Lucy’s Substack
Red SiliconMy personal Substack
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 38 | Founded | a year ago | Last Issue | 5 days ago |
| Active | Language |  ZH-TW | |||
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當程式碼不再稀缺，資本真正願意付錢的是什麼？
AI 正在快速降低寫程式的成本，但沒有同步降低 review、測試、安全與長期維護的成本。美國創投資金並未離開 AI，而是高度集中到少數超大型輪次；對其他團隊而言，證據門檻反而提高。開源社群的價值不只在分發，而在於用公開治理、持續行為與責任升級，生產一種可被客戶與資本共同驗證的信任。Star 只能代表能見度；真正有價值的是生產採用、重複貢獻與淨維護負擔。
上週，我在北京的 Community Over Code Asia 2026 做了一場 keynote。
這是 Apa...
梁文鋒 2026年 投資人會議完整逐字稿（英文版/中文版）
- leaked 4-hour investor meeting transcript for DeepSeek founder Liang Wenfeng English Version
- 梁文鋒與投資人的4小時對話錄音逐字稿
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Anthropic 每月燒 12.5 億租算力，中國模型卻用 1/30 的價格逼近盈利
AI 基礎設施每年投入上萬億美金，但帳到底算不算得過？我用「每 GW 算力」當統一單位拆了一遍：美國付費推論層每 GW 成本約 $130 億、收入約 $300 億，毛利 50%+。Anthropic 願意用 3 倍溢價租 SpaceX 的 GPU 集群，因為收入撐得住。但中國模型以 1/30 的 API 定價、靠 MoE 架構 + 國家算力補貼，正在逼近同等水準的單位經濟效率。這篇文章把多空雙方的帳都算清楚，給創業者、投資人、和用模型的人各一個判斷框架。
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今年一...
LLM 把舊世界的「不可能三角」鬆動了
剛從台北的 ComputeX 回到上海，第一次參加 ComputeX 的我有許多感觸，其中一個是：展會上台灣硬體那一側的能力是壓倒性的。
從晶片到模組到整機，供應鏈密密麻麻地咬合在一起，每一環都做到了極致。
但軟體那一側，我看到的大多是「一層貼在硬體之上的軟體」，目的是把某個硬體規格或者某項硬體功能「用起來」。我認為這種產品設計的邏輯是「從下往上」的：硬體能做什麼，於是配一個軟體，把這個能力暴露出來。
這和我在中國親歷的那套產品思維，是相反的。移動互聯網和 SaaS 那...
我飛去台北參加 GTC 2026，讓我停下腳步的，不是 Robin 的 C2C 架構——而是宇樹。
舞台上究竟發生了什麼
黃仁勳這次發布的不只是一顆新晶片，而是一個全新的平台。
Isaac GR00T 人形機器人參考系統，將輝達的 Jetson Thor 運算模組——基於 Blackwell 架構打造——與宇樹 H2 機體配對，作為標準參考硬體。機身擁有 31 個自由度，雙手各 25 個自由度，由 Sharpa 製造，本機算力達到 2,070 TOPS（FP4 精度）。
這不是一台展示用的概念機，而是一套研究級、可量產出貨的系統，目前已確認將交付給史丹佛大...
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As an expert in investment and strategy, I specialize in China's tech innovation landscape and business development. I provide strategic, actionable insights on technology market trends and value generation opportunities across these sectors.
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