
My personal Substack
| Platform | Pricing | Only free issues | Publishes | Weekly | |
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| Issues | 96 | Founded | a year ago | Last Issue | 7 days ago |
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在上篇與中篇裡,我們一路把信任從「可不可信」拆成幾個更具體的問題:部位要放多大、閾值要怎麼調、判斷工具本身會不會失準。
但這裡還有一個沒有處理的問題:就算你現在同意這些原則,真正做決策的那一刻,你未必會照著做。高代價的信任決策,很少發生在最冷靜的時候。它通常發生在中篇結尾說的那些狀態裡——壓力、誘惑、連勝自信、沉沒成本,一起發生的時候。
所以這篇不再談更多判斷框架。框架再漂亮,到了現場用不出來,也只是紙上談兵。這篇想談的是另一件比較不討喜的事:
你能不能在自己還清醒的時...
上篇談到,信任不是一個非黑即白的判斷題。一旦進入部位決策,事情就變得複雜多了:你要信到什麼程度?你要讓這個人、這家公司、這段關係,影響你多少資源?
這些要估計的東西,幾乎都沒有明確標價,也不會寫在紙上。更令人感到麻煩的是:就算我可以把這些概念量化,我又怎麼知道自己腦中那套判斷工具,本身是不是早就失準了?
我那筆失敗的教育股投資,最讓我不甘心的地方不是我完全沒做功課。現實狀況剛好相反,我的研究做了、訪談做了、財務數字看了、管理層也接觸了。每一個動作在當下都說得通,甚至如果你...
多數人在處理信任問題時,問的都是同一個問題:這個人可不可信?
這個問題很古老。從面相、識人術、到 FBI 教的微表情判讀,每一代都有自己的工具。大家都想從某個眼神、某句話、某個細節裡,看出對方到底是什麼人。
這種努力不能說沒用。但它把信任過度簡化成一道是非題:可信,或不可信。
我後來發現,真正讓人付出高代價的信任錯誤,通常不是你完全看錯一個人。更常見的情況是,你讓這個人、這家公司、這段關係,或這個機會,影響了太多你的資源:錢、時間、名聲、情感、職涯選擇、機會成本。你交出...
如果有人告訴你,決定未來十年 AI 產業利潤歸屬的關鍵變數,可能不是模型能力,也不是 GPU 架構,而是「每一度電的物理位置」。你會當它是認真的產業判斷,還是科幻小說的開場?
這樣的問題之所以值得當成產業分析,是因為幾家最有能力重塑算力供給曲線的玩家,已經開始以不同方式測試這條路的邊界:SpaceX 在 IPO 前把太空資料中心納入它們的長期敘事;Amazon 的 Bezos 公開談論 GW 級太空算力;Google 的 Project Suncatcher 已經開始研究低...
每隔一段時間,各種財富排行榜都會引發討論。人們比較那些數字,猜測富豪的資產組成,推算財富增速,然後很自然地把財富等同於資源、能力,甚至影響力。
這個推論不算全錯,但它只看見了已經被結算的力量。排行榜看得到的是存量,看不到的是調度未來的能力——那種在結果還沒發生前,有多少人願意先把資源押在一個人身上的情況。
後者,往往比帳面財富更值錢。
這次我想談的,就是這種能力背後最核心的來源:信用。很多時候,財富不是力量真正的起點。它只是信用被成功兌現後留下的沉澱。
▉ 錢是 po...
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The writers behind this newsletter.
* Clairvoyance Capital (2007-2012) * JP Morgan Asset Management (2013-2024) * Associate Professor of Practice, Chang Gung University (2025-current) * Consultant of several startups
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