红杉资本：2026将是AGI元年，编程智能体已经打响了第一枪！

华尔街见闻01-19

通用人工智能（AGI）不再是遥远的未来，而是已经随着“长时程智能体”（Long-horizon agents）的出现成为了现实。据红杉资本合伙人Pat Grady和Sonya Huang 14日发布的文章《2026：这就是AGI》（2026: This is AGI），尽管技术层面对于AGI的定义仍有分歧，但从功能层面看，具备自主解决问题能力的人工智能已正式落地，2026年将是属于它们的一年。 ...

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AGI），尽管技术层面对于AGI的定义仍有分歧，但从功能层面看，具备自主解决问题能力的人工智能已正式落地，2026年将是属于它们的一年。\n...</p>\n\n<a href=\"https://wallstreetcn.com/articles/3763600\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"LU0828237510.HKD","symbol_name":"SCHRODER ISF BIC (BRAZ IN CH)\"A\" (HKD) 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agents）是AGI落地的首个实例，且更多类型的智能体正在涌现。与早期的对话式AI不同，新一代的长时程智能体能够像人类一样，基于基线知识进行推理，并通过不断的自我迭代来达成目标。这一能力的跨越，标志着人工智能从单纯的“对话者”向能够实际交付工作的“执行者”转型。\n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。\n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。\n功能性定义：AGI即“自行解决问题”的能力\n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“自行解决问题的能力”。对于想要成事的企业而言，AI如何实现目标并不重要，重要的是它能否真正完成任务。\n文章将具备这种能力的AI拆解为三个核心要素：\n\n\n基线知识（预训练）： 这是2022年ChatGPT时刻的核心驱动力。\n推理能力（推理时计算）： 随着2024年底o1模型的发布而实现。\n迭代能力（长时程智能体）： 这是最新的突破，即AI能够像通用智能的人类一样，在数小时内自主工作，修正错误，并在没有具体指令的情况下决定下一步行动。\n\n\n从指令到自主：智能体的工作闭环\n为了阐述何为“自行解决问题”，文章以招聘场景为例：当创始人需要寻找一位既懂技术又活跃于社交媒体的开发者关系负责人时，传统的做法是发布职位描述。而智能体则能自主执行复杂的搜索闭环。\n据文章描述，智能体可以在31分钟内完成人类招聘专家的心理循环：它不仅会在LinkedIn上搜索Datadog、Temporal等竞对公司的相关职位，还会转向YouTube筛选高互动率的演讲者，并进一步交叉比对Twitter上的活跃度与内容质量。智能体甚至能通过分析发帖频率的下降来敏锐地捕捉潜在的离职信号，最终筛选出最佳人选并起草个性化的联系邮件。\n这种在模糊环境中通过建立假设、测试、试错并调整方向直至达成目标的能力，正是长时程智能体的核心特征。尽管它们目前仍会产生幻觉或迷失方向，但其发展轨迹已不可逆转，且错误正变得日益可修正。\n技术路径：强化学习与代理架构的双重驱动\n关于如何实现这一跨越，红杉资本指出，让模型进行长时间思考并非易事。目前两种技术路径已被证明有效且具备扩展性：\n一是强化学习（Reinforcement Learning），主要由研究实验室主导。通过在训练过程中不断的“敲打”和引导，教导模型在长时间内保持专注。目前在多智能体系统和工具使用可靠性方面已取得显著进展。\n二是代理架构（Agent Harnesses），属于应用层的范畴。开发者通过设计特定的脚手架（如记忆交接、压缩等）来规避模型的已知局限。目前市场上备受好评的产品，如Manus、Claude Code和Factory’s Droids，均得益于其卓越的架构设计。\n根据METR对AI完成长时程任务能力的追踪，这一领域的进展呈指数级增长。按照目前的趋势推算，智能体在2028年将能可靠地完成人类专家需耗时一整天的工作，到2034年将能完成一整年的工作量。\n商业变革：从软件到“数字员工”\n“你能否雇佣一个智能体？”红杉资本认为这是检验AGI的试金石。目前的市场格局表明，从医药领域的OpenEvidence、法律领域的Harvey，到网络安全领域的XBOW，各行各业的专用智能体正在迅速涌现。\n这对创业者意味着巨大的范式转移。2023年和2024年的AI应用多为“对话者”，影响有限；而2026年及以后的应用将是“执行者”。这一转变使得“销售工作”成为可能。创始人需要重新思考：在这一新范式下，哪些需要持续关注的任务可以被智能体接管？如何针对“结果”而非“工具”进行定价和包装？\n文章最后呼吁市场“备好马鞍”（Saddle Up），迎接长时程智能体的指数级增长。虽然今天的智能体可能只能可靠地工作约30分钟，但很快它们就能承担一整天的工作量，最终甚至能处理相当于人类一世纪工作量的任务。\n这意味着，曾经被视为过于宏大的路线图——例如交叉比对20万个临床试验数据，或重构整个美国税法代码——如今已变得切实可行。在AGI元年，雄心勃勃的规划正逐渐转变为现实的商业计划。","kind":"highlight","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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