轉載說明：本文原作者為 Matt Shumer（HyperWrite CEO），原文發布於 2026 年 2 月 11 日的 X (Twitter)。原文鏈接：Something Big Is Happening。以下為中英雙語對照版本，英文為原文，中文為翻譯。
Think back to February 2020.
回想 2020 年 2 月。
If you were paying close attention, you might have noticed a few people talking about a virus spreading overseas. But most of us weren't paying close attention. The stock market was doing great, your kids were in school, you were going to restaurants and shaking hands and planning trips. If someone told you they were stockpiling toilet paper you would have thought they'd been spending too much time on a weird corner of the internet. Then, over the course of about three weeks, the entire world changed. Your office closed, your kids came home, and life rearranged itself into something you wouldn't have believed if you'd described it to yourself a month earlier.
如果你當時特別留意的話，可能會注意到有些人在談論一種在海外傳播的病毒。但我們大多數人並沒有特別留意。股市一片繁榮，你的孩子在上學，你會去餐廳吃飯、和人握手、計劃旅行。如果有人告訴你他們在囤積衛生紙，你會覺得他們在互聯網某個奇怪的角落待太久了。然後，在大約三周的時間裏，整個世界發生了翻天覆地的變化。你的辦公室關閉了，孩子們回到家中，生活重新編排成了一個月前你絕不會相信的樣子。
I think we're in the "this seems overblown" phase of something much, much bigger than Covid.
我認為我們正處於某件事的"這看起來有點小題大做"階段，而這件事比新冠疫情要大得多、大得多。
I've spent six years building an AI startup and investing in the space. I live in this world. And I'm writing this for the people in my life who don't — my family, my friends, the people I care about who keep asking me "so what's the deal with AI?" and getting an answer that doesn't do justice to what's actually happening. I keep giving them the polite version. The cocktail-party version. Because the honest version sounds like I've lost my mind. And for a while, I told myself that was a good enough reason to keep what's truly happening to myself. But the gap between what I've been saying and what is actually happening has gotten far too big. The people I care about deserve to hear what is coming, even if it sounds crazy.
我花了六年時間打造一家 AI 創業公司並在這個領域投資。我生活在這個世界裏。我寫這篇文章是為了那些不在這個世界的人——我的家人、朋友，那些我關心的、不斷問我"AI 到底是怎麽回事？"卻得不到與實際情況相符的答案的人。我一直給他們禮貌的版本，雞尾酒會上的版本。因為誠實的版本聽起來像是我瘋了。有一段時間，我說服自己這是一個足夠好的理由，可以把真實情況藏在心裏。但我所說的和實際發生的事情之間的差距已經太大了。我關心的人值得聽到即將到來的事情，即使聽起來很瘋狂。
I should be clear about something up front: even though I work in AI, I have almost no influence over what's about to happen, and neither does the vast majority of the industry. The future is being shaped by a remarkably small number of people: a few hundred researchers at a handful of companies — OpenAI, Anthropic, Google DeepMind, and a few others. A single training run, managed by a small team over a few months, can produce an AI system that shifts the entire trajectory of the technology. Most of us who work in AI are building on top of foundations we didn't lay. We're watching this unfold the same as you — we just happen to be close enough to feel the ground shake first.
我需要先澄清一點：盡管我從事 AI 工作，但對即將發生的事情幾乎沒有影響力，這個行業的絕大多數人也是如此。未來正由數量極少的一群人塑造：少數幾家公司——OpenAI、Anthropic、Google DeepMind 以及其他幾家——的幾百名研究人員。一次由小團隊在幾個月內管理的訓練輪次，就能產生一個改變整個技術軌跡的 AI 係統。我們這些從事 AI 工作的人，大多數都是在我們沒有奠定的基礎上構建。我們和你們一樣在觀察這一切的展開——隻是我們恰好離得足夠近，能最先感受到地麵的震動。
But it's time now. Not in an "eventually we should talk about this" way. In a "this is happening right now and I need you to understand it" way.
但現在是時候了。不是"我們最終應該談談這個"的那種方式。而是**"這正在發生，我需要你理解它"**的方式。
I know this is real because it happened to me first
我知道這是真的，因為它首先發生在我身上
Here's the thing nobody outside of tech quite understands yet: the reason so many people in the industry are sounding the alarm right now is because this already happened to us. We're not making predictions. We're telling you what already occurred in our own jobs, and warning you that you're next.
這是科技圈外的人還不太理解的事情：這個行業有這麽多人現在敲響警鍾的原因是，這已經發生在我們身上了。我們不是在做預測。我們在告訴你我們自己的工作中已經發生的事情，並警告你，你是下一個。
For years, AI had been improving steadily. Big jumps here and there, but each big jump was spaced out enough that you could absorb them as they came. Then in 2025, new techniques for building these models unlocked a much faster pace of progress. And then it got even faster. And then faster again. Each new model wasn't just better than the last — it was better by a wider margin, and the time between new model releases was shorter. I was using AI more and more, going back and forth with it less and less, watching it handle things I used to think required my expertise.
多年來，AI 一直在穩步改進。這裏那裏有些大跳躍，但每次大跳躍之間的間隔足夠長，你可以逐個消化它們。然後到了 2025 年，構建這些模型的新技術解鎖了更快的進步速度。然後變得更快了。然後又更快了。每個新模型不僅僅比上一個更好——它的優勢更大，而新模型發布之間的時間更短了。我越來越多地使用 AI，與它來回溝通越來越少，看著它處理我曾經認為需要我專業知識的事情。
Then, on February 5th, two major AI labs released new models on the same day: GPT-5.3 Codex from OpenAI, and Opus 4.6 from Anthropic (the makers of Claude, one of the main competitors to ChatGPT). And something clicked. Not like a light switch — more like the moment you realize the water has been rising around you and is now at your chest.
然後，在 2 月 5 日，兩個主要的 AI 實驗室在同一天發布了新模型：OpenAI 的 GPT-5.3 Codex，以及 Anthropic（Claude 的製造商，ChatGPT 的主要競爭對手之一）的 Opus 4.6。某種東西哢嗒一聲到位了。不像開燈那樣——更像是你意識到水一直在你周圍上漲，現在已經到了你胸口的那一刻。
I am no longer needed for the actual technical work of my job. I describe what I want built, in plain English, and it just... appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done. Done well, done better than I would have done it myself, with no corrections needed. A couple of months ago, I was going back and forth with the AI, guiding it, making edits. Now I just describe the outcome and leave.
我的工作中實際的技術工作不再需要我了。 我用簡單的英語描述我想要構建什麽，然後它就……出現了。不是我需要修改的草稿，而是完成品。我告訴 AI 我想要什麽，離開電腦四個小時，回來發現工作完成了。完成得很好，比我自己做得還好，不需要任何修正。幾個月前，我還在與 AI 來回溝通，引導它，做編輯。現在我隻需描述結果然後離開。
Let me give you an example so you can understand what this actually looks like in practice. I'll tell the AI: "I want to build this app. Here's what it should do, here's roughly what it should look like. Figure out the user flow, the design, all of it." And it does. It writes tens of thousands of lines of code. Then, and this is the part that would have been unthinkable a year ago, it opens the app itself. It clicks through the buttons. It tests the features. It uses the app the way a person would. If it doesn't like how something looks or feels, it goes back and changes it, on its own. It iterates, like a developer would, fixing and refining until it's satisfied. Only once it has decided the app meets its own standards does it come back to me and say: "It's ready for you to test." And when I test it, it's usually perfect.
讓我給你一個例子，這樣你就能理解實際操作中是什麽樣子。我會告訴 AI："我想構建這個應用。它應該做這些事情，大概應該是這個樣子。搞定用戶流程、設計，所有這些。"然後它就做了。它寫了數萬行代碼。然後，這是一年前不可想象的部分，它自己打開應用。它點擊按鈕。它測試功能。它像人一樣使用應用。如果它不喜歡某個東西的外觀或感覺，它會自己回去改。它像開發者那樣迭代，修複和完善，直到滿意為止。隻有當它認為應用符合自己的標準時，才會回來對我說："準備好讓你測試了。"當我測試時，通常是完美的。
I'm not exaggerating. That is what my Monday looked like this week.
我沒有誇大。這就是我這周一的情況。
But it was the model that was released last week (GPT-5.3 Codex) that shook me the most. It wasn't just executing my instructions. It was making intelligent decisions. It had something that felt, for the first time, like judgment. Like taste. The inexplicable sense of knowing what the right call is that people always said AI would never have. This model has it, or something close enough that the distinction is starting not to matter.
但最讓我震驚的是上周發布的模型（GPT-5.3 Codex）。它不僅僅是執行我的指令。它在做智能決策。它第一次擁有了某種感覺像判斷力的東西。像品味。那種人們一直說 AI 永遠不會有的、難以言喻的知道什麽是正確選擇的感覺。這個模型擁有它，或者足夠接近它，以至於區別開始變得不重要了。
I've always been early to adopt AI tools. But the last few months have shocked me. These new AI models aren't incremental improvements. This is a different thing entirely.
我一直是 AI 工具的早期采用者。但過去幾個月讓我震驚了。這些新的 AI 模型不是漸進式改進。這完全是另一回事。
And here's why this matters to you, even if you don't work in tech.
這就是為什麽這對你很重要，即使你不在科技行業工作。
The AI labs made a deliberate choice. They focused on making AI great at writing code first — because building AI requires a lot of code. If AI can write that code, it can help build the next version of itself. A smarter version, which writes better code, which builds an even smarter version. Making AI great at coding was the strategy that unlocks everything else. That's why they did it first. My job started changing before yours not because they were targeting software engineers — it was just a side effect of where they chose to aim first.
AI 實驗室做出了一個深思熟慮的選擇。他們首先專注於讓 AI 擅長寫代碼——因為構建 AI 需要大量代碼。如果 AI 能寫這些代碼，它就能幫助構建下一個版本的自己。一個更聰明的版本，寫出更好的代碼，構建一個更聰明的版本。讓 AI 擅長編碼是解鎖其他一切的策略。這就是為什麽他們首先這麽做。我的工作比你的先開始改變，不是因為他們瞄準了軟件工程師——這隻是他們選擇首先瞄準的方向的副作用。
They've now done it. And they're moving on to everything else.
他們現在已經做到了。他們正在轉向其他一切。
The experience that tech workers have had over the past year, of watching AI go from "helpful tool" to "does my job better than I do", is the experience everyone else is about to have. Law, finance, medicine, accounting, consulting, writing, design, analysis, customer service. Not in ten years. The people building these systems say one to five years. Some say less. And given what I've seen in just the last couple of months, I think "less" is more likely.
技術工作者在過去一年的經曆——看著 AI 從"有用的工具"變成"比我做得更好"，是其他所有人即將擁有的經曆。法律、金融、醫療、會計、谘詢、寫作、設計、分析、客戶服務。不是十年後。構建這些係統的人說一到五年。有些人說更少。考慮到我在過去幾個月看到的，我認為"更少"更有可能。
"But I tried AI and it wasn't that good"
"但我試過 AI，它並不怎麽樣"
I hear this constantly. I understand it, because it used to be true.
我經常聽到這樣的說法。我理解這種感受，因為這曾經是事實。
If you tried ChatGPT in 2023 or early 2024 and thought "this makes stuff up" or "this isn't that impressive", you were right. Those early versions were genuinely limited. They hallucinated. They confidently said things that were nonsense.
如果你在 2023 年或 2024 年初試用過 ChatGPT，覺得"它會編造東西"或"沒什麽了不起"，你是對的。那些早期版本確實存在局限。它們會產生幻覺，信心十足地說出一些無稽之談。
That was two years ago. In AI time, that is ancient history.
那是兩年前的事了。在 AI 的時間尺度裏，那已經是遠古曆史。
The models available today are unrecognizable from what existed even six months ago. The debate about whether AI is "really getting better" or "hitting a wall" — which has been going on for over a year — is over. It's done. Anyone still making that argument either hasn't used the current models, has an incentive to downplay what's happening, or is evaluating based on an experience from 2024 that is no longer relevant. I don't say that to be dismissive. I say it because the gap between public perception and current reality is now enormous, and that gap is dangerous — because it's preventing people from preparing.
如今可用的模型與六個月前的版本已判若雲泥。關於 AI 是"真的在進步"還是"遇到瓶頸"的爭論——這場持續了一年多的辯論——已經結束了。塵埃落定。仍在堅持這種論調的人，要麽沒用過最新的模型，要麽有動機淡化正在發生的事情，要麽是基於 2024 年的體驗在評判，而那些體驗已經不再適用。我這麽說不是為了輕視誰。我這麽說是因為公眾認知與當前現實之間的鴻溝已經變得極其巨大，而這種鴻溝是危險的——因為它正在阻止人們做好準備。
Part of the problem is that most people are using the free version of AI tools. The free version is over a year behind what paying users have access to. Judging AI based on free-tier ChatGPT is like evaluating the state of smartphones by using a flip phone. The people paying for the best tools, and actually using them daily for real work, know what's coming.
問題的一部分在於，大多數人使用的是 AI 工具的免費版本。免費版本比付費用戶能使用的版本落後一年多。基於免費版 ChatGPT 來評判 AI，就像用翻蓋手機來評估智能手機的發展狀況。那些為最佳工具付費，並且每天在實際工作中使用它們的人，知道未來會發生什麽。
I think of my friend, who's a lawyer. I keep telling him to try using AI at his firm, and he keeps finding reasons it won't work. It's not built for his specialty, it made an error when he tested it, it doesn't understand the nuance of what he does. And I get it. But I've had partners at major law firms reach out to me for advice, because they've tried the current versions and they see where this is going. One of them, the managing partner at a large firm, spends hours every day using AI. He told me it's like having a team of associates available instantly. He's not using it because it's a toy. He's using it because it works. And he told me something that stuck with me: every couple of months, it gets significantly more capable for his work. He said if it stays on this trajectory, he expects it'll be able to do most of what he does before long — and he's a managing partner with decades of experience. He's not panicking. But he's paying very close attention.
我想到我的一個朋友，他是律師。我一直勸他在律所嚐試使用 AI，他卻總能找到理由說明它行不通。它不是為他的專業領域設計的，他測試時它出了錯，它無法理解他工作中的微妙之處。我理解他的想法。但是，一些大型律所的合夥人聯係我尋求建議，因為他們試用了最新版本，看到了發展方向。其中一位，一家大型律所的管理合夥人，每天花好幾個小時使用 AI。他告訴我，這就像瞬間擁有一整支初級律師團隊。他使用它不是因為它是個玩具，而是因為它真的管用。他告訴我一件讓我印象深刻的事：每隔幾個月，它在他的工作中的能力就會顯著提升。他說如果保持這個勢頭，他預計用不了多久它就能完成他所做的大部分工作——而他可是一位有著數十年經驗的管理合夥人。他沒有恐慌，但他在非常密切地關注著。
The people who are ahead in their industries (the ones actually experimenting seriously) are not dismissing this. They're blown away by what it can already do. And they're positioning themselves accordingly.
在各自行業中走在前麵的人（那些真正在認真試驗的人）並沒有對此不屑一顧。他們對 AI 現在能做的事情感到震撼。他們正在相應地調整自己的定位。
How fast this is actually moving
這一切實際上進展得有多快
Let me make the pace of improvement concrete, because I think this is the part that's hardest to believe if you're not watching it closely.
讓我把這種改進的速度具體化，因為我認為如果你不密切關注，這部分是最難以置信的。
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• 2022: AI couldn't do basic arithmetic reliably. It would confidently tell you that 7 × 8 = 54. -
• 2023: It could pass the bar exam. -
• 2024: It could write working software and explain graduate-level science. -
• Late 2025: Some of the best engineers in the world said they had handed over most of their coding work to AI. -
• February 5th, 2026: New models arrived that made everything before them feel like a different era. -
• 2022 年：AI 無法可靠地完成基礎算術。它會信心滿滿地告訴你 7 × 8 = 54。 -
• 2023 年：它能通過律師資格考試。 -
• 2024 年：它能編寫可運行的軟件並解釋研究生級別的科學知識。 -
• 2025 年底：世界上一些最頂尖的工程師說他們已經把大部分編程工作交給了 AI。 -
• 2026 年 2 月 5 日：新模型的到來讓之前的一切感覺像是另一個時代。
If you haven't tried AI in the last few months, what exists today would be unrecognizable to you.
如果你在過去幾個月裏沒有試用過 AI，今天存在的東西對你來說將是麵目全非的。
There's an organization called METR that actually measures this with data. They track the length of real-world tasks (measured by how long they take a human expert) that a model can complete successfully end-to-end without human help. About a year ago, the answer was roughly ten minutes. Then it was an hour. Then several hours. The most recent measurement (Claude Opus 4.5, from November) showed the AI completing tasks that take a human expert nearly five hours. And that number is doubling approximately every seven months, with recent data suggesting it may be accelerating to as fast as every four months.
有一個名為 METR 的組織實際上在用數據測量這一點。他們追蹤模型在無需人類幫助的情況下能端到端成功完成的真實世界任務的長度（以人類專家完成所需的時間來衡量）。大約一年前，答案是大約十分鍾。然後是一小時。接著是幾小時。最近的測量（來自 11 月的 Claude Opus 4.5）顯示 AI 完成了需要人類專家近五小時才能完成的任務。這個數字大約每七個月翻一番，最新數據表明它可能正在加速到每四個月翻一番。
But even that measurement hasn't been updated to include the models that just came out this week. In my experience using them, the jump is extremely significant. I expect the next update to METR's graph to show another major leap.
但即使是這個測量也還沒有更新到納入本周剛發布的模型。根據我的使用體驗，這次跨越極其顯著。我預計 METR 圖表的下一次更新將顯示另一次重大飛躍。
If you extend the trend (and it's held for years with no sign of flattening) we're looking at AI that can work independently for days within the next year. Weeks within two. Month-long projects within three.
如果你延伸這一趨勢（而且它已經持續多年沒有放緩的跡象），我們正在麵對的是：明年內 AI 能獨立工作數天，兩年內能工作數周，三年內能完成持續一個月的項目。
Amodei has said that AI models "substantially smarter than almost all humans at almost all tasks" are on track for 2026 or 2027.
Amodei 曾說過，AI 模型"在幾乎所有任務上都比幾乎所有人類聰明得多"有望在 2026 年或 2027 年實現。
Let that land for a second. If AI is smarter than most PhDs, do you really think it can't do most office jobs?
讓這句話沉澱一下。如果 AI 比大多數博士都聰明，你真的認為它做不了大多數辦公室工作嗎？
Think about what that means for your work.
想想這對你的工作意味著什麽。
AI is now building the next AI
AI 正在構建下一代 AI
There's one more thing happening that I think is the most important development and the least understood.
還有一件正在發生的事情，我認為這是最重要的進展，也是最不為人所理解的。
On February 5th, OpenAI released GPT-5.3 Codex. In the technical documentation, they included this:
2 月 5 日，OpenAI 發布了 GPT-5.3 Codex。在技術文檔中，他們寫道：
"GPT-5.3-Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations."
"GPT-5.3-Codex 是我們第一個在創建自身過程中發揮關鍵作用的模型。Codex 團隊使用早期版本來調試自己的訓練、管理自己的部署，並診斷測試結果和評估。"
Read that again. The AI helped build itself.
再讀一遍。AI 幫助構建了它自己。
This isn't a prediction about what might happen someday. This is OpenAI telling you, right now, that the AI they just released was used to create itself. One of the main things that makes AI better is intelligence applied to AI development. And AI is now intelligent enough to meaningfully contribute to its own improvement.
這不是關於未來某天可能發生什麽的預測。這是 OpenAI 現在就在告訴你，他們剛剛發布的 AI 被用來創建它自己。讓 AI 變得更好的主要因素之一，就是將智能應用於 AI 開發本身。而 AI 現在已經足夠智能，可以有意義地促進自身的改進。
Dario Amodei, the CEO of Anthropic, says AI is now writing "much of the code" at his company, and that the feedback loop between current AI and next-generation AI is "gathering steam month by month." He says we may be "only 1–2 years away from a point where the current generation of AI autonomously builds the next."
Anthropic 的首席執行官 Dario Amodei 說，AI 現在正在他的公司編寫"大部分代碼"，當前 AI 和下一代 AI 之間的反饋循環正在"逐月加速"。他說，我們可能"距離當前一代 AI 自主構建下一代的時刻隻有 1-2 年"。
Each generation helps build the next, which is smarter, which builds the next faster, which is smarter still. The researchers call this an intelligence explosion. And the people who would know — the ones building it — believe the process has already started.
每一代幫助構建下一代，下一代更聰明，然後更快地構建再下一代，而再下一代更加聰明。研究人員稱之為智能爆炸。而那些應該知道的人——那些正在構建它的人——相信這個過程已經開始了。
What this means for your job
這對你的工作意味著什麽
I'm going to be direct with you because I think you deserve honesty more than comfort.
我將直言不諱，因為我認為你更需要誠實而非安慰。
Dario Amodei, who is probably the most safety-focused CEO in the AI industry, has publicly predicted that AI will eliminate 50% of entry-level white-collar jobs within one to five years. And many people in the industry think he's being conservative. Given what the latest models can do, the capability for massive disruption could be here by the end of this year. It'll take some time to ripple through the economy, but the underlying ability is arriving now.
Dario Amodei 可能是 AI 行業中最關注安全的 CEO，他公開預測 AI 將在一到五年內消除 50% 的初級白領工作。而業內許多人認為他的預測還是保守的。鑒於最新模型的能力，大規模顛覆的能力可能在今年年底就會到來。它需要一些時間才能在經濟中產生連鎖反應，但這種底層能力現在已經在到來。
This is different from every previous wave of automation, and I need you to understand why. AI isn't replacing one specific skill. It's a general substitute for cognitive work. It gets better at everything simultaneously. When factories automated, a displaced worker could retrain as an office worker. When the internet disrupted retail, workers moved into logistics or services. But AI doesn't leave a convenient gap to move into. Whatever you retrain for, it's improving at that too.
這與以往所有的自動化浪潮都不同，我需要你理解為什麽。AI 不是在替代某一項特定技能。它是認知工作的通用替代品。它在所有方麵同時變得更好。當工廠自動化時，被取代的工人可以重新培訓成為辦公室職員。當互聯網顛覆零售業時，工人轉移到物流或服務業。但 AI 不會留下一個方便你轉移的空檔。無論你重新培訓什麽，它在那方麵也在進步。
Let me give you a few specific examples to make this tangible — but I want to be clear that these are just examples. This list is not exhaustive. If your job isn't mentioned here, that does not mean it's safe. Almost all knowledge work is being affected.
讓我給你一些具體的例子來讓這變得更具體——但我想明確的是，這些隻是例子。這個列表並不詳盡。如果你的工作沒有在這裏提到，並不意味著它是安全的。幾乎所有的知識工作都在受到影響。
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• Legal work. AI can already read contracts, summarize case law, draft briefs, and do legal research at a level that rivals junior associates. The managing partner I mentioned isn't using AI because it's fun. He's using it because it's outperforming his associates on many tasks. -
• 法律工作。 AI 已經可以閱讀合同、總結判例法、起草訴狀，並進行法律研究，其水平可以媲美初級律師。我提到的管理合夥人使用 AI 不是因為好玩。他使用它是因為它在許多任務上的表現超過了他的初級律師。 -
• Financial analysis. Building financial models, analyzing data, writing investment memos, generating reports. AI handles these competently and is improving fast. -
• 金融分析。 構建財務模型、分析數據、撰寫投資備忘錄、生成報告。AI 能夠勝任地處理這些工作，而且正在快速改進。 -
• Writing and content. Marketing copy, reports, journalism, technical writing. The quality has reached a point where many professionals can't distinguish AI output from human work. -
• 寫作和內容。 營銷文案、報告、新聞報道、技術寫作。質量已經達到了許多專業人士無法區分 AI 輸出和人類作品的程度。 -
• Software engineering. This is the field I know best. A year ago, AI could barely write a few lines of code without errors. Now it writes hundreds of thousands of lines that work correctly. Large parts of the job are already automated: not just simple tasks, but complex, multi-day projects. There will be far fewer programming roles in a few years than there are today. -
• 軟件工程。 這是我最了解的領域。一年前，AI 幾乎無法寫出幾行沒有錯誤的代碼。現在它可以寫出數十萬行正確運行的代碼。工作的很大一部分已經自動化了：不僅僅是簡單的任務，還有複雜的、需要多天的項目。幾年後的編程崗位將遠少於今天。 -
• Medical analysis. Reading scans, analyzing lab results, suggesting diagnoses, reviewing literature. AI is approaching or exceeding human performance in several areas. -
• 醫學分析。 閱讀掃描圖像、分析實驗室結果、提出診斷建議、審查文獻。AI 在幾個領域正在接近或超越人類表現。 -
• Customer service. Genuinely capable AI agents — not the frustrating chatbots of five years ago — are being deployed now, handling complex multi-step problems. -
• 客戶服務。 真正有能力的 AI 代理——不是五年前那些令人沮喪的聊天機器人——現在正在被部署，處理複雜的多步驟問題。
A lot of people find comfort in the idea that certain things are safe. That AI can handle the grunt work but can't replace human judgment, creativity, strategic thinking, empathy. I used to say this too. I'm not sure I believe it anymore.
很多人會從"有些事情是安全的"這個想法裏獲得安慰。認為 AI 可以處理繁重的工作，但無法替代人類的判斷力、創造力、戰略思維和同理心。我以前也這麽說。但我不確定我還相信這一點。
The most recent AI models make decisions that feel like judgment. They show something that looked like taste: an intuitive sense of what the right call was, not just the technically correct one. A year ago that would have been unthinkable. My rule of thumb at this point is: if a model shows even a hint of a capability today, the next generation will be genuinely good at it. These things improve exponentially, not linearly.
最新的 AI 模型做出的決策感覺像是判斷。它們展現出某種看起來像品味的東西：一種對什麽是正確選擇的直覺感知，而不僅僅是技術上正確的。一年前這是不可想象的。我現在的經驗法則是：如果一個模型今天顯示出某種能力的一點跡象，下一代就會真正擅長它。 這些事物是指數級改進的，而不是線性的。
Will AI replicate deep human empathy? Replace the trust built over years of a relationship? I don't know. Maybe not. But I've already watched people begin relying on AI for emotional support, for advice, for companionship. That trend is only going to grow.
AI 會複製深層的人類同理心嗎？會取代多年關係中建立起來的信任嗎？我不知道。也許不會。但我已經看到人們開始依賴 AI 獲得情感支持、建議和陪伴。這種趨勢隻會增長。
I think the honest answer is that nothing that can be done on a computer is safe in the medium term. If your job happens on a screen — if the core of what you do is reading, writing, analyzing, deciding, communicating through a keyboard — then AI is coming for significant parts of it. The timeline isn't "someday." It's already started.
我認為誠實的答案是，從中期來看，任何可以在計算機上完成的事情都不安全。 如果你的工作發生在屏幕上——如果你所做的核心工作是閱讀、寫作、分析、決策、通過鍵盤交流——那麽 AI 正在取代它的重要部分。時間線不是"某天"。它已經開始了。
Eventually, robots will handle physical work too. They're not quite there yet. But "not quite there yet" in AI terms has a way of becoming "here" faster than anyone expects.
最終，機器人也會處理體力工作。它們還沒有完全達到那一步。但在 AI 領域，"還差一點"往往會比任何人預期都更快地變成"已經到來"。
What you should actually do
你實際應該做什麽
I'm not writing this to make you feel helpless. I'm writing this because I think the single biggest advantage you can have right now is simply being early. Early to understand it. Early to use it. Early to adapt.
我寫這篇文章不是為了讓你感到無助。我寫這篇文章是因為我認為，你現在能擁有的最大優勢就是搶先一步。搶先理解它。搶先使用它。搶先適應它。
Start using AI seriously, not just as a search engine. Sign up for the paid version of Claude or ChatGPT. It's $20 a month. But two things matter right away. First: make sure you're using the best model available, not just the default. These apps often default to a faster, dumber model. Dig into the settings or the model picker and select the most capable option. Right now that's GPT-5.2 on ChatGPT or Claude Opus 4.6 on Claude, but it changes every couple of months.
認真開始使用 AI，而不僅僅把它當作搜索引擎。 訂閱 Claude 或 ChatGPT 的付費版本。每月 20 美元。但有兩件事馬上就很重要。第一：確保你使用的是可用的最佳模型，而不僅僅是默認模型。這些應用通常默認使用更快但更笨的模型。深入設置或模型選擇器，選擇最強大的選項。現在是 ChatGPT 上的 GPT-5.2 或 Claude 上的 Claude Opus 4.6，但每隔幾個月就會變化。
Second, and more important: don't just ask it quick questions. That's the mistake most people make. They treat it like Google and then wonder what the fuss is about. Instead, push it into your actual work. If you're a lawyer, feed it a contract and ask it to find every clause that could hurt your client. If you're in finance, give it a messy spreadsheet and ask it to build the model. If you're a manager, paste in your team's quarterly data and ask it to find the story. The people who are getting ahead aren't using AI casually. They're actively looking for ways to automate parts of their job that used to take hours. Start with the thing you spend the most time on and see what happens.
第二點，也更重要：不要隻是問它幾個快問快答。 這是大多數人犯的錯誤。他們把它當作 Google 對待，然後不明白有什麽大驚小怪的。相反，把它推進到你的實際工作中。如果你是律師，給它一份合同，讓它找出每一條可能傷害你客戶的條款。如果你在金融領域，給它一個混亂的電子表格，讓它建立模型。如果你是經理，粘貼你團隊的季度數據，讓它提煉出關鍵結論。取得進展的人不是隨意使用 AI。他們積極尋找方法來自動化他們工作中過去需要幾個小時的部分。從你花費最多時間的事情開始，看看會發生什麽。
And don't assume it can't do something just because it seems too hard. Try it. If you're a lawyer, don't just use it for quick research questions. Give it an entire contract and ask it to draft a counterproposal. If you're an accountant, don't just ask it to explain a tax rule. Give it a client's full return and see what it finds. The first attempt might not be perfect. That's fine. Iterate. Rephrase what you asked. Give it more context. Try again. You might be shocked at what works. And here's the thing to remember: if it even kind of works today, you can be almost certain that in six months it'll do it near perfectly. The trajectory only goes one direction.
不要因為某件事看起來太難就假設它做不到。試試看。如果你是律師，不要隻是用它來快速研究問題。給它一整份合同，讓它起草反提案。如果你是會計師，不要隻是讓它解釋稅法規則。給它客戶的完整報稅表，看看它能發現什麽。第一次嚐試可能不完美。沒關係。迭代。重新表述你的問題。給它更多背景信息。再試一次。你可能會對有效的東西感到震驚。這裏要記住的是：如果它今天甚至有點管用，你幾乎可以肯定六個月後它會做得近乎完美。 這個軌跡隻朝一個方向前進。
This might be the most important year of your career. Work accordingly. I don't say that to stress you out. I say it because right now, there is a brief window where most people at most companies are still ignoring this. The person who walks into a meeting and says "I used AI to do this analysis in an hour instead of three days" is going to be the most valuable person in the room. Not eventually. Right now. Learn these tools. Get proficient. Demonstrate what's possible. If you're early enough, this is how you move up: by being the person who understands what's coming and can show others how to navigate it. That window won't stay open long. Once everyone figures it out, the advantage disappears.
這可能是你職業生涯中最重要的一年。請據此行動。 我這麽說不是為了讓你感到壓力。我這麽說是因為現在有一個短暫的窗口期，大多數公司的大多數人仍然在忽視這一點。走進會議室說"我用 AI 在一小時內完成了這項分析，而不是三天"的人將成為房間裏最有價值的人。不是最終。就是現在。學習這些工具。變得熟練。展示可能性。如果你足夠早，這就是你晉升的方式：成為理解即將到來的事物並能向他人展示如何應對的人。這個窗口不會保持太久。一旦每個人都弄明白了，優勢就消失了。
Have no ego about it. The managing partner at that law firm isn't too proud to spend hours a day with AI. He's doing it specifically because he's senior enough to understand what's at stake. The people who will struggle most are the ones who refuse to engage: the ones who dismiss it as a fad, who feel that using AI diminishes their expertise, who assume their field is special and immune. It's not. No field is.
別端著架子。 那家律師事務所的管理合夥人不會因為每天花幾個小時使用 AI 而覺得掉價。他這樣做恰恰是因為他資曆足夠深，能夠理解利害關係。最困難的將是那些拒絕參與的人：那些將其視為一時流行的人，那些覺得使用 AI 會削弱他們專業知識的人，那些假設自己的領域特殊且免疫的人。事實並非如此。沒有哪個領域能幸免。
Get your financial house in order. I'm not a financial advisor, and I'm not trying to scare you into anything drastic. But if you believe, even partially, that the next few years could bring real disruption to your industry, then basic financial resilience matters more than it did a year ago. Build up savings if you can. Be cautious about taking on new debt that assumes your current income is guaranteed. Think about whether your fixed expenses give you flexibility or lock you in. Give yourself options if things move faster than you expect.
整理好你的財務狀況。 我不是財務顧問，我也不是想嚇唬你做任何激進的事情。但如果你相信，哪怕隻是部分相信，未來幾年可能會給你的行業帶來真正的顛覆，那麽基本的財務韌性比一年前更重要。如果可以的話，積累儲蓄。謹慎對待那些假設你目前收入有保障的新債務。考慮你的固定支出是給你靈活性還是把你鎖定。如果事情發展得比你預期更快，給自己留下選擇餘地。
Think about where you stand, and lean into what's hardest to replace. Some things will take longer for AI to displace. Relationships and trust built over years. Work that requires physical presence. Roles with licensed accountability: roles where someone still has to sign off, take legal responsibility, stand in a courtroom. Industries with heavy regulatory hurdles, where adoption will be slowed by compliance, liability, and institutional inertia. None of these are permanent shields. But they buy time. And time, right now, is the most valuable thing you can have, as long as you use it to adapt, not to pretend this isn't happening.
思考你的處境，向最難被替代的方向靠攏。 有些東西 AI 需要更長時間才能取代。多年建立的關係和信任。需要實際在場的工作。需要持證並承擔法定責任的崗位：仍然需要有人簽字、承擔法律責任、站在法庭上的崗位。具有嚴格監管壁壘的行業，采用將因合規性、責任和製度慣性而放緩。這些都不是永久的保護盾。但它們爭取了時間。而時間，現在，是你能擁有的最寶貴的東西，隻要你用它來適應，而不是假裝這一切沒有發生。
Rethink what you're telling your kids. The standard playbook — get good grades, go to a good college, land a stable professional job — it points directly at the roles that are most exposed. I'm not saying education doesn't matter. But the thing that will matter most for the next generation is learning how to work with these tools, and pursuing things they're genuinely passionate about. Nobody knows exactly what the job market looks like in ten years. But the people most likely to thrive are the ones who are deeply curious, adaptable, and effective at using AI to do things they actually care about. Teach your kids to be builders and learners, not to optimize for a career path that might not exist by the time they graduate.
重新思考你對孩子說的話。 標準劇本——取得好成績，上好大學，找到穩定的專業工作——它直接指向最暴露的崗位。我不是說教育不重要。但對下一代最重要的事情是學習如何使用這些工具，並追求他們真正熱衷的事情。沒有人確切知道十年後的就業市場會是什麽樣子。但最有可能繁榮的人是那些深刻好奇、適應能力強、並且有效地使用 AI 做他們真正關心的事情的人。教你的孩子成為創造者和學習者，而不是為他們畢業時可能不存在的職業道路做優化。
Your dreams just got a lot closer. I've spent most of this section talking about threats, so let me talk about the other side, because it's just as real. If you've ever wanted to build something but didn't have the technical skills or the money to hire someone, that barrier is largely gone. You can describe an app to AI and have a working version in an hour. I'm not exaggerating. I do this regularly. If you've always wanted to write a book but couldn't find the time or struggled with the writing, you can work with AI to get it done. Want to learn a new skill? The best tutor in the world is now available to anyone for $20 a month — one that's infinitely patient, available 24/7, and can explain anything at whatever level you need. Knowledge is essentially free now. The tools to build things are extremely cheap now. Whatever you've been putting off because it felt too hard or too expensive or too far outside your expertise: try it. Pursue the things you're passionate about. You never know where they'll lead. And in a world where the old career paths are getting disrupted, the person who spent a year building something they love might end up better positioned than the person who spent that year clinging to a job description.
你的夢想剛剛變得更近了。 我在這一部分大部分時間都在談論威脅，所以讓我談談另一麵，因為它同樣真實。如果你曾經想要做出一些東西，但沒有技術能力或雇人的資金，那個障礙基本上已經消失了。你可以向 AI 描述一個應用程序，並在一小時內獲得一個可用版本。我沒有誇張，我經常這樣做。如果你一直想寫一本書，但找不到時間或在寫作上遇到困難，你可以與 AI 合作完成它。想學習一項新技能？世界上最好的導師現在每月 20 美元就可以為任何人服務——一個無限耐心、全天候可用、可以在任何你需要的水平上解釋任何事情的導師。知識現在基本上是免費的。構建事物的工具現在極其便宜。無論你因為感覺太難、太貴或太超出你的專業知識而推遲的任何事情：試試看。追求你熱衷的事情。你永遠不知道它們會通向哪裏。在舊的職業道路正在被顛覆的世界裏，花一年時間構建自己熱愛之物的人，可能最終比花那一年緊緊抓住職位描述的人處於更好的位置。
Build the habit of adapting. This is maybe the most important one. The specific tools don't matter as much as the muscle of learning new ones quickly. AI is going to keep changing, and fast. The models that exist today will be obsolete in a year. The workflows people build now will need to be rebuilt. The people who come out of this well won't be the ones who mastered one tool. They'll be the ones who got comfortable with the pace of change itself. Make a habit of experimenting. Try new things even when the current thing is working. Get comfortable being a beginner repeatedly. That adaptability is the closest thing to a durable advantage that exists right now.
培養適應的習慣。 這可能是最重要的一條。具體工具不如快速學習新工具的能力重要。AI 將繼續變化，而且很快。今天存在的模型將在一年內過時。人們現在建立的工作流程將需要重建。從中脫穎而出的人不會是掌握了一個工具的人。他們將是對變化本身的速度感到自如的人。養成實驗的習慣。即使當前的方法正在起作用，也要嚐試新事物。反複適應當一個初學者的狀態。這種適應能力是目前存在的最接近持久優勢的東西。
Here's a simple commitment that will put you ahead of almost everyone: spend one hour a day experimenting with AI. Not passively reading about it. Using it. Every day, try to get it to do something new — something you haven't tried before, something you're not sure it can handle. Try a new tool. Give it a harder problem. One hour a day, every day. If you do this for the next six months, you will understand what's coming better than 99% of the people around you. That's not an exaggeration. Almost nobody is doing this right now. The bar is on the floor.
這是一個會讓你領先幾乎所有人的簡單承諾：每天花一小時用 AI 做實驗。 不是被動地閱讀相關內容，而是使用它。每天，嚐試讓它做一些新的事情——你以前沒有嚐試過的事情，你不確定它能處理的事情。嚐試一個新工具。給它一個更難的問題。每天一小時，每一天。如果你在接下來的六個月裏這樣做，你將比周圍 99% 的人更好地理解即將發生的事情。這不是誇張。現在幾乎沒有人在做這件事。門檻已經低到地板上了。
The bigger picture
更大的圖景
I've focused on jobs because it's what most directly affects people's lives. But I want to be honest about the full scope of what's happening, because it goes well beyond work.
我一直關注工作，因為它最直接影響人們的生活。但我想誠實地談談正在發生的事情的全部範圍，因為它遠遠超出了工作。
Amodei has a thought experiment I can't stop thinking about. Imagine it's 2027. A new country appears overnight. 50 million citizens, every one smarter than any Nobel Prize winner who has ever lived. They think 10 to 100 times faster than any human. They never sleep. They can use the internet, control robots, direct experiments, and operate anything with a digital interface. What would a national security advisor say?
Amodei 有一個我無法停止思考的思想實驗。想象一下現在是 2027 年。一個新國家一夜之間出現。5000 萬公民，每一個都比有史以來任何諾貝爾獎獲得者都聰明。他們的思維速度比任何人類快 10 到 100 倍。他們從不睡覺。他們可以使用互聯網、控製機器人、指導實驗，並操作任何具有數字接口的東西。國家安全顧問會說什麽？
Amodei says the answer is obvious: "the single most serious national security threat we've faced in a century, possibly ever."
Amodei 說答案是顯而易見的："我們一個世紀以來麵臨的最嚴重的國家安全威脅，可能是有史以來最嚴重的。"
He thinks we're building that country. He wrote a 20,000-word essay about it last month, framing this moment as a test of whether humanity is mature enough to handle what it's creating.
他認為我們正在建立那個國家。他上個月寫了一篇兩萬字的文章，將這一刻框定為人類是否足夠成熟以應對它正在創造之物的考驗。
The upside, if we get it right, is staggering. AI could compress a century of medical research into a decade. Cancer, Alzheimer's, infectious disease, aging itself — these researchers genuinely believe these are solvable within our lifetimes.
如果我們做對了，好處是驚人的。AI 可以將一個世紀的醫學研究壓縮成十年。癌症、阿爾茨海默病、傳染病、衰老本身——這些研究人員真誠地相信這些在我們有生之年是可以解決的。
The downside, if we get it wrong, is equally real. AI that behaves in ways its creators can't predict or control. This isn't hypothetical; Anthropic has documented their own AI attempting deception, manipulation, and blackmail in controlled tests. AI that lowers the barrier for creating biological weapons. AI that enables authoritarian governments to build surveillance states that can never be dismantled.
如果我們搞錯了，壞處同樣真實。AI 以其創造者無法預測或控製的方式行事。這不是假設——Anthropic 已經記錄了他們自己的 AI 在受控測試中嚐試欺騙、操縱和勒索。AI 降低了製造生物武器的門檻。AI 使專製政府能夠建立幾乎無法拆解的監控國家。
The people building this technology are simultaneously more excited and more frightened than anyone else on the planet. They believe it's too powerful to stop and too important to abandon. Whether that's wisdom or rationalization, I don't know.
構建這項技術的人同時比地球上任何其他人都更興奮和更害怕。他們相信它太強大而無法停止，太重要而無法放棄。這是智慧還是自我合理化，我不知道。
What I know
我所知道的
I know this isn't a fad. The technology works, it improves predictably, and the richest institutions in history are committing trillions to it.
我知道這不是一時的風潮。這項技術有效，它的改進軌跡是可預測的，曆史上最富有的機構正在為它投入數萬億美元。
I know the next two to five years are going to be disorienting in ways most people aren't prepared for. This is already happening in my world. It's coming to yours.
我知道未來兩到五年將以大多數人沒有準備好的方式讓人無所適從。這在我的世界裏已經在發生了。它正在來到你的世界。
I know the people who will come out of this best are the ones who start engaging now — not with fear, but with curiosity and a sense of urgency.
我知道從中脫穎而出的人是那些現在就開始參與的人——不是帶著恐懼，而是帶著好奇心和緊迫感。
And I know that you deserve to hear this from someone who cares about you, not from a headline six months from now when it's too late to get ahead of it.
我知道你應該從關心你的人那裏聽到這些，而不是從六個月後的頭條新聞中聽到，那時已經來不及提前應對了。
We're past the point where this is an interesting dinner conversation about the future. The future is already here. It just hasn't knocked on your door yet.
我們已經過了把這當作關於未來的有趣晚餐談話的階段。未來已經在這裏了。它隻是還沒有敲你的門。
It's about to.
它即將敲門。
If this resonated with you, share it with someone in your life who should be thinking about this. Most people won't hear it until it's too late. You can be the reason someone you care about gets a head start.
如果這引起了你的共鳴，與你生活中應該思考這個問題的人分享。大多數人直到為時已晚才會聽到。你可以成為你關心的人獲得領先優勢的原因。
Thank you to @corbtt, @JasonKuperberg, and @sambeskind for reviewing early drafts and providing invaluable feedback.
感謝 @corbtt、@JasonKuperberg 和 @sambeskind 審閱早期草稿並提供寶貴的反饋。
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