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hot_img SK Hynix: The competition in AI data centers is shifting from single chips to overall infrastructure architecture

SK Hynix stated in a recent article that the competition in AI is shifting from the performance of individual chips to the design and operation of the entire infrastructure architecture. The competitiveness of AI data centers no longer depends on individual components, but on whether the five key elements of computing, memory, storage, networking, and power cooling can be seamlessly integrated.The article points out that the continuous expansion of AI model scales has led to a surge in demand for computing power and data movement. Training requires repeatedly reading massive datasets, while inference relies on quickly retrieving user request information, both of which place higher demands on the system architecture of data centers. At the memory level, HBM, server DRAM, and others have formed a hierarchical system, each undertaking different bandwidth and capacity tasks. At the networking level, as large-scale training and inference rely on multi-server parallel processing, networking has become a key factor determining the scalability of data centers. System design is shifting from single-server to whole rack and cluster-level expansion.According to Omdia's forecast, the AI data center chip market will grow from $123 billion in 2024 to $207 billion in 2025, reaching $286 billion by 2030. SK Hynix also mentioned that Microsoft's Fairwater data center in Wisconsin is about the length of five football fields, indicating that infrastructure is being deployed on a larger scale. SK Hynix emphasizes that memory is becoming a key layer connecting computing and data.

hot_img SemiAnalysis: Gemini has exited the frontier competition, and GCP is accelerating the sale of TPUs to third parties for profit

The research organization SemiAnalysis released an analysis indicating that Google DeepMind is no longer among the leading AI laboratories. A week prior, DeepMind co-founder Demis Hassabis stepped back from daily operations, and key members such as Google Chief Scientist Jeff Dean and Gemini co-lead Oriol Vinyals left to establish a new lab called Discovery Loop. The analysis suggests that the long-term struggle within Google over computing power allocation between Gemini and GCP has concluded with GCP emerging victorious.SemiAnalysis stated that Gemini 3.5 Pro has been canceled, and Gemini 3.6 Flash's performance is inferior to that of leading Chinese open-source models and Grok 4.5. Currently, Gemini has fallen to the 8th or 9th position in the large model rankings. Meanwhile, GCP is selling a large number of TPUs to competitors like Anthropic, having secured long-term leasing and sales contracts for hundreds of thousands of TPUs over the past nine months. The Tokenomics model estimates that Gemini's own ARR is about $12 billion, while GCP's third-party AI cloud service revenue is expected to exceed $73 billion by the end of 2027, with TPU system sales contributing an additional over $120 billion. GCP's latest quarterly growth rate is 82%, and it is expected to accelerate to over 100% by 2027 due to TPU system sales, contributing approximately $3 to Google's earnings per share.

Bernstein reiterates optimism for Circle: Q2 performance alleviates concerns over stablecoin competition, maintains target price of $140

According to The Block, research firm Bernstein reaffirmed its "Outperform" rating and maintained a target price of $140 after Circle announced its Q2 2026 financial results, believing that the company's latest performance constitutes a "reverse validation" of the market's bearish views. Bernstein analysts stated that the market currently underestimates USDC's long-term growth potential and Circle's advantages in distribution channels, liquidity, and regulatory compliance, due to two major core concerns regarding Circle—intensifying competition in stablecoins and changes in the interest rate environment that may affect reserve income.Investors may not have fully accounted for the future revenue opportunities from transaction fees, partner ecosystems, and the Arc blockchain that Circle could generate. The firm specifically pointed out that several infrastructure initiatives recently advanced by Circle, including obtaining a national trust bank license in the U.S., expanding the Circle Payments Network, and the planned launch of the Arc public chain mainnet on September 16, could all become future growth drivers. Additionally, Bernstein noted that Circle has raised its guidance for other revenues and profit margins after deducting distribution costs for 2026, expecting to confirm approximately $180 million in Arc token presale revenue.Analysts believe that future staking yields, gas fees, and ecosystem partnership revenues from Arc have not been fully reflected in current valuation expectations. As of the end of Q2, the circulating supply of USDC was $73.3 billion, a decrease of 5% from the previous quarter but an increase of 19% year-over-year. Bernstein believes that Circle is shifting from a purely crypto trading infrastructure to payments, real-world asset (RWA) tokenization, and broader financial infrastructure, which will drive USDC into the next phase of growth. Circle's stock closed at $63.28 on Wednesday, and Bernstein's target price of $140 implies a potential upside of about 121%.

hot_img "Fortune": The AI competition has shifted from "US-China confrontation" to "open source versus closed source."

Fortune magazine recently published a commentary article pointing out that with the rapid rise of China's open-source AI models, the competitive landscape of the global AI field is changing. The real contest has shifted from U.S.-China rivalry to the battle between open-source and closed-source models. The article cites data from the independent evaluation platform Artificial Analysis, stating that the intelligence index of DeepSeek V4 Flash is only 1 point lower than that of GPT-5.6 Luna. Even though OpenAI has reduced prices by 80%, the former's single-task cost is still 60% lower.The author notes that while U.S. export control policies aim to limit China's AI development, they have objectively stimulated innovation at the architectural level among Chinese companies, forcing them to shift towards open-source and low-cost routes. Chinese models attract global developers to participate in improvements by opening weights, while transferring inference costs to overseas cloud service providers, achieving a low-cost global layout. Regarding the viewpoint that open-source models "cannot be profitable," the article argues that their profit model is similar to that of open-source software: users pay for hosting services, and most companies are not inclined to self-deploy.The article also points out that recently, several figures in the U.S. tech community, including former White House AI official David Sacks, have begun to support open-source, and NVIDIA CEO Jensen Huang has also called for the industry to embrace open-source. The author urges both China and the U.S. to cooperate in the field of AI security, viewing open-source AI as a "global public good," rather than falling into a zero-sum competition that costs trillions of dollars.
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