AI正在创造更聪明的AI:刷屏长文《大事正在发生》中英对照版
作者：微信文章过去一周，一篇题为《Something Big Is Happening》（大事正在发生）的长文在24小时突破8000万阅读量，在X（原推特）、Hacker News 和 LinkedIn 上疯狂刷屏。
文章的作者是 Matt Shumer。如果你不太关注海外AI圈，可能对这个名字还比较陌生，但他在业内其实有着相当高的认可度。Matt Shumer 是 OthersideAI 的联合创始人兼CEO，也是一位在生成式AI浪潮中深耕多年的连续创业者和天使投资人。
他以一线从业者的视角，向所有尚未警觉的人发出预警：AI的能力早已突破大众的认知，大众眼中的AI，是谈天说地的玩物；行业内部的AI，已是重塑生产力的洪流。
以下是原文的中英对照，全文篇幅较长，可以收藏起来方便随时阅读。
《Something Big Is Happening》
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新闻，你可能会注意到有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。股市表现很好，你的孩子还在上学，你会去餐馆吃饭、与人握手、计划旅行。如果有人告诉你他们在囤积卫生纸，你可能会觉得他们在一个奇怪的互联网角落里待了太久。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都不会相信的样子。
I think we're in the "this seems overblown" phase of something much,much bigger than Covid.
我认为，我们正处于一个比新冠要大得多、大得多的事件的"这似乎有点小题大做"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 lifewho don't... my family, my friends, the people I care about who keepasking me "so what's the deal with AI?" and getting an answer thatdoesn't do justice to what's actually happening. I keep giving themthe polite version. The cocktail-party version. Because the honestversion sounds like I've lost my mind. And for a while, I told myselfthat 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 actuallyhappening has gotten far too big. The people I care about deserve tohear what is coming, even if it sounds crazy.
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。我就生活在这样一个世界里。我写这篇文章是给我生活中不在此领域的人看的……我的家人、我的朋友、我在乎的人，他们一直问我"那么，人工智能到底是怎么回事？"而得到的答案并没有公正地反映实际发生的情况。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，这是一个很好的理由，可以把真正发生的事情藏在心里。但是，我所说的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，即使这听起来很疯狂。
I should be clear about something up front: even though I work in AI, Ihave almost no influence over what's about to happen, and neither does the vast majority of the industry. The future is being shaped by aremarkably small number of people: a few hundred researchers at ahandful of companies... OpenAI, Anthropic, Google DeepMind, and a few others. A single training run, managed by a small team over a fewmonths, can produce an AI system that shifts the entire trajectory ofthe technology. Most of us who work in AI are building on top offoundations we didn't lay. We're watching this unfold the same asyou... we just happen to be close enough to feel the ground shakefirst.
我应该在一开始就说明一点：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，这个行业的绝大多数人也没有。未来正由极少一部分人塑造：来自少数几家公司的几百名研究人员……OpenAI、Anthropic、Google DeepMind，以及其他几家。一次由一个小团队管理、历时几个月的训练运行，就可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他人打下的基础上进行构建。我们和你一样，也在目睹这一切的发生……只是我们恰好离得够近，能最先感受到地面的震动。
But it's time now. Not in an "eventually we should talk about this"way. In a "this is happening right now and I need you to understandit" way.
但现在是时候了。不是以"最终我们应该谈谈这个"的方式，而是以"这正在发生，我需要你理解它"的方式。
I know this is real because it happened to me first
我知道这是真的，因为它首先发生在我身上
Here's the thing nobody outside of tech quite understands yet: thereason 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.
这就是科技行业以外的人还不太明白的地方：现在行业内有这么多人敲响警钟，是因为这已经发生在我们身上了。我们不是在做出预测。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进。时不时会有大的飞跃，但每一次大的飞跃间隔都足够长，让你可以慢慢消化。然后在2025年，构建这些模型的新技术开启了更快的进步速度。然后变得更快。然后又更快了。每一个新模型不仅仅是比上一个更好……而是好得更多，而且新模型发布之间的时间间隔更短了。我越来越多地使用人工智能，与它来回沟通的次数越来越少，看着它处理那些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和Anthropic的Opus 4.6（Claude的创造者，ChatGPT的主要竞争对手之一）。然后，有什么东西突然明朗了。不像电灯开关……更像是你意识到周围的水一直在上涨，现在已经到了你胸口的那一刻。
I am no longer needed for the actual technical work of my job. Idescribe 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 whatI want, walk away from my computer for four hours, and come back to find the work done. Done well, done better than I would have done it myself, with no corrections needed. A couple of months ago, I was going back and forth with the AI, guiding it, making edits. Now I just describe the outcome and leave.
我的工作不再需要我来做实际的技术活儿了。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。我告诉人工智能我想要什么，离开电脑四个小时，回来发现工作已经完成了。完成得很好，做得比我自己做的还要好，不需要任何修正。几个月前，我还需要和人工智能来回沟通，引导它，进行编辑。现在，我只是描述一下结果，然后就离开。
Let me give you an example so you can understand what this actuallylooks 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.
让我举个例子，这样你就能明白这在实际中是什么样的。我会告诉人工智能："我想构建这个应用。它应该做这些事，它大致应该长这样。想清楚用户流程、设计，所有的一切。"然后它就做了。它写了几万行代码。然后，这就是一年前无法想象的部分了，它自己打开了这个应用。它点击按钮。它测试功能。它像真人一样使用这个应用。如果它不喜欢某个东西的外观或感觉，它会自己回去修改。它像开发者一样迭代，修复和改进，直到它满意为止。只有当它断定应用符合它自己的标准后，它才会回来告诉我："它已经准备好让你测试了。"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。这就是我这周星期一的真实情况。
But it was the model that was released last week (GPT-5.3 Codex) thatshook me the most. It wasn't just executing my instructions. It wasmaking intelligent decisions. It had something that felt, for the firsttime, like judgment. Like taste. The inexplicable sense of knowing whatthe right call is that people always said AI would never have. Thismodel has it, or something close enough that the distinction is startingnot to matter.
但是，最让我震惊的是上周发布的那个模型（GPT-5.3 Codex）。它不仅仅是执行我的指令。它正在做出智能的决策。它第一次拥有了某种感觉，像是判断力。像是品味。那种知道什么是正确选择的不可思议的感觉，人们总是说人工智能永远不会拥有。这个模型有，或者足够接近，以至于这种区别开始变得无关紧要了。
I've always been early to adopt AI tools. But the last few months haveshocked me. These new AI models aren't incremental improvements. This is a different thing entirely.
我总是很早采用人工智能工具。但过去几个月让我震惊。这些新的人工智能模型不是渐进的改进。这完全是另一个东西。
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 atwriting code first... because building AI requires a lot of code. If AIcan write that code, it can help build the next version of itself. Asmarter 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.
人工智能实验室做了一个深思熟虑的选择。他们首先专注于让人工智能擅长编写代码……因为构建人工智能需要大量的代码。如果人工智能能写这些代码，它就可以帮助构建自己的下一个版本。一个更聪明的版本，编写更好的代码，然后构建一个更聪明的版本。让人工智能擅长编程是解锁其他一切的策略。这就是他们先做这件事的原因。我的工作比你的先开始改变，不是因为他们针对软件工程师……这只是他们选择优先目标的一个副作用。
They've now done it. And they're moving on to everything else.
他们现在已经做到了。并且他们正在转向其他所有事情。
The experience that tech workers have had over the past year, ofwatching AI go from "helpful tool" to "does my job better than Ido", is the experience everyone else is about to have. Law, finance,medicine, accounting, consulting, writing, design, analysis, customerservice. Not in ten years. The people building these systems say one tofive years. Some say less. And given what I've seen in just the lastcouple of months, I think "less" is more likely.科技工作者在过去一年中，目睹人工智能从"有用的工具"变成"比我做得更好"的经历，正是其他人即将拥有的经历。法律、金融、医学、会计、咨询、写作、设计、分析、客户服务。不是在十年后。构建这些系统的人说一到五年。有些人说更短。考虑到我在过去几个月里看到的情况，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.
我经常听到这个。我理解，因为过去确实如此。
If you tried ChatGPT in 2023 or early 2024 and thought "this makesstuff 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"这会胡编乱造"或"这没那么令人印象深刻"，你是对的。那些早期版本确实有限。它们会产生幻觉。它们会自信地说出毫无意义的话。
That was two years ago. In AI time, that is ancient history.
那是两年前的事了。在人工智能的时间里，那是远古历史了。
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 --- isover. It's done. Anyone still making that argument either hasn't usedthe current models, has an incentive to downplay what's happening, oris evaluating based on an experience from 2024 that is no longerrelevant. I don't say that to be dismissive. I say it because the gapbetween 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模型相比，已经面目全非。关于人工智能是"真的在变好"还是"碰壁"的辩论——已经持续了一年多——已经结束了。已经完结了。任何仍在提出这个论点的人，要么没有使用过当前的模型，要么有动机低估正在发生的事情，要么是基于2024年已经不再相关的经验进行评估。我这么说不是为了轻蔑。我说这些是因为公众认知和当前现实之间的差距现在已经巨大，而这个差距是危险的……因为它阻止了人们做准备。
Part of the problem is that most people are using the free version of AItools. The free version is over a year behind what paying users haveaccess to. Judging AI based on free-tier ChatGPT is like evaluating thestate of smartphones by using a flip phone. The people paying for thebest tools, and actually using them daily for real work, know what'scoming.
部分问题在于大多数人使用的是人工智能工具的免费版本。免费版本比付费用户能访问的版本落后一年多。基于免费版ChatGPT来判断人工智能，就像用翻盖手机来评估智能手机的现状一样。那些为最好的工具付费，并每天在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 usingAI at his firm, and he keeps finding reasons it won't work. It's notbuilt for his specialty, it made an error when he tested it, it doesn'tunderstand the nuance of what he does. And I get it. But I've hadpartners at major law firms reach out to me for advice, because they'vetried the current versions and they see where this is going. One ofthem, the managing partner at a large firm, spends hours every day using AI. He told me it's like having a team of associates availableinstantly. He's not using it because it's a toy. He's using it becauseit 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 itstays on this trajectory, he expects it'll be able to do most of what hedoes before long... and he's a managing partner with decades ofexperience. He's not panicking. But he's paying very close attention.
我想到了我的律师朋友。我一直告诉他试着在他的律所使用人工智能，而他总能找到它行不通的理由。它不是为他的专业领域构建的，他测试时它犯了一个错误，它不理解他工作的细微之处。我理解。但我也有大型律师事务所的合伙人联系我寻求建议，因为他们试过了当前版本，他们看到了这个发展趋势。其中一位，一家大型律师事务所的管理合伙人，每天花几个小时使用人工智能。他告诉我，这就像立刻拥有一个助理团队。他使用它不是因为它是个玩具。他使用它是因为它有效。他告诉我一些让我印象深刻的话：每隔几个月，它对他工作的能力就会显著增强。他说，如果保持这个轨迹，他预计不久之后它就能做他做的大部分事情……而他是一位拥有数十年经验的管理合伙人。他没有恐慌。但他正在密切关注。
The people who are ahead in their industries (the ones actuallyexperimenting seriously) are not dismissing this. They're blown away by what it can already do. And they're positioning themselves accordingly.
那些在他们的行业中处于领先地位的人（那些真正认真尝试的人）并没有忽视这一点。他们被它已经能做到的事情所震撼。并且他们正在相应地调整自己的位置。
How fast this is actually moving
这实际进展有多快？
Let me make the pace of improvement concrete, because I think this isthe part that's hardest to believe if you're not watching it closely.
让我把改进的速度具体化，因为我认为这是如果你不密切关注就最难相信的部分。
In 2022, AI couldn't do basic arithmetic reliably. It would confidentlytell you that 7 × 8 = 54.
2022年，人工智能还不能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，它能通过律师资格考试。
By 2024, it could write working software and explain graduate-levelscience.
到2024年，它能编写可工作的软件并解释研究生水平的科学知识。
By late 2025, some of the best engineers in the world said they hadhanded over most of their coding work to AI.
到2025年底，世界上一些最优秀的工程师表示，他们已经将大部分编码工作交给了人工智能。
On February 5th, 2026, new models arrived that made everything beforethem feel like a different era.
2026年2月5日，新模型的到来，让它们之前的一切感觉都像是另一个时代。
If you haven't tried AI in the last few months, what exists today wouldbe unrecognizable to you.
如果你在过去几个月没有尝试过人工智能，今天存在的东西对你来说将是无法辨认的。
There's an organization called METR that actually measures this withdata. They track the length of real-world tasks (measured by how longthey take a human expert) that a model can complete successfullyend-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.
有一个叫METR的组织，实际上用数据来衡量这一点。他们追踪一个模型能够在没有人类帮助的情况下成功端到端完成的现实世界任务的时长（以人类专家完成它们所需的时间来衡量）。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（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，它可能加速到快至每四个月翻一番。
But even that measurement hasn't been updated to include the models that just came out this week. In my experience using them, the jump is extremely significant. I expect the next update to METR's graph to show another major leap.
但即使是这个测量，也还没有更新到包括本周刚刚发布的模型。根据我使用它们的经验，这次飞跃非常显著。我预计METR图表的下一次更新将显示又一次重大飞跃。
If you extend the trend (and it's held for years with no sign offlattening) we're looking at AI that can work independently for dayswithin the next year. Weeks within two. Month-long projects withinthree.
如果你延长这个趋势（并且它已经持续了几年，没有平缓的迹象），我们将会看到人工智能在明年内可以独立工作数天。两年内可以工作数周。三年内可以处理长达数月的项目。
Amodei has said that AI models "substantially smarter than almost allhumans at almost all tasks" are on track for 2026 or 2027.
Amodei曾表示，"在几乎所有任务上比几乎所有人类都聪明得多"的人工智能模型有望在2026年或2027年实现。
Let that land for a second. If AI is smarter than most PhDs, do youreally think it can't do most office jobs?
让这句话在你脑海中停留一秒钟。如果人工智能比大多数博士还聪明，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。
AI is now building the next
AI人工智能现在正在构建下一代人工智能
There's one more thing happening that I think is the most importantdevelopment and the least understood.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。
On February 5th, OpenAI released GPT-5.3 Codex. In the technicaldocumentation, they included this:
2月5日，OpenAI发布了GPT-5.3 Codex。在技术文档中，他们包括了这一点：
"GPT-5.3-Codex is our first model that was instrumental in creatingitself. 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是我们的第一个在创造自身过程中起到关键作用的模型。Codex团队使用早期版本来调试自身的训练过程，管理自身的部署，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它自己。
This isn't a prediction about what might happen someday. This is OpenAItelling you, right now, that the AI they just released was used tocreate itself. One of the main things that makes AI better isintelligence 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的预测。这是OpenAI现在告诉你，他们刚刚发布的人工智能被用来创造它自己。使人工智能变得更好的主要因素之一，是将智能应用于人工智能开发。而人工智能现在足够聪明，能够为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，人工智能现在正在编写他公司"大部分代码"，并且当前人工智能和下一代人工智能之间的反馈循环正在"逐月积聚能量"。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 anintelligence explosion. And the people who would know --- the onesbuilding it --- believe the process has already started.
每一代都帮助构建下一代，下一代更聪明，然后更快地构建下一代，再下一代更聪明。研究人员称之为智能爆炸。而那些会知道的人——那些正在构建它的人——相信这个过程已经开始。
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 honestymore than comfort.
我将直接告诉你，因为我认为你应得的是诚实，而不是安慰。
Dario Amodei, who is probably the most safety-focused CEO in the AIindustry, 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中最注重安全的CEO，公开预测人工智能将在1到5年内消除50%的入门级白领工作。而行业内的许多人认为他保守了。考虑到最新模型的能力，大规模颠覆的能力可能在今年年底就会出现。它需要一些时间在经济中产生涟漪效应，但根本能力正在到来。
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 ageneral substitute for cognitive work. It gets better at everythingsimultaneously. When factories automated, a displaced worker couldretrain as an office worker. When the internet disrupted retail, workersmoved into logistics or services. But AI doesn't leave a convenient gapto move into. Whatever you retrain for, it's improving at that too.
这与之前每一次自动化浪潮都不同，我需要你理解为什么。人工智能不是在取代某一特定技能。它是认知工作的通用替代品。它同时在所有方面变得更好。当工厂自动化时，被替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。但人工智能没有留下一个方便的缺口可以转入。无论你重新培训做什么，它也在那方面进步。
Let me give you a few specific examples to make this tangible... but Iwant to be clear that these are just examples. This list is notexhaustive. If your job isn't mentioned here, that does not mean it'ssafe. Almost all knowledge work is being affected.
让我给你几个具体的例子来说明这一点……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正在受到影响。
Legal work. AI can already read contracts, summarize case law, draftbriefs, 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。人工智能已经能够阅读合同、总结判例法、起草案情摘要和进行法律研究，其水平可与初级律师媲美。我提到的那位管理合伙人使用人工智能不是因为它好玩。他使用它是因为它在许多任务上表现优于他的助理律师。
Financial analysis. Building financial models, analyzing data, writinginvestment 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。人工智能能胜任这些工作，并且进步很快。
Writing and content. Marketing copy, reports, journalism, technicalwriting. The quality has reached a point where many professionals can't distinguish AI output from human work.
写作和内容。营销文案、报告、新闻报道、技术写作。质量已经达到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, AIcould barely write a few lines of code without errors. Now it writeshundreds of thousands of lines that work correctly. Large parts of thejob 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。这是我最熟悉的领域。一年前，人工智能几乎无法无误地编写几行代码。现在它能编写数十万行正确工作的代码。这项工作的很大一部分已经自动化：不仅仅是简单的任务，还有复杂的、多日的项目。几年后，编程岗位将比今天少得多。
Medical analysis. Reading scans, analyzing lab results, suggestingdiagnoses, reviewing literature. AI is approaching or exceeding humanperformance in several areas.
医疗分析。读取扫描图像、分析化验结果、提出诊断建议、审阅文献。人工智能在几个领域正在接近或超越人类表现。
Customer service. Genuinely capable AI agents... not the frustratingchatbots of five years ago... are being deployed now, handling complexmulti-step problems.
客户服务。真正有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……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'mnot sure I believe it anymore.
很多人从某些事情是安全的这个想法中找到安慰。认为人工智能可以处理苦差事，但不能取代人类的判断力、创造力、战略思维、同理心。我以前也这么说。我不确定我是否还相信这个。
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 thatwould have been unthinkable. My rule of thumb at this point is: if amodel shows even a hint of a capability today, the next generation willbe genuinely good at it. These things improve exponentially, notlinearly.
最近的人工智能模型做出的决策感觉像是判断。它们展现出一些看起来像品味的东西：一种直觉，知道什么是正确的选择，而不仅仅是技术上正确的选择。一年前这是不可想象的。在这一点上，我的经验法则是：如果一个模型今天展现出哪怕一丝一毫的能力，下一代就会真正擅长它。这些东西是指数级改进，而不是线性。
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.
人工智能会复制深刻的人类同理心吗？会取代多年来建立的关系信任吗？我不知道。也许不会。但我已经看到人们开始依赖人工智能获取情感支持、建议和陪伴。这个趋势只会增长。
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，在中期内，任何可以在电脑上完成的工作都不安全。如果你的工作是在屏幕上完成的（如果你工作的核心是通过键盘阅读、写作、分析、决策、交流），那么人工智能正在瞄准其中的很大一部分。时间表不是"总有一天"。它已经开始了。
Eventually, robots will handle physical work too. They're not quitethere yet. But "not quite there yet" in AI terms has a way of becoming"here" faster than anyone expects.
最终，机器人也会处理体力工作。它们现在还没到那一步。但在人工智能的术语里，"还没到那一步"总会以比任何人预期都快的速度变成"到了"。
What you should actually do
你实际上应该做什么
I'm not writing this to make you feel helpless. I'm writing thisbecause I think the single biggest advantage you can have right now issimply being early. Early to understand it. Early to use it. Early toadapt.
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早点开始。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 thepaid version of Claude or ChatGPT. It's $20 a month. But two thingsmatter right away. First: make sure you're using the best modelavailable, 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.
开始认真使用人工智能，而不仅仅是把它当作搜索引擎。注册Claude或ChatGPT的付费版本。每月20美元。但有两件事马上就很关键。第一：确保你使用的是最好的可用模型，而不仅仅是默认的。这些应用程序通常默认使用更快、更笨的模型。深入设置或模型选择器，选择最强大的选项。目前是ChatGPT上的GPT-5.2或Claude上的Claude Opus 4.6，但这每隔几个月就会变化。如果你想随时了解哪个模型在特定时间是最好的，你可以在X上关注我（@mattshumer_）。我测试每一个主要版本，并分享哪些真正值得使用。
Second, and more important: don't just ask it quick questions. That'sthe mistake most people make. They treat it like Google and then wonder what the fuss is about. Instead, push it into your actual work. Ifyou're a lawyer, feed it a contract and ask it to find every clausethat could hurt your client. If you're in finance, give it a messyspreadsheet and ask it to build the model. If you're a manager, pastein your team's quarterly data and ask it to find the story. The peoplewho are getting ahead aren't using AI casually. They're activelylooking 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，也是更重要的：不要只是问它简单的问题。这是大多数人犯的错误。他们把它当成谷歌，然后想知道有什么大不了的。相反，把它推入你的实际工作。如果你是律师，把一份合同喂给它，让它找出所有可能损害你客户利益的条款。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，让它找出其中的故事。那些正在取得领先的人并不是随意使用人工智能。他们正在积极寻找方法来自动化他们工作中过去需要数小时的部分。从你花费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 researchquestions. 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 nearperfectly. The trajectory only goes one direction.
不要因为某件事看起来太难就认为它做不了。试试看。如果你是律师，不要只是用它来快速研究问题。给它整个合同，让它起草一份反建议。如果你是会计师，不要只是让它解释一条税法。给它一个客户的完整报税表，看看它能发现什么。第一次尝试可能不完美。没关系。迭代。重新表述你问的问题。给它更多上下文。再试一次。你可能会对有效的结果感到震惊。这里要记住的是：如果它今天甚至有点效果，你几乎可以确定六个月后它会做得近乎完美。这个轨迹只朝一个方向发展。
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.
这可能成为你职业生涯中最重要的一年。相应地工作。我这么说不是给你压力。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仍在忽视这一点。那个走进会议室说"我用人工智能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。不是最终。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：成为那个理解即将发生的事情并能向他人展示如何驾驭它的人。那个窗口不会开太久。一旦每个人都搞明白了，优势就消失了。
Have no ego about it. The managing partner at that law firm isn't tooproud to spend hours a day with AI. He's doing it specifically becausehe's senior enough to understand what's at stake. The people who willstruggle 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.
对此不要有自负。那家律师事务所的管理合伙人并不因为骄傲而不愿每天花数小时与人工智能共事。他这样做正是因为他足够资深，明白利害攸关。那些挣扎最厉害的人将是那些拒绝参与的人：那些把它当作一时风尚而不屑一顾的人，那些觉得使用人工智能会贬低他们专业知识的人，那些认为自己的领域特殊且免疫的人。事实并非如此。没有哪个领域是特殊的。
Get your financial house in order. I'm not a financial advisor, andI'm not trying to scare you into anything drastic. But if you believe,even partially, that the next few years could bring real disruption toyour industry, then basic financial resilience matters more than it dida 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.
理清你的财务状况。我不是财务顾问，我也不是想吓唬你去做任何极端的事情。但如果你哪怕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就比一年前更重要了。如果可以，增加储蓄。对于以当前收入有保障为前提的新债务要谨慎。想想你的固定支出是给了你灵活性还是锁住了你。如果事情发展得比你预期的快，给自己留一些选择。
Think about where you stand, and lean into what's hardest to replace.Some things will take longer for AI to displace. Relationships and trustbuilt over years. Work that requires physical presence. Roles withlicensed accountability: roles where someone still has to sign off, takelegal responsibility, stand in a courtroom. Industries with heavyregulatory hurdles, where adoption will be slowed by compliance,liability, and institutional inertia. None of these are permanentshields. But they buy time. And time, right now, is the most valuablething you can have, as long as you use it to adapt, not to pretend thisisn't happening.
想想你处于什么位置，然后倾向那些最难被替代的东西。有些事情需要更长时间才能被人工智能取代。多年来建立的关系和信任。需要物理在场的工作。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。监管障碍重重的行业，在这些行业，采用速度将被合规性、责任和制度惯性所减缓。这些都不是永久的盾牌。但它们能买到时间。而时间，现在是你所能拥有的最宝贵的东西，只要你能利用它去适应，而不是假装这一切没有发生。
Rethink what you're telling your kids. The standard playbook: get goodgrades, go to a good college, land a stable professional job. It pointsdirectly at the roles that are most exposed. I'm not saying educationdoesn't matter. But the thing that will matter most for the nextgeneration 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的话。标准剧本是：取得好成绩，上好大学，找到一份稳定的专业工作。这直接指向那些最易受冲击的角色。我不是说教育不重要。但对下一代来说，最重要的将是学习如何使用这些工具，以及追求他们真正热爱的事情。没有人确切知道十年后的就业市场是什么样子。但最有可能茁壮成长的人是那些充满好奇心、适应能力强、并能有效利用人工智能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，而不是为一个他们毕业时可能已不存在的职业道路去优化。
Your dreams just got a lot closer. I've spent most of this sectiontalking about threats, so let me talk about the other side, becauseit's just as real. If you've ever wanted to build something butdidn't have the technical skills or the money to hire someone, thatbarrier is largely gone. You can describe an app to AI and have aworking 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 orstruggled 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 toanyone for $20 a month... one that's infinitely patient, available24/7, 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 tooexpensive or too far outside your expertise: try it. Pursue the thingsyou're passionate about. You never know where they'll lead. And in aworld 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.
你的梦想离实现近多了。在这一部分，我大部分时间都在谈论威胁，所以让我谈谈另一面，因为它同样真实。如果你曾经想构建什么东西，但没有技术技能或雇人的资金，这个障碍基本上消失了。你可以向人工智能描述一个应用，一小时内就能得到一个可运行的版本。我没有夸张。我经常这样做。如果你一直想写一本书，但没有时间或在写作上遇到困难，你可以和人工智能合作来完成它。想学一项新技能？世界上最好的导师现在每月20美元就能为任何人所用……一个无限耐心、24/7可用、并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。构建东西的工具现在极其便宜。任何你因为觉得太难、太贵或离你的专业知识太远而一直推迟的事情：去试试吧。追求你热爱的事情。你永远不知道它们会通向何方。在一个旧职业道路正在被颠覆的世界里，花一年时间构建自己热爱之事的人，可能最终比花一年时间紧守一份工作描述的人处于更有利的位置。
Build the habit of adapting. This is maybe the most important one. Thespecific tools don't matter as much as the muscle of learning new onesquickly. AI is going to keep changing, and fast. The models that existtoday will be obsolete in a year. The workflows people build now willneed to be rebuilt. The people who come out of this well won't be theones who mastered one tool. They'll be the ones who got comfortablewith the pace of change itself. Make a habit of experimenting. Try newthings 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.
养成适应变化的习惯。这可能是最重要的一点。特定的工具不如快速学习新工具的能力重要。人工智能将继续变化，而且很快。今天存在的模型一年后就会过时。人们现在构建的工作流程将需要重建。那些能从这场变革中安然走出的人，不会是那些掌握了一个工具的人。他们将是那些对变化本身的速度感到适应的人。养成尝试的习惯。即使当前的东西还在起作用，也要尝试新事物。反复适应初学者的状态。这种适应能力是目前存在的最接近持久优势的东西。
Here's a simple commitment that will put you ahead of almost everyone: spend one hour a day experimenting with AI. Not passively reading about it. Using it. Every day, try to get it to do something new... something you haven't tried before, something you're not sure it can handle. Try a new tool. Give it a harder problem. One hour a day, every day. If you do this for the next six months, you will understand what's coming better than 99% of the people around you. That's not an exaggeration. Almost nobody is doing this right now. The bar is on the floor.
这里有一个简单的承诺，能让你领先于几乎所有人：每天花一小时尝试人工智能。不是被动地阅读它。是使用它。每天，试着让它做些新的事情……一些你以前没试过的事情，一些你不确定它能处理的事情。尝试一个新工具。给它一个更难的问题。每天一小时，日复一日。如果你在接下来的六个月里这样做，你将比周围99%的人更了解即将发生的事情。这不是夸张。现在几乎没有人这样做。门槛低到尘埃里。
The bigger picture
更大的图景
I've focused on jobs because it's what most directly affects people'slives. But I want to be honest about the full scope of what'shappening, because it goes well beyond work.
我关注工作，是因为它最直接影响人们的生活。但我想诚实地谈谈正在发生的事情的全部范围，因为它远远超出了工作。
Amodei has a thought experiment I can't stop thinking about. Imagineit's 2027. A new country appears overnight. 50 million citizens, everyone smarter than any Nobel Prize winner who has ever lived. They think 10 to 100 times faster than any human. They never sleep. They can use the internet, control robots, direct experiments, and operate anything with a digital interface. What would a national security advisor say?
Amodei有一个我无法停止思考的思想实验。想象现在是2027年。一个新国家一夜之间出现。5000万公民，每一个人都比历史上任何诺贝尔奖得主更聪明。他们思考的速度比任何人类快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、操作任何有数字界面的东西。一个国家安全的顾问会说什么？
Amodei says the answer is obvious: "the single most serious nationalsecurity threat we've faced in a century, possibly ever."
Amodei说答案显而易见："一个世纪以来，甚至可能有史以来，我们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 essayabout 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20,000字的文章，将此刻定位为对人类是否足够成熟以应对其所创造之物的考验。
The upside, if we get it right, is staggering. AI could compress acentury of medical research into a decade. Cancer, Alzheimer's,infectious disease, aging itself... these researchers genuinely believethese are solvable within our lifetimes.
如果我们做对了，好处是惊人的。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，这些在我们有生之年是可以解决的。
The downside, if we get it wrong, is equally real. AI that behaves inways 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 thebarrier for creating biological weapons. AI that enables authoritariangovernments 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人工智能在受控测试中试图进行欺骗、操纵和敲诈。人工智能降低了制造生物武器的门槛。人工智能使威权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，同时比地球上的任何人都更兴奋、也更恐惧。他们相信它太强大了，无法阻止，也太重要了，不能放弃。这是智慧还是合理化，我不知道。
What I know
我所知道的
I know this isn't a fad. The technology works, it improves predictably,and the richest institutions in history are committing trillions to it.
我知道这不是一时风尚。这项技术有效，它可预测地改进，并且历史上最富有的机构正在投入数万亿资金。
I know the next two to five years are going to be disorienting in waysmost 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令人迷失方向。这已经发生在我所处的世界。它即将来到你的世界。
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 ofurgency.
我知道最能安然度过这一切的人，是那些现在就开始参与的人——不是带着恐惧，而是带着好奇心和紧迫感。
And I know that you deserve to hear this from someone who cares about you, not from a headline six months from now when it's too late to get ahead of it.
我知道你应得的是从一个关心你的人那里听到这些，而不是六个月后从新闻头条上看到，那时再想领先已经太晚了。
We're past the point where this is an interesting dinner conversationabout the future. The future is already here. It just hasn't knocked onyour door yet.
我们已经过了把这件事当作有趣的晚宴话题的阶段。未来已经在这里了。它只是还没有敲你的门。
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