펜실베이니아대 와튼스쿨 제시카 워처 교수의 분석에 따르면, 하이퍼스케일러들의 AI 데이터센터 투자는 2027년까지 약 1.1조 달러에 달하며, 2030년까지 손익분기점에 도달하려면 생산성을 2.7배 끌어올려야 합니다. 올해 AI 매출은 1,500억~2,000억 달러에 불과해 투자 대비 수익 격차가 크며, 실패 시 '역사상 최대 자본 오배분'이 될 위험이 있습니다.
번역된 본문
펜실베이니아대 와튼스쿨의 재무 교수인 제시카 워처(Jessica Wachter)가 향후 수년간 AI가 경제에 미칠 영향을 평가하려 했을 때, 그녀는 수많은 비즈니스 및 기술적 불확실성에 직면했다. 그래서 그녀는 논란의 여지가 없는, 그녀가 말하는 '주목할 만한 사실'에서 출발했다: 소위 하이퍼스케일러(hyperscaler)라 불리는 소수의 기업들이 AI 데이터센터 구축에 막대한 자금을 투자하고 있다는 것이다. AI 모델이 얼마나 유용하고 광범위하게 배포될지 예측하는 대신, 그녀는 단순히 지출이 그녀와 공동 연구자의 추산에 따라 약 1.1조 달러에 도달할 2027년까지의 지출을 정당화하려면 하이퍼스케일러들의 수익이 얼마나 빠르게 성장해야 하는지 물었다. 이는 오늘날 역사적인 AI 인프라 구축 붐을 이해하기 위한 절제된 회계적 접근법이다. 그 결과는 놀라운데: 자본 비용과 15% 수익률, 자산 감가상각을 고려하면 AI 기업들은 2030년까지 손익분기점을 맞추려면 자체 생산성을 2.7배 향상시켜야 한다. 워처는 이것이 불가능한 것은 아니라고 말한다. 그 결과는 1990년대 중반부터 약 10년간 이어진 미국 IT 붐 시기와 비슷한 경제 성장으로 이어질 것이다. 하지만 그녀는 2030년까지 그런 성장이 실현되려면 '많은 성장을 몇 년 안에 압축해야 한다'고 지적한다. 그렇다면 하이퍼스케일러들이 이러한 수익 목표를 달성하지 못하면 어떻게 될까? 전직 SEC 수석 경제학자이자 경제·리스크 분석국장을 지낸 워처는 '이자 지급에 차질이 생기고 파산 위험이 커질 것'이라고 말한다. 그녀와 공동 저자는 연구 논문에서 생산성 붐이 '실현되지 못하면 현재의 인프라 구축은 역사상 최대의 자본 오배분이 될 것'이라고 결론짓는다. AI 인프라에 대한 현재의 대규모 투자에 거대한 위험이 따른다는 사실은 초지능이 아니어도 알 수 있다. 하이퍼스케일러들은 올해 전국 곳곳에 거대한 데이터센터를 짓는 데 약 7,500억 달러를 지출할 것이다. 그리고 이 지출 행진은 둔화 조짐을 보이지 않고 있다. 일부 전망에 따르면, 하이퍼스케일러 기업들—알파벳(Alphabet), 마이크로소프트(Microsoft), 아마존(Amazon), 메타(Meta), 그리고 OpenAI와 파트너십을 맺은 오라클(Oracle)—의 총 AI 자본 투자는 향후 4년간 5조 달러를 넘을 수 있다. 이는 역사상 어느 산업의 자본 투자보다도 가장 큰 규모 중 하나다. 그러나 주의를 기울이는 사람이라면 누구나 알 수 있는 문제가 있다. 바이든 행정부 시절 SEC 의장을 지냈고 현재 MIT 슬로언스쿨 교수로 재직 중인 게리 겐슬러(Gary Gensler)에 따르면, 하이퍼스케일러들이 수조 달러를 지출할 계획인 반면 올해 총 AI 매출은 약 1,500억~2,000억 달러 수준이다. '문제는 지출에 상응하는 매출이 아직 없다는 것이다. 이것은 사실'이라고 그는 말한다. '그리고 문제는 이것이 미래에 회수될 투자냐는 것이다.' 이 천문학적인 질문을 두고 거대 AI 기업들의 재무 건전성과 미국 경제 전체—투자 규모는 곧 GDP의 약 3%에 육박할 수 있다—가 걸려 있다. 그 답은 초고가 데이터센터 자체의 운명을 결정할 수도 있다. 이 수십억 달러짜리 거대 시설들이 앞으로 얼마나 수익성 있고 유용할지는 아무도 확실히 모른다. AI 모델이 지난 몇 년간 눈부신 발전을 이뤘음에도, 우리에게 얼마나의 컴퓨팅 용량이 필요할지는 아무도 모르는 일이다. 기술이 더 효율적이 되어 원시적인 컴퓨팅 파워에 대한 의존도가 줄어들 수도 있다. 아니면 AI 제품에 대한 수요가 둔화되거나, 고객들이 더 저렴한 모델로 눈을 돌릴 수도 있다. 올해 들어 이런 AI 기업들이 더 많은 데이터센터를 짓기 위해 막대한 차입을 시작하면서 투자자와 경제 모두에 대한 위험은 더욱 커졌다. 하이퍼스케일러 그룹의 프리캐시플로우(영업현금흐름에서 자본지출을 뺀 값)는 곧 마이너스로 떨어질 것으로 예상된다. 막대한 현금을 창출하고 축적하는 것으로 유명한 알파벳조차 최근 분기에 거의 1,200억 달러에 달하는 인상적인 매출이 AI 인프라 지출에 잡아먹혔다고 보고했다.
When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called hyperscalers are investing huge amounts of money to build AI data centers. Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when—she and her collaborator estimate—expenditures will reach nearly $1.1 trillion. It's a no-nonsense accounting approach to making sense of today’s historical AI buildout. The results are eye-opening : The AI companies will need to increase their own productivity by a factor of 2.7 to break even by 2030, accounting for the cost of capital and a 15% return, and depreciation of the assets. Not impossible, says Wachter. The result would lead to the kind of economic growth that we saw during the US IT boom over a period of about 10 years starting in the mid-1990s. But, she says, for it to happen by 2030 “that’s a lot of growth compressed into a few years.” And if the hyperscalers cannot meet such profit goals? “Then they will fall behind on their interest payments, and that risks bankruptcy,” says Wachter, who was previously the SEC’s chief economist and director of its division of economic and risk analysis. If a productivity boom “fails to materialize,” she and her coauthor conclude in their research paper, “the current buildout will be the largest misallocation of capital in history.” It doesn’t take superintelligence to realize that today’s large investments in the infrastructure for artificial intelligence come with huge risks. The hyperscalers will spend about $750 billion this year, building massive data centers scattered across the country. And the spending spree shows no signs of slowing. According to some projections, total AI capital investments from the hyperscaler companies—Alphabet, Microsoft, Amazon, Meta, and Oracle (which partners with OpenAI)—could be more than $5 trillion over the next four years . It’s one of the largest capital investments by any industry in history. But there’s a problem that’s obvious to anyone paying attention. While the hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion this year, says Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT’s Sloan School. “The challenge is that the spending does not have commensurate revenues yet . That’s a fact,” he says. “And then the question is, is that an investment that will be paid off in the future?” At stake in that trillion-dollar question is the financial health of the giant AI companies and the overall US economy—the investments could soon balloon to around 3% of GDP. The answer could also determine the fate of the hugely expensive data centers themselves. No one really knows how profitable and useful these multibillion-dollar behemoths will be down the road. Though AI models have made dazzling progress over the last few years, it’s anyone’s guess how much compute capacity we will need. The technology could become more efficient and therefore less dependent on raw computational power. Or demand for AI products could slow, or customers could turn to cheaper models. The risks, both to investors and to the economy, have become even greater this year, as these AI companies have begun borrowing large amounts of money to build more and more data centers. Free cash flow—operating cash flow minus capital expenditures—is expected to soon dip into negative territory for the group. Even Alphabet, known for generating and hoarding huge amounts of cash, reports in the latest quarter that its impressive revenues of nearly $120 billion were devoured by AI infrastructure spending, leaving it with a free cash deficit of some $5.9 billion—its first shortfall since Google went public in 2004. In the near term, it’s not a big financial worry for most of the companies. They make a lot of money and have very deep pockets. But debt is expensive, and some investors are losing patience . If future demand for the data centers’ computation power drops, the companies will still be on the hook to pay back the borrowed money. What’s more, the risks are spreading to the rest of the economy as the loans get passed along via various financial mechanisms. It won’t be enough to simply cover the enormous price tags of the new data centers. Hyperscalers will also have to pay for the rising costs of capital as they borrow more money. They will need returns that are impressive enough to justify all their spending to investors and creditors. And to add to those concerns, they will have to make up for the depreciation of billions of dollars in chips housed within the facilities—a ticking time bomb buried in the investments. Performance of the expensive GPU chips at the core of the data centers—such compute electronics represent some 60% of costs—is roughly doubling every two years or so. The pace of progress helps explain the increasing wizardry of the AI models, but it comes with a cost. Owners of AI data centers that come online this year and next will need to spend billions more on the next generation of chips by the end of the decade if they want to stay competitive. Without the investments, says Mihir Kshirsagar at Princeton’s Center for Information Technology Policy, the data centers risk becoming “hulks,” stranded assets “scattered all over the place.” To put it bluntly: The AI companies need to start making a lot more money. And they need to do it fast. But juicing their earnings alone still won’t be enough to sustain their data-center investments for the long term. Productivity is everything At some point, AI is also going to have to create broad economic growth to justify continuing the hyperscalers’ spending spree. Sloan’s Gensler describes today’s large investments into AI infrastructure as “a parlay bet by the capital markets and the economy.” That means success will require winning three related but independent wagers: Hyperscalers must generate massive revenues, AI must boost widespread economic growth, and both must happen while the powerful but expensive so-called frontier models that rely on the data centers fend off cheaper versions, which many businesses might find good enough. What makes this so tricky is that each wager depends on the other two but also poses its own challenges. If the hyperscalers continue to spend huge amounts of money on data centers into the next decade, revenues will need to skyrocket into the trillions. Stijn Van Nieuwerburgh, a finance professor at Columbia Business School, bases his estimates on a scenario in which about 183 gigawatts of planned AI compute capacity is built between 2025 and 2032; he calculates that each gigawatt costs about $41 billion. Assuming a 10% return—the minimum that would be acceptable to most investors—“required” annual revenues will be roughly $3.7 trillion by 2032, he says. Others get a similar number . Winning the second part of the bet—productivity growth across the economy—will be crucial to achieving such numbers. For a few years, AI companies could likely boost their revenues by simply selling subscriptions and tokens to all the businesses clamoring to get into AI. But eventually—and this might be happening already—those paying customers will need to justify their expenses by seeing bottom-line benefits from the technology. AI will need to fulfill its promise of making workers more productive and making businesses more efficient and profitable while expanding their products and services. In economic jargon, that means customers will need to see productivity growth. Taken together, these results will mean the country is prospering and growing. “If you don’t