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LATEST NEWS

  • Marijan Hassan - Tech Journalist

Google's carbon footprint grows by almost 50% as AI adoption increases


Google's commitment to a greener future is facing a new challenge – the energy demands of artificial intelligence (AI). The company’s 2024 Environmental Report has revealed that greenhouse gas emissions rose by nearly 50% over the past five years and this can be attributed to the power-hungry data centers required to run AI systems.



The annual report detailing Google's progress towards achieving carbon neutrality by 2030, revealed that the company released 14.3 million metric tons of carbon dioxide in 2023 which was 48 percent higher than in 2019, and 13 percent higher than a year before.


While the report highlights the company’s usage of renewable energy sources, the surge in emissions paints a concerning picture. Google acknowledged the potential roadblock saying, "As we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands associated with the expected increases in our technical infrastructure investment."


This news comes amidst a growing conversation about the environmental impact of AI. Last month, Microsoft, which also pledged to go “carbon negative” by the end of this decade, reported that its greenhouse gas emissions had risen nearly 30 percent since 2020 due to the construction of data centers.


The immense computational power required to train and run complex AI models translates to a significant energy footprint. A 2023 research by AI startup Hugging Face and Carnegie Mellon University found that generating a single image using artificial intelligence can use as much energy as charging a smartphone. The findings correlate with another forecast that AI would “double the rate of US electricity demand growth and total consumption could outstrip current supply in the next two years,”


However, other experts including Bill Gates have noted that the issue of AI energy consumption will not be a major problem. Gates went on to suggest that, ultimately, AI itself could solve the problem by delivering more efficient ways to organize the energy grids.

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