Generative AI and climate change are on a collision course

In 2025 artificial intelligence and climate change, two of the biggest societal disruptors we face, will collide.

Summer of 2024 broke the record for the hottest day on Earth since data collection began, sparking widespread media coverage and public debate. This also happens to be the year that both Microsoft and Googletwo of the leading big tech companies investing heavily in artificial intelligence research and development have missed their climate targets. While this has also made headlines and sparked outrage, AI’s impact on the environment is still far from common knowledge.

In reality, the current “bigger is better” paradigm of AI – epitomized by tech companies’ pursuit of ever larger, more powerful large language models that are presented as the solution to every problem – comes at a very significant cost to the environment. These range from generating colossal amounts of energy to power the data centers that run tools like ChatGPT and Midjourney, to the millions of gallons of fresh water that are pumped through those data centers to ensure they don’t overheat, and the tons of rare earth metals needed to build the hardware they contain.

Data centers already use 2 percent of global electricity. In countries such as Ireland, this figure is as high as a fifth of the electricity generated, prompting the Irish government to announce moratorium in effect of new data centers by 2028. While much of the energy used to power data centers is officially “carbon neutral,” this relies on mechanisms like renewable energy credits that technically offset the emissions generated by generating that electricity, but don’t change the way. on which it was generated.

Places like Data center alley‘ in Virginia are powered primarily by non-renewable energy sources such as e.g natural gasand energy providers are delaying the retirement of coal-fired power plants to keep up increased requirements of technologies such as AI. Data centers siphon vast amounts of fresh water from scarce aquifers, pitting local communities against data center providers in locations ranging from Arizona to Spain. c TaiwanThe government has chosen to allocate precious water resources to chip-making facilities to keep pace with rising demands, rather than allowing local farmers to use them to irrigate their crops amid the worst drought the country has seen in more than a century .

My recent research shows that moving from older standard AI models – trained to perform a single task, such as answering questions – to the new generative models can use up to 30 times more energy just to answer the same questions. Tech companies, which are increasingly adding generative AI models to everything from search engines to word processing software, also don’t disclose the carbon costs of these changes – we still don’t know how much energy is used during a ChatGPT conversation or when generating an image with the Google Gemini.

Much of the Big Tech discourse about the impact of artificial intelligence on the environment follows two trajectories: either it’s not really a problem (according to Bill Gates), or an energy breakthrough will come and magically fix things (cf Sam Altman). What we really need is more transparency about the impact of AI on the environment through voluntary initiatives such as AI Energy Star a project I lead that would help users compare the energy efficiency of AI models to make informed decisions. I predict that in 2025 voluntary initiatives such as these will begin to be enforced through legislation, from national governments to intergovernmental organizations such as the United Nations. In 2025, with more research, public awareness and regulation, we will finally begin to understand The Ecological Footprint of AI and take the necessary actions to reduce it.