文章Articles
传统行业 AI 落地的实战洞察与避坑经验。Field notes on making AI work in traditional industries.
公司的资料，到底能不能发给 AI？付费版也可能拿数据去训练Can company material go into AI? Even paid plans may train on your data
员工多半已经在用 AI 了，公司要做的是把这件事管起来：先分资料，定下哪些不能放进任何外部的 AI；再定工具，优先选条款写明不拿数据训练的；最后管账号，工作资料只放在公司的正式账号里。Your staff are probably using AI already, so the job is to govern how it's used: first sort the material, deciding what must never go into any external AI; then choose the tools, preferring ones whose terms say your data won't be used for training; then control the accounts, keeping work material in company accounts only.
DeepSeek 这次开源的 Harness 不是模型，是骨架What DeepSeek open-sourced this time isn't a model — it's a harness
DeepSeek 上个月开源了 DeepSeek Harness。对企业最有用的信息在名字里：开源的不是模型，是骨架——包在模型外面那层运行系统。它做对了三件事，也划了两条边界。Last month DeepSeek open-sourced DeepSeek Harness. The most useful thing about it for a company is already in the name: what shipped is not a model, it's the harness around one. Three of its design calls are worth borrowing — and so is the boundary it draws around itself.
传统企业缺的不是模型，是一套新的操作系统What traditional companies lack isn't a model — it's a new operating system
工具买了、会员开了、供应商也找了，可业务该怎么转还怎么转。我看过的传统企业，缺的从来不是模型——缺的是数据底座、组织架构、方法论这三样。The tools are bought, the subscriptions are live, the vendors have been met — and the business runs exactly as it did before. What these companies lack was never the model. It's three things: a data foundation, an org structure, and a method.
传统企业的 AI 项目，为什么大多数都白做了？Why most AI projects at traditional companies end up wasted
传统企业的 AI 项目失败，绝大多数不是败在技术，是败在"落地方法"——一个 8 年 CTO 总结的三个最常见的坑。Most AI projects at traditional companies don't fail on technology — they fail on execution. Three traps I keep seeing, from 8 years as a CTO.