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7NEWS
5 days ago
- Science
- 7NEWS
Being polite to AI could be harmful to the environment
Whether it's answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people's lives. ut a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up. Each word in an AI prompt is broken down into clusters of numbers called 'token IDs' and sent to massive data centres — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations. The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute. So, for each prompt you give AI, what's the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers. In addition, 'smarter' LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported. 'This shows us the tradeoff between energy consumption and the accuracy of model performance,' Maximilian Dauner, a doctoral student at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday, said. Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models. 'You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,' Dauner said. What you can do to reduce your carbon footprint Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said. 'AI expends a lot of energy being polite, especially if the user is polite, saying 'please' and 'thank you',' Dauner said. 'But this just makes their responses even longer, expending more energy to generate each word.' For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don't need an explanation at all. Most important, Dauner's study highlights that not all AI models are created equally, Sasha Luccioni, the climate lead at AI company Hugging Face, said. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task. 'Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,' Luccioni said. If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator. Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said. When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks. Why it's hard to measure AI's environmental impact Putting a number on the environmental impact of AI has proved challenging. The study noted that energy consumption can vary based on the user's proximity to local energy grids and the hardware used to run AI models. That's partly why the researchers chose to represent carbon emissions within a range, Dauner said. Furthermore, many AI companies don't share information about their energy consumption — or details like server size or optimisation techniques that could help researchers estimate energy consumption, Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI's water consumption, said. 'You can't really say AI consumes this much energy or water on average — that's just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,' Ren said. One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested. 'Generally, if people were more informed about the average (environmental) cost of generating a response, people would maybe start thinking, 'Is it really necessary to turn myself into an action figure just because I'm bored?' Or 'do I have to tell ChatGPT jokes because I have nothing to do?'' Dauner said. Additionally, as more companies push to add generative AI tools to their systems, people may not have much choice how or when they use the technology, Luccioni said. 'We don't need generative AI in web search. Nobody asked for AI chatbots in (messaging apps) or on social media,' Luccioni said. 'This race to stuff them into every single existing technology is truly infuriating, since it comes with real consequences to our planet.' With less available information about AI's resource usage, consumers have less choice, Ren said, adding that regulatory pressures for more transparency are unlikely to the United States anytime soon. Instead, the best hope for more energy-efficient AI may lie in the cost efficacy of using less energy. 'Overall, I'm still positive about (the future). There are many software engineers working hard to improve resource efficiency,' Ren said. 'Other industries consume a lot of energy too, but it's not a reason to suggest AI's environmental impact is not a problem. 'We should definitely pay attention.'
Time of India
20-06-2025
- Science
- Time of India
AI chatbots using reason emit more carbon than those responding concisely, study finds
HighlightsA study revealed that carbon emissions from chat-based generative artificial intelligence can be up to six times higher when processing complex prompts, such as abstract algebra or philosophy, compared to simpler prompts like high school history. The research, conducted by Maximilian Dauner at Hochschule Munchen University of Applied Sciences, found that reasoning-enabled models produced significantly more carbon dioxide emissions than concise response models, with emissions reaching up to 50 times higher. The findings suggest a clear accuracy-sustainability trade-off in large-language model technologies, with the most accurate model, Cogito, achieving nearly 85 percent accuracy while generating three times more carbon dioxide emissions than smaller models. A study found that carbon emissions from chat-based generative AI can be six times higher when responding to complex prompts, like abstract algebra or philosophy, compared to simpler prompts, such as high school history. "The environmental impact of questioning trained ( large-language models ) is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions," first author Maximilian Dauner, a researcher at Hochschule Munchen University of Applied Sciences, Germany, said. "We found that reasoning-enabled models produced up to 50 times more (carbon dioxide) emissions than concise response models ," Dauner added. The study, published in the journal Frontiers in Communication, evaluated how 14 large-language models (which power chatbots), including DeepSeek and Cogito, process information before responding to 1,000 benchmark questions -- 500 multiple-choice and 500 subjective. Each model responded to 100 questions on each of the five subjects chosen for the analysis -- philosophy, high school world history, international law, abstract algebra, and high school mathematics. "Zero-token reasoning traces appear when no intermediate text is needed (e.g. Cogito 70B reasoning on certain history items), whereas the maximum reasoning burden (6.716 tokens) is observed for the Deepseek R1 7B model on an abstract algebra prompt," the authors wrote. Tokens are virtual objects created by conversational AI when processing a user's prompt in natural language. More tokens lead to increased carbon dioxide emissions. Chatbots equipped with an ability to reason, or ' reasoning models ', produced 543.5 'thinking' tokens per question, whereas concise models -- producing one-word answers -- required just 37.7 tokens per question, the researchers found. Thinking tokens are additional ones that reasoning models generate before producing an answer, they explained. However, more thinking tokens do not necessarily guarantee correct responses, as the team said, elaborate detail is not always essential for correctness. Dauner said, "None of the models that kept emissions below 500 grams of CO₂ equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly." "Currently, we see a clear accuracy-sustainability trade-off inherent in (large-language model) technologies," the author added. The most accurate performance was seen in the reasoning model Cogito, with a nearly 85 per cent accuracy in responses, whilst producing three times more carbon dioxide emissions than similar-sized models generating concise answers. "In conclusion, while larger and reasoning-enhanced models significantly outperform smaller counterparts in terms of accuracy, this improvement comes with steep increases in emissions and computational demand," the authors wrote. "Optimising reasoning efficiency and response brevity, particularly for challenging subjects like abstract algebra, is crucial for advancing more sustainable and environmentally conscious artificial intelligence technologies," they wrote.
Economic Times
19-06-2025
- Science
- Economic Times
AI chatbots using reason emit more carbon than those responding concisely, study finds
A study found that carbon emissions from chat-based generative AI can be six times higher when responding to complex prompts, like abstract algebra or philosophy, compared to simpler prompts, such as high school history. "The environmental impact of questioning trained (large-language models) is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions," first author Maximilian Dauner, a researcher at Hochschule Munchen University of Applied Sciences, Germany, said. "We found that reasoning-enabled models produced up to 50 times more (carbon dioxide) emissions than concise response models," Dauner study, published in the journal Frontiers in Communication, evaluated how 14 large-language models (which power chatbots), including DeepSeek and Cogito, process information before responding to 1,000 benchmark questions -- 500 multiple-choice and 500 model responded to 100 questions on each of the five subjects chosen for the analysis -- philosophy, high school world history, international law, abstract algebra, and high school mathematics. "Zero-token reasoning traces appear when no intermediate text is needed (e.g. Cogito 70B reasoning on certain history items), whereas the maximum reasoning burden (6.716 tokens) is observed for the Deepseek R1 7B model on an abstract algebra prompt," the authors wrote. Tokens are virtual objects created by conversational AI when processing a user's prompt in natural language. More tokens lead to increased carbon dioxide equipped with an ability to reason, or 'reasoning models', produced 543.5 'thinking' tokens per question, whereas concise models -- producing one-word answers -- required just 37.7 tokens per question, the researchers tokens are additional ones that reasoning models generate before producing an answer, they more thinking tokens do not necessarily guarantee correct responses, as the team said, elaborate detail is not always essential for said, "None of the models that kept emissions below 500 grams of CO₂ equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly." "Currently, we see a clear accuracy-sustainability trade-off inherent in (large-language model) technologies," the author most accurate performance was seen in the reasoning model Cogito, with a nearly 85 per cent accuracy in responses, whilst producing three times more carbon dioxide emissions than similar-sized models generating concise answers."In conclusion, while larger and reasoning-enhanced models significantly outperform smaller counterparts in terms of accuracy, this improvement comes with steep increases in emissions and computational demand," the authors wrote. "Optimising reasoning efficiency and response brevity, particularly for challenging subjects like abstract algebra, is crucial for advancing more sustainable and environmentally conscious artificial intelligence technologies," they wrote.
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Business Standard
19-06-2025
- Science
- Business Standard
AI chatbots that reason emit more carbon than ones with simple reply: Study
A study found that carbon emissions from chat-based generative AI can be six times higher when responding to complex prompts, like abstract algebra or philosophy, compared to simpler prompts, such as high school history. "The environmental impact of questioning trained (large-language models) is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions," first author Maximilian Dauner, a researcher at Hochschule Mnchen University of Applied Sciences, Germany, said. "We found that reasoning-enabled models produced up to 50 times more (carbon dioxide) emissions than concise response models," Dauner added. The study, published in the journal Frontiers in Communication, evaluated how 14 large-language models (which power chatbots), including DeepSeek and Cogito, process information before responding to 1,000 benchmark questions -- 500 multiple-choice and 500 subjective. Each model responded to 100 questions on each of the five subjects chosen for the analysis -- philosophy, high school world history, international law, abstract algebra, and high school mathematics. "Zero-token reasoning traces appear when no intermediate text is needed (e.g. Cogito 70B reasoning on certain history items), whereas the maximum reasoning burden (6.716 tokens) is observed for the Deepseek R1 7B model on an abstract algebra prompt," the authors wrote. Tokens are virtual objects created by conversational AI when processing a user's prompt in natural language. More tokens lead to increased carbon dioxide emissions. Chatbots equipped with an ability to reason, or 'reasoning models', produced 543.5 'thinking' tokens per question, whereas concise models -- producing one-word answers -- required just 37.7 tokens per question, the researchers found. Thinking tokens are additional ones that reasoning models generate before producing an answer, they explained. However, more thinking tokens do not necessarily guarantee correct responses, as the team said, elaborate detail is not always essential for correctness. Dauner said, "None of the models that kept emissions below 500 grams of CO2 equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly." "Currently, we see a clear accuracy-sustainability trade-off inherent in (large-language model) technologies," the author added. The most accurate performance was seen in the reasoning model Cogito, with a nearly 85 per cent accuracy in responses, whilst producing three times more carbon dioxide emissions than similar-sized models generating concise answers. "In conclusion, while larger and reasoning-enhanced models significantly outperform smaller counterparts in terms of accuracy, this improvement comes with steep increases in emissions and computational demand," the authors wrote. "Optimising reasoning efficiency and response brevity, particularly for challenging subjects like abstract algebra, is crucial for advancing more sustainable and environmentally conscious artificial intelligence technologies," they wrote.
Indian Express
19-06-2025
- Science
- Indian Express
AI chatbots using reason emit more carbon than those responding concisely, study finds
A study found that carbon emissions from chat-based generative AI can be six times higher when responding to complex prompts, like abstract algebra or philosophy, compared to simpler prompts, such as high school history. 'The environmental impact of questioning trained (large-language models) is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions,' first author Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences, Germany, said. 'We found that reasoning-enabled models produced up to 50 times more (carbon dioxide) emissions than concise response models,' Dauner added. The study, published in the journal Frontiers in Communication, evaluated how 14 large-language models (which power chatbots), including DeepSeek and Cogito, process information before responding to 1,000 benchmark questions — 500 multiple-choice and 500 subjective. Each model responded to 100 questions on each of the five subjects chosen for the analysis — philosophy, high school world history, international law, abstract algebra, and high school mathematics. 'Zero-token reasoning traces appear when no intermediate text is needed (e.g. Cogito 70B reasoning on certain history items), whereas the maximum reasoning burden (6.716 tokens) is observed for the Deepseek R1 7B model on an abstract algebra prompt,' the authors wrote. Tokens are virtual objects created by conversational AI when processing a user's prompt in natural language. More tokens lead to increased carbon dioxide emissions. Chatbots equipped with an ability to reason, or 'reasoning models', produced 543.5 'thinking' tokens per question, whereas concise models — producing one-word answers — required just 37.7 tokens per question, the researchers found. Thinking tokens are additional ones that reasoning models generate before producing an answer, they explained. However, more thinking tokens do not necessarily guarantee correct responses, as the team said, elaborate detail is not always essential for correctness. Dauner said, 'None of the models that kept emissions below 500 grams of CO2 equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly.' 'Currently, we see a clear accuracy-sustainability trade-off inherent in (large-language model) technologies,' the author added. The most accurate performance was seen in the reasoning model Cogito, with a nearly 85 per cent accuracy in responses, whilst producing three times more carbon dioxide emissions than similar-sized models generating concise answers. 'In conclusion, while larger and reasoning-enhanced models significantly outperform smaller counterparts in terms of accuracy, this improvement comes with steep increases in emissions and computational demand,' the authors wrote. 'Optimising reasoning efficiency and response brevity, particularly for challenging subjects like abstract algebra, is crucial for advancing more sustainable and environmentally conscious artificial intelligence technologies,' they wrote. PTI KRS KRS MPL
