What’s at stake in AI’s trillion-dollar gamble
What’s at stake in AI’s trillion-dollar gamble
人工智能万亿赌局的利害关系
EXECUTIVE SUMMARY 执行摘要
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. 当宾夕法尼亚大学沃顿商学院的金融学教授杰西卡·瓦赫特（Jessica Wachter）想要评估人工智能在未来几年对经济的影响时，她面临着一长串商业和技术上的不确定性。因此，她从一个她称之为“显著事实”且毋庸置疑的现象入手：少数所谓的“超大规模云服务商”（hyperscalers）正在投入巨资建设人工智能数据中心。
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. 她没有试图预测人工智能模型会有多大用处或普及程度如何，而是直接计算这些超大规模云服务商的盈利需要以多快的速度增长，才能证明其截至2027年的支出是合理的——她与合作者估计，届时支出将达到近1.1万亿美元。这是一种务实的会计方法，旨在理清当今史无前例的人工智能基础设施建设。
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.” 结果令人震惊：这些人工智能公司需要将其自身生产率提高2.7倍，才能在2030年实现盈亏平衡（计入资本成本、15%的回报率以及资产折旧）。瓦赫特表示，这并非不可能。其结果将带来类似于20世纪90年代中期开始、持续约10年的美国IT繁荣时期的经济增长。但她指出，要在2030年之前实现这一目标，“意味着在短短几年内压缩了大量的增长”。
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.” 如果这些超大规模云服务商无法达到这样的利润目标呢？“那么他们将无法按时支付利息，从而面临破产风险，”曾任美国证券交易委员会（SEC）首席经济学家及经济与风险分析部门主管的瓦赫特说道。她在研究论文中与合著者总结道，如果生产力繁荣“未能实现”，“当前的建设将成为历史上最大规模的资本错配。”
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. 无需超人工智能也能意识到，当今对人工智能基础设施的大规模投资伴随着巨大的风险。这些超大规模云服务商今年将花费约7500亿美元，在全国各地建设庞大的数据中心。而且这种疯狂的支出丝毫没有放缓的迹象。据一些预测，未来四年，Alphabet、微软、亚马逊、Meta和甲骨文（与OpenAI合作）等超大规模云服务商在人工智能上的总资本投资可能超过5万亿美元。这是历史上任何行业最大规模的资本投资之一。
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?” 但任何留心观察的人都会发现一个显而易见的问题。曾在拜登政府时期执掌SEC、现任麻省理工学院斯隆管理学院教授的加里·根斯勒（Gary Gensler）表示，虽然这些巨头计划投入数万亿美元，但今年人工智能的总收入预计仅在1500亿至2000亿美元之间。“挑战在于，这些支出尚未产生相应的收入。这是一个事实，”他说，“那么问题就在于，这笔投资在未来能否获得回报？”
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. 这个万亿美元问题的利害关系在于这些人工智能巨头的财务健康状况以及整个美国经济——这些投资很快可能膨胀至GDP的3%左右。答案还可能决定这些极其昂贵的数据中心本身的命运。没有人真正知道这些耗资数十亿美元的庞然大物在未来会有多大的盈利能力和实用价值。尽管人工智能模型在过去几年取得了令人眼花缭乱的进步，但我们到底需要多少计算能力仍是未知数。技术可能会变得更高效，从而减少对原始计算能力的依赖；或者对人工智能产品的需求可能会放缓，亦或是客户可能会转向更便宜的模型。
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. 今年，投资者和经济面临的风险变得更大，因为这些人工智能公司已经开始借入大量资金来建设更多的数据中心。该行业的自由现金流（经营现金流减去资本支出）预计很快将陷入负值。即使是以产生并囤积巨额现金而闻名的Alphabet，在最新季度报告中也显示，其近1200亿美元的惊人收入被人工智能基础设施支出所吞噬，导致其自由现金流出现约59亿美元的赤字——这是自谷歌2004年上市以来的首次亏损。
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. 短期内，这对大多数公司来说并不是什么大的财务担忧。他们赚了很多钱，财力雄厚。但债务成本高昂，一些投资者正在失去耐心。如果未来对数据中心计算能力的需求下降，这些公司仍需偿还借款。更重要的是，随着贷款通过各种金融机制传递，风险正在蔓延到经济的其他领域。
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. 仅仅支付新数据中心的巨额账单是不够的。随着借款增加，这些超大规模云服务商还必须承担不断上升的资本成本。他们需要获得足够可观的回报，以向投资者和债权人证明其所有支出的合理性。此外，他们还必须弥补设施内价值数十亿美元芯片的折旧——这是埋在这些投资中的一颗定时炸弹。
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.” 作为数据中心核心的昂贵GPU芯片（此类计算电子设备约占成本的60%）的性能大约每两年翻一番。这种进步速度有助于解释人工智能模型日益强大的魔力，但代价高昂。如果今年和明年上线的数据中心所有者想要保持竞争力，到本十年末，他们将需要再花费数十亿美元购买下一代芯片。普林斯顿大学信息技术政策中心的米希尔·克什萨加尔（Mihir Kshirsagar）表示，如果没有这些投资，数据中心可能会变成“空壳”，成为“散落在各处”的搁浅资产。
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 econom 直言不讳地说：这些人工智能公司需要开始赚更多的钱，而且必须快。但仅靠榨取利润还不足以长期维持其数据中心投资。生产力就是一切。在某个阶段，人工智能还必须创造广泛的经济效益。