执行变廉价之后，什么能力还值钱 AI 改变的不是效率，是定价机制 AI 让很多事情变快了，但职业差距在拉大。 junior 被 AI 放大，senior 更值钱，但 senior 更难被组织看见。 所有人都能用 AI 写代码、做分析、出报告的时候，执行效率就不再稀缺了。市场在重新定价：什么能力真正稀缺？什么能力 AI 替不了？ 我想讨论的就一件事：在 AI 时代，什么才是真正的长期定价能力。不聊怎么用 AI，不聊 prompt 工程，不聊求职技巧。 职业价值的本质：降低不确定性 从第一性原理讲，不确定环境中职业价值的公式是： 价值 = 正确性 x…
AI makes many things faster, but the professional gap is widening. Juniors get amplified. Seniors become more valuable but harder for organizations to recognize.
When everyone can use AI to write code, run analysis, and generate reports, execution efficiency stops being scarce. The market reprices: what capabilities are truly scarce? What can’t AI replace?
We keep saying “production environment” like it’s just another deployment target. It’s not. Demo systems optimize for success. Production systems optimize for survival. Completely different species.
When dashboards are all green but users are already down, you’re facing a control system failure, not an observability problem.
02:17 AM. A major customer reports massive request timeouts in the support channel.
02:19. Oncall gets paged.
02:21. Grafana opens. All green. CPU normal. Memory normal. Error rate near zero. The system looks completely healthy.
02:25. More customers report timeouts. Business leaders join the call: How many customers affected? What’s the impact scope? Do we need to rollback? How long to recover?
No one can answer. Monitoring systems exist, but they provide nothing usable for decisions.
仪表盘一片绿色，用户已经宕机了。你遇到的不是可观测性问题，是控制系统故障。 这篇文章来自一次真实事故的复盘。不是监控入门，是设计审查：为什么生产组织在最需要可见性的时候反而看不见。 那次凌晨两点的事故 现场 凌晨 02:17，客户支持群冒出第一条消息：某大客户请求大量超时。 02:19，Oncall 被拉醒。 02:21，打开 Grafana，一片绿色。CPU 正常，Memory 正常，Error rate 接近 0。系统看上去啥事没有。 02:25，更多客户报告超时。业务负责人进会议，开始问：影响多少客户？范围多大？要不要回滚？多久能恢复？…
AI can write well. Give it a topic, and it generates articles with clear structure, smooth logic, decent prose. So why write yourself?
It depends on what you’re optimizing for.