Nilay Patel: ‘Beware Software Brain’

John Gruber

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AI 導讀 technology AI 重要性 4/5

9 億用戶卻人人厭惡:Patel 用「軟體腦」解釋 AI 反彈的真正病因

  • Gen Z 是 AI 最重度用戶,但對 AI 抱希望者只剩 18%,感憤怒者升至 31%,越用越厭
  • 軟體腦把世界看成資料庫,AI 本質上是要求人配合軟體,這是普通人抗拒的根源
  • OpenAI 花兩億做廣告是診斷錯誤——問題不是不了解 AI,而是用過了不喜歡

ChatGPT 現有 9 億週活用戶,但超過八成美國人對 AI 感到擔憂,Gen Z 對 AI 抱有希望的比例更跌到只剩 18%。The Verge 總編輯 Nilay Patel 認為這不是行銷問題,而是科技業患了一種叫「軟體腦」的思維病——它正在讓整個業界看不見普通人真正的感受。

ChatGPT 9 億用戶,Gen Z 只有 18% 抱有希望

Patel 開篇就列出一組讓科技業難堪的民調數字。Quinnipiac 發現超過半數美國人認為 AI 弊大於利;超過 80% 受訪者對 AI「非常擔憂」或「有些擔憂」;只有 35% 的人對 AI 感到興奮。NBC 新聞的調查更顯示,AI 的好感度甚至比移民執法局(ICE)還差,只略高於伊朗戰爭和民主黨整體評價。

最弔詭的是 Gen Z 的數據。Gallup 調查顯示,Z 世代對 AI 抱有希望的比例從去年的 27% 跌到只剩 18%,同期憤怒的比例則從 22% 升至 31%。而這個世代,正是 AI 工具最重度的使用者。換言之,用得越多,越不開心——而不是越用越愛。暴力也開始出現:政治人物因支持資料中心建設遭到槍擊,Sam Altman 的住所被丟汽油彈。

Gen Z 對 AI 情緒變化(Gallup 2025 → 2026)

使用最多的世代,越來越不開心

「軟體腦」:把 Zillow 到 DOGE 都看成資料庫

所謂「軟體腦」,Patel 的定義是:把整個世界看成一組可以用結構化程式碼控制的資料庫。這種思維方式確實孕育了許多偉大公司——Zillow(房屋搜尋平台)是房屋資料庫,Uber 是司機與乘客的資料庫,YouTube 是影片資料庫。Marc Andreessen(矽谷著名創投人)早在 2011 年就在《華爾街日報》宣告「軟體正在吞噬世界」,如今被 AI 進一步放大。

但軟體腦有其邊界。Patel 舉了 Elon Musk 和 DOGE(Department of Government Efficiency,政府效率部)的案例:他們進入政府後第一件事就是接管大量資料庫,卻撞上了一個無法迴避的現實——資料庫不等於現實,政府也不是軟體。這次行動以顯著的失敗告終,說明人不是電腦,人的生活無法被整齊封裝進可自動化的迴圈裡。

Sam Altman 花兩億打廣告:AI 沒有行銷問題

面對排山倒海的負面民調,科技業的診斷是:這是個行銷問題。OpenAI 斥資兩億美元贊助 TBPN podcast,希望改善 AI 的公眾形象。Sam Altman 甚至公開說,如果 AI 是政治候選人,它將是「史上最不受歡迎的候選人」,他認為只要行銷夠好就能解決。Anthropic 執行長 Dario Amodei 則在公開場合坦言,他擔心 AI 將先「擴大」、再「取代」金融、諮詢、科技等領域的入門白領工作,可能引發嚴重的就業危機。

Patel 直接反駁:「AI 沒有行銷問題。」ChatGPT 有 9 億週活用戶,每個人都在 Google 搜尋裡看過 AI Overview,也在社群動態裡見識過大量 AI 生成的垃圾內容(slop)。廣告無法說服人不相信自己的親身體驗——人們已充分感受過 AI,他們就是不喜歡。這正是軟體腦的根本認知盲點:科技業以為人們只是不了解 AI,但問題根本不在那裡。

美國民眾對 AI 的民調數字彙整
調查機構指標數值
Quinnipiac認為 AI 弊大於利超過 50%
Quinnipiac非常或有些擔憂 AI超過 80%
Quinnipiac對 AI 感到興奮35%
GallupGen Z 對 AI 抱有希望18%(去年 27%)
GallupGen Z 對 AI 感到憤怒31%(去年 22%)
OpenAIChatGPT 週活用戶9 億,趨近 10 億

資料來源:NBC News、Quinnipiac、Gallup

法律腦與軟體腦的神似:模糊性才是兩者的核心

Patel 本人曾是律師,妻子至今仍是律師,他提出了一個精彩的類比:律師腦與軟體腦驚人地相似。兩者都依賴大量先例——法律有判例法,工程師有程式碼函式庫;兩者都試圖用結構化語言(法律條文 vs. 程式碼)引導複雜系統走向可預測的結果。Larry Lessig 在 2000 年出版的《Code and Other Laws of Cyberspace》已點出這層關係。

然而法律的核心恰恰是模糊性——這是讓律師存在的理由,也是讓人厭惡律師的原因,因為任何案件都可以從另一個角度辯論,灰色地帶永遠存在。密西根州最高法院前首席大法官 Bridget McCormack 曾在 Decoder 節目上提倡全自動 AI 仲裁系統,理由是人們覺得傳統法律太不公平,寧可接受 AI 給出的「較差但感覺公平」的結果——Patel 認為,這本身就是軟體腦的教科書案例:試圖讓真實世界像電腦一樣運作。

「人們不渴望被扁平化」:AI 真正的阻力所在

現代商業世界確實大量依賴「收集資料→分析→重複迴圈」的模式,AI 在這裡有真實的商業價值。Anthropic 聚焦企業客戶、OpenAI 轉向商業應用,都是因為企業擁有自己的資料、能要求所有系統整合。廣告與行銷的最前線已是 AI 自動化,不再是創意人才。

但「不是所有事都是生意,不是所有事都是迴圈」。Patel 引用 Ezra Klein 的矽谷觀察:科技業人士為了讓 AI 更有用,正瘋狂地把郵件、行事曆、訊息、文件全部接上 AI,試圖讓自己「對 AI 更易讀(legible)」。Patel 認為這是反人性的邏輯:電腦應該適應人,不是讓人配合電腦、把自己變成資料庫。Apple、Google 和 Amazon 花超過十年試圖讓普通人在乎智慧家居自動化,始終失敗。「人們就是不在乎」,AI 也改變不了這一點。對有軟體腦的人而言,AI 是令人興奮的工具;對其他所有人而言,它只是一個「要求很多的爛泥怪獸(slop monster)」——一種威脅,而非機會。

人們討厭 AI 不是因為不了解它,而是因為用過它——廣告解決不了真實體驗造成的排斥。

Abstract

Nilay Patel, in a terrific essay (and Decoder one-sider) at The Verge: In fact, the polling on this is so strong, I think it’s fair to say that a lot of people hate AI, and that Gen Z in particular seems to hate AI more and more as they encounter it. There’s that NBC News poll showing AI with worse favorability than ICE and only a little bit above the war in Iran and the Democrats generally. That’s with nearly two thirds of respondents saying they used ChatGPT or Copilot in the last month. Quinnipiac just found that over half of Americans think AI will do more harm than good, while more than 80 percent of people were either very concerned or somewhat concerned about the technology. Only 35 percent of people were excited about it. Poll after poll shows that Gen Z uses AI the most and has the most negative feelings about it. A recent Gallup poll found that only 18 percent of Gen Z was hopeful about AI, down from an already-bad 27 percent last year. At the same time, anger is growing: 31 percent of those Gen Z respondents said they feel angry about AI, up from 22 percent last year. A good friend texted me a few weeks ago that “the phrase ‘software is eating the world’ sure hits differently now” than when Marc Andreessen coined the term back in 2011. (Patel, in fact, references Andreessen’s seminal essay.) That same friend texted me a link to this piece by Patel this morning. Something is profoundly off in the computer industry when it comes to software broadly and AI specifically. It’s up for debate what exactly is off and what should be done about it, but the undeniable proof that something is profoundly off is the deep unpopularity surrounding everything related to AI. You can’t argue that the public always turns against groundbreaking technology. The last two epoch-defining shifts in technology were the smartphone in the 2000s, and the Internet/web in the 1990s. Neither of those moments generated this sort of mainstream popular backlash. I’d say in both of those cases, regular people were optimistically curious. The single most distinctive thing about “AI” today is the vociferous public opposition to it and deeply pessimistic expectations about what it’s going to do. You can’t advertise people out of reacting to their own experiences. This is a fundamental disconnect between how tech people with software brains see the world and how regular people are living their lives. So what is software brain? The simplest definition I’ve come up with is that it’s when you see the whole world as a series of databases that can be controlled with the structured language of software code. Like I said, this is a powerful way of seeing things. So much of our lives run through databases, and a bunch of important companies have been built around maintaining those databases and providing access to them. Zillow is a database of houses. Uber is a database of cars and riders. YouTube is a database of videos. The Verge’s website is a database of stories. You can go on and on and on. Once you start seeing the world as a bunch of databases, it’s a small jump to feeling like you can control everything if you can just control the data. But that doesn’t always work. “Software brain” is a good term — a tidy two-word encapsulation of a sprawling worldview that is currently very much in vogue. Take some time to read Patel’s whole piece carefully. It feels important, and it’s really well considered.  ★