Is it sustainable that we continue on our current path of technological development?
After the week 2 lecture, I have been reflecting on whether our current trajectory in technology, particularly artificial intelligence (AI), is sustainable. What stood out most from the readings and tutorials was Google’s water use and the way they have attempted to downplay the scale of their consumption. They claim that a single Gemini prompt only uses a few drops of water, yet experts argue this masks the broader issue of indirect water and energy use (Rainey, 2025). It feels like a hidden truth, carefully concealed from the public eye. (Changed phrasing from “invisible truth, hidden from the public, and not by accident” to formalise tone). This concerns me because, in education and wider society, we are encouraged to embrace AI tools without much questioning of their environmental cost. (Changed in line with feedback about informal tone)
I feel conflicted. On one hand, AI can create efficiencies and new opportunities for learning. On the other, it leaves me uneasy. What happens if there is a significant power outage or another disruption and we suddenly lose access? Such reliance on vulnerable systems seems risky. (Rephrased from “It feels dangerous” to a more formal statement) I also question whether companies genuinely prioritise sustainability over profit. My concerns are shaped by my own lifestyle, I grow vegetables, raise chickens, and live rurally, which grounds me in the rhythms of nature. Yet I have also worked in digital marketing and photography, so I appreciate the possibilities technology brings. For me, balance is critical. (Formal tone without the lose of my personal voice)
The advantages of AI are undeniable, but the costs are becoming clearer too. Overreliance may lead to detachment from the natural world, loss of practical life skills, and unseen environmental pressures. Research supports this unease. MIT News (2025) highlights how generative AI is resource-intensive, noting that training GPT-3 alone may have required about 700,000 litres of fresh water, with global AI water demand projected to reach billions of cubic metres by 2027 (Li et al., 2023). These numbers are staggering, and the ecological implications are real. If we can create technology this advanced, surely we can also develop solutions that make it sustainable. Yet this would demand genuine commitment rather than profit-driven motives. (More formal wording added “ecological implications are real” to heighten academic tone and make analysis clearer as per feedback)
As a future teacher, I see the need to model balance. I will use technology in the classroom, but I will also ensure students engage in practical, hands-on experiences. (Elaborated on previously underdeveloped point, as per feedback) For example, students could explore how AI might be applied to outdoor education, such as designing a sustainable garden. AI could recommend the best crops for a location, provide step-by-step fertilisation methods, and generate an action plan to maximise success. Students would then test these plans in real life, learning not only digital skills but also how to grow food and live more self-sufficiently. (Added in an example of vegetable gardening and expanded it to fully explain how this looks in a classroom context) This approach would show them how technology can complement, rather than replace, essential life skills. (Expanded my sustainable living theme with AI and a real life classroom example)
Personally, I now recognise the need to value both worlds. I enjoy being an early adopter of technology, yet I also find grounding in nature and family. AI can be a useful tool, even to support sustainable living, but it must not become a substitute for human connection or ecological responsibility. Ignoring the balance between technology and the natural environment risks devastating consequences, both in terms of resources and in our relationship to the world around us. (Formal tone for conclusion, less personal)
References
Li, P., Yang, J., Islam, M. A., & Ren, S. (2023). Making AI less “thirsty”: Uncovering and addressing the secret water footprint of AI modelshttps://doi.org/10.48550/arXiv.2304.03271
MIT News. (2025, January 17). Explained: The environmental impact of generative AI. Massachusetts Institute of Technology. https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117
Rainey, C. (2025, August 6). They’re just hiding the critical information. Google’s latest AI efficiency claims spark backlash over hidden environmental costs. Windows Central. https://www.windowscentral.com/artificial-intelligence/google-gemini-ai-efficiency-claims-debate