{"id":4604,"date":"2024-10-07T21:59:38","date_gmt":"2024-10-07T19:59:38","guid":{"rendered":"https:\/\/www.rivistaeco.com\/?p=4604"},"modified":"2024-10-07T21:59:38","modified_gmt":"2024-10-07T19:59:38","slug":"data-centres-are-hungry-for-energy","status":"publish","type":"post","link":"https:\/\/www.rivistaeco.com\/en\/2024\/10\/07\/data-centres-are-hungry-for-energy\/","title":{"rendered":"Data Centres Are Hungry for Energy"},"content":{"rendered":"

Data processing centres, fundamental for the development of AI, require enormous amounts of electricity. The increase in consumption forces tech companies, once at the forefront of emission reduction efforts, to review their climate commitments. Solutions to a problem that should not be underestimated involve energy efficiency and the search for clean energy sources.<\/span><\/i><\/p>\n

Before climate change became front-page news, there was one sector\u2014the tech companies\u2014that had already sensed the direction the world was heading. Until a few years ago, giants like Google, Apple, and Microsoft were cited as virtuous examples for their carbon neutrality policies. Today, that path seems to have been interrupted, and the culprit has a precise name: artificial intelligence. The fierce competition among high-tech companies to develop new and increasingly efficient AI software has suddenly driven up energy consumption and consequently climate-altering emissions.<\/span><\/p>\n

The ecological and digital transitions have often been described as the two directions to follow to build the economy of tomorrow. So much so that many European Union initiatives are dedicated to the twin transitions. In recent years, however, the alliance seems to have encountered an obstacle. Those same companies once praised for emission reduction are now forced to review their climate commitments. The most exemplary case is Google. The California-based company reached carbon neutrality\u2014the balance between CO2 emissions produced and those absorbed\u2014in 2007. Yet in 2023, its emissions increased by 48% compared to 2019, causing it to lose the coveted carbon-neutral label. This is not an isolated case. Microsoft’s emissions rose by 29% compared to 2020, while Meta’s emissions grew by 66% between 2021 and 2023.<\/span><\/p>\n

The Hunger for Energy (and Water)<\/b><\/h3>\n

There is a fundamental reason behind the growth in emissions from tech companies: the insatiable energy appetite of data centres indispensable for developing artificial intelligence. According to a report by the International Energy Agency (IEA), data centres consumed around 460 terawatt-hours in 2022, about 2% of global electricity production. The forecast is that by 2026, this figure could more than double, surpassing 1000 terawatt-hours, roughly equivalent to Japan’s annual electricity consumption. These estimates do not account for the fact that training processes have not yet been optimised, and there is significant room for improvement in making the process more efficient. A single Meta data centre, the IEA estimates, consumes daily the equivalent of seven million computers running eight hours a day for an entire year. Considering ChatGPT, the most famous AI-based chatbot, the data is equally impressive. A simple question posed to the language model developed by OpenAI requires about ten times more electricity (29 Wh) than a Google search (0.3 Wh).<\/span><\/p>\n

Energy consumption by these systems begins during the learning phase. “The training of GPT-3 requires the use of supercomputers with thousands of graphics processing units (GPUs) for about a month with an energy consumption of over 200 MWh,” explains Cristina Silvano, a full professor of Computer Architecture at the Politecnico di Milano. “Estimating that a family consumes about 10 MWh per year on average, the training of GPT-3 is equivalent to the average energy consumption of a family over twenty years,” continues the expert. But training language models like ChatGPT is not the only factor impacting AI energy consumption. Other examples mentioned by Cristina Silvano include supercomputing systems that simulate the behaviour of physical and climatic phenomena, financial prediction models, and cloud computing services offered by major platforms like Microsoft and Amazon. All these activities, the professor explains, “require the parallel processing of large amounts of data” and consequently involve “high energy consumption.”<\/span><\/p>\n

However, energy hunger is not the only dark side of artificial intelligence. A study by the Riverside campus of the University of California revealed that executing AI queries also has a significant water footprint. The training of GPT-3, for example, consumed 700,000 litres of fresh water. To prevent machines from overheating, data centres need complex cooling systems. In 2022, the three major tech companies\u2014Google, Meta, and Microsoft\u2014extracted over 2 billion cubic meters of fresh water. It is estimated that by 2027, the demand for generative artificial intelligence could raise this figure to 66 billion cubic meters.<\/span><\/p>\n

Electricity Consumption of AI Systems<\/b><\/p>\n

 <\/p>\n

\"\"
Source: International Energy Agency.<\/figcaption><\/figure>\n

Renewables, Nuclear, and the Return to Fossil Fuels<\/b><\/h3>\n

There are no official and detailed data on the number of data centres worldwide. According to Statista, updated as of March 2024, there are 10,593 data centres globally. The United States dominates the ranking with 5,381 centres (more than a third of the total), followed by Germany (521) and the United Kingdom (514). China ranks fourth (449) and Italy is twelfth (170). To meet the enormous energy needs of these structures, especially those dedicated to AI, tech companies face two options: draw energy from the existing grid or build new power plants. In West Virginia, the shutdown of coal plants has been postponed to an unspecified future to ensure the flow of energy to new data centres built in neighbouring Virginia.<\/span><\/p>\n

Looking at the entire United States, the demand for electricity from data processing facilities is leading to the growth of new gas infrastructure, which is less polluting than oil and coal but still a fossil fuel. According to a Goldman Sachs study, the increase in energy consumption by data centres in the United States will require an additional daily capacity of nearly a billion cubic meters of gas by 2030. This amount, the report emphasises, can be guaranteed “through the construction of new pipelines.” However, things do not necessarily have to go this way. By placing servers in states with higher renewable potential, for example, there would be no need for additional gas to cover the increased energy demand.<\/span><\/p>\n

Some of the companies most involved in the AI sector are also considering alternative solutions. Sam Altman, CEO of OpenAI and the “father” of ChatGPT, has invested in Helion Energy, a startup aiming to produce nuclear fusion energy by 2028, a deadline that is almost impossible to meet. Microsoft, on the other hand, has decided to rely on renewable sources, announcing a $10 billion plan to build wind and solar farms to cover the energy demand of its data centres. Google has chosen a different path. For its new data centre in Nevada, the company has focused not only on renewables but also on geothermal energy, which uses underground heat to produce energy without carbon dioxide emissions. There are similar examples in Italy, especially in Tuscany, the cradle of geothermal energy.<\/span><\/p>\n

 <\/p>\n

Ranking of Countries by Number of Data Centres<\/b><\/p>\n

\"\"
Source: Statista.com.<\/figcaption><\/figure>\n

A Network Under Stress<\/b><\/h3>\n

According to the International Energy Agency, about 2% of the electricity consumed worldwide in 2022 went to data centres. However, this figure also includes cloud computing, search engines, and other digital services. A study by economist Alex de Vries, a researcher at the VU Amsterdam School of Business and Economics, estimates that servers dedicated to artificial intelligence alone could account for 0.5% of global electricity consumption by 2027. In absolute terms, this is between 85 and 134 terawatt-hours, the same amount of energy consumed in a year by countries like Argentina or Sweden. Goldman Sachs’ analysis paints an even more worrying scenario for the United States, where data centres already account for about 3% of total electricity consumption. By 2030, the New York bank estimates, this percentage could rise to 8%.<\/span><\/p>\n

At the end of March 2024, Reuters revealed that Microsoft and OpenAI plan to build a massive data centre with a $100 billion investment. The supercomputer will be called Stargate and could come into operation in 2028, requiring five gigawatts of power, equivalent to about five nuclear reactors. Currently, the economy surrounding data centres is growing at a much faster rate than the electrical grid infrastructure, which is increasingly under stress. This situation could have a significant impact on climate-altering emissions, nullifying the good work done so far by tech companies to achieve carbon neutrality.<\/span><\/p>\n

Possible Solutions<\/b><\/h3>\n

To address the energy hunger of data centres, researchers and companies are focusing primarily on one solution: greater energy efficiency. “One of the challenges of the microelectronics industry is designing high-efficiency microchips based on specialised processing units to perform the calculations required by AI algorithms,” explains Cristina Silvano. Among the other “optimised” solutions cited by the Politecnico di Milano professor are small, low-cost accelerators that allow companies to run AI algorithms locally without relying on a data centre several kilometres away.<\/span><\/p>\n

However, advancements in energy efficiency alone will not solve the problem. Another area where researchers are focusing their efforts is data centre cooling technologies. In recent years, alternative and more efficient methods like air-assisted liquid cooling or immersion cooling, which is still in development, have started to spread. Then there is the issue of grid load. Some companies are experimenting with systems in their data centres that allow energy consumption to be adjusted based on the grid’s needs. This means that the structures can schedule the most energy-intensive computing activities during off-peak hours so as not to overload the grid.<\/span><\/p>\n

Another solution involves a comprehensive rethink of the data centre industry. To reduce their impact on electricity demand, some suggest focusing on “edge data centres,” smaller and distributed processing centres that can be built near the data generation sites. The enormous electricity consumption by data centres is a significant problem that certainly cannot be solved by abandoning all AI research projects. In fact, AI development can be extremely useful in the fight against climate change, as demonstrated by weather prediction models or intelligent energy flow management systems. What is really needed is to accompany the race to artificial intelligence with an equally robust development of clean energy sources and energy efficiency solutions. Only in this way can the digital and ecological transitions run side by side again.<\/span><\/p>\n

Bio<\/b><\/em><\/p>\n

Gianluca Brambilla is a journalist for Open. He writes about the environment, climate, and energy.<\/span><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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