四大结构性难题制约 大模型规模化落地遇阻

Core Insights - The AI industry is entering its "next decade" driven by the goal of achieving general artificial intelligence, but the pace of practical application is lagging behind the advancements in model capabilities [1] - Key structural challenges hindering the large-scale application of AI include high costs, lack of high-quality industry data, insufficient engineering capabilities, and misconceptions about the boundaries of large model capabilities [1][3] Group 1: Structural Challenges - High costs associated with training and using large models are a significant barrier, with industry experts noting that the narrative of scaling models leads to increased expenses [3] - The lack of high-quality data, particularly industry-specific corpora, is a critical shortcoming, as many sectors still face issues with data quality and quantity [4] - Insufficient engineering capabilities are seen as the "last mile" obstacle to successful AI deployment, requiring comprehensive system delivery, hardware-software integration, and large-scale customization [4] Group 2: Industry Dynamics - The balance between open-source and commercialization is evolving, with open-source being crucial for the AI industry's development and contributing to commercial value [6] - The AI entry point is shifting from cloud-based solutions to end-user devices, with smart terminals becoming key interfaces for human-machine interaction [7] - The understanding of what constitutes valuable AI is changing, focusing on its ability to improve core business metrics rather than merely providing additional features [7]