5条提升AI写作效果的规则

如果你用AI来写一篇评论文章，那会不会让你的想法贬值？

If you use AI to write an op ed, does that devalue your thoughts?

信不信由你，这可能是过去几天AI社区讨论得最多的问题。

Believe it or not, that's been maybe the biggest question the AI community has been discussing for the last couple of days.

当金融界最有影响力的声音之一在《华尔街日报》发表了一篇批评财政部长和当前货币政策的评论文章时，他可能没想到大家讨论的重点会是他怎么写这篇文章的。

When one of finance's biggest voices dropped an op ed in The Wall Street Journal critiquing the treasury secretary and current monetary policy, he probably didn't think that the big discourse was going to be about how he wrote the op ed.

但事实就是这样，因为那篇文章，你看，很明显是AI写的。

And yet, that's what it was because the piece, you see, was very, very clearly written by AI.

大家的反应就像这是什么大丑闻一样。

People reacted like it was some big scandal.

但后来，那位著名的金融家和《华尔街日报》的评论编辑都说，是的，他当然用了AI。

Except then both the famous financier and the opinion editor at the Wall Street Journal said, yeah, of course he used AI.

所以，如果我们现在生活在一个用AI写作完全正常的时代，我们怎么能让它真正写得好呢？

So if we now live in a world where AI writing is totally normal, how can we make it actually good?

《AI每日简报》是一个每日播客和视频节目，讨论AI领域最重要的新闻和话题。

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

好了，朋友们。

Alright, friends.

在我们深入之前，先快速说几个通知。

Quick announcements before we dive in.

首先，感谢今天的赞助商：KPMG、Blitzy、Section 和 Hyperagent。

First of all, thank you to today's sponsors KPMG, Blitzy, Section, and Hyperagent.

想要收听无广告版本，请访问 patreon.com/aideallybrief。

To get an ad free version of the show, go to patreon.com/aideallybrief.

想了解更多关于赞助节目或节目的其他信息，请访问 aideallybrief.ai。

And to learn more about sponsoring the show or really to find out anything else about the show, go to aideallybrief.ai.

那里有赞助商信息。

There's sponsor information there.

还有我们正在进行的教育项目的信息，当然，还有每集的完整介绍，方便你查找和分享最感兴趣的部分。

There's information about education programs we're doing, and of course, a full breakdown of every episode that will make it much easier to reference and share the specific parts that are most interesting to you.

再次提醒，所有这些都可以在 aidailybrief.ai 上找到。

Again, you can find it all on aidailybrief.ai.

信不信由你，过去几天关于AI最大的讨论之一就是AI写作，特别是它的合法性、相关的披露要求，以及这些东西可能会怎么变。

Believe it or not, one of the biggest conversations around AI for the last couple of days has been around AI writing, specifically the legitimacy of it, disclosures around it, and how all of those things might be changing.

具体背景是投资者斯坦利·德鲁肯米勒在《华尔街日报》上发表的一篇专栏文章。

The specific context for this was an op ed published in The Wall Street Journal by investor Stanley Druckenmiller.

那篇名为《让债券市场说话》的专栏文章，对财政部长斯科特·贝森特进行了相当激烈的批评。

The op ed called Let the Bond Market Speak was a fairly full throated critique of Treasury Secretary Scott Besant.

我在这里不打算深入探讨德鲁肯米勒的具体论点，因为那是另一个完全不同的话题，但值得注意的有两点：一是德鲁肯米勒在金融界有着传奇地位，他管理的对冲基金在1981年到2010年间没有一年亏损，这在之前和之后都是绝无仅有的；二是这篇文章关注的不是次要或边缘话题，而是直击当前宏观经济政策的核心。

Now I'm not going to get into the substance of Drunkenmiller's specific argument as that's a totally different show than this one, but the things that are worth noting are one, that Druckenmiller has a legendary status among finance folks, among other achievements running a hedge fund that had zero losing years between 1981 and 2010, which is completely unprecedented before or afterwards, and two, that the focus of this piece is not some secondary or tertiary topic, but cuts to the quick of macroeconomic policy right now.

换句话说，从内容实质来看，这篇专栏文章很重要。

In other words, the op ed, from a substance perspective at least, matters.

然而，很快人们讨论的焦点就不在内容本身了。

And yet extremely quickly, the substance was not what people were talking about.

大家关注的是，这篇文章里充斥着各种现在常见的AI写作痕迹。

Instead it was the fact that the piece is replete with just about every egregious AI ism that exists today.

比如，里面到处都是“不是这个，而是那个”的句式。

It is, for example, full of it's not this, it's that.

如果三十年期国债必须以5.5%的收益率才能卖出去，那这不是危机，而是一张账单。

If the thirty year must trade at 5.5% to clear, that isn't a crisis, it's an invoice.

这不是流动性管理，而是价格管理，而且这个错误比40亿美元所暗示的要大得多。

This wasn't liquidity management, it was price management, and a mistake far larger than 4,000,000,000 suggests.

像Pangram这样的AI检测工具很快就识别出这篇文章有100%的可能性是AI生成的，这让一些人很不高兴。

AI detectors like Pangram quickly identified the chance that it was AI generated at 100%, and some folks were not happy.

JB在X上写道：我不明白人们是怎么看出这是AI写的，我是说除了用Pangram检测之外。

JB on X writes: I don't understand how people sniff this out as AI, I mean apart from running it through Pangram.

德鲁肯米勒为什么要署自己的名在一篇AI垃圾文章上？

Why would Druckenmiller put his name on AI slop?

他这么做没问题，即使这些据说不是他自己的话？

He's fine with doing that even though these allegedly aren't his words?

尼尔·塞巴特写道：为什么《华尔街日报》没有披露德鲁肯米勒用AI写了这篇文章？

Neil Seibart writes: Why did The Wall Street Journal not disclose that Druchenmiller used AI to write his piece?

这种披露应该是强制性的！

Such disclosures should be mandatory!

知名市场评论员杰西·利弗莫尔写道：把AI生成的内容当作自己的作品来展示，就是抄袭，没有商量余地。

Widely followed markets commentator Jesse Livermore writes: Presenting AI slop as if it were your own writing is plagiarism, full stop.

你是在为那些不是你写的词句邀功。

You're taking credit for words and phrases you did not write.

所以，德鲁肯米勒被揭穿后，应该会不好意思地承认确实用了AI，然后道歉，说下次会做得更好，对吧？

So Druckenmiller, having been caught out, admitted sheepishly that yes indeed he had used AI and apologized and said he'd do better next time, right?

错了。

Wrong.

他的回应反而是：“兄弟，我当然用了AI。”

His response instead was Bro, of course I used AI.

除了那句“我当然用了AI”之外，德鲁肯米勒还补充说：“这就是我从英语专业转到经济学专业的原因。”

In addition to the literal line of course I used AI, Druchen Miller added: There's a reason I moved from an English major to being an Economics major.

我并不为此感到难为情。

I'm not embarrassed by it.

我现在所有东西都用AI来写，就像做数学题时用计算器一样。

I write everything using AI now for the same reason I use a calculator when I do math problems.

华尔街观点编辑保罗·贾戈特支持德鲁肯米勒，他说：AI是现代生活的一个事实。

Wall Street opinion editor Paul Jaigault backed Druckenmiller up with Jaigault saying: AI is a fact of modern life.

人们会用AI来辅助工作和写作，包括研究、检查语法、编辑等等。

People will use it to assist their work and their writing, including with research, checking grammar, editing and more.

对我们来说，问题在于我们发表的投稿是否反映了作者原创的论点，以及作者是否有立场和可信度来提出这个论点。

The question for us is whether what we publish from contributors reflects an author's original argument and if the author has the standing and credibility to make it.

就斯坦·德鲁肯米勒而言，我们和他有多年的关系，没有人能怀疑他的专栏文章是他真实的观点。

In Stan Druckenmiller's case, we have had a relationship with him for many years and no one can doubt that his op ed is his genuine opinion.

所以很明显，这家报纸的专栏立场是，重要的是思想的真诚，而不是用来表达思想的词语的独特人文味道。

So clear as day here, the journal's op ed position is that what matters is the sincerity of the ideas, not the unique human flavor of the words used to express them.

从那里，对话很快转移了，虽然一些批评者可能声音很大，但也有很多人指出，这与长期以来知名人物撰写专栏的方式并没有太大不同。

And from there, the conversation quickly moved and while some of the critiquers might have been pretty loud, there were also a lot of folks pointing out this wasn't all that different from how leading figures had done op eds for quite some time.

彭博社的乔·韦森塔尔写道：在AI之前，有很多知名人士以自己名义发表专栏，但完全由下属代写，我认为这并没有引起太多争议。

Bloomberg's Joe Weisenthal wrote: Prior to AI, there were plenty of high profile people publishing op eds under their own name that were entirely written by some underling, and I don't think there was much controversy about it.

为了证实这一点，记者莎伦·戈德曼写道：从2015年到2020年，我为顶级高管撰写了数十篇专栏文章，发表在科技或行业出版物上。

Validating that, journalist Sharon Goldman writes: From 2015 to 2020, I wrote dozens of op eds for top executives that ran in tech or trade publications.

这是很常见的做法，我从来没有收到过署名，也不想要署名。

It was common practice and I never received or wanted a bylinecredit.

对于一个需要做很多种工作来付账单的自由撰稿人来说，这算是一份不错的活儿。

It was a decent gig for a freelance writer who had to do lots of types of work to pay the bills.

我通常和高管谈一次话，再加上一些笔记。

I usually had one conversation with the executive as well as some notes.

其他人也认同德鲁肯米勒的观点，认为这实际上只是一个工具。

Others picked up on Druckenmiller's argument that effectively this was just a tool.

对于德鲁肯米勒说当然用了AI，投资者斯科特·菲利普斯写道：好！

Reflecting on Druckenmiller saying that of course he used AI, investor Scott Phillips wrote: Good!

你能想象因为用打字机、电脑或互联网做研究而不是用百科全书而道歉吗？

Can you imagine having to apologize for using a typewriter or a computer or the Internet for research rather than encyclopedias?

AI已经存在了，我们应该问为什么人们不用它，而不是批评他们用。

AI is here to stay, we should be asking why people aren't using it not criticizing them for doing so.

当All In的杰森·卡利卡尼斯写道：如果你用AI写公开文章，你应该在第一句话中披露这一点。

When all in's Jason Kallikanis wrote: If you write a public piece with AI, you should disclose that in the first sentence.

这篇文章是AI帮我写的。

AI wrote this for me.

公众人物发表AI写的文章，不在第一句明确披露，是不可原谅的。

Unforgivable for a public figure to publish an AI written piece without a clear disclosure in the first sentence.

这完全破坏了发表观点文章的意义，因为我们分不清哪些是你的想法，哪些是那个神奇黑匣子的想法。

Undermines the entire premise of publishing a thought piece because we can't tell what's yours and what's the magic black box's thoughts.

然而，他的搭档查马斯·帕里哈皮蒂亚反驳说：这是一种愚蠢的新式道德作秀。

However, his co host Chamath Palihapitiya bit back: This is a dumb new form of virtue signaling.

你在X上随便发表意见时，会披露你读过的每一篇影响你观点的文章吗？

Do you disclose every article you've ever read that gives you an opinion when you spout off an X?

当然不会！

No!

AI的工作原理也是这样，他不如在写的每个字里都声明一下整个互联网。

This is how AI also works, he might as well disclaim the entire internet in every written word.

如果他署名了，那就是他的观点。

If he puts his name behind it, it's his opinion.

另外，查马斯还发帖说：如果斯坦·德鲁肯米勒都不觉得当个肉替身丢人，你也不该觉得丢人。

Separately, Chamath also posted: If Stan Druckenmiller isn't embarrassed to be a meat proxy, you shouldn't be either.

这就像火柴发明之后，还在庆祝费劲钻木取火的辛苦过程。

It's like after matches was invented, still celebrating the arduous time to rub two sticks together.

太蠢了！

Dumb!

更简单点说，投资人安德鲁·斯坦沃德写道：人们因为别人用AI写作而生气，这真是老古董思维。

Even simpler, investor Andrew Steinwald wrote: People getting mad at others for writing with AI is boomercoded.

大概两年后，没人会在乎你用不用AI。

In like two years, no one will care if you use AI or not.

阿里亚德·德尼兹指出了价值转移的方向，写道：谁在乎特拉克和米勒是否用AI来帮助表达想法？

Pointing out where value is shifting, Ariad Deniz writes: Who cares if Truck and Miller used AI to help articulate his thoughts?

AI只是一个工具。

AI is a tool.

如果它能帮助投资者把几十年的经验、判断力和模式识别能力更清晰地表达出来，那是件好事。

If it helps an investor take decades of experience, judgment, and pattern recognition and articulate those thoughts more clearly, that's a good thing.

价值在于思考。

The value is in thinking.

你得习惯这一点！

Get used to it!

但对其他人来说，这个话题要复杂得多。

But for others this was a much more nuanced conversation.

Spectrum Markets的Brent Donnelly写道：关于AI写作的争论归结为一个问题：写作是艺术，还是像代码一样的功能性沟通工具？

Spectrum Market's Brent Donnelly wrote: The AI writing debate comes down to one question: Is writing art or is it a functional communication tool like code?

这就像问汽车是用来从A点到B点的，还是用来在好时候找乐子的。

This is like asking if a car is to get from A to B or for excitement in good times.

这纯粹是个人看法。

It's just a personal thing.

对我来说，写作，即使是非虚构作品，也是艺术，而汽车是用来玩的。

To me writing, even non fiction, is art and cars are for fun.

但很多人不同意，没有标准答案。

But many don't agree, there's no right answer.

大卫·麦克马洪写道：这里确实存在合理的争论，德鲁肯米勒是个聪明人。

David McMahon wrote: There's a legitimate debate here and Druckenmiller is a smart guy.

我毫不怀疑那篇AI写的社论反映了他真实的想法。

I have no doubt the AI written editorial reflected his actual thinking.

但太多人把AI当作严肃思考的替代品了。

But too many people are using AI as a substitute for serious thinking.

看看这个网站上那些回复狂人，到处用AI生成垃圾内容刷屏，简直泛滥成灾。

Just look at the epidemic of reply guys on this site flooding posts with AI generated garbage.

这种用法只配被嘲笑。

That type of use deserves nothing but mockery.

再深入一点，有人认为问题不在于AI写作本身，而在于AI写社论。Dirty Texas Hedge发帖说：Drunk和Jiggo说的在描述上百分之百正确。

Going deeper on the idea that part of the problem was not AI writing in general but AI writing for op eds, dirty Texas Hedge posted: What Drunk and Jiggo say is 100% true descriptively.

问题不在于他们错了，而在于这种做法表达了对社论形式和读者的蔑视。

The problem is not that they're wrong, the problem is that it represents an expression of contempt of the format of an op ed and of its audience.

还有人开始探讨这里是否有差异，认为AI写作可能在某些方面有用，但在其他方面不行。

And others started to reach for whether there were some variance here, that AI writing might be good for some things but not for others.

CNBC的Deirdre Bosa写道：我经常想到这个问题，但记不清是谁说的了。

CNBC's Deirdre Bosa wrote: I think about this often but can't remember who said it.

AI在中间环节表现很好。

AI is great for the middle.

开头需要人来定。

Beginning needs a human.

核心想法是什么，你到底想表达或构建什么？

What's the idea and what are you actually trying to say or build?

中间环节可以让AI来研究、整理、起草、重写，甚至帮忙挑毛病。

Middle let AI research, organize, draft, rewrite, Titan poke holes in it.

结尾也需要人来把关，这写得好吗？

End needs to be human too is this any good?

真实吗？

True?

我信吗？

Do I buy it?

我觉得德鲁肯米勒在结尾部分做得不够好。

I think that Drunkenmiller didn't do the end part as well as he could have.

为了完整起见，她提到的那个想法来自巴拉吉·斯里尼瓦桑，他在2025年6月发帖说：AI不做端到端，它做中到中。

Now for completeness, the thought that she was referencing came from Balaji Srinivasan who back in June 2025 posted: AI doesn't do end to end, it does middle to middle.

新的瓶颈在于提示和验证。

The new bottlenecks are prompting and verifying.

所以，考虑到所有这些，作为一个对此思考颇多的人，我想整理一份关于我如何看待AI写作的速查表。

So with all of this in mind, and as someone who's thought about this quite a bit, I wanted to put together kind of a crib sheet on how I think about AI writing.

为了取一个吸引人的播客标题，我称之为“AI写作五规则”，除了这些准则，我还想分享我在几种不同具体情境下对写作的看法。

For the sake of a catchy podcast title, it is called Five Rules for AI Writing, and in addition to those maxims, I also want to share about how I think about writing in a few different specific contexts.

这一切的基础是，我倾向于同意AI写作现在已经成为现实。

So the foundation for all of this is that I tend to agree that AI writing is now a fact of life.

如果你需要任何证据，只需看看OpenAI最近发布的企业数据，关于用户如何使用ChatGPT。

If you need any evidence of this, just go look at OpenAI's recently released enterprise data about how users are using ChatGPT.

在几乎所有部门中，最主要的用途就是写作。

The preponderance of it across almost every department is writing.

这不仅仅适用于通讯部门，招聘、市场营销、客户支持、法务、销售、政策，你说得出来的部门都是这样。

And that's not just for comms, that's recruiting, marketing, customer support, legal, sales, policy, you name it.

AI写作已经来了，而且会一直存在。

AI writing is here and it's here to stay.

但这并不意味着所有的AI写作都是一样的。

But that doesn't mean that all AI writing is the same.

事实上，对我来说，第一条规则就是不同类型的写作对应不同的规则。

In fact, rule number one for me is that different types of writing mean different types of rules.

电子邮件和战略备忘录不一样。

An email is not the same as a strategy memo.

战略备忘录和LinkedIn上的帖子也不一样。

A strategy memo is not the same as a post on LinkedIn.

LinkedIn上的帖子又和评论文章不一样。

A post on LinkedIn is not the same as an op ed.

这些不同类型的写作追求的目标不同，因此我们与AI互动的方式也会相应不同。

These different types of writing are trying to achieve different things, and the way that we engage with AI around those writings will consequently be different.

我下一条关于AI写作的原则是，我也同意这种所谓的“纯度测试”阶段会像渡渡鸟一样消失。

My next Maxim of AI writing is that I also agree that this purity test sort of phase will go the way of the dodo.

我觉得那些说这个话题几年后就会过时的人，坦白说，很可能是对的。

I think the folks who are saying that this conversation is going to be quaint in a couple of years are frankly probably right.

但如果纯度测试会消失，我认为质量测试不会。

But if the purity test will go, I think the quality test won't.

事实上，我觉得恰恰相反，写作输入变得越容易，对写作输出的要求反而会更高。

In fact, I think if anything, the increased easiness of the inputs of writing will increase the burden on the outputs of writing even more.

我不认为仅仅因为人们开始接受别人用AI写作，就意味着他们会接受由此产生的烂文章。

I don't think that just because people start to accept that people are using AI to write means that they'll accept bad writing as a consequence.

具体来说，这就是第三条原则。

To get specific on that is Maxim three.

对大多数人来说，质量是和努力程度或者感知到的努力程度挂钩的，而有没有努力，通常一眼就能看出来。

For most people quality is corresponding with effort or the perception of effort and effort or its absence tends to be pretty obvious.

我觉得很多人对德鲁肯米勒那篇专栏的负面反应，主要是因为那些AI痕迹太明显了，而且很容易改掉，看起来他连花点时间改几个句子的语法都不愿意。

I think a lot of the negative response to Druckenmiller's op ed was that the AI isms were so glaring and so fixable that it seemed like he didn't even care to take the time to change the syntax on a couple of sentences.

如果他连这点时间都不愿意花，我们真的觉得他会对他想表达的观点投入那么多思考吗？

And if he didn't care to take that time, do we really think that he put all that much thought into the point that he's trying to make?

在这种情况下，我觉得这就是一个例子，说明人们感知到写作本身缺乏努力，这更广泛地反映出论点构建上的不用心。

In that case, I think we have an example of people's perception of lack of effort on the writing itself being more broadly expressing of a lack of effort in the construction of the argument underneath.

毕马威和德克萨斯大学奥斯汀分校的一项新研究发现，当人们与AI合作时，相似的技能并不能保证相似的成果。

A new study from KPMG and the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes.

研究人员研究了500多名早期职业专业人士，发现表现最好的人通过引导、评估和完善AI的输出，持续放大了AI的价值。

Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs.

这些顶尖表现者，被称为“AI放大器”，他们的定义不仅仅在于他们知道什么，而在于他们如何与AI合作。

These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI.

想了解更多关于AI放大器与其他人的区别，请访问kpmg.com/us/aiaamplifiers。

Learn more about what separates AI amplifiers from everyone else at kpmg.com/us/aiaamplifiers.

这就是为什么大多数遗留系统现代化项目失败的原因：负责工作的AI无法大规模理解代码库。

Here's why most legacy modernization projects fail: the AI doing the work can't understand codebases at scale.

它只能看到一小部分上下文，检查语法，却错过了遍布全球应用生态系统的多年决策。

It sees a small slice of context, examines syntax, and misses years of decisions distributed across the global application ecosystem.

Blitzy解决这个问题的方式和它解决其他问题的方式一样。

Blitzy solves this the way it solves everything.

在迁移开始之前，Blitzy的智能代理会基于你的代码，将整个遗留系统逆向工程成一个持久的知识图谱。

Grounded in your code before any migration begins, Blitzy's agents reverse engineer the entire legacy system into a persistent knowledge graph.

每一个依赖、每一个约束、每一条曾经只存在于某个工程师脑子里的隐性知识。

Every dependency, every constraint, every piece of tribal knowledge that used to live in one engineer's head.

基于这些理解，Blitzy自主执行语言迁移、框架升级以及从单体架构到微服务的转换，并且全程进行端到端验证。

From that understanding, Blitzy autonomously executes language migrations, framework upgrades, and monolith to microservices transformations, all validated end to end.

一个Blitzy客户在十六周内用编码代理现代化了一个价值一千万美元的单体保险系统，而基线时间是一百三十七周。

One Blitzy customer modernized a $10,000,000 monolithic insurance stack in sixteen weeks against a one hundred and thirty seven week baseline with coding agents.

这相当于压缩了九倍的时间。

That's 9x compression.

在加速你的路线图的同时，消除技术债务。

Retire technical debt while accelerating your roadmap.

访问blitzy.com了解更多。

See how at blitzy.com.

这就是 blitzy.com。

That's blitzy.com.

说句实话：你的公司可能花了成千上万甚至上百万美元在AI工具上，但这些工具被严重浪费了。

Here's a harsh truth: Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized.

一半的公司有AI工具，但只有12%的公司用它们创造商业价值。

Half of companies have AI tools, but only 12% use them for business value.

大多数员工还在用AI来总结会议纪要。

Most employees are still using AI to summarize meeting notes.

如果你在公司负责AI的落地，那你需要Section。

If you're the one responsible for AI adoption at your company, you need Section.

Section是一个平台，帮你管理整个组织的AI转型。

Section is a platform that helps you manage AI transformation across your entire organization.

它指导员工在实际场景中使用AI，追踪谁在用AI创造商业影响，并让你清楚看到AI在哪里创造价值，在哪里没有。

It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value.

结果呢？

The result?

你从部署工具转向推动可衡量的AI价值。

You go from rolling out tools to driving measurable AI value.

你的员工从写会议纪要转向解决实际业务问题，而且你能证明投资回报率。

Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI.

别再猜测你的AI投资是否有效了。

Stop guessing if your AI investment is working.

去sectionai.com看看Section吧。

Check out Section at sectionai.com.

就是sectionai.com。

That's sectionai.com.

本期《AI每日简报》由Hyperagent赞助，你可以在上面运行你的团队可以共同管理的智能体集群。

This episode of the AI Daily Brief is brought to you by Hyperagent, where you run fleets of agents your team can manage together.

新用户可以获得1000美元的推理额度。

New users get $1,000 in inference.

忘掉那些本地智能体和等待你笔记本电脑提示的聊天工作流吧。

Forget local agents and chat workflows waiting on your laptop to be prompted.

Hyperagent 在云端部署始终在线的智能体，在你团队已经使用的工具中完成实际工作。

Hyperagent deploys always on agents in the cloud, doing real work across the tools your team already uses.

营销部门的智能体把竞争对手的动作变成落地页。

Marketing's agent turns competitor moves into landing pages.

销售部门的智能体丰富潜在客户信息、起草邮件，并更新 CRM。

Sales's agent enriches leads, drafts emails, and updates the CRM.

运营智能体处理文书工作，跟踪预算。

Ops agent chases the paperwork and tracks the budget.

每个智能体都能访问共享上下文，并遵循你关于范围和审批的规则。

Every agent has access to shared context and follows your rules about scope and approvals.

是时候让你的智能体像队友一样了。

It's time you add agents that feel like teammates.

去 Hyperagent 雇佣你的智能体吧，它由 Airtable 团队打造。

Hire yours at Hyperagent built by the team at Airtable.

在 hyperagent.com/aideallybrief 领取你的 1000 美元推理额度。

Claim your $1,000 in inference at hyperagent.com/aideallybrief.

第四条准则，我觉得这条对企业听众来说感受最深：在AI写作中，内容越长并不代表越好，实际上往往恰恰相反。

Maxim four, and I think this one will be most acutely felt among enterprise listeners: When it comes to AI writing, longer is not better and in fact usually the opposite is true.

我们现在之所以有“工作废话”这种说法，是因为过去几年我们用的很多AI模型，更擅长长篇大论，而不是简洁地说到点子上。

The reason we have phrases like Workslop now is that a lot of the AI models that we've used over the last couple of years have been much better at saying a lot than saying the right thing succinctly.

有意思的是，虽然AI写作让这个问题变得严重得多，但优秀的作家一直都知道这个道理。

And what's funny is that although this problem has been greatly exacerbated by the rise of AI writing, great writers have always known this to be true.

十七世纪布莱兹·帕斯卡有一句名言，后来被很多作者引用和改编，他在一封信里写道：我写这封信比平时长，是因为我没有时间把它写短。

There's a famous quote from Blaise Pascal in the seventeenth century that has been cribbed and updated by many authors since where in a letter he wrote: I've made this longer than usual because I have not had time to make it shorter.

第一波AI写作的特点就是文档又长又啰嗦，看得人眼睛都疼。

The first wave of AI writing was defined by endlessly long, eye bleeding length documents.

下一代AI写作不会再这样了。

The next generation will not be.

最后，第五条准则。

And lastly, Maxim five.

很多人都指出过，在很多情况下，写作本身就是思考。

As so many people have pointed out, in so many cases writing is thinking.

确定你的论点是什么、支持论据是什么、构建叙事，甚至考虑一个措辞的影响，这些过程。

The process of deciding what your thesis is, what the supporting arguments are, narrative construction, even considering the impact of a turn of phrase.

这些练习的主要目的最终不是落在纸上的文字，而是支撑这些文字背后的论证实质。

These are exercises whose main point is ultimately not the words that end up on the page, but the substance of the argument that runs underneath them.

这并不意味着使用AI写作就等同于外包你的思考，但这是你在使用AI写作时需要时刻警惕的风险，因为太容易陷入其中了。

That does not mean a priori that using AI for writing means outsourcing your thinking, but it is the constant risk that needs to hover in your field of view when you are using AI writing because it is so easy to fall into.

我说这些完全没有评判的意思。

And I say this with absolutely no judgment.

我感受到这种紧张感的一个地方，就是当我为像这一集这样的节目构思新概念的时候。

One of the places that I feel this tension is when I'm ideating on a new concept for an episode like this one.

我完全可以告诉Claude或者ChatGPT，我想做一集叫《AI写作五条法则》的节目，我知道其中几条法则，比如写作就是思考，但你可以接手这个想法，帮我构建出我能在节目中讲解的内容。

I could have very easily told Claude or Chatchippy Tee I want to do an episode called Five Rules of AI Writing and I know a couple of the rules like writing is thinking, but you take and run with that idea and build me the asset that I can talk over in the show.

那会比我现在做的要快得多——我坐在Notion里，真正思考这五条法则，和自己辩论是否要改成六条，或者是否要把第一条当作基础，但在这种情况下，更手动的方法才是正确的。

That would have been a much faster process than what I did, which is sitting in Notion and actually thinking through each of these five, debating with myself around whether I wanted it to be six or whether I wanted to consider that first one just the foundations, but for this particular context, the more manual approach was the right one.

这就引出了一份关于不同类型写作负担的速查表。

Which gets us to a crib sheet for the burden on different types of writing.

记住，第一条准则就是不同类型的写作，有不同的规则。

Remember, Maxim one is different types of writing, different types of rules.

这意味着，AI能不能写或者应不应该写，在大多数情况下可能都不是对的问题。

And what that means is can or should AI write this is probably in most cases the wrong question.

那我们来看几个不同的例子吧。

So let's talk about a few different examples.

先从最明显的说起，比如邮件。

Starting with some of the most obvious like emails.

邮件很明显是适合AI来写的。

Emails are pretty clearly safe for AI.

因为绝大多数邮件，从来没有人会去评判它的文笔好不好。

In that for the vast, vast majority of emails, no one has ever judged them on the quality of the prose.

虽然用AI写邮件确实有外包思考的风险，但邮件通常涉及的都是很小、很简洁的思维单元，所以那种我们天生倾向于把思考外包出去的风险，在这里就变得不那么突出了。

And while, yes, there could be the risk of outsourcing your thinking with email writing, emails tend to focus on such small, concise units of thought that the risk that any of our inherent tendency towards intellectual outsourcing becomes prominent gets reduced.

话虽如此，我觉得总的来说，我们正在走向一个用AI写邮件在很多情况下可能有点大材小用的世界。

That said, I think in general we're moving to a world where using AI for emails might be in many cases overkill.

不过有些情况下用AI写邮件还是有意义的，比如我们有一个针对赞助请求的标准回复模板。但对于互动性的邮件，那些你本来可能会通过Slack或即时通讯应用来处理的内容，我觉得其他加快流程的方法，比如用Whisper Flow这样的工具来口述，可能比用AI更有效。

Now maybe there are some types where it makes sense, for example, we have a standard response email for sponsorship requests that come through, but for interactive emails the type of things that you might otherwise be doing via Slack or a messaging app I tend to think that other ways of speeding up your process like just dictating with something like Whisper Flow are going to be even more effective than using AI.

接下来，我们聊聊会议纪要总结，这也是AI写作中非常常见的用途。

Next up, let's talk about meeting notes summary another extraordinarily common use of AI writing.

这个对AI来说同样非常安全，因为从很多方面来看，这甚至不算是真正的写作。

This once again is very safe for AI because in many ways this isn't even really writing.

它更像是压缩，把一堆话提炼成简洁的内容。

It's about compression, it's about taking a bunch of other words and turning them into something succinct.

不过，即使是在会议纪要上，AI也有一个失败模式，那就是过于详尽。

Still, there is an AI failure mode even with meeting notes, which is the AI being too exhaustive.

一个AI如果参加了会议，听了六十分钟，并且有每个人的完整转录，它可能很擅长总结每个人说了什么，但可能不如你擅长指出哪些是最重要的内容。

An AI that was sitting in a meeting and listened for sixty minutes and has the transcript of everything that everyone said is probably going to be very good at summarizing what everyone said, but might not be as good as you as explaining the most important things that anyone said.

所以，即使你可以把大部分会议纪要总结这类工作外包给AI，也值得在它产出的基础上，补充说明那一两个真正重要的点，作为对AI产出的补充。

And so I think even if you can outsource the vast majority of meeting notes summarization and things like that to AI, it's worth coming in over the top with your clarification of the one or two things that are actually important as a complement to what it produces.

那内部战略备忘录呢？

What about an internal strategy memo?

我觉得这个对AI来说可能暗藏风险，虽然你当然可以用AI来辅助。

This, I think, can be sneakily bad for AI, although certainly you can use AI to assist.

一方面，这看起来像是AI的绝佳应用领域，因为这也是一个文笔好坏不太重要的场景。

On the one hand, it seems like this would be a great area for AI because it's another one where the strength of the prose doesn't really matter that much.

不过，这正是“写作即思考”这条准则发挥作用的好例子。

That said, this is a great example of where that fifth maxim that writing is thinking comes in.

如果让AI来写内部战略备忘录，除非你对它要传达的具体战略以及它不应该传达的内容有极其精确的指示，否则AI很容易偏离轨道，加入自己的内容，或者只是基于它的训练数据给出泛泛的建议，而不是针对你组织的实际情况，这其实就是糟糕的思考。

If an AI is tasked with writing some internal strategy memo, unless you are hyper precise about exactly the strategy that it is communicating and the things that it is meant not to communicate, it is very, very easy for AI to go off the rails and add its own additions or simply to provide bad thinking in the form of generic advice that is based on its set of training data rather than the specific context of your actual organization.

这意味着，内部战略类写作是迪德丽提到的那个中间地带的一个很好的例子。

What that means is that internal strategy type writing is a great example of I think the middle that Diedre was talking about.

在个人层面，你需要彻底地思考清楚战略。

You're going to want to, on a personal level, exhaustively think through the strategy.

你甚至可能需要在两个完全不同的层面上考虑与AI的互动。

And you might even want to think about your interaction with AI on two totally different levels.

第一个层面是用AI来支持思考，比如列要点、列大纲、迭代，而最终的写作则是一个完全独立的过程。

The first is using AI to support that thinking, bulletin, outlining, iteration, and then the final writing as an entirely separate process.

我觉得如果大纲很严密，包含了所有的思考，那么让AI来写具体的文字通常完全没问题。

I think if the outline is tight and contains all of that thinking effort, it will often be totally fine to let AI write the actual words.

那社交媒体文案这种非常常见的用途呢？

Now what about a very common use case in social media copy?

这个情况就有点复杂了，好坏参半。

This one is interestingly mixed.

一方面，我觉得AI在很多社交媒体文案上做得不错，特别是如果你把它调教得远离那些最明显的AI腔调，比如“不是这个，而是那个”这种句式、自我吹嘘，以及其他那些会让人一眼看穿、分散注意力的东西。

On the one hand, I think that AI does a fine job on a lot of social copy, especially if you have tuned it away from the most egregious AI isms like it's not this, it's that, the self congratulation, and other things that are just going to be glaring and distract people from whatever it is that you're trying to say.

总的来说，内容越短，AI表现越好。

I think in general, it does better the shorter the medium is.

换句话说，AI可能更容易写好一条老式的单行推特，而不是一篇完整的领英长文，因为那篇长文有更多空间让它的AI腔调暴露出来。

In other words, AI is probably gonna have an easier time with an old school single line post on x than it is with a full LinkedIn essay because that full LinkedIn essay just has more room for its AI isms to shine through.

AI写社交媒体内容更大的问题是，社交媒体已经不再仅仅奖励简洁有力的文字了。

The bigger problem with AI writing for social media is that to the extent that it ever did, social media no longer simply rewards pithy writing.

事实上，社交媒体要求的是互动和参与。

In fact, social media demands engagement.

它希望发帖的人不只是自动发布内容来填充那个无底洞，还希望这些人去评论别人的帖子，总体上提供互动。

It wants posters not just automating their posting to provide more content into the MAW, it wants those same posters and commenting on other people's posts and generally providing engagement.

那种宝贵的、真实的人类信号，让平台能够因为你在上面花更多时间而投放广告。

The sweet, sweet human signal that allows those platforms to serve ads because you spend more time on the platform.

我觉得，要让AI在社交媒体上写得更好，需要分两部分。

I think the way to do AI writing better for social media then is two parts.

首先，你愿意前期投入越多时间来调整语气，即使AI写的那部分，结果也会越好。

First, the more time you're willing to take to invest upfront to tune the voice, the better your results are going to be even with the AI written part.

第二部分就是，要明白不管AI写作调得多好，在社交媒体上它能帮你的也就那么多。

The second part is simply to appreciate that AI writing, no matter how tuned it is, is only going to get you so far with social media.

这一点我感受特别深，因为我现在已经建了一个完整的流程，从把每集内容拆解到aideallybrief.ai网站上，再到生成一系列帖子发到Twitter和LinkedIn。

And this is one where I feel it acutely as I've now built a full pipeline from the disaggregation of each episode that ends up on the aideallybrief.ai website to a set of generated posts go to Twitter and LinkedIn.

这很有价值。

It has been valuable.

它增加了分享量。

It's increased sharing.

它确实帮我做了很多我想在社交媒体上做的事，尤其是考虑到我之前在社交媒体上几乎什么都不做，但几周之内，我就明显撞上了它能为《AI每日简报》在社交媒体上做到多少的墙。

It's done a lot of the things that I wanted it to do for social, especially considering I was doing exactly zero social before, but within weeks so clearly slammed up against the walls of how much it can do for the AI Daily Brief on social media.

所以从某些方面来说，AI写作的教训就是要有现实的期望。

So in some ways, the lesson of AI writing here is to just have realistic expectations.

在我们结束之前，还有两个要点要讲。

Two more to focus on before we get out of here.

首先是营销文案，然后我们以专栏文章结尾，因为那是我们开始对话的地方。

First, marketing copy, and then we'll end with op eds given that that's where the conversation started.

我发现AI写营销文案糟糕得让人恼火。

I have found AI writing with marketing copy to be annoyingly bad.

我说糟糕得让人恼火，因为这是我希望它表现好的领域之一。

I say annoyingly bad because this is one of the areas where I want it to be good.

即使我是一个痴迷于描述我项目的网站文字的人，我也希望它足够好，这样我就不用再那样痴迷了。

Even as someone who obsesses over the words on the websites that describe my projects, I want it to be good enough to allow me to not do that sort of obsessing.

然而，营销恰恰是我发现最令人发指的AI腔调出现的地方。

And yet marketing is the area where I find the most egregious AI isms turn up.

可能是因为营销这个领域需要的是独特性和差异化，而不是千篇一律，所以那些常见的AI腔调在这里代价更高。

Maybe it's because marketing is an area where you want uniqueness and distinction, not sameness and commonality, and so the cost of those common AI isms is even higher than in other contexts.

我还发现，AI在写营销文案时，即使你在跟它互动，也会有一些特别糟糕的表现。

I also find there are some specifically bad things that AI does when it comes to marketing copy even as you are interacting with it.

我最常遇到的情况是，当我给模型提出修改意见时。

The one that I run across most often is when I give the model a correction.

比如，像“别对读者做太多假设”这样的意见，它往往会把这条意见本身变成文案。

For example, something like stop presuming so much about the reader, it tends to turn that correction itself into copy.

所以我看下一版的时候，就会在某处标题里看到“我们对读者不做任何假设”这样的话。

So I'll look at the next version and find some headline somewhere that says we assume nothing about the reader.

这真的让我抓狂，而且这种情况经常发生。

It drives me absolutely batty, and it happens all the time.

所以，关于如何让AI把营销文案写得更好，我的简短回答是，不幸的是，我真的没能做到。

So when it comes to how to make AI writing better for marketing copy, my short answer unfortunately is that I genuinely haven't been able to.

但如果我非得深究的话，我会尝试的做法是，给AI大量提供过去符合品牌调性的文案示例，以及尽可能多地提供我针对自己品牌喜欢和不喜欢的例子，试图至少让它稍微调校得好一点。

How I would try, however, if I had to really dig down on this, would be to extensively provide as many examples of past on brand writing as well as as many examples as possible of what I like and don't like for my brand specifically, to the AI to try to tune it at least a little bit better.

不过，我觉得现在AI在营销文案上更好的用途是当快速迭代工具。

Still, think for me right now, a better use of AI for marketing copy is as a rapid iterator.

比如，如果我想找一个特别喜欢的标语，让AI想出20个，这真的是激发我自己灵感的好办法。

For example, if I'm trying to figure out a tagline that I really like, asking AI to come up with 20 is a really great way to spark something for myself.

最后，说说专栏文章和说服性文章。

Lastly, on op eds and convincing essays.

还记得Texas Hedge对Druckenmiller那篇文章的评价吗？

Remember what Texas Hedge wrote about the Druckenmiller piece.

他说，问题在于那篇文章表达了对专栏文章形式及其读者的蔑视。

He said the problem is that it represents an expression of contempt of the format of an op ed and of its audience.

我觉得这正好印证了关于努力的那些准则。

And this I think gets to the maxims about effort.

专栏文章的全部意义在于说服别人。

The whole point of an op ed is to convince someone of something.

当读者几乎一眼就能看出这是敷衍了事时，他们自然会失去兴趣。

When readers can more or less instantly tell it was low effort, they inherently switch off.

如果你连花时间把论点讲清楚都不愿意，那我凭什么要在乎你的论点？

If you couldn't be bothered to take the time to make your argument convincingly, why should I care about your argument?

就像我说的，我觉得其实没人真正在意德鲁肯米勒那篇专栏文章的文笔好不好。

As I said, no one actually cared I don't think about the quality of the writing in Druchenmiller's op ed.

问题在于，他们觉得那篇文章写得太敷衍，这反而削弱了它背后的论点。

The issue was that the perceived laziness of it, in their estimation, reduced the strength of the underlying argument.

而这也许就是“思考即写作”最明显的例子。

And this is perhaps the easiest example of thinking is writing.

你得先思考，然后再动笔。

You just gotta do the thinking first.

当你想说服别人时，你得先把论点和论据想得清清楚楚，然后再交给AI。

When you're trying to convince someone of something, you have to get extremely clear on both the thesis and the supporting points before you turn it over to AI.

这就是小学时学的五段式作文，宝贝。

This is the five paragraph essay from grade school, baby.

你只需要去做就行了。

You just gotta do it.

为了把整个流程做完整，在你把内容交给AI之后，还要再检查一遍，确保没有那些特别明显的AI腔调。

And to complete the effort sandwich, after you have turned it over to the AI, go through and make sure it doesn't have those incredibly obvious AI isms.

总的来说，AI写作不会消失，而且我觉得它会带来很多机会。

To sum up, AI writing is not going anywhere, and I think it will unlock a lot of opportunity.

以前不习惯用写作来沟通的人，也会开始尝试。

People who haven't used writing for communication before will start to.

但有一点不会变，就是工作中的懒惰，最终还是会体现在作品里。

What it won't change is that laziness in work comes across as laziness in work.

想把事情做好，终究没有捷径，即使我们能善用新工具，更快更好地完成。

There are no shortcuts ultimately for doing things well, even if we can make great use of new tools that help us do that well faster and better.

今天的AI每日简报就到这里。

For now, that's gonna do it for today's AI Daily Brief.

一如既往，感谢你的收听或收看，下次再见，祝一切顺利。

Appreciate you listening or watching as always, and until next time, peace.