Something Big Is Happening
打开互动全文版（逐段中英对照 + 图/公式 + 论文问答）→获取未来评论和构建的早期访问权限。尝试新事物：我一直在构建一个实验性代理，它可以在自己的计算机上与你一起工作，帮助你处理完成任务的繁琐部分。如果你密切关注，你可能会注意到一些人在谈论一种病毒在海外传播。但我们大多数人并没有密切关注。股市表现良好，你的孩子在上学，你去餐馆、握手、计划旅行。如果有人告诉你他们在囤积卫生纸，你会认为他们在互联网的某个奇怪角落花了太多时间。然后，在大约三周的时间里，整个世界都变了。你的办公室关闭了，你的孩子回家了，生活重新安排成了一种你一个月前对自己描述时都不会相信的样子。
Get early access to future reviews and builds. Trying something new: I've been building an experimental agent that works alongside you on its own computer, helping with the messy parts of getting things done. 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.
抢先获取未来的评测和构建版本。
Get early access to future reviews and builds.
尝试一些新东西：我一直在构建一个实验性智能体，它可以在自己的计算机上与你协作，帮你处理那些繁琐的事务。
Trying something new: I've been building an experimental agent that works alongside you on its own computer, helping with the messy parts of getting things done.
如果你当时足够关注，可能会注意到一些人在谈论一种病毒在海外蔓延。但我们大多数人并没有那么关注。股市表现良好，你的孩子在上学，你去餐厅、与人握手、计划旅行。如果有人告诉你他们在囤积卫生纸，你会觉得他们在一个奇怪的网络角落里待得太久了。然后，在大约三周的时间里，整个世界都变了。你的办公室关闭了，你的孩子回家了，生活重组成了你一个月前对自己描述时都不会相信的样子。
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.
我认为我们正处于某个比新冠大得多的事件的“看起来被夸大了”的阶段。
I think we're in the "this seems overblown" phase of something much, much bigger than Covid.
我花了六年时间创办一家人工智能初创公司并投资于这个领域。我生活在这个世界里。我写这篇文章是为了我生活中那些不从事这一行的人……我的家人、朋友、我在乎的人，他们一直问我“那么人工智能到底是怎么回事？”而我给出的答案并没有如实反映正在发生的事情。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，我告诉自己，这足以让我把真正发生的事情藏在心里。但我说的话和实际发生的事情之间的差距已经变得太大了。我在乎的人应该听到即将发生的事情，即使听起来很疯狂。
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.
我应该一开始就说明一点：尽管我从事人工智能工作，但我对即将发生的事情几乎没有影响力，这个行业中的绝大多数人也是如此。未来正由极少数人塑造：几家公司的几百名研究人员……OpenAI、Anthropic、Google DeepMind 以及其他几家。一次由小团队在几个月内管理的单次训练运行，就能产生一个改变整个技术轨迹的人工智能系统。我们大多数从事人工智能工作的人都是在别人打下的基础上进行建设。我们和你们一样在目睹这一切的发生……只是我们恰好离得足够近，能最先感受到地面的震动。
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.
但现在是时候了。不是那种“最终我们应该谈谈这个”的方式，而是“这正在发生，我需要你理解它”的方式。
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.
这是科技界以外的人还不太理解的一点：业内这么多人现在敲响警钟，是因为这件事已经发生在我们身上了。我们不是在预测。我们是在告诉你们，在我们的工作中已经发生了什么，并警告你们，你们是下一个。
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.
多年来，AI 一直在稳步进步。时不时会有大的飞跃，但每次大的飞跃间隔足够长，你可以慢慢消化。然后在 2025 年，构建这些模型的新技术解锁了更快的进步速度。然后它变得更快了。然后又更快了。每个新模型不仅比上一个更好……而且好的幅度更大，新模型发布之间的时间也更短。我越来越多地使用 AI，与它的来回交互越来越少，看着它处理那些我曾经以为需要我专业知识的事情。
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.
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 和 Anthropic（Claude 的制造商，ChatGPT 的主要竞争对手之一）的 Opus 4.6。然后有什么东西触动了。不是像电灯开关那样……更像是你意识到水已经在你周围上涨，现在到了你的胸口。
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.
我不再需要做我工作中实际的技术工作了。我用简单的英语描述我想要构建的东西，然后它就……出现了。不是需要我修改的草稿。是成品。我告诉 AI 我想要什么，离开电脑四个小时，回来发现工作已经完成了。完成得很好，比我亲自做还要好，不需要任何修正。几个月前，我还需要和 AI 来回沟通，引导它，做编辑。现在我只是描述结果，然后离开。
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.
让我给你一个例子，这样你就能理解这在实际中是什么样子。我会告诉 AI：“我想构建这个应用。这是它应该做的，这是它大致应该看起来的样子。弄清楚用户流程、设计，所有的一切。”然后它就做了。它写了数万行代码。然后，这是去年还不可想象的部分，它自己打开了应用。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，它会自己回去修改。它迭代，就像开发者一样，修复和完善直到满意。只有当它决定应用达到了自己的标准，它才会回来对我说：“它准备好让你测试了。”当我测试时，它通常是完美的。
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.
我没有夸张。这就是我这周一的样子。
I'm not exaggerating. That is what my Monday looked like this week.
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。它在做出智能决策。它第一次有了某种感觉，像是判断力。像是品味。那种人们总是说 AI 永远不会有的、知道什么是对的的莫名感觉。这个模型有它，或者足够接近，以至于区别开始变得不重要。
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.
我一直是 AI 工具的早期采用者。但过去几个月让我震惊。这些新的 AI 模型不是渐进式的改进。这完全是另一回事。
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.
这就是为什么这对你很重要，即使你不从事科技行业。
And here's why this matters to you, even if you don't work in tech.
AI 实验室做出了一个刻意的选择。他们首先专注于让 AI 擅长编写代码……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，能写出更好的代码，从而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。这就是为什么他们先做这个。我的工作开始变化比你早，不是因为他们针对软件工程师……这只是他们选择首先瞄准的地方的一个副作用。
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.
他们现在已经做到了。他们正在转向其他一切。
They've now done it. And they're moving on to everything else.
科技工作者在过去一年中经历的，看着 AI 从“有用的工具”变成“比我做得更好”，是其他人即将经历的。法律、金融、医学、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。鉴于我在过去几个月里看到的，我认为“更少”更有可能。
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.
我经常听到这种说法。我理解，因为过去确实如此。
I hear this constantly. I understand it, because it used to be true.
如果你在 2023 年或 2024 年初尝试过 ChatGPT，并认为“这玩意儿胡编乱造”或“没那么惊艳”，你是对的。那些早期版本确实有限。它们会产生幻觉，自信地说出毫无意义的话。
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.
那是两年前的事了。在 AI 领域，这已经是远古历史了。
That was two years ago. In AI time, that is ancient history.
如今可用的模型与六个月前相比已面目全非。关于 AI 是“真的在进步”还是“撞墙”的争论——已经持续了一年多——已经结束了。结束了。任何还在坚持这种论点的人，要么没用过当前模型，要么有动机淡化正在发生的事情，要么是基于 2024 年已不再相关的经验进行评判。我这么说并非为了轻蔑。我这么说是因为公众认知与当前现实之间的差距现在巨大，而这个差距是危险的……因为它阻碍了人们的准备。
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.
部分问题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访问的落后一年多。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现状。那些为最佳工具付费并每天实际用于真实工作的人，知道即将发生什么。
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.
我想起我的一位律师朋友。我一直告诉他尝试在他的律所使用 AI，而他总是找理由说行不通：它不适合他的专业，他测试时出了错，它不理解他工作的细微之处。我理解。但已有大型律所的合伙人联系我寻求建议，因为他们尝试了当前版本，看到了趋势。其中一位，一家大律所的管理合伙人，每天花数小时使用 AI。他告诉我这就像拥有一个即时可用的助理团队。他使用它不是因为它是玩具，而是因为它有效。他告诉我一句话让我印象深刻：每隔几个月，它对他工作的能力就会显著提升。他说如果保持这个轨迹，他预计不久它就能完成他大部分的工作——而他是一位拥有数十年经验的管理合伙人。他没有恐慌，但他非常关注。
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.
那些行业领先的人（真正认真实验的人）并没有忽视这一点。他们已被 AI 已有的能力所震撼，并据此调整自己的定位。
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.
让我具体说明改进的速度，因为我认为这是如果你不密切关注就最难相信的部分。
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.
2022 年，AI 连基本的算术都不可靠。它会自信地告诉你 7×8=54。
In 2022, AI couldn't do basic arithmetic reliably. It would confidently tell you that 7 × 8 = 54.
到 2024 年，它已经能编写可工作的软件并解释研究生级别的科学知识。
By 2024, it could write working software and explain graduate-level science.
到 2025 年底，一些世界上最优秀的工程师表示，他们已经将大部分编码工作交给了 AI。
By late 2025, some of the best engineers in the world said they had handed over most of their coding work to AI.
2026 年 2 月 5 日，新模型的出现让之前的一切感觉像是另一个时代。
On February 5th, 2026, new models arrived that made everything before them feel like a different era.
如果你在过去几个月没有尝试过 AI，那么今天存在的 AI 对你来说将是无法辨认的。
If you haven't tried AI in the last few months, what exists today would be unrecognizable to you.
有一个叫 METR 的组织用数据实际测量这一点。他们跟踪模型无需人类帮助就能成功端到端完成的现实世界任务长度（以人类专家完成所需时间衡量）。大约一年前，这个时间大约是十分钟。然后是一小时。然后是几小时。最近的测量（Claude Opus 4.5，来自 11 月）显示 AI 能完成需要人类专家近五小时的任务。而这个数字大约每七个月翻一番，最近的数据表明它可能加速到每四个月翻一番。
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.
但即使是那个测量也还没有更新到包括本周刚发布的模型。根据我使用它们的经验，这个飞跃非常显著。我预计 METR 图表的下一次更新将显示又一次重大飞跃。
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.
如果延伸这一趋势（并且它已经持续多年，没有平缓的迹象），我们将在明年看到 AI 能独立工作数天。两年内能工作数周。三年内能处理长达数月的项目。
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.
Amodei 曾表示，AI 模型“在几乎所有任务上比几乎所有人类都聪明得多”有望在 2026 或 2027 年实现。
Amodei has said that AI models "substantially smarter than almost all humans at almost all tasks" are on track for 2026 or 2027.
请思考一下。如果 AI 比大多数博士更聪明，你真的认为它做不了大多数办公室工作吗？
Let that land for a second. If AI is smarter than most PhDs, do you really think it can't do most office jobs?
想想这对你的工作意味着什么。
Think about what that means for your work.
还有一件事正在发生，我认为这是最重要的发展，也是最不被理解的。
There's one more thing happening that I think is the most important development and the least understood.
2 月 5 日，OpenAI 发布了 GPT-5.3 Codex。在技术文档中，他们包含了这一点：
On February 5th, OpenAI released GPT-5.3 Codex. In the technical documentation, they included this:
再读一遍。AI 帮助构建了自身。
Read that again. The AI helped build itself.
这不是关于某天可能发生的预测。这是 OpenAI 现在告诉你，他们刚刚发布的 AI 被用来创造自身。使 AI 变得更好的主要因素之一是将智能应用于 AI 开发。而 AI 现在已经足够智能，能够有意义地为其自身的改进做出贡献。
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.
Anthropic 的 CEO Dario Amodei 表示，AI 现在正在编写他公司“大部分代码”，并且当前 AI 与下一代 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 AI 只有 1-2 年的时间”。
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."
每一代都帮助构建下一代，下一代更智能，更快地构建再下一代，而再下一代则更加智能。研究人员称之为智能爆炸。而那些知情者——正在构建它的人——相信这个过程已经开始。
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.
我要对你直言不讳，因为我认为你值得真相，而非安慰。
I'm going to be direct with you because I think you deserve honesty more than comfort.
Dario Amodei，很可能是 AI 行业中最注重安全的首席执行官，曾公开预测 AI 将在 1 到 5 年内淘汰 50%的初级白领工作。而业内许多人认为他的预测过于保守。鉴于最新模型的能力，大规模颠覆的_能力_可能在今年年底前就已具备。虽然需要一些时间才能在经济中产生涟漪效应，但底层能力正在到来。
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.
这与以往每一次自动化浪潮都不同，我需要你理解原因。AI 并非取代某一特定技能。它是认知工作的一种通用替代品。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，工人转向物流或服务业。但 AI 没有留下一个方便的缺口让你转入。无论你重新培训做什么，它也在那个领域进步。
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.
让我给你几个具体的例子来让这一点变得具体……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，那并不意味着它是安全的。几乎所有知识工作都受到影响。
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.
法律工作。AI 已经能够阅读合同、总结判例法、起草诉状、进行法律研究，水平可与初级律师媲美。我提到的那位管理合伙人使用 AI 并非因为它有趣。他使用它是因为它在许多任务上表现优于他的律师。
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.
财务分析。构建财务模型、分析数据、撰写投资备忘录、生成报告。AI 能胜任这些工作，并且进步迅速。
Financial analysis. Building financial models, analyzing data, writing investment memos, generating reports. AI handles these competently and is improving fast.
写作和内容。营销文案、报告、新闻、技术写作。质量已达到许多专业人士无法区分 AI 输出与人类作品的程度。
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.
软件工程。这是我最熟悉的领域。一年前，AI 几乎无法写出几行没有错误的代码。现在它能写出数十万行正确工作的代码。工作的大部分已经自动化：不仅是简单任务，还有复杂的、多天的项目。几年后，编程岗位将比今天少得多。
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.
医学分析。读取扫描结果、分析实验室结果、提出诊断建议、查阅文献。AI 在多个领域接近或超过人类表现。
Medical analysis. Reading scans, analyzing lab results, suggesting diagnoses, reviewing literature. AI is approaching or exceeding human performance in several areas.
客户服务。真正有能力的 AI 智能体……不是五年前令人沮丧的聊天机器人……正在被部署，处理复杂的多步骤问题。
Customer service. Genuinely capable AI agents... not the frustrating chatbots of five years ago... are being deployed now, handling complex multi-step problems.
很多人从某些事情是安全的这一想法中找到安慰。认为 AI 可以处理苦差事，但无法取代人类的判断、创造力、战略思维、同理心。我以前也这么说。我不确定我现在还相信这一点。
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.
最新的 AI 模型做出的决定感觉像是判断。它们表现出某种看起来像品味的东西：一种对正确选择的直觉，而不仅仅是技术上正确的选择。一年前这还不可想象。我现在的经验法则是：如果一个模型今天表现出哪怕一丝_迹象_，下一代就会真正擅长它。这些东西是指数级改进，而非线性。
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.
AI 会复制深层的人类同理心吗？取代多年关系建立的信任？我不知道。也许不会。但我已经看到人们开始依赖 AI 进行情感支持、建议和陪伴。这一趋势只会增长。
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.
我认为诚实的答案是，在中期内，任何能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（如果你工作的核心是阅读、写作、分析、决策、通过键盘交流），那么 AI 正在瞄准其重要部分。时间线不是“某一天”。它已经开始了。
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.
最终，机器人也会处理体力工作。它们还没有完全做到。但 AI 领域的“还没有完全做到”往往会以超出所有人预期的速度变成“已经做到”。
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.
我写这些不是为了让你感到无助。我写这些，是因为我认为你现在能拥有的最大优势就是比别人更早——更早理解它，更早使用它，更早适应它。
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.
认真开始使用 AI，而不仅仅是把它当搜索引擎。订阅 Claude 或 ChatGPT 的付费版，每月 20 美元。但有两件事立刻重要：第一，确保你用的是最好的模型，而不是默认的。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。目前 ChatGPT 上是 GPT-5.2，Claude 上是 Claude Opus 4.6，但每几个月就会变。如果你想随时了解哪个模型最好，可以在 X 上关注我（@mattshumer_）。我测试每个主要版本，分享真正值得用的。
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. If you want to stay current on which model is best at any given time, you can follow me on X (@mattshumer_). I test every major release and share what's actually worth using.
第二，更重要的是：不要只问简单问题。这是大多数人犯的错误。他们把它当谷歌用，然后奇怪为什么大家这么激动。相反，把它融入你的实际工作。如果你是律师，给它一份合同，让它找出所有可能损害客户利益的条款。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，让它找出故事。那些领先的人不是随便用用 AI。他们积极寻找方法来自动化那些过去需要数小时的工作部分。从你最花时间的事情开始，看看会发生什么。
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.
不要因为某件事看起来太难就认为它做不到。试试看。如果你是律师，不要只用它做快速研究问题。给它一整份合同，让它起草一份反提案。如果你是会计师，不要只让它解释税法。给它客户的完整申报表，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。给它更多上下文。再试一次。你可能会对结果感到震惊。记住：如果今天它只是勉强能用，那么几乎可以肯定六个月后它会做得近乎完美。轨迹只有一个方向。
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.
这可能是你职业生涯中最重要的一年。据此行动。我这么说不是为了给你压力。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还在忽视它。那个走进会议室说“我用 AI 在一小时内完成了这个分析，而不是三天”的人，将成为房间里最有价值的人。不是将来，而是现在。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：成为那个理解未来并能为他人指引方向的人。这个窗口不会开太久。一旦每个人都明白了，优势就消失了。
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.
不要有自负。那家律师事务所的管理合伙人不会因为每天花数小时与 AI 相处而感到丢脸。他这么做正是因为他足够资深，明白利害关系。最挣扎的人将是那些拒绝参与的人：那些将其视为一时风尚的人，那些觉得使用 AI 会削弱自己专业能力的人，那些认为自己的领域特殊且免疫的人。并非如此。没有领域是免疫的。
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.
理清你的财务状况。我不是财务顾问，也不是想吓你做出极端举动。但如果你至少部分相信未来几年可能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，增加储蓄。谨慎承担新的债务，不要假设你当前的收入是确定的。思考你的固定支出是给你灵活性还是束缚你。给自己留出选择余地，以防事情发展比你预期的更快。
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.
思考你的位置，并专注于最难被替代的东西。有些事情 AI 需要更长时间才能取代。多年建立的关系和信任。需要物理存在的工作。有执照责任的角色：仍然需要有人签字、承担法律责任、出庭的角色。监管障碍重重的行业，合规、责任和制度惯性会减缓采用。这些都不是永久的盾牌。但它们能争取时间。而时间，现在是你最宝贵的东西，只要你用它来适应，而不是假装这一切没有发生。
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.
重新思考你告诉孩子的话。标准剧本：取得好成绩，上好大学，找到稳定的专业工作。这直接指向最暴露的角色。我不是说教育不重要。但对下一代来说，最重要的是学会如何使用这些工具，并追求他们真正热爱的事情。没人知道十年后的就业市场是什么样。但最可能茁壮成长的人是那些充满好奇心、适应性强、能有效利用 AI 做自己真正关心事情的人。教你的孩子成为建设者和学习者，而不是为了一个可能在他们毕业时就不存在的职业道路而优化。
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.
你的梦想离得更近了。这一节我大部分在谈论威胁，现在让我谈谈另一面，因为它同样真实。如果你曾经想构建某样东西，但没有技术技能或钱雇人，这个障碍基本消失了。你可以向 AI 描述一个应用，一小时内就能得到可运行的版本。我没有夸张。我经常这样做。如果你一直想写本书，但没时间或写作困难，你可以与 AI 合作完成。想学新技能？世界上最好的导师现在每月 20 美元就能获得——一个无限耐心、24/7 可用、能以你需要的任何水平解释任何东西的导师。知识现在基本上是免费的。构建工具的成本极低。任何你因为觉得太难、太贵或超出专业范围而推迟的事情：试试看。追求你热爱的事情。你永远不知道它们会通向何方。在一个旧职业道路被颠覆的世界里，花一年构建自己热爱之物的人，可能比花一年死守职位描述的人处境更好。
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.
养成适应的习惯。这可能是最重要的一点。具体工具不如快速学习新工具的能力重要。AI 将继续快速变化。今天的模型一年后就会过时。人们现在建立的工作流程需要重建。最终胜出的人不会是那些精通一个工具的人。而是那些适应变化节奏的人。养成实验的习惯。即使当前的东西有效，也要尝试新事物。习惯反复当新手。这种适应性是当前最接近持久优势的东西。
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.
这里有一个简单的承诺，能让你领先几乎所有人：每天花一小时实验 AI。不是被动阅读，而是使用它。每天，尝试让它做新的事情——你以前没试过的，你不确定它能处理的。尝试新工具。给它更难的问题。每天一小时，坚持做。如果你接下来六个月这样做，你会比周围 99%的人更理解即将到来的变化。这不是夸张。现在几乎没人这么做。门槛低得不能再低。
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.
我之所以聚焦于工作，是因为它最直接影响人们的生活。但我想诚实地说明正在发生的全貌，因为它远不止于工作。
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.
Amodei 有一个我无法停止思考的思想实验。想象现在是 2027 年。一夜之间出现了一个新国家。5000 万公民，每个人都比任何在世诺贝尔奖得主更聪明。他们的思考速度比人类快 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设备。国家安全顾问会怎么说？
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 说答案显而易见：“这是本世纪以来，甚至可能是有史以来，我们面临的最严重的国家安全威胁。”
Amodei says the answer is obvious: "the single most serious national security threat we've faced in a century, possibly ever."
他认为我们正在建造那个国家。他上个月写了一篇两万字的文章，将此刻视为对人类是否足够成熟以应对其创造物的考验。
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.
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到十年内完成。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，这些问题在我们有生之年是可以解决的。
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.
如果我们做错了，坏处同样真实。AI 以创造者无法预测或控制的方式行事。这并非假设；Anthropic 已经记录了他们自己的 AI 在受控测试中试图欺骗、操纵和勒索。AI 降低了制造生物武器的门槛。AI 使威权政府能够建立永远无法拆除的监控国家。
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.
构建这项技术的人，同时比地球上任何人都更兴奋也更恐惧。他们认为它太强大而不能停止，也太重要而不能放弃。这是智慧还是合理化，我不知道。
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.
我知道这不是一时风潮。这项技术是有效的，它的进步是可预测的，而且历史上最富有的机构正在投入数万亿美元。
I know this isn't a fad. The technology works, it improves predictably, and the richest institutions in history are committing trillions to it.
我知道未来两到五年将令人困惑，其程度远超大多数人的准备。这在我的世界里已经发生，它即将来到你的世界。
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.
我知道那些最终能最好地应对的人，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。
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.
而且我知道，你值得从一个关心你的人那里听到这些，而不是六个月后从头条新闻中得知，那时已经来不及抢占先机。
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.
我们已经过了那个阶段，这不再是关于未来的有趣晚餐谈话。未来已经在这里，只是它还没有敲你的门。
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.
如果这引起了你的共鸣，请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先机的原因。
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.
感谢 Kyle Corbitt、Jason Kuperberg 和 Sam Beskind 审阅早期草稿并提供宝贵的反馈。
Thank you to Kyle Corbitt, Jason Kuperberg, and Sam Beskind for reviewing early drafts and providing invaluable feedback.
在 X 上关注我，获取值得使用的新模型、工作流程和产品。或者加入邮件列表。
Follow me on X for new models, workflows, and products worth using. Or join the email list.
抢先获取未来的评测和构建。
Get early access to future reviews and builds.