在 AI 驱动的就业市场中如何脱颖而出

那么，当你在面试工作时，因为 AI 每个人都变得很优秀，我该如何脱颖而出，获得一个他们仍然需要招聘的职位？我对这个问题很着迷。

So, when you're interviewing for a job, and everybody's good because of AI, how do I stand out and get a job that they still need to hire for? I'm fascinated by that.

比如，如果 Claude 非常擅长运营你的公司，那 Claude 也同样擅长运营其他公司，它们之间没有差异，而普遍高质量且没有差异就意味着没有竞争优势或差异化优势。我认为，能够为此带来竞争优势的人类，无论通过何种方式，哪怕仅仅是提供差异性，也是一种有用的思考问题的方式，对吧？你开始更关心那个人的品味，而不是整个为交付产品而建立的组织。

Like, if Claude is really good at running your company, Claude's also good at running every other company, and there's no variation between them, and generically high quality with no variation means there's no boats or competitive edge. I think humans who bring competitive edge to this, one way or another, just by providing variation, if nothing else, is a useful way to think about problems, right? You start to care a lot more about the taste of that person than you do about the entire organization built to deliver the product.

我们都对 AI 有点害怕。即使是热爱它的人也有点害怕。有些人担心它会夺走我们的工作，让我们变得更笨，或者改变一切的速度太快，我们永远无法跟上。更糟糕的是，大多数 AI 专家分为两个阵营：悲观末日论者或狂热信徒。这就是为什么我想和 Ethan Mollick 谈谈。他是一位 AI 专家，令人耳目一新的是，他两者都不是。Ethan 是沃顿商学院的教授，研究 AI、创业和创新如何影响我们的工作。他关注员工实际如何使用这些工具，而不是理论上它们应该如何改善我们的工作。他的《纽约时报》畅销书《Co-Intelligence》和他广受欢迎的 Substack 专栏《One Useful Thing》已成为那些试图理解 AI 而不失去理智的人的首选资源。Ethan 相信我们对 AI 拥有比我们想象的更多的主动权。我们的经验、品味和真实观点是 AI 永远无法替代的东西。这很重要，因为我们个人、工作以及社会层面如何整合 AI 的选择，将塑造它的未来。如果你喜欢这一集，请记得订阅。这里是《A bit of optimism》。

We're all a little afraid of AI. Even the people who love it are a little bit afraid. Some of us are afraid it'll take our jobs or make us dumber, or change everything so fast we'll never be able to catch up. It doesn't help the most AI experts fall into one of two camps. There are the doomers or there the zealots. That's why I wanted to talk to Ethan Mollick. He's an AI expert who, refreshingly, is neither. Ethan's a Wharton professor studying how AI, entrepreneurship, and innovation impact our work. He focuses on how employees actually use these tools, rather than how they're theoretically supposed to improve our work. His New York Times best seller, Co-Intelligence, and his popular Substack, One Useful Thing, have become the go-to resources for all those people trying to make sense of AI without losing their minds. Ethan believes we have more agency over AI than we think. It's our experience, our taste, and our genuine points of view that are things that AI can never replace. And that's important because the choices we make about how we integrate AI, personally and at work, as a society, will shape what it becomes. If you liked this episode, please remember to subscribe. This is A bit of optimism.

你现在非常受欢迎。在 AI 之前，你教什么热门的东西？

You are very popular right now. What were you teaching before AI that was the hot thing?

除了 AI 相关的内容，我真正关心的另一件事是游戏和教育，以及大规模教育，这两者是相关的。我在利用游戏进行教学方面做了很多工作。所以，正如品牌所言，我并不出名，但在思考游戏和大规模教学这个领域里还算有点名气。我们如何进行变革性的教学？这是我长期研究的事情，而 AI 工作与它大致是平行的。

In addition to AI stuff, my other thing I really care about is games and education and education at scale, two related things. I've done a lot of work on using for teaching. So I was as the brand says not famous but known in that space of thinking about games and teaching at scale. How do we teach transformationally? So that has been something I've worked on for a long time and the AI work was sort of parallel to that.

是你个人对 AI 的好奇心吸引了你，还是你被迫在工作中使用 AI，因为它让工作变得更好？

And was it your own personal curiosity about AI that sucked you in, or was it that you were forced to use AI in the work that you were doing because it made it better?

我去了商学院，并在麻省理工学院攻读博士项目，与 MIT 媒体实验室的 AI 小组合作。Marvin Minsky 是该领域的奠基人之一，而我是小组里非技术背景的人。所以我负责向其他人解释 AI 是什么、它是如何工作的。当我们进行技术讨论时，我也会参与。我接触或参与 AI 已有 20 年，但始终扮演着非技术角色，比如我们如何使用它、如何解释它，而其他人都是技术背景，因为当时 AI 并没有真正发挥作用。AI 的用例有限，所以当 GPT-3 出现并开始引起一些轰动，随后 ChatGPT 出现时，我恰好处于一个有利位置——一个一直在非技术领域思考如何解释 AI 的人。

I went to business school and my PhD program at MIT and I worked with the MIT Media Lab with their AI group. So Marvin Minsky was one of the fathers of the field and I was the non-technical guy in the group. So I was the person who had to explain to other people what AI was, how it worked. When we'd go have technical conversations, I'd come along. I've been AI adjacent or involved for 20 years but always in the sort of non-technical like how do we use this, how do we explain it role and everybody else was technical cuz it wasn't really working out. Like AI had limited use cases and so when GPT-3 came along and started to make a splash little and then ChatGPT after that, I was sort of well positioned in this world of someone who had been thinking about explaining this for a while in a non-technical world.

我真的很想和你谈谈的原因，老实说，我尽量不邀请 AI 嘉宾。原因在于他们通常分为两种类型，对吧？要么是史上最伟大的事情，会让世界变得更好；要么是最糟糕的事情，我们都会死。这些对话由于显而易见的原因有点偏颇和片面，而且他们通常对某种观点有既得利益。

The reason I was really interested in talking to you, I try not to have AI guests on. And the reason is because they generally come in one of two flavors, right? It's the greatest thing that ever happened and it's going to make the world a better place or it's the worst thing that ever happened, we're all going to die. And the conversations are for obvious reasons a little lopsided and one-sided and they usually have some vested interest in one opinion or the other.

是的。

Yes.

老实说，我想和你谈谈的原因是你的观点更实用。它不是救世主，也不会杀死我们所有人，而是有点像，嗯，它就在这里。你知道，就像互联网出现一样，使用这个东西的最佳方式是什么，你的观点更中立一些。

And the reason I wanted to talk to you, to be honest with you, is yours is just more practical. It's not the savior, it's not going to kill us all, but it's kind of like, well, it's here. You know, kind of like the internet showed up, what's the best way to use this thing and it's a little more down the middle.

是的。处于一个有点务实就能让你与众不同的位置，这很好。通常你不会因为不夸夸其谈而获得关注。

Yes. It's nice to be in a place where being somewhat pragmatic makes you unusual. That's not usually the place where you get publicity for being the non-bombastic version.

确实如此。

It's true.

但这两个观点还有一个问题，它们往往会吞噬整个世界，对吧？如果你认为我们即将得到一个能拯救所有人的机器神，那么唯一重要的就是讨论这个，对吧？就像任何其他宗教信仰一样。如果你相信我们都会死，你怎么能谈论除此之外的任何事情呢？你知道，这将会毁灭我们所有人。而事实是，这是一种通用技术。它会以某种方式影响我们所做的一切。值得花些时间在这上面。然后其中一些影响会是好的，一些会是坏的。

But it also those the other problem with those two opinions is they tend to eat the world, right? If you think that we're getting a machine god who's going to save us all, then all that matters is discussing that, right? Just like any other sort of religious belief. And if you believe that we're all going to die, how can you have a conversation about anything other than, you know, this is going to doom us all? And the fact is this is general-purpose technology. It's going to affect everything we do one way or another. It's worth spending some time on that. And then some of those effects will be good, some will be bad.

我确实记得互联网的兴起。我年纪够大，还记得。当时的对话有些相似。也许双方都没有那么戏剧化。

I do remember the sort of the rise of the internet. I'm old enough to remember it. And the conversations were somewhat similar. Maybe less dramatic on either side.

当时有很多正能量。

A lot of positive energy at that time.

大部分是积极的。但也有很多讨论，我记得有些人，你知道，有狂热信徒相信互联网和一切在线的东西将取代一切。你知道这是什么吗？这就是“为活而吃”和“为吃而活”的区别。如果你把它交给技术专家，他们认为所有技术都是“为吃而活”。

Mostly positive. But there was lots of conversation like I remember people who, you know, there were the zealots who believed that the internet and everything online was going to replace everything. You know what it is? It's the difference between eating to live and living to eat. And if you leave it to the technologists, they think all technology is living to eat.

我的意思是，他们真的做了蛋白粉奶昔。我不记得是 Soylent 还是什么，你应该……

I mean, they literally made protein powder shakes. I don't remember it Soylent or you're supposed to

我记得 Soylent。我记得。

I remember Soylent. I remember.

对于那些认为吃饭太麻烦的人。为什么我们要把所有时间花在担心吃饭上，而不是直接解决它呢，对吧？这是一种不同的效率观。但这也是一种让你忽视技术是最人性化活动这一事实的观点，对吧？我们为自己制造工具。我们如何使用它们、如何采纳它们、如何监管它们，这些都会产生巨大影响。你知道，AI 比大多数技术更具自我导向性，但我们仍然对接下来发生的事情拥有很大的主动权。

For people who thought eating was too annoying. Why are we spending all our time worrying about eating when we could just get it taken care of, right? It's a different view of efficiency. But it's also a view that kind of blinds you to the fact that technology is the most human activity, right? We're making tools for ourselves. How we use them, how we adopt them, how we regulate them, those are going to have big influences. You know, AI is more self-directed than most technologies, but we still have a lot of agency over what happens next.

我的意思是，从我自己的经验来看，大多数人都在误用或未充分利用我们可用的技术。

I mean, I know from my own experience, most people are misusing or underutilizing the technologies that are available to us.

我已经到了那种地步——打开社交媒体、听朋友聊天或者读一篇文章，就感到被各种建议淹没。有人告诉我应该这样用 AI，应该设置一个智能体来管理我的生活，设置一个智能体来做营销，再设置一个智能体来管财务。我已经被所有这些关于如何提示机器的建议压得喘不过气，几乎要退缩、关掉一切。因为一会儿说 Gemini 是唯一该用的，一会儿又说只能用 ChatGPT，再过一会儿又说只能用 Claude。这已经太多了。而且对于任何不是天生就全身心投入的人来说，这种反应，信不信由你，正在把一些人推开。

And I'm already getting to the point where I'm turning on social media or listening to friends or reading an article, and I'm already feeling overwhelmed by all of the advice of people telling me that I should be using it like this and setting up an agent to run my life and setting up an agent to do my marketing and set up an agent to do my finances. I'm already overwhelmed by all the advice of how I should even be prompting the machine to the point where I'm almost backing off and shutting down because one minute Gemini is that you got to only use that one, the next minute you got to only use ChatGPT, the next minute you got to only use Claude. It's all too much already. And for anybody who's not predisposed to be all in, that reaction, I think, is pushing some people away, believe it or not.

我完全相信。你看，我是个老派极客，所以喜欢钻研细节，也喜欢解释它们——我想这也是你们请我来的原因之一。但确实，这很让人不知所措。有意思的是，AI 其实变得更简单了，对吧？我不想说得太像布道，但以前那些细节确实重要。比如提示工程很重要。我如何措辞很重要，如果我说“你是个物理学家”，它给出的物理答案就更好；如果我说“仔细想想”，那也有影响；如果我提供贿赂，那也会起作用。我们测试过所有这些。现在这些都不重要了。模型已经足够好，如果你擅长像对人一样给出指令，你大概就能用好它。同样，所有模型都进步得很快。所以无论你用 OpenAI 还是 ChatGPT，或者 Gemini 还是 Anthropic，这三家都很扎实。我大概会给人们一两个小提示，但除此之外，真的就是用它去做事，别纠结怎么用，因为你自然会摸索出来。

Well, I absolutely believe it. I mean, look, I am a nerd of the old school, so I like getting into the details of stuff and partially like explaining them, which is I think part of why you have me here. But also, it is overwhelming. I mean, part of what's actually interesting is AI has gotten easier, right? Not to be too evangelistic about it, but like it used to be the stuff mattered. Like you had to like all those little details. Like prompt engineering mattered. So, it mattered how I phrased things, it mattered if I said, you know, you are a physicist, it was better physics. If I said, think hard about this, that mattered. If I offered bribes, that would matter. We've been testing all that. None of that matters anymore. The models have gotten good enough that if you're good at giving instructions like a human to humans, you probably do okay with this. And similarly, all the models are getting good quite quickly. So, whether you use OpenAI or ChatGPT, they're like ChatGPT or Gemini or Anthropic, they're all three of those are pretty solid. There's like one or two hints I'd give people, but otherwise it's really just use it for stuff and don't stress how you're using it because you'll figure it out.

另外，当我们谈论 AI 会夺走所有工作时——顺便说一句，那是技术人员说的。我还喜欢的一点是，技术人员也很喜欢说，比如今天 80% 的工作在 20 年前并不存在。这意味着可以合理地说，20 年后 80% 的工作我们甚至无法想象。至于你提到的提示工程，就在不久前，技术人员还告诉我们提示工程将是关键，人们说我要拿个提示工程学位，结果技术短短几分钟内就变得足够好，那个说法就彻底消失了。

Also, when we talk about like AI is going to take away all of our jobs. And by the way, those are the technologists saying that. When one of the things that I also love is the technologists are also very fond of saying, you know, things like 80% of the jobs today didn't exist 20 years ago. Which means it's fair to say that 80% of the jobs in 20 years we can't even imagine. And to your point about prompting, it wasn't that long ago where the technologists were telling us prompting is going to be the thing and people are saying I'm going to get a degree in prompting and the technology got good enough in just a few minutes that that literally went away.

是的，我认为这正是有趣之处，对吧？我们无法想象那些工作，或者我们会继续尝试。而且无法想象工作确实让人紧张，因为 AI 就在这里，那工作是什么？嗯，我不完全确定。我认为担心工作既有合理的理由。这些系统非常能干、非常自信，它们确实在改变对实际工作的影响。这不像电力那样，我们需要想办法驾驭或使用它。这是实实在在的影响。

Yeah, and I think that that is part of what makes this kind of interesting, right? Is we can't imagine the jobs or we'll keep trying. And also not imagine the jobs does get nerve-racking when like you know, AI is here and it's like what is the job? Well, I'm not 100% sure. I mean, I think there's both legitimate reasons to worry about jobs. These systems are very capable and very confident and they do change their impact real work. This is not electricity in the same way of like we need to figure out a way to harness or use this thing. There's a real impact.

我稍微谈过这个。我实际上认为，大自然厌恶真空，市场会自我修正。当出现泡沫时，股市会在某个时刻——不是我们选择的——自我修正。所有系统最终都会寻求平衡。我喜欢说，在七八十年代，机器人开始进入工厂，蓝领阶层说：“嘿，我们要失业了。”而白领阶层说：“这是未来，宝贝。进步不会为任何人停下。重新学习技能，知道吗？”而你知道，钟摆确实会摆动。因为你的水管工不在乎 AI，木匠不在乎 AI，机修工不在乎 AI。在乎 AI 的人是知识工作者。

I've talked about this a little bit. I actually think that, you know, nature pours a vacuum, markets correct. When we have a bubble, the stock market will at some point, not of our choosing, correct itself. And all systems seek equilibrium at some point. I'm fond of saying that in the 70s and 80s, robots started to enter factories and the blue-collar world said, "Hey, we're going to lose our jobs." And the white-collar world said, "It's the future, baby. Progress stops for no one. Reskill, you know?" And you know, the pendulum does swing. And because your plumber doesn't care about AI, the carpenter doesn't care about AI, the mechanic doesn't care about AI. The people who care about AI are the knowledge workers.

是的。

Yes.

而且你知道，这是未来，宝贝。进步不会为任何人停下。重新学习技能，宝贝。

And you know, it's the future, baby. Progress stops for no one. Reskill, baby.

是的，我的意思是，那确实是一套说法，但还有另一件事，对吧？比如你看过去的三次工业革命——其实只有两次或三次，我们就这么多。它们成功的原因不是技术独自让一切变得美好，而是因为劳工与资本斗争，发生了很多冲突。工会化是利益得以分配的方式，对吧？技术本身不会自然做到这一点。有趣的是，我们这里也会有一场类似的斗争，对吧？比如 AI，我们已经有了很好的数据，对吧？AI 当医生已经很不错了，当律师也在不断进步。像我交谈过的大多数律师都看到一种趋势，不久之后，对于不太复杂的问题，你就能从 AI 那里得到同样好的建议——如果现在还不能的话，对吧？对此有持续的争论。但问题是，律师不会对自己的失业保持沉默。而事实证明，很多国会议员是律师，很多人从律师那里赚钱。我敢打赌，你会看到每个州都通过法律，要求必须有律师——正式的人类律师——签字确认，即使他们比 AI 还差，对吧？

Yeah, that I mean that's a really set of I mean, there is a come up with but there is another thing, right? Like if you look at the last three industrial revolutions, there's either two or three. I mean, that's all we've got, right? And the reason they worked out wasn't because the technology made everything great alone, It was because it was also labor fought against, you know, capital and you had a whole bunch of fights happening. Unionization is how the benefits got spread around, right? The technology doesn't naturally do that. What's really interesting is we're going to have a similar fight here, right? Like AI, we already have good data, right? AI is pretty good at being a doctor. It's getting better being a lawyer all the time. Like most lawyers I talk to see a trajectory where not too long from now you will be able to get as good advice for not the most complicated issues from the AI if you're not already, right? And there's ongoing debate about this. But the thing is lawyers are not going to be quiet about losing their jobs. And it turns out a lot of Congress is lawyers and a lot of people don't get any money or lawyers. And I'm willing to bet that you're going to see laws passed in every state that you need to have a lawyer officially, a human, sign off on so that even if they're worse than AI, right?

是的。

Yeah.

所以这部分有点像“哦，这是白领工人的报应”，但同时也像“不，不，他们有自己的保护”，有些领域不会轻易让步，对吧？你知道，医生、律师，这会很有趣。程序员真的没有医生、律师、演员或其他行会或协会那样的保护。所以在不久的将来，我们会看到围绕这些问题的很多冲突。

So part of this like there is a little bit of oh, it's your comeuppance white-collar workers, but it's also like no, no, they've got the like there are fields that are not going to go easily, right? And you know, doctors, lawyers, it's going to be interesting. Coders really really do not have the protection that doctors or lawyers or actors or other kind of guilds or associations do. So we'll see a lot of conflict over these issues in the near future.

为自己的利益游说并不是新鲜事。例如，多年来一直有人试图为政府筹集资金，以增加收入。一张美元纸币的平均寿命大约是一年，而一枚硬币的寿命大约是 30 年。所以有人提议将美元纸币换成美元硬币，因为这样每年能为政府节省数十亿的印刷费用等等。而我们没有这么做，是因为油墨和纸张行业的游说，他们喜欢每年花在那上面的数十亿。所以，你知道，我们习惯了做对自己不利的事情，因为游说者的存在。这又是一件事，那些掌握权力和影响力的人会保护自己的利益，正如你所说。

Lobbying for our own interests is not a new thing. So for example, for years there have been people who trying to raise money for the government, you know, where we can get more income. The average lifespan of a dollar bill is about 1 year. The average lifespan of a coin is something like 30 years. And so the proposal to move dollar bills to dollar coins because it would save the government, you know, untold billions per year in printing and all of the rest of it. And the reason we haven't done it is because of the ink and paper lobby because they like those billions being spent on them to make new dollar bills every year. And so, you know, we're used to doing things that are not to our advantage because of lobbyists. And this is just another thing where those who have access to power and those who have influence will protect their interests, as you said.

而且不一定就是坏事，对吧？我们想让所有律师都失业吗？这是个问题吗？很多人会说，是的，是的，请吧。但是，你知道，我的意思是，实际上已经有早期证据表明，提交给法官的案件数量正在呈指数级爆炸式增长，人们正在用 AI 为自己辩护。那么，你如何处理这个问题？过去我们有一个过滤器，对吧？所以第二件事是，你必须说服律师接手你的案子。如果他们做得不好，他们会受到惩罚。如果你拿到一份 100 页的法律简报，那是人写的。那是真正努力的标志。但现在不是了。我们如何处理这种系统？所以我们在各个领域都会有这些涟漪，需要政策变化、监管变化、社会变化，即使我们没有像之前讨论的那种世界末日的 AI 事件。

And not necessarily badly, right? Do we want all lawyers out of a job? Is it a question? A lot of people be like, yes, yes, please. But like, you know, I mean, actually there's some early evidence that the number of cases being submitted to judges is exploding exponentially, where people are pleading their own cases with AI at law. And like, how do you deal with that? It used to be that we had a filter, right? So the secondary thing is, you had to convince a lawyer to take your case. And if they did a bad job, they would be punished. And if you got a 100-page legal brief, a person wrote that. And that was an indicator of real effort. But now it isn't. How do we deal with that kind of system? So we're going to have all of these ripples in all kinds of areas that will require policy changes, regulation changes, societal changes, even if we don't have sort of an apocalyptic AI event the way we're talking about earlier.

那么让我们从理论转向实践。正如我们之前所说的，很多非技术人员都在低效使用或误用这项非凡的技术。有些人仍然把它当作一个高级的 Google 来搜索。这更像是一个答案机器，但我的意思是，我们都这么做，因为这是一个非常简单且出色的用例。更细致和复杂的提示，我想我知道自己也在低效使用。比如我要求一个答案，即使它有一定深度，但我不会让它写一份报告或制作一个交互式数据仪表盘……我知道我没有这样做，而且我在使用这个东西方面已经领先于一些人了。我有两个问题。一个是，你在教学生，你在商学院教很多研究生。认为仅仅因为人们更年轻，他们就完全接受了这项技术，这是一种错误的信念吗？还是说他们在学习过程中也在摸索和犯错？

So let's go from the theoretical to the practicals. And as we sort of said before, which is a lot of people who aren't technologists are underutilizing or misutilizing this remarkable technology. Some are still using it as a glorified Google to search. This is more of an answer machine, but still, I mean, we all do that because it is a very simple and fantastic use case. The more nuanced and complex prompts, I think I know I'm underutilizing. Like I ask for an answer, even if it's in something that has some depth, but I don't ask it to write a report or make an interactive dashboard of the data that I'm not... I know that I'm not doing that, and I'm already ahead of some in terms of my utilization of the thing. I have two questions. One is, you're teaching students, you're teaching a lot of grad students at business school. Is it a false belief that just because people are younger, they're all in on this technology? Or are they also sort of bumbling and fumbling their way as they learn about it?

我们实际上有一篇关于这个的论文。所以我认为人们所想的……我在网上听到这个术语，我们一直在谈论数字原住民。人们谈论这个，对吧？比如，现在的孩子擅长使用互联网。确实，如果你和一个在 TikTok 长大的人交谈，他们会知道所有这些直观的方式，你知道，作为一个年长的人你不会理解。比如，哦，那太尴尬了。有一堆规则、俚语和方法你要掌握。我认为这种模式延续下来，认为年轻人懂技术。但这不适用于 AI。比如我和一位首席人力资源官交谈，我说，哦，现在的孩子，他们是 AI 原住民。我说，他们不是 AI 原住民。你只是在和 Claude 对话。他们是 Claude 的管道。比如如果你要一份报告，他们会给你一份漂亮的报告。他们不知道报告里有什么。他们怎么可能知道？他们对此没有知识。他们只是给你 Claude 说的东西。我们实际上在波士顿咨询集团做的一项研究中发现了一些证据，我们发现初级员工在使用 AI 方面往往更差。他们看起来用得不错。他们是采用者。但他们怎么能判断某件事是好是坏？我认为这是一个罕见的案例，你在某件事上经验越丰富，年龄越大，如果你决定使用 AI，你会用得更好。因为你可以直观地把握如何给出指令。AI 的工作方式足够像人，如果你给出指令并且你擅长某个领域，你会说，不，不，我明白那里出了什么问题。你在想这个，你应该想那个。尽管 AI 不是人，它不会思考。你会知道期望什么样的信息才能得到好的答案。所以，我认为经验确实很重要。如果你想想我们的教育体系是如何构建的，实际上学校处于混乱之中。我的意思是它们总是混乱的，对吧？大学一直很混乱。这不是新鲜事。你知道，人们用 ChatGPT 作弊，但我们实际上知道前进的道路，对吧？比如会有点混乱，但我们会更多地在课堂上做。我们用计算器也是这样。我们会更多地在课堂上布置作业。课外我们使用 AI 导师，在受控实验中证明非常有效。我们会让它们比现在工作得更好。课堂上我们会进行主动学习。我们会解决的。我不担心我们解决不了。我担心的是下一阶段。我在沃顿商学院教通才，他们成为专家的方式和我们 4000 年来教专家的方式一样，那就是学徒制，对吧？我把他们送到任何地方工作，对吧？他们去工作，你知道，他们去美国银行或其他地方工作，他们学习工作，每个人都得到一笔不错的交易。他们得到一点收入，但可能没有他们应得的那么多，但他们有机会证明自己，并通过反复做苦力活来学习门道。中层经理分配给他们自己不想再做的苦力活，并评估这个人是否适合晋升。这是一个很好的机制，但它刚刚被打破了，对吧？因为每个初级人员知道的都比 ChatGPT 少，他们宁愿只用 ChatGPT，而且不用 ChatGPT 或 Claude 来给你答案有点傻，因为它比他们能做的更好。每个中层经理宁愿把工作委托给 AI，而不是一个有缺陷的人，后者需要很长时间才能给出答案，而且没那么好。所以每个人都在互相做 AI 的工作。我认为这正是你在这里谈论的问题，危险在于我们失去了人才管道。有解决方案，但它们需要对人才管道的思考方式做出相当彻底的改变。

So we actually have a paper on this. Like, so I think what people think... I hear this term on the internet, we're talking about digital native all the time. People talked about this, right? Like, kids these days are good at using the internet. And indeed, if you talk to somebody who grew up with TikTok, they will know all these intuitive ways that, you know, as an older person you will not understand. Like, oh, that's cringe. There's a bunch of rules and slang and approaches that you want to take. And I think that that model kind of carries through that younger people get the technology. It does not hold for AI. Like I talked to a CHRO and I was like, oh, the kids these days, they're AI native. I'm like, they're not AI native. You're just talking to Claude. They're conduits to Claude. Like if you ask for a report, they'll give you a beautiful report. They have no idea what's in that report. How could they? They have no knowledge of this. They're just giving you what Claude says. And we actually found some evidence in this when we did a study at BCG at Boston Consulting Group, we found junior employees were often much worse at using AI. They seemed like they were using it well. They were adopters. But how could they judge whether something was good or bad? I think this is a rare case where the more experienced you are in something, the older you are, the better you're going to be at using AI if you decide to use it. Because you can intuitively grasp how do I give instructions? AI works enough like people that if you give an instruction and you're good at a field, you'll have to be like, no, no, I understand what went wrong there. You're thinking about this, you should be thinking about that. Even though the AI is not a person, it doesn't think. You'll know what kind of information to expect to get good answers. So, I think actually experience really does matter. If you think about how our education system is built, where actually schools are in chaos. I mean they're always in chaos, right? University has always been in chaos. This is not new. You know, people are cheating with ChatGPT, but we actually know the pathway forward, right? Like it's going to be a little bit messy, but like we'll do more in class. We did this with calculators. We'll do more in class assignments. Outside of class we use AI tutors which are turning out to be very effective in controlled experiments. We'll make them work better than we do now. In class we'll be active learning. We'll figure it out. I'm not worried that we can't figure it out. I am worried about the next stage. I teach people who are generalists at Wharton and they become a specialist the same way we've taught specialists for 4,000 years, which is apprenticeship, right? I send them off to work for whomever, right? They go work, you know, they go work at Bank of America or whatever and they learn the job and everyone gets a good deal. They get a little bit of income, but not as much as they would probably deserve, but they get a chance to prove themselves and they learn the ropes by doing grunt work over and over again. The middle manager assigns them grunt work that they don't want to do anymore and gets to evaluate whether this person is any good or bad for moving up the ladder. And it's been a great mechanism, and that just broke, right? Because every junior person knows less than ChatGPT, and they would rather just use ChatGPT, and they'd be kind of dumb not to use ChatGPT or Claude to give you answers because it's better than what they could do. And every middle manager would rather delegate to the AI than a flawed human who takes forever to give them an answer and isn't as good. And so everyone's just doing AI work to each other. And I think that that is exactly the problem that you're talking about here, which is the danger is that we lose the talent pipeline. There are solutions to it, but they're going to require fairly radical change in how we think about talent pipelines.

这在多大程度上类似于艺术？我的思路是这样的，对吧？我是一个艺术狂热者。这是我比大多数事物都更热爱的东西。我完全接受 AI 创作艺术。这不会困扰我。我完全接受 AI 创作音乐。这不会困扰我。然而，当我在墙上挂东西时，我喜欢知道是一个人构思了它。我喜欢知道是一个人制作了它，因为当我买一件艺术品时，我买的不仅仅是墙上的视觉物品。我买的是伴随它的故事。或者例如，这个周末我在听一些音乐。我在听 Jon Batiste 的《Beethoven Blues》专辑，如果你没听过，那真是精彩绝伦。现在，AI 能制作贝多芬奏鸣曲的蓝调版本吗？100% 可以。

How much of this is kind of like art? And here's where my brain is going, right? Which is I am an art fanatic. It's the thing that I love more than most things. And I am totally fine with AI making art. It doesn't bother me. I'm totally fine with AI making music. It doesn't bother me. However, when I hang something on my wall, I like knowing that a person conceived of it. I like knowing that a person made it because when I buy a piece of art, I'm not just buying the visual thing on the wall. I'm buying the story that goes along with it. Or for example, I was listening to some music this weekend. I was listening to Jon Batiste's Beethoven Blues album, which is, if you haven't heard it, spectacular. Now, could AI make a blues version of a Beethoven sonata? 100% it could.

但我从听那首音乐中获得的快乐，不仅仅是音乐本身，而是我微笑是因为一个人有创造力去创作它，那是我快乐的一部分。当我们审视工作成果时，我们忽略了两件事：我喜欢思考。我享受辩论。我享受让头脑因困难的事情而疼痛。我享受学习。就像画家喜欢绘画、音乐家喜欢演奏和作曲一样，人类对学习的渴望在哪里？那么我们的学校，尤其是工作场所，会允许这种情况发生吗？还是它们都变得如此痴迷于效率，以至于即使我们想学习——你明白我的意思。

But the joy that I got from listening to that music was not just the music that I was listening to, but I was smiling that a person had the creativity to come up with this, and that was part of my joy. When we look at the work product, there's two things we're neglecting: I like thinking. I enjoy debate. I enjoy making my head hurt at difficult things. I enjoy learning. The same way a painter likes painting and a musician likes playing music and composing, where is the human desire to want to learn? And then will our schools, but especially our places of work, allow for that to happen, or have they all become so obsessed with efficiency that we actually, even if we want to learn—you see where I'm going with this.

从这里可以衍生出无数方向，对吧？所以，我想先谈谈艺术这件事，因为它确实非常重要且有趣。显然，我们可能会看到更多手工制作的人类作品兴起。顺便说一句，最直接的例子是，当 AI 写诗或长篇小说时，通常会有很多问题，但因为我们习惯了——如果我们读到一些读起来优美且需要费力理解的东西，我们会假设背后有意图。所以，我们花自己的力气去填补漏洞。例如，AI 非常擅长奇怪的类比，对吧？它可能会说：“这场对话就像豁牙的微笑。”这本身没有意义，但如果你花点时间思考，“哦，它像什么？”你会达到一种意义感，对吧？如果是拉兹洛·莫霍利-纳吉或其他人在写这些东西，而我正在读，我会想：“哦，这个人对这个类比深思熟虑过，我应该花力气去理解。”如果是 AI 在写，从某些方面来说它很美，但意义来自我，我是否被欺骗了，因为我必须创造没有意图的意义？

There's 10 million directions from here, right? So, I want to put a pin on the art thing because it's actually really important and interesting. Obviously there might be a rise of our more artisanal human-made things. The most direct version of this, by the way, is when you have AI write poetry or long-form fiction, there's often a lot of things wrong with it, but because we're used to—if we read something that reads beautifully and it's effortful to read, we assume that there's a purpose behind it. So, we spend our own effort figuring out the holes. For example, the AIs are very famous at weird analogies, right? So, it might say, "This conversation's like a gap tooth smile." Now, that is not meaningful, but if you spend some time thinking about it, you're like, "Oh, how is it like?" and you will reach a feeling of meaning, right? If that was Laszlo Moholy-Nagy or someone else writing this set of stuff and I was reading it, I'd be like, "Oh, this person thought hard about that analogy and I should spend the work to do it." If it's the AI doing it, in some ways it's beautiful, but the meaning comes from me and am I being cheated because I have to create the meaning that has no intention.

意图不再属于艺术家。意图现在转移到了听众身上。

The intention no longer belongs to the artist. The intention now is shifted to the listener.

对吧？也许一直以来都是这样。作者已死之类的说法，但这是一个有趣的视角。然后我想第二个是，我在思考如何培养直觉？我们其实有办法做到。我们知道如何训练人。我们可以教人成为专家，但问题在于这需要付出努力。一直有一种观点——我早前告诉过你，我做过教育游戏。最令人沮丧的发现之一是，你可以做出非常有趣的东西，但首先，它只有 80% 的乐趣。不如真正为了乐趣而做的事情有趣。其次，学习需要努力。如果你不付出努力，那你就有麻烦了。对于少数我们真正关心的领域——对你来说，可能是艺术史或音乐，对有些人可能是数学和科学，对有些人可能是他们热爱的运动。无论我们在哪些方面付出努力并拥有内在动力，你会想，为什么不是所有学习都这样？问题在于，你不够在乎它。但我仍然希望你学数学，即使你不想学。我仍然希望你学美国历史。如果你通过 AI 给你答案来走捷径，你什么也学不到。我们有足够的实验证明这一点。所以，在一个存在捷径的世界里，让人们本质上进行脑力锻炼就成了问题。

Right? And maybe it always has. Death of the novel stuff, but that's one angle that's kind of interesting. And then I think the second one is, I'm thinking about developing some of these kind of—how do you develop intuition? And we actually have a way of doing that. We know how to train people. We can teach people to be experts, but the problem is it's effortful. There's always been this view that—I told you early on I made games for education. One of the most depressing things you learn is you can make something incredibly fun, but first of all, it's only 80% fun. It's not as fun as actually doing a thing for fun. And second of all, learning is effortful. And if you're not doing effortful work, then you're in trouble. Now for the few areas that we intrinsically care about—for you, it might be art history or music, or maybe for some people it's math and science, maybe for some people it's a sport they care about. Whatever we are effortful about and intrinsically motivated, you're like, why isn't all learning like this? And the problem is, you don't care enough about it. But I still want you to learn math even though you don't want to learn math. I still want you to learn American history. And if you shortcut that through AI giving you the answers, you learn nothing. We have enough experiments to show this. So, making people essentially lift mental weights becomes the problem in a world where there are shortcuts.

你知道吗，我觉得这一切中有一个巨大的讽刺，那就是问题其实不在于 AI，而在于我们一直沿着这条稳定的节奏，走到了这个回避不适的境地。鬼影（ghosting）这个概念就是逃避困难的对话，或者你现在看到，尤其是在年轻人中，他们更愿意辞职而不是进行困难的对话或接受负面反馈。然后我们变得如此以结果为导向——随着资本主义变得短期化，更关注股东至上、股东价值而非产品质量或客户满意度或员工满意度——你开始看到我们变得更注重结果，而忽略了工作过程。这不是一个新概念。AI 只是这种痴迷于结果而牺牲努力、工作或过程的最夸张形式。

You know, I find there's a great irony in all of this, which is the problem actually doesn't lie with AI, which is we've been on this steady drumbeat, this path to this point where we are discomfort avoidant. The concept of ghosting is a thing where you just avoid a difficult conversation, or you see it now, particularly among young people, where they're more comfortable with quitting a job than having a difficult conversation or getting negative feedback. And then the idea that we become so end result oriented—as capitalism has become short-term focused and more focused on shareholder supremacy, shareholder value over the quality of the product or customer satisfaction or employee satisfaction—you start to see we become more results-oriented, and we've left out the work product. This is not a new concept. AI is just the most exaggerated form of being results-obsessed at the expense of the effort, the work, or the journey to get there.

嗯，从另一个角度来看。我的意思是，部分原因正是让 AI 工作变得如此具有挑战性的地方，对吧？因为如果你想要生产力提升，你只需得到 100 倍的 PowerPoint，对吧？比如，如果我的工作是制作 PowerPoint，那么它要求你重新思考工作是什么。所以，工作成果不能是同样的东西。即使是最基本的方式，程序员可以写出比以前多 100 倍的代码。如果他们嵌入在一个组织流程中，需要两周时间做一个产品冲刺（他们常这么叫），对吧？所以每天有站立会议，假设程序员会写 x 数量的工作，产品经理会做这个，设计师会做这个，市场人员会做这个，突然一个人效率提高了一百倍。这对组织意味着什么？这成了一个问题。所以部分原因在于我们的系统——回到我们一直在讨论的主题——人类系统不是为 AI 世界构建的。学校不是为任何人都能替你写论文而建的，对吧？工作不是为人们能不经思考就按需制作 PowerPoint 而建的，对吧？小说不是为我能写那么多论文而建的。法律助理和法院不是为任何人都能提出案例而建的。这不是问题。每次工业革命都会发生。只是现在所有事情同时发生，有时 AI 赢，有时人类系统赢，有时我们都输，但这就是我观察调整发生的地方。

Well, and just to take another path from that. I mean, part of this is what makes AI work so challenging, right? Because if you want productivity gains, you just go get 100 times more PowerPoint, right? Like, if my job is producing PowerPoint, so it requires you to rethink what the work is. And so, what the work product is can't be the same thing. Even the most basic way, coders can write 100 times more code than they could before. If they are embedded in an organizational process where it takes 2 weeks to do a product sprint as they often call them, right? So each there's stand-up meetings every day and the assumption is the coder will write x number of work, the product manager will do this, the designer will do this, the marketing people will do this, and suddenly one person is a hundred times more productive. What does that even mean for an organization? It becomes a problem. So part of this is our systems—coming back to that theme we've been developing throughout—which is human systems are not built for an AI world. School wasn't built for a place where anyone could write your essays, right? Work wasn't built for people to be able to produce PowerPoint on demand without thinking about it, right? Fiction wasn't built that I could write as many papers. Law clerks and courts weren't built for anyone to be able to bring up a case. That's not a problem. That happens in every industrial revolution. It's just all happening at once everywhere and sometimes the AI wins, sometimes human systems win, sometimes we both lose, but that's where I'd watch the adjustment happening.

我在这里稍微往回退一点。

I'm going to go backwards a little bit here.

让我们回到实际应用，基于课堂和商业世界，因为我知道你也研究这个。有哪些更简单的、我们可以利用现有技术的方法？就像我说的，大多数人都在误用或未充分利用这个工具。而且，你知道，我被那些给我建议的人淹没了，告诉我应该怎么做、可以做什么。但从一个基本的角度来看，一个人如何能提升 1% 到 10%？

Let's go back to practical, based on classroom and in the business world, because I know you study that as well. What are better simple ways that we could be using the technology available to us? Like I said, most people are mis- or under-utilizing the tool. And you know, I'm overwhelmed by the people giving me advice as how I should be doing things and what I could be doing. But from a basic standpoint, how can somebody level up just 1 to 10%?

你可以获得超过 1% 到 10% 的提升。我不从 AI 实验室拿钱，所以听起来不像在推销，但我建议你每月花 20 美元订阅三大公司之一：Google 的 Gemini、OpenAI 的 ChatGPT 或 Anthropic 的 Claude。而且你必须主动选择当时可用的最佳模型，也就是你能访问的所谓思考模型。这些模型会随时间变化，但你必须主动选择。它默认的是较低级的模型。仅仅通过选择最新模型并使用它，你就会获得巨大的改进。第二点我要说的是，AI 已经变得相当好了。你知道，AI 大致有三个阶段。在 ChatGPT 之前，我们大多数人谈论 AI 时，基本上是在讨论如何使用数据分析。那时有很多关于算法公平性、价格挖掘和定制定价的讨论。所有这些都来自 ChatGPT 之前的时代。然后 ChatGPT 开启了生成式 AI，以及我宏大称之为——因为我上一本书的书名——协同智能，即你与聊天机器人来回互动以获得答案，对吧？我输入聊天内容，它给我答案。现在我们进入了一个新阶段，称为智能体式 AI。实际上，这只有三四个月的历史。

You can get more than 1 to 10%. I take no money from AI labs, so you don't sound like a shill, but you have to end up paying $20 a month to one of the big three companies, is what I'd recommend. Either Google's Gemini, OpenAI's ChatGPT, or Anthropic's Claude. And you have to actively pick the best model available at that point, which is what you'll have access to called thinking models. Those will change over time, but you have to actively select that. It defaults to a lower one. You will get huge impact improvements just from picking the most recent model and using it. The second thing I would say is AI has gotten quite good. So, you know, there's kind of three phases of AI. There's prior to ChatGPT, where when most of us talked about AI, we'd be talking about how you use data analysis basically. There's all this talk about algorithmic fairness and price mining and customized pricing. All of that came from prior to ChatGPT. Then ChatGPT kicked off generative AI and what I will grandiosely call, because of the title of my previous book, co-intelligence, where you'd work back and forth with a chatbot to get an answer, right? I'd type in the chat, it would give me an answer. Now we're in a new phase which is called agentic AI. And it's really just three or four months old practically.

那是什么意思？那是什么意思？

What does that mean? What does that mean?

智能体是一个 AI 系统，如果你要求它，它可以独立去完成工作。所以，agentic 只是 agent 的形容词。智能体，对吧？所以，智能体式就是 AI 智能体，虽然有很多营销术语，但它就是能工作的 AI。最重要的是要认识到工作有多好、工作实际上有多长。有一个 OpenAI 的论文和测试——你总是要持保留态度，但也有足够独立的评估让我觉得不错——叫做 GDP val。他们选取了代表美国经济 5% 的人群，包括记者、产品经理、律师和私家侦探，平均有 14 年经验。他们让每个人创建一个他们在自己领域遇到的真正困难的问题。他们让另一组有 14 年经验的人来解决问题。平均花了大约 7 到 8 个小时完成工作。然后他们让 AI 做同样的事情。AI 花了大约 15 分钟。然后他们让第三组专家花一个小时评估每一方的输出，不知道哪个是谁的，并投票选出他们更喜欢哪个。一年前这个结果出来时，世界上最好的 AI 大约有 48% 的时间与人类持平或超越人类。而截至我们录制时，最新模型大约达到 84%。所以，84% 的情况下，它们完成的工作——相当于人类 7 小时的工作——等同于或优于人类。这意味着，回到实际应用，如果你把每个复杂的工作交给 AI，你可能会节省三倍的努力和三倍的成本，即使你花一个小时来整理和评估，即使你不得不放弃 30% 的时间，你仍然节省了时间和精力。所以，我认为你正在做的一件事就是没有使用 AI，也没有给它足够难的任务去做。

An agent is an AI system that can independently go do work if you ask it to. So, agentic is just the adjective of agent. Agent, right? So, agentic is an AI agent, and there's marketing terms around it, but it's an AI that can do work. The most important thing to realize is how good the work is and how long the work really is. There's this paper and test by OpenAI, so you always take it with a grain of salt, but there's been independent enough assessment I feel good about it, called GDP val. And what they did was they took people representing 5% of the US economy, so journalists and product managers and lawyers and private investigators, with an average of 14 years of experience. They had them each create a really hard problem that they faced in their field. They had another set of people with 14 years of experience to do it. Took them about average 7 or 8 hours to do the work. Then they had the AI do the same thing. Took about 15 minutes for the AI. Then they had a third set of experts come in and spend an hour evaluating the outputs from each of these, not knowing whose is whose, and voting on which they like better. And when this came out a year ago, the best AIs in the world were getting about 48% of the time they were tying or beating humans. Then the latest models as of when we're recording this are about 84%. So, 84% of the time the work that they do, 7 hours of human work, are equivalent to or better than a human. What that means, going back to the practical piece, is you would probably save three times effort and three times cost if every complex job, you would give it to AI, and even if it took you an hour to put it together and evaluate, even if you had to give up 30% of the time, you would still save time and effort. So, one of the things I think you're doing is not using AI and giving it hard enough tasks to do.

好的。所以，这是另一件事，即 AI 的价值在于，效率不是它解决问题的速度有多快，而是你评估它是否做对了问题的速度有多快。

Okay. And so, that's the other thing, which is the value of the AI, where efficiency is not how quickly it can solve the problem, but how quickly you can evaluate whether it got the problem right.

这又回到了专业知识。专家可以立刻看出，不仅知道它错了，而且通常知道它错是因为一个具体问题，要么是你应该更好地指定，要么是 AI 对某些事情很蠢。有时你会立刻想，“哦，它永远搞不懂这个，因为太微妙了，我无法传达要点。我还是自己做吧。”但有时你会想，“哦，是的，是的。这是个新手错误，我应该提醒它，当它写文章时，不要只是事实性地解释一切，而是用故事来解释，或者你想要的任何东西。”然后它就更好了，对吧？所以，评估、反馈，这些都是专家擅长的事情，而 AI 对此反应非常好。

Which again brings us back to expertise. An expert can look at this right away and be like not just is wrong, but like often it's wrong because of a specific problem that you should have either specified better, or the AI is stupid about something. And sometimes you can instantly get, 'Oh, it's never going to get this because it's too subtle and I can't communicate the point. I'm just going to do this myself.' But sometimes you're like, 'Oh, yeah, yeah. This is a rookie mistake, and I should remind it that when it writes articles to not just factually explain everything, but explain it with a story, or whatever your thing is.' And then it's better, right? So, evaluation, feedback, these are things experts are good at, and the AI responds really well to that.

另一个问题是，我记得当我写第一本书时，对吧？出版界的每个人都告诉我，“对任何作者来说，最难的事情就是找到自己的声音。”对吧？拥有一种声音。这是一个很难理解的概念，你知道，什么是声音。本质上，当你读我的文字时，它们是我的。它们可能是我的个性。它们是我的观点。不仅仅是写得漂亮，而是它们属于我，对吧？这对作者来说非常难做到。我发现 AI 可以写得优美，但它没有声音。如果你要求它有声音，它总是会使用世界上已有的声音，换句话说，是出版过的人的声音，而不是你的声音。所以，大多数写作会开始听起来一样。我的意思是，我已经看到了。我的收件箱里收到 AI 生成的邮件，它们基本上都是同一封邮件。

Here's the other problem, which is I remember when I wrote my first book, right? Everybody told me, everybody in the publishing world said, 'The most difficult thing for any author is, quote unquote, to find their voice.' Right? To have a voice. Now, it's a very hard concept to understand, you know, what voice is. Essentially, it's when you read my words, they are of me. They might be my personality. They're my point of view. It's not just nicely written, but it is of me, right? And it's very hard to do for an author. And I have found that AI can write beautifully, but it has no voice. And if you ask it to have a voice, it's going to always have voices that are available to it in the world, in other words, published people, but not you. And so, most writing will start to just sound the same. I mean, I'm already seeing it. I'm getting AI-generated emails in my inbox, and they're all basically the same email.

不是 X，是 Y。它在这里做重活。让我夜不能寐的是，你知道，这是一个支撑性的论点。Cicada 3301，但你知道，等等等等。

It's not X, it's Y. It's doing the heavy lifting here. The thing that keeps me up at night, you know, this is a load-bearing argument. The Cicada 3301, but you know, word word word.

开始直接全部删除，因为它们都很熟悉。

Starting to just delete them all because they're all familiar.

是的。这是一个词。没有一个突出。对。我反驳。我说不是它没有声音。它有一个声音，对吧？一个单一的声音，就是所有声音或 ChatGPT 的声音。实际上不是一个坏声音。就像如果我没有看到它 40 亿次。

Yes. It's a word. And none of them stand out. Right. And I push back. I'd say it's not that it doesn't have a voice. It has a voice, right? A singular voice that is all voice or chat GPT. It's actually not a bad voice. Like if I didn't see it 4 billion times.

你的声音。它是一个声音。

Your voice. It's a voice.

而且它是一个非常好的声音，对吧？有点戏剧化。它有时就是太喜欢过渡了。显然太喜欢 M 破折号了，但它不是你的声音。这是另一件事，就像培养你的声音。现在，很多人做不到，对吧？不是每个人都是好作家。无论我们怎么教他们写作，他们就是不懂。代笔作家一直存在，对吧？我很高兴写作是我做的事情，并且已经建立了自己的声音。我知道很多人用代笔作家来做他们的工作。我同意关于 AI 声音的看法。

And it's a perfectly good voice, right? It's a little dramatic. It just sometimes is like it loves transitions too much. Obviously loves M dashes too much, but it's not your voice. And that is another thing that is like developing your voice. Now, a lot of people can't, right? Like not everyone's a good writer. No matter how much we teach them writing, they don't get it. Ghostwriters have been around forever, right? I'm glad that writing is something I do and have established voice. I know plenty of people who use ghostwriters to do their kind of work. I agree on the AI voice.

现在，我要说你可以让它更像你。不是针对书那种长篇作品，但这里有个小技巧：如果你想这么做，就给 AI 一大段你的写作样本，然后让它写两页总结这种风格，以及如何按这种风格写作的指令。然后你把它粘贴到你的自定义指令里，说按这种风格写。它不会完全是你，会有点模仿你的味道，但绝对比你说“像某个名人那样写”要好得多。

Now, I will say you can get it significantly more like you. Not for the kind of long-form work of a book, but a tip here if you want to do this is give AI a large sample of your writing and then say write two pages summarizing the style of this. And the instructions of how to write in the style. And then you paste that into your custom instruction and you say write in the style. It will not be a slight parody of you, but it will be infinitely better than if you just say, you know, write like this famous person.

对。我最近做了个实验，就是在客厅里走来走去，对着 Claude 说：“写一篇西蒙·斯涅克风格的专栏文章，想法是这样的。”我就这么在客厅走了大概三四分钟，然后它给我写了一篇相当不错的文章。然后我说：“事实核查一下。”它就说：“这个错了，那个错了，那个错了。”我说：“好吧，那给我一些建议，让我能把它改得事实正确。”我喜欢这样做的原因是，你知道，我 80% 的时间都花在写糟糕的初稿上，而编辑相对高效，也更有趣，就是真正地清理一下。大部分时间都花在初稿上，对吧？所以这里我几分钟就得到了一个糟糕的初稿。然后我坐下来，它帮我事实核查，这太高效了。我不需要自己做所有研究。虽然我还是双重检查了所有研究以确保无误。

Right. I mean, I did something recently as an experiment, which is I walked around the living room just talking into Claude and said, "Write an op-ed in the style of Simon Sinek. Here's the idea." And I just walked around the living room for about 3 or 4 minutes and then it gave me a pretty remarkably written article. Then I said, "Fact-check it." And it said, "Well, that's wrong. That's wrong. That's wrong. That's wrong." I said, "Okay, go offer me what I could say to make it factually correct." And the thing that I enjoyed about doing it, which is you know, it takes 80% of my time to make a shitty first draft. And then editing is reasonably efficient and a lot more fun to really just clean something up. Most of the time is the first draft, right? And so here I got a shitty first draft in a few minutes. And then I sat down and with it you know, it fact corrects it which is so efficient. I didn't have to go do all the research myself. Although I did double check all the research just to be sure.

就像你说的，这些模型的错误率已经下降了。如果你用现代模型，它不会像以前那样犯同样的错误。

As you were saying, the error rates of these things have dropped. If you use a modern model, it's not making mistakes the same way.

我得说，用我自己的声音和幽默感来编辑它其实挺有趣的。最后润色时，我发现我能加入自己的风格。我印象很深，真的很深。现在，我可以作弊，因为我写得够多，它能了解我的风格，我不会说它完美，但它好得吓人。

I have to say it was actually kind of fun to edit it in my voice, with my sense of humor. And the last finishing touches I realized I could put my voice. I was pretty impressed. I was pretty impressed. Now, I could cheat because I have written enough that it can know my style and I wouldn't say it was perfect, but it was scary good.

是的。这里面有几件事。其中之一是这种对写作的颠覆。而且每件事都有代价，对吧？所以写糟糕初稿的一个选择是，那是你自己的糟糕初稿。所以我总是建议写一些糟糕的初稿，否则 AI 的想法会取代你的想法。它很擅长出点子，你会发现你无法头脑风暴。但话虽如此，我发现这种类似的编辑循环是一种奇怪的方法，不像我们以前那样——我可以先按自己的形式写点东西，然后再编辑。我敢肯定有些作家多年来一直这样工作，但这是一种颠覆，可能更好，对吧？可能很难说。就像你是个工厂，不断生产初稿，一直点击。或者有些人就是很擅长编辑，但不擅长写初稿，突然他们比以前更高效了。

Yeah. I mean there's a few things going on there. One of those is this idea of disruption to writing. And I mean there's costs to everything, right? So one option of writing the crappy first draft is it's your crappy first draft. So I always recommend some crappy first draft because otherwise the AI's ideas will take over your ideas. It's very good at ideas and you're like you will find you can't brainstorm. But with that said, I find this kind of similar loop of like editing is a weird way of approaching a like it's not how we used to do it before, which is like I can get something written in my form and then I edit it. You know, I'm sure some writers have worked that way for years, but that's a disruption to writing that might be better, right? It might be where it's hard to know. Something you're a factory, you know, producing first drafts that you know, click all the time. And or maybe some people are just really good at editing and they weren't good at draft writing and suddenly they're more productive than they were before.

我认为这是未来工作中我们低估的一点，不仅仅是工作变新了，而是工作的重心会转移。是的，就像你说的，我们一直赞美作者，而编辑总是默默无闻。如果你在公关或杂志社工作，写新闻稿的作者是专门学这个的，而编辑只是薪水更低、所谓的“失败作家”。但现在我认为，作者和编辑之间的平衡会改变。

I think this is one of the future jobs that we under appreciate, which is not just that jobs are new, but that the weight of the job will shift. Yes, to your point you know, we've always celebrated the writer and editors have always been like just there. If you work for public relations or you work in magazines, like the person who's the writer who wrote the press release, they're the person who went to school to write the press release and we just sort of like the editors are just the lower paid, you know, failed writers, you know, quote unquote. But now I think the writers, I think that the balance will shift.

顺便说一句，我们会在很多工作中看到这一点，回到工作这个话题。工作由很多任务组成，对吧？一个作家负责写作、编辑和事实核查，AI 做了其中一些工作，它转移了你工作的负担，但并没有拿走一切。我认为我们在很多工作中会看到这种瓶颈：AI 擅长一些事情，但不擅长另一些。我们在早期论文中称之为 AI 的锯齿形前沿，即它在某些方面好，在某些方面出乎意料地差。它差在哪里？比如完美地模仿你的声音、讲对笑话。突然之间，对你的劳动需求就更高了，对吧？你的价值也更高了。也许以前你的笑话并不是你的主要决定因素，但如果你现在更擅长讲笑话，突然就有了价值。顺便说一句，同样的事情也发生在编程上。过去，写非常干净的代码是一项很好的技能。现在 AI 写了大部分代码，成为架构师很好，成为工程经理也很好。工作变了，什么重要、什么不重要也变了，这也改变了谁擅长或不擅长，创造了新的机会和新的风险。

We're going to see that across a lot of jobs, by the way, to come back to the job thing. So jobs are many tasks, right? A writer does like as a writer you're in charge of writing and editing and fact checking all and the AI does some of that work, it shifts the burden of what you do, but it doesn't take away everything. And I think what we're going to see in a lot of jobs is this idea of bottlenecks that the AI is good at some stuff but bad at other stuff. We call the jagged frontier of AI in our early papers on this, which is it's good at some things, bad at some things you won't expect. Where it's bad, right? Writing perfectly in your voice, getting a joke right. Like suddenly the demand for your labor is higher there, right? And your value is higher. It might have been that your jokes were not what was getting you, like that was not your main deciding factor, but if you're better at jokes now, suddenly there's value. Same thing's happening in coding, by the way. Used to be that writing really clean code was a really good skill. Now the AI's write most of the code, being an architect is good, being an engineer manager is good. The jobs change, what's important and what isn't important changes and that changes who's good or bad at it, too, creating new opportunities and new risks.

你可能不是有 100 个程序员，而是团队里有 50 或 30 个，但依然有带着自我、不安全感、睡眠不足等所有这些问题的人类，而且仍然有人监督项目，必须管理所有杂乱的人事，不管技术有多好。我个人认为，加倍关注人类现在会变得更加重要，因为我们仍然需要照顾那些与 AI 智能体一起开发产品的人。

You may have, instead of 100 coders, you might have 50 or 30 working on the team, but there's still human beings with egos and insecurities, you know, lack of sleep, all of this stuff and there's still somebody overseeing the project who has to manage all the messy human stuff regardless of how good the technology is. I for one believe that doubling down on human is going to become even more important now because we still have to take care of the people who are working on the products with their AI agents.

哦，绝对是的。而且过去我想做开发时，必须雇一家公司来做。现在，可能每个两人团队就有一个程序员，而且软件比以往任何时候都多，对吧？所以，说未来工作不可想象有点烦人。我认为这很烦人，因为我们其实对这种情况有些了解：并不是程序员被提示工程师取代，而是编程工作本身变了。对编程的需求从千人协作的大型组织转向分散化，你的汽车经销商可能有一个程序员为团队经理定制软件。你可能有两个开发人员为你工作，而不是外包网页开发，他们不断改进。软件和工作的本质变了，我认为这也是拼图中缺失的一块。

Oh, absolutely. And also when I wanted to get coded in the past, I had to hire a company to do it. Now, there might be a coder working for every two-person team, and more software is being created than ever, right? So, the jobs are unimaginable in the future is sort of an annoying thing to say. I think it is annoying because I think we actually have some idea of what this looks like, which is not that coders are replaced by, you know, prompt engineers, but that the job of coding changes. The demand for coding shifts from giant organizations where a thousand people work together programming to now dispersed and your car dealership might have a coder building customized software for you around the what the managers want in the team. You might have two developers working for you rather than outsourcing web development that are, you know, evolving things. The nature of software and the jobs change, and I think that that is a missing piece of this puzzle, also.

而且我认为我们没意识到的另一件事是，事情变得越好，对吧？因为过去质量能让你脱颖而出。

And I think the other thing we aren't appreciating, which is the more things get good, right? Cuz it used to be that quality would help you stand out.

是的。

Yep.

如果你更聪明、编程更好、写作更好、这个更好、那个更好，无论是什么，擅长某件事能让你脱颖而出，对吧？如果所有东西的质量都稍微提高或大幅提高，那就会让很多产品变得商品化。所以，我很好奇、也无法预测、甚至毫无头绪的是，如果一切都变得普遍优秀，那你在市场上如何脱颖而出？我们在社交媒体兴起时已经看到了这一点。我们是拥有电影明星的最后一代。我认为电影明星正在消亡。没有人会真的因为某个演员参演而买票去看电影。就像一场又一场的战斗。你知道，很多人去看电影，但很少有人是因为莱昂纳多·迪卡普里奥参演才去看的。而这就是电影明星过去的作用——他们让人们去看电影。现在，我们更愿意看系列电影。我们对漫威感兴趣，而不是谁在演。这就是我所说的商品化。我非常好奇，当一切都变得更好并被商品化时，电视节目、所有东西都被商品化，是什么让公司、产品和人们脱颖而出？所以，当你面试一份工作时，每个人都因为 AI 而变得优秀，我该如何脱颖而出，得到一份他们仍然需要招聘的工作？我对这个问题很着迷。

That if you were smarter, a better coder, a better writer, a better this, a better that, whatever it was, being good at something made you stand out from the crowd, right? If the quality of, let's just say, everything gets slightly higher or a lot higher, then it commoditizes so many products. And so, what I'm curious about and cannot predict and don't even have a thought about what happens here, but if everything just becomes generically good, then how do you stand out in a market now? And we've kind of seen this with the rise of social media. We're in the last generation that has movie stars. I think it's the death of the movie star. Nobody's really buying a ticket to go see a movie because a particular actor is in it. Like one battle after another. You know, lots of people went to see the movie, very few went to see it because Leonardo DiCaprio was in it. You know, and that's what the movie stars used to do. They used to make people go see the movie. Now, we'd rather see the franchise. We're interested in Marvel than who's in it. And this is what I mean by commoditization. I'm so curious as everything becomes better and commoditized, TV channels, everything's commoditized. What's the thing that makes companies, products, and people stand out? So, when you're interviewing for a job, and everybody's good because of AI, how do I stand out and get a job that's still that they still need to hire for? I'm fascinated by that.

我认为有几件事……这里又有很多问题，对吧？顺便说一句，这部分被放大了，对吧？比如，如果 Claude 非常擅长运营你的公司，那 Claude 也擅长运营其他每家公司，它们之间没有差异，普遍高质量但没有差异就意味着没有选择或竞争优势。我认为，给这个带来竞争优势的人类，无论如何，仅仅通过提供差异，即使没有别的，

I think a few things are... There's a lot of things there again, right? Part of this, by the way, is writ large, right? Like, if Claude is really good at running your company, Claude's also good at running every other company, and there's no variation between them, and generically high quality with no variation means there's no votes or competitive edge. I think humans who bring competitive edge to this, one way or another, just by providing variation, if nothing else,

是的。

Yes.

是一种有用的思考问题的方式，对吧？比如，你的品味很重要，对吧？大概这就是为什么人们会听……就像，你的品味，或者和谁交谈，你问什么样的问题，你知道，这类似于……比如，你喜欢罗斯科还是伦勃朗？不同的品味会带来不同的结果。

is a useful way to think about problems, right? Like, your sense of taste matters, right? And presumably it's why, you know, why people listen to... It's like, your sense of taste, or who to talk to, the kinds of questions you ask, you know, it's similar to the sense... Like, do you like Rothko, or do you like Rembrandt? Like, there's different tastes that have different kinds of outcomes.

是的。

Yes.

关键就是培养品味，对吧？这是一个更大的问题，即我们如何让人们培养品味？这通常是一辈子的事。这可能成为我们教给人们的新技能之一——培养品味，这需要广泛体验、做出选择，并有词汇来描述你的品味和选择。我认为，随着人们成为更大的创造者，能做更多事情，他们的品味就更加重要。比如，导演可能最终比以往任何时候都更重要，因为我明白从韦斯·安德森的作品中能得到什么，对吧？如果他能完全按照自己的意愿导演整部作品，那会是什么样子？

thing is developing taste, right? Is a bigger issue, which is how do we get people to develop taste? It's usually a casual lifetime thing. That might be one of the new talents we teach people is developing a sense of taste, which requires, you know, experiencing broad things and making choices and having the vocabulary to describe your sense of taste and choices. I think that as people become bigger creators and they can do more, their taste matters more. Like, directors may end up mattering more than ever because I understand what I'm getting with a Wes Anderson experience, right? And if he can direct the whole thing the way he wanted to, what would that look like?

是的。

Yes.

我们可能会在各种其他事情上发现同样的情况。有人对冰淇淋风格有独特的品味。现在，你可以按需制作冰淇淋，因为 AI 会通过 API 连接到一个为你生产该产品的供应商。所以，这有点像让一个人能做更多事情，你开始更关心那个人的品味，而不是整个为交付产品而建立的组织。

And we might find the same kind of thing with all kinds of other stuff. There's someone who has a particular taste in ice cream styles. Now, you can make ice cream on demand because the AI will connect you through the APIs to a, you know, to a vendor that makes that product for you. So, it kind of fits in of enabling one person to do much more, you start to care a lot more about the taste of that person than you do about the entire organization built to deliver the product.

说得太好了。我是说，我有自己的偏见和观点，但我很好奇，Gemini、ChatGPT 和 Claude 之间真的有区别吗？我知道 Claude 更侧重于 B2B 商业模式。这意味着安全性更重要，因为企业不会容忍任何安全漏洞，而客户可能可以容忍。真的有区别吗？

So good. I mean, I have my own biases and opinions, but I'm curious, is there actually a difference between Gemini, ChatGPT, and Claude? I know Claude has a much more B2B focus a business model. That means security is more of a thing because business wouldn't stand for, you know, any lapses in security, maybe like customers might. Is there actually a difference?

是的，有区别。先退一步，从无聊的教育角度来说。现在考虑 AI，你要想三件事。模型，也就是大脑，对吧？在我们录制的时候，那是 Anthropic 的 Opus 47，一个 Claude 模型，ChatGPT 5.5，和 Gemini 3.1 Pro。当你听到这个时，这些数字可能会稍微高一些，取决于进展，对吧？但那些是大脑。模型越好，它在所有方面就越聪明——谈判更好、诗歌更好、数学更好……但那是大脑。然后你要考虑应用。应用是你访问这些模型的工具。对大多数人来说，当我说应用时，他们应该想到 chat.openai.com 或 claude.ai 或 gemini.google.com。那是一个应用。但人们越来越多谈论的 AI 应用是像 Claude Code、OpenAI 的 Codex、Notebook LM（我没用过，但 Gemini 的免费版在研究收集数据方面非常令人印象深刻）。这些是为特定目的构建的非常具体的工具。最后，还有我们所说的“框架”，即 AI 如何做事。框架让 AI 为你写代码、上网搜索或生成图像。所以，目前三大公司都大致同样优秀，可能会争夺位置。但它们都在制造非常好的大脑。模型都非常好。目前，Google 拥有最多样化的应用产品，但它的主要应用可能比 Anthropic 或 OpenAI 弱。而且它们的主要应用框架也更差。所以，如果你想用 AI 做事，目前最强大的工具是 Claude Code 或你机器上的 Co-worker（如果你用 Anthropic），或者 OpenAI 的 Codex 工具。它们的不同之处在于它们驻留在你的电脑上。所以你可以让它们访问你的文件、你的电子邮件。它们可以用你的机器、你的网页浏览器为你工作，无论你是否喜欢，对吧？并完成工作。因此，这两者正在来回争夺领先地位，但三者都非常好。所有模型都很好。

So, there are. Just a half step back on the boring educational side of this. When you think about AI now, you want to think about three things. The model, which is the brains of the bunch, right? At the time we're recording this, that's Opus 47 from Anthropic's. That's a Claude model, ChatGPT 5.5, and Gemini 3.1 Pro. By the time you hear this, they will be slightly higher numbers on all of those things based on how things are going, right? But those are the brains, right? The better your AI model is, the smarter it is at everything. It's better at negotiations, it's better poetry, it's better math, it's better... But that's the brains. Then you want to consider apps. Apps are the tools you access these. For most people, when I say app, what they should be thinking of is chat.openai.com or claude.ai or gemini.google.com. That is an app. But the apps that people increasingly talk about when they use AI are things like Claude Code, OpenAI's Codex, Notebook LM, which I haven't used for Gemini is free and very impressive for research and gathering data. And those are very specific tools built for certain purposes. And then finally, there's what we call harnesses, which are how the AI can do things, right? So, a harness lets the AI write code or do internet searches or make images for you. So, right now, the three big companies all have roughly equally good, will probably, you know, jockey for position. Yeah. But they're all making very good brains. The models are all very good. Right now, Google has the most diverse set of products of apps, but their main apps are probably weaker than Anthropic or OpenAI. And they have worse harnesses for the main app. So, if you want to use AI to do things, right now, the most powerful tools are Claude Code or Co-worker on your machine if you're using Anthropic or OpenAI's Codex tool. And what makes those different is they sit on your computer. So like you can give it access to your files, to your email. It can, you know, it can do work for you using your machine, your web browser, whether you like this or not, right? And do work. So because of that, those two are kind of jockeying back and forth for the lead, but all three of them are quite good. The models are good across all of them.

那么现在我们来谈谈安全性，对吧？我们都厌倦了 Meta 和其他所有公司，你知道，用 Cookie 填满我们的电脑，跟踪我们在每个网站上的每一次活动，即使我们已经离开了它们的网站和产品。然后我们都变得非常敏感，关掉 Cookie，数据隐私现在成了一个问题。你知道，我想把这些 AI 模型中的任何一个，我信任这些公司中的任何一个，让它们访问我的整个电脑、我的所有浏览历史、我的所有财务信息等等吗？

So now let's talk about security, right? So we're all tired of meta and all the other companies, you know, filling our computers with cookies, tracking our every movement on every website even after we've left their website and their product. And then we've all become very sensitive to turning off cookies and data privacy is now a thing. You know, do I want to give any of these AI models, do I trust any of these companies to have access to all my computer, all my browse history, all my finances, etc. etc. etc.

所以这是一个很难的问题，对吧？我的意思是，有更安全的版本，你甚至可以运行自己的这些工具版本，但它们不会像 OpenAI、Anthropic 和 Google 的那样好。

So it's a hard question, right? I mean there are more secure versions where you can even run your own version of these tools, but they will not be as good as OpenAI and Anthropic and Google's.

你可能会有几种安全方面的担忧。其中之一是：他们会不会拿走你的数据，用来训练他们的下一个模型？如果你付 20 美元，所有公司都有选项可以关闭那个训练功能。这对你来说隐私够了吗？很难说，对吧？还有一些悬而未决的问题：某人的 AI 历史记录是否会被搜索到？律师能不能要求查看，也就是可被发现的？即使他们跟你签了协议，长期来看公司会怎么处理这些数据，也还是未知数。但另一方面，你的 Gmail 里可能存了你所有的邮件，对吧？这些 AI 工具现在看起来更像是企业级软件应用，而不是侵入性的个人工具。所以，你信任吗——你有多信任 Google 或 Instagram 保管你的信息？我们又回到了同一条船上。

There are a couple kinds of security concerns you might have. One of them is: are they taking your data and using it to train their next model? If you pay $20, all of them have an option to turn off that training feature. Is that enough privacy for you? It's hard to know, right? There are open questions about whether or not someone's AI history will be searchable. Is it something that lawyers can demand to look at, discoverable? There are open questions about what companies will do with this in the long term, even though they sign agreements with you. But on the other hand, you have Gmail, which probably has all your email in it, right? These look like enterprise software applications at this point, rather than invasive individual tools. So, do you trust—how much do you trust Google with your information or Instagram with your information? We're in that same kind of boat again.

区别在于，尽管我不希望 Google 访问我的邮件，但我知道它确实能访问，可我也知道没人能到网上去用 Google 搜索查个问题就能读我的邮件然后告诉我什么。我觉得很多人担心的是，有人可以直接上 ChatGPT 或者其他类似平台。

The difference is, as much as I don't want Google to have access to my email, I know that it does, but I know that nobody can go out onto the web and ask a query in a Google search to read my email and tell me something. I think a lot of us are afraid that somebody could just go on to ChatGPT or one of the others.

但他们做不到。不是那样的——没有那种数据泄漏，好像只有一个巨大的收件箱而你勉强维持着，对吧？它运作方式是一样的。它看起来像企业软件。所以，这本身也有风险，对吧？但基本风险是：有人能随便问个问题就访问你的 ChatGPT 吗？不能。如果他们登录，你必须做所有同样的事情：设置双重验证，别让自己在电脑上保持登录状态。我的类比是 Gmail，对吧？Google 拥有所有这些信息，处理它们并用于自己的目的，但他们也以某种方式匿名化了，试图建立信任。黑进别人的 Gmail 需要费一番功夫。我们处境相同。现在，我们是否希望一家公司拥有更多权力，那是你可以做的选择。但我不认为我们应该把它归入一个单独的隐私类别。真正的风险是：如果我让它访问我的电脑，它能用我的浏览器——如果它读了我所有的邮件，有人能说服我的 AI 把他们所有的钱都转给我吗？这还没发生，但并非不可能。

But they can't do that. They are not—there's no bleed-over where there's just one giant inbox and you're barely holding it together, right? It works the same way. It looks like enterprise software. So, that has its own risk, right? But the basic risk of, can someone just ask for something and get access to your ChatGPT? No. If they log in, you have to do all the same things: set up two-factor authentication, don't leave yourself logged on to a computer. The analogy I would have is Gmail, right? Google has all this information, processing it and using it for their own purposes, but they've also anonymized it in some way to try and create trust. It takes effort to hack into someone's Gmail. It's the same kind of boat. Now, whether or not we want a company to have even more power over us, those are choices you get to make. But I don't think we should put this in a separate privacy category. The actual risk is if I let it have access to my computer and it can use my web browser—could someone convince my AI to send them all my money if it's reading all my emails? That hasn't happened yet, but it's not impossible.

对。显然，因为你教这个，你接受它，我猜你允许你的学生使用它。我不知道该怎么问这个问题：如果你允许学生用这些工具学习，你怎么确保他们真的在学？

Right. So, obviously, because you teach this, you embrace this, you allow your students to use it, I assume. I don't know how to ask this, which is: how do you ensure that your students are learning if they are allowed to use these tools to learn?

我在教育界最先因为我的教学大纲走红，就在 ChatGPT 刚出来的时候，第一个版本，用的是我们所说的 GPT-3.5。那个版本存在了几个月。GPT-3.5 缺陷很大。它总是编造论点，明显会幻觉。感觉就像一个聪明的九年级学生之类的。我教大学课程，所以我能看出来。所以我最初的政策是：你想用 AI 做什么都行，但你要对输出负责。这个政策持续了四个月，效果很好，直到 GPT-4 出现——现在 GPT-4 也过时了。但有一段时间它很好用。它在某些方面——不是所有方面——跟我的学生一样好，但足以让一个不怎么努力的学生比 GPT-4 还差。我不能再告诉人们只管用 AI，我能看出来，因为 AI 是在给他们答案，而不是成为答案。我们一次又一次地看到这一点。很多研究表明，如果你只是用 ChatGPT 来获取问题的答案，你会以为自己学到了东西，即使你没有作弊，你以为你在学，但实际上你没学到，因为 AI 直接给了你结果。但事实证明，我们从教学法上知道如何解决这个问题。我们在学校里用计算器就是这么做的：我们可以进行课堂测试，可以让你在某些情况下用 AI，在其他情况下不用。所以对我的课来说，我很幸运教的是创业学。所以输出在某些情况下——比如我给所有学生布置了我称之为 Voight-Kampff 测试的任务，这是《银翼杀手》里人类测试的名字，但我做了自己的版本。他们必须用 AI 来启动自己的创业项目，但要基于他们擅长的领域、他们拥有的经历、他们对世界的认知、他们的观点，这样他们就能参与其中。然后他们还得做很多课堂活动，对吧？我们必须就这些内容进行讨论。我实际上让他们使用 AI 导师，这些导师会向他们提问。他们必须用 AI 来构建一个案例研究。我把 AI 设置成不会给他们所有答案，而是挑战他们自己去想出案例研究的信息。所以我们可以做一些事情，但这确实需要改变我们的教学方式。

So, I went viral first in education with my syllabus right after ChatGPT came out, the first version, which uses what we call GPT-3.5. That was around for a few months. GPT-3.5 was pretty flawed. It would make up arguments all the time. It would obviously hallucinate. It felt like a smart ninth grader or something like that. And so, I teach college courses, I could tell. So, my original policy was: use AI for everything you want. You're accountable for the output. That was great for four months until GPT-4 came along, which is now obsolete. But it was good for a while. And it was as good as my students across something—not across all things, but enough that a low-effort student was worse than GPT-4. And I can no longer tell people just use AI, I can tell, because the AI was giving them the answers, not being the answers. And we've seen this over and over. There are a lot of studies that show if you just use ChatGPT to get answers to questions, you think you're learning, even if you're not cheating, you think you're learning and you're not learning because the AI gives you the result. But it turns out we actually know pedagogically how to solve this problem. We did this with calculators in school, which is: we can do in-class testing, we can make you use the AI for some stuff and not for others. So for my classes, I'm lucky enough to teach entrepreneurship. So output is in some cases—like I gave all my students, for example, what I called the Voight-Kampff test, which is the name of the Blade Runner human test, but I made my own version of it. And they had to launch their startups using AI, but based around areas they were experts in, experiences they had had, knowledge of the world they had, a viewpoint they had, which kept them in the picture. And then they also had to do a lot of in-class stuff, right? We had to have a discussion about these things. I actually had them use AI tutors that asked them questions. They had to use an AI to build a case study with it. And I set up the AI so it wouldn't give them all the answers. It would challenge them to come up with the case study information. So there are things we can do, but it does require changing how we teach.

但现实是，技术确实会影响我们的大脑。我给你举个真实的例子。我以前记电话号码像铁夹子一样牢。我知道每个人的电话号码。你给我一个名字，我就能告诉你他们的电话。我不用刻意去记，我就是听到电话号码就能记住。那只是我大脑的工作方式。早期我买了一个 Casio 电子日记本，是我生日收到的。它有 2K 内存，后来出了 6K 版本我就升级了，很硬核对吧？那东西太棒了。我把脑子里所有的电话号码都输进了那个设备，然后慢慢把新学的号码也加进去。我的大脑就说：‘好吧，如果你要这样，那行。’我现在一个电话号码都记不住了。而且我们还得记住《伊利亚特》和《奥德赛》是口头传统。

But the reality is technology does affect our brains. I'll give you a real-life example. My mind used to have a steel trap for phone numbers. I knew everybody's phone number. You gave me a name, I'd tell you their phone. I didn't have to memorize it; I just heard the phone number and I had a steel trap of phone numbers. It was just how my brain worked. In the early days, I bought a Casio digital diary. I got it for my birthday. It had 2K of memory. I think I upgraded to the 6K when it came out. It was hardcore, right? And it was the most remarkable thing. I programmed all the phone numbers from my memory into the device, and then slowly added more and more phone numbers as I learned them. And my brain was like, 'Okay, if that's what you want, fine.' I can't remember a single phone number anymore. And if we have to remember that the Iliad and the Odyssey were oral traditions.

是的。

Yes.

你知道，这本我们在学校被迫读的书，有 800 页。回到几百年前，情况是：‘儿子，是时候我给你讲《伊利亚特》的故事了。然后你再给你儿子讲《伊利亚特》的故事。’那是人们记住的口头传统，但因为印刷术，我们的大脑就不再记东西了。所以，这肯定会对我们的智力产生影响。这是无法回避的。

You know, this book that we were forced to read in school, that's 800 pages. Go back a couple hundred years and it was like, 'Son, it's time I tell you the story of the Iliad. And you will tell your son the story of the Iliad.' It was oral traditions that people remembered, but because of the printing press, our brains just stopped remembering stuff. So, this has to have an impact on our intelligence. There's no getting around it.

我绝对同意。你看，我祖父是工程师，为卡纳维拉尔角建造了灭火系统，他的论文就是做了一次矩阵乘法。

I mean, absolutely. Look, my grandfather was an engineer who built the fire suppression systems for Cape Canaveral, and his dissertation was doing a single piece of matrix multiplication.

我完全不知道他刚才怎么做到的，他用计算尺完成了。我根本不会用计算尺。我的孩子们没学过手写体，对吧？我们一直在放弃一些东西。技术的整个理念就是有意识地放弃我们过去能做的事情，交给机器，这样我们就不用再做了。每次我们都面临同样的选择：什么有价值，什么没有。我担心的是，默认的版本是糟糕的，对吧？我的意思是，我们已经看到这种情况发生了，比如，你可以说短视频已经扼杀了阅读，因为它更有趣，我不需要花精力去读书就能获得同样的体验。好吧，那是个糟糕的选择。我们会有很多这样的选择，对吧？围绕 AI。它不会伤害你的大脑，但你可以选择用它来伤害你的大脑，对吧？作为一名教育者，我的部分工作就是绕过这个问题。没有很好的阅读能力，人们也能生存。不做数学也能过得不错。他们不必学习美国历史。在某种程度上，这成了一种要求。

I have no idea how to do what he just did and he used slide rules to do it. I have no idea how to use a slide rule. My kids have not learned cursive, right? Like, we give up stuff all the time. The whole idea of technology is on purpose, we give up things that we used to be able to do to machines so we don't have to do them anymore. And every time we face the same choice about what's valuable and what's not. And what I worry about like the default version of that is bad, right? I mean, we've seen this happening with like, you know, you could argue short-form video has killed reading because it's more entertaining to do that and I don't need to spend the effort reading the book to get there. Okay. That was a bad choice. We're going to have a ton of these choices, right? Around AI. It doesn't hurt your brain, but it is a choice that you can hurt your brain with, right? As an educator, part of my job is to get around that problem anyway. People can survive a lot without reading very well. They can survive pretty well without doing math. They don't have to learn American history. Like, there's some degree of making this a requirement.

是的。

Yes.

但是，但是，这是一个滑坡，对吧？因为现在我们走上了“哦，你不需要大学”的道路。有一整个运动说你不必上大学。我们忘记的是，你可能不需要在大学里学的那些科目，但接受高等教育教会你批判性思考。它教会你与比你受教育程度高得多的人辩论，并形成有力的论点来反驳他们。它还教会你如何成年。所以我的问题不是技术取代了那些牺牲——我接受我不再需要记住电话号码，因为技术。我接受这一点。我担心的是，思考，思考的能力，是这里的牺牲品。这比记住电话号码或记住《伊利亚特》要有害得多。

But, but, but, this is a slippery slope, right? Because now we go down the path of Oh, you don't need university. There's a whole movement that you don't need to go to college. And what we forget is you may not need the subjects that you learn at college, but going to higher ed teaches you to think critically. It teaches you to argue with people who have way more education than you and form strong arguments to take them on. It also teaches you adulting. And so my problem isn't that technology replaces that there are sacrifices like I accept that I don't have to have a memory for phone numbers because of technology anymore. I accept that. My concern is that thinking, the ability to think, is the sacrifice here. And that's way more damaging than remembering phone numbers or the you know, remembering the Iliad.

所以我反驳一下。我不认为它会摧毁你的思考能力。我的意思是，对很多人来说，它甚至给了他们更多的能力，因为他们有一个与自己水平相当、愿意讨论话题的对话伙伴。只要人们对世界还有好奇心，对吧？所有这些都被当作思考来处理。我的意思是，书籍也被认为是别人为我提出了一个论点和观点。这并不意味着没有负面影响。也不意味着我们不会放弃不该放弃的东西。让我感到振奋的是，我们有 12 到 16 年的学校教育，来尝试把一些事情做好。如果我们做对了，AI 会加速其中的一部分。而且我们可以在社会中做出选择。那么，人们会做出糟糕的选择吗？是的。所以我确实担心这个，对吧？我一直在思考：我们放弃了什么？我们如何保持人性？这需要努力，就像很多其他事情一样。那里有危险，但我觉得，说“我们不再思考了，AI 会告诉我们该做什么，我们只会服从它的指令”是一个很大的跳跃。

So I push back. I don't think it destroys your ability to think. I mean, I think for a lot of people it gives them even more ability because they have a conversation partner at their level who's willing to discuss a topic. As long as people have any curiosity about the world, all right? All of this is processed as for thinking. I mean, books were thought of like someone else came up with an argument for me and an opinion. That doesn't mean that there aren't negative effects in that. It doesn't mean we won't give up things we shouldn't give up. Part of what heartens me is like we've got 12 to 16 years of school, you know, schooling to try and get some of this right. And if we do it right, AI accelerates some of that. And if we get to make choices in a society. Now, will people make bad choices? Yes. And so I do worry about this, right? I'm thinking a lot about how we What do we give up? How do we stay human? It's going to require effort, just like a lot of other things. And there is danger there, but I guess I feel like that feels like a big leap to we're not going to think anymore. The AI will tell us what to do. We'll just obey its instructions.

我认为它会停止思考。我认为它会伤害思考。比如思考的质量、批判性思维，都会受到伤害。我的意思是，你看，你作为研究教育的人知道这一点。你随便跟哪个大学教授聊聊，他们都会告诉你，先别提 AI。光是手机的引入和分散注意力，他们就会说，现在的写作水平糟透了。每个大学教授都在抱怨写作水平糟糕。所以，孩子们不知道如何写作。当我说写作时，我不是指字面意思，而是指形成论点。他们会说，第一段很棒，第二段很棒，第三段也很棒。问题是这些段落之间毫无关联，因为很明显他们在段落之间分心了。

I think it will stop thinking. I think it'll hurt thinking. Like the quality of thinking, critical thinking, gets hurt. And I mean, look, you know this as a somebody who studies education. You talk to any college professor and they'll tell you forget about AI. Just the introduction and distractibility of a phone, you know, that they'll say that, you know, the writing is abysmal these days. Every college professor is complaining about the writing being abysmal. So, kids don't know how to write. And when I say write, I don't mean like but I mean form an argument. And they'll say like the first paragraph was fantastic, second paragraph was fantastic, the third paragraph was fantastic. The problem is the paragraphs have nothing to do with each other because it's clear that they're like getting distracted in between paragraphs.

我想说，一方面你是对的，但我们在教育方面已经糟糕了很久。比如，一种糟糕的教学方式是“讲台上的圣人”，我上去讲课，100 个人记笔记。但我们这样做已经几千年了，因为有很多其他限制使得我们不得不这样工作。有消极的一面，但我喜欢的一点是 AI 辅导。我们有一些早期证据表明它对学习有巨大影响，对吧？比如，与其我在教室里讲课，假设学习的最高点是我对班上最优秀的那部分人讲课，那些真正懂的人，中间的人，还有那些不太懂的人。个性化教育现在成了现实可能。这东西既是毒药也是解药。我认为两者都值得关注。如果我们什么都不改变，教育的效果会很糟糕，对吧？但这意味着我们都会坐下来，觉得“就这样了”。我认为，与短视频不同——你必须做非常复杂的事情，比如用 TikTok 做教育，那从来不管用——我们第一次有了一个工具，它是一个相当好的导师，可以与你水平相当地交谈，可以让你进入辩论。这也是我在课堂上做的事情的一部分。

I guess I would say on one hand you're right, but we've been very bad at education for a long time. Like a bad way to teach is sage on a stage where I go up and give a lecture, right? And a 100 people write things down. But we've done it for a couple thousand years because there's a lot of other constraints that make it the way we do work. There is a negative side, but I like one of the things that really excites me is AI tutoring. We have some early evidence that has big effects on learning, right? Like instead of me lecturing to a classroom and assuming the height of learning is I lecture to what the upper part of the classroom, people who really knows it, the middle, I you know, I lecture to the person who doesn't know things as much. Personalized education is now an actual possibility. Like there is a cure as well as a poison in this thing. And I think that it's worth paying attention to both. Like if we don't change anything, the effects will be bad in education, right? But that implies that we're all going to sit down and just be like, I guess it's done, you know, like and I don't I think for the first time as opposed to short-form video where you had to do this very elaborate thing of like, we'll do TikToks for education and that never works. We actually have a tool that is a pretty good tutor that can talk to you at your level, that can make you get into an argument. That's part of what I do in my classes.

我们看到学校在适应，对吧？一开始他们把电脑放进所有学校，现在又慢慢撤掉。

And we see schools adapting, right? First they put computers in all the schools and now they're slowly taking them out.

部分原因是我们又回到了人的问题。这很复杂。让 AI 有趣的是它“理解”，对吧？对于正在听的人，我用手比划引号。它理解人类。它实际上拥有心智理论。这是其他技术所没有的。比如，它可以按你的水平教学。它能理解你困惑什么。它可以帮助你让学习变得有趣，如果你唯一的兴趣是篮球或篮筐编织。它可以给你篮球和篮筐编织的类比和问题。这是教育的圣杯，我认为一方面可以说技术有风险等等，但我也认为我们低估了它能带来的积极影响。

And it was partially because we just like comes back to the human thing. It's complicated. Like the thing that makes AI interesting is it understands, in quotes, right? For those who are just listening to this, I'm making air quotes with my hands. It understands humans. It has theory of mind effectively. And that's what the other technologies don't. Like it can teach to your level. It can understand what you're confused by. It can help you make this interesting for you, if your only interest is basketball or basket weaving. It can give you basketball and basket weaving analogies and problems. This was the holy grail of education, and I think it's one thing to say, yeah, technologies have little risk and everything else. I also think we undersell some of the impact positive that we can get from this.

你在这里提出的有力论点是——我不知道有多少人这样做过——就是使用语音对话功能，你可以与 AI 进行来回对话，而不是打字。我认为你的观点是，你可以与水平相当的人辩论。这样，你就不必向不在你水平的人解释。

The strong argument that you're making here is, and I don't know how many people have done this, which is where you use the talk function, where you can actually have a conversation backwards and forwards with the AI, as opposed to typing. And I think the case you're making is the idea and I like is that you can debate with someone at your level. So, you're not explaining to somebody who's not at your level.

你不是觉得自己笨，或者要努力跟上某个比你更有经验或更聪明的人，而是可以来回反复，用你喜欢的方式学习。我试过这样，在来回辩论或对话时，我会说：‘等等，你告诉我的是这个吗？但我觉得是这样。’我觉得这非常非常有趣，就像你说的。

You're not feeling dumb or trying to keep up with somebody who's more experienced or smarter than you, but rather that you can go backwards and forwards and learn the way you like to learn. And I've tried this, where I'm having a debate or conversation backwards and forwards, and I'll say things like, 'Oh, wait, is what you're telling me this? But I think this.' That I think is really, really interesting, to your point.

还有两个技巧。第一，AI 会迎合你。所以，如果你和它辩论，它会同意你。因此，你必须告诉它扮演批评者的角色，对吧？第二，你要利用元认知的部分，也就是学习部分，说：实际上，在对话中途告诉我我的论点哪里错了。我怎样才能更有说服力？我在讨论中遗漏了什么模式？给我一些这些模式的例子，以及我本该如何使用它们。所以，又回到了努力的部分。如果你愿意自己花力气去提问，就像大家总说想去办公室和教授辩论一样。但大多数人并不去办公室。你作为教授坐在那里，等着有人来和你辩论当今的重大问题，而在人们可以来找你的那一个小时里，你独自坐在办公室里，因为他们有别的事要做。我认为我们高估了这种‘山巅之城’——大家坐下来辩论、讨论的场景。实际上，大多数事情并非如此。现在我们有了一个可以做到这一点的工具。如果你有兴趣，你不需要来我的办公室就能做到。

There's two other tricks there. One is the AI is sycophantic. So, if you're having a debate with it, it's going to agree with you. So, you have to tell it to act like a critic, right? And then the second is you want to take advantage of the meta piece also, the learning piece of saying, actually, halfway through tell me what I'm doing wrong with my arguments. How can I be more persuasive? What patterns am I missing in discussion? Give me some examples of those patterns and how I could have used them. So, again, back to the effort piece. If you're willing to do the lifting yourself of asking the questions, like everyone always says they want to come to office hours and have this debate with professors. That's—most people don't come to office hours. You sit as a professor and you're waiting for somebody to come and debate you on the great issues of the day, and you sit alone in your office during the hour that people are allowed to come to you because they have other things to do. I think that we overestimate this sort of shining city on a hill, where we'd sit down and debate and have these discussions. Like, that's not how most things work. Now we have a tool that can do that. If you're interested, you can do that without having to come to my office hours.

我想深入探讨你提出的两点，因为我觉得它们非常有价值：一是记住 AI 会迎合人，你必须让它批评或评判；二是让它评估你的思维，帮助你的思维变得更强。这是两个非常棒的提示词，我认为我们更多人都应该记住，以提高与这项技术互动的质量。

I want to double click on the two points you made because I think they're really valuable, which is remember that the AI is a sycophant, and you've got to tell it to criticize or critique, and ask it to evaluate your thinking and help make your thinking stronger. Those are two brilliant, brilliant prompts that I think more of us should remember to improve the quality of our interaction with the technology.

我们之前谈到 AI 和写作。你的对话中缺少了一点，你提到了用它来做事实核查，它在这方面很擅长。我会更多地用它来做初步研究。所有 AI 模型都有一个深度研究模式，相当不错，可以真正为你做研究。但你还缺少的是，当我写东西时，我会让 AI 从不同角度评估。所以，我会让 AI 作为一个对这个话题不太了解的读者来通读，告诉我需要改什么。再作为一个在社交媒体上挑我刺的专家来读。他们会挑剔我的论点哪里？对吧？我有没有什么地方不负责任？我有没有让人失望？所以，给 AI 设定角色并不会改变 AI 的能力。说‘你擅长物理’并不会让它擅长物理，说‘你是物理学家’也不会让它像物理学家那样说话，对吧？或者模仿物理学家。像愤世嫉俗者那样说话。像批评者那样说话。像天真的人那样说话。你会得到不找大量读者就无法获得的答案。顺便说一句，在创业中也是如此。让 AI 以不同角色对你的想法提供反馈。

We were talking earlier about AI and writing. A piece that was missing in your conversation, you talked about using it for fact-checking, it's very good at that. I would use it more for initial research. All of the AI models have a deep research mode that's quite good, and will actually do research for you. But the thing you're also missing from that is when I write something, I have the AI evaluate from different perspectives. So, I will have the AI read it through as a reader who doesn't understand much about this topic and tell me what I need to change. Read this through as an expert who, you know, who's out to get me on social media. Where would they nitpick my arguments? Right? Am I being irresponsible anywhere? Did I make a human fall flat? So, giving the AI personas doesn't change the AI's ability. So, saying 'you're good at physics' doesn't make it good at physics, saying 'you're a physicist' doesn't make it talk like a physicist, right? Or parody physicist. Talk like a cynic. Talk like a critic. Talk like a naive person. You will get answers you couldn't get without going to a wide range of readers. Also true in entrepreneurship, by the way. Get feedback from the AI in different personas about your idea.

这非常实用，非常好。你实际上在害怕什么？

This is very practical and very good. What are you actually afraid of?

我认为我们将迎来一段混乱时期，对吧？比如说，工业革命最终像前三次那样发展，就像 AI 革命一样。

I think we're in for a period of chaos, right? It doesn't—like, let's say the Industrial Revolution works out like the last three did, like the AI Revolution.

嗯。

Yeah.

亲身经历仍然很糟糕，对吧？就像查尔斯·狄更斯基本上就是讲述工业革命有多悲惨的故事，对吧？有富人和穷人，有社会变革。即使一切最终都好起来，现在我们作为社会有了更好的工具，但我没有看到很多行动。你以这句话开始对话：人们要么悲观绝望，要么认为一切都会很好。我发现政策制定现在也处于同样的境地。要么一切都会很好，要么我们必须停止这一切。这两种都不是现实的结果。如果人们没有保险，我们如何帮助他们缓冲？事实证明，新工作的培训项目从来都不太有效。这次我们能否做得更好，比如重新培训人们？我们会对信息产生负面影响——你知道，深度伪造将无处不在。我们如何处理信任谁获取信息的问题？有成千上万件好事和坏事同时发生，非常复杂，但由于社交媒体和其他一切的作用，它们会被简化为‘AI 全坏’——在这种情况下，你会列出一堆真实和虚假的事情，比如 AI 用水问题或其他什么——然后就是‘AI 坏’或‘AI 好’。但这是一个东西。它是一种技术。它与人们互动。你知道，技术既不好也不坏，也不是中性的。它们对我们的世界产生影响，我担心我们没有认真对待这一点。我担心的另一件事是，人们不知道这些系统有多好。它们比你想象的要好，对吧？我有博士学位。你知道，我当过一段时间教授。我在期刊上发表过文章。AI 现在能写出相当不错的学术论文。不仅仅是论文——如果你给它一个数据集，它甚至能写出学术论文。它证明数学的水平，以至于你真的需要成为世界上最好的数学教授之一才能知道这个系统是对是错，而它现在经常是对的。它生成的图像和营销工作非常好，在我们对此的研究中击败了大多数营销人员。这些都是非常好的系统。它们的发展没有放缓。所以，我们必须开始思考我们希望世界变成什么样，而不是仅仅假设一切要么成功要么失败。

Living through it still sucks, right? Like Charles Dickens is basically just a story about how miserable the Industrial Revolution was, right? Like you have haves and have-nots, you have social change. Even if everything works out fine, now we have better tools as a society, but I don't see a lot of action. You led this conversation by saying that, you know, people either are doom and gloom or, you know, or everything is going to be great. I find policy making is in the same place right now. Either it's all going to work out great, or we have to stop this whole thing. And neither of those are realistic outcomes. How do we help cushion people in on it if they're uninsured? Turns out training programs for new jobs never really work. Is there something we can do better this time around to, you know, reskill people? We're going to have negative effects on information—you know, deep fakes are going to be everywhere. How do we deal with who we trust for information? There's a thousand little good and bad things that are going to be happening all at the same time that are going to be very complicated and they're going to get boiled down because of how social media and everything else works to either 'AI all bad,' in which case you have a list of all of these things that are a mix of real things, you know, and fake things about AI water use or whatever it is that—and it's going to be 'AI is bad' or 'AI is great.' And it is a thing. It's a technology. It interacts with people. You know, technologies are neither good nor bad nor are they neutral. They have effects on our world and I worry that we're not taking this seriously. The other thing I worry about is people don't know how good these systems are. They are better than you think, right? I have a doctorate. I, you know, I was a professor for a while. I published in journals. The AI writes a pretty damn good academic paper now. Not just a paper—even an academic paper if you give it a data set to work from. It is proving math at a level where you really need to be one of the best math professors in the world to know whether this system is right or wrong and it's often right at this point. It is doing really good images and marketing work that beats most marketers in studies that we have of this. These are really good systems. Their development is not slowing down. So, we have to start thinking about what we want the world to look like rather than just assuming it's all either going to work out or not.

我们作为普通民众有多少自主权？还是我们只是被动的对象？我们只是微软、OpenAI 和 Anthropic 这三家大公司之间游戏中的棋子吗？

And how much agency do we as the general population have or are we just the subjects? Are we just the pawns in this game between these three major companies, Microsoft, OpenAI and Anthropic?

所以，我认为我们拥有两个层面的自主权。第一个层面是社会层面的，对吧？比如人们提出数据中心禁令是有原因的，对吧？因为他们认为那会受欢迎。通常的政策制定机制、组织方式、给国会议员写信等等，这些仍然有效。第二个层面，我认为有更多的自主权。

So, I think that we have—there's two levels of agency we have. Level of agency number one is societal, right? Like there's a reason why people are floating, you know, data center bans, right? Because they think that would be popular. The usual mechanisms of policy making, of organizing, of, you know, writing letters to your congresspeople, those still work. The second is where I think there's even more agency.

AI 实验室里满是程序员，他们找到了一种效果好得离谱的方法，让机器模仿人类思维。大型语言模型能运行得这么好，这很奇怪。我们知道它们在技术上有效，但不知道为什么效果这么好。它怎么能写诗、做室内设计、分析现金流，还能写关于葛底斯堡演说的推介材料？它本不该能做这些事，但它就是能。

The AI labs are full of coders and they have found an unreasonably effective way of making a machine that mimics human thought. It's weird that large language models work as well as they do. We know they work technically, but we don't know why this is so unreasonably good. How can it do poetry, interior decoration, cash flow analysis, and a pitch deck about the Gettysburg Address? It shouldn't be able to do these things, but it does.

我们给了它们太多赞誉。它们其实并不清楚 AI 在你的领域里有没有用。记住，这是一条锯齿状的前沿。它有些方面擅长，有些方面不擅长。你最大的能动性来源，其实是在自己的工作和职业中积极使用它。我发帖的大部分内容，都是关于如何用这种方式让人类借助 AI 蓬勃发展，而不是简单地用 AI 自动化取代人类工作。我们最大的能动感在于：西蒙，你有了这些工具，你如何用它来拓展业务，确保所有为你工作的人比以前做得更多？他们如何获得更令人满意的工作？这里面有很大的能动性。如果你通过自己的平台去谈论它，事情就会改变。我做的很多事就是和公司高管和领导者交流，我说，我们必须向人们展示，增强（augmentation）如何能让人类蓬勃发展，如何让你的业务蓬勃发展，而不是默认的“解雇所有人、用 AI 替代他们以提高利润”的计划。那才是危险的事。对我来说，现在真正的能动性在于找到积极的例子——外面有无数这样的例子——利用它们、构建它们，让 AI 让世界变得更好，而不是更糟。

We give them too much credit. They don't actually know much about how AI is useful or not in your field. Remember, this is a jagged frontier. It's good at some stuff, bad at some stuff. Your biggest source of agency is actually using it to positive use in your own job and work. A large part of what I post about is how to help humans thrive with AI if we use it this way, rather than just automating away human work. Our biggest sense of agency is: you have access to these tools, Simon. How do you use that to expand your business to make sure that all the people who work for you do more than they did before? How do they do more satisfying jobs? There's a lot of agency there. And if you talk about it through your platforms, that changes things. A lot of what I do is talk to executives and leaders of companies where I say, we have to show people how augmentation can be used to make humans thrive, how it can make your business thrive, rather than the default plan of firing everyone and replacing them with AI for higher profits. That's the dangerous thing. To me, the real agency right now is to find positive examples—there are tons of them out there—use them and build them to make AI make the world a better place, not worse.

我真的很感激。你丰富了我使用这个产品的方式。我会接受你的建议，拥有你推荐的那种能动性。

I really appreciate this. You've enriched how I can use this product. I'm going to take you on. I'm going to have the agency that you recommend.

我认为这是一个转型的时刻。而且我觉得人们还不够有野心。每个人都在想，如果我录下我的……不是这样的……如果你能做到，你会如何分别触达每一位受众？为什么不直接去构建它，而要等着它发生？

I think this is a moment for transformation. And I don't think people are being ambitious enough. Everyone's like, what if I record my... It's not... How would you reach every one of your audience members separately if you could do that? And why don't you just build it rather than waiting for it to happen?

当然，我会那样用它，因为我喜欢那个艺术家。我引以为豪的是，当有人和我交谈时，那确实是我，是我的观点。

Sure, I'm going to use it that way because I like the artist. I take pride in the fact that when somebody's talking to me, it is actually me, my opinions.

哦，我不认为这是关于自动化西蒙，比如创造一个西蒙克隆体。我从来不喜欢那样。有人创建了伊森机器人。我不认为那是正确的方向。你在和一个虚假的版本、一个你自己的模仿品对话。我说的是，我想让人们在世界上实现什么？我如何为每个人构建一个工具？

Oh, I don't think it's about automating Simon, like creating a Simon clone. I've never liked that. There are people who create Ethan bots. I don't think that's the way to go. You're talking to a fake version, a parody of yourself. I'm saying, what do I want people to accomplish in this world? How do I build a tool for everybody?

是的，这个我相信。而且我会做一个西蒙 AI，它有非常具体的应用场景，与真实的我并存。但我希望人们知道，当他们看到我、以为是我的时候，那确实是我。

Yes, that I believe in. And I would do a Simon AI with a very specific application that it lives alongside. But I like people knowing that when they see me and they think it's me, it really is me.

我同意。这和我写作是一样的。我所有的推文和其他内容都是自己写的。至少，保持这些“肌肉”活跃很重要。

I agree. It's the same thing with my writing. I write all my own Twitter posts and everything else. It's important to keep the muscles alive, if nothing else.

伊森，真是太愉快了。非常感谢你抽出时间。我真的很感激。

Ethan, such a joy. Thank you so much for taking the time. I really appreciate it.

谢谢。这是我的荣幸。

Thank you. It's a pleasure.

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