转载说明：本文原作者为 Matt Shumer（HyperWrite CEO），原文发布于 2026 年 2 月 11 日的 X (Twitter)。原文链接：Something Big Is Happening。以下为中英双语对照版本，英文为原文，中文为翻译。
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.
如果你当时特别留意的话，可能会注意到有些人在谈论一种在海外传播的病毒。但我们大多数人并没有特别留意。股市一片繁荣，你的孩子在上学，你会去餐厅吃饭、和人握手、计划旅行。如果有人告诉你他们在囤积卫生纸，你会觉得他们在互联网某个奇怪的角落待太久了。然后，在大约三周的时间里，整个世界发生了翻天覆地的变化。你的办公室关闭了，孩子们回到家中，生活重新编排成了一个月前你绝不会相信的样子。
I think we're in the "this seems overblown" phase of something much, much bigger than Covid.
我认为我们正处于某件事的"这看起来有点小题大做"阶段，而这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.
我花了六年时间打造一家 AI 创业公司并在这个领域投资。我生活在这个世界里。我写这篇文章是为了那些不在这个世界的人——我的家人、朋友，那些我关心的、不断问我"AI 到底是怎么回事？"却得不到与实际情况相符的答案的人。我一直给他们礼貌的版本，鸡尾酒会上的版本。因为诚实的版本听起来像是我疯了。有一段时间，我说服自己这是一个足够好的理由，可以把真实情况藏在心里。但我所说的和实际发生的事情之间的差距已经太大了。我关心的人值得听到即将到来的事情，即使听起来很疯狂。
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.
我需要先澄清一点：尽管我从事 AI 工作，但对即将发生的事情几乎没有影响力，这个行业的绝大多数人也是如此。未来正由数量极少的一群人塑造：少数几家公司——OpenAI、Anthropic、Google DeepMind 以及其他几家——的几百名研究人员。一次由小团队在几个月内管理的训练轮次，就能产生一个改变整个技术轨迹的 AI 系统。我们这些从事 AI 工作的人，大多数都是在我们没有奠定的基础上构建。我们和你们一样在观察这一切的展开——只是我们恰好离得足够近，能最先感受到地面的震动。
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.
但现在是时候了。不是"我们最终应该谈谈这个"的那种方式。而是**"这正在发生，我需要你理解它"**的方式。
I know this is real because it happened to me first
我知道这是真的，因为它首先发生在我身上
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.
这是科技圈外的人还不太理解的事情：这个行业有这么多人现在敲响警钟的原因是，这已经发生在我们身上了。我们不是在做预测。我们在告诉你我们自己的工作中已经发生的事情，并警告你，你是下一个。
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.
多年来，AI 一直在稳步改进。这里那里有些大跳跃，但每次大跳跃之间的间隔足够长，你可以逐个消化它们。然后到了 2025 年，构建这些模型的新技术解锁了更快的进步速度。然后变得更快了。然后又更快了。每个新模型不仅仅比上一个更好——它的优势更大，而新模型发布之间的时间更短了。我越来越多地使用 AI，与它来回沟通越来越少，看着它处理我曾经认为需要我专业知识的事情。
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.
然后，在 2 月 5 日，两个主要的 AI 实验室在同一天发布了新模型：OpenAI 的 GPT-5.3 Codex，以及 Anthropic（Claude 的制造商，ChatGPT 的主要竞争对手之一）的 Opus 4.6。某种东西咔嗒一声到位了。不像开灯那样——更像是你意识到水一直在你周围上涨，现在已经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.
我的工作中实际的技术工作不再需要我了。 我用简单的英语描述我想要构建什么，然后它就……出现了。不是我需要修改的草稿，而是完成品。我告诉 AI 我想要什么，离开电脑四个小时，回来发现工作完成了。完成得很好，比我自己做得还好，不需要任何修正。几个月前，我还在与 AI 来回沟通，引导它，做编辑。现在我只需描述结果然后离开。
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.
让我给你一个例子，这样你就能理解实际操作中是什么样子。我会告诉 AI："我想构建这个应用。它应该做这些事情，大概应该是这个样子。搞定用户流程、设计，所有这些。"然后它就做了。它写了数万行代码。然后，这是一年前不可想象的部分，它自己打开应用。它点击按钮。它测试功能。它像人一样使用应用。如果它不喜欢某个东西的外观或感觉，它会自己回去改。它像开发者那样迭代，修复和完善，直到满意为止。只有当它认为应用符合自己的标准时，才会回来对我说："准备好让你测试了。"当我测试时，通常是完美的。
I'm not exaggerating. That is what my Monday looked like this week.
我没有夸大。这就是我这周一的情况。
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.
但最让我震惊的是上周发布的模型（GPT-5.3 Codex）。它不仅仅是执行我的指令。它在做智能决策。它第一次拥有了某种感觉像判断力的东西。像品味。那种人们一直说 AI 永远不会有的、难以言喻的知道什么是正确选择的感觉。这个模型拥有它，或者足够接近它，以至于区别开始变得不重要了。
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。但过去几个月让我震惊了。这些新的 AI 模型不是渐进式改进。这完全是另一回事。
And here's why this matters to you, even if you don't work in tech.
这就是为什么这对你很重要，即使你不在科技行业工作。
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 实验室做出了一个深思熟虑的选择。他们首先专注于让 AI 擅长写代码——因为构建 AI 需要大量代码。如果 AI 能写这些代码，它就能帮助构建下一个版本的自己。一个更聪明的版本，写出更好的代码，构建一个更聪明的版本。让 AI 擅长编码是解锁其他一切的策略。这就是为什么他们首先这么做。我的工作比你的先开始改变，不是因为他们瞄准了软件工程师——这只是他们选择首先瞄准的方向的副作用。
They've now done it. And they're moving on to everything else.
他们现在已经做到了。他们正在转向其他一切。
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.
技术工作者在过去一年的经历——看着 AI 从"有用的工具"变成"比我做得更好"，是其他所有人即将拥有的经历。法律、金融、医疗、会计、咨询、写作、设计、分析、客户服务。不是十年后。构建这些系统的人说一到五年。有些人说更少。考虑到我在过去几个月看到的，我认为"更少"更有可能。
"But I tried AI and it wasn't that good"
"但我试过 AI，它并不怎么样"
I hear this constantly. I understand it, because it used to be true.
我经常听到这样的说法。我理解这种感受，因为这曾经是事实。
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 年初试用过 ChatGPT，觉得"它会编造东西"或"没什么了不起"，你是对的。那些早期版本确实存在局限。它们会产生幻觉，信心十足地说出一些无稽之谈。
That was two years ago. In AI time, that is ancient history.
那是两年前的事了。在 AI 的时间尺度里，那已经是远古历史。
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.
如今可用的模型与六个月前的版本已判若云泥。关于 AI 是"真的在进步"还是"遇到瓶颈"的争论——这场持续了一年多的辩论——已经结束了。尘埃落定。仍在坚持这种论调的人，要么没用过最新的模型，要么有动机淡化正在发生的事情，要么是基于 2024 年的体验在评判，而那些体验已经不再适用。我这么说不是为了轻视谁。我这么说是因为公众认知与当前现实之间的鸿沟已经变得极其巨大，而这种鸿沟是危险的——因为它正在阻止人们做好准备。
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.
问题的一部分在于，大多数人使用的是 AI 工具的免费版本。免费版本比付费用户能使用的版本落后一年多。基于免费版 ChatGPT 来评判 AI，就像用翻盖手机来评估智能手机的发展状况。那些为最佳工具付费，并且每天在实际工作中使用它们的人，知道未来会发生什么。
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.
我想到我的一个朋友，他是律师。我一直劝他在律所尝试使用 AI，他却总能找到理由说明它行不通。它不是为他的专业领域设计的，他测试时它出了错，它无法理解他工作中的微妙之处。我理解他的想法。但是，一些大型律所的合伙人联系我寻求建议，因为他们试用了最新版本，看到了发展方向。其中一位，一家大型律所的管理合伙人，每天花好几个小时使用 AI。他告诉我，这就像瞬间拥有一整支初级律师团队。他使用它不是因为它是个玩具，而是因为它真的管用。他告诉我一件让我印象深刻的事：每隔几个月，它在他的工作中的能力就会显著提升。他说如果保持这个势头，他预计用不了多久它就能完成他所做的大部分工作——而他可是一位有着数十年经验的管理合伙人。他没有恐慌，但他在非常密切地关注着。
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.
在各自行业中走在前面的人（那些真正在认真试验的人）并没有对此不屑一顾。他们对 AI 现在能做的事情感到震撼。他们正在相应地调整自己的定位。
How fast this is actually moving
这一切实际上进展得有多快
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.
让我把这种改进的速度具体化，因为我认为如果你不密切关注，这部分是最难以置信的。
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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 无法可靠地完成基础算术。它会信心满满地告诉你 7 × 8 = 54。 -
• 2023 年：它能通过律师资格考试。 -
• 2024 年：它能编写可运行的软件并解释研究生级别的科学知识。 -
• 2025 年底：世界上一些最顶尖的工程师说他们已经把大部分编程工作交给了 AI。 -
• 2026 年 2 月 5 日：新模型的到来让之前的一切感觉像是另一个时代。
If you haven't tried AI in the last few months, what exists today would be unrecognizable to you.
如果你在过去几个月里没有试用过 AI，今天存在的东西对你来说将是面目全非的。
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.
有一个名为 METR 的组织实际上在用数据测量这一点。他们追踪模型在无需人类帮助的情况下能端到端成功完成的真实世界任务的长度（以人类专家完成所需的时间来衡量）。大约一年前，答案是大约十分钟。然后是一小时。接着是几小时。最近的测量（来自 11 月的 Claude Opus 4.5）显示 AI 完成了需要人类专家近五小时才能完成的任务。这个数字大约每七个月翻一番，最新数据表明它可能正在加速到每四个月翻一番。
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.
但即使是这个测量也还没有更新到纳入本周刚发布的模型。根据我的使用体验，这次跨越极其显著。我预计 METR 图表的下一次更新将显示另一次重大飞跃。
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.
如果你延伸这一趋势（而且它已经持续多年没有放缓的迹象），我们正在面对的是：明年内 AI 能独立工作数天，两年内能工作数周，三年内能完成持续一个月的项目。
Amodei has said that AI models "substantially smarter than almost all humans at almost all tasks" are on track for 2026 or 2027.
Amodei 曾说过，AI 模型"在几乎所有任务上都比几乎所有人类聪明得多"有望在 2026 年或 2027 年实现。
Let that land for a second. If AI is smarter than most PhDs, do you really think it can't do most office jobs?
让这句话沉淀一下。如果 AI 比大多数博士都聪明，你真的认为它做不了大多数办公室工作吗？
Think about what that means for your work.
想想这对你的工作意味着什么。
AI is now building the next AI
AI 正在构建下一代 AI
There's one more thing happening that I think is the most important development and the least understood.
还有一件正在发生的事情，我认为这是最重要的进展，也是最不为人所理解的。
On February 5th, OpenAI released GPT-5.3 Codex. In the technical documentation, they included this:
2 月 5 日，OpenAI 发布了 GPT-5.3 Codex。在技术文档中，他们写道：
"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 是我们第一个在创建自身过程中发挥关键作用的模型。Codex 团队使用早期版本来调试自己的训练、管理自己的部署，并诊断测试结果和评估。"
Read that again. The AI helped build itself.
再读一遍。AI 帮助构建了它自己。
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.
这不是关于未来某天可能发生什么的预测。这是 OpenAI 现在就在告诉你，他们刚刚发布的 AI 被用来创建它自己。让 AI 变得更好的主要因素之一，就是将智能应用于 AI 开发本身。而 AI 现在已经足够智能，可以有意义地促进自身的改进。
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 的首席执行官 Dario Amodei 说，AI 现在正在他的公司编写"大部分代码"，当前 AI 和下一代 AI 之间的反馈循环正在"逐月加速"。他说，我们可能"距离当前一代 AI 自主构建下一代的时刻只有 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.
每一代帮助构建下一代，下一代更聪明，然后更快地构建再下一代，而再下一代更加聪明。研究人员称之为智能爆炸。而那些应该知道的人——那些正在构建它的人——相信这个过程已经开始了。
What this means for your job
这对你的工作意味着什么
I'm going to be direct with you because I think you deserve honesty more than comfort.
我将直言不讳，因为我认为你更需要诚实而非安慰。
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 行业中最关注安全的 CEO，他公开预测 AI 将在一到五年内消除 50% 的初级白领工作。而业内许多人认为他的预测还是保守的。鉴于最新模型的能力，大规模颠覆的能力可能在今年年底就会到来。它需要一些时间才能在经济中产生连锁反应，但这种底层能力现在已经在到来。
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.
这与以往所有的自动化浪潮都不同，我需要你理解为什么。AI 不是在替代某一项特定技能。它是认知工作的通用替代品。它在所有方面同时变得更好。当工厂自动化时，被取代的工人可以重新培训成为办公室职员。当互联网颠覆零售业时，工人转移到物流或服务业。但 AI 不会留下一个方便你转移的空档。无论你重新培训什么，它在那方面也在进步。
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.
让我给你一些具体的例子来让这变得更具体——但我想明确的是，这些只是例子。这个列表并不详尽。如果你的工作没有在这里提到，并不意味着它是安全的。几乎所有的知识工作都在受到影响。
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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 已经可以阅读合同、总结判例法、起草诉状，并进行法律研究，其水平可以媲美初级律师。我提到的管理合伙人使用 AI 不是因为好玩。他使用它是因为它在许多任务上的表现超过了他的初级律师。 -
• Financial analysis. Building financial models, analyzing data, writing investment memos, generating reports. AI handles these competently and is improving fast. -
• 金融分析。 构建财务模型、分析数据、撰写投资备忘录、生成报告。AI 能够胜任地处理这些工作，而且正在快速改进。 -
• 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. -
• 写作和内容。 营销文案、报告、新闻报道、技术写作。质量已经达到了许多专业人士无法区分 AI 输出和人类作品的程度。 -
• 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. -
• 软件工程。 这是我最了解的领域。一年前，AI 几乎无法写出几行没有错误的代码。现在它可以写出数十万行正确运行的代码。工作的很大一部分已经自动化了：不仅仅是简单的任务，还有复杂的、需要多天的项目。几年后的编程岗位将远少于今天。 -
• Medical analysis. Reading scans, analyzing lab results, suggesting diagnoses, reviewing literature. AI is approaching or exceeding human performance in several areas. -
• 医学分析。 阅读扫描图像、分析实验室结果、提出诊断建议、审查文献。AI 在几个领域正在接近或超越人类表现。 -
• Customer service. Genuinely capable AI agents — not the frustrating chatbots of five years ago — are being deployed now, handling complex multi-step problems. -
• 客户服务。 真正有能力的 AI 代理——不是五年前那些令人沮丧的聊天机器人——现在正在被部署，处理复杂的多步骤问题。
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.
很多人会从"有些事情是安全的"这个想法里获得安慰。认为 AI 可以处理繁重的工作，但无法替代人类的判断力、创造力、战略思维和同理心。我以前也这么说。但我不确定我还相信这一点。
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 模型做出的决策感觉像是判断。它们展现出某种看起来像品味的东西：一种对什么是正确选择的直觉感知，而不仅仅是技术上正确的。一年前这是不可想象的。我现在的经验法则是：如果一个模型今天显示出某种能力的一点迹象，下一代就会真正擅长它。 这些事物是指数级改进的，而不是线性的。
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 会复制深层的人类同理心吗？会取代多年关系中建立起来的信任吗？我不知道。也许不会。但我已经看到人们开始依赖 AI 获得情感支持、建议和陪伴。这种趋势只会增长。
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.
我认为诚实的答案是，从中期来看，任何可以在计算机上完成的事情都不安全。 如果你的工作发生在屏幕上——如果你所做的核心工作是阅读、写作、分析、决策、通过键盘交流——那么 AI 正在取代它的重要部分。时间线不是"某天"。它已经开始了。
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.
最终，机器人也会处理体力工作。它们还没有完全达到那一步。但在 AI 领域，"还差一点"往往会比任何人预期都更快地变成"已经到来"。
What you should actually do
你实际应该做什么
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.
我写这篇文章不是为了让你感到无助。我写这篇文章是因为我认为，你现在能拥有的最大优势就是抢先一步。抢先理解它。抢先使用它。抢先适应它。
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.
认真开始使用 AI，而不仅仅把它当作搜索引擎。 订阅 Claude 或 ChatGPT 的付费版本。每月 20 美元。但有两件事马上就很重要。第一：确保你使用的是可用的最佳模型，而不仅仅是默认模型。这些应用通常默认使用更快但更笨的模型。深入设置或模型选择器，选择最强大的选项。现在是 ChatGPT 上的 GPT-5.2 或 Claude 上的 Claude Opus 4.6，但每隔几个月就会变化。
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.
第二点，也更重要：不要只是问它几个快问快答。 这是大多数人犯的错误。他们把它当作 Google 对待，然后不明白有什么大惊小怪的。相反，把它推进到你的实际工作中。如果你是律师，给它一份合同，让它找出每一条可能伤害你客户的条款。如果你在金融领域，给它一个混乱的电子表格，让它建立模型。如果你是经理，粘贴你团队的季度数据，让它提炼出关键结论。取得进展的人不是随意使用 AI。他们积极寻找方法来自动化他们工作中过去需要几个小时的部分。从你花费最多时间的事情开始，看看会发生什么。
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.
不要因为某件事看起来太难就假设它做不到。试试看。如果你是律师，不要只是用它来快速研究问题。给它一整份合同，让它起草反提案。如果你是会计师，不要只是让它解释税法规则。给它客户的完整报税表，看看它能发现什么。第一次尝试可能不完美。没关系。迭代。重新表述你的问题。给它更多背景信息。再试一次。你可能会对有效的东西感到震惊。这里要记住的是：如果它今天甚至有点管用，你几乎可以肯定六个月后它会做得近乎完美。 这个轨迹只朝一个方向前进。
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.
这可能是你职业生涯中最重要的一年。请据此行动。 我这么说不是为了让你感到压力。我这么说是因为现在有一个短暂的窗口期，大多数公司的大多数人仍然在忽视这一点。走进会议室说"我用 AI 在一小时内完成了这项分析，而不是三天"的人将成为房间里最有价值的人。不是最终。就是现在。学习这些工具。变得熟练。展示可能性。如果你足够早，这就是你晋升的方式：成为理解即将到来的事物并能向他人展示如何应对的人。这个窗口不会保持太久。一旦每个人都弄明白了，优势就消失了。
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.
别端着架子。 那家律师事务所的管理合伙人不会因为每天花几个小时使用 AI 而觉得掉价。他这样做恰恰是因为他资历足够深，能够理解利害关系。最困难的将是那些拒绝参与的人：那些将其视为一时流行的人，那些觉得使用 AI 会削弱他们专业知识的人，那些假设自己的领域特殊且免疫的人。事实并非如此。没有哪个领域能幸免。
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.
整理好你的财务状况。 我不是财务顾问，我也不是想吓唬你做任何激进的事情。但如果你相信，哪怕只是部分相信，未来几年可能会给你的行业带来真正的颠覆，那么基本的财务韧性比一年前更重要。如果可以的话，积累储蓄。谨慎对待那些假设你目前收入有保障的新债务。考虑你的固定支出是给你灵活性还是把你锁定。如果事情发展得比你预期更快，给自己留下选择余地。
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.
思考你的处境，向最难被替代的方向靠拢。 有些东西 AI 需要更长时间才能取代。多年建立的关系和信任。需要实际在场的工作。需要持证并承担法定责任的岗位：仍然需要有人签字、承担法律责任、站在法庭上的岗位。具有严格监管壁垒的行业，采用将因合规性、责任和制度惯性而放缓。这些都不是永久的保护盾。但它们争取了时间。而时间，现在，是你能拥有的最宝贵的东西，只要你用它来适应，而不是假装这一切没有发生。
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.
重新思考你对孩子说的话。 标准剧本——取得好成绩，上好大学，找到稳定的专业工作——它直接指向最暴露的岗位。我不是说教育不重要。但对下一代最重要的事情是学习如何使用这些工具，并追求他们真正热衷的事情。没有人确切知道十年后的就业市场会是什么样子。但最有可能繁荣的人是那些深刻好奇、适应能力强、并且有效地使用 AI 做他们真正关心的事情的人。教你的孩子成为创造者和学习者，而不是为他们毕业时可能不存在的职业道路做优化。
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.
你的梦想刚刚变得更近了。 我在这一部分大部分时间都在谈论威胁，所以让我谈谈另一面，因为它同样真实。如果你曾经想要做出一些东西，但没有技术能力或雇人的资金，那个障碍基本上已经消失了。你可以向 AI 描述一个应用程序，并在一小时内获得一个可用版本。我没有夸张，我经常这样做。如果你一直想写一本书，但找不到时间或在写作上遇到困难，你可以与 AI 合作完成它。想学习一项新技能？世界上最好的导师现在每月 20 美元就可以为任何人服务——一个无限耐心、全天候可用、可以在任何你需要的水平上解释任何事情的导师。知识现在基本上是免费的。构建事物的工具现在极其便宜。无论你因为感觉太难、太贵或太超出你的专业知识而推迟的任何事情：试试看。追求你热衷的事情。你永远不知道它们会通向哪里。在旧的职业道路正在被颠覆的世界里，花一年时间构建自己热爱之物的人，可能最终比花那一年紧紧抓住职位描述的人处于更好的位置。
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.
培养适应的习惯。 这可能是最重要的一条。具体工具不如快速学习新工具的能力重要。AI 将继续变化，而且很快。今天存在的模型将在一年内过时。人们现在建立的工作流程将需要重建。从中脱颖而出的人不会是掌握了一个工具的人。他们将是对变化本身的速度感到自如的人。养成实验的习惯。即使当前的方法正在起作用，也要尝试新事物。反复适应当一个初学者的状态。这种适应能力是目前存在的最接近持久优势的东西。
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.
这是一个会让你领先几乎所有人的简单承诺：每天花一小时用 AI 做实验。 不是被动地阅读相关内容，而是使用它。每天，尝试让它做一些新的事情——你以前没有尝试过的事情，你不确定它能处理的事情。尝试一个新工具。给它一个更难的问题。每天一小时，每一天。如果你在接下来的六个月里这样做，你将比周围 99% 的人更好地理解即将发生的事情。这不是夸张。现在几乎没有人在做这件事。门槛已经低到地板上了。
The bigger picture
更大的图景
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.
我一直关注工作，因为它最直接影响人们的生活。但我想诚实地谈谈正在发生的事情的全部范围，因为它远远超出了工作。
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 有一个我无法停止思考的思想实验。想象一下现在是 2027 年。一个新国家一夜之间出现。5000 万公民，每一个都比有史以来任何诺贝尔奖获得者都聪明。他们的思维速度比任何人类快 10 到 100 倍。他们从不睡觉。他们可以使用互联网、控制机器人、指导实验，并操作任何具有数字接口的东西。国家安全顾问会说什么？
Amodei says the answer is obvious: "the single most serious national security threat we've faced in a century, possibly ever."
Amodei 说答案是显而易见的："我们一个世纪以来面临的最严重的国家安全威胁，可能是有史以来最严重的。"
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.
他认为我们正在建立那个国家。他上个月写了一篇两万字的文章，将这一刻框定为人类是否足够成熟以应对它正在创造之物的考验。
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.
如果我们做对了，好处是惊人的。AI 可以将一个世纪的医学研究压缩成十年。癌症、阿尔茨海默病、传染病、衰老本身——这些研究人员真诚地相信这些在我们有生之年是可以解决的。
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.
如果我们搞错了，坏处同样真实。AI 以其创造者无法预测或控制的方式行事。这不是假设——Anthropic 已经记录了他们自己的 AI 在受控测试中尝试欺骗、操纵和勒索。AI 降低了制造生物武器的门槛。AI 使专制政府能够建立几乎无法拆解的监控国家。
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.
构建这项技术的人同时比地球上任何其他人都更兴奋和更害怕。他们相信它太强大而无法停止，太重要而无法放弃。这是智慧还是自我合理化，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.
我知道这不是一时的风潮。这项技术有效，它的改进轨迹是可预测的，历史上最富有的机构正在为它投入数万亿美元。
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.
我知道未来两到五年将以大多数人没有准备好的方式让人无所适从。这在我的世界里已经在发生了。它正在来到你的世界。
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.
我知道从中脱颖而出的人是那些现在就开始参与的人——不是带着恐惧，而是带着好奇心和紧迫感。
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.
我知道你应该从关心你的人那里听到这些，而不是从六个月后的头条新闻中听到，那时已经来不及提前应对了。
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.
我们已经过了把这当作关于未来的有趣晚餐谈话的阶段。未来已经在这里了。它只是还没有敲你的门。
It's about to.
它即将敲门。
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.
如果这引起了你的共鸣，与你生活中应该思考这个问题的人分享。大多数人直到为时已晚才会听到。你可以成为你关心的人获得领先优势的原因。
Thank you to @corbtt, @JasonKuperberg, and @sambeskind for reviewing early drafts and providing invaluable feedback.
感谢 @corbtt、@JasonKuperberg 和 @sambeskind 审阅早期草稿并提供宝贵的反馈。
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