마이크론은 씨티의 목표가 175달러 언급으로 3.05% 상승(2024.08.15)

2024.08.15

미국 시간 8월14일 마이크론은 씨티의 목표가 175달러 언급으로 3.05% 상승한 100.41달러로 마감.

향후 75% 상승 여력. 현재 시총은 1113.3억달러로 한화 약 153조원(1달러=1350원 계산) 정도다.

씨티의 목표가 175달러로 계산한 마이크론의 시총은 267.7조원에 달한다.

 

사업 구조가 비슷한 SK하이닉스의 현재 시총은 135조원(현재가 186,700원)으로 마이크론의 목표가 175달러에 해당하는 하이닉스의 시총 267.7조원은 현재 하이닉스 시총 135조원의 약 2배에 해당하고, 이 시총에 해당하는 하이닉스의 주가는 

약 373,000원에 해당한다.

 

 

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Micron Technology Inc (MU): The Best Data Center Stock According to Wall Street Analysts? (yahoo.com)

 

Micron Technology Inc (MU): The Best Data Center Stock According to Wall Street Analysts?

마이크론 테크놀러지는 월스트리트 애널리스트들이 선정한 최고의 데이터 센터 주식인가요?

 

We recently compiled a list of the 15 Best Data Center Stocks To Buy According to Jefferies, Citi and Wall Street Analysts. In this article, we are going to take a look at where Micron Technology Inc (NASDAQ:MU) stands against the other data center stocks.

 

최근 우리는 씨티의 Jefferies와 월스트리트 애널리스트들이 '매수'로 선정한 15개의 최고의 데이터 센터 주식을 정리했습니다. 이 기사에서는 Micron Technology Inc (NASDAQ:MU)가 다른 데이터 센터 주식들 사이에서 어떤 위치에 있는지 살펴보겠습니다.

 

The boom in the interest surrounding artificial intelligence has not only affected semiconductor stocks, even though they're the biggest beneficiaries. While the chips that these companies make are indispensable for running AI workloads, they have to be housed somewhere, and this is where data centers come into play.

인공지능에 대한 관심이 급증하면서 반도체 주식이 가장 큰 수혜를 입고 있지만, 이러한 관심은 반도체 주식에만 국한되지 않습니다. AI 워크로드를 실행하기 위해 필수적인 반도체 칩들은 어디에선가 보관되어야 하며, 이때 데이터 센터가 중요한 역할을 합니다.

 

In fact, for the data center space, AI has only accelerated the growing demand this industry is seeing. Prior to AI GPUs and accelerators, enterprise computing chips made by the same companies that are now making AI chips were seeing hefty demand. This demand led to gaming GPU companies effectively being transformed into enterprise computing firms, with sectors such as the SaaS and cloud computing industries relying on these products. For some SaaS stocks, you should check out 10 Best SaaS Stocks To Buy Now.

 

사실, AI는 데이터 센터 분야에서 이 산업이 겪고 있는 수요 증가를 가속화시켰습니다. AI GPU와 가속기 이전에도, 현재 AI 칩을 만드는 회사들이 제조한 엔터프라이즈 컴퓨팅 칩들은 이미 높은 수요를 보였습니다. 이 수요는 게임 GPU 회사들이 실질적으로 엔터프라이즈 컴퓨팅 기업으로 변모하게 했으며, SaaS와 클라우드 컴퓨팅 산업 같은 분야가 이러한 제품들에 의존하게 되었습니다. 일부 SaaS 주식에 대해 알고 싶다면, 지금 사야 할 10개의 최고의 SaaS 주식 목록을 확인해보세요.

 

This pre AI demand for data centers is visible in statistics too. Data from Jefferies shows that the demand growth for data centers has jumped by between 10% to 20% for the last 15 years, or before AI GPUs hit the market. As expected, AI has accelerated this demand, with the demand for data center space outpacing 30% in most markets for the past two years. This growth in real estate requirements also means that while the computing industry might be able to scale up by providing products like networking gear and cables, tertiary industries like energy generation will take some time to catch up.

 

AI 이전의 데이터 센터 수요 증가도 통계에서 확인할 수 있습니다. Jefferies의 데이터에 따르면, AI GPU가 시장에 나오기 전인 지난 15년 동안 데이터 센터 수요 증가율이 10%에서 20% 사이로 상승했습니다. 예상대로 AI는 이 수요를 가속화시켰으며, 지난 2년 동안 대부분의 시장에서 데이터 센터 공간에 대한 수요가 30%를 초과했습니다. 이러한 부동산 요구의 증가는 컴퓨팅 산업이 네트워크 장비와 케이블과 같은 제품을 제공함으로써 확장할 수 있을지라도, 에너지 생성과 같은 3차 산업은 따라잡는 데 시간이 걸릴 것임을 의미합니다.

 

If you're a believer in AI, then the optimistic line of thought would suggest that these tertiary firms will only grow in the future as they scale up their operations to meet the growth in AI data centers. After all, data from Goldman shows that a query made to ChatGPT consumes ten times as much energy as a Google search query - understandable since ChatGPT is parsing through data and drawing insights to generate a response. By 2030, AI is expected to grow data center power demand by as much as 160%, as data centers potentially account for 4% of global energy consumption and Europe in particular needs more than $1 trillion to power its AI grid.

 

AI 이전의 데이터 센터 수요 증가도 통계에서 확인할 수 있습니다. Jefferies의 데이터에 따르면, AI GPU가 시장에 나오기 전인 지난 15년 동안 데이터 센터 수요 증가율이 10%에서 20% 사이로 상승했습니다. 예상대로 AI는 이 수요를 가속화시켰으며, 지난 2년 동안 대부분의 시장에서 데이터 센터 공간에 대한 수요가 30%를 초과했습니다. 이러한 부동산 요구의 증가는 컴퓨팅 산업이 네트워크 장비와 케이블과 같은 제품을 제공함으로써 확장할 수 있을지라도, 에너지 생성과 같은 3차 산업은 따라잡는 데 시간이 걸릴 것임을 의미합니다.

 

If you're a believer in AI, then the optimistic line of thought would suggest that these tertiary firms will only grow in the future as they scale up their operations to meet the growth in AI data centers. After all, data from Goldman shows that a query made to ChatGPT consumes ten times as much energy as a Google search query - understandable since ChatGPT is parsing through data and drawing insights to generate a response. By 2030, AI is expected to grow data center power demand by as much as 160%, as data centers potentially account for 4% of global energy consumption and Europe in particular needs more than $1 trillion to power its AI grid.

 

만약 당신이 AI의 가능성을 믿는다면, 이러한 3차 산업들이 AI 데이터 센터의 성장을 따라잡기 위해 운영을 확장하면서 앞으로 더욱 성장할 것이라는 낙관적인 전망을 가질 수 있습니다. 실제로 Goldman의 데이터에 따르면, ChatGPT에 의해 수행되는 쿼리는 Google 검색 쿼리보다 10배 더 많은 에너지를 소비한다고 합니다. 이는 ChatGPT가 데이터를 분석하고 통찰을 도출하여 응답을 생성하기 때문에 이해할 수 있는 부분입니다. 2030년까지 AI는 데이터 센터의 전력 수요를 최대 160%까지 증가시킬 것으로 예상되며, 데이터 센터가 전 세계 에너지 소비의 4%를 차지할 수 있으며, 특히 유럽은 AI 그리드를 구동하기 위해 1조 달러 이상의 비용이 필요할 것입니다.

 

Naturally, since the US is responsible for ushering in AI, AI energy consumption in America is higher than that in other countries. According to the Boston Consulting Group, by 2030, AI power consumption will account for 16% of all of America's energy use. It is expected to grow by 15% to 20% annually and touch as much as 130 GW, or the amount of electricity that's used by 100 million homes.

미국이 AI를 선도하고 있는 만큼, 미국 내 AI 에너지 소비는 다른 국가들보다 더 높습니다. 보스턴 컨설팅 그룹에 따르면, 2030년까지 AI 전력 소비는 미국 전체 에너지 사용량의 16%를 차지할 것으로 예상됩니다. AI 전력 소비는 매년 15%에서 20%씩 증가하여 최대 130GW에 이를 것으로 보이는데, 이는 1억 가구가 사용하는 전력량과 맞먹는 수준입니다.

 

AI chip companies are also aware of these trends, with the latest AI chips promising to improve energy efficiency by 25x. Improving AI performance at the semiconductor level is important especially since some areas where data centers are growing are being forced to turn to coal power to reduce the power gap.

 

AI 칩 제조사들도 이러한 트렌드를 인식하고 있으며, 최신 AI 칩은 에너지 효율성을 25배 개선할 것을 약속하고 있습니다. 반도체 수준에서 AI 성능을 개선하는 것은 중요하며, 특히 데이터 센터가 성장하고 있는 일부 지역에서는 전력 부족을 줄이기 위해 석탄 발전에 의존해야 하는 상황이 발생하고 있기 때문에 더욱 그렇습니다.

 

Nowhere is this clearer than in Northern Virginia, where data centers process 70% of the world's internet traffic. With more than 300 data centers that churn out more than $700 million in taxes annually, the region's computing centers are expected to require a whopping 11,000 megawatts of electricity annually by 2035 according to estimates by the local regulator. This demand has also spurred a $5.2 billion effort to lay down new transmission lines and keep coal power plants open for longer than initially planned.

 

이 점이 가장 분명하게 드러나는 곳은 전 세계 인터넷 트래픽의 70%를 처리하는 버지니아 북부입니다. 연간 7억 달러 이상의 세금을 창출하는 300개 이상의 데이터 센터가 있는 이 지역의 컴퓨팅 센터들은 2035년까지 연간 11,000메가와트의 전력을 필요로 할 것으로 예상되며, 이는 지역 규제 기관의 추정에 따른 것입니다. 이 수요는 또한 새로운 송전선 구축과 초기 계획보다 더 오랜 기간 동안 석탄 발전소를 운영하기 위한 52억 달러 규모의 노력을 촉진했습니다.

 

Not only does AI need real estate and power, but it also needs water. Since energy can neither be created nor destroyed, all the megawatts of power that AI chips need have to go somewhere. For the chips, it is dissipated in the form of heat, and cooling this requires copious amounts of water. Estimates show that not only does training GPT-3 evaporate a whopping 700,000 liters of drinkable water, but global AI demand by 2027 could end up using anywhere between 4.2 billion to 6.6 billion cubic meters of water.

 

AI는 부동산과 전력뿐만 아니라 물도 필요로 합니다. 에너지는 생성되거나 파괴될 수 없기 때문에, AI 칩이 필요로 하는 수 메가와트의 전력은 어디론가 흘러가야 합니다. 칩의 경우, 이 에너지는 열의 형태로 방출되며, 이를 냉각하기 위해서는 막대한 양의 물이 필요합니다. 추정에 따르면, GPT-3를 훈련시키는 것만으로도 70만 리터의 음용수가 증발하며, 2027년까지 글로벌 AI 수요로 인해 42억에서 66억 입방미터의 물이 사용될 수 있다고 합니다.

 

Coming back to real estate, it might be the easiest way for the AI savvy investor to cash in on the world's thirst for computing. Citi believes that the "development and construction of hyper-scale data center capacity will grow meaningfully over the next 7 years," as global industrial giants expand into the data center space. Not only are industrial firms actually converting their warehouses into data centers, but Citi adds that the associated power demand for these computing facilities will grow between the mid teens annually until 2030.

 

부동산으로 돌아와서, AI에 능통한 투자자들이 세계의 컴퓨팅 수요에 따라 수익을 올릴 수 있는 가장 쉬운 방법이 될 수 있습니다. Citi는 글로벌 산업 거대 기업들이 데이터 센터 분야로 확장함에 따라 "하이퍼스케일 데이터 센터 용량의 개발 및 건설이 향후 7년 동안 크게 성장할 것"이라고 믿고 있습니다. 산업 기업들이 실제로 창고를 데이터 센터로 전환하고 있을 뿐만 아니라, Citi는 이러한 컴퓨팅 시설의 관련 전력 수요가 2030년까지 매년 중간 두 자릿수 비율로 증가할 것이라고 덧붙였습니다.

 

So, as these Wall Street firms lay out a maze of industries that will profit from AI, we decided to look at the top data center stocks to buy according to analysts.

 

따라서 월스트리트 기업들이 AI로부터 이익을 얻을 다양한 산업을 제시함에 따라, 우리는 애널리스트들이 추천하는 최고의 데이터 센터 주식을 살펴보기로 했습니다.

 

Our Methodology  우리의 방법론

 

To make our list of the top data center stocks to buy, we ranked the US listed holdings of Global X's data center ETF and the stocks chosen by Jefferies and Citi by the average analyst share price target percentage upside and picked out the stocks with the highest upside.

최고의 데이터 센터 주식을 선정하기 위해, 우리는 Global X의 데이터 센터 ETF에 포함된 미국 상장 보유 주식과 Jefferies 및 Citi가 선택한 주식을 평균 애널리스트 목표 주가 대비 상승률에 따라 순위를 매겼으며, 가장 높은 상승 잠재력을 가진 주식들을 선정했습니다.

 

For these stocks, we also mentioned the number of hedge funds that had bought the shares in Q1 2024. Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. Our quarterly newsletter’s strategy selects 14 small-cap and large-cap stocks every quarter and has returned 275% since May 2014, beating its benchmark by 150 percentage points (see more details here).

 

이러한 주식들에 대해서는 2024년 1분기 동안 해당 주식을 매수한 헤지 펀드의 수를 언급했습니다. 왜 우리가 헤지 펀드들이 대거 매수한 주식에 관심을 가지는 걸까요? 이유는 간단합니다. 우리의 연구에 따르면, 최고의 헤지 펀드들이 선택한 주요 주식을 모방함으로써 시장을 능가할 수 있다는 것을 보여주었습니다. 우리의 분기별 뉴스레터 전략은 매 분기마다 14개의 소형주와 대형주를 선정하며, 2014년 5월 이후 275%의 수익률을 기록하여 벤치마크를 150%포인트 초과했습니다 (자세한 내용은 여기를 참조하세요).