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ETF盘中资讯|科创人工智能为何遭遇调整?589520盘中跌近2%?AI亟需自主可控!资金迎来逢跌布局机会?
Sou Hu Cai Jing·2025-08-26 02:38

Core Viewpoint - The domestic AI industry chain, particularly the Sci-Tech Innovation Artificial Intelligence ETF (589520), is experiencing market adjustments, with significant fluctuations in key stocks, reflecting both market sentiment and individual stock performance [1][3]. Group 1: Market Performance - On August 26, the Sci-Tech Innovation Artificial Intelligence ETF (589520) saw a decline of 1.96%, with major components like Chipone Technology dropping over 8% and Cambricon Technologies falling more than 3% [1]. - The ETF attracted 45.62 million yuan in a single day on August 25, accumulating 150 million yuan over the past 60 days, indicating strong capital inflow into domestic AI alternatives [1]. Group 2: Stock Adjustments - The adjustment in the ETF is attributed to the decline of major weighted stocks, particularly Cambricon Technologies and Chipone Technology, which faced significant sell-offs [3]. - The decline in Chipone Technology is linked to a large discounted share sale by its shareholders, although this is not indicative of the company's business deterioration [3]. Group 3: Domestic Chip Development - The development of domestic computing chips is characterized by two main technical routes: one focusing on GPGPU compatibility with NVIDIA's CUDA, and the other aiming to establish an independent ecosystem outside of NVIDIA's influence [4][5]. - The domestic computing market share is expected to grow significantly, driven by product advancements and the increasing competitiveness of domestic manufacturers [5]. Group 4: Market Outlook - Analysts predict that the urgency for domestic computing chip replacement will continue to rise, with expectations of a doubling market capacity by 2025 due to rapid growth in domestic demand [5]. - The AI chip market is anticipated to expand significantly, with local brands like Cambricon expected to capture a 40% market share by 2025, driven by increased demand for inference and training computing power [5].