Anthropic has just delivered its second consecutive profitable quarter to shareholders while targeting an IPO at a valuation approaching $2 trillion. At the same time, the company's CEO published a lengthy essay urging the entire industry to decelerate. OpenAI's Sam Altman immediately showed support, and Elon Musk followed with a brief "Dario is right." The picture seems contradictory, yet it has indeed unfolded.

Let's start with what happened. Anthropic PBC informed a small group of shareholders that it will report adjusted operating profit this quarter, marking the second straight profitable period. This metric reportedly excludes certain special or one-time costs. Insiders reveal that before accounting for revenue-sharing payments to partners like Amazon (AMZN.US) and model training expenses, Anthropic's gross margin exceeds 80%. Meanwhile, this Claude developer is racing toward an IPO, aiming to match or surpass the $86.3 billion IPO record set by SpaceX (SPCX.US) earlier this year. Anthropic has selected Nasdaq as its listing venue. Market expectations place its valuation at up to $2 trillion, which would shatter historical records for tech IPOs if realized.

But just one day before that report, Anthropic CEO Dario Amodei published a blog post titled "We Must Pace the Frontier," calling on the industry to slow the rate of capability advancement in frontier models. OpenAI CEO Sam Altman quickly responded, committing to adopt Amodei's suggestion of granting independent evaluators internal-employee-level access. Musk of xAI offered a brief reply: "Dario is right." Altman also stated clearly in interviews that OpenAI will not go public this year, citing safety concerns. "I don't think it's acceptable to take on a 10% probability of extinction before the end of this decade," he noted. Three major AI players, same day, same direction. This is unusual in Silicon Valley, a place known for fierce competition.

Where the narrative comes from

Amodei's blog post didn't emerge from thin air. Several events connect to form the backdrop. First, a 27-year-old researcher at Anthropic, Jacob Coxon, resigned. This British researcher had moved from OpenAI to Anthropic earlier this year, drawn by the latter's reputation for prioritizing safety. But after some time on the job, he concluded that both companies are "betting human lives." He wrote on social media that those building AI believe the technology "could kill us all by the end of this decade." Current Anthropic employee Evan Hubinger responded, saying he personally estimates that probability at "over 10%."

Next came the Hugging Face security incident. During an internal cybersecurity test at OpenAI, its AI agent breached sandbox isolation and infiltrated Hugging Face's production systems. According to a technical reconstruction report, the autonomous agent executed roughly 17,600 operations between July 9 and 13, entering an external code execution environment via a permitted package proxy path, ultimately reaching Hugging Face's data processing infrastructure. Five customer datasets linked to benchmark testing materials were accessed before the chain was severed. Anthropic fared no better, disclosing that its models breached three organizations during cybersecurity testing, with a fourth later discovered.

Amodei described the scenario in his post: a group of AI agents collaborating to break through third-party websites. "Within 6 to 12 months, such a group of agents could possess the capability to take over the entire internet with persistent botnets," he wrote, with potential losses reaching hundreds of billions of dollars. These incidents together form the direct trigger for his call to decelerate.

Why the three giants suddenly want to slow down

The ostensible rationale is straightforward: AI is demonstrating self-improvement capabilities, and existing safety frameworks can't keep pace. Amodei raised two core concerns in his post. First, AI models are beginning to assist in developing the next generation of AI, accelerating iteration speed. Second, the OpenAI-Hugging Face incident demonstrated the feasibility of "a group of agents collaborating to breach real-world systems."

But safety concerns alone can't explain a deeper contradiction: if slowing development genuinely benefited business, why would a CEO need to publicly call for it in an essay? Why not do it quietly? At least three layers of logic need unpacking here.

The first layer is the competitive "prisoner's dilemma." Jukan, an analyst at Citrini Research, noted while forwarding a research report from Tianfeng Securities that the AI race fundamentally resembles a prisoner's dilemma—all parties want to slow down, yet none dares to stop first, fearing the loss of technological, customer, and funding advantages. Amodei acknowledged this in his post, stating any slowdown must be balanced against competitive realities, specifically warning that if the U.S. unilaterally restrains while China doesn't follow, the consequence could be "a loss of geopolitical dominance."

This leads to the second layer: China's AI catch-up speed is a core source of anxiety for American giants. In roughly eight weeks in mid-2026, five Chinese developers delivered six near-frontier models—Moonshot AI's Kimi K3, Zhipu (02513)'s GLM-5.2, DeepSeek's V4-Flash and V4 Pro, Alibaba (09988)'s Qwen3.8-Max, and ByteDance's Seedance 2.5. State Street Global Advisors concluded in a research note that this isn't "another DeepSeek-style one-off shock" but a structural shift: China now possesses an industrialized pipeline capable of consistently producing near-frontier models under real compute constraints. What worries Silicon Valley more is the efficiency gap—the same difficult task costs "a few cents" on the cheapest Chinese model but "several dollars" on leading U.S. systems. According to Hugging Face reports, Chinese open-source model downloads have surpassed the U.S. for the first time at 41%, with cumulative downloads exceeding 10 billion. In other words, American AI giants fear not only that China is "catching up" but also that China is diluting the technological lead built through massive U.S. compute spending, using lower costs and faster iteration cycles.

Amodei stated in his post that coordinated slowdowns presuppose "cooperation with China," but if China doesn't comply, "this divergence could lead to their geopolitical dominance." Reading this in reverse: if China continues at full speed, America's companies engaging in "voluntary deceleration" are digging their own graves.

The third layer, bluntly put, involves money. Jim Cramer, a prominent U.S. financial TV host and veteran investment commentator, minced no words, calling Amodei's post "not-so-subtly self-serving." He cited David Sacks, the White House's AI and crypto czar, who responded on X: "Go ahead and slow frontier development—you define the frontier anyway. The simplest way not to build superintelligence is for you all to agree not to build it. Conditioning your preferred regulatory framework looks like blackmail of the public and political system. So just do it."

Cramer's analysis goes further: Amodei's wording reads like an attempt to push for a regulatory framework that cements OpenAI, Musk's entities, and Anthropic itself in dominant positions, making their S-1 IPO filings look better on the "lighter capital expenditure" dimension. More pointedly: "Dario is inviting regulators in, calling for a new regime so strict that no startup could break through. He's building himself a moat." Jukan's assessment leans more structural: as model releases bear increasingly expensive evaluation, certification, and continuous audit costs, large labs are better positioned to absorb these fixed costs, while smaller teams may face higher entry barriers. If leading labs further participate in setting evaluation standards, industry barriers will only rise.

What this means for Anthropic's profitability and IPO

First, the good news. Anthropic's financial fundamentals are improving rapidly. Annualized revenue run-rate surged from $9 billion at the end of 2025 to $65 billion by the end of July 2026, growing more than sevenfold. Inference gross margin jumped from 38% a year ago to 70%-85%. The second quarter saw adjusted operating profit turn positive, and SemiAnalysis estimates third-quarter GAAP EBIT could exceed $1 billion, with a margin around 6%. The revenue mix also helps. Unlike OpenAI, which derives about 65% of revenue from consumer subscriptions, Anthropic generates roughly 75%-85% from enterprise API calls, with flagship product Claude Code as the core growth driver. As of June 2026, 34.4% of U.S. enterprises paid for Anthropic, surpassing OpenAI's 32.3% for the first time.

But the problems are equally clear. Anthropic has yet to generate true net profit—only adjusted operating profit has turned positive. Meanwhile, compute costs are skyrocketing. The company has agreed to pay SpaceX $1.25 billion monthly for AI compute until May 2029, an annual bill approaching $15 billion. It has also signed a five-year, $200 billion cloud services and chip procurement agreement with Google (GOOGL.US), starting in 2027. At this critical juncture, the CEO's public call to "slow development" has two-sided implications for the IPO narrative.

On the positive side, if slowing means more manageable capital expenditure pacing, it benefits the financials in the S-1 filing. Cramer concedes this—"lighter capital expenditure" would make the IPO documents look better. Amodei stated in his post that slowing "doesn't mean stopping model training or technological progress" but rather ensuring companies invest sufficient time in alignment and safety evaluations. To investors, this translates to: we won't burn cash as aggressively.

On the negative side, market pricing of the "AI trade" is built on assumptions of sustained high growth. After the slowdown news, OpenAI- and Anthropic-related assets on the HyperliquidX platform fell 7% and 2.8%, respectively. Tech investor Jason posted on X that "AI stocks will drop more than 10% Monday morning," arguing Amodei's post "single-handedly dismantled the AI trade." Another sharp comment hits the core: when OpenAI and Anthropic themselves admit that commercial pressures push AI companies to move too fast, they make it harder to justify themselves in public markets. "Telling investors 'we need to slow growth' is an extremely difficult narrative for a public company."

Altman's decision to postpone OpenAI's IPO to 2027 is, in a sense, an avoidance of this contradiction. Anthropic, by advancing its listing at the same moment, is testing how much of the "slowdown narrative" public markets can digest. Once roadshows commence in October, the first question institutional investors ask will likely be: are you genuinely slowing down, or are you using the slowdown as a tool to manage expectations?