資料來源#
- Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next
- How Anthropic's product team moves faster than anyone else | Cat Wu (Head of Product, Claude Code)
- The Founder's Playbook: Building an AI-Native Startup
摘要#
Anthropic 的知識工作 agent 產品,是 Claude Code 的姊妹產品。Claude Code 鎖定產出為程式碼的工作,Cowork 則鎖定產出為非程式碼的工作:投影片簡報、收件匣分流、客戶檔案、發布文件、會議準備。產品由 Cat Wu 共同領導。
定位(Cat Wu 的框架)#
- Claude Code(CLI) — 一次性的程式設計任務;CLI 最先取得新功能;「所有 tools 中最強大的」
- Claude desktop/IDE 中的 Claude Code — 想要預覽窗格的前端工作;更適合不習慣終端機的非技術使用者
- Claude Code mobile/web — AFK 啟動任務;在推出之前是「缺失的產品」——人們會在戶外把筆電綁定到手機上
- Cowork — 任何產出不是程式碼的工作
Cat Wu 如何使用它#
連接 Google Calendar、Slack、Gmail、Google Drive——「context 越多越好」。範例工作流程:晚上把草稿大綱加上相關的 Twitter/launch-room 連結餵給 Cowork,隔天早上回來就有一份 20 頁打磨過的投影片簡報,再經一輪回饋反覆修改。載入 Anthropic 的設計系統後,簡報看起來就像設計師親手做的。
人們為何使用它(根據 Cat Wu)#
「Applied AI」團隊——僅次於工程部門的第二大內部 token 消耗者——用 Cowork 來做:
- 客戶會議準備(「dossier」工作流程:昨天的問題、行動項目、ETA 研究)
- 收件匣歸零/Slack 歸零的分流
- 發布計畫、內部文件、從 Salesforce + Gong + Slack 拉取資料、為客戶量身打造的簡報
它如何銜接更大的 Anthropic 路線圖#
- Boris Cherny 將 Cowork 視為通用型 MCP and computer use 整合的舞台——與 Claude AI 相同的 MCP 連接器,加上 Anthropic 在 computer use 上的領先優勢,用於缺乏 MCP 的軟體(在 Opus 4.7 上「相當不錯」)。
- 「我們公司任何地方都不再有人工撰寫的程式碼」——Cowork 涵蓋了這項主張中的非程式碼那一半(SQL、簡報、營運文件全部由模型生成)。
在創辦人手冊中(2026 年 5 月)#
The Founder's Playbook 將 Cowork 定位為 AI 原生新創在每個階段的營運層:
- 構想階段 — 自動化客戶觸及與排程。Cowork 利用已驗證的目標輪廓研究潛在客戶、草擬個人化的觸及訊息、透過 MCP 連接 Gmail/Calendar、管理對話串、執行第 7 天的跟進。
- MVP 階段 — 回饋循環的後勤:觸及早期使用者、安排回饋會談、結構化的 bug 與功能收集、每週彙整。
- 發布階段 — 在營運層取代創辦人的注意力:稽核創辦人的重複性任務、分類為自動化/委派/僅限創辦人、為適合自動化的項目建立工作流程邏輯。PM 作業系統:衝刺排程、bug 分流路由、每週指標彙編。
- 規模化階段 — 企業級營運:工單路由、升級工作流程、由產品變更觸發的文件、續約追蹤、報告節奏。再加上 GTM 戰術執行:內容管線、外撥序列、分析師簡報後勤、CRM 整備。
手冊的框架:Cowork + Claude Code 兩者結合,能給「一個小團隊一個大得多的組織才有的支援態勢」。Skills 被強調為將重複性創辦人工作流程編碼化的介面。
重要引文#
- (Cat Wu)「如果我做的東西產出是程式碼,我會用 Claude Code 或 desktop 或手機上的 Claude Code。而如果產出是任何不是程式碼的東西,我就會用 Cowork 來做。」
- (Boris Cherny)「我認為 Claude design 是個很好的例子。它今天已經相當不錯了。它還會變得[更好]很多。」(在他談願景的回答中,指涉的是 Cowork 類的產品。)
相關連結#
- Claude Code — 姊妹產品
- Claude Design — 姊妹的 Anthropic Labs 產品;Cowork 的產出是簡報/文件,Claude Design 的是視覺設計/原型;Boris Cherny 在此把 Claude Design 標記為一個願景範例
- Cat Wu — 產品負責人
- Boris Cherny — 將其點名為一個投資領域
- Anthropic — 供應商
- Engineer PM Convergence — Cowork 加上 Claude Code 正是讓「人人都寫程式」+「人人都透過 agents 跑營運」得以橫跨非工程職能的關鍵
- AI Employee Framing — Cowork 的部署介面(Gmail、Slack、Calendar、Drive)正是做出「AI 即員工」框架決策的場域;HBR 研究指出,這些如何被框架化會改變問責與審查品質
- Human-AI Accountability Redesign — 非程式碼的 agent 產出(簡報、客戶檔案、收件匣分流)缺乏編譯器/測試驗證,所以問責重新設計比起 Claude Code 來說更重要,而非更不重要
- Agentic Misalignment (AM) — Cowork 式的部署(長 context、使用 tool、每個動作的監督薄弱)正是 AM 評估的情境
- AI-Native Startup Lifecycle — Cowork 是橫跨全部四個創辦人階段的營運層
- Founder as Agent Orchestrator — Cowork 加上 MCP 整合,正是讓單人創辦人也能勝任協調者角色的關鍵
- Compounding Data Moat — Cowork 運行企業支援層,在規模化階段複利累積工作流程的鎖定
- Problem-Solution Fit Discipline — Cowork 在構想階段的角色是研究、訪談框架稽核與訪談後彙整——驗證紀律的營運層
- Fiona Fung — 領導 Cowork 的工程+產品(與 Claude Code 一起);親自把小型企業客戶導入 Cowork,以親身感受導入的痛點(Dogfooding as Product Discipline)
待解決的問題#
- Cowork 的 harness 與 Claude Code 的相比如何?兩者都暴露 skills、MCP、sub-agents——但非程式碼產出的失效模式不同(沒有測試套件、沒有編譯器、沒有 diff 可審查)。
- Cowork 類產出的評估紀律是什麼?Cat Wu 說 memory 從 evals 中獲益良多;但投影片簡報的品質如何衡量並不清楚。
資料來源#
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