Can AI answer the $3 trillion question?

AI 能回答这 3 万亿美元的问题吗？

Three years ago, Sequoia partner David Cahn was one of the first people to do the math and put a number on the implications of Silicon Valley’s titanic spend on AI infrastructure. In 2023, he was reacting to Nvidia’s reported annual GPU revenue of $50 billion. Starting with that figure, and adding in the implied costs of operating the data centers and the margins for their operators, he deduced that $200 billion in revenue would be required to pay back the up-front investment. He took it as a challenge, asking entrepreneurs to come up with AI products and services to make use of, and generate revenue from, all that infrastructure. 三年前，红杉资本（Sequoia）合伙人 David Cahn 是最早通过计算，量化硅谷在人工智能基础设施上巨额支出影响的人之一。2023 年，他针对英伟达（Nvidia）公布的 500 亿美元年度 GPU 收入做出了回应。他以该数字为起点，加上运营数据中心的隐含成本以及运营商的利润率，推断出需要 2000 亿美元的收入才能收回前期投资。他将此视为一项挑战，呼吁创业者开发人工智能产品和服务，以利用这些基础设施并从中创造收入。

Fast-forward to today, adding up three years of hyperscaling, and Cahn’s got a new number on AI infrastructure spending for 2026: $1.5 trillion. All told, he calculates that the AI industry will have to earn $3 trillion to justify all those chips and other data center expenditures. And that’s probably an underestimate — the rising costs of memory and the increasing use of exotic or inference-specific chips will drive that number up. “Recently,” he writes, “the required revenue per GW of CapEx has sharply increased due to these bottleneck dynamics and rising costs of construction.” 快进到今天，经过三年的超大规模扩张，Cahn 对 2026 年人工智能基础设施支出的预测有了新数字：1.5 万亿美元。总而言之，他计算出人工智能行业必须赚取 3 万亿美元，才能证明所有这些芯片和其他数据中心支出的合理性。而且这可能还是低估了——内存成本的上升以及对特殊或推理专用芯片的使用增加，将推高这一数字。他写道：“最近，由于这些瓶颈动态和建筑成本的上升，每吉瓦（GW）资本支出所需的收入已大幅增加。”

On the other side of the ledger, Anthropic is thought to have hit $60 billion in ARR, while OpenAI reportedly earned $13 billion in 2025 (although in November 2025, it said it was at $20 billion ARR) and is presumably making more this year. But there’s clearly a large gap to be closed. Someone minding that gap is Torsten Slok, the chief economist at Apollo, the giant asset manager. In a recent note, he points out that the hyperscalers — Google, Meta, Microsoft, and Amazon — are all predicting massive accelerations in their free-cash flow in 2028. That is, they expect to see the payback from all those chips they bought. 在账目的另一端，据信 Anthropic 的年度经常性收入（ARR）已达到 600 亿美元，而 OpenAI 据报道在 2025 年赚取了 130 亿美元（尽管其在 2025 年 11 月称其 ARR 为 200 亿美元），且预计今年会赚得更多。但显然，仍有一个巨大的缺口需要填补。关注这一缺口的人是大型资产管理公司阿波罗（Apollo）的首席经济学家 Torsten Slok。他在最近的一份报告中指出，超大规模云服务商——谷歌、Meta、微软和亚马逊——都预测其 2028 年的自由现金流将大幅加速增长。也就是说，他们期望看到所购买的所有芯片带来的回报。

What if they don’t? Slok notes a risk we’re currently seeing across AI usage: More organizations turning to cheaper open weight models, often Chinese, not those built by the frontier labs, and overall token prices falling. OpenAI’s latest model, per CEO Sam Altman, is 54% more token efficient on coding tasks. That’s good for users fretting about the cost of their AI agents, but it may be bad for companies building token factories should users not wildly increase their overall token usage with them. 如果他们做不到呢？Slok 指出了我们目前在人工智能使用中看到的一个风险：越来越多的组织转向更便宜的开放权重模型（通常是中国模型），而不是由前沿实验室构建的模型，且整体 Token 价格正在下降。据 OpenAI 首席执行官 Sam Altman 称，其最新模型在编码任务上的 Token 效率提高了 54%。这对担心人工智能代理成本的用户来说是好事，但如果用户没有随之大幅增加其整体 Token 使用量，这对那些建立“Token 工厂”的公司来说可能不是好消息。

Slok worries that if hyperscalers don’t meet their cash-flow goals, the market reaction could be severe — “with so much riding on so few names,” he writes, “a slower payoff wouldn’t just be a sector problem, it would risk tipping the economy into recession and the S&P 500 into a correction.” Just something to keep in mind as you’re herding your AI agents toward cheaper tokens. Slok 担心，如果超大规模云服务商无法实现其现金流目标，市场反应可能会很严重。他写道：“由于太多的赌注押在少数几家公司身上，回报放缓不仅是一个行业问题，还可能导致经济陷入衰退，并使标普 500 指数陷入回调。”在你引导人工智能代理使用更便宜的 Token 时，请记住这一点。