Anthropic Races Toward IPO: Net Loss of $80.7 Billion in the First Half of 2026, DeepSeek's Funding Raised to 100 Billion Yuan

In the past few years, Anthropic has gradually become one of the most closely watched companies in the enterprise AI market. It has established an advantage in the programming field, embedding model capabilities into developer workflows and providing higher-value services to enterprise customers. According to data disclosed by Reuters, Anthropic's revenue reached $4.59 billion in 2025 and further increased to $16.29 billion in the first half of 2026.

But the prospectus also reveals another side: in the first half of 2026, Anthropic's net loss reached $80.7 billion. Of that, about $72.4 billion came from changes in the fair value of convertible notes and does not reflect cash outflows. The actual operating loss was about $693 million.

But what makes it hard for investors to breathe easy is that Anthropic stated in its prospectus at least $518 billion in computing power procurement commitments over the next decade, of which about 80% cannot be canceled or must be paid regardless of usage.

In this IPO, Anthropic plans to raise $100 billion. Since its founding, Anthropic has raised a total of $150 billion.

The U.S. stock market was already showing concern about massive spending on artificial intelligence infrastructure, and Anthropic's listing has further strained the market's fragile nerves.

Industry insiders commented that this listing is of great importance, and if it does not go smoothly, the entire market will be dragged down.

OpenAI, which once followed Anthropic, has also frequently shifted direction recently. On the one hand, it is turning to low-cost models. On October 26, OpenAI released a new generation mid-range model, GPT-6.1 Terra, making lower call prices and computer operation capabilities important selling points.

On the other side of the ocean, DeepSeek, which has always focused on the cost-effectiveness route, has repeatedly raised its financing plan for this round. The latest news says its new round of financing exceeded 100 billion yuan, and it has already been boldly embarking on a path of major expansion. Several people familiar with the matter told Phoenix Net Technology that DeepSeek's team expansion has recently gained strong momentum, "after all, the money raised has to be spent in reasonable places," and the more financing it obtains, the more things DeepSeek means to do.

Commercialization model Anthropic, huge loss of $80.7 billion

In the global AI industry, Anthropic has always been a highly representative reference point.

Unlike products that mainly rely on consumer traffic, Anthropic started from programming and enterprise services and gradually established its own business path. Claude has accumulated users among developers, while enterprise customers purchase model capabilities through API calls, paid subscriptions and other means. The model is not just a chat window, but a production tool that enters software development, knowledge processing and business processes.

The appeal of this path is direct: enterprises are willing to pay for tools that can improve production efficiency, and high-value scenarios usually have a clearer payment logic than ordinary consumer applications.

Especially in the programming field, the value of AI is no longer merely reflected in whether answers are accurate, but in whether it can complete code writing, debugging, testing and complex task execution. For enterprises, if the model can shorten development cycles and reduce repetitive work, payment has a relatively concrete basis.

A CEO in the AI sector, when communicating with Phoenix Net Technology, said he considers Anthropic an important sample of the AI business model. "Everyone first discovered that AI coding has strong commercialization conversion ability, and this earliest also came from Anthropic. Now Anthropic has started doing AI pharmaceuticals, and in the future there will certainly be people following."

But the quality of revenue and the quality of profit are two different things.

According to Reuters' review of Anthropic's prospectus, the company's operating loss in 2025 was about $8.06 billion, and its operating loss in the first half of 2026 was about $693 million. Its computing and infrastructure spending in the same period was close to $9 billion, a sharp increase from about $2.3 billion in the same period last year.

This means that Anthropic's business is indeed expanding rapidly, but the resource costs supporting business expansion cannot be ignored either.

Every token sold by a model company does not come from nowhere. Training requires computing power, inference requires computing power, and the expansion of user scale and the increase in task complexity will also continuously increase the consumption of computing resources. Especially as AI moves from simple question answering to programming agents and complex task execution, models may require more rounds of reasoning, tool calls and repeated verification, and the cost structure behind a single task also changes accordingly.

Enterprises being willing to pay does not mean that this business naturally has a sufficiently high profit margin. If revenue growth can only be maintained by continuously investing more computing power, then there may not necessarily be a simple positive correlation between scale expansion and profitability improvement.

Another pressure facing Anthropic is long-term computing power procurement commitments. Reuters reported that the company has signed huge cloud service, computing resource and infrastructure agreements with multiple partners, with related commitments over the next decade exceeding $500 billion.

Anthropic's operations are not without progress. On the contrary, revenue growth and enterprise customer expansion show that the market is paying for its model capabilities. But what puzzles the outside world is the business model itself: as model capabilities continue to improve, can inference costs decline in tandem? As more work is handed over to AI, can the prices customers are willing to pay cover the full costs of models, computing power and service delivery?

Supported by various "chain leaders," another path that remains open source

If Anthropic represents the path of entering from high-value scenarios and gradually building commercial revenue, then another lesson DeepSeek brings to the industry is that cost itself can also become part of a model's competitiveness.

One of the important reasons DeepSeek initially shook the industry was that it made the market re-examine the cost of training and using large models. Outside attention once focused on its models' performance and disclosures related to training costs. But for commercialization, what matters more than any single training figure is whether low cost can extend to model deployment, inference and actual use.

According to OpenRouter data, in terms of call volume, DeepSeek's models have ranked first on the platform's weekly chart for three consecutive weeks. In addition, models from MiniMax, Zhipu and Tencent have also long ranked in the platform's top five by call volume.

DeepSeek's path offers a direction worth studying: model capability does not have only one implementation path of continuously increasing parameters and stacking computing power. Architecture design, training methods, inference efficiency and resource scheduling can all affect the final unit cost.

More importantly, low cost will change the way models enter the market.

These will be two completely different development approaches.

The U.S. Department of Homeland Security recently ordered federal agencies to fully remove DeepSeek products and third-party services developed based on its models, while DeepSeek almost simultaneously announced that it would fully open-source its internal inference framework DeepInfer, compatible with the two major computing platforms Nvidia and Huawei Ascend. Behind the dramatic contrast points to the same fact: DeepSeek has already been placed in the front row of global AI competition, and the way it has chosen to respond is not to contract, but to spread its technology foundation more widely.

Recently, multiple media outlets reported that DeepSeek is about to complete about 100 billion yuan in financing. Since September, the scale of this financing round has been "chased higher" all the way: initially planned at 50 billion, raised midway to 80 billion, and finally settled at 100 billion. Adding the first round of about 51 billion yuan in June, DeepSeek has raised a cumulative total of about 150 billion yuan within half a year, with its pre-money valuation maintained at 500 billion yuan.

The investor lineup is unprecedentedly lavish. According to a tally by the self-media outlet "Tides of Money," this round of financing brought together more than 20 investment institutions. Existing shareholders from the previous round, including IDG Capital, Shixiang Technology, Zhengxin Valley Capital, Monolith, CATL, Tencent and Guozhitou, continued to participate in this round. New investors include China Mobile, China Telecom, China Life Investment, GigaDevice's Shixi Capital, SMIC Capital, Boyu Capital, CPE Yuanfeng, Gaorong Capital, Legend Capital, CITIC Investment, Meituan Dragon Ball, Hengdian Capital, Geely Capital and other institutions. It is also understood that this round of financing attracted a large number of listed companies and industrial capital to participate indirectly, including Muyuan Foods, Zhihu, Century Huatong, Perfect World, Giant Network, Dahua Technology and others; the participants are too numerous to list one by one.

It is not hard to see that Liang Wenfeng particularly values industrial value when choosing investors. For example, in energy, there is the CATL group with solid reserves, and the GigaDevice group, which is closely tied to domestic memory chips.

In the face of huge investment, DeepSeek is also facing the worry of "spending money." A person close to DeepSeek told Phoenix Technology that DeepSeek is planning a new growth route for investors, including a major expansion of its team.

Just after the first round of about 51 billion yuan in financing was settled in June, DeepSeek issued a recruitment announcement on June 25, explicitly proposing to "expand all departments by at least double," opening 33 positions at once, covering seven major sectors: technology R&D, computing infrastructure, product implementation, frontier algorithm research, and comprehensive functions. It is even recruiting data center infrastructure positions in Ulanqab.

By September, Cui Tianyi, head of the DeepSeek Harness team, released about 150 recruitment slots at once on social platforms. In his own words, the demand was "unprecedented." DeepSeek is also generous with employee benefits. Reports say DeepSeek offered an intern from Tsinghua University's Yao Class a pre-tax daily wage of 5,500 yuan, more than 120,000 yuan a month; the monthly salary range for campus recruitment core system R&D engineer positions is 60,000 to 90,000 yuan.

But Liang Wenfeng has always remained plain and low-key. A person close to DeepSeek told Phoenix Technology that DeepSeek's offices are mainly in Beijing and Hangzhou, while Liang Wenfeng has long rented a place in Beijing, and the room layout has always been very simple.

Cost-effectiveness begins to dominate everything

Among the many AI giants, OpenAI has always been the most wavering one.

They initially pursued the upper limit of model intelligence, and at one point wanted to capture the consumer market, even fantasizing about building "the WeChat of the AI era" to connect everything.

But in the face of huge losses and revenue that was overtaken, OpenAI at one point learned from Anthropic, conquering territory in the AI coding field in hopes of better commercialization conversion.

Now, OpenAI's approach is becoming more and more like DeepSeek. On September 23 this year, GPT-6 Sol and Luna were released simultaneously, and Luna's API input price was pushed down to $0.1 per million tokens, a rock-bottom price.

Half a month later, on October 7, OpenAI pushed Luna directly to free users, covering ChatGPT's entire user base; paying users get Sol, free users get Luna.

GPT-6.1 Terra, released on October 26, is positioned in OpenAI's mid-range model lineup, focusing on computer operation and daily office tasks. Its standard API price is 50% lower than the previous generation GPT-5.6 Terra; OpenAI also claims that in some task tests, Terra can complete work at a cost far lower than flagship models.

However, some industry insiders believe that "OpenAI is still in a wavering stage. You can see it from the fact that they are working on embodied intelligence models. Compared with Anthropic, OpenAI is often very wavering, but it will soon be educated by the market to do business that makes money."

After all, the involution in the large model market is obvious to all. If a company cannot continuously build advantages and do its main business well, it will soon be a flash in the pan.