当AI不再只是PPT里的幻象,投资者该如何捕捉真实的利润增长?Lena 和 Miles 带你拆解“芯网存”底层逻辑与物理AI新范式,助你在这场价值质变中找准硬核赛道。

AI正在从‘概念验证’全面迈向‘价值创造’,现在的逻辑是,谁能把AI塞进具体的业务场景里,谁能真正产生利润,谁才是赢家。
Investment and technology








2026年的科技投资已从对“可能性”的崇拜转向对“价值创造”的追求。过去几年市场关注的是AI写诗、画画等概念验证,而现在资本更看重AI能否进入具体业务场景并产生利润。投资主线已进入由“场景深度”定义“商业溢价”的下半场,拼的是谁能利用技术解决硬核问题并实现业绩兑现,单纯讲故事的时代已经过去。
物理AI是指AI与物理硬件系统的融合,使智能体能够通过传感器和执行器感知、推理并与现实世界互动。与传统写死代码的机械臂不同,物理AI通过在虚拟环境中进行数百万次模拟训练,具备了对物理规律的“直觉”和感知决策能力。随着劳动力短缺和传感器成本下降,物理AI正从实验室走向车间,成为触达生产力核心的重头戏。
随着AI原生环境的发展,传统的“人适应机器”模式正在向“意图驱动”的交互范式转变。未来用户不再需要频繁切换不同的APP,而是通过智能代理(Agent)直接表达意图,由AI自动调动后台协议完成任务。这种变革催生了“生成式用户体验”,界面将根据用户的实时行为和情绪动态生成,实现超大规模的“一人一面”。
过去AI发展主要依赖GPU算力的提升,但随着模型规模扩大和推理需求激增,单一算力已无法解决所有问题。如果带宽(网)不够或存储(存)读取太慢,就会产生“木桶效应”,限制整体效能。2026年的AI基建已演变为一个复杂的系统工程,涵盖了数据中心设计、电力传输、冷却系统乃至能源供应,这种全方位的协同是支撑AI价值创造的地基。
语境架构(Context Engineering)解决了通用大模型不了解企业私有数据和业务背景的痛点。通过知识图谱、语义层以及如MCP(模型上下文协议)等标准,企业可以将最相关的私有数据在正确的时间喂给AI。这让AI从一个“盲人摸象”的聪明大脑变成了一个真正懂业务、能提供精准商业建议的操作系统,从而构建起企业的技术护城河。
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Creato da alumni della Columbia University a San Francisco
