Anthropic S-1似乎为公司尽可能多地花费在计算上提供了财务理由。 Anthropic是否在尽力建立机器神，做一连串看起来像是你试图建造机器神的东西
Anthropic的业务计划：建立上帝或死亡尝试
Anthropic面临激烈的竞争压力和巨大的固定成本。 Claude能否快速建设自己以获得成功？.
Anthropic的业务计划：建立上帝或死亡尝试
Anthropic面临激烈的竞争压力和巨大的固定成本。 Claude能否快速建设自己以获得成功？.
别管机器神了至少在我的S-1读物中，Anthropic似乎正将公司的未来推向戏剧性的成长；为了支付固定成本，它必须这样做。 你不必怀疑人工智能，但如果你怀疑剧变自我改善是可能的 — — 以商业计划看来需要的方式 — — 这看起来不是个好投资。
让我们从Anthropic粉丝的好消息开始。 2026年上半年至2025年上半年，公司收入增长14倍，达到163亿美元。 虽然损失高达800亿美元，2026年上半年的调整收入为16.2亿美元。 Anthropic的S-1的答案似乎是至少现在是的。
Anthropic只命名为OpenAI和Google作为对手
Anthropic的纪律性比OpenAI或伊隆·穆斯克要强得多，似乎已经有所回报。 (Anthropic只将OpenAI和Google作为对手命名。 祝你下次好运伊隆公司从来没有发布的AI图像生成器，AI TikTok克隆，或者AI waifu. 相反，它专门从事编码和网络安全等任务，将激光聚焦在计算机最容易完成的事情上：更多的计算机。 它关注的是企业市场，它不像消费者那么脆弱。 其净收入留存率为900%. 不错！这些收入也不是消费者。 2026年上半年约四分之三的客户每年至少花费10万美元。
另一方面，Anthropic受到激烈的竞争压力——来自其他前沿实验室和开源模型。 其他前沿实验室也都急着赶到尖端 Claude Opus 5.5目前坐落在Epoch能力指数，但领导板经常变化。 任何不在乎自己的软件已经落后四个月的人
Anthropic对Google这样的对手不利，Google也有。 Google还可以将其AI包装到已经使用的其他产品企业中。 记得Microsoft队吃Slack的时候吗？并不是因为Teams是更好的产品；Microsoft只是有更好的分配，因为Teams已经与其他程序公司捆绑在一起，已经支付费用。 拥有最佳模式对网络安全更重要——一个领域Anthropic的目标是增长——而不是即时通讯软件，但很难在发行上竞争。
有些长期承诺突出了Anthropic的扩张压力有多大。
另外，收入也以闪电形式出现，通常在新型号为发布之后。 在每个小说模式之后的竞争和使用增长之间，Anthropic永远无法停止训练，它必须比它更大的竞争更有效率。
因此，一个重要的问题是：Anthropic能否继续提供我们在S-1中看到的结果？
有些长期承诺突出了Anthropic的扩张压力有多大。 当然，第64页有关于购买承诺和租赁协议的方便空白，第137页有关于预计在2030年12月31日之前花费的金额的又一个粗略空白。 然而，我们知道2045年的承诺。
幸运的是，有一个“承诺和突发事件”部分可以帮助我们一点。 有固定的最低不可撤销购买承付款总额2656亿美元，期限为1至20年。 该公司的租赁协议“主要涉及计算和基础设施能力”，截至6月30日未经审计的数字，该协议尚未启动3,214亿美元。 之后，Anthropic又签订了新的计算协议和基础设施协议，总金额约达2,450亿美元，再次以1年到20年为期限。 Anthropic估计耗资1,250亿美元至1,500亿美元。 还有一些其他计算承诺是可变的。
“S-1确实及时冻结了一个快乐的时期，当时最初的型号在使用中猛增。”
未来20年里，承诺金额至少将达8500亿美元，如果交易尚未敲定，假设我没有遗漏任何东西
因此，为了履行这些承诺，Anthropic的增长必须以惊人的速度继续。 Anthropic并未给出我们未来承诺的支付时间表，但PitchBook的分析师Harrison Rolfes在2027至2030年的年薪中，他写道：“成长对这家公司来说不是可选的。” “合同承担了这一责任。”
与客户有关的计算和基础设施(以及其他一些项目)在今年上半年耗资23亿美元。 模型开发(和少数其他项目)成本大幅提高；Anthropic为此花费97亿美元。 但Anthropic同时获得了更高的效率——根据罗尔夫斯的说法，在计算上花费较少的钱来带来每美元的收入。
网络安全界的每个人都得继续跑才能待在同一个地方就像Anthropic
这是长期的趋势吗？难曰。 我们面前没有第三季度的号码。 “S-1确实及时冻结了一个快乐的时期，最初的型号在使用上猛增
作为企业软件，Anthropic以公司的IT预算为目标是合理的。 简而言之，网络安全是一个地方，如果你不时更新，你绝对会得到你的狗屎信息——那里每个人都必须不停地运行，以便停留在同一地方，就像Anthropic一样。 当然，人工智能是问题的一部分；黑客可以利用它来进行他们以前无法做的事情。 此外，这些产品本身也由于合作伙伴在AI agents周围草率破坏安全协议而引起头痛。
边疆实验室需要高边际产品，以避免在象征性定价上竞争到底部，指出 Burke. Opus 5.5使客户成本比Opus 5降低40%。 现在，Anthropic 称，降低成本意味着更多的使用量，这让公司保持其利润率。 如果你假设每一次新型号是发布时成本都会降低，很快你就会以一小部分的价格完成任务。
Claude已经在人类指导下建造了Claude
称 Gil Luria是DA Davidson的分析师，Amazon网络服务公司和其他云端供应商“每年效率高得多
但增长的压力是不可否认的。 这才是在极少或没有监督的情况下进行递归自我改进的地方。 “更有能力的模型可以帮助我们的研究人员更快地发现这些改进；反过来，更强大的计算乘数使我们能够培训更有能力的模型并更有效地加以部署。” 根据Anthropic关于启示录或什么的一般言论，它预期会很快地发生戏剧性的递归自我改进。
Claude已经在人类指导下建设Claude. Anthropic 警告的风险因素是，这与一个明确的权衡结果是一致的，因为使用AI来开发更多的AI“可能导致我们模型的发展能力比我们开发、验证和执行相应的安全、配合和安全措施的能力更快”。 如果我读到这个权利，Anthropic说，它的业务依赖于如此快的建造新模型，使用它可能不完全理解的工作，它本质上是被迫的，出于商业原因，做可能对每个人有风险的事情。 因此我们得到了一些关于“灾难性”危害的存在危害以及风险因素中“危险形式”的发展的怪异东西。
超越人类监督界限的戏剧性的自我改进是否可能？没有若不是，Anthropic更难兑现现有的计算承诺
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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.
Anthropic’s business plan: Build God or die trying
Anthropic faces intense competitive pressure and huge fixed costs. Can Claude build itself fast enough to succeed?
Anthropic’s business plan: Build God or die trying
Anthropic faces intense competitive pressure and huge fixed costs. Can Claude build itself fast enough to succeed?
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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