红杉资本认为： 2026年为AGI元年，其核心标志是"长时程智能体"的成熟。AI已从简单的对话者演变为具备 自主推理与迭代能力的执行者，能像人类一样在模糊环境中解决复杂问题。商业范式将 从"销售软件"转向"销售工作成果"，智能体正成为能全天候工作的"数字员工"。通过强化学 习与代理架构驱动，其能力每7个月翻倍，正彻底重塑生产力边界。 这一转变将对商业和投资领域产生深远影响。红杉资本分析认为，随着智能体能力的指数级增长，创始 人构建产品的逻辑将发生根本性变化——从销售软件转向直接"销售工作成果"。未来的AI应用将不再仅 仅是辅助工具，而是能够作为"同事"全天候并行工作的实体，用户将从独立贡献者转变为智能体团队的 管理者。 随着Claude Code和其他编程智能体在近期跨越了关键的能力阈值，市场对于AGI的认知已被重塑。文章 强调，通过强化学习和代理架构的优化，智能体处理复杂任务的能力正在以每7个月翻一番的速度增 长，这将彻底改变企业的人才结构与生产力边界。 01 功能性定义：AGI即"自行解决问题"的能力 红杉资本表示，作为投资者，他们无意介入AGI的技术定义之争，而是提出了一个务实的功能性定义： AGI就是 ...

Core Insights - General Artificial Intelligence (AGI) is no longer a distant future but has become a reality with the emergence of Long-horizon agents, marking 2026 as a pivotal year for AGI [1] - The transition from conversational AI to Long-horizon agents signifies a shift from mere dialogue to actual task execution, fundamentally altering business and investment landscapes [1][7] Technological Developments - The capabilities of agents, particularly coding agents, have crossed critical thresholds, with their ability to handle complex tasks doubling approximately every seven months [2] - AGI is defined functionally as the ability to autonomously solve problems, focusing on the outcome rather than the technical definitions [3] - Long-horizon agents possess the ability to hypothesize, test, and adjust strategies in ambiguous environments, although they still face challenges such as generating hallucinations [4] Methodologies - Two primary technological paths are driving the development of Long-horizon agents: reinforcement learning and agent architectures [5][6] - Reinforcement learning focuses on maintaining long-term attention through iterative training, while agent architectures involve designing frameworks to overcome known limitations of models [6] Business Implications - The emergence of specialized agents across various sectors, such as pharmaceuticals and legal fields, indicates a significant paradigm shift for entrepreneurs [7] - The future of AI applications will transition from being mere tools to becoming "digital employees," prompting founders to rethink task delegation and pricing strategies based on outcomes rather than tools [7] - The potential for agents to handle extensive workloads, such as analyzing vast clinical trial data or reconstructing complex legal codes, is becoming increasingly feasible, transforming ambitious plans into actionable business strategies [7]