AI「下沉」是 Web3 的机遇吗?

marsbitPublished on 2025-07-01Last updated on 2025-07-02

最近观察 AI 行业,发现个越来越「下沉」的变化:从原先拼算力集中和「大」模型的主流共识中,演变出了一条偏向本地小模型和边缘计算的分支。

这一点,从 Apple Intelligence 覆盖 5 亿设备,到微软推出 Windows 11 专用 3.3 亿参数小模型 Mu,再到谷歌 DeepMind 的机器人「脱网」操作等等都能看出来。


会有啥不同呢?云端 AI 拼的是参数规模和训练数据,烧钱能力是核心竞争力;本地 AI 拼的是工程优化和场景适配,在保护隐私、可靠性和实用性上会更进一步。(主要通用模型的幻觉问题会严重影响垂类场景渗透)


这其实对 web3 AI 会有更大的机会,原来大家拼「通用化」(计算、数据、算法)能力时自然被传统 Giant 大厂垄断,套上去中心化的概念就想和谷歌、AWS、OpenAI 等竞争简直痴人说梦,毕竟没有资源优势、技术优势,也更没有用户基础。


但到了本地化模型 + 边缘计算的世界,区块链技术服务面临的形势可就大为不同了。


当 AI 模型运行在用户设备上时,如何证明输出结果没有被篡改?如何在保护隐私的前提下实现模型协作?这些问题恰恰是区块链技术的强项...


有注意到一些 web3 AI 相关新项目,诸如最近由 Pantera 零投 10M 的 @Gradient_HQ 推出的数据通信协议 Lattica,来解决中心化 AI 平台的数据垄断和黑箱问题;@PublicAI_ 脑电波设备 HeadCap​​采集真实人类数据,构建「人工验证层」,已经实现了 14M 的收入;其实,都在尝试解决本地 AI 的「可信性」问题。


一句话:只有当 AI 真正「下沉」到每个设备时,去中心化协作才会从概念变成刚需?


#Web3AI 项目与其继续在通用化赛道里内卷,不如认真思考怎么为本地化 AI 浪潮提供基础设施支持?

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