns. 

  1. Massive AI systems could in fact be compressed to transfer its database into a smaller model which would require less power.
  1. Being more selective and choosing only the required information, or adapting current models for a new purpose, could bring down the cost of training a model.
  1. Those developing AI models should first estimate its environmental impact. 
  2. Developers should ensure that they are tapping renewable energy sources to reduce carbon dioxide emissions. 


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