[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-why-jensen-huang-is-wrong-about-agi-being-achieved-zh":3,"tags-why-jensen-huang-is-wrong-about-agi-being-achieved-zh":23,"related-lang-why-jensen-huang-is-wrong-about-agi-being-achieved-zh":24,"related-posts-why-jensen-huang-is-wrong-about-agi-being-achieved-zh":28,"series-industry-53fcc0da-62a4-4863-a169-d4ba7cb49db2":64},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":10,"language":12,"translated_content":10,"views":13,"is_premium":14,"created_at":15,"updated_at":15,"cover_image":11,"published_at":16,"rewrite_status":17,"rewrite_error":10,"rewritten_from_id":18,"slug":19,"category":20,"related_article_id":21,"status":22,"google_indexed_at":10,"x_posted_at":10},"53fcc0da-62a4-4863-a169-d4ba7cb49db2","Why Jensen Huang Is Wrong Abo…","\u003Cp>\u003Ca href=\"\u002Fnews\u002Fwhy-jensen-huang-is-wrong-about-agi-zh\">Jens\u003C\u002Fa>en Huang is wrong: today’s AI is impressive, but it is not AGI.\u003C\u002Fp>\u003Cp>His own words expose the problem. On Lex Fridman’s podcast, Huang said, “I think we’ve achieved AGI,” then minutes later admitted t\u003Ca href=\"\u002Fnews\u002Fopenai-chatgpt-images-2-0-launch-zh\">hat\u003C\u002Fa> “the probability that 100,000 of those \u003Ca href=\"\u002Fnews\u002Fwhy-gpt-image-2-matters-more-than-another-ai-image-launch-zh\">age\u003C\u002Fa>nts build NVIDIA is zero percent.” That is not a trivial nuance. It is a direct acknowledgment that the system he called AGI cannot sustain a complex organization, cannot carry responsibility across time, and cannot reliably perform open-ended work at scale. A model that can briefly produce a viral app is useful. A system that deserves the label AGI must do far more than impress in a demo.\u003C\u002Fp>\u003Ch2>第一個論點：Huang 對 AGI 的門檻太低\u003C\u002Fh2>\u003Cp>第一個問題很簡單：他把一個狹窄的成功案例，包裝成一般智能。AI 會寫程式、寫文案、協助產品上線，這些都很有價值，但它們證明的是工具能力，不是一般性智能。Huang 舉的例子如果只是「AI 能做出一個熱門 app、創造收入、然後消失」，那更像是高效自動化流程，不是能跨情境學習、推理、調整目標的 AGI。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777079923343-wyz8.png\" alt=\"Why Jensen Huang Is Wrong Abo…\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>把標準拉回嚴肅定義，差距就很清楚。《Artificial Intelligence: A Modern Approach》把 AGI 描述為能「理解、學習並執行人類能執行的任何智力任務」的系統；OpenAI 的公開說法則是「在大多數經濟上有價值的工作中超越人類的高度自主系統」。對照今天的模型，這些門檻都還沒被跨過。它們是能力很廣的窄型系統，不是能自主處理任務全貌的通用智能。\u003C\u002Fp>\u003Ch2>第二個論點：失敗模式仍然太多，且太核心\u003C\u002Fh2>\u003Cp>如果 AGI 已經到來，我們就不會還把 hallucination、脆弱推理、長鏈條規劃失敗，當成產品的主要風險。事實正好相反，這些問題至今仍是日常。模型可以通過考試、摘要大量文本、在熟悉範式下寫出像樣的程式碼，但一碰到因果推理、長期記憶、真實世界約束與一致性決策，表現就迅速下滑。這不是邊角問題，而是核心能力缺口。\u003C\u002Fp>\u003Cp>產業界和研究界其實都看得很清楚。Yann LeCun 一再強調，單靠擴大大型語言模型不會自然長出人類級智能，還需要 world models；Gary Marcus 也長期主張，LLM 距離 AGI 還非常遠。連 Satya Nadella 都說，我們「甚至還沒接近」。爭論的焦點不是 AI 有沒有用，而是能不能把「很有用」誤認成「已經通用」。把能在幾分鐘內討好使用者的系統叫做 AGI，就像把計算機叫成數學家，因為它算得比人快。\u003C\u002Fp>\u003Ch2>反方可能怎麼說\u003C\u002Fh2>\u003Cp>最強的反方論點是：AGI 本來就不是一個清楚、固定的標準。人類自己也不是什麼完美的「通用智能」；我們有偏見、有限、依賴工具，而且高度專門化。從這個角度看，一個能寫作、能編碼、能搜尋、能摘要、能跨任務推理的系統，已經「夠通用」了。若硬要它像科幻作品中的全知機器，反而會錯過真正的里程碑。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777079927009-61uy.png\" alt=\"Why Jensen Huang Is Wrong Abo…\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>另一個反方主張更務實：市場不在乎哲學定義，只在乎能不能做事。如果模型已經能在很多經濟價值高的工作上替代人力，那企業就會把它當成 AGI 來部署，政策與勞動市場也會跟著改變。既然影響是真實的，名稱上的爭論似乎就不重要。\u003C\u002Fp>\u003Cp>但這個說法最後還是站不住腳，因為術語會直接影響風險判斷。若把 AGI 一路下修到「非常好用的軟體」，這個詞就失去辨識重大技術轉折的功能，只剩宣傳效果。商業自動化的門檻，和人工通用智能不是同一件事。兩者混在一起，只會抬高期待、掩蓋限制，最後讓產品、投資與政策都建立在錯誤前提上。今天的 AI 的確會改變世界，但這不等於它已經是 AGI。\u003C\u002Fp>\u003Ch2>你能做什麼\u003C\u002Fh2>\u003Cp>如果你是工程師、PM 或創辦人，請停止把 AGI 當成行銷捷徑，改用能力導向的語言：它能做什麼、在哪裡失敗、幻覺率多高、需要多少人工覆核。產品設計上，優先追求可驗證的可靠性，而不是模糊的通用性；決策上，把今天的模型視為強大但有邊界的系統，預留監督、驗證與回滾機制。真正成熟的做法，不是爭論 AGI 到底到了沒，而是按「它還沒到」來設計。\u003C\u002Fp>","Jensen Huang is wrong: today’s AI is impressive, but it is not AGI, and calling it that only muddies the public 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