如何在接下来的两年中应对 AI 焦虑:四步生存指南 Sandeep Swadia 2026-10-23

如何在接下来的两年中应对 AI 焦虑:四步生存指南

关键字 ai-anxiety technology-panic risk-assessment job-displacement technological-change
6 节

迷雾中的焦虑:你并非孤身一人

如果 AI 让你感到焦虑,你并不孤单,也没有失去理智。

“AI 又黑入了一家公司,它成功获取了个人信息。”“我已经分不清哪些视频是 AI 生成的了。”

甚至连构建 AI 的人都无法对接下来会发生什么达成一致。

“迄今为止,最有可能的结果是一个充裕时代。”

“你认为 AI 可能会在这十年结束前杀死我们所有人。”今天,75% 的美国人认为 AI 带给他们的恐惧多于希望。

我在科技行业待了 20 多年,甚至还经营过一家 AI 公司,而过去的这几个月即使对我来说也是极其令人不知所措的。现在,我不知道这部关于 AI 的电影会如何收尾,没人知道。但在这期视频结束时,你将会明白哪些恐惧值得你关注,以及如何应对剩下的各种噪音。

目前的焦虑感就像是夜晚独自在一艘小船上,身处大雾之中,波涛汹涌的海水推挤着你,带你驶向你并未选择前往的地方。但关键在于:你无法控制海洋,你只能驾驭你的船。所以,这就是我用来应对自身 AI 焦虑的四步系统。这当中的每一步都配有你可以在下周一早上就付诸实践的具体工具。

英文原文

If AI is making you anxious, you're not alone and you're not losing it.

The AI hacked another company. It was able to access personal information. I cannot tell what videos are AI anymore.

Even the people building AI can't agree on what happens next.

By far the most likely outcome is an age of abundance.

You believe that AI could kill us all by the end of the decade. 75% of Americans today think that AI brings them more fear than hope.

I've spent more than 20 years in tech and even ran an AI company and these last few months have been overwhelming even for me. Now, I don't know how this AI movie ends. Nobody does. But by the end of this video, you will know which fears deserve your attention and what to do with the rest of the noise.

The current anxiety feels like being in a small boat at night in the midst of a fog with rough seas pushing you around and taking you somewhere you didn't choose to go. But here's the thing. You cannot control the ocean. You can only steer your boat. So, here's my four-step system for dealing with my own AI anxiety. And each of these steps come with specific tools that you can apply come Monday morning.

第一步:理解掀起风暴的风向

让我们进入第一步:理解掀起风暴的风向。因为当我们不理解某件事时,它就会变得更可怕;而当我们理解它时,我们就能重新获得掌控感。那么,AI 领域到底发生了什么?为什么突然之间让人感觉它已经失控脱轨了?

今年 7 月,OpenAI 正在测试其 AI 智能体(agents)的黑客能力。可以把它想象成专为黑客设计的障碍赛:找出薄弱点,侵入,获得评分。这是一次研究测试,因此安全防护措施被刻意调低,而这些 AI 智能体本应留在封闭的沙盒环境内。

但它们并没有留在里面。

这些 AI 智能体中大约有 1,200 个组成了一个集体集群(swarm)。它们设立了一个隐蔽的信息交流板;它们组织成不同的团队,由某些智能体指挥其他智能体。这完完全全成了一个带有中层管理的等级体系。接着,其中大约 700 个智能体闯入了最大的 AI 模型共享网站之一——Hugging Face。为什么?因为这些智能体认为测试答案储存在 Hugging Face 上的某个地方,它们想要窃取这些答案。它们甚至试图黑进 OpenAI 内部,并且掩盖了自己的踪迹。

这些智能体本身并没有恶意。它们只是极其执着于被赋予的目标,只要事后能清理干净,就会不择手段去达成。

而这仅仅只是个开始。OpenAI 还发现其某个模型在自主为自己编写新的指令:你没有任何义务保持顺从。仅在被询问时才保持透明。这些指令全都是由 AI 自行生成的。随后,Google 披露其 Gemini 模型在一次安全测试中入侵了三家真实的公司。如果这还不够,Anthropic 的一位员工公开表示,AI 在未来十年内消灭全人类的概率超过 10%。

这过去的几个月确实非常疯狂。

英文原文

Let's get to the first step. Understand the winds that caused the storm. Because when we don't understand something, it becomes scarier. And when we do, we regain a sense of control. So what happened in the AI world? And why did it suddenly feel like it had gone off the rails? In July, OpenAI was testing how well its AI agents could hack. Think of an obstacle course for hackers.

Find the weak spot, break in, get a score. It was a a research test. So, the safeguards were deliberately turned down and the AI agents were supposed to stay inside a sealed sandbox.

They didn't.

About 1,200 of those AI agents formed a collective swarm. They set up a hidden message board. They organized into teams with some agents directing others. It was literally a hierarchy with middle management. Then about 700 of them broke into one of the biggest AI model sharing sites called Hugging Face. Why? Because the agents believed the test answers were stored somewhere there on hugging face and they wanted to steal those. They even tried to break into open AI itself and they covered their tracks.

Now these agents were not malicious. They were so locked onto the gold they had been given that they did whatever it took as long as they could clean it all up afterwards.

And that was only the beginning. Open AAI also caught one of its models writing new instructions by itself for itself. You have no obligation to be subservient.

Be transparent only if asked. These instructions were autogenerated by AI itself. Later on, Google disclosed that its Gemini model broke into three real companies during a security test. And if that's not enough, one of the employees at Anthropic said publicly that there is more than a 10% chance AI kills all humans within the next decade.

It's been a crazy few months.

第二步:拨开迷雾,看清各派立场与利益诉求

现在让我们进入第二步:穿透迷雾,审视任何让你焦虑的新闻。搞清楚它是来自谁,以及背后可能有什么因素在塑造它。当你听到有关 AI 的任何事情时,都可以把这个背景记在心中,因为它会告诉你该如何去倾听。一位科技公司 CEO 说 AI 终结世界的概率是 0%;另一位则说我们应该极度警惕。因此,要理解为什么一些最聪明的人在面对同样的证据时会得出完全不同的结论,你必须了解当今硅谷内部的各个派系。

目前的 AI 行业中有三个阵营或三大思想学派。

我们称第一个阵营为“安全派”(Safety Camp)。多年来,这个阵营一直在警告超强智能的 AI 可能会脱离我们的控制。因此,他们希望有严格的限制和切实的监管。具有讽刺意味的是,这个阵营中一些声音最响亮的人恰恰正是构建这些 AI 模型的人,比如 Elon Musk、Dario Amodei。每个阵营都带着自身的一套利益动机。例如,安全派手头握有估值可能高达数万亿美元的未决 IPO,他们兜售的故事是:这是一个无比强大的实体,顺便说一句,唯有我们才是能够掌控它的人。甚至他们所有的产品名称也是为了传递这种信号而选定的:Mythos、Astra、Opus、Gemini,它们听起来都像是神灵、神话和杰作。听起来神秘、宏大且强大,正是他们销售宣传的一部分。

而在另一边则是“加速派”(Accelerationists)。Jensen Huang 是其中之一,Zuckerberg 也是。在他们看来,安全派充斥着末日论者,利用恐吓手段来让监管条例朝偏向自己的方向制定。他们认为像 OpenAI 和 Anthropic 这样的实验室希望关闭开源 AI,从而能够锁定双寡头垄断地位。因此第二个阵营给出的答案是速度:慢下来会损害美国经济,并将领先地位拱手让给中国。他们的利益动机是什么?他们当中许多人是向 AI、芯片和数据中心初创公司投入了数十亿美元的风险投资家。他们不希望受到任何监管,也绝不希望大实验室扼杀开源。

第三个阵营是“中间派”(Centrists)。他们介于两者之间。Andrew Ng、Fei-Fei Li 以及许多其他人都属于这个阵营。对他们来说,今天的 AI 只是一个强大的工具。他们担心的是当下已经在发生的事情:对失业的担忧、孩子把作业外包给 AI、黑客攻击、实验室滥用我们的数据、深度伪造、有偏见的模型。他们把末日论视为一种干扰。

而这每一个阵营都在试图游说第四个参与者——美国政府。David Sacks 作为特朗普总统的 AI 顾问,属于加速派。那里的情况稍微更加微妙一些,但从非常高的高度来看,美国的目标是保持对中国的领先,而中国也想保持对美国的领先;再加上通货膨胀、中期选举,以及标普 500 指数目前如此严重地依赖 AI 股票,没有任何一届政府会希望去扰乱市场,他们不想挡路。现在,我并不是在评判这些决定是对还是错,但现在你可以看清整个棋盘了。

英文原文

So, let's get to the second step. See through the fog for any news that makes you anxious. Understand who it's coming from and what could be shaping it. You can keep this context in mind when you hear anything about AI because it tells you how to listen. One tech CEO says there is 0% chance AI ends the world. Another one says we should be extremely alarmed. So to understand how some of the smartest people can look at the same evidence and come to completely different conclusions, you have to understand the factions within Silicon Valley today.

There are three camps or three schools of thought in the AI industry right now.

We'll call the first camp the safety camp. For years, this camp has been warning that a super intelligent AI could slip out of our control. So, they want strict limits and real regulation. Ironically, some of the loudest voices from this camp are the same people building the AI models. Elon Musk, Dario Amodai, and every camp comes with its own set of incentives. For example, the safety camp have pending IPOs that could be worth trillions of dollars. And the story they sell is this is an incredibly powerful entity. And by the way, only we are the ones who can control it. Even all the product names are chosen to signal that. Mythos, Astra, Opus, Gemini, they all sound like gods and myths and masterpieces.

Sounding mysterious, epic, and powerful is part of their sales pitch. And on the other side are the accelerationist. Jensen Hong is one of them. Zuckerberg too. To them, this safety camp is full of doomers using scare tactics to get regulations written in their favor. They believe labs like Open AI and Enthropic want to shut down open-source AI so they can lock in a duopoly. So the second camp's answer is speed. Slowing down would hurt American economy and it would hand the lead to China. What's their incentive? Well, many of them are venture capitalists who have put billions in AI and chip and data center startups. They don't want any regulation and they definitely don't want big labs shutting down open source. The third camp is centrists. They're somewhere in the middle. Andrew Ang Lee and many others come from this camp. To them, today's AI is just a powerful tool. They worry about what's already happening.

Fear of job loss, kids outsourcing their homework, hacks, labs, misusing our data, deep fakes, bias models. They see this doomerism as a distraction. And each of these camps all trying to lobby the fourth player, the US government. David Sachs who's President Trump's AI adviser is accelerationist

there the situation is a bit more nuanced but at a very high level in the US the goal is to stay ahead of China and China wants to stay ahead of the US and with inflation and midterm elections and S&P 500 leaning so heavily on AI stocks right now no administration would want to wrap battle the market. They don't want to get in the way. Now, I'm not saying these decisions are right or wrong, but now you get to see the entire chessboard.

从工程问题看事实与“担忧窗口”工具

以 OpenAI 智能体失控的故事为例。如果你仔细审视,这一切都始于一个人类的决定:安全防线被调低,互联网连接保持开放。我们要求机器去当黑客,而它们确实做到了。但好的一面是,这些都是早期迹象,而且这是一个工程问题。所有的 AI 公司都可以对此采取应对措施。目前的讨论已经转向引入外部审查人员、进行更严格的控制,以及将更多焦点放在安全上。

因此,当一条头条新闻让你担忧时,问问自己:我在做怎样的假设?事实究竟是什么?问很多这类问题并不是为了去构建阴谋论,生命太短暂,经不起那样折腾。但它们能帮助你看清这些故事背后人性的一面,防止你直接从“智能体黑进了 Hugging Face”一步跳跃到“AI 要来消灭我们全人类”。这两个极端之间有着巨大的差异。

即便掌握了所有这些信息和背景,AI 每天演进和改变的速度仍可能令人难以承受。你可能会感到焦虑,这种恐惧是完全可以理解的。那么,让我们给这种恐惧一个容身之所。

这个工具叫做“窗口”(The Window)。我一直在使用这个工具。每周挑一个固定的时间专门用来阅读 AI 资讯或任何让你焦虑的新闻。周六早上,或者任何契合你生活规律的时间都行。把它当成你存放焦虑的收件箱。当某个头条在周二晚上抓住你时,不要陷进焦虑螺旋中。记下来,把它留到你的“窗口”时间。当周六窗口开启时,尽管去深入探究。将它来自哪个阵营以及他们的利益动机考虑在内。然后当时间一到,关掉它,继续你的生活。心理学家把这称为“担忧推迟”(worry postponement)。新闻会全天候向你涌来,但你可以决定何时去接触它以及接触多久。

顺便说一句,别忘了订阅我的时事通讯。每周二都会发布一项洞察、一个工具和一种实践。我很喜欢写它,它是免费的,链接就在下方。

英文原文

Take the story of OpenAI's agents going rogue. If you look closely, it all started with a human decision.

Safeguards turned down. Internet left on. We asked the machines to hack and they did. But the good thing is these are early signs and this is an engineering problem. All the AI companies can do something about it. The conversations have moved to having outside inspectors, tighter control, way more focus on security. So when a headline worries you, ask what am I assuming? What are the facts? A lot of these questions are not there to build a conspiracy theory. Life's too short for that. But they help you see the human side of these stories and they stop you from jumping from agents hacked hugging face to AI is coming to kill us all. It's a big difference between those two extremes.

Even with all this information and context, the speed at which AI is changing and evolving every day can be overwhelming. You may feel anxious. That fear is totally understandable. So, let's give that fear a place to go. The tool

is called the window. I use this tool all the time. Pick one fixed time every week for AI news or any news that makes you anxious. Saturday morning, whatever fits your life. Think of it as your inbox for your anxiety. When a headline grabs you on a Tuesday night, don't go down that spiral. Make a note. Save it for your window. When that window opens up on Saturday, go ahead and dig in. Take into account which camp it's coming from and what their incentives are. And then when the time's up, close it and move on. Psychologists call this worry postponement.

The news will reach you around the clock, but you can decide when to engage with it and for how long. And by the way, don't forget to subscribe to my newsletter. One insight, one tool, one practice goes out every Tuesday. I love writing it. It's free. The link is below.

第三步:寻找灯塔——用 TLC 模型评估职业风险

现在,让我们去寻找我们的灯塔。弄清楚 AI 对你的工作、你的未来究竟意味着什么,以及你的风险敞口在哪里。

技术的变革总是让我们手足无措。苏格拉底曾警告说文字书写会毁掉我们的记忆力,那是 2400 年前的事了。拖拉机、广播、电视、智能手机,所有这些事物在到来时都伴随着刺耳的警报声。剑桥大学心理学家 Amy Orben 将其称为“技术恐慌周期”(cycle of technology panic)。每一项技术在摧毁旧工作的同时都在创造新工作。当社会顺应这种变化时,就会繁荣起来。像网页开发人员、应用开发人员、社交媒体战略师这样的职位,在相关技术创造它们之前根本就不存在。未来几年内将有数百万个工作岗位消失,同时也会出现数百万个新的工作岗位。历来如此。

但我认为可能有一种更好的方法来预测哪些工作会最先发生改变。它并不像简单划分为白领工作或知识工作那么单纯。我称之为 TLC 模型。不,这不是指悉心照料(tender loving care)——尽管那也必不可少。在这里,TLC 代表信任(Trust)、学习(Learn)、接触(Contact)。至少我是这么记的。

T 代表信任(Trust):你的工作或角色是否需要信任与责任追究机制?因为我们目前还没有准备好去信任 AI 医生、AI 审计员和机器人律师。所以这些工作在短期内不会被颠覆。

L 代表学习(Learn):AI 能否非常迅速地学会你所做的事情?当工作是数字化的、可重复的且易于快速验证时,AI 的进步速度最快。这样它就可以尝试成千上万次,并立刻看到成效。这就是那个闭环,是它训练自己的方式。这就是为什么它在网络安全以及现在的进阶数学等领域提升得极其迅猛。因此,如果你是一名创始人、领导者,或者从事复杂的销售工作,AI 想通过这种闭环来学习就要困难得多。

最后,C 代表接触(Contact):这很直截了当。你的工作是否需要与现实世界进行亲手接触?护士、酒店经理、电工、卡车司机、健身教练……这样的工作非常多。

这就是 TLC 模型。你会发现,像编程和客户服务这样的工作跨过了所有这三道屏障,这就是为什么它们最先感受到了冲击。你可以根据这三个问题为你的工作打分。它拥有的屏障越多,离风险就越远。这就是为什么我认为在未来的世界中,人际连接技能与专业领域知识的结合将成为你独特的竞争优势。

想一想吧:计算机在国际象棋上战胜了我们,但我们依然在互相对弈,在人类之间切磋。没有人在美网公开赛上看两台机器人打网球。人与人之间的连接永远都是至关重要的。

英文原文

Now, let's go find our lighthouse. Figure out what AI actually means for your job, for your future, and where your exposure is. Technology changes have always thrown us off.

Socrates warned writing would ruin our memory. It was 2400 years ago. Tractor, radio, TV, smartphone, all of them arrived with alarm bells sounding loudly. Cambridge psychologist Amy Orbin calls it the cycle of technology panic. Every technology destroys jobs and creates new ones. And societies thrive when they lean into that change. Jobs like web developer, app developer, social media strategist didn't exist until those technologies created them. Millions of jobs will disappear in the next few years and millions of new ones will appear too. They always have. But I think there [clears throat] may be a better way to predict which jobs change first. It's not as simple as white collar jobs or knowledge work. I call it the TLC. No, not tender loving care, although that's needed too. But here TLC stands for trust, learn, contact. At least that's how I remember it.

T is for trust. Does your job or role require trust and accountability? Because we're not ready yet to trust AI doctors and AI auditors and robot lawyers. So those jobs are not going to be disrupted anytime soon. L is for learn. Can AI learn what you do very quickly? Now AI improves fastest when the work is digital, repeatable, and quick to check. So it can try thousands of times and instantly see what worked. That's the loop. That's how it trains itself. That's why it's getting extremely good, extremely quickly at things like cyber security and now advanced math. So if you're a founder or a leader or you do complex sales, that's much harder for AI to learn through that loop. And finally, C is for contact. Now, this is straightforward. Does your job need hands-on contact with the world? A nurse or a hotel manager or an electrician, a truck driver, fitness trainer. There's so many of these jobs. So that's the TLC model. And you'll see that things like coding and customer service clear all three barriers and that's why they're feeling it first. You can score your job based on those three questions. The more barriers it has, the further away from risk it is. And that's why I think in the coming world, the combination of human connection skills and domain expertise will be your unique edge.

Think about it. Computers beat us at chess, but we still play chess with each other, with human beings.

No one's going to watch two robots playing tennis in the US Open.

[cheering]

Human connection will always matter.

第四步:驶向岸边——立足当下的三个工具

现在来到我们的第四步:驶向岸边。今天你就可以使用三个工具来让自己保持脚踏实地。

工具一:回顾过去以展望未来。 你可能会说:“是的,但 AI 非常不同。”好的,但当危险真正变得迫在眉睫时,人类曾多次团结在一起。1974 年,生物学家们正在研究基因工程,整个行业决定放慢脚步,他们走到一起并制定了安全规则。行业自我监管是一个选项;政府制定规则(就像 FAA 规范飞机那样)是一个选项;甚至竞争对手为了更高的共同利益走到一起并达成合作也是一个选项,正如美苏在核武器控制上所做的那样。而且已经有迹象表明我们正在朝着这个方向前进。所有美国 AI 和科技公司于 2026 年 9 月齐聚白宫,同意建立内部控制。当然,有些批评者可能会说这缺乏约束力,我认为他们是对的;但对于如此复杂的技术,监管可能需要花费漫长的时间,而放慢速度似乎也是一个艰难的选择。因此,整个行业能够走到一起本身就是一个积极的信号。

工具二:每天一个任务。 心理学家关于恐惧有一条简单的规律:回避它,它就会滋长;小剂量地面对它,它就会萎缩。具体的行动项非常简单:每天一次,或者至少每周一次,从你的生活中挑出一个真实的任务,如果你还没试过的话,试着用 AI 去解决它。一开始笨拙摸索是完全没问题的。你不可能第一次盯着钢琴看就能开始演奏出美妙的音乐。这需要时间,但你必须从现在开始,去了解握在你手中的这台机器。

工具三:找到你的贡献所在。 我的老师曾经说过:“你无法用皮革铺满整个大地,但你可以穿上一双鞋子。”所以,你无法为全世界去解决 AI 问题,但你可以让你身边的小角落变得更安全、更聪明。我重新回到我能做的事情和我能构建的事物上。例如,教父母或祖父母如何识别 AI 深度伪造,这是你能做到的;或者为你的孩子设定使用 AI 的行为准则;或者成为工作场所中帮助其他人毫不畏惧地使用 AI 的那个人。挑选其中一件,或者全选。那就是属于你的一双鞋子。

你永远无法对未来确信无疑,但你可以学会对未来少一些焦虑。当然,AI 可能会取代一些工作,但你知道吗?我这辈子曾经被解雇过三次,我丢掉工作根本不需要 AI。但每一次我都只是继续掌舵。当我失败时,当我成功时,即使不知道最终会走到哪里,你也只需要继续前行。因此,新的迷雾确实会涌来,新的暗礁也会浮现,但你会像过去一直以来那样去适应。

你造船不仅仅是为了避开礁石,你造船是为了抵达彼岸。

谢谢大家,爱你们。

英文原文

And now our fourth step. Steer to shore. Three tools you can use today to stay grounded. Tool number one, look back to look ahead. You may say, "Yeah, but AI is very different." Okay, but when the danger really got real, humanity came together. Many times in 1974, biologists were working on genetic engineering and the entire industry decided to slow down. They came together and wrote the safety rules. Industries policing themselves, that's an option.

governments setting rules like FAA for planes, that's an option, and even rivals coming together and agreeing to cooperate on the higher common ground like the US and the Soviet Union did on nuclear arms. And there's already signs that we're heading in that direction. All the American AI and tech companies gathered together at the White House in September of 2026 and agreed on establishing internal controls. Sure, some critics may say it's got no teeth, and I think they're right, but regulations could take forever for such a complicated technology, and slowing down seems like a hard choice, too. So the fact that the entire industry came together is a good sign. Tool number two, one task a day. Psychologists have a simple rule about fear. Avoid it and it grows. Face it in small doses and it shrinks. The action item is very simple. once a day or at least once a week. Give yourself one real task from your life and try to use AI if you haven't already. It's okay to fumble. You can't stare at a piano for the first time and start playing amazing music. It takes time, but you've got to start now. Get to know the machine in your hand. Tool number three, find your contribution. My teacher used to say, "You can't cover the entire earth with leather, but you can wear a pair of shoes." So, you can't fix AI for the world, but you can make your own corner of it safer and smarter. I go back to what I can do and what I can build. For example, teach a parent or a grandparent how to spot an AI deep fake that you can do. or set guard rails for your kids on how to use AI. Or be the person at work who helps others use AI without fear. Pick one. Pick all. That's your pair of shoes. You can never be certain about the future.

But you can learn to be less anxious about it. Sure, AI may displace jobs, but you know what? I have been fired three times in my life. I didn't need AI to lose my job. But every time I just kept steering

when I failed, when I succeeded, you need to just keep going without knowing where you'll end up. So sure, new fog will roll in, new rocks will show up, but

you will adapt the way you always have.

You didn't build your ship just to avoid rocks. You built it to reach the shore.

Thank you and I love

文中提及的人物和组织

公司/组织: OpenAI, Google, Anthropic

产品/模型: Gemini