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New York Times
10 hours ago
- Science
- New York Times
Can You Choose an A.I. Model That Harms the Planet Less?
From uninvited results at the top of your search engine queries to offering to write your emails and helping students do homework, generative A.I. is quickly becoming part of daily life as tech giants race to develop the most advanced models and attract users. All those prompts come with an environmental cost: A report last year from the Energy Department found A.I. could help increase the portion of the nation's electricity supply consumed by data centers from 4.4 percent to 12 percent by 2028. To meet this demand, some power plants are expected to burn more coal and natural gas. And some chatbots are linked to more greenhouse gas emissions than others. A study published Thursday in the journal Frontiers in Communication analyzed different generative A.I. chatbots' capabilities and the planet-warming emissions generated from running them. Researchers found that chatbots with bigger 'brains' used exponentially more energy and also answered questions more accurately — up until a point. 'We don't always need the biggest, most heavily trained model, to answer simple questions. Smaller models are also capable of doing specific things well,' said Maximilian Dauner, a Ph.D. student at the Munich University of Applied Sciences and lead author of the paper. 'The goal should be to pick the right model for the right task.' The study evaluated 14 large language models, a common form of generative A.I. often referred to by the acronym LLMs, by asking each a set of 500 multiple choice and 500 free response questions across five different subjects. Mr. Dauner then measured the energy used to run each model and converted the results into carbon dioxide equivalents based on global most of the models tested, questions in logic-based subjects, like abstract algebra, produced the longest answers — which likely means they used more energy to generate compared with fact-based subjects, like history, Mr. Dauner said. A.I. chatbots that show their step-by-step reasoning while responding tend to use far more energy per question than chatbots that don't. The five reasoning models tested in the study did not answer questions much more accurately than the nine other studied models. The model that emitted the most, DeepSeek-R1, offered answers of comparable accuracy to those that generated a fourth of the amount of emissions. Source: Dauner and Socher, 2025 Note: A.I. models answered 500 free-response questions By Harry Stevens/The New York Times Grams of CO2 emitted per answer Source: Dauner and Socher, 2025 Note: A.I. models answered 100 free-response questions in each category By Harry Stevens/The New York Times Want all of The Times? Subscribe.
Gizmodo
10 hours ago
- Science
- Gizmodo
Why Some AI Models Spew 50 Times More Greenhouse Gas to Answer the Same Question
Like it or not, large language models have quickly become embedded into our lives. And due to their intense energy and water needs, they might also be causing us to spiral even faster into climate chaos. Some LLMs, though, might be releasing more planet-warming pollution than others, a new study finds. Queries made to some models generate up to 50 times more carbon emissions than others, according to a new study published in Frontiers in Communication. Unfortunately, and perhaps unsurprisingly, models that are more accurate tend to have the biggest energy costs. It's hard to estimate just how bad LLMs are for the environment, but some studies have suggested that training ChatGPT used up to 30 times more energy than the average American uses in a year. What isn't known is whether some models have steeper energy costs than their peers as they're answering questions. Researchers from the Hochschule München University of Applied Sciences in Germany evaluated 14 LLMs ranging from 7 to 72 billion parameters—the levers and dials that fine-tune a model's understanding and language generation—on 1,000 benchmark questions about various subjects. LLMs convert each word or parts of words in a prompt into a string of numbers called a token. Some LLMs, particularly reasoning LLMs, also insert special 'thinking tokens' into the input sequence to allow for additional internal computation and reasoning before generating output. This conversion and the subsequent computations that the LLM performs on the tokens use energy and releases CO2. The scientists compared the number of tokens generated by each of the models they tested. Reasoning models, on average, created 543.5 thinking tokens per question, whereas concise models required just 37.7 tokens per question, the study found. In the ChatGPT world, for example, GPT-3.5 is a concise model, whereas GPT-4o is a reasoning model. This reasoning process drives up energy needs, the authors found. 'The environmental impact of questioning trained LLMs is strongly determined by their reasoning approach,' study author Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences, said in a statement. 'We found that reasoning-enabled models produced up to 50 times more CO2 emissions than concise response models.' The more accurate the models were, the more carbon emissions they produced, the study found. The reasoning model Cogito, which has 70 billion parameters, reached up to 84.9% accuracy—but it also produced three times more CO2 emissions than similarly sized models that generate more concise answers. 'Currently, we see a clear accuracy-sustainability trade-off inherent in LLM technologies,' said Dauner. 'None of the models that kept emissions below 500 grams of CO2 equivalent achieved higher than 80% accuracy on answering the 1,000 questions correctly.' CO2 equivalent is the unit used to measure the climate impact of various greenhouse gases. Another factor was subject matter. Questions that required detailed or complex reasoning, for example abstract algebra or philosophy, led to up to six times higher emissions than more straightforward subjects, according to the study. There are some caveats, though. Emissions are very dependent on how local energy grids are structured and the models that you examine, so it's unclear how generalizable these findings are. Still, the study authors said they hope that the work will encourage people to be 'selective and thoughtful' about the LLM use. 'Users can significantly reduce emissions by prompting AI to generate concise answers or limiting the use of high-capacity models to tasks that genuinely require that power,' Dauner said in a statement.
