#Data & training

US data company sells data worth about $500 million to Chinese AI labs, including reinforcement learning environments to help improve model performance in specific fields.

US data company sold about $500 million data to Chinese AI labs, including reinforcement learning environments to help improve model performance in fields such as coding, networking, finance, law, and biology. These environments simulate the world to allow models to train repeatedly and use scoring standards to optimize output. Chinese labs can save over 70% of inference computing costs annually by purchasing the same RL environments as US companies like Anthropic and OpenAI.

Stargate Data Center in Inner Mongolia, China: Scale, Cost, and the Remarkable Speed of Vertical Integration

Envision Group's Galaxy Data Center Phase I 120MW project in Wulanchabu, Inner Mongolia, China, was delivered in one year at one-fifth the cost of the US, with the entire 800MW campus custom-built for two major internet giants. The center adopts an 800V DC architecture and liquid-cooled server design, reflecting the speed, scale, and depth of vertical integration in China's data center construction, where Envision not only builds data centers but also constructs wind farms and supporting power facilities.

Ramp data shows AI spending stagnation, as Anthropic's IPO is approaching.

Ramp's report shows that corporate token spending only increased by 1.5% from August to September, while it grew by 42% month-on-month in July, and token usage is still rising. Ara Kharazian, chief economist at Ramp, believes that the price cuts by OpenAI and Anthropic and corporate discounts make AI usage cheaper, and the top 1% of spending companies are slightly below the monthly per capita peak of $10,000 in summer. Ramp data shows AI spending stagnation, coinciding with Anthropic's upcoming IPO. Ramp's spending data gives a price signal opposite to usage growth, which can be used to observe the tension between model companies' revenue and IPO valuations.

How does Claude 4.7 balance cutting-edge intelligence and efficiency?

Microsoft, Google, Oracle, and other five tech giants have pledged to invest more than 3 trillion US dollars in AI infrastructure.

According to Bloomberg, Alphabet, Meta, Microsoft, Amazon, and Oracle have pledged to invest more than 3 trillion US dollars in data centers and other fields in the coming years, with a total of 1.3 trillion US dollars in lease payment commitments and a total of 1.9 trillion US dollars in other procurement commitments.

Fujifilm is increasing tape production capacity by 5 times to meet the growing demand for archives driven by AI.

Anthropic files 335-page prospectus, targets record IPO

Anthropic officially filed its IPO application with the Nasdaq, with the stock code ANT, and its 335-page prospectus was also released on the SEC website. Revenue in the first half of 2026 was $16.286 billion, with a net loss of $80.748 billion, of which about $72.4 billion came from changes in the fair value of financing instruments. The prospectus discloses a commitment to over $650 billion in computing power over the next ten years and states that it is not currently developing image and video generation models, focusing computing power on programming, Agent capabilities, and security research.

Hidden storage costs of AI: data storage overheads overlooked behind reasoning

In corporate AI inference costs, data storage overhead is often overlooked. After each model response, a large amount of backend data needs to be stored, including logs, caches, backups, etc., which is 130 times the size of the output files users see. McKinsey predicts that by 2030, AI inference demand will account for 40% of total data center demand, and nearly 95% of IT decision-makers report that data volume has increased by generative AI in the past year.

Tech companies are buying data from failed startups to train AI models

Tech companies like SimpleClosure and Sunset are buying code and workspace data from failed startups to sell to AI labs, as AI training labs face limitations with public and synthetic datasets.