AI 導入後，為什麼 Senior 反而更累？
導入 AI 後團隊速度沒變快，Senior 卻每天加班——junior 用 Cursor 一小時產出 300 行，Senior 要花兩小時 review、補測試、抓邊界。產出曲線往上，認知負擔曲線更陡。從 code review 結構、責任邊界、AI 信任分層三個視角拆，看為什麼瓶頸從「寫得慢」變成「審得慢」，以及怎麼把 Senior 從人肉 linter 救回來。
Technical Debt
Why Senior Engineers End Up More Exhausted After AI Adoption
Your team adopted AI coding tools and shipped faster—but your seniors are burning out. Juniors push 300 lines an hour with Cursor; seniors spend two hours reviewing, patching tests, and chasing edge cases the AI didn’t see. Throughput went up, cognitive load went up steeper. Three lenses on why the bottleneck shifted from writing to reviewing—code review structure, ownership boundaries, AI trust tiers—and how to stop using your seniors as human linters.
Your Team Uses AI to Code. Why Isn’t It Faster?
85% of developers use AI coding tools, yet 43% of enterprises abandon AI projects due to ‘lack of technical maturity.’ The problem isn’t the tool—it’s that your codebase isn’t ready. Here’s a 5-level engineering foundation checklist.
團隊開始用 AI 寫程式，為什麼效率沒提升？
85% 開發者在用 AI 工具，但 43% 企業因「技術成熟度不足」放棄 AI 專案。問題不在工具，而是 codebase 還沒準備好。5 層工程地基檢查，讓你知道團隊卡在哪一層。