红杉AI峰会六大关键议题解读（5）：AI商业化范式转移，从“模型调用”迈向“组织结构调用”

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The consensus at the 2025 Sequoia AI Summit is the shift in AI commercialization from "model invocation" to "organizational structure invocation" [1][6] - The AI industry currently prioritizes computational scale and parameter stacking as core competencies, but the real constraints are in organizational structures, processes, and toolchains that are not adapted for intelligent operations [1][6] - Future competition will focus on building more efficient collaborative networks and smarter organizational structures [1][6][10] Summary by Sections AI Application Development - The focus of AI application development is shifting from isolated model optimization to organizational alignment, which will redefine workflows and technical pathways [2][7] - The industry is currently trapped in a "technical obsession," prioritizing computational power while neglecting the deeper logic of deployment [2][7] Limitations of Model Invocation - Model invocation is limited in achieving true business synergy and value closure, which organizational structure invocation can resolve through systemic integration [3][8] - Model-centric strategies often isolate optimization at the algorithmic level, failing to address fragmented workflows and data silos [3][8] Organizational Structure Invocation - Organizational structure invocation integrates AI with existing resources, processes, and personnel through structural optimization, creating a closed loop from model application to value creation [3][8] - Claude Code's implementation by Anthropic demonstrates the transformative power of organizational adaptation, achieving over 70% of production code submissions internally [4][9] Future of AI Competition - The future of AI competition will pivot from model performance to optimizing organizational structures and collaboration networks, reshaping industry dynamics [4][10] - Enterprises will leverage high-density agent-driven logic for end-to-end intelligent operations, moving away from reliance on labor scale [4][10]