Anthropic 的商业计划：造出“机器神明”，或者至死方休

Anthropic 的 S-1 招股书似乎为该公司竭尽所能地将资金砸在算力上提供了财务上的正当理由。如果你一直在怀疑 Anthropic 是否在一边极力试图打造一个“机器神明”，一边向华尔街推销自己做的是企业 IT 合同生意，那么 Anthropic 刚刚给出了一个现成的答案：采取一系列看起来像是在打造“机器神明”的行动，是它避免破产的唯一途径。

暂且把“机器神明”放在一边。至少在我对这份 S-1 招股书的解读中，Anthropic 似乎将公司的未来押注在了极其惊人的爆发式增长上——为了覆盖其高昂的固定成本，它不得不这么做。你根本不需要是一个 AI 怀疑论者，就会对这些数据感到瞠目结舌。但如果你怀疑这种商业计划所依赖的剧烈“递归自我提升”是否真的可行，那么这看起来绝不是一笔稳妥的投资。

让我们先从利好 Anthropic 支持者的消息说起。在 2025 年上半年至 2026 年上半年之间，该公司的营收增长了14 倍，达到 163 亿美元。虽然账面上出现了 800 亿美元的亏损，但这很大程度上是一种与核心业务无关的会计处理问题。如果剔除该项以及包括员工薪酬在内的其他费用，我们在 2026 年上半年看到的是 16.2 亿美元的调整后营业利润。我此前曾发问过 AI 是否能够赚到足够维持自身的资金，而 Anthropic 的 S-1 文件给出的答案——至少对这家公司而言，至少在眼下——似乎是肯定的。

Anthropic 仅将 OpenAI 和谷歌列为竞争对手

相比 OpenAI 或埃隆·马斯克（Elon Musk），Anthropic 要克制得多，而且这种克制似乎收到了回报。（Anthropic 只把 OpenAI 和谷歌列为竞争对手。下次加油吧，埃隆！）该公司从未推出过 AI 图像生成器、AI 版 TikTok 仿制品或 AI 虚拟伴侣（AI waifu）。相反，它专注于代码编写和网络安全等任务，高度聚焦于对计算机而言最得心应手的事情：更多的计算。并且它专注于比个人消费者更少多变的企业级市场。其净收入留存率（Net Revenue Retention）达到了惊人的 900%。表现相当出色！而且这些收入并非来自个人用户，在 2026 年上半年，大约四分之三的客户年支出至少达到 10 万美元。

但另一方面，Anthropic 承受着极其激烈的竞争压力——既来自其他前沿实验室，也来自开源模型。其他前沿实验室同样在拼命冲刺技术前沿。Claude Opus 5.5 目前在 Epoch 能力指数（Epoch Capabilities Index）上名列榜首，但这一排行榜变动频繁。而对于那些不在意自己的软件落后四个月的人来说，还有开源模型可供选择。

更重要的是，面对像谷歌这样的对手，Anthropic 处于劣势。它缺乏多元化的变现途径，而谷歌拥有。谷歌还可以将其实时 AI 能力嵌入到企业已在使用的其他产品中。还记得当年微软 Teams 如何蚕食 Slack的吗？那并不是因为 Teams 是一款更好的产品，而是因为微软拥有更强大的分发渠道，Teams 直接与企业付费购买的其他软件进行了捆绑。在 Anthropic 重点布局的网络安全领域，拥有最优秀的模型确实比在即时通讯软件中重要得多，但在分发渠道上的劣势依旧难以逾越。

巨额长期承诺凸显 Anthropic 面临的扩张压力有多沉重

此外，营收增长往往是脉冲式的，通常在新模型发布后爆发。面对竞争对手的步步紧逼以及每次新模型推出后使用量的激增，Anthropic 一刻也不能停止模型训练，而且其训练效率必须高过体量庞大得多的竞争对手。

因此，一个至关重要的问题摆在面前：Anthropic 能否持续维持我们在 S-1 中看到的高速业绩？

一些长期承诺清晰地展现了 Anthropic 承受着多么沉重的扩张压力。诚然，第 64 页中关于采购承诺和租赁协议的部分被巧妙地隐去，第 137 页中关于预计在 2030 年 12 月 31 日前支出的金额也留下了一个耐人寻味的空白。不过，我们已知这些承诺的周期延续至 2045 年。

所幸的是，“承诺与或有事项”一节为我们提供了一些线索。不可撤销的固定最低采购承诺总额达 2656 亿美元，期限在 1 至 20 年之间。截至未经审计的 6 月 30 日数据，该公司“主要与算力及基础设施容量相关”但尚未生效的租赁协议总额达 3214 亿美元。此后，Anthropic 又达成了总计约 2450 亿美元的新算力和基础设施协议，期限同样在 1 至 20 年之间。还有一些尚未敲定的交易，Anthropic 估计其成本在 1250 亿至 1500 亿美元之间。此外，还存在其他不固定的算力承诺。

“S-1 文件定格了一段美好时光，当时他们早期模型的使用量暴涨。”

如果把所有这些加起来，在未来 20 年里，Anthropic 面临着至少约 8500 亿美元的承诺支出；若将尚未最终敲定的交易按当前估计成本计入，这一数额将上升至约 1 万亿美元。而这还是建立在我没有遗漏任何项目的前提下——而我极有可能有所遗漏。

因此，为了兑现这些承诺，Anthropic 必须保持惊人的增长速度。虽然 Anthropic 没有披露未来承诺的还款计划表，但 PitchBook 分析师 Harrison Rolfes 测算，从 2027 年到 2030 年，该公司每年的承诺支出约为 900 亿美元，这意味着它需要达到 1880 亿美元的年营收，才能维持当前成本与算力之间的稳定比例。“对这家公司而言，增长不是可选项，”他写道，“合同条款本身就预设了这一点。”

今年上半年，与客户相关的算力与基础设施（及其他少数项目）支出为 23 亿美元。模型研发（及其他少数项目）的成本则高得多，Anthropic 为此投入了 97 亿美元。但根据 Rolfes 的分析，Anthropic 的效率同时也在提升——每获取一美元营收所消耗的算力资金有所减少。

网络安全领域的每个人都必须拼命奔跑才能留在原地，正如 Anthropic 一样

这是一种可持续的长期趋势吗？目前很难断言，毕竟第三季度的数据尚未揭晓。“S-1 文件定格了一段美好时光，当时他们早期模型的使用量暴涨，且由于算力供不应求，他们能够对自己拥有的算力收取更高溢价，”Futurum Group 分析师 Brendan Burke 指出。

作为企业级软件，Anthropic 瞄准企业的 IT 预算合情合理。巧合的是，网络安全正是一个只要稍不紧跟前沿就会惨败的领域——那里的每一个人都必须拼命奔跑才能留在原地，正如 Anthropic 自身一样。当然，AI 本身也是制造问题的一部分；黑客可以利用它暴力攻破以往无法突破的防线。此外，由于合作伙伴在针对 AI Agent 的安全协议上处理粗糙，产品本身也正在引发一系列麻烦。

Burke 指出，前沿实验室需要高利润率的产品，以避免陷入 Token 定价的恶性低价竞争。这让我注意到了另一件事：Opus 5.5 使客户成本相比 Opus 5 降低了 40%。Anthropic 声称，成本下降意味着使用量增加，从而能够维持公司的利润率。但如果你假设每次新模型发布都会带来这种幅度的成本下降，很快这些任务的处理价格就会变得微不足道。

在人类指导下，Claude 已经在构建 Claude

这并不一定是个不可逾越的障碍，部分原因是效率的提升并不总是会削减利润率，DA Davidson 分析师 Gil Luria 表示。亚马逊 AWS 和其他云服务商“每年效率都大幅提高并逐年降价，但他们依然保持了稳定增长和利润率上升，”他说。

但增长的压力是毋庸置疑的。这正是极少甚至无需人工监督的“递归自我提升”（recursive self-improvement）登场的地方。“更强大的模型有助于我们的研究人员更快发现改进途径；反过来，更强大的算力杠杆使我们能够训练出能力更卓越的模型，并更高效地进行部署。”从 Anthropic 针对末日危机或类似论调的惯用措辞来看，它预计剧烈的递归自我提升即将到来，甚至可能就在不久之后。

在人类的指导下，Claude 已经在参与构建 Claude。在招股书的风险因素中，Anthropic 警告称其已被锁定在这种开发模式中。这伴随着明显的权衡取舍，因为利用 AI 开发更多 AI“可能导致模型能力的进化速度超出我们开发、验证和实施相应安全性、对齐机制及防护措施的能力”。如果我的理解没有偏差，Anthropic 的潜台词是：其商业模式依赖于如此快速地打造新模型，以至于必须使用自己可能尚未完全理解的成果，出于商业利益，它在本质上被迫去做一些可能对所有人都充满风险的事情。这也就是为什么我们在风险因素中看到了大量关于“灾难性”危害的生存威胁以及开发出“某种形式的自我意识”等匪夷所思的警示条款。

脱离人类监督框架的剧烈递归自我提升真的可能实现吗？无人知晓。但如果答案是否定的，Anthropic 想要兑现其现有的算力采购承诺并扭亏为盈，恐怕将难如登天。

The Anthropic S-1 seems to have provided the financial justification for the company to spend as much as it possibly can on compute. If you are the kind of person who wonders if Anthropic is doing its level best to build a machine god while convincing Wall Street it’s in the business of selling IT contracts, Anthropic has just offered a handy solution: Doing a bunch of things that look like you’re trying to build the machine god is the only way to avoid going bankrupt.

Forget about the machine god for a minute. At least in my read of the S-1, it seems like Anthropic is pinning the future of its company to dramatic growth; it has to, in order to meet its fixed costs. You don’t have to be an AI skeptic to lift an eyebrow at these numbers. But if you are skeptical that dramatic recursive self-improvement is possible — in the way the business plan seems to require — this doesn’t look like a good investment.

Let’s start with the good news for Anthropic fans. The company’s revenue grew fourteen times to $16.3 billion between the first half of 2026 and the first half of 2025. There’s an $80 billion loss, but it’s largely an accounting thing that isn’t relevant to the underlying business. If we remove that, and some other charges, including employee compensation, we’re looking at $1.62 billion in adjusted income in the first half of 2026. I’ve asked before if AI can make enough money to pay for itself, and the answer from Anthropic’s S-1 appears to be, at least for this company, at least right now, yes.

Anthropic names only OpenAI and Google as rivals

Anthropic was considerably more disciplined than OpenAI or Elon Musk, and it seems to be paying off. (Anthropic names only OpenAI and Google as rivals. Better luck next time, Elon!) The company has never released an AI image generator, an AI TikTok clone, or an AI waifu. Instead, it specialized in tasks like coding and cybersecurity, staying laser-focused on the thing it is easiest for a computer to do: more computer. And it focused on the enterprise market, which is less fickle than consumers. Its net revenue retention is 900 percent. Pretty good! That revenue isn’t consumers, either. About three-quarters of customers in the first half of 2026 are spending at least $100,000 a year.

On the other hand, Anthropic is under intense competitive pressure — from both other frontier labs and open-source models. The other frontier labs are all rushing for the cutting edge, too. Claude Opus 5.5 currently sits atop the Epoch Capabilities Index, but that leaderboard changes frequently. And for anyone who doesn’t care that their software is four months behind, there are the open-source ones.

What’s more, Anthropic has a disadvantage against a rival like Google. It doesn’t have multiple paths to revenue — and Google does. Google can also wrap its AI into other products businesses already use. Remember when Microsoft Teams ate Slack? It wasn’t because Teams was the better product; Microsoft just had better distribution, since Teams was bundled with other programs companies were already paying for. Having the best model matters more for cybersecurity — one area Anthropic is targeting for growth — than for instant messaging software, but it’s hard to compete on distribution.

There are some long-term commitments that highlight how intense the expansion pressure is on Anthropic

And also, revenue comes in spurts, usually after a new model is released. Between the competition and growth in use after each novel model, Anthropic can never stop training, and it has to do so more efficiently than its much larger competition.

So one important question is: Can Anthropic continue to provide the results we see in the S-1?

There are some long-term commitments that highlight how intense the expansion pressure is on Anthropic. Sure, there’s a convenient blank on its purchase commitments and lease agreements on page 64, and another kicky little blank on the amounts it’s expecting to spend before December 31st, 2030, on page 137. However, we know the commitments run through 2045.

Fortunately, there’s a “Commitments and contingencies” section that can help us out a little. There’s a fixed minimum of non-cancelable purchase commitments that total $265.6 billion, with terms between one and 20 years. The company has lease agreements “primarily related to compute and infrastructure capacity” that haven’t yet commenced of $321.4 billion, as of the unaudited June 30th figures. After that, Anthropic entered new compute and infrastructure agreements totaling about $245 billion, again with terms between one and 20 years. There’s some other transactions that haven’t yet been finalized, which Anthropic estimates as costing $125 billion to $150 billion. There are also other compute commitments that are variable.

“The S-1 does freeze in time a happy period when their initial models skyrocketed in usage.”

If I add all that up, I’m looking at a minimum of around $850 billion of commitments over the next 20 years, which will rise to about a trillion dollars if the transactions that haven’t been finalized are added in at the current estimated cost. And that’s assuming I’m not missing anything, which I may very well be.

So to meet those commitments, Anthropic’s growth must continue at breakneck speed. Anthropic doesn’t give us a payment schedule for its future commitments, but analyst Harrison Rolfes of PitchBook models it at about $90 billion a year from 2027 to 2030, which means $188 billion in revenue to keep the current ratio of cost to compute stable. “Growth is not optional for this company,” he writes. “The contracts assume it.”

Compute and infrastructure (and a few other items) associated with customers cost $2.3 billion in the first half of this year. Model development (and a few other items) was significantly more expensive; Anthropic spent $9.7 billion on it. But Anthropic simultaneously got more efficient — spending less money on compute to bring in each dollar of revenue, according to Rolfes.

Everyone in cybersecurity has to keep running just to stay in the same place, exactly like Anthropic

Is that a long-standing trend? Hard to say. We don’t have the third quarter numbers in front of us. “The S-1 does freeze in time a happy period when their initial models skyrocketed in usage and they didn’t have enough compute so they are able to charge more for what they had,” says Brendan Burke, an analyst at Futurum Group.

As enterprise software, it makes sense for Anthropic to target companies’ IT budgets. Conveniently, cybersecurity is a place where you will absolutely get your shit rekt if you are not constantly up to date — everyone there has to keep running just to stay in the same place, exactly like Anthropic. Of course, AI is part of the problem; hackers can use it to brute-force stuff they couldn’t do before. Plus, the products themselves are also causing headaches thanks to a partner’s sloppy fuck-up of security protocols around AI agents.

Frontier labs need high-margin products to avoid a race to the bottom on token pricing, notes Burke. That made something else stand out to me: Opus 5.5 reduced customer costs by 40 percent over Opus 5. Now, Anthropic says that lower cost means more usage, which lets the company preserve its margins. If you assume that cost reduction happens every time a new model is released, pretty soon you wind up with tasks at a fraction of the price.

Claude is already building Claude under human direction

This isn’t necessarily an insurmountable problem, in part because increased efficiency doesn’t always slash margins, says Gil Luria, an analyst with DA Davidson. Amazon Web Services and other cloud providers ”have become far more efficient every year and reduce their prices every year and they have had steady growth and rising margins,” he says.

But the pressure for growth is pretty undeniable. That’s where recursive self-improvement, with minimal or no supervision, comes in. “More capable models help our researchers discover these improvements faster; in turn, more powerful compute multipliers enable us to train even more capable models and deploy them more efficiently.” Based on the general rhetoric Anthropic has about the apocalypse or whatever, it expects to hit dramatic recursive self-improvement, maybe soon.

Claude is already building Claude under human direction. In its risk factors, Anthropic warns that it’s locked in to that approach. That comes with a clear tradeoff, since using AI to develop more AI “may cause the capabilities of our models to advance more rapidly than our ability to develop, validate, and implement corresponding safety, alignment, and security measures.” If I am reading that right, Anthropic is saying that its business relies on building new models so fast, using work it may not fully understand, that it is essentially forced, for business reasons, to do stuff that may be risky to everyone. Which is why we’ve got a bunch of weird stuff about the existential hazards of “catastrophic” harm and the development of “forms of sentience” in the risk factors.

Is dramatic recursive self-improvement that escapes the bounds of human supervision possible? Dunno. But if it isn’t, that makes it much harder for Anthropic to meet its existing compute commitments and turn a profit.

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