2025年度AI十大趋势报告-量子位

Core Insights - The report outlines the top ten core trends in the AI field for 2025, emphasizing the transformation from computational infrastructure to industrial application, highlighting China's rise in open-source ecology and self-controlled routes [1][3]. Group 1: Infrastructure - The core pillars of AI infrastructure are the establishment of computational power and the AI-native architecture of chips. Major global tech companies are investing heavily in large-scale data center construction, with projects like Google's "Stargate" and Microsoft's AI super park exceeding $10 billion [1][3]. - The shift in the chip sector is moving from general computing to AI-native architectures, with GPUs remaining central to training while NPUs become standard for edge devices. Domestic chips have achieved self-sufficiency in training models with hundreds of billions of parameters, breaking foreign technology monopolies [1][3]. Group 2: Model Evolution - The evolution of models focuses on breakthroughs in efficiency and capability. Innovations in pre-training architectures, such as the MoE (Mixture of Experts) model, balance performance and cost, with domestic models like GLM-4.6 and Qwen3 adopting this architecture [1][3]. - Upgrades in inference capabilities are driving the development of adaptive inference and heterogeneous computing technologies, with embodied intelligence becoming a popular area, as humanoid robots begin to enter industrial and household scenarios [1][3]. Group 3: Application Landscape - The application landscape shows a characteristic of "full-scene penetration," with the Agentic internet reshaping traffic entry points from "people finding services" to "services finding people." Multi-Agent collaboration frameworks lower development barriers and promote the execution of complex tasks [2][3]. - The rapid proliferation of AI hardware, including AI PCs, smart wearables, and AI toys, is reshaping human-computer interaction methods, with edge AI gaining popularity due to its low latency and high privacy advantages [2][3]. Group 4: China's Route - China's approach highlights a dual drive of open-source ecology and independent innovation. Open-source AI is entering a "China time," with models like DeepSeek and Qwen achieving high download rates in global open-source communities, establishing international influence [2][3]. - The national strategy incorporates AGI into top-level design, with tech giants and startups shifting focus from applications to core technology development, creating a full-stack ecosystem of "domestic chips + self-developed models + independent SDKs" [2][3].