AI 및 데이터센터의 에너지 수요

### 디지털 라이프의 에너지 수요: 친환경 솔루션에 대한 준비가 되어 있을까요?
* 📊 은퇴한 학교 교사인 Kathy는 **친환경 에너지가 에너지 시스템에 대한 AI, 암호화폐, 클라우드 스토리지의 증가하는 수요**를 충족시킬 수 있는지에 대한 중요한 질문을 제기했습니다.
* 💻 디지털 서비스에 대한 의존도가 높아지면서 특히 **수천 대의 서버를 지속적으로 운영하는 데이터 센터**에서 상당한 전력을 필요로 합니다.
* 🏢 2022년 데이터 센터, AI, 암호화폐는 전 세계 전력 수요의 약 2%를 차지했으며, **2026년에는 스웨덴과 같은 국가 전체의 에너지 소비량과 맞먹는 두 배로 증가**할 것으로 예상됩니다.
* ⚡ GPT-3와 같은 대형 AI 모델을 학습시키는 데는 **약 1300메가와트시의 전력이 소모**되는데, 이는 **130가구가 1년에 사용하는 전력량**과 맞먹는 양입니다.
* 🌍 2030년까지 탄소중립을 달성하겠다는 구글과 마이크로소프트 같은 거대 기술 기업의 약속에도 불구하고 **데이터센터 에너지 소비는 크게 증가**하여 이러한 지속가능성 목표를 달성하는 데 어려움을 겪고 있습니다.
* 🌬 데이터센터의 급속한 건설은 재생 에너지 인프라의 발전 속도를 앞지르며 **백업 전력을 위한 화석 연료 의존도**를 높이고 있습니다.
* 🔍 전문가들은 **기술 기업의 투명성을 높이고 데이터센터의 에너지 효율성을 개선**하는 것이 지속 가능한 디지털 성장을 위한 중요한 단계라고 말합니다.
* 🌱 궁극적으로 우리는 디지털 생활의 급증하는 수요를 충족하는 동시에 모두를 위한 지속가능성을 보장하기 위해 **재생 에너지원을 시급히 확대**해야 합니다.

데이터 센터의 에너지 수요 증가는 AI와 클라우드 스토리지에 대한 의존도가 높아지는 가운데 기후 목표를 위협하고 있습니다.

I spent some time recently reading through a big spreadsheet of questions submitted by Vox's audience members. And one of them caught my eye. It was from Kathy, a retired schoolteacher in New York City. What is the question that you wanted us to answer? So the question is, can green energy even begin to handle the increased demands that I. And, crypto and cloud storage are going to put on our energy system. It's a good question. I've done some reporting on AI, but I've never thought much of the climate impact of all the AI products we're increasingly using, or all of our digital belongings, like photos and documents and emails getting stockpiled in servers around the world. They need a lot of electricity, and the electricity has to come from somewhere. This is all happening while the climate crisis demands we use less energy, not invent new ways to use more of it. I think our climate goals already feel pretty impossible to me, but now it's almost like we haven't changed the goalposts.

We've changed the entire game. So let's get to the bottom of this. Within Kathy's big question is a more basic one about how much electricity our digital lives require. At first, I was thrown off that Kathy mentioned things like cloud storage and AI and cryptocurrency in one category. But then I realized that their electricity demands happen at the same place: data centers. Ultimately, you're talking about machines loaded up in large facilities that generate computations. And they need power. A lot of it. They need water. They need space. I spoke to Alex de Vries. He runs a research site called Digiconomist, where he's dug into this exact topic. Data centers are massive, often windowless warehouses that house thousands of servers that run virtually nonstop. Some of the bigger data centers are as big as four football fields and use as much electricity at any given time as 80,000 households. There are more than 8,000 data centers around the world, and the US has more than any other country.

In 2022, data centers, artificial intelligence, and cryptocurrencies made up about 2% of total global electricity demand. But by 2026, that number is expected to double, which is like adding the amount of electricity used by the entire country of Sweden. I'll explain why in a minute. All right, support for this video comes from Klaviyo. Klaviyo works with businesses to turn their data into meaningful connections with their customers through AI-powered email, text messages, and more. According to Klaviyo, over 150,000 brands trust their data and marketing platform to build smarter digital relationships with their customers during the holiday season and beyond. Klaviyo has no editorial influence over our work, but they make videos like this possible. Learn more at the link below. That big jump from 2022 to 2026 is thanks to rising cloud storage and cryptocurrency electricity demands, but it's also because of the AI boom. We know AI requires a ton of computational power,

but it turns out that really difficult question to answer. AI is a huge umbrella term that includes everything from basic statistical models that detect patterns in data to generative AI that creates text and images and videos. That's the most computationally intensive kind. The thing is, the handful of private tech companies that dominate the, I feel, don't really disclose how much of their energy use is dedicated to AI. Specifically. If you look at Google's latest environmental report, it clearly states they absolutely don't want to make a distinction between regular workloads and AI-specific workloads. In these companies, AI models are mostly closed source, meaning no one knows exactly how they are built. This has left some researchers to try to piece it together on their own. Researchers looked at an open-source large language model called Bloom that has roughly the same amount of parameters as GPT-3 and found that training something like GPT-3 required almost 1300 megawatt hours of electricity.

About as much power as consumed by 130 homes in the US for one year. Today, large language models like GPT-4 have hundreds of billions of parameters, if not a trillion. And researchers say that the computational power required to train these models is expected to double every nine months. So far, it has mostly been large language models driving the AI energy boom. Of course, that could change going forward. Now we see AI on the rise for image generation and also specifically video generation. So far, we talked about training a large language model. Researchers also looked at energy use from people actually using it. It's been estimated, by myself and others, that a single GPT interaction would take like three watt hours, which is comparable to running a low-lumen LED bulb for one hour or so on itself. It doesn't sound like a whole lot, but of course, hey, it's the volume that matters, the system times more than the standard Google search. And of course, if you're talking about millions

or billions of interactions, the numbers start to stack up quickly. Alex took another research approach by looking at the hardware used for eye training and use. Over 95% of the AI industry uses servers made by the company Nvidia. They could sell 1.5 million of their servers by 2027. He multiplied that by the information Nvidia publicizes about each of their servers' energy demand. He found that data centers devoted to AI alone could consume around 100 terawatt hours of electricity per year, or about the same as his home country of the Netherlands. There's a big part of Cathy's question I haven't gotten to yet. From the world's data centers? The good news is that using green energy is the stated goal of a lot of these companies. Both Google and Microsoft have made pledges to be net zero by 2030, but there are signs that AI is disrupting those plans. That's because solar and wind energy can't produce electricity all of the time,

and these data centers need to be running all of the time. In most cases, they will just have a backup connection to the power grid, which will have fossil fuels on it. It's not just that data centers are being built at a rate that renewable energy infrastructure can't keep up with. It can take a year to build a data center, but many more years to get a solar or wind farm on an electrical grid. Google's 2024 sustainability report showed that the company's emissions rose by 48% from 2019 to 2023, in large part due to its data center energy consumption, suggesting that integrating AI into their products could make reducing their emissions challenging. There's already evidence in the US that coal plants that were meant to close are staying open because of data centers' electricity demands, and that state utilities are building new natural gas plants for the same reason. But even if these tech companies can look good on their sustainability reports and get to net zero,

there's still a problem. The thing is that our renewable energy supply globally is limited. So if we attribute an increasing part of that to the data center industry, the consequence is that there's less renewables available for everything else. That probably will mean that on the whole, we will end up using more fossil fuels anyway. With all this context, the answer to Cathy's question is that for right now, we aren't prepared for renewable energy to meet the increasing demand of the world's data centers. About this? As users, it would be extremely difficult to opt out of backing up our data on the cloud or even refrain from using AI. Think AI is embedded in so many things that I'm not sure I will have the option to say I'm not using it, you know, I'm out. Researchers like Alex say the best place to start is to force more transparency from these tech companies. In the EU that I asked doesn't really for us to companies.

to disclose anything with regard to the environment. And that's the EU not even talking about the U.S. yet, which is lagging behind a bit on this matter. Some environmental organizations and local communities are calling for moratoriums on data centers. And some researchers have proposed the idea of an energy efficiency rating so companies and consumers can choose data centers that are the most sustainable. We could also hope that the servers and data centers will keep getting more energy efficient. But more than anything, this issue emphasizes how desperately we need to be scaling up renewable energy and fast, not only to meet the ever-increasing data center demands, but so there's plenty of renewable energy to go around. If you liked this video, you'll love Vox's new podcast. Explain it to me. I'm the host, JQ, and every week I call up a listener with a question, get them some answers, and we have some fun along the way. You can find a link to the podcast in the description.

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디지털 요구가 에너지 시스템에 미치는 영향

은퇴한 학교 교사인 Kathy는 친환경 에너지가 암호화폐, 클라우드 스토리지, AI의 증가하는 수요를 충족할 수 있을지 의문을 품었습니다.

이러한 디지털 서비스는 상당한 양의 전기, 물, 공간을 필요로 하는 수천 대의 서버를 수용하는 대규모 시설인 데이터센터에 의존합니다.

미국은 전 세계에서 가장 많은 데이터 센터를 보유하고 있으며, 일부 시설은 8만 가구가 한꺼번에 사용할 수 있는 양의 전기를 사용합니다.

데이터센터와 AI가 전 세계 전력 수요에 미치는 영향력 확대

데이터 센터, AI, 암호화폐는 현재 전 세계 전력 수요의 2%를 차지하고 있으며, 2026년에는 그 비중이 두 배로 증가할 것으로 예상됩니다.

이러한 증가는 클라우드 스토리지, 암호화폐 수요의 증가, 특히 계산 집약적인 제너레이티브 AI 모델로 인한 AI 붐에 따른 것입니다.

Google과 같은 기술 기업은 AI 관련 에너지 사용량을 공개하지 않기 때문에 연구원들은 GPT-3와 같은 모델을 훈련하는 데 약 1300메가와트시의 전력이 필요하다고 추정하고 있습니다.

GPT-4와 같은 대규모 언어 모델은 9개월마다 계산 능력이 두 배로 증가할 것으로 예상되는 등 상당한 AI 에너지 붐을 일으키고 있습니다.

단일 GPT 상호 작용은 약 3와트시를 소비하지만, 상호 작용의 양은 빠르게 증가하여 상당한 에너지 사용으로 이어집니다.

AI 업계가 엔비디아 서버에 의존하면서 데이터센터가 연간 약 100테라와트시의 전력을 소비할 수 있으며, 구글과 마이크로소프트 같은 기업은 2030년까지 순 제로 에너지를 목표로 하고 있지만 AI의 수요로 인해 이러한 계획이 차질을 빚고 있습니다.

데이터센터가 재생 에너지에 미치는 영향

데이터 센터는 재생 에너지 인프라의 발전 속도를 앞지르며 빠르게 성장하고 있습니다.

데이터 센터 에너지 소비로 인해 2019년부터 2023년까지 Google의 탄소 배출량은 48% 증가하여 화석 연료 사용에 미치는 영향에 대한 우려가 커지고 있습니다.

데이터 센터의 전력 수요 증가는 재생 에너지 공급에 부담을 주고 있으며, 잠재적으로 화석 연료에 대한 의존도를 높일 수 있습니다.

EU는 데이터센터가 환경에 미치는 영향을 해결하기 위한 조치를 취하고 있지만 미국은 뒤처지고 있습니다.

데이터센터에 대한 사용 유예와 지속 가능성 증진을 위한 에너지 효율 등급 제안이 요구되고 있습니다.

이 이슈는 데이터센터의 증가하는 수요를 충족하기 위해 재생 에너지를 확대해야 한다는 시급한 필요성을 강조합니다.