minutes read
February 21, 2024

The Cost of Intelligence. Resources consumption by AI industry

As we delve into the burgeoning world of Artificial Intelligence (AI), it's becoming increasingly clear...

As we delve into the burgeoning world of Artificial Intelligence (AI), it's becoming increasingly clear that the industry's growth comes with substantial resource demands, particularly in energy consumption. This article aims to shed light on the multifaceted impacts of AI on the energy sector, cloud technologies, hardware, and the data industry. Notably, the vast data requirements for training AI algorithms underscore the need for specialized roles such as data annotators, scientists, and engineers.

The Surging Energy Demands of AI

By 2027, the energy consumption of the AI industry is projected to rival that of a country like the Netherlands. This staggering comparison highlights a significant shift in resource utilization. NVIDIA, a major player in the field, is estimated to supply about 95% of the AI processing kits required by the industry, marking its pivotal role in this development. The expected delivery of these computing systems by 2027 leads to an estimated AI energy consumption range of 85-134 terawatt-hours (TWh) annually.

AI's Intensive Energy Phase: Training and Beyond

The energy usage of AI extends beyond the training phase. For instance, AI applications like ChatGPT are reported to consume approximately 564 MWh of electricity daily. This significant energy expenditure occurs even during the data generation phase based on user prompts.

Google's integration of generative AI into various services, including email and search, serves as a prime example of AI's expanding footprint. According to estimates, if AI were employed in every Google search, the annual power requirement would be around 29.2 TWh, equating to the yearly electricity consumption of Ireland. This scenario, while currently unlikely, could become more feasible with the rapid expansion of AI server production.

AI's Intensive Energy Phase: Training and Beyond

AI’s Escalating Energy Challenge

Looking ahead to 2027, the global electricity consumption attributed to AI could surge between 85 to 134 TWh per year. This range is comparable to the energy needs of countries like Argentina and Sweden. The push for more efficient AI and the adaptation of computer chips for AI purposes could further amplify this demand. 

The Extensive Use of GPUs in AI

The popularity of platforms like ChatGPT, with 590 million visits in just one month, implies a significant reliance on GPUs to meet user demand. This reliance is indicative of a broader trend where companies, to remain competitive or even viable, are increasingly integrating AI into their operations, often utilizing cloud servers.

AI and Renewable Energy: A Potential Solution

As AI extends into various domains like video production, gaming, and filmmaking, its energy consumption is expected to reach monumental levels. However, the adoption of renewable energy sources, such as nuclear power, offers a viable path to neutralize this energy impact.

A Look Towards 2030: The Expanding Energy Footprint of Technology

By 2030, it's estimated that computing and communication technologies could account for 8-21% of global electricity consumption, with data centers contributing a significant portion of this usage.

The AI industry is on a trajectory of rapid growth and innovation, but this comes with an escalating demand for energy and specialized expertise in data management. As we navigate this landscape, the balance between technological advancement and sustainable resource consumption will be crucial.


Artificial intelligence stands at the forefront of global technological transformation, offering unprecedented potential. Yet, this progress is inextricably linked to a significant energy consumption challenge. As AI continues to expand in scope and sophistication, it brings forth a spectrum of concerns, from economic pressures for its users to substantial environmental impacts due to its large-scale energy demands.

Fortunately, the AI sector is increasingly cognizant of the need for a more eco-friendly approach. Innovations in software efficiency, embracing renewable energy sources, implementing carbon offset initiatives, and advancements in both algorithms and hardware are being pursued to harmonize technological growth with ecological responsibility.

This journey towards an eco-conscious AI future necessitates a collective effort. Businesses, governmental bodies, and academic researchers are called upon to place a high priority on ecological sustainability in the AI realm. This collaborative approach aims to marry the revolutionary prowess of AI with a commitment to nurturing a legacy of global innovation, all while safeguarding the environment for future generations.

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