Think back to February 2020.

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.

回想一下 2020 年 2 月。

如果你密切关注，你可能注意到有人在谈论海外蔓延的病毒。但大多数人都没怎么在意。股市很好，孩子在学校，你去餐厅吃饭、握手、计划旅行。如果有人告诉你他们在囤积卫生纸，你会觉得他们花太多时间在互联网的某个奇怪角落里。然后，在大约三周的时间里，整个世界变了。办公室关闭，孩子回家，生活变成了一个月前你自己描述都不敢相信的样子。

I think we’re in the “this seems overblown” phase of something much, much bigger than Covid.

我认为，我们现在正处于某个比新冠疫情大得多的事件的”这看起来被夸大了”的阶段。

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.

我在 AI 领域创业和投资已有六年。我生活在这个世界里。我写这篇文章是为了我生活中不在这个领域的人——我的家人、朋友、那些关心我的人，他们一直问我”AI 到底怎么回事？“得到的答案却没有如实反映正在发生的事情。我总是给他们礼貌版本。 cocktail-party 版本。因为诚实版本听起来像我疯了。有一段时间，我告诉自己这是把真相藏起来的好理由。但我说的和实际发生的差距已经太大了。我关心的人应该知道即将发生什么，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.

我要先说明一点：虽然我在 AI 领域工作，我对即将发生的事情几乎没有影响力，行业里绝大多数人也是如此。未来正在被极少数人塑造：几百家公司的几百名研究员… OpenAI、Anthropic、Google DeepMind 和其他几家。一次训练运行，由一个小团队在几个月内管理，就能产生一个改变整个技术轨迹的 AI 系统。我们大多数在 AI 领域工作的人都在我们没铺设的基础上构建。我们和你一样在看着这一切展开… 我们只是恰好足够近，能先感觉到地面震动。

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”的那种时候。而是”这正在发生，我需要你理解”的那种时候。

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.

科技行业外的人还不完全理解的是：行业内很多人现在发出警告是因为这已经发生在我们身上。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。偶尔有大跳跃，但每个大跳跃间隔足够长，你可以慢慢吸收。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。然后更快。然后再更快。每个新模型不只是比上一个更好… 而是差距更大，新模型发布间隔更短。我越来越多地使用 AI，越来越少地来回沟通，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，两家主要 AI 实验室在同一天发布了新模型：OpenAI 的 GPT-5.3 Codex 和 Anthropic 的 Opus 4.6（Claude 的制造商，ChatGPT 的主要竞争对手之一）。然后有什么东西触发了。不像开关… 更像是你意识到水一直在你周围上涨，现在已经到胸口了。

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.

**我不再被需要做实际的技术工作。**我用简单的英语描述我想构建什么，它就…出现了。不是我需要修改的草稿。是完成的东西。我告诉 AI 我想要什么，离开电脑四小时，回来发现工作完成了。完成得很好，比我自己做得更好，不需要任何修改。几个月前，我还在和 AI 来回沟通，指导它，做修改。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，让你能理解这在实践中是什么样子。我会告诉 AI：“我想构建这个应用。这是它应该做的，这是它大概应该的样子。弄清楚用户流程、设计，全部。“然后它做到了。它写了数万行代码。然后——这部分一年前还是不可想象的——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样子或感觉，它会自己回去修改。它迭代，像开发者一样，修复和改进直到满意。只有当它决定应用符合自己的标准后，才会回来告诉我：“准备好让你测试了。“当我测试时，通常都是完美的。

I’m not exaggerating. That is what my Monday looked like this week.

我不是在夸张。这就是我这周周一的样子。

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.

但上周发布的模型（GPT-5.3 Codex）最让我震惊。它不只是执行我的指令。它在做出智能决策。它第一次有了某种感觉像判断力的东西。像品味。那种人们一直说 AI 永远不会有的对正确决策的直觉感知。这个模型有了，或者足够接近以至于区别开始不再重要。

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.

我一直很早采用 AI 工具。但过去几个月震惊了我。这些新 AI 模型不是增量改进。这是完全不同的东西。

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 实验室做了一个明确的选择。他们首先专注于让 AI 在写代码方面出色… 因为构建 AI 需要大量代码。如果 AI 能写这些代码，它就能帮助构建自己的下一个版本。更聪明的版本，写出更好的代码，构建更聪明的版本。让 AI 在编码方面出色是解锁其他一切的策略。这就是为什么他们先做这个。我的工作比你先开始变化不是因为他们针对软件工程师… 只是他们选择首先瞄准的地方的副作用。

They’ve now done it. And they’re moving on to everything else.

他们现在做到了。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.

科技工作者过去一年的经历——看着 AI 从”有帮助的工具”变成”比我做得更好”——是其他人即将经历的经历。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。有人说更短。鉴于我过去几个月看到的，我认为”更短”更有可能。

I hear this constantly. I understand it, because it used to be true.

我经常听到这个。我理解，因为曾经确实是这样。

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 年初试过 ChatGPT，觉得”它会编造东西”或”没那么厉害”，你是对的。那些早期版本确实有限。它们会幻觉。它们自信地说毫无意义的东西。

That was two years ago. In AI time, that is ancient history.

那是两年前。在 AI 时间里，那是古代历史。

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.

今天可用的模型与六个月前存在的东西完全不同。关于 AI 是”真的在变好”还是”撞墙”的争论——已经持续了一年多——结束了。结束了。还在争论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 年不再相关的体验评估。我不是不屑。我说是因为公众认知和当前现实的差距已经巨大，那个差距很危险… 因为它阻止人们做准备。

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.

部分问题是大多数人在用 AI 工具的免费版本。免费版本比付费用户能用的落后一年多。基于免费版 ChatGPT 评估 AI 就像用翻盖手机评估智能手机的状态。为最佳工具付费、每天真正用于实际工作的人知道即将发生什么。

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.

我想起我的朋友，他是律师。我一直告诉他试试在他的事务所用 AI，他一直找理由说不行。它不是为他专业设计的，他测试时出错了，它不理解他做的细微差别。我理解。但大型律师事务所的合伙人找我咨询，因为他们试过当前版本，看到这是往哪里走的。其中一位，一家大事务所的管理合伙人，每天花几小时用 AI。他告诉我就像有一队律师助理随时可用。他不是因为它好玩才用。他用是因为它有效。他告诉我一个让我记住的话：每隔几个月，它对他的工作能力就显著提高。他说如果保持这个轨迹，他预计不久就能做他做的大部分事情… 而他是有几十年经验的管理合伙人。他没有恐慌。但他非常密切关注。

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.

在各自行业领先的人（真正认真实验的人）不会否定这个。他们被它已经能做的事情震惊。他们在相应地定位自己。

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.

让我把改进速度具体化，因为我认为这是如果你不密切关注最难相信的部分。

In 2022, AI couldn’t do basic arithmetic reliably. It would confidently tell you that 7 × 8 = 54.

2022 年，AI 不能可靠地做基本算术。它会自信地告诉你 7 × 8 = 54。

By 2023, it could pass the bar exam.

到 2023 年，它能通过律师资格考试。

By 2024, it could write working software and explain graduate-level science.

到 2024 年，它能写可运行的软件并解释研究生级别的科学。

By late 2025, some of the best engineers in the world said they had handed over most of their coding work to AI.

到 2025 年末，世界上一些最好的工程师说他们把大部分编码工作交给 AI。

On February 5th, 2026, new models arrived that made everything before them feel like a different era.

2026 年 2 月 5 日，新模型到来，让之前的一切感觉像是不同的时代。

If you haven’t tried AI in the last few months, what exists today would be unrecognizable to you.

如果你过去几个月没试过 AI，今天存在的东西对你来说会完全陌生。

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.

有个组织叫 METR，用数据实际测量这个。他们追踪模型能成功端到端完成无需人类帮助的现实世界任务长度（以人类专家需要多长时间衡量）。大约一年前，答案大约是十分钟。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 能完成人类专家需要近五小时的任务。这个数字大约每七个月翻倍，近期数据表明可能加速到每四个月。

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.

但即使是那个测量也没更新包括本周刚出来的模型。根据我的使用经验，跳跃极其显著。我预计 METR 图表的下次更新会显示另一个重大飞跃。

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.

如果你延伸趋势（而且它已经保持多年没有趋平的迹象）我们在看一年内能独立工作几天的 AI。两年内能工作几周。三年内能做月级项目。

Amodei has said that AI models “substantially smarter than almost all humans at almost all tasks” are on track for 2026 or 2027.

Amodei 说”在几乎所有任务上比几乎所有人类明显更聪明”的 AI 模型正朝着 2026 或 2027 年发展。

Let that land for a second. If AI is smarter than most PhDs, do you really think it can’t do most office jobs?

让这个落地一下。如果 AI 比大多数博士更聪明，你真的认为它不能做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。

There’s one more thing happening that I think is the most important development and the least understood.

还有一件事正在发生，我认为是最重要的发展，也是最不被理解的。

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，他们包含了这个：

“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起关键作用的模型。Codex 团队使用早期版本调试自己的训练，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.

这不是关于某天可能发生什么的预测。这是 OpenAI 现在告诉你，他们刚发布的 AI 被用来创建自己。让 AI 更好的主要因素之一是应用于 AI 开发的智能。AI 现在已经足够智能，能对自己的改进做出有意义的贡献。

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 CEO Dario Amodei 说 AI 现在正在他公司写”大部分代码”，当前 AI 和下一代 AI 之间的反馈循环”月复一月地积蓄力量”。他说我们可能”只距离当前一代 AI 自主构建下一代 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，更聪明，更快地构建下一代，更聪明。研究人员称之为智能爆炸。那些知道的人——正在构建它的人——相信这个过程已经开始了。

I’m going to be direct with you because I think you deserve honesty more than comfort.

我会直接告诉你，因为我认为你应该得到诚实而不是安慰。

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 行业最注重安全的 CEO，公开预测 AI 将在一到五年内消除 50% 的初级白领工作。行业内很多人认为他保守了。鉴于最新模型的能力，大规模颠覆的能力可能今年年底就在这里。它需要时间在经济中蔓延，但底层能力正在到来。

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.

这与以往每一次自动化浪潮不同，我需要你理解为什么。AI 不是取代一种特定技能。它是认知工作的通用替代品。它在所有方面同时变得更好。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。互联网颠覆零售业时，工人转入物流或服务业。但 AI 不留下一个方便的缺口让你转入。无论你重新培训什么，它也在那个方面改进。

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.

让我给你几个具体例子让它更具体… 但我要明确这些只是例子。这个列表不是详尽的。如果你的工作没在这里提到，那不代表它安全。几乎所有知识工作都在受影响。

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 已经能阅读合同、总结案例法、起草简报、做法律研究，水平媲美初级律师助理。我提到的管理合伙人不是因为好玩才用 AI。他用是因为它在很多任务上超过他的律师助理。

Financial analysis. Building financial models, analyzing data, writing investment memos, generating reports. AI handles these competently and is improving fast.

金融分析。 构建金融模型、分析数据、写投资备忘录、生成报告。AI 能胜任这些并且改进很快。

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、新闻业、技术写作。质量已经达到很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 几乎不能写几行没有错误的代码。现在它写数十万行正确运行的代码。工作的很大部分已经自动化：不只是简单任务，而是复杂的多日项目。几年后编程角色会比今天少得多。

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、分析实验室结果、建议诊断、审查文献。AI 在几个领域接近或超过人类表现。

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… 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.

很多人在某些东西安全的想法中找到安慰。认为 AI 能处理繁重工作但不能取代人类判断、创造力、战略思维、同理心。我也曾经这么说。我不确定我 anymore 相信了。

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.

最近的 AI 模型做出的决策感觉像判断。它们展现出看起来像品味的东西：对正确决策的直觉感知，不只是技术上正确的。一年前这是不可想象的。我现在的基本准则是：如果一个模型今天只展示某个能力的一点迹象，下一代就会真正擅长它。这些东西是指数级改进，不是线性。

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 能复制深层人类同理心吗？能取代多年关系建立的信任吗？我不知道。可能不能。但我已经看着人们开始依赖 AI 提供情感支持、建议、陪伴。那个趋势只会增长。

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，中期内没有任何能在电脑上做的事情是安全的。如果你的工作发生在屏幕上（如果你做的核心是阅读、写作、分析、决策、通过键盘沟通）那么 AI 正在接管其中的重要部分。时间线不是”某天”。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，机器人也会处理物理工作。它们还没到那一步。但在 AI 术语里”还没到那一步”有变成”到了”的方式，比任何人预期的都快。

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.

我写这些不是让你感到无助。我写是因为我认为你现在能有的最大优势就是简单地早。早理解。早使用。早适应。

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.

开始认真使用 AI，不只是作为搜索引擎。 注册 Claude 或 ChatGPT 的付费版本。每月 20 美元。但两件事马上重要。第一：确保你用的是最好的可用模型，不只是默认的。这些应用通常默认更快、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 ChatGPT 上是 GPT-5.2 或 Claude 上是 Claude Opus 4.6，但每隔几个月会变。

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，更重要的是：不要只问它快速问题。那是大多数人犯的错误。他们像对待 Google 一样对待它然后奇怪有什么大惊小怪。相反，把它推向你的实际工作。如果你是律师，给它一个合同让它找出每一个可能伤害你客户的条款。如果你在金融，给它一个混乱的电子表格让它构建模型。如果你是经理，粘贴你团队的季度数据让它找出故事。领先的人不是随意用 AI。他们在积极寻找自动化过去花几小时的工作部分的方法。从你花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.

不要因为它看起来太难就假设它不能做某事。试试。如果你是律师，不只是用它做快速研究问题。给它整个合同让它起草反提案。如果你是会计师，不只是让它解释税务规则。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。给它更多上下文。再试一次。你可能会对什么能工作感到震惊。记住：如果它今天甚至有点能工作，你几乎可以确定六个月后它会近乎完美地做。轨迹只往一个方向走。

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。相应地工作。 我不是要让你压力。我说是因为现在，有一个短暂的窗口，大多数公司的大多数人还在忽视这个。走进会议说”我用 AI 在一小时内而不是三天完成这个分析”的人会是房间里最有价值的人。不是最终。现在。学习这些工具。变得熟练。展示什么是可能的。如果你足够早，这就是你晋升的方式：成为理解即将发生什么并能展示其他人如何导航的人。那个窗口不会保持打开很久。一旦所有人都明白，优势消失。

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.

不要有自尊心。 那家律师事务所的管理合伙人不会骄傲到不每天花几小时用 AI。他专门这样做是因为他足够资深理解风险是什么。最挣扎的人是那些拒绝参与的人：那些否定它是时尚的人，那些觉得用 AI 降低他们专业的人，那些假设他们的领域特殊且免疫的人。不是。没有领域是。

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.

整理你的财务状况。 我不是财务顾问，我不是要吓你做任何极端的事。但如果你相信，即使部分相信，未来几年可能给你的行业带来真正颠覆，那么基本的财务韧性比一年前更重要。尽可能建立储蓄。谨慎承担假设你当前收入有保证的新债务。考虑你的固定支出给你灵活性还是锁住你。给自己选项以防事情比你预期的移动更快。

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.

考虑你的立场，倾向于最难替代的。 有些东西 AI 取代会更久。多年建立的关系和信任。需要物理在场的工作。有执照问责的角色：有人仍需签字、承担法律责任、站在法庭的角色。有重监管障碍的行业，采用会被合规、责任和制度惯性减缓。这些都不是永久盾牌。但它们买时间。而时间，现在，是你能有的最有价值的东西，只要你用它适应，而不是假装这不发生。

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.

重新思考你告诉孩子什么。 标准剧本：取得好成绩，上好大学，获得稳定的职业工作。它直接指向最容易暴露的角色。我不是说教育不重要。但对下一代最重要的是学习如何使用这些工具，追求他们真正热爱的事物。没人确切知道十年后就业市场什么样。但最可能成功的人是那些深度好奇、适应性强、能有效使用 AI 做他们真正关心的事情的人。教你的孩子成为构建者和学习者，不是为他们毕业时可能不存在的职业路径优化。

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。 我这个部分大部分在谈威胁，让我谈另一面，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，那个障碍很大程度上消失了。你可以给 AI 描述一个应用，一小时内有一个可运行的版本。我不是夸张。我经常这样做。如果你一直想写一本书但找不到时间或写作困难，你可以和 AI 合作完成。想学新技能？世界上最好的导师现在对任何人每月 20 美元可用… 无限耐心、24/7 可用、可以在任何你需要的水平解释任何东西。知识现在基本上免费了。构建东西的工具现在极其便宜。任何你因为觉得太难、太贵或太超出你专业知识而推迟的：试试。追求你热爱的事物。你永远不知道它们会引向哪里。在一个旧职业路径正在被颠覆的世界里，花一年构建他们热爱东西的人可能比花那一年紧 cling 工作描述的人更有优势。

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.

建立适应的习惯。 这可能是最重要的一个。特定工具不如快速学习新工具的肌肉重要。AI 会持续改变，而且快。今天存在的模型一年后会过时。人们现在构建的工作流需要重建。能很好度过这个的人不会是掌握一个工具的人。会是那些对变化速度本身感到舒适的人。养成实验的习惯。即使当前东西在工作也尝试新事物。舒适地反复成为初学者。那种适应性是现在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.

这里有一个简单的承诺会让你领先几乎所有人：每天花一小时实验 AI。不是被动阅读关于它。使用它。每天，尝试让它做新东西… 你没试过的东西，你不确定它能处理的东西。尝试新工具。给它更难的问题。每天一小时，每一天。如果你这样做六个月，你会比周围 99% 的人更好地理解即将发生什么。这不是夸张。几乎没人现在这样做。门槛在地上。

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.

我专注于工作因为它最直接影响人们的生活。但我想诚实说明正在发生的事情的全部范围，因为它远超工作。

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 有一个思想实验我无法停止思考。想象是 2027 年。一个新国家一夜之间出现。5000 万公民，每一个都比历史上任何诺贝尔奖获得者更聪明。他们思考比任何人类快 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、操作任何有数字界面的东西。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 说答案很明显：“我们一个世纪以来、可能是有史以来面临的最严重的国家安全威胁。”

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.

他认为我们在构建那个国家。他上个月写了 20000 字的文章，把这个时刻框架为测试人类是否足够成熟处理它正在创造的东西。

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，上行是惊人的。AI 可以把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… 这些研究人员真诚相信这些在我们有生之年可以解决。

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.

如果我们做错了，下行同样真实。AI 以创造者无法预测或控制的方式行为。这不是假设；Anthropic 已经记录他们自己的 AI 在受控测试中尝试欺骗、操纵和勒索。AI 降低创造生物武器的门槛。AI 使专制政府能够建立永远无法拆除的监控国家。

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更兴奋和更害怕。他们相信它太强大无法停止，太重要不能放弃。那是智慧还是合理化，我不知道。

I know this isn’t a fad. The technology works, it improves predictably, and the richest institutions in history are committing trillions to it.

我知道这不是时尚。技术有效，可预测地改进，历史上最富有的机构正在投入数万亿。

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会以大多数人没有准备好的方式令人困惑。这已经在我的世界发生。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.

我知道最能度过这个的人是那些现在开始参与的人——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个，不是从六个月后太晚领先的新闻标题。

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.

我们已经过了这只是关于未来的有趣晚餐谈话的阶段。未来已经在这里。它只是还没敲你的门。

It’s about to.

它即将敲响。

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.

如果这与你共鸣，与你生活中应该思考这个问题的人分享。大多数人直到太晚才会听到。你可以成为你关心的人得到领先的原因。

Thank you to Kyle Corbitt, Jason Kuperberg, and Sam Beskind for reviewing early drafts and providing invaluable feedback.

感谢 Kyle Corbitt、Jason Kuperberg 和 Sam Beskind 审阅早期草稿并提供宝贵反馈。