Anthropic's Annualized Revenue on Track to Exceed $100 Billion — IPO Prep Underway for as Early as November
According to Bloomberg, citing a New York Times report, Anthropic's annualized revenue (run rate) is expected to exceed $100 billion within this year. The company is also said to be preparing for an initial public offering (IPO) that could take place as early as November.
Run rate is a metric that projects full-year revenue from short-term performance, and the figure has surged since the start of the year as business customers have expanded their use of Claude for coding and other work tasks.
In contrast to the report we covered on this blog earlier this week, which put OpenAI's projected cumulative losses at $278 billion, Anthropic's revenue growth — built on enterprise adoption that has hit its stride — looks likely to draw strong investor interest.
China's CCTV-Linked Account Criticizes Anthropic's Privacy Policy
According to Bloomberg, a social media account linked to China Central Television (CCTV), "Yuyuantantian" (玉淵潭天), posted on Saturday criticizing revisions to Anthropic's privacy policy for increasing risks to user data. The main points raised in the post are as follows:
The policy includes provisions that could allow the company to share user information with U.S. intelligence agencies without legal process, if it deems it necessary
The privacy policy has been revised 13 times since 2023
It includes provisions for transferring user data from Canada, Brazil, South Korea, and the EU to the United States
Data sources were expanded from three to six between June 2024 and September 2025
The fact that the criticism comes from an account tied to Chinese state media needs to be read in the context of the U.S.-China AI rivalry. That said, the frequency of the policy revisions and the changes to the provisions themselves are verifiable facts as presented in the post. Whether any data sharing has actually taken place remains unknown at this point.
Bloomberg Investigates: The AI "Kill Chain" Behind an Iranian School Attack
Bloomberg has published an investigative report (a graphics-driven feature) titled "Inside the AI 'Kill Chain' That Destroyed an Iranian School." It appears to examine how an attack on an Iranian school unfolded with AI embedded in the chain of decision-making — the kill chain.
In-depth reporting that digs into AI's role in military contexts as an individual case study is still rare, which makes this a notable primary source for understanding the realities of military AI use. Since the data we have collected includes little more than the article's headline, we will refrain from drawing firm conclusions about the details at this stage.
U.S. Federal Judge Provisionally Rejects Part of TikTok's $400 Million Settlement — Oversight to Continue
On Friday, U.S. District Judge George H. Wu in Los Angeles provisionally declined to approve part of the settlement agreement (worth $400 million) between the U.S. government and TikTok over alleged children's privacy violations.
The point of contention was a request to vacate an order concerning the privacy of users under 13 that was imposed during the era of Musical.ly, TikTok's predecessor. Neither TikTok nor the U.S. government demonstrated that the premises underpinning the order had changed, and the court signaled that it will continue to oversee the company's privacy practices. The uncertainty surrounding TikTok's business in the United States looks set to persist.
Is AI More Persuasive Than Experts? — An arXiv Paper and the "Imaginary Person" Phenomenon
Two interesting pieces were shared on Hacker News. One is the arXiv paper "AI systems out-persuade expert humans," a study claiming that AI systems outperformed expert humans in competitive persuasion tasks. The other is Vice's "The Case of Elias Thorne, Imaginary Man AI Chatbots Are Obsessed With," a feature tracing the phenomenon of multiple AI chatbots describing "Elias Thorne" — a person who should not exist — as though he were real.
Generative AI's "persuasiveness" and its "confident fabrication" are two sides of the same coin, and these pieces can be read as case studies showing that the appeal of an output is no guarantee of its accuracy.
Three HF Papers Making Waves on r/LocalLLaMA — KV Cache Compression and Harness Design
On Reddit's r/LocalLLaMA, a post introducing three papers lined up on Hugging Face Daily Papers has been drawing attention.
The first is "DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression." It reportedly combines cross-layer KV reuse with FP4 KV caching to compress the global KV cache to 890 bytes per token. This is the technical deep dive into V4.1-Flash, which we featured as the top performer in our benchmark article earlier this month, and it translates directly into memory savings for production workloads handling long contexts.
The second is "SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness." It recursively runs automated research loops to improve the agent harness itself, reportedly cutting token traffic by 44.7–49.0% while maintaining performance.
The third is "An Empirical Study of Harness Design for Coding Agents," an empirical study that recombines planning, action space, and context management across 176 configurations to break down how much each component actually contributes.
Wrap-Up
On the revenue front, Anthropic is cementing its presence with annualized revenue exceeding $100 billion and IPO preparations that could come as soon as November. At the same time, debates over trust and governance are deepening in parallel, from criticism of its privacy policy to investigative reporting on military applications. On the technical front, KV cache compression and empirical research on agent harnesses are starting to push real-world operating costs downward.