张宏江外滩大会分享：基础设施加速扩张，AI步入“产业规模化”

Core Insights - The "Scaling Law" for large models remains valid, indicating that higher parameter counts lead to better performance, although the industry perceives a gradual slowdown in pre-trained model scaling [3] - The emergence of reasoning models has created a new curve for large-scale development, termed "reasoning scaling," which emphasizes the importance of context and memory in computational demands [3] - The cost of using large language models (LLMs) is decreasing rapidly, with the price per token dropping significantly over the past three years, reinforcing the scaling law [3] - AI is driving massive infrastructure expansion, with significant capital expenditures expected in the AI sector, projected to exceed $300 billion by 2025 for major tech companies in the U.S. [3] - The AI data center industry has experienced a construction boom, which is expected to stimulate the power ecosystem and economic growth, reflecting the core of "AI industrial scaling" [3] Industry Transformation - Humanity is entering the "agent swarm" era, characterized by numerous intelligent agents interacting, executing tasks, and exchanging information, leading to the concept of "agent economy" [4] - Future organizations will consider models and GPU computing power as core assets, necessitating an expansion of computing power to enhance model strength and data richness [4] - The integration of "super individuals" and agents is anticipated to bring about significant structural changes in enterprise processes [4]