The Verge 解读 Anthropic S-1：巨额算力支出被描绘为避免破产的唯一路径

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.

Follow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.

• Elizabeth Lopatto

原文版权归 The Verge · AI 及原作者所有，本站转载仅供阅读。如需删除，请联系 support@aisa.one。