Frequent, large-scale optimization of systems based on artificial intelligence (AI) threatens to aggravate the pollutant crisis generated by the technology industry. The emerging sector could produce e-waste equivalent to more than 13 billion iPhone 15 Pro units by the beginning of the next decade. The projection is one more warning of the impacts that solutions like ChatGPT or Gemini have on the environment.
The figure is the product of research by the University of Cambridge and the Institute of Urban Environment of the Chinese Academy of Sciences. The work published in the magazine Nature Computational Science explains that the accelerated advancement of AI demands increasingly sophisticated computational requirements. The useful life of servers is reduced at the same speed.
Researchers have created a model that provides initial crude estimates of the waste flow related to the hardware of AI. “Our work does not seek to accurately predict the amount of garbage that artificial intelligence servers will generate in the future. The objective is to offer approximate calculations to show the magnitude of the looming problem and explore possible solutions,” they explain.
The team took the Nvidia DGX H100 server as a reference. The computing platform is made up of eight graphics processing units (GPUs). Today, it supports most of the major next-generation digital services. The authors proposed four possible scenarios for the growth and adoption of AI:
- Limited (41% growth): takes as a reference the rates of increase in the use of AI recorded between 2022 and 2023. It establishes a non-massive adoption of the new technology.
- Conservative (85%): considers that AI could adopt a gradual and sustained pace of penetration and development, similar to that of voice assistants.
- Moderate (115%): suggests that the popularity of AI will increase rapidly and widely thanks to its integration into commonly used digital platforms such as social media.
- Aggressive (136%): assumes that large language models will become “a ubiquitous tool in people’s daily lives.” Therefore, AI would be used in a widespread, massive and constant way.
The circular economy could reduce the impact of AI
The conclusions of the experiment indicate that waste generation would grow from 2,600 tons documented last year to 2.5 million tons in 2030. The volume would be equivalent to discard between 2.1 and 13.3 billion iPhone 15 Pro units. The calculation predicts that in the next five years no forceful measures will be implemented to reduce the garbage generated by the digital industry.
The analysis adds that restrictions on imports and exports of semiconductors could aggravate the situation. Various manufacturers have improved the efficiency of their chips and servers by integrating technologies that guarantee the same performance with fewer resources. Blockades such as those imposed by the United States limit the global adoption of these improvements. The situation could cause a 14% increase in the number of obsolete AI servers and add around 5.7 million tons of waste in 2030.
Scientists say the AI industry urgently needs to adopt circular economy mechanisms to reduce its environmental footprint. They claim that reusing GPU communication, memory and battery modules could reduce electronic waste by more than 40%. “The implementation of strategies [esta naturaleza] along the value chain of generative AI could reduce the production of electronic waste between 16 and 86%,” they add.
The environmental impact of AI is still uncertain. Dozens of specialists have recognized the potential of technology to make the fight against the climate crisis more efficient. The United Nations Educational, Scientific and Cultural Organization has said that “big data, artificial intelligence and digital transformation can play an essential role in ensuring environmental sustainability and sustainable development.” Despite this, environmental defenders demand that the development of these resources consider the inherent ecological footprint they cause. They ask companies to modify their processes to reduce their waste, emissions, water and energy consumption.
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