{"slug": "professor-warns-ai-use-wastes-physical-resources", "title": "Professor Warns AI Use Wastes Physical Resources", "summary": "Professor Seyedali Mirjalili of Western Sydney University warned in The Conversation that habitual use of large AI models for trivial tasks wastes significant physical resources, citing an International Energy Agency projection that global data center electricity use could roughly double to 945 TWh by 2030 driven primarily by AI. He called for a 'right tool for the task' mindset and urged governments to require mandatory reporting of data center electricity use, water consumption, and emissions.", "body_md": "# Professor Warns AI Use Wastes Physical Resources\n\nProfessor Seyedali Mirjalili, writing in The Conversation, argues that everyday AI use is wasteful when powerful cloud models like ChatGPT are applied to trivial tasks. Using an analogy of a truck delivering a single envelope, he notes that while one small request has negligible impact, millions of unnecessary requests scale into significant resource consumption. He cites the IEA's projection that global data center electricity use could roughly double to around 945 TWh by 2030, driven primarily by AI. The piece calls for a 'right tool for the task' mindset and offers practical tips: write clearer prompts, request only what you need, and prefer lighter-weight models for simple work. Mirjalili also urges governments to require data centers to report electricity use, water consumption, and emissions.\n\n### What happened\n\nProfessor Seyedali Mirjalili (Western Sydney University) published an opinion piece in The Conversation arguing that habitual reliance on large AI models for minor tasks is quietly wasteful. The analogy he uses: running ChatGPT to polish a short sentence is like using a large truck to deliver one envelope across the street -- the outcome is the same, but the resource overhead is disproportionate.\n\n### The resource case\n\nThe piece cites the International Energy Agency's \"Energy and AI\" report, which projects that global data center electricity consumption could roughly double to around 945 TWh by 2030, with AI as the primary growth driver. Mirjalili emphasizes that the \"cloud\" is not virtual -- it is data centers filled with servers, chips, cables, and cooling systems -- and that every request, however small, carries a real cost in electricity, water, and land use.\n\n### Practical guidance from the article\n\nMirjalili offers four recommendations:\n\n- •choose the right tool for the task, not always the most powerful model\n- •write clear, specific prompts to reduce unnecessary back-and-forth\n- •ask only for what you need, since shorter output means less compute\n- •treat media files (images, audio, video) with extra care, as they typically require substantially more compute than text\n\n### Governance and policy angle\n\nOn the organizational side, Mirjalili urges companies to evaluate whether AI is genuinely needed before adoption rather than adding it for optics. On regulation, he notes that Australia has released voluntary guidelines for data center developers but calls for mandatory reporting on electricity use, water consumption, emissions, and e-waste as part of planning approvals.\n\n### Context\n\nThis is an opinion piece, not original research. Mirjalili frames AI resource use as analogous to household energy habits -- most people know not to leave lights on all day, and a similar mindset, he argues, should apply to AI queries. The piece is relevant to engineers and data scientists who control model selection and API usage patterns at scale.\n\n## Scoring Rationale\n\nAn opinion piece from a well-credentialed AI researcher in The Conversation, grounded in IEA data projecting data center electricity use doubling to 945 TWh by 2030. The practical guidance on matching model size to task complexity is relevant to practitioners, but the piece offers no new research or findings of its own. Score reflects educational value and topic relevance to the AI/DS community without overstating the news significance of an opinion article.\n\nPractice interview problems based on real data\n\n1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.\n\n[Try 250 free problems](/problems)", "url": "https://wpnews.pro/news/professor-warns-ai-use-wastes-physical-resources", "canonical_source": "https://letsdatascience.com/news/professor-warns-ai-use-wastes-physical-resources-75bcef93", "published_at": "2026-06-17 03:23:21.018563+00:00", "updated_at": "2026-06-17 03:23:23.548026+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-ethics", "ai-policy", "ai-infrastructure"], "entities": ["Seyedali Mirjalili", "Western Sydney University", "The Conversation", "International Energy Agency", "ChatGPT"], "alternates": {"html": "https://wpnews.pro/news/professor-warns-ai-use-wastes-physical-resources", "markdown": "https://wpnews.pro/news/professor-warns-ai-use-wastes-physical-resources.md", "text": "https://wpnews.pro/news/professor-warns-ai-use-wastes-physical-resources.txt", "jsonld": "https://wpnews.pro/news/professor-warns-ai-use-wastes-physical-resources.jsonld"}}