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XPeng Motors announced that its self-developed Turing AI chip, designed to support Level 4 autonomous driving capabilities, is slated for mass production in 2025.

Xiaopeng Motors chairman He Xiaopeng recently announced that the company's fully self-developed Turing AI chip will be mass-produced and launched in the second quarter of this year. This progress comes as the automotive industry accelerates the application of end-to-end intelligent driving technology and the scale of AI large models continues to expand. Xiaopeng Motors is building its strongest AI brain by simultaneously developing a world base model with 35 times the parameters of mainstream VLA models, and a self-developed chip with computing power equivalent to three Nvidia Orin Xs, which is about to be mass-produced.

Nvidia recently announced plans to establish over one million square feet of manufacturing space in Arizona and Texas for the production and testing of AI chips. This represents a significant move by Nvidia to bring some of its manufacturing back to the United States. Nvidia's Blackwell chip is already in production at TSMC's Arizona facility. Additionally, Nvidia is establishing "supercomputer" manufacturing plants in Texas, partnering with Foxconn in Houston and Wistron in Dallas. In Arizona, Nvidia is collaborating with Amkor and others.

AI chip giant Nvidia announced it will partner with manufacturing collaborators to design and build its first AI supercomputer on US soil, marking a significant step in its supply chain strategy. Nvidia has commissioned over one million square feet of manufacturing space for production and testing of its latest Blackwell AI chips in Arizona, and for the manufacturing and testing of the AI supercomputer in Texas. Nvidia's ecosystem partners are expected to invest $500 million to support the construction of this AI infrastructure. While the...

In a recent annual letter to shareholders, Amazon CEO Andy Jassy highlighted the company's significant investment in artificial intelligence (AI). He noted that while the development and deployment costs of AI remain high, future AI usage costs are expected to decrease significantly as technology advances. Image Note: Image generated by AI, image licensing provider Midjourney. Jassy revealed that Amazon plans to invest up to $100 billion in capital expenditures in 2025.

Japan's state-backed Rapidus Corp. has begun adjusting its chipmaking equipment and is expected to start test production of advanced semiconductors by the end of this month. This is a crucial step for Rapidus as it pushes to enter the artificial intelligence (AI) components market. The two-year-old startup aims for mass production of 2nm semiconductors by 2027, aiming to rival Taiwan Semiconductor Manufacturing Company (TSMC), a leading semiconductor manufacturer. Image Caption: Image

Faster data transfer speeds are crucial for advancements in AI. Lightmatter, a Mountain View-based startup valued at $4.4 billion, has announced two new technologies to accelerate connections between AI chips. Having raised $850 million in venture capital, Lightmatter is part of the burgeoning silicon photonics trend in Silicon Valley. Their innovation lies in using optical fiber to transmit data via light signals instead of traditional electrical signals.

Reports suggest that Nvidia, the global semiconductor giant, is nearing a deal to acquire the startup Lepton AI for potentially hundreds of millions of dollars. Founded two years ago, Lepton AI focuses on leasing servers equipped with Nvidia AI chips to other businesses. According to The Information, the deal is progressing rapidly, although Nvidia has yet to officially comment.

Former Intel CEO Pat Gelsinger recently criticized Nvidia's pricing strategy for its AI GPUs on the Acquired podcast during Nvidia's 2025 GPU Technology Conference, arguing that the high cost makes them unsuitable for large-scale AI inference tasks. Gelsinger highlighted that inference is crucial for deploying AI models and that the industry should focus more on inference, a domain where Nvidia's technology falls short in terms of cost-effectiveness. Image source omitted.

Bloomberg, citing sources, reports that Ant Group has made a significant breakthrough in AI, reducing AI model training costs by 20% using chips from Alibaba and Huawei. This injects new momentum into China's drive for technological self-reliance. The report further indicates that Ant Group's internal tests show these Chinese-made AI chips rival Nvidia's offerings in performance. Widespread validation and adoption of this could significantly alter the global AI landscape.

South Korean media reports that FuriosaAI, a startup specializing in AI application chips, recently rejected an $800 million acquisition offer from tech giant Meta. FuriosaAI stated it will continue focusing on independent R&D and production of its AI chips. The report indicates that the breakdown of acquisition talks stemmed from disagreements on business strategy and organizational structure, not the acquisition price itself. Similar to many tech companies building large language models (LLMs), Meta...

According to Bloomberg, OpenAI is making a massive investment in its first infrastructure project, dubbed "Stargate," with an estimated cost of $100 billion. The project's first data center complex, located in the small Texas town of Abilene, is planned to house up to 400,000 powerful Nvidia AI chips. Once fully operational, it will be one of the world's largest AI computing centers. Image Note: Image generated by AI, image licensing service Midjourney.

Nvidia has unveiled its next-generation AI chip, Rubin, promising a performance leap of up to 900 times that of its current flagship Hopper architecture. This significant advancement points towards a new era in AI processing capabilities.

Nvidia recently announced that its next-generation AI chip will be named after renowned astronomer Vera Rubin. Born in Philadelphia in 1928, Vera Rubin's exceptional achievements and profound impact on the scientific community serve as the inspiration behind this name.

Social media giant Meta is facing unprecedented AI infrastructure costs, with projected AI-related spending soaring to a staggering $65 billion, potentially contributing to a total annual expenditure of $119 billion! Faced with this astronomical bill, the tech giant has decided to develop its own AI chips, and has already made significant progress. Recent reports indicate that Meta is about to begin a small-scale deployment of its custom chips, signaling a gradual move away from Nvidia and its reliance on their GPUs.