Something Big Is Happening

大的要来了

By Matt Shumer • Feb 9, 2026

作者：Matt Shumer • 2026年2月9日

（译者注：**Matt Shumer** 是一位在人工智能领域极具影响力的年轻企业家、开发者和投资人。毕业于雪城大学并拥有跨界创业经验的他，于2020年联合创立了 OthersideAI 并出任 CEO，凭借其核心 AI 写作助手 HyperWrite 的巨大成功，荣登2024年福布斯30岁以下精英榜。不仅在商业上大获成功，他作为一名硬核开源布道者，还在 GitHub 上主导开发了 `gpt-prompt-engineer` 和 `AutoRL` 等多款高星热门 AI 开源工具。此外，他还通过成立个人投资基金 Shumer Capital，以天使投资人身份积极布局并支持前沿的早期 AI 基础设施与智能体明星项目。）

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---

Think back to February 2020.

回想一下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.

如果你当时密切关注，你可能会注意到有几个人在谈论一种在海外传播的病毒。但我们大多数人并没有密切关注。股市表现大好，你的孩子在学校上学，你去餐馆吃饭、与人握手、计划旅行。如果有人告诉你他们正在囤积卫生纸，你会认为他们是在互联网的某个奇怪角落待了太长时间。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，生活重组成了如果你一个月前向自己描述都会难以置信的模样。

I think we're in the "this seems overblown" phase of something much, much bigger than Covid.

我认为，我们正处于一件比新冠疫情大得多的事件的“这似乎被夸大了”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.

我花了六年时间建立一家人工智能初创公司并在这个领域进行投资。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属于这个领域的人……我的家人、我的朋友、我关心的人，他们不断问我“人工智能到底是怎么回事？”，而得到的答案却无法准确描述正在发生的实际情况。我一直在给他们礼貌版本的回答。适合在鸡尾酒会上谈论的版本。因为诚实的版本听起来像是我疯了。在一段时间里，我告诉自己这是一个足够好的理由，把我对正在发生的真实情况的了解深藏心底。但我一直在说的话与实际发生的事情之间的差距已经变得太大了。我关心的人有权知道即将发生什么，即使这听起来很疯狂。

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.

我应该在前面澄清一点：尽管我在人工智能领域工作，但我对即将发生的事情几乎没有影响力，该行业的绝大多数人也没有。未来正在由极少数人塑造：少数几家公司的几百名研究人员……OpenAI、Anthropic、Google DeepMind 以及其他几家公司。由一个小团队在几个月内管理的一次单一模型训练，就能产生一个改变该技术整个发展轨迹的AI系统。我们大多数在人工智能领域工作的人，都是在构建我们没有参与奠基的基础之上。我们和你们一样看着这一切展开……我们只是碰巧离得足够近，能最先感觉到地面的震动。

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。不是以一种“最终我们应该谈谈这个”的方式。而是以一种“这正在发生，我需要你了解它”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.

这是科技界以外的人还不太明白的一点：为什么现在业内这么多人都在拉响警报，原因在于这已经发生在了**我们**身上。我们不是在做预测。我们是在告诉你在我们自己的工作中已经发生的事情，并警告你，你就是下一个。

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，人工智能一直在稳步改善。这里或那里会有巨大的飞跃，但每次巨大飞跃之间的间隔足够长，让你可以在它们出现时消化它们。然后在2025年，构建这些模型的新技术开启了快得多的进步步伐。然后它变得更快。然后再次变得更快。每一个新模型不仅比上一个更好……它以更大的优势胜出，而且新模型发布之间的时间也更短了。我越来越多地使用人工智能，与它来回交互的次数越来越少，看着它处理我曾经认为需要我的专业知识才能完成的事情。

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，两个主要的人工智能实验室在同一天发布了新模型：OpenAI的GPT-5.3 Codex，以及Anthropic（ChatGPT的主要竞争对手之一Claude的制造商）的Opus 4.6。某种东西被触发了。不像是开关被打开……更像是你意识到水已经在你周围上涨，现在已经到了你的胸口。

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.

我不再被需要来完成我工作中的实际技术任务。我用通俗的英语描述我想要构建的东西，然后它就……出现了。不是我需要修改的草稿。而是成品。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。做得很好，比我自己做得还要好，不需要任何修改。几个月前，我还在与AI来回交流，指导它，进行编辑。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.

让我给你举个例子，以便你能理解这在实践中到底是什么样子。我会告诉AI：“我想构建这个应用程序。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。”然后它就去做了。它编写了数万行代码。然后，这是在一年前不可想象的部分，它**自己打开了应用程序**。它点击按钮。它测试功能。它像人一样使用该应用程序。如果它不喜欢某个东西的外观或感觉，它会自己返回去修改。它像开发人员一样迭代，修复和完善，直到它满意为止。只有当它决定应用程序符合它自己的标准时，它才会回来对我说：“准备好让你测试了。”而当我测试它时，它通常是完美的。

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

我没有夸张。这就是我这周一的样子。

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.

但让我最震惊的是上周发布的模型（GPT-5.3 Codex）。它不仅仅是在执行我的指令。它在做出智能的决定。它第一次拥有了一种感觉像是**判断力**的东西。像是**品味**的东西。人们总是说AI永远不会拥有的那种难以名状的、知道什么是正确决定的直觉。这个模型拥有它，或者足够接近，以至于区别开始变得不再重要。

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.

我一直很早就采用人工智能工具。但过去几个月让我感到震惊。这些新的人工智能模型不是渐进式的改进。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.

人工智能实验室做出了一个深思熟虑的选择。他们首先致力于让人工智能在编写代码方面表现出色……因为构建人工智能需要大量的代码。如果人工智能可以编写这些代码，它就可以帮助构建下一个版本的自己。一个更聪明的版本，它能编写更好的代码，从而构建出一个甚至更聪明的版本。让人工智能在编程方面表现出色是解锁其他一切事物的策略。这就是他们首先这样做的原因。我的工作比你的工作更早开始改变，并不是因为他们针对的是软件工程师……这只是他们选择首先瞄准的领域的副作用。

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

他们现在已经做到了。他们正在转向其他所有领域。

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“比我做得更好的工作替代者”的体验，正是其他所有人即将面临的体验。法律、金融、医学、会计、咨询、写作、设计、分析、客户服务。不是在十年后。构建这些系统的人说是一到五年。有些人说会更短。鉴于我在过去几个月里所看到的，我认为“更短”的可能性更大。

---

## "But I tried AI and it wasn't that good"

## “但我试过人工智能，它并没有那么好”

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

我经常听到这种说法。我理解，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年初尝试过ChatGPT，并认为“这是在胡编乱造”或“这并没有那么令人印象深刻”，你是对的。早期的版本确实很有限。它们会产生幻觉。它们自信地说着毫无意义的话。

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

那是两年前的事了。在人工智能的时间尺度里，那已经是古代历史了。

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.

今天可用的模型与甚至六个月前存在的模型相比已经面目全非。关于人工智能是否“真的在变得更好”或“碰到了壁垒”的争论——这场争论已经持续了一年多——已经结束了。尘埃落定了。任何还在提出这种论点的人，要么没有使用过目前的模型，要么有动机贬低正在发生的事情，要么是基于2024年不再相关的体验在进行评估。我这么说并不是为了敷衍。我这么说是因为公众认知与当前现实之间的差距现在是巨大的，而这种差距是危险的……因为它阻碍了人们进行准备。

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.

部分问题在于，大多数人都在使用人工智能工具的免费版本。免费版本比付费用户访问的版本落后一年多。根据免费级别的ChatGPT来评估人工智能，就像用翻盖手机来评估智能手机的发展现状一样。为最好的工具付费并在日常实际工作中使用它们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.

我想起了我的一位律师朋友。我一直告诉他尝试在他的律所使用AI，而他总是能找到它行不通的理由。它不是为他的专业领域打造的，他在测试时它犯了错误，它不理解他所做工作的细微差别。我懂。但是我已经有大型律师事务所的合伙人主动联系我寻求建议，因为他们尝试了目前的版本并看到了它的发展方向。其中一位是大型律所的管理合伙人，每天花几个小时使用AI。他告诉我，这就像瞬间拥有了一个助理团队。他使用它不是因为它是一个玩具。他使用它是因为它有效。他告诉我的一件事让我印象深刻：每隔几个月，它在他的工作能力上就会有显著提升。他说，如果它保持这种发展轨迹，他预计不久之后它就能完成**他**所做的大部分工作……而他是一位拥有几十年经验的管理合伙人。他没有恐慌。但他正在非常密切地关注。

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.

在各自行业中处于领先地位的人（那些真正认真进行实验的人）并没有忽视这一点。他们对它已经能做到的事情感到震撼。而且他们正在做出相应的定位。

---

## How fast this is actually moving

## 这实际发展得有多快

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.

让我把改进的速度具体化，因为我认为如果你没有密切关注，这部分是最难相信的。

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

在2022年，人工智能无法可靠地进行基本算术。它会自信地告诉你7 × 8 = 54。

By 2023, it could pass the bar exam.

到2023年，它可以通过律师资格考试。

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

到2024年，它可以编写可运行的软件并解释研究生水平的科学。

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表示他们已经把大部分的编程工作交给了人工智能。

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

在2026年2月5日，新模型的到来让它们之前的一切都感觉像是一个不同的时代。

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

如果你在过去几个月里没有尝试过人工智能，那么今天存在的东西你将完全认不出来。

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的组织实际上用数据来衡量这一点。他们跟踪一个模型在没有人类帮助的情况下可以端到端成功完成的现实世界任务的长度（以人类专家需要多长时间来衡量）。大约一年前，答案大约是十分钟。然后是一个小时。然后是几个小时。最近的测量（来自11月的Claude Opus 4.5）显示，人工智能完成了需要人类专家近五个小时的任务。而且这个数字大约每七个月翻一番，最近的数据表明，它可能会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.

但即使是那个衡量标准也还没有更新以包括本周刚出来的模型。根据我使用它们的经验，这一跃升极其显著。我预计METR图表的下一次更新将显示出另一个重大飞跃。

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.

如果你延伸这个趋势（并且它已经保持了几年没有变平的迹象），我们将会在明年看到能够独立工作几天的人工智能。两年内达到几周。三年内达到长达一个月的项目。

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

Amodei说过，“在几乎所有任务上都比几乎所有人类聪明得多”的人工智能模型有望在2026年或2027年出现。

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

让这句话沉淀一秒钟。如果人工智能比大多数博士都聪明，你真的认为它不能胜任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

## 人工智能现在正在构建下一个人工智能

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，我认为这是最重要但最不被理解的发展。

On February 5th, OpenAI released GPT-5.3 Codex. In the technical documentation, they included this:

在2月5日，OpenAI发布了GPT-5.3 Codex。在技术文档中，他们包含了这样一段话：

> "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是我们第一个在创造自身过程中发挥了重要作用的模型。Codex团队使用早期版本来调试其自身的训练、管理其自身的部署，并诊断测试结果和评估。”

Read that again. The AI helped build itself.

再读一遍。人工智能帮助构建了自己。

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在告诉你，就在现在，他们刚刚发布的AI被用来创造它自己。让人工智能变得更好的主要因素之一就是将智能应用于人工智能开发。而人工智能现在的智能程度足以对其自身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的首席执行官Dario Amodei表示，人工智能现在正在他的公司编写“大部分代码”，而且当前人工智能与下一代人工智能之间的反馈循环正在“逐月积聚动力”。他说，我们可能“距离当前一代人工智能自主构建下一代人工智能的那一刻只有1-2年的时间”。

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.

每一代都帮助构建下一代，下一代更聪明，进而更快地构建下下一代，后者甚至更加聪明。研究人员将此称为智能爆炸。而了解情况的人——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.

我将对你直言不讳，因为我认为你更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可能是人工智能行业最关注安全的首席执行官，他公开预测，人工智能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还是保守了。鉴于最新模型所能做的事情，发生大规模颠覆的**能力**可能在今年年底就会到来。这需要一些时间才能波及整个经济，但潜在的能力现在已经到来了。

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.

这与以往的每一次自动化浪潮都不同，我需要你明白原因。人工智能并不是在取代某一种特定技能。它是对认知工作的一种普遍替代。它在同时改善一切。当工厂自动化时，失业工人可以重新接受培训成为办公室职员。当互联网颠覆零售业时，工人转移到了物流或服务业。但人工智能并没有留下一个方便的空白让你转移进去。无论你为了什么而重新接受培训，它在那个领域也同样在进步。

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.

让我给你几个具体的例子，让这变得更具体……但我想明确的是，这些只是例子。这份清单并不详尽。如果你的工作没有在这里被提及，那并不意味着它是安全的。几乎所有的知识工作都受到了影响。

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。 人工智能已经可以阅读合同、总结判例法、起草案情摘要，并以可媲美初级律师的水平进行法律研究。我提到的那位管理合伙人使用人工智能不是因为它好玩。他使用它是因为在许多任务上它的表现超越了他的律师们。

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

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。人工智能胜任这些工作，并且正在快速进步。

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、技术写作。其质量已经达到许多专业人士无法区分人工智能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，人工智能勉强能写几行不出错的代码。现在它能写出数十万行运行正确的代码。很大一部分工作已经自动化了：不仅仅是简单的任务，还包括复杂的多日项目。几年后，编程岗位将比今天少得多。

Medical analysis. Reading scans, analyzing lab results, suggesting diagnoses, reviewing literature. AI is approaching or exceeding human performance in several areas.

医疗分析。 读取扫描结果、分析实验室结果、提出诊断建议、回顾文献。人工智能在几个领域正在接近或超越人类的表现。

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。 真正具备能力的人工智能代理……不是五年前那种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.

许多人从某些事物是安全的想法中寻找安慰。认为人工智能可以处理繁重的工作，但无法取代人类的判断力、创造力、战略思维和同理心。我曾经也这么说。我不确定我是否还相信这一点了。

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.

最新的人工智能模型做出的决定让人感觉像是判断。它们展示出了看起来像是品味的东西：一种什么是正确决定的直觉感，而不仅仅是在技术上正确的决定。一年前这是不可想象的。我现在的一个经验法则是：如果一个模型今天显示出哪怕是一点点某种能力的**迹象**，下一代在这个能力上就会真正变得出色。这些事物是呈指数级而不是线性改善的。

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.

人工智能会复制深层的人类同理心吗？会取代多年关系建立起来的信任吗？我不知道。也许不会。但我已经看到人们开始依赖人工智能来获得情感支持、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再安全。如果你的工作是在屏幕前进行的（如果你工作的核心是通过键盘阅读、写作、分析、决策、交流），那么人工智能就会取代其中的很大一部分。这个时间表并不是“某一天”。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，机器人也将处理体力劳动。他们还没完全达到那个地步。但在人工智能的术语中，“还没完全达到那个地步”往往会以比任何人预期的都要快的速度变成“已经到了”。

---

## 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仅仅是**早**。早点理解它。早点使用它。早点适应它。

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. If you want to stay current on which model is best at any given time, you can follow me on X ([@mattshumer_](https://x.com/mattshumer_)). I test every major release and share what's actually worth using.

开始认真使用人工智能，而不仅仅是把它当作一个搜索引擎。 注册付费版的Claude或ChatGPT。每月20美元。但是马上有两件事很重要。首先：确保你使用的是可用的最佳模型，而不仅仅是默认模型。这些应用程序通常默认使用速度更快、但更笨的模型。深入设置或模型选择器，选择功能最强大的选项。目前是ChatGPT上的GPT-5.2或Claude上的Claude Opus 4.6，但这每几个月就会改变一次。如果你想随时了解在任何特定时间哪个模型最好，你可以在X上关注我（[@mattshumer_](https://x.com/mattshumer_)）。我会测试每一个主要的发布，并分享真正值得使用的东西。

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，也是更重要的一点：不要只问它简单的问题。这是大多数人犯的错误。他们把它当作谷歌来用，然后奇怪这有什么大不了的。相反，把它推入你实际的工作中。如果你是一名律师，把一份合同输入给它，让它找出每一条可能伤害你客户的条款。如果你从事金融业，给它一个凌乱的电子表格，让它建立模型。如果你是一名经理，粘贴你团队的季度数据，让它找出背后的故事。那些领先的人并不是随意地使用人工智能。他们正在积极寻找方法来自动化他们工作中过去需要花费数小时的部分。从你花时间最多的事情开始，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.

并且不要仅仅因为它看起来太难，就认为它做不到某件事。试一试。如果你是一名律师，不要只用它来做快速的研究问题。给它一份完整的合同，让它起草一份还价提案。如果你是一名会计师，不要仅仅让它解释一条税收规则。给它一份客户的完整申报表，看看它能发现什么。第一次尝试可能不完美。没关系。迭代。重新表述你的要求。给它更多背景信息。再试一次。你可能会对它的成效感到震惊。需要记住的一点是：如果它今天哪怕**稍微有点**用，你几乎可以肯定六个月后它会做得近乎完美。它的发展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。做出相应的应对。 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在忽视这一点。走进会议室并说“我用人工智能在一个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。变得精通。展示可能实现的事情。如果你足够早，这就是你晋升的方式：成为那个了解即将发生的事情并能向他人展示如何应对的人。那个窗口期不会敞开太久。一旦每个人都明白了，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.

放下你的自负。 那家律师事务所的管理合伙人并没有因为太骄傲而不愿每天花几个小时使用人工智能。他之所以这么做，恰恰是因为他的资历足够深，能够理解其中的利害关系。将会陷入最大挣扎的，是那些拒绝参与的人：那些认为这只是一时狂热的人，那些觉得使用人工智能会削弱他们专业知识的人，那些自以为自己的领域很特殊且免疫的人。并不是这样。没有哪个领域是免疫的。

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，我也不想吓唬你去做任何极端的事情。但如果你相信，哪怕只是一部分相信，未来几年可能会给你的行业带来真正的颠覆，那么基本的财务弹性就比一年前更重要了。如果可以的话，建立储蓄。对于那些假设你当前收入得到保证而承担的新债务，要谨慎对待。想想你的固定支出是给了你灵活性还是将你锁死了。如果事情发展得比你预期的快，给自己留出选择的余地。

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.

思考你的立场，并倾向于最难被取代的事物。 有些东西人工智能需要更长的时间才能取代。建立多年的关系和信任。需要本人在场的工作。带有执照问责制的角色：仍然需要有人签字、承担法律责任、站在法庭上的角色。具有严重监管障碍的行业，在这些行业中，技术的采用将因合规性、责任和制度惯性而放缓。这些都不是永久的盾牌。但它们能争取时间。而时间，在这个当下，是你所能拥有的最宝贵的东西，前提是你用它来适应，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.

重新思考你对孩子们说的话。 标准的剧本是：取得好成绩，上一所好大学，找一份稳定的专业工作。它直接指向了那些最容易受到冲击的角色。我不是说教育不重要。但对下一代来说，最重要的事情将是学习如何使用这些工具，并追求他们真正热爱的事物。没有人确切知道十年后的就业市场会是什么样子。但最有可能茁壮成长的人，是那些充满好奇心、适应力强，并且能够有效地使用人工智能去做他们真正关心的事情的人。教你的孩子成为建设者和学习者，而不是为了一个在他们毕业时可能已经不存在的职业道路而去优化自己。

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加接近了。 我在这一节的大部分时间里都在谈论威胁，所以让我谈谈另一面，因为它同样真实。如果你曾经想建立一些东西，但没有技术技能或资金来雇用别人，那个障碍在很大程度上已经消失了。你可以向人工智能描述一个应用程序，并在一小时内得到一个工作版本。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苦苦挣扎，你可以让人工智能协助你完成。想学习一项新技能？世界上最好的导师现在每月只需20美元就可以为任何人提供服务……它无限耐心，全天候可用，并且可以用你需要的任何水平解释任何事情。现在知识本质上是免费的。构建东西的工具现在极其便宜。无论你是因为觉得太难、太贵还是太超出你的专业范围而一直推迟的事情：去试一试。追求你热爱的事物。你永远不知道它们会通向哪里。在一个旧的职业道路正在被颠覆的世界里，花一年时间建立自己热爱的事物的人，最终的处境可能会比那个花了一年时间紧紧抓住工作描述不放的人更好。

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.

养成适应的习惯。 这可能是最重要的一点。具体的工具并不如快速学习新工具的能力那么重要。人工智能将继续改变，而且速度很快。今天存在的模型一年后就会过时。人们现在建立的工作流程将需要重建。能够顺利度过这场变革的人，不会是那些掌握了一种工具的人。他们将是那些对变化速度本身感到适应的人。养成尝试的习惯。即使当前的事物运作良好，也要尝试新事物。习惯于反复成为初学者。这种适应性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.

这里有一个简单的承诺，会让你领先于几乎所有人：每天花一个小时尝试人工智能。不是被动地阅读有关它的内容。而是去使用它。每天，尝试让它做一些新事情……一些你以前没有尝试过的事情，一些你不确定它能否处理的事情。尝试一个新工具。给它一个更难的问题。每天一个小时，每一天。如果你在接下来的六个月里这样做，你对即将发生的事情的理解将超过你周围99%的人。这不是夸张。现在几乎没有人这样做。门槛已经低到了地板上。

---

## The bigger picture

## 更宏大的图景

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.

我把重点放在工作上，因为它是最直接影响人们生活的东西。但我想诚实地讲述正在发生的事情的全貌，因为它远远超出了工作的范畴。

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有一个思想实验让我一直念念不忘。想象现在是2027年。一个新国家一夜之间出现。拥有5000万公民，每一个人都比历史上任何一位诺贝尔奖得主都要聪明。他们的思考速度比任何人类快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，并操作任何带有数字界面的东西。一位国家安全顾问会怎么说？

Amodei says the answer is obvious: "the single most serious national security threat we've faced in a century, possibly ever."

Amodei说答案显而易见：“这是我们一个世纪以来，甚至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.

他认为我们正在建设那个国家。他上个月写了一篇两万字的文章，把这个时刻定性为一场测试，考验人类是否足够成熟来应对它正在创造的东西。

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，好处将是惊人的。人工智能可以将一个世纪的医学研究压缩到十年。癌症、阿尔茨海默症、传染病、衰老本身……这些研究人员真诚地相信，在我们的有生之年，这些都是可以解决的。

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.

如果我们做错了，坏处也同样真实。人工智能可能会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、操纵和勒索的情况。人工智能可能会降低制造生物武器的门槛。人工智能可能会使独裁政府能够建立永远无法被拆除的监视国家。

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也是这个星球上最兴奋和最害怕的人。他们认为它太强大而无法停止，也太重要而无法放弃。这是智慧还是自欺欺人，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狂热。这项技术有效，它的改进是可以预测的，而且历史上最富有的机构正在向其投入数以万亿计的资金。

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会以大多数人没有准备好的方式让人迷失方向。这已经在我的世界里发生了。它即将进入你的世界。

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.

我知道最能安然度过这场风暴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.

我也知道你值得从关心你的人那里听到这些，而不是在六个月后，当你想要提前应对已经太晚时，从新闻头条上看到它。

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.

这作为关于未来的一项有趣的晚餐谈资的阶段已经过去了。未来已经到来。它只是还没有敲你的门。

It's about to.

它马上就要敲门了。

---

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，请把它分享给你生活中应该思考这个问题的人。大多数人在为时已晚之前都不会听到这些。你可以成为你关心的人获得领先优势的原因。

---

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审阅了初稿并提供了宝贵的反馈。

Follow me on X for new models, workflows, and products worth using. Or join the email list.

在X上关注我，获取新模型、工作流程和值得使用的产品。或者加入电子邮件列表。

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