AI原生初创企业：5款产品、七位数收入、100%由AI编写的代码 | Dan Shipper（Every联合创始人兼CEO）

你正在创建的业务、组建的团队、运营的方式，正是企业在人工智能时代力求达到的最前沿实践。

The business you're building, the team you're building, the way you're operating is the very bleeding edge of how companies are trying to operate in this AI era.

我们有一位AI运营主管。她持续不断地构建提示词和工作流程，让我和团队其他成员能尽可能实现自动化操作。

We have a head of AI operations. She's just constantly, like, building prompts and building workflows so that I and everyone else on the team are just automating as much as possible.

关于AI，你有哪些与众不同的见解？

What are some things that you believe about AI that most people don't?

我厌恶那些宣称AI会取代初级岗位的耸动标题。每当我看到有年轻人使用Chegupti工具时，我就震惊于他们将比任何同事都高效得多。我们团队有个小伙子，仅用两个月就完成了一年的进度——因为每次我指导他如何构思故事、拟定标题时，他都会录下所有内容转化为提示词，从此再未重复犯错。

I hate the headlines that are like entry level jobs are taken away by AI. Whenever I see a kid with Chegupti, I'm like, holy shit. They're gonna go so much faster than any other person that I work with. We have this guy. He made like a year's worth of progress in two months because every time I sat down with him and told him, okay, here's how you tell a story.

Here's how you think about a headline. Like, he recorded all of it, put it into a prompt, and he never made the same mistake twice.

Here's how you think about a headline. Like, he recorded all of it, put it into a prompt, and he never made the same mistake twice.

现在有种趋势：我们将进入完全无需编写代码的时代。比如你的产品团队可能根本不需要写代码。

There's this sense. We're getting to a place where you don't have to write any code. Like, you have a product team not writing code at all.

手工编码已成过去式。像我们这样走在技术前沿的组织，正在实践那些三年后才会普及的工作方式。

No one is manually coding anymore. Organizations like ours, people who are playing at the edge, we're doing things that in, like, three years, everybody else is gonna be doing.

今天的嘉宾是Dan Shipper。作为Avery公司的联合创始人兼CEO，Dan带领着这家站在AI技术最前沿的企业。他们仅15人的团队已开发并发布了四款产品，每日出版新闻简报，还设有帮助企?#业采用最新AI最佳实践的咨询部门。其产品团队工程师完全不手写代码，而是依靠智能代理体系来制定需求并构建产品。

Today, my guest is Dan Shipper. Dan is the cofounder and CEO of Avery, which is a company that is at the very bleeding edge of what is possible with AI. Their team of just 15 employees has built and shipped four different products. They publish a daily newsletter, and they have a consulting arm that helps companies adopt the latest AI best practices. On their product team, their engineers don't handwrite a single line of code and instead use an arsenal of agents who help them craft requirements and build their products.

他们的内容部门运用AI实现更高效的优质内容产出，甚至设有专职人员帮助每位员工通过最新AI工作流程提升效率。在对话中，Dan分享了提升员工效能的内部策略、个人AI工具栈、判断企业能否通过AI实现生产力飞跃的关键指标、独特的公司运营理念，以及对AI未来发展的多项预测。若喜欢本期节目，别忘了在常用播客平台或YouTube订阅关注。年度订阅我的新闻通讯还可免费获赠Superhuman、Linear、Notion、Perplexity、Bolt、Granola等超值产品一年使用权，详情请访问lenny'snewsletter.com点击bundle查看。

Their editorial arm uses AI to publish better work faster, and they even have a person whose entire job is to help every employee at the company become more efficient using the latest AI workflows. In our conversation, Dan shares a bunch of tactics that they use internally to increase the leverage of their own employees, his personal AI tool stack, the one predictor that he's found for whether a company will successfully find huge productivity gains through AI, how he's building his company in a really unique way, a bunch of predictions for where AI is going, and so much more. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. And also, if you become an annual subscriber of my newsletter, you get a bunch of amazing products for free for one year, including superhuman, linear, notion, perplexity, bolt, granola, and more. Check it out at lenny'snewsletter.com and click bundle.

现在有请Dan Shipper。本期节目由AI代码审查平台Coderabbit赞助——它正在改变工程团队在保障代码质量前提下加速交付的方式。代码审查至关重要却耗时，Coderabbit作为AI协作者，能即时提供PR审查意见并评估潜在影响。不仅能发现问题，还可一键修复缺陷，并支持通过AST grep模式自定义代码质量规则，捕捉传统静态分析工具易遗漏的细微问题。

With that, I bring you Dan Shipper. This episode is brought to you by Coderabbit, the AI code review platform, transforming how engineering teams ship faster with AI without sacrificing code quality. Code reviews are critical but time consuming. Coderabbit acts as your AI copilot, providing instant code review comments and potential impacts of every pull request. Beyond just flagging issues, Coderabbit provides one click fix suggestions and lets you define custom code quality rules using AST grep patterns, catching subtle issues that traditional static analysis tools might miss.

Codebabbit还提供直接在IDE中进行的免费AI代码审查服务，支持VS Code、Cursor和Windsurf。截至目前，Codebabbit已审查超过1000万次PR，安装在100万个代码库上，并被超过7万个开源项目使用。访问codebabbit.ai并使用优惠码Lenny，即可免费获得Codebabbit一整年的使用权。网址是codebabbit.ai。

Codebabbit also provides free AI code reviews directly in the IDE. It's available in Versus Code, Cursor, and Windsurf. Codebabbit has so far reviewed more than 10,000,000 PRs, installed on 1,000,000 repositories, and is used by over 70,000 open source projects. Get Codebabbit for free for an entire year at codebabbit.ai using code Lenny. That's codebabbit.ai.

本期节目由DX赞助播出。如果您是工程团队负责人或平台团队成员，CEO迟早会向您索要生产力指标。但衡量工程组织效率很困难，我们都认同简单的PR数量或提交次数无法反映全貌。这正是DX的用武之地。DX是由顶尖研究人员设计的工程智能解决方案，这些专家也是DORA和SPACE框架的幕后推手。

Today's episode is brought to you by DX. If you're an engineering leader or on a platform team, at some point, your CEO will inevitably ask you for productivity metrics. But measuring engineering organizations is hard, and we can all agree that simple metrics like the number of PRs or commits doesn't tell the full story. That's where DX comes in. DX is an engineering intelligence solution designed by leading researchers, including those behind the DORA and SPACE frameworks.

它结合了开发者工具的量化数据与开发者的质性反馈，为您提供工程生产力及其影响因素的完整视图。了解为何Etsy、Dropbox、Twilio、Vercel和Webflow等世界知名企业都信赖DX。访问getdx.com/lenny。丹，非常感谢你来做客，欢迎来到播客节目。

It combines quantitative data from developer tools with qualitative feedback from developers to give you a complete view of engineering productivity and the factors affecting it. Learn why some of the world's most iconic companies, like Etsy, Dropbox, Twilio, Vercel, and Webflow rely on DX. Visit DX's website at getdx.com/lenny. Dan, thank you so much for being here, welcome to the podcast.

谢谢邀请。我长期以来都是节目的忠实听众，能来参加真是莫大的荣幸。

Thank you for having me. I've obviously been a huge fan for a long time, and so it's an honor to be here.

这是我的荣幸，丹。感觉这期节目命中注定就该实现。终于能和你畅所欲言实在太棒了，我们有太多太多可以探讨的话题了。

It's my honor, Dan. I feel like this is a podcast that was meant to be. I'm so happy we're finally doing this. There's so damn much that I wanna talk about. There's so damn much we can talk about.

我想以几个犀利观点开场会很有趣，选择这个开场是因为在我看来，你花在思考AI、构建AI、使用AI和评估AI上的时间几乎比我认识的任何人都多，因此我非常尊重你对AI发展趋势的见解。让我直接抛出这个问题：关于AI工具的应用，有哪些是你深信不疑但多数人并不认同的观点？

I thought it'd be fun to start with just some hot takes, and the reason I want to start here is I feel like you spend more time thinking about AI, building with AI, using AI, evaluating AI, than anyone else I know nearly, and so I really respect your insights and your perspectives on where things are going. So let me just ask you this kind of question and see where this goes. What are some things that you believe about AI, using AI tools that most people don't believe?

我要说出我最激进的观点——这也是我最缺乏证据支撑的观点。先说清楚这点，之后我会给出更有理有据的看法。我认为AI可能是促使美国就业岗位回流的最大驱动力之一。虽然人们都担心它会导致失业，它确实会改变岗位所需的技能。

I'm gonna go with my hottest take, and this is the take that I have the least evidence for. So let's just start with that. Have other more well reasoned takes to give you, but this is my hottest one, which is I think that AI may be a one of the biggest force for reshoring American jobs. And so I think everyone is worried about it, unemploying people. And for sure, it will change the skills needed to do the jobs that you're doing.

但我认为它实际上可能促使大量岗位回流本土。这主要通过两种方式实现：一是当前富人和大企业购买的许多昂贵服务（如内部法务、呼叫中心等），廉价智能将使中小企业和个人也能负担得起这些服务，从而刺激需求。

But I think it may actually reshore a lot of jobs. And it'll do that in two ways. One is, there are a lot of expensive services that rich people and big companies pay for right now. So like in house counsel or call center or whatever. And what cheap intelligence does is it makes those kinds of things affordable for small companies and individuals.

二是它能让从业人员服务更多客户。以客服为例，AI或许不会消灭这个岗位，但可能让10个中西部呼叫中心员工服务数十万乃至数百万客户（或许这个数字夸张了），但肯定会远超传统电话接线的工作效率。这使得美国企业雇佣本土员工变得更具成本效益。

So it stimulates demand. The other thing that it does is it allows people who are in those jobs to serve more people cheaply. So if you're so it may not get rid of customer service, for example, but it may allow, you know, 10 people in the Midwest who would normally be working at a call center to serve hundreds of thousands or millions of people. Maybe maybe that's maybe that's too much, but, like, a lot more people than they would ordinarily if they were the ones on the on the phone all the time. And so it becomes much more cost effective for American companies to hire people in The US.

我认为美国人在使用AI工具完成工作方面往往更具优势。因此，让这些岗位由本土员工运用AI工具来完成可能更高效——况且AI模型公司也大多在美国。你可以自行评判这是否是好事，但在关于‘AI是否取代工作’的讨论中，这个视角被严重忽视了。

And I think the people in The US are gonna be better in in a lot of cases at using these AI tools to do work. And so I think it may actually make it more effective to have drop those jobs in The US run by people sitting in The US who are using it to get to get work done, and also the model companies are here too. So there's there's a lot of American stuff happening. And you can you can decide whether or not you think that's a good thing, but I think it's quite it's quite lost in the conversation over whether AI will get rid of jobs.

我喜欢关于AI的乐观看法，这很棒。而且，正如你所说，目前尚不确定这对其他国家是否有利，但对美国肯定有益。你还有什么其他高见？

I like optimistic takes about AI, so this is great. And, like, to your point, I want TBD if this is good for other countries, but good for The US. What else what else you got? What other hot takes?

另一个重磅观点。这次并非标新立异，而是觉得人们真的低估了它的价值——我认为非程序员群体严重低估了Cloud Code（云端代码）的强大。这个观点还可以扩展到Google刚推出的Gemini CLI命令行界面等类似工具。

Another another big hot take. And this is this isn't less like contrarian and more just like, think people are truly sleeping on it. I think people are truly sleeping on how good Cloud Code is for non coders. And I'll extend this to not just Cloud Code, but Google just came out with the Gemini CLI command line interface. So things like that.

为不了解Cloud Code的听众解释一下：它本质上就是个命令行界面——就是程序员用的那种黑色终端窗口。你可以启动这个界面，它能访问你的文件系统。

And I'll tell you about for people who are listening that don't know what Cloud Code is. Cloud Code is just the command line interface. So it's, you know, those black terminals that programmers use. It's a command line interface that you can boot up. It has access to your file system.

它能执行任何终端命令，还能浏览网页等。你可以派它执行任务，它能自主运行二三十分钟完成任务——尤其是刚发布的Cloud Opus四代，简直是AI自主工作能力的巨大飞跃。Cloud Code甚至能生成多个子代理并行处理任务，对程序员来说极其好用。

It knows how to use any kind of terminal command, and it knows how to like browse the web, all that kind of stuff. You can give it something to do, and it will go off and it will run for like twenty or thirty minutes and complete a task, like autonomously, agentically. It's a especially with Cloud Opus four that just came out, it's like this gigantic leap forward in AI's ability to work by itself. And and Cloud Code can even spawn multiple sub agents that do a bunch of tasks in parallel. And it's incredibly useful for programmers.

现在行业内人人都在全天候使用，像嗑了代理药似的——大家都有15个代理在处理各种事务，疯狂吧？但非程序员因为畏惧终端而不敢尝试。

Like, everybody inside of every is using it all day every day. Like, everyone's agent pill. They've got, like, 15 agents doing all this kind of stuff. It's crazy. But non programmers don't use it because it's intimidating to use the terminal.

其实你可以这样做：下载所有会议笔记到文件夹，然后命令它'阅读我所有会议笔记并分析我的行为模式'——比如我发现它总能指出我微妙回避冲突的时刻。它会自建待办清单和小笔记，逐条处理文件后通过多轮交互给出总结报告，而不像普通聊天工具那样把所有内容塞进上下文。

But you can like download, for example, you can download all your meeting notes and put it in a folder and just be like, okay, I want you to read every single one of my meeting notes and tell me something that I do, for example, is tell me all the time that I subtly avoided conflict. And it will it writes a little to do list for itself. It can have, a little notebook. It can, like, go and read each little thing and then, like, write into its notebook, go down its do list and give you a summarized answer over multiple turns. So it's not just like stuffing everything into context, which is what you'd be doing with like a, you know, chatty bitty chat or a regular quad chat.

它真正在处理你给的每个文件。我认为这对需要处理大量文本的任务极具颠覆性。

It's like actually processing every single file that you give it. And so I think it's incredibly powerful for any kind of task that involves processing a lot of text.

简而言之，这就是个能读取本地文件并听你差遣的本地代理？

So as a simple way to think about this, you basically have an agent on your local computer that can read your local files and do your bidding.

完全正确。而且它能长时间稳定运行不失控。

Yes, Exactly. And it can do that for long amounts of time without going off the rails.

有意思。非技术人员只需克服使用终端发指令这个小门槛，之后就能用自然语言指挥它工作

Interesting. And so there's like a small hurdle that nontechnical people have to overcome, which is using their terminal and giving commands. But once they get it running, it's just you talk to it in English and ask it to do

没错，正是这样。

stuff. Exactly.

所以这里的独到见解是，大多数人认为专为工程师设计的Claude代码，实际上是非技术人员最被低估的工具。没错，正是如此。你能想到人们还有哪些使用场景吗？这个会议记录的例子真的很酷。

So the hot take here is just Claude code, which most people think is for engineers, is the most underrated tool for nontechnical people. Yep. Exactly. What are some other ways you imagine people seeing this? This meeting note example is really cool.

我能想象人们会这样使用它。你还见过哪些其他应用场景或者——

And I could see people do it using this. What else have you seen or

——在思考什么吗？这是我经常做的一件事。作为职业写作者，比如我特别喜爱——我知道你会问我喜欢的书籍——先透露一下，我最爱《战争与和平》。

thinking about? Something that I've done a lot. So I'm a writer for a lot of my job. And for example, I love and I know you're gonna ask me about books I love. So I'm gonna give you a sneak peek, which is I love War and Peace.

我刚第三次读完它。

I just read it for the third time.

哇，那可是部巨著。

Wow. That's a long book.

篇幅确实超长，但精彩绝伦。我认为托尔斯泰是位天才作家。我曾想在自己的写作中融入他的文风特色——他最擅长用微妙句子通过行为展现角色心理，比如面部微表情，或是声音语调与眼神流露的矛盾感，诸如此类的细节刻画。

It's so it's so long, but it's so good. I think Tolstoy is a brilliant writer. And one thing that I wanted to do is I was like, I wanna inflect some of my writing with some of Tolstoy's style. And the way I did that is I think he's incredible at these little subtle sentences where he shows you what a character is thinking and feeling just by how they behave. Like how they move their face, or like the mismatch between the intonation in their voice and the expression in their eyes, like all that kind of stuff.

他就像是人类行为与心理学的顶级观察家。我把公版资源的《战争与和平》电子版下载到电脑，让Claude研读前三章并提取所有人物描写，最终生成了一份'托尔斯泰式角色描写指南'。虽然普通指令也能实现部分功能，但不可能塞入整部《战争与和平》——这需要更多人工引导，而Claude近乎自主完成了全过程。

Like he's just like a an incredible student of human behavior and psychology. And so I just downloaded War and Peace to my computer, which you can do because it's public domain. And then I had Claude read like the first three chapters of War and Peace and pull out all of those descriptions. And make then make a guide for itself for like how to do descript like character descriptions like Tolstoy. And you could totally do this with, like, a regular, like, Opus command, but you couldn't put all of War and Peace into it.

后来我还下载了俄语原版与英译本进行对照，让它分析我钟爱的场景中可能遗失的翻译细节。无论你痴迷哪个领域，都能这样深度挖掘。处理大量客户访谈或数据时同样适用——对于从庞大数据集中提炼洞见简直威力无穷。

It would take a lot more hand holding to get it to do this, and it just sort of did this by itself, like, without my, like, really intervening. It also ended up, like, downloading I I had to download a Russian version of War and Peace and the English version and then start comparing different scenes that I love to, like, tell me about things that I might have missed in the translations. So that you can get as deep and weird and nerdy for whatever subfield you care about as you want to. Same thing for like if you've got tons of customer interviews or or like tons of customer data you wanna go through, it's like incredibly powerful for for going and figuring stuff out stuff out from big data sets like that.

你启发了我——虽然用途不同但也很酷，可能听起来很书呆子气：我正在读托翁的另一部作品《安娜·卡列尼娜》。

You actually inspired me to use, this is not what you're describing, but it's also something that's very cool. This is gonna sound so nerdy. I'm reading Anna Karenina right now. Yes. Based on also Tolstien.

这是之前一位播客嘉宾推荐的，所以我想，好吧，我得读读这本书。而且真的很长。在我的Kindle上，读到13%时我就在想，天啊，我已经读了几个月了。

This is recommended by a previous podcast guest, and so I was like, alright. I gotta read this. Also, very long. On my Kindle, I'm just like, alright, 13% in, I've been reading for months.

个人观点，我觉得《温暖碎片》比《时空错位者》更好，尤其对科技从业者而言，不过两本都不错。

Hot take, I think Warm Piece is better than Anachronino, especially for like a tech person, but they're both good.

好了，说到点子上了。我看到你推特分享的这个用法——我超爱这个方式——就是边读书边让ChatTPG语音待命，随时提问。因为你不需要输入书籍内容，它知道整本书的内容。Anthropic刚透露（不确定是他们主动公开还是有人从法律简报里发现的），他们其实购买了海量书籍并自行扫描。是的，这就是他们实现合理使用的方式。

Okay, there we go, there's my year. I saw you tweet this use case that I love that I've been using which is just while I'm reading having ChatTPG voice sitting around and then just asking it questions because you don't actually have to feed it the book, it knows the whole book. Anthropic just shared this, I don't know if they shared or someone found this in their legal briefings that they actually bought tons of books and scanned them themselves. Yeah. It's how they did fair use.

所以它掌握了所有上下文。你可以坐着问它‘俄罗斯社会里这玩意儿到底是什么’——超级有趣。回到你刚才的犀利观点...

And so it has all this context. So just sitting there asking it like what the heck is this thing in Russian society It's super fun. Okay. So this is awesome. So the tip here is just coming back to your hot take.

重点在于：你完全可以使用本地文件的智能代理在电脑上完成各种酷炫操作，而不必把内容上传到项目或提示词里。没错，太酷了。所以我猜未来的趋势是，人们会发现并开始日常使用这个功能。

The tip is you basically can have an agent using local files and doing all kinds of cool stuff on your computer versus having to upload it into into projects or into your prompts and things like that. Yeah. Super cool. So I guess the bet here is that people are gonna discover this and start using this just day to day.

他们绝对会的。而且我认为模型公司也会让这个功能更易用。就像Cloud Code等工具带来的变革会渗透到所有场景——无论是网页还是其他平台。最初所有AI应用都只是在现有界面里塞个聊天框，比如Copilot在IDE里的自动补全，Cursor的侧边栏聊天...

I think they absolutely will and I also think probably the model companies are gonna start making this more accessible. Like, I think one of the things that will just come from Cloud Code and other things like it into their everything else you use, whether it's on the web or wherever, is the original all of the original AI apps were pasting a chat box into an existing UI. So, you know, you've got Copilot. It's got a little it's got, like, the autocomplete in the in the IDE. You've got cursor.

但Cloud Code的不同在于你根本不用看代码——它不是为手动编码设计的，而是让你说‘我要实现某个功能’，它就去执行。

It's got a little sidebar with a little chat. And the difference with Cloud Code is you never look at the code. It's not meant for coding. It's not meant for coding by hand. It's meant for you to say, I want you to get something done, and it goes and does it.

我觉得我们正进入这样一个阶段：对于几乎所有常见应用场景，AI将足够成熟，让我们可以基本摆脱那些需要深入操作细节的界面，你更多是在委派任务，让它去完成。

And I think we're just getting to a point where for pretty much all of these, you know, all the usual applications, AI is going to be good enough that we can get rid of the interfaces more or less where you're like digging into all the things that it's actually doing and and it's you're you're sort of interleaved with its execution, and you're more just like, I'm delegating. It's gonna go do it.

没错。我采访过Cursor CEO迈克尔·托雷尔，他的终极愿景就是‘后代码时代’。确实如此。最近还采访了Base44创始人（他创办的公司以8000万美元卖给Wix），他说公司最近三个月里，自己一行前端代码都没碰过——全用Cursor和其他工具搞定。

Yeah. I had cursor CEO Michael Thorell on the podcast, and this is his big vision is what comes after code. Mhmm. And way we should be like, exactly, exactly. And I also just had the founder of Base44 on the podcast who sold, you know, built this company, sold $80,000,000 to Wix and he shared that for the, so he's been around for six months, the company for the last three months, he hasn't touched a single line of front end code, all base 44, and, or sorry, all cursor, and other tools he's using.

变革正在发生。

So this is happening.

对于Every内部的人来说也是如此，就像没有人再手动编写代码了。

Same thing for people inside of Every, like no one is manually coding anymore.

好的，这个绝对值得探讨。在那之前，你还有什么其他高见想分享吗？

Okay. Definitely need to talk about that. Before we do, any other hot takes that you want to throw out there?

我还有另一个观点，就是我对AGI（人工通用智能）有个定义。众所周知，AGI很难定义——究竟什么才算是真正的人工通用智能？图灵测试曾是标准之一，但我们在很多方面已远超图灵测试的范畴。目前缺乏公认的好标准。我注意到，可以通过放手让AI自主工作的时间长短，来判断它的进步程度。

I have one other hot take, which is I have a definition for AGI. And so AGI is like famously hard to define, like what it what does it mean for it to be artificial artificial general intelligence? The Turing test was one, but like we pretty much blown past the Turing test in a lot of ways. So we we have no good one. And so what I have noticed is that you can tell how much better AI is getting by how long a leash you can give it to do work.

比如Copilot时代就像按Tab键补全代码，那只是起点。ChatGPT能回答问题返回结果，可能比Tab补全稍强些。如今到了Claude Opus四代和Gemini这类模型，配合深度研究，它们能持续工作二三十分钟。这个'放手时长'在延长，人类需要干预的间隔也在拉大。这让我联想到儿童心理学家温尼科特的理论。

So with Copilot, it was like a you can tab complete, and that was like the beginning. With ChatGPT, you ask it a question, and it it returns a response, and that's like maybe slightly better than a tab complete. And then now with with Cloud Opus four and Gemini and all that kind of stuff, like it can go off and and work for also with Deep Research, it can go off and work for like twenty or thirty minutes. So that leash is getting longer where where you have to intervene. And I was thinking about this, and it reminded me of Winnicott, who's a child psychologist.

他写过《游戏与现实》这本书。他认为从婴儿成长为成人的过程，就是逐步'被放下'的过程——刚出生时你与母亲或照料者完全共生，成长就是学会在能承受的时刻逐渐体验分离。

He wrote this book called Playing in Reality. And his conceptualization for what it means to become an adult, what it means to go from being an infant to a child to an adult, is when you're when you're first born, you're effectively fused with usually your mother, your caregiver. Like, there's no difference between you and her, or you and whoever your caregiver is. And growing up is this process of being gradually, like, let down in certain moments where you can handle being let down. So you learn that there's a separation between you and your caregiver.

对婴儿来说，从全天候贴身照顾到偶尔独处（比如哭闹时暂不抱起），这种体验教会你与父母是独立个体。养育孩子就是要把握何时该适当放手让他们独立。人类发展也有类似的'放手时长'概念，能独处的时间越来越长。目前AI大概处于二三十分钟水平...

So for infants, it's like instead of being fused at the hip for every hour of every day, you get left alone. Maybe it's like you get left left alone to cry it out. Like, who who knows if that's like the right thing to do with infants? A lot of consternation there. But like, that's teaching you that there's a separation between you and your mom or you and your dad.

当然不可能让学步儿童独处二三十分钟，但可能比那个阶段稍成熟些。

Like, there's there's not gonna always be someone to pick you up. And raising a child is about knowing when they're ready to be let down a little bit and have to stand up on their own. So I think there's that same leash with human development. It's like you get longer and longer periods of time where you can be on your own. So we're still in the kind of like twenty to thirty minutes is like maybe maybe I don't know.

大概二三十秒吧。

I guess you probably can't leave a toddler alone for twenty to thirty minutes. But like, you know, it's a little bit older than a toddler.

对学步儿童来说，可以共处一室但不需要时刻互动，有时能持续二十分钟。我认为AGI也存在类似的'放手法则'。因此我对AGI的定义是：当人类经济上合算到可以永久运行智能体时，它就达到了AGI——永远不需要关机。

Maybe twenty, thirty seconds.

You you can with a toddler, it's like you can be in the same room, but not interacting with them total like, every single second for for twenty for twenty minutes sometimes. So it's it's around there. And I think there's a similar I think that we have that similar leash with AGI. And so I think a good definition of AGI is when does it become economically profitable for people to run agents indefinitely? So it just never turns off.

You you can with a toddler, it's like you can be in the same room, but not interacting with them total like, every single second for for twenty for twenty minutes sometimes. So it's it's around there. And I think there's a similar I think that we have that similar leash with AGI. And so I think a good definition of AGI is when does it become economically profitable for people to run agents indefinitely? So it just never turns off.

这是一段永远在运行的云端代码，它时刻在运作。你从不需要关闭它，因为这值得持续运行——它永远不会等你发出‘下一步’指令才行动。

It's a cloud code that's always running. It's always doing something. You just never turn it off, and you don't need to because, like, you know that it's worthwhile to keep it to keep it on. It's never waiting for you to be like, okay. Next thing.

当你需要时它总能响应‘下一步’指令，但本质上它像青少年般自主运转，而这正是其价值所在。你更希望它主动作为，而非等待指令。这非常有趣。

It'll always respond to you when you're like, okay. Next thing. But it's off just essentially living its life like a teenager, and that is profitable for you. You'd rather have it do that than just wait for you to tell it what to do next. And Interesting.

我认为这就是通用人工智能（AGI）的精确定义。

I think that's the good definition of AGI.

其中盈利性还涉及运行成本与持续运作的平衡

And the profitable piece is also just the cost of running that thing and having it

这既是成本问题也是价值问题。当然你可以耍小聪明——比如让Claude无限循环运行——但我指的是更广泛层面：全天候工作的智能体被大规模采用。

It's it's partly the cost and partly the value. Right. And, obviously, you can, like, game this a little bit and be like, cool. I'm just gonna, like, tell Claude to, like, run-in a loop forever, but, like, I'm talking about more than that. Like, more widespread more widespread adoption of agents that that work all the time.

我认同盈利标准：当运行需少量成本且保持盈利时，意味着它必须持续提供实质价值才值得留存。

And and I like the profitable thing because if it costs a little bit of money and we're we're the bar is profitability, then there's, like, a it has to actually be doing something useful for you to keep it on.

有趣的是，这完全对应资深员工的隐喻——自主性越强，所需指导越少，绩效评估越简略，与其资历深度直接相关。太准确了。还有其他相关见解吗？

It's interesting how that also is very the metaphor of a of a senior employee and autonomy, and essentially the more autonomous they are, the less instruction you have to give, the less reviews you have to do is also just directly correlated with how senior they are. Totally. Okay. Great. Anything else along these lines?

观点很多。我特别反感‘取代工作’或‘让三分之二劳动力失业’的标题党，这根本不现实。也厌恶‘使用ChatGPT会让人停止思考’这类论调。

I mean, have plenty of them. I think I'm generally like, I hate the headlines that are like, it's gonna replace jobs, or like it's going to unemploy like two thirds of the workforce. Like, don't think that's true. I hate headlines that are like, you don't use your brain when you use Chachi B. T.

还有更典型的标题：‘单独医生、医生+AI或纯AI哪个更好？AI胜出意味医生将淘汰’。这类言论极其愚蠢。以医疗为例，使用AI本身就是项技能。

Or like there another another good headline is like doctors alone, doctors plus AI or just AI, like which one is better? AI is better. Therefore, like doctors are gonna be outmoded. Like all that stuff is, I think, pretty dumb. So for the doctors plus AI example, I think it's important to recognize that using AI is a skill.

若研究完全不熟悉AI的医生，确实可能制造出‘纯AI更优’的假象。虽然某些场景确实如此，但医疗决策涉及大量复杂情境，单次研究根本无法定论。更何况在技术飞速发展的当下，不能要求医生立即精通AI。但我敢断言：五到十年后局面将彻底不同。

And so if you study doctors in a vacuum that, like, don't really have a lot of experience with AI, yeah, you could probably create a situation such that, like, it's better to just to just use an AI. And sometimes it is going be better, but there's a lot. There's so many contexts that doctors need to make decisions and do things that it's really hard to take one study and make any sort of conclusion about that. And it's especially hard when you're dealing with a technology that's developing so rapidly that doctors can't really be expected to be experts at it yet. But I would guess in five or ten years that would be totally and completely different.

以学生为例，或者就像那个‘AI让你大脑停转’的例子，我认为关键在于理解科技发展史上总是如此——为了获得某些能力，你必须放弃另一些技能。比如，柏拉图 famously 对书写持怀疑态度，他认为这会损害记忆力，事实的确如此。相比古人通过记忆长篇史诗来娱乐彼此的年代，我们的记忆力确实有所退步。但用稍微衰退的记忆力换取书写能力，我认为是值得的。AI的情况也类似，在某些任务上你的投入度可能会降低。

For the student example, or like the, you know, AI turns your brain off example, I think it's really important to understand that in the history of technology, it has always been the case that you give up certain skills in order to get other ones. So for example, Plato was famously very skeptical of writing because he thought it would harm your memory, and it did. We don't remember things quite as well as they did back in the day because they had to remember long epic poems to entertain each other. But I think writing is a worthwhile trade for having a slightly worse memory. And I think something similar is going on with with with AI where, yeah, you may you may be slightly less engaged in certain tasks.

但如果正确使用AI，你将在获得更强能力的其他任务上投入更多精力。你可以设计实验证明使用AI时大脑连接性下降，就像能设计实验证明掌握书写技能的人记忆力更差一样。但我想没人愿意回到全民文盲的时代。

But if you use it right, you're going be way more engaged in other tasks where you have much more power. And so you can construct a study that says brain connectivity goes down when you use AI in the same way that you could construct a study that says people's memory is were are worse when they have writing skills. But I don't think anyone would want to go back to a world where no one was literate.

这太有趣了。尼日利亚等地的研究都显示了AI对学生学习的助益，以及人们进步神速的效果。在这个语境下，你指出的核心观点很重要——我们会失去某些东西，但希望获得的远多于失去，目前看来确实如此。

That is super interesting. There's all these studies that are showing the benefits of AI to students with these studies in Nigeria and just how fast people progress. So I I think it's really important in this context. You're showing up that you will lose some things, but the gain the hope is the gain is much higher, and so far it seems like it will be.

是啊。人们总是——尤其是在技术炒作周期或范式变革初期——容易低估变革速度。我常举的例子是：我住在布鲁克林，街尾的裁缝店至今不接受信用卡。信用卡已存在很久了，但即使最理想情况下，这类技术的普及也需要漫长过程。

Yeah. I think people always, especially at the beginning of a tech hype cycle or a revolution paradigm shift, it's always easy to underestimate how quickly things are gonna change. And the example I always use is, I live in Brooklyn, and the tailor down the road down the street from me, like, doesn't accept credit cards. Like, credit cards have been around for a long time. So it takes a long time for technology like this to be adopted even in the best case.

人类擅长处理的特定场景复杂度很容易被低估。虽然在测试中取得高分很惊人——我热爱AI，它确实令人惊叹——但这并不意味你能直觉感知替代具体工作环节的真实难度。举个直观例子：一个月前我用周末做了个小实验，测试AI能否预测我在会议中的发言。

And I think it's really easy to underestimate how complex specific contexts are that humans know how to, like, deal with. And just because you can get a really good score on a test, it it it's incredible. I love AI. It's so incredible, but it doesn't it doesn't actually give you an intuition for how difficult it is to actually be replacing specific parts of work or activities that you do. I think a really good thing to give you a maybe like a little bit of an intuition for it is I built this thing over a weekend, like a month ago, that was, can o three, can it predict what I'm going to say in a meeting?

这就像个基准测试——我称之为CEO基准测试。灵感来自OpenAI的黄金测试标准：他们用内部代码库测试模型强度，因为那些代码从未公开。我的会议记录同样未上网（虽然部分发言内容有重叠），于是我用前沿模型测试这些原始记录，结果相当糟糕。

It's like, we that's a benchmark. It's a it's the CEO benchmark. And the reason I did that is because OpenAI is the gold standard for OpenAI for testing how powerful a model is, is they test it on their internal code base. So they say, how good is the new model at predicting what comes next in our internal code base? Because that's not anywhere out on the internet.

确实很糟糕。但这并非因为模型不够聪明。Spotify的Toby提出了'情境工程'概念——即在正确时间为模型提供正确情境信息，这至少占模型表现的50%。我认为这个观点100%正确。

So it's a really good benchmark for that. And so I was like, well, my meeting transcripts aren't anywhere on the Internet. A lot of what I say is on the Internet Internet, and some of the there's some overlap, but be kind of interesting. And so I ran a bunch of the Frontier models on this on just like my granola transcripts. And they're pretty bad.

三年来我一直在写相关文章，当时称为'知识编排'。现在觉得'情境工程'可能更贴切。这是极其复杂的问题，绝非单纯扩大上下文窗口就能解决。我相信会逐步改进，但当AI能预测我会议发言时，我将直接把它作为工具使用——而这又会彻底改变我的发言动态。

They are pretty bad. And it's not because they're not smart. There's this real push now. Toby from Spotify coined this term called context engineering, which is like getting the context to the model, the right context at the right time, like is at least half the performance. And I think that's a 100% true.

事情没表面那么简单。

It's something that I've been writing about for like three years. At the time I called it knowledge orchestration. I think context engineering is is a better probably a better term, but, like, it's totally true, and and and it's that's a very, very hard problem to solve. It's not just, a one shot problem where it's, like, you know, gigantic context window and we're done. It's go it's I think it's going to get better over time, but the minute it gets good at predicting what's what's gonna what I'm gonna say next in a meeting, I'm just gonna use it as a tool, and that's gonna change the entire dynamic of what I say next in a meeting.

So it's not as easy as it seems.

So it's not as easy as it seems.

有意思。我想你可以基于此构建一个GPT模型，这样就不再需要与丹开会，直接和它对话并协助决策就行了。

Interesting. I imagine you can build a GPT from that, and then instead of having a meeting with Dan, now just talk to this thing and help make decisions.

确实如此。其实我们已经在小范围实践了——虽然它不能完全预测我在会议中要说的每句话。但如果你是CEO、创始人或管理者，会发现工作中很大部分是在重复自己。而这轮AI革命最棒的一点，就是能让你摆脱这种重复。

Definitely. And I I I mean, we do this a little bit. It's not the same as it's not the same as having being able to predict exactly what I'm gonna say in a meeting. But I think if you're a CEO or founder or manager, it's really stunning how much of your job is just repeating yourself. And that is one of the best things about this AI, particular AI revolution, is that you don't have to repeat yourself.

比如上个季度，我设定了一两个季度目标，其中最重要的就是'不要重复自己'。我尽量避免在会议中说重复内容。我们有个每日简报，这是运营中的重要部分...

And so we had it like last quarter. I I tend to set like one or two quarterly goals. And like one of my big goals for us last quarter was don't repeat yourself. So I don't wanna ever say the same thing in a meeting twice if I if I can help it. So for us at every like one of the big parts of every is we have a daily newsletter.

我花大量时间给标题提建议、指导如何写引言或评估创意价值。后来我们把这类反馈都编成提示词——虽不能完全复刻我的表达，但能将我的审美标准前置。这样即使写作者无法直接联系我，他们在提交前已经与某种'模拟版的我'交流过，这效果非常惊人。

And I'm spending a lot of time like giving feedback on headlines or giving feedback on how do you write an intro or like how is this what is this idea any good? Like that kind of stuff. And we started to codify all that into prompts that basically it's not the same as mimicking me. It can't exactly say exactly what I'm gonna say in a meeting, but it pushes my taste out to the edge so that writers who are not able to talk to me, like, by the time I see it, they've already talked to, like, some simulation of a simulation of me. And that's incredibly powerful.

让我们深入这个话题——这正合我意。你们打造的商业模式、团队运作方式，简直代表着AI时代企业运营的最前沿。你们全力拥抱AI优先战略，这与你们的写作理念高度契合。

Let's follow this thread. This is exactly where I wanted to go. I feel like the business you're building, the team you're building, the way you're operating is the very bleeding edge of how companies will operate and are trying to operate in this AI era. You guys are trying to be super AI first. It's super aligned with just so much of how you're writing.

你们的研究价值巨大，真的感谢分享——这对所有人都有启发。首先请告诉大家Every究竟是什么，再分享些你们的运作心得。

There's just like so much reason to study what you guys are doing. Thank you. And this is benefiting all of us. So thank you. So first of all, just tell people what the heck every is, and then share a few insights into just how you operate.

你笑得很耐人寻味。

It's funny that you laugh.

总有人这么问，因为我们的公司形态确实特殊——历史上虽有类似案例，但在AI赋能下才真正可行。我通常这样定义Every：我们在AI前沿探索创意与应用。核心业务是运营了五年的每日简报...

Everyone asks that because it's just, it's like a, it's a very, it's just, it's a very weird shape of a company that you can actually see other companies that have this shape from earlier eras, but they're, it's a little bit, it's less common. It doesn't make as much sense, and I think it's newly enabled by AI, and, and we can talk about why. But the way, the way that I typically talk about, Every, is, we do ideas and apps at the edge of AI. So, the core of the business is we have a daily newsletter. We've been doing it for about five years.

目前约有10万订阅用户，顶尖AI实验室的研究员都是我们的读者。每当OpenAI或Anthropic发布新模型，我们都能提前试用并撰写评测——这简直是我的理想工作，热爱至极。

We have about a 100,000 subscribers. All the people from the top AI labs read us. Anyone who's who's basically interested in or working in AI at the frontier and wants to know what's going on reads us. We do a lot of like, for example, whenever whenever OpenAI or or Anthropic drop a new model, like, get our hands on it early, and then we get to play with it and write about it, which is it's like my ideal job. I I love it.

简直太棒了。虽然不该在播客里说脏话...但真他妈完美。

It's the best. Don't about the curse on this podcast, Perfect. But it's the fucking

运用得很棒。你们管那些叫氛围检查吗？就是那个

Excellent use. And you call those vibe checks? Is that the

对，我们称之为氛围检查。氛围检查。

Yeah. We call them vibe checks. Vibe checks.

很喜欢这个概念。

Love those.

我认为这非常重要，因为这涉及到我们工作的下一个部分——应用程序相关的内容。进行氛围检查并称之为氛围检查很关键，因为它们关注的是使用这个东西时的感受，以及在工作或生活中正常使用它时的体验。我觉得这捕捉到了标准基准测试无法真正衡量的东西。而最适合撰写氛围检查的人，正是那些实际在一线使用它的人。久而久之我们发现，最优秀的技术文章和内容往往来自真正在使用和构建它的人。

Which I think is really important because and this gets to the next part, the the apps part of of what we do. I think it's really important to do vibe checks and and to call them vibe checks because they're about how does it feel to use this thing, and how does it feel to use it for work, for things that you would normally use it for, like, in your job or in your life. Because I think that captures something that standard benchmarks just don't capture and really can't. And the best people to tell you to write a vibe check are people that are actually at the edge using it for stuff. And so what we found over time is we have we we love, we think the best writing and content about technology is from people that are actually using it and building with it.

因此我们始终保持着这种功能——除了写作外，我们还在不断构建小型实验项目，这帮助我们产出优质内容。如今这些已发展成一套内部运行的应用程序，开发者们同时也是作家，他们参与氛围检查等内容的创作。这样你就能深入了解这些为日常使用者打造的产品是如何构建的。我们的应用套件中有一个叫Quora的产品，就在录制当天刚刚公开发布，这太棒了。

And so we've always had this sort of function where we're always building little experiments in addition to our writing, and that that helps us write great stuff. And that has turned into a suite of apps that we run internally, and the people who are people who are building those apps are also writers, and they're contributing to things like vibe checks. So you get a really inside look into how is this stuff being built for people who are actually using it every day. And the suite of apps that we have, one's called Quora. We just launched Quora publicly on the day that we're recording this, which is really awesome.

恭喜。谢谢。你可以把它想象成邮件AI总管，用人工智能帮你管理邮件，非常酷。

Congratulations. Thank you. You can think of it like a chief of staff, an AI chief of staff for your email. It helps manage email with AI. It's very cool.

详情我们稍后可以深入探讨。我们还有一款叫Sparkle的AI文件清理工具，另一个叫Spiral的产品用AI实现内容自动化。最初我们孵化了Lex——一个AI文档撰写工具，后来它独立成为公司，由我的联合创始人Nathan运营。基本上我们将所有产品打包成套。

We can go into more of it later. We have another one called Sparkle, which is an AI file cleaner. We have another one called Spiral that does content automation with AI. We originally incubated Lex, which is an AI document writer, which we spun out into its own company, and my every co founder Nathan runs that. And basically we bundle everything together.

只需支付一次费用，就能使用我们开发的所有软件，我们会持续往套件里添加新产品。关于我们偏好孵化什么类型项目及孵化方式——这里有很多特别有趣的东西——我可以再详细介绍，不过我已经说了很多，就先到这里吧。

So you pay one price, and you get access to all of the software that we make, and we're constantly putting new stuff in the bundle. And I can tell you more about, like, what kinds of things we like to incubate and how do we like to incubate it, because I think there's there's a lot of there's some really interesting special things in there, but I've been blabbing for a while, so I'll stop there.

还有个我想讨论的咨询公司业务，不过我们先聊...

There's also consulting firm which I wanna talk about, but let's talk We off

我们确实有咨询业务，这是公司业务的第三支柱。虽然它和我的应用流理念不完全契合，但我们花大量时间帮助大企业转型为AI优先：培训员工使用人工智能，这部分工作非常酷且充满乐趣，是我们业务中极其重要的一环。

have consulting. We also do that, and that is another, that's like the third leg of the stool in the business. It doesn't fit quite as nicely into my ideas in app streaming, but we spend a lot of time with big companies where we teach them how to basically how to be AI first. We train all the people on how to use AI, and it's it's very cool. It's it's really it's really fun and and very a very important part of what we do.

这感觉像是价值十亿美元的生意。想回头再谈谈这个。我也这么认为。因为大家都想学这个。好的，分享一下你们团队运作的几种方式。

That feels like a billion dollar business right there. Wanna come back to it. I think so. Because everybody wants to learn this. Okay, so share a few ways that you guys operate.

你提到团队不写任何代码。那你们是如何高效运作的？我知道你们团队很小。你们有每日通讯，三四个产品，还有咨询业务。整个团队有多少人？

You mentioned that your team doesn't write any code. What are just some ways that allow you to operate this efficiently? I know your team's really small. You have a daily newsletter, you three, four products, you have a consulting arm. How big is the team of everybody?

我们有15个人。

We have 15 people.

15人，好的。那么请给我们讲讲你们处于前沿的一些运作方式。

15 people, okay. Yeah. So just give us insight into some of the ways you operate that are kind of at the bleeding edge.

好的，说几点。首先——我认为每个人都该这么做——我们设立了AI运营主管。我每周和她碰头，每当发现重复性工作时，我们就列入待办清单。她持续构建提示词和工作流，让我和团队成员尽可能实现自动化。这带来了重大突破，因为如果整天忙于救火，你会纠结：是用熟悉的老方法，还是冒险尝试可能失败的新方法？比如花大量时间在Zapier上搭建无代码自动化。

Okay, so a couple things. One, and I think everyone should do this, is we have an AI a head of AI operations. I sit with her once a week, and every time I'm doing something repetitively, I'm like we put it in a to do list, and she's just constantly, like, building prompts and building workflows and stuff like that so that I and everyone else on the team is are just automating as much as possible. And I think that has been a big unlock because it's really hard to if you're working in a job all day, you're fighting fires and like you're you're like, am I gonna do this in the way that I know how, or am I gonna do it in the new way that might not work? I'm gonna spend a bunch of time in Zapier building some no code automation.

我不想那样。AI运营主管能识别这些问题并解决，而无需实际执行者额外耗时，这大大提高了落地可能性。关键技巧在于确保方案被采用，本质上是在内部开发小型应用。如果你擅长开发人们爱用的工具，效果会非常好。

I don't wanna do that. And having an AI operations lead lets you basically identify those things and have them solved without people who are doing the work actually getting in getting like having to take time to do it, which I think makes it much more likely it happens. There's always a trick with that where it's like, you have to make sure it gets used. So it's basically you're developing little applications internally. But if you're good at making applications people use, it's great.

强烈建议设立AI运营主管。

Highly recommend having an AI AI operations lead.

我猜你看到Quora CEO的推文了，他正想招聘这类人才。显然这是趋势。按照你的观点，这个人应该脱离公司日常事务，专注用AI提升团队效率？

I imagine you saw the CEO of Quora tweeted about this wanting to hire exactly this sort of person. Yeah. So clearly this is a trend. So the idea is this per, like your point that this needs to be somebody who's, who's outside of the day to day work of the company, and is specifically focused on helping the team be more efficient with AI. Yeah.

是的。

Yeah.

那这个人主要是帮你自动化，还是也能协助其他人？不是。

And then is this person mostly just you automating you, or can they help other people? No.

她基本上帮助所有人。每个人。好的。我们现在从编辑部门开始着手。编辑工作中有太多事情需要处理，比如我或者我们的主编凯特，凯特经常做一些小的文案修改，确保所有内容都符合风格规范。

She helps she helps everyone basically. Everyone. Okay. Where we're starting right now is with the editorial operation. So there's so much stuff in the editorial operation where I or our our our editor in chief Kate, like, Kate is constantly doing, like, little small copy edits to make sure everything is, like, in every style.

这每天要花好几个小时。现在Opus已经发展到这样一个阶段：你给它一个风格指南和提示，它就能检查你写的任何东西并进行文案编辑，这真是太棒了。关键在于，不仅仅是构建这个功能，你还要让凯特养成习惯，每次有人给她东西时都会问‘这个用过提示了吗？’

It takes, like, hours hours a day. And so now Opus is at a point where you can give it a style guide and a prompt, and it'll go through go through anything you're writing and copy edit it, which is amazing. The trick is, it's not just building that. You also have to get Kate to be like, did you put this through the prompt yet? Anytime someone gives her something.

所以还需要一些行为习惯上的改变，我觉得这是个非常有趣的组织挑战。对我们来说可能稍微容易些，因为团队里的每个人都以AI为先，都很愿意尝试。我们没有什么人会说‘我不知道，我不想做这个’，而这在很多组织里都是个难题。

So there's a little bit of, like, behavioral update too that has to happen, which I think is a really interesting organizational challenge. I think for us, it's a little easier because everybody inside the org is very AI first and just wants to go do it. We don't have anyone really who's like, I don't know. I don't really wanna do this. And that's a whole different challenge, which I think a lot of organizations face.

但总会有如何让人们使用它的问题。

But there's always a problem of getting people to use it.

这太酷了。那位负责AI运营的人背景是什么？

That is super cool. What is her background, this AI operations person?

她叫凯蒂·帕罗特。她为我们做了很多代笔工作。Every团队里的建设者们通常自己写东西，但有时需要帮助时，她会协助他们撰写正在做的工作内容。她就是这样加入我们的。现在她依然做这个，但也花很多时间在AI运营上。

She her name is Katie Parrott. She does a lot she actually does a lot of ghostwriting for us. So she also when when people inside of Every who are builders, often they just write themselves, but sometimes they want help, and she'll help them write about whatever they're working on. So that's how she started with us. She still does that, but she also spends a lot of time doing the AI operations stuff.

之前她在Animals工作，那是一家顶级的内容营销机构。他们非常注重流程。我觉得凯蒂这么出色的原因在于，她非常擅长流程性工作，同时她也是个优秀的写作者，而且对AI充满热情。她喜欢钻研尝试。

And then before that, she was, she worked at Animals, which is a content marketing agency, like one of the top content marketing agencies. And they're very process oriented. And I think the reason Katie is so good is because she's incredibly good at that kind of process stuff, or like thinking about that. But she's also a great writer, and she's also just incredibly excited about AI. She just wants to tinker and wants to ease it.

正是这点让我决定，与其只做代笔，不如让她来负责这个。结果非常棒。所以至少你需要一个愿意钻研、喜欢构建的人。

That was the thing that got me to be like, okay, you should just come and do that instead of just ghost writing. We should add this to your plate. And it's it's been really fantastic. So I think that's a at minimum, you really just want someone who's just like, I wanna tinker. I wanna build stuff.

如果这个人还有点流程意识会更好，了解他们为之构建的领域技能也很重要。

There's also people who have a little bit more of that process orientation. I think that is important. And to the extent they understand the craft of the thing that they're trying to build for, that also helps a lot.

这个建议太棒了。感觉大家都要开始招聘这样的...

This is an amazing tip. I feel like everyone's gonna start hiring these I

我也这么认为。还有其他人讨论过这个话题。我听过Rachel Woods的观点，她对AI领域思考很多。她提到这种现象正在形成趋势，而且我认为这极为重要，它会渗透到组织的每个角落。比如我们虽然在编辑部门内部推行，但Quora上发布的文案数量庞大。

I think so. There's there's a couple other people who talk about this. I heard Rachel Woods, who's another sort of she thinks a lot about AI stuff. She's she's talking about I think it's becoming, like, it's becoming a thing, and and I think it's I think it's really important, and and it just, like, bleeds out into every other part of the org. So, like, we're doing this inside of the editorial org, but there's a lot of copy that goes out on Quora.

顺便说下，Quora拼写是c o r a，和q u o r a不同——确实容易混淆。无论是Quora、Spiral还是Sparkle平台，我们都需要保持同等质量标准的文案输出。经常有工程师给Kate发消息说：'这是Figma文件，能帮忙做文案校对吗？'

And by the way, Quora is spelled c o r a, so it's different from q u o r a. Slightly confusing. There's a lot of copy that goes on a Quora or Spiral or Sparkle that we want to have that same every quality bar for. And so we have, you know, engineers sending Kate like, here's the Figma file. Can you go and, like, do copy edits?

这对所有人都是负担。Kate只有一个人，实在难以应付。于是我们程序员Nitesh开发了Cloud Code指令——它能自动调用提示词扫描整个代码库的文案，在GitHub创建pull request并发送给Kate。现在她只需审核PR判断合理性就行。这样就能把提示词转化为工程师可用的格式，整个工程团队突然都能按既定风格撰写营销文案了。

And that sucks for everybody. And Kate is one person and it's just really hard to do that. So one thing that we did, Nitesh, who's one of the programmers engineers on Quora, built a Cloud Code command that just uses that prompt, and checks through the entire code base for all the copy edits, and then creates a pull request on GitHub, and then sends the pull request to Kate. So she's just like looking at the pull request and being like, does this make sense? And so you can translate that prompt into, for example, a format that engineers can use, and suddenly your engineering team is writing marketing copy in the style you want.

我觉得这太酷了。

I think that's so cool.

确实惊艳。我想稍微延伸下话题——你不断提到Claude，我很好奇你们团队日常使用哪些工具栈？Claude似乎是核心组件？

That is extremely cool. I wanna take I'm gonna take this on a little tangent. Keep mentioning Claude, and I'm I'm curious just what is kind of in the stack of tools that you find yourself using, your team ends up using? This seems like Claude is a core part of it.

我确实钟爱Claude。不过日常首选其实是O3，我就像个ChatGPT男孩。O3质量极高，特别适合写作场景。

I do love Claude. I would say I'm generally my first thing that I open is O3. I'm like a Chachu tea boy. And I think O3 is super high quality. I think it's great for writing.

它编码和各类任务都很出色。相比Claude最大的优势在于具备记忆功能。我花了大量时间训练ChatGPT：'文字要犀利简洁'——现在它已深谙此道。当我让它撰写内容时，水平可能超越普通ChatGPT用户。我还常用它进行自我反思和个人成长分析，它很了解我。

It's great for coding. It's great for all that stuff. And what it has that really makes a difference still from Claude is it has memory. And I just love that. I've spent so much time yelling at Chatuchibi Tea about, like, I need my writing to be punchy and concise.

比如发送会议记录问表现如何，它会指出：'你老毛病又犯了，但某方面进步显著'——这种反馈很棒。所以O3是我的日常主力工具。

You know? And it just knows that now. So I think when I ask it to write something for me, it's, like, actually better than yours or maybe not yours, but, like, you your average your average Chatuchibi Tea user. And I also find like, I I use it a lot for self reflection and personal growth type stuff. So it knows me.

Claude Opus方面...Claude Code基本是我们的标配，开发时绝对离不开它。Gemini刚推出新功能，我很期待尝试——因为这是我们应用内置最常用的模型。

So when I send it a meeting transcript, and I'm like, how did I do? It's like, well, you did that thing that you normally do, but you're way better on this other thing. And I I like that. I think that's I think that's really great. So day to day, o three, that's my go to.

I think Claude Opus is, first of all, Claude code. Everyone inside every, that's basically what we use. If you're building something, you're using Cloud Code, it's crazy. It's so good. Gemini just came out with something, so I'm very excited to try that, because I think that that's the model that we use most for the apps that we build, like inside the apps.

I think Claude Opus is, first of all, Claude code. Everyone inside every, that's basically what we use. If you're building something, you're using Cloud Code, it's crazy. It's so good. Gemini just came out with something, so I'm very excited to try that, because I think that that's the model that we use most for the apps that we build, like inside the apps.

它无比强大且价格极其低廉，这太棒了。所以我打算试试他们推出的CLI工具。我们也稍微用了下Codecs，那是OpenAI的编程工具，适合那种一次性的、独立的小功能需求。我还用什么？回过头来继续用Claude。

It's incredibly powerful and it's incredibly cheap, which is great. So I wanna try the CLI tool they came out with. We also use Codecs a bit, which is OpenAI's coding tool, and that's for like, I want a one off self contained, I want to pick off this little feature. What else do I use? Going back to Claude.

Claude Opus四能做到其他模型都无法做到的事——除了另一个我不能提及的模型外。它是唯一具备这种能力的模型。

Claude Opus four can do something that no other model, except one other model that I can't talk about. Can do something that no other model can do.

我们就不深究那个了，不想给你惹麻烦。继续说吧，不过...

We won't go there. We don't want get you in trouble. Okay, go on. But

没错，其他模型都做不到这点。早期版本的Claude，包括其他模型的普遍情况是，当你问'这篇文章写得如何？'时，Claude总是给个B+。如果同一对话中你又说'我修改过了'，它会立刻提到A-。再来一轮对话，它可能就给A了。这就像缺乏那种直觉判断力...

yeah, no other model can do this, which is earlier versions of Claude, and I think generally versions of other models, when you ask them, is this piece of writing any good? Claude, example, would always give it a b plus. And then if you chain if if you did another turn of the same conversation, you're like, I updated this, it would always go to a minus. And then if you get it another turn, it would go to like a, you know? So it like doesn't have the same kind of gut.

它太过考虑你想听什么了。虽然有些提示工程技巧可以缓解这个问题，比如给它模板之类的，但始终缺少那种本质能力——它真能判断文字是否有趣或优秀吗？它具备那种直觉感知吗？

It's like, it's sort of thinking about what you probably wanna hear too much. And there's various methods that you can use to like prompt prompt engineer around this. Like, give it a template or, like, whatever. And they sorta worked, but it just still doesn't doesn't have that thing where it's like, can it tell if writing is interesting or any good? Does it have that gut sense?

而Opus四具备了。这实在太神奇了，我认为这极其重要，因为由此开启了语言模型作为评判者的各种应用场景。比如我们正在开发新版本的内容自动化产品Spiral——你之前用过——本质上是在做Claude代码版的内容风格产品。

And Opus four has it. It's really wild. And I think that's I think that's super important because it opens up all these use cases where you might wanna use a language model as a judge. So for us, for example, we're working on a new version of our product Spiral, which does content automations. You've used that in the past.

当你说'我想试发推文'并给出文档后，它会建立待办清单自主创作。最妙的是现在它能自我评判：'我写了三条推文，先判断质量如何'，在提交前自我改进。这个突破太关键了——我们之前花了三个月试图搭建复杂的评判系统。

And we're doing a essentially Claude code, but for content style product, where you know, you say, I want to try to tweet, you give it all the documents, it has a bunch of memories, it creates a to do list for itself, and then it goes and writes. And one of the things that is so interesting is, now because it can it can judge things, Part of its to do list is, okay. I wrote three tweets. I'm gonna, like, judge whether I think these are any good, and then it can improve before it comes back to you. And that's just like a huge, huge unlock that we were struggling for, like, three months to, like, build this, like, crazy system to, like, try to get it to judge writing.

结果Opus四一次就搞定了，我们当即决定立即发布这个产品。就冲这点我太爱它了。

And then Opus four just, one shot at it, we're like, great. This product works. Let's, like, let's start shipping it. So, yeah, I love it for that.

还有其他你常用的AI工具吗？你提到过granola，除了这些之外，有没有什么被大众低估的工具？

Are there any other AI tools that you just use regularly? You mentioned granola, even outside of the bottles. So what are were some that you think maybe people are sleeping on?

我用granola。以前用Super Whisper和Whisper Flow，它们很棒。我们内部有个叫Monologue的类似工具即将发布，现在我在用。我认为语音转文本接口是未来，应该被更广泛应用。此外我们高频使用Notion，特别是它的会议记录功能。

I use granola. So I used to use Super Whisper and Whisper Flow, which I think are fantastic. We have an internal version of that called Monologue that will be shipping in like a month or so that I I use now, but you can think of them as roughly equivalent. And I think like generally speech to text interfaces are the future and more people should be using them and more people should be building them as affordances. I use I use we use Notion all the time, and I specifically use their meeting recording.

我想那基本就是我认为那差不多是好的。

I think that's most I think that's mostly the Okay.

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她是叫这个名字吧？对。好的。还有呢？你们还做了哪些你认为其他公司应该效仿或终将开始做的事？

Was that her name? Yep. Okay. What else? What else do you do that you think other companies should be doing or will eventually start doing?

Quora团队就是基兰和尼泰什两人，本质上这就是整个团队。

So the Quora team, which is Kieran and Nitesh, basically team. Two people. That's the team.

嗯。

Yeah. Well,

准确说是基兰、尼泰什加上15个Claude代码实例。所以这比听起来强大得多。

it's Quora it's it's Kiran, Nitesh, and 15 Claude code instances. So it's, you know, it's more powerful than you think.

这真是...我太喜欢这种对未来惊鸿一瞥的感觉了。

This is I love that this is just, again, a glimpse into the future.

我们做的一件很酷的事是他们发明的——和我无关——叫复合工程。核心理念是：每个工作单元都应该让下一个单元更容易完成。比如在Claude代码环境中，当你不需要大量编码时，反而会花很多时间撰写PRD文档——详细列出需要实现的需求。

One of the things that we do that I think is really cool, and they basically invented this. Like, I had nothing to do with this, is they invented the idea of compounding engineering. So basically, for every unit of work, you should make the next unit of work easier to do. So an example is, in a Claude code world, where you're not coding a lot, you end up spending a lot of time essentially typing PRDs. Like, here's a document with exactly the stuff that I need to I need to do.

对吧？然后你就可以说，好吧，酷，这就是我现在的工作了。我就专门写PRD（产品需求文档）。每个后续的PRD工作量其实是一样的。

Right? And so you could just be like, okay, cool. That's my job now. I'm gonna just like write PRDs. And so each successive PRD, it's the same amount of work.

或者你可以花点时间思考——存在一种PRD的柏拉图式理想模板。我要做的是写一个提示词，能把我零散的想法转化成规范的PRD。这样你前期投入少量工作，就能让之后所有的PRD写作变得更轻松，因为实际需要手动撰写的内容变少了。每次构建新东西时，找到这些能加速后续同类工作的小技巧，我认为能让你的工程团队获得更大的杠杆效应。比如我们现在有Kiran和Nitesh，还有刚公开的Quora项目——

Or you could spend a little bit of time being like, there's a sort of platonic ideal of a PRD. And what I'm gonna do is write a prompt that can take my rambling thoughts and then turn that into a PRD. And so you spend a little bit of work to make all of the next, like, PRDs that you're doing easier to easier to write because you're writing less of them. And so finding those little speed ups where every time you're building something, you're doing you're making it easier to do that that same thing next time, I think, gets you a lot more leverage in your engineering team. And so, like, yeah, we have Kiran and Nitesh, and, you know, Quora has it just came out of it just became public.

它之前处于内测阶段，现在已有2500名活跃用户，处理着数百万封邮件。我们这家15人的公司能做出这样的产品，确实有点疯狂。

It was in private beta. It has 2,500 active users, and, like, there's, like, millions of emails going through it, and, like, that's one of the products that we do as a 15 person company. It's it's kinda crazy.

确实疯狂。你们具体怎么实现这种加速？是靠不断优化提示词吗？

It is crazy. How do you do this speed up thing? Is it prompts that they continue to refine?

大部分是通过提示词和自动化流程之类的，没错。

A lot of it is prompts and automations and stuff like that. Yeah.

明白了。说到自动化工具，你们具体用什么工具来实现自动化？

Got it. For automations, what's the tool what's the tool you use for automating automations?

他们主要用Cloud Code。可以通过斜杠命令调用预设的重复性提示词模板。

What they're using a lot of is is Cloud Code. So you can do slash commands in Cloud Code, which are like repeated prompts that you're that you're doing.

懂了。所以本质上他们是在建立一个提示词库，把'我想构建什么'快速转化为能直接输入Cloud Code的、更准确高效的PRD文档对吧？

Got it. Okay. So basically they're building a library of prompts that make the process of here's what I wanna build to a good solid PRD that you can feed into clot code. Yeah. More correct and more efficient.

完全正确，这太有意思了。他们是把这些存在文件里还是放进项目管理系统？

Exactly. Super interesting. And and they just keep like a file or they put this into a project. Is that how they

存在GitHub上，类似GitHub的共享仓库。还有个很酷的做法：他们同时运行多个云服务，还搭配使用三个其他智能代理。

store this? It's a GitHub. It's like a GitHub. It's like in their GitHub where they can they can like share it with each other. Another thing that they do, which I think is very cool, is they they use a bunch of clouds at once, but then they're also using like three other agents.

所以他们特别喜欢一个叫Friday的智能体。

So they love there's there's an agent called Friday that they love.

那个像是...那是一个叫Friday的AI智能体产品？对，对。之前没听说过这个。好的。

That's like a that's a that's an AI agent product called Friday? Yeah. Yeah. Haven't heard of that. Okay.

有意思。他们还有个特别喜欢的叫Charlie的智能体。具体来说，我觉得他们喜欢Charlie的原因...我们为此专门做了期视频，我可以发给你看。

Interesting. There's another one called Charlie that they really love. And in particular, I think the thing they like about Charlie we have a whole video about this, which I can send to you.

好，我会留意的。

Yeah. I'll point to it.

他们还做了个AI智能体分级榜单，从S级到F级，特别有趣。我特别喜欢Charlie的一点是它直接集成在GitHub里——当你收到pull request时，直接就能@Charlie让它帮忙审查。这种让不同智能体提供差异化视角的模式效果很好，就像不同的人会带着不同观点和审美来看问题。

They did like a, you know, s tier through f tier of AI agents, which I think is so funny. And one of the things I really like about about Charlie is that it lives in GitHub. So you can when you get a when you get a pull request, you can just be like, at Charlie, like, can you can you check this out? And that seems to seems to work really well to have, like, different agents that have, like, maybe slightly different perspectives. It's like different people, you know, that have different perspectives and have different taste.

比如基兰就是个典型的Rails狂热粉，他特别痴迷Rails的那种感觉。所以他对智能体风格特别敏感，比如某个智能体给人的感觉简洁专业，带有特定风格——这可能是他喜欢的类型，而Claude又是另一种风格。最有趣的是这些智能体真的有个性，这会直接影响你使用场景的选择，甚至同时调用三个不同智能体的理由。

Like, you can I Kieran is he's like a one of those, like, like, serious rails files who are just they just love rails, and they love the way that rails feels? And so I think he has a real sensitivity to, okay, this agent, you know, for example, it's very it feels very terse and minimal and and professional and so and and it has a particular kind of style that maybe he likes versus, I don't know, Claude is a slightly different style. And I think that's I think all of that is so interesting that they that these things have personalities and that those that that changes what you might wanna use it for or why you might wanna use three of them at once.

太迷人了。这让我想起彼得·蒂尔那场访谈，他谈到招聘策略时说关键是要组建'复仇者联盟'——每个成员在某方面都是顶尖的，组合起来就是完美团队，而不是追求全能的超人。有意思的是你现在完全可以用不同公司的智能体来做到这点。

That is so fascinating. It makes me think about Peter Ding's conversation again, where he talks about his hiring strategy and one of his key lessons. And he ended up hiring, like, the current head of product for ChatGPT, the current head of marketing at ChatGPT, the current head of engineering, like, because he hires these incredible people. And his philosophy is to hire a team of Avengers where everyone is strong at certain things and together they're the perfect team versus everyone versus like the best at everything. And it's interesting that you can almost do that with different product different agents from different companies.

绝对可以。这让我感觉市场潜力比想象中更大——人们会想要不同公司的智能体，而不是全都用Devin或者Kodak。

You definitely can. And it makes me feel like there's a bigger market than people think, potentially, where people will want different companies' agents, not just all Devins or not all Kodak.

我完全同意。根本不存在什么'一统江湖的万能智能体'。啊，这个观点真有意思。

I think there really is. It's definitely not like one one agent rule them all. Oh, interesting.

对了，Quora团队那两个人是什么背景？都是工程师还是？

Yeah. Oh my god. The two people on the Quora team are what's their background? Are they both engineers or what are they?

他们俩都是工程师。好吧，基兰的背景相当惊人——他们俩的背景都非常有意思。基兰曾是一家初创公司的工程副总裁，实际上相当于那家或可能两家初创公司的首席技术官，还是创始人之一。

They're both engineers. Okay. Kieran's got this like crazy background where they both have really interesting backgrounds. Kieran's got this crazy background where he was previously VP eng at a startup. So was effectively the CTO of startup or maybe two startups and was one of the founders.

在此之前，他是位职业作曲家，再之前还当过面包师。去年我们在法国搞团队静修时，他教我们所有人做可颂面包。我做的可颂惨不忍睹，而他做的简直精美绝伦。

And then before that, he was a composer, a professional composer. And before that, he was a baker. So we did, like, a team retreat in France last year, and he, taught us all how to make croissants. My croissant was horrible. His was, like, beautiful.

听起来这真是非常出色

Seems that was that's a very good

总的来说，我认为这种多维度的天赋正是我最希望在团队中看到的特质，因为我们都是通才。大家都想用AI去做各种新奇酷炫的创意项目。有这样背景的人不仅对智能体有独到见解，还能对落地页设计等事务有良好品味——在试图将15人的通才团队扩展到五个产品时，这种能力愈发重要。至于尼泰什的背景...说实话我有点嫉妒，他直到ChatGPT问世才开始学编程。

And generally, I think, like, that kind of multidimensional type of talent is the kind of person that I love having at every because we're all generalists. We all wanna use AI for all these weird, awesome, creative things. And someone who has that background is going to have a good taste for not only agents, but what should the landing page look like or whatever, which I think is increasingly important where you're trying to scale a team of generalists of 15 people to, like, five products. So that's Kieran's background. Nitesh's background is I'm jealous because he only started learning to code when ChatGPT came out.

他一直想学编程，但全程都处在AI时代。我总跟他说：老兄，我可是初中时靠看书学的编程，得专门去巴诺书店买书。那时候遇到函数报错连谷歌都用不了，根本无处查错。

He'd wanted to learn to code forever, and he's only known how to code in an AI era. And I keep telling him, dude, like, I I learned to program in middle school from books. Like, I had to go to Barnes and Noble and, like, buy a book. And there was nothing I couldn't Google anything about, like, how this how this why why this function wasn't working.

那时候连Stack Overflow都没有呢

No Stack Overflow even back then.

没错。只有些奇怪的BD Net论坛之类，我12岁就混那些地方可能不太合适。但说真的，他的进步速度远超AI时代前的任何工程师。

Yeah. It yeah. There wasn't Stack Overflow. There was, weird BD Net forums and stuff that, like, I was like 12 and I probably shouldn't have been on there, whatever. So it's he has gone so much faster than any other engineer, I think, like in a pre AI era.

我在公司其他人身上也看到同样现象。关于AI取代初级岗位的讨论很多，虽然这值得警惕，但每当我看到年轻员工用AI工具时，我的反应都是：天呐，他们的成长速度会远超我合作过的任何人。

And I see the same thing in the rest of the company. Like, I think there's this huge question about what happens when kids like, entry level jobs are taken away by AI. And my take is, like, that that's worth thinking about. And it's it's possible that that might be a problem at some point. But my take is whenever I see a kid with LGBT, I'm like, holy shit.

比如和我们共事的亚历克斯·达菲，他负责Context Window的文案。刚完成了AI玩外交游戏的超酷项目——整个系统都是他搭建的，才华横溢得可怕。一年前刚加入时，他就像我见过无数次的典型案例：点子很棒但文笔欠佳。

They're gonna go so so much faster than any other person that I've worked with. Like, we have this guy, Alex Duffy, who works with us. He writes for context window, and he he just launched we taught AIs how to how to play diplomacy with each other, which is really cool. He did that whole thing, and he's I think he's really, really, really, really talented. And when he came to us, I guess almost a year ago now, it was one of those classic cases which I've seen, like, over and over at every, which is you have great ideas, but you're not a good writer yet.

通常这种情况下我只能安排些小任务等人慢慢进步。但在他身上，我发现他用两个月就取得了一年才能达到的成长——每次我教他叙事技巧或标题创作，他会全部录下来转化成提示词，同样的错误绝不会犯第二次。

And it's really hard for me to do anything with you until you're good enough at it. So I have to give you like small little things until you get better and blah blah blah whatever. And what I noticed with him is he was just making a year like, he made like a year's worth of progress in like two months because every time I sat down with him and told him, okay, here's how you tell a story. Here's how you think about a headline. Like, he recorded all of it, put it into a prompt, and, like, he never made the same mistake twice.

我认为由于这些技术的加持，他的成长速度远超预期。我在组织其他部门也看到了类似现象，尼泰什就是另一个很好的例子。总体而言，人们终将意识到——只要给予适当指导，那些订阅Chachi Beauty的20岁年轻人也能爆发出惊人潜力。我觉得这太棒了。

And I think he's so much accelerated from where he would have been because of this stuff. And I see that in lots of other parts of the org. So Nitesh is another good example. And so I think generally people are gonna figure out that, like, some 20 year old with Chachi Beauty subscription is, like, super powerful if you just, like, mentor them. And I think that's great.

老兄，这里面可探讨的角度太多了。比如有人担心初级岗位正在消失，年轻人根本没有入行机会——如果新人无法从基层学起，我们未来哪来的资深人才？而你的观点是，ChatGPT这类工具能让人快速进阶，根本不需要长期待在底层岗位。

Man, there's so many threads I could follow here. Like, there's all this fear of entry level people will never like, the roles are disappearing for entry level people. And so how will we ever have senior people if these people can't learn to do things as an entry level person? And what you're saying is ChatGPT and these tools help you accelerate really quickly, so you don't really need to be at the bottom rung for a long time.

没错。本质上你从一开始就在学习如何超越初级水平。这正好印证我的分配经济理论——在AI时代，管理类技能将变得极有价值。如今是人类管理者，未来人人都要成为模型管理者。

Yeah. You're effectively, like, learning how to be one level above Mhmm. The entry level from the beginning. And you have to and this is sort of my my whole allocation economy thesis where when you look at what skills are gonna be valuable in the AI era, one big group of skills are the skills of managers. Today they're human managers, tomorrow everyone's a model manager.

目前管理技能尚未普及是因为成本太高。现有8%的劳动力是管理者，但未来管理成本将大幅降低，更多人必须掌握这项技能。所以现在的年轻人，20岁左右的这批，就要开始学习这个。

Right now, is not right now management skills are not broadly distributed because it's very expensive. Another expensive thing that, so 8% of the workforce is managers. It's now going to be much cheaper to manage. So more people are going to have to do it. And so that's the thing that kids, 20 year olds, whatever I sees now are gonna start to have to learn.

不仅如此，你不能简单说'去干吧'就完事了。必须能深入具体工作并提供改进建议——他们正在同步学习管理和实操技能，两者兼备才能游刃有余。

In addition to, you know, they're it's not like you can just say, like, okay, go do it and then come back. Like, you have to be able to go into the work that's being done and help make it better, but they're learning both at the same time. They're learning how to manage and how to do the actual work, so that they're good at it.

这里说的管理是指管理智能体对吧？

And the managing here is managing agents, right?

对，管理AI。

Yeah, managing AI, yeah.

这正好回到你核心团队的观点，你说现在没人写代码了，零编码，全靠管理智能体来生成代码。

And so this is a good, coming back to your point about how this core team, and I guess you said everyone doesn't write code, zero code written, now it's just managing agents that are writing code for you.

没错。

Yeah.

哇，我从没见过这个阶段的公司，太酷了！工作流程就是：提出需求→用你们开发的提示词库优化→智能体编写代码→团队时间主要花在代码审查和输出效果评估上→持续迭代。Cursor的Michael几个月前跟我说，他预计一年后行业会发展到这个阶段——你们现在就实现了！

Okay, I've never heard of a company at this stage, so this is extremely cool. So the workflow is they give it, here's what I want, I refine it using this cool prompts library that they build on, and agents build code, write the code, then basically the time is spent reviewing code, and then reviewing the output, what does it look like, how does it feel like, and then continuing to refine. Wow. So you guys are at where Michael from Cursor said we will be, so which I chatted with him a few months ago. He said in a year, this is where he thinks thing will be.

我们现在不再看代码了。你们已经到达那个阶段了。虽然你们还在看代码。好吧，你们依然在查看。

We're we're not looking at code anymore. You guys are already there. Although you're looking at code. Okay. You're still looking

他们绝对是在看代码。是的。所以，你们知道，在进行代码审查之前你们还没准备好做任何事。而且我认为，比如Danny运营的Spiral——就是我在说的那个我们正在构建的云端内容代码工具——他花了几天时间深入研究某个我们感兴趣的第三方库的内部原理，仅仅因为这有助于了解。

They at they definitely are looking at code. Yeah. So, you know, you're doing a code review before you Not ready. Anything. And I do think, like, Danny, runs Spiral, which is the cloud code for content tool I was talking about that we're building, you know, he spent a couple of days, like, digging into the internals of some third party library that we were interested in Just because it's like, it's helpful to know.

理解这些东西确实有帮助，但一旦他弄懂了，实际上并不会动手写代码。他只是告诉云端代码该做什么。我认为这非常非常重要。

It's helpful to like understand those things, but then he's not actually like writing any code once he understands it. He's just like off telling Cloud Code what to do. And I think that's I think that's that's really that's really important.

我们正达到一个疯狂的里程碑。就像，有这样一种感觉，我们正在进入一个你不需要真正懂代码、也不用写任何代码的境界——我们会到达那里，而你们已经在那里了。我觉得这太容易被忽视其疯狂之处：一个产品团队完全不写代码。

This is an insane milestone we're hitting here. Like, there's this, you know, sense, we're getting to a place where you don't need to really understand code, you don't have to write any code, like we'll get there, and that, like you guys are there. I think this is like so easy to overlook how wild this is. You have a product team not writing code at all.

这确实很疯狂。我认为尤其疯狂的是，一个小团队里每个人都多才多艺，具备各种技能，全是通才，人人都精通AI。在这样的环境下，一个小团队能做的事是惊人的，你们几乎在发明全新的协作原则、工程方法等等。这正是我喜欢这种模式的原因——由此产出的内容质量极高，因为我们可以基于实践经验来探讨。但我要补充：目前Every的员工如果不懂编码，仍无法胜任他们的工作。

It is really wild. I think it's really wild in particular just like having a small group of people that have, everyone's multidimensional, everyone has all these different skills, everyone's a generalist, everyone's AI forward. So what you can do in an environment like that with just still a small team is crazy, and you're kind of inventing all these new principles for how do we work together, how do we do engineering, all that kind of stuff. And I think that's what makes the writing that's why I like doing that, is because the writing that we do from that, I think is really good, because we can talk about it from a sort of position of experience. But I do want to say something else, is we're not at a point yet where the people that work at Every could do what they do if they didn't know how to code.

对，这正是我想问的。

Yeah. This is what I was gonna ask.

这完全是另一个门槛。我认为在未来很长时间内，掌握编码技能仍将很有价值。但这种演进并非新鲜事——比如我初中学编程时，新潮的是Python和JavaScript这类脚本语言。但当时真正的程序员需要懂底层C语言。

Which is a a different bar. And I think for a long time, it's going to be valuable to know how to code for a long time. But this has been this is this is like a a progression that is not a new progression. So for example, when I was middle school learning to code, the the new hot thing was scripting languages, which is like Python and JavaScript. And if you were but if you were a real programmer, you would understand the language underlying Python and JavaScript, which was which is what's written in C.

脚本语言当时被认为不够正宗，要做有意思的事必须掌握整个技术栈。就像70年代C语言刚出现时，程序员还得会写汇编语言。而英语现在就像是脚本语言之上的又一层抽象。

And scripting language was, like, weren't, like, weren't totally real. And in order to, like, really do anything interesting, you had to be be able to learn both parts of the stack. Same thing for C programmers when, I guess, in the seventies C was invented. It was like, you gotta learn you gotta be able to write assembly. And English is just like a layer on top of scripting languages.

这些观点在过渡期都是对的——深入底层的能力很重要，但随着时间推移，这种需求会逐渐减少，不过过程非常漫长。即便对JavaScript或Python程序员，了解底层实现原理至今仍有价值，只是重要性已大不如前——这花了二三十年时间。编程技能的演变也会遵循同样规律。

So I think all of those all of those things were right in the sense that there's especially during transitions, there's a lot of reasons why it's important to be able to go down a layer in the stack. And it gets less and less frequent over time, but that still takes a long time. And there's sometimes when even if you're a JavaScript or a Python programmer, it's useful to know, like, how how all that how that stuff works, how it's written, see how it's how it's implemented. It's today, it's much less important than it used to be, but that took, like, ten or twenty years. And I think that's the same thing is gonna be true for programming.

掌握编程技能极其重要，能让你快速成长。虽然未来其重要性会逐渐降低，但我们离那个阶段还很遥远。

Like, having that skill is super important and will accelerate you significantly. It will sort of start to get less important over time, but we're not close to that yet.

好的，这一点非常重要。我很高兴你提到了这一点。那么，你认为我们距离雇佣非工程师人员开发另一个产品还有多远？

Okay. That's a really important point. I'm glad you went there. So do you have a sense of how far we might be from you hiring someone to build another product that isn't an engineer?

像真正的SaaS产品吗？因为

Like a real SaaS product? Because it

比如说，嘿，我们有个想法，想找个人来实际领导这个项目。

So, like, hey, we have this idea. We wanna bring someone on to actually lead it.

非常遥远，甚至看不到影子。但有许多事情可以成为产品，它们比那个低一个层次，我认为你现在几乎就可以做。比如，我们之前谈论的Dia，那款来自浏览器公司的新AI浏览器。Dia有一种叫做‘技能’的东西，本质上就像是在浏览器中运行的小型AI应用。

Very far. Like, not even not within sight. But there's a lot of things that could be products that are a layer a level down from that that I think that you could do almost now. So, like, an example, we were talking about Dia, the browser from the the new AI browser from the browser company. Dia has these things called skills, which are effectively like little AI apps that you can run-in the browser.

你可以通过提示让它们在网页上运行并为你工作。非技术人员也能构建这个。ChatGPT的自定义GBT也是同理，非技术人员完全可以实现。所以我认为，虽然我们远未达到让任何人在零编程知识的情况下构建传统SaaS应用的程度（除了演示版），

You can prompt them and they run on the web page and do work for you. A non technical person could build that. Same thing for custom GBTs from ChatGPT. The non technical person can definitely build that. So I think while I will I will definitely maintain that we're not anywhere close to anybody being able to, like, build a conventional SaaS app with zero programming knowledge, aside from just like a demo.

但未来会出现其他形式的软件。我的观点之一是：软件正在变成内容。将会出现不同于现今形态的软件形式，非技术人员即使不懂编程也能启动并作为业务运营——这在不久后就会实现（实际上已初现端倪）。只是它们看起来不像你所询问的那种形态，更像是好莱坞电影与YouTube视频的区别。

There are going to be other forms of software. One of my things, like, software is becoming content. There's gonna be other forms of software that don't look like the software today, but you can run start and run as a business as a nontechnical person even if you don't know how to code, and that'll happen very soon if I I mean, it's already kind of happening. It's just it doesn't look like the thing that you're asking about. It's like it's sort of like the difference between a Hollywood movie and, like, a YouTube video.

明白了。这对很多人来说真的很令人安心。本质上，你看到的是AI极大地赋能那些具备技能的人，让他们能做得更多。好的。

Okay. I think that's really reassuring to a lot of people. Basically, what you're seeing is AI just supercharges people who have a skill and allows them to do a lot more. Yeah. Okay.

你们团队是否有其他特别有趣的运作方式值得分享？那些能帮助你们快速行动、事半功倍的方法？

Is there any other way that you guys operate that is really interesting that might be worth sharing that helps you operate really quickly, helps you do more with less?

我...我是说，我很乐意谈谈我们构建产品的思维方式。比如决定开发什么产品？我们最终构建了什么？因为我觉得这其中有些特别之处，可能对他人有借鉴意义的模式。

I I mean, I I would love to talk about our like, how we think about building products. Mhmm. Like, what products to build? Like, what do we end up building? Because I think there's something sort of special about it that probably there's a playbook that is useful for people.

关于这点，我是最近才理清思路的——之前很多都是凭直觉行事。但回顾我们孵化的项目类型，本质上回归到最初的观点：那些历史上极其昂贵、只有富人或大公司才能购买的服务。比如为你邮箱配备的私人助理（就像行政秘书），我认为治疗师或律师也是有趣的例子。

So when I think about this is this is only sort of snapped into focus recently. So a lot of this was just like doing it intuitively without really a thought for it. But when I think about the kind of things that we have ended up incubating, it's basically it goes back to something I said at the beginning, which is there are these things that were historically really expensive that only rich people or big companies could buy. So a chief of staff for your a chief of staff for your email. I think a therapist or, like, a lawyer is another interesting example.

比如有人帮你整理衣柜或整理电脑文件就是另一个例子。代笔服务也正变得极其便宜，让每个人都能使用，即使你只是个小初创公司。基本上，当你像我们这样运营一家以AI为先的公司时，你会不断遇到各种小状况，心想‘要是有个代笔就好了’。但代笔服务很贵。或者‘要是有个律师就好了’，但请律师动辄就要2.5万美元。

Someone to, like, organize your closet or organize your organize your computer is another example. Someone to ghostwrite for you that are becoming orders of magnitude cheaper so that everyone can use them, even if you're at a small startup. And so basically, like when you're running, like we are sort of this AI first company, you're running into these, all these little things where you're like, I wish I had a ghost writer right now. But ghost writers are really expensive. Or I wish I had a lawyer, but it wouldn't cost me like $25,000 Lawyers are really expensive.

这类服务的需求远超过供给，因为它们太昂贵了。而AI的作用就是让你能说‘哦，这个可以用云端服务解决’或‘这个可以用Chechiuti处理’。于是你就能实现那些原本因价格望而却步的需求——比如请不起律师，雇不起代笔——还有很多事我们想做却负担不起。

There's a lot more demand for those services than can be fulfilled because they're so expensive. And what AI does is it allows you to be like, oh, I could just use cloud for that. I can use Chechiuti for that. And so you're you're able to you're able to use the the demand that you have that, like, can we can afford a lawyer. We have ghostwriters, but like, there's a lot more that we can't do because we can't afford it.

所以我们仍然聘用律师和代笔，但借助AI能完成更多这类工作。于是我们开始尝试用ChachiBT和Claude这些通用工具，测试它们是否真的有用、能否奏效。

So we still have our lawyer and we still have our ghostwriters, but we just do a lot more of that stuff. And so we noticed that. We start to then use, like, ChachiBT and Claude first, these general purpose tools to try it and see, is this useful? Does this actually work? All that kind of stuff.

如果效果理想，我们就会将其拆分出来做成独立应用。这次变革最特别的是整个游戏规则被彻底重置——五年前你只能做又一个笔记应用，再做一款B2B SaaS软件，本质上都是换汤不换药。而现在完全是新大陆，所有人都在边探索边创造。

And then if it does, we will, like, unbundle it into its own separate thing that becomes an app. And and I think what's really special about this time is the entire game board has been like totally reset in terms of things you can build. Where, you know, five years ago, it was like, you're gonna build another notes app. Like we've been building notes app for forever. Like another B2B SaaS app.

就像电子表格刚问世时，人们摸索各种新工作流程那样。这些后来都拆分为B2B SaaS服务，ChaiWT和Cloud也会经历同样过程。最棒的是当你发现‘用ChaiWT处理这个超好用’时，你可能就是最早意识到这个应用场景的人之一。

Like it's all the same stuff, like slightly different packaging. And now it's like totally new territory. No one knows what's going on. No, like everyone's inventing it as it happens, right? All these new workflows are being created in a very similar way to, I don't know, for example, when spreadsheets were first a thing on computers, like we were figuring out all these new workflows on spreadsheets.

由于Every团队全员都是AI优先的实践者，他们自然成为我们的首批用户。我们通过‘这产品在内部是否爆火’来衡量成功——比如之前提到的Monologue应用，大家突然都开始使用，我们就知道挖到宝了。

They got unbundled into B2B SaaS. Same thing for ChaiWT and Cloud. And what's really cool is you can be like, cool, I'm using ChaiWT for this. It's really useful for me. And you might be like one of the first people to like really notice that.

更妙的是，Every的读者群体与我们有相似气质，他们会成为下一批用户。这形成了一种独特的产品孵化管道。当前完全是未被开垦的处女地，你现在构思的东西很可能就是全新领域。

And then because everybody that works at Every is AI first and came to us because they reads Every, they read Every. So they all have the we all have the same vibe. We're all kind of doing similar stuff. They become our users. So we measure the success of the product by like, is it a banger inside of Every?

像我们这样走在前沿的组织，正在做的事情三年后将成为主流。现在可能显得小众，但当大众产生同样需求时，就会变成大事。

Monologue, app that I was talking to you about, everyone just started using it. And we're like, okay, we've got something here. And what's what's really interesting then is if everyone inside of Every is it and people read Every, they have a similar vibe to us too. So they become the next set of users. And that's a really, I think, interesting like, pipeline for building applications or building apps.

这太酷了。我听到的重点是：GPT说唱歌手是个值得开发的好点子。

It's a totally new, like, greenfield so that all the stuff you're thinking about, like, it's probably new, which is really cool. And over time, what I think is organizations like ours, people who are playing at the edge, we're doing things that in like three years, everybody else is gonna be doing. So it may be kind of niche for now, but it will be a big deal in three years when everyone else has the same needs that we do.

That is really cool. What I'm hearing is GPT rappers are a good idea and are worth building.

That is really cool. What I'm hearing is GPT rappers are a good idea and are worth building.

我百分之百认为AI说唱歌手棒极了，他们无缘无故遭受了太多非议，人们根本不明白他们有多么无可替代的价值。

I I a 100% think g g GPT rappers are amazing, and they've been much maligned for absolutely no reason, and people don't understand how absolutely valuable they are.

我觉得还有一点，你们已经完成了种子轮融资。现在或许是个好时机聊聊这个话题——这些产品不必非得成为数十亿级的爆款。你们手上有公司组合，有内容业务，所以在我看来，关键在于这些项目需要做到多大才算成功。不如展开说说...

I think there's also just, you guys are, you raised the sip seed round. I wanna, so this is a good time to maybe talk about that, just like these products don't have to become some mega billion dollar hit. Yeah. You kind of have this portfolio of companies, you have the content business, so I think there's a really interesting approach to how big these need to get to be successful. Maybe just talk Yeah.

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我真正希望Every能成为一个教导人们如何用科技（特别是AI）过更有温度生活的机构。既要通过文字和内容教学，也要开发实用工具。但我觉得建立机构的核心在于——至少对我而言——内部要像个创意游乐场，让我们有机会冒险尝试那些看似毫无逻辑的疯狂点子。可能无法向任何人解释，但就是觉得有趣。我始终在机构庄重感与...

I I really want Every to be an institution that teaches people how to live a better, more human life with technology, particularly with AI. And both, like, teaches them how to do it with writing and the content we make, and then builds tools for them to do that. And but I think fundamental to building an institution is, at least for me, the way I would like to do it, is I want internally it to feel like this creative playground where we have the opportunity to take risk and do stuff and do weird stuff that just doesn't make any sense. We can't justify anyone, but we just feel like it would be fun. And so I think I'm always playing with that dynamic tension between institutions serious.

既要建立持久重要的影响力，又要保持玩乐心态。这种张力非常珍贵。所以我总对大规模融资犹豫不决，因为那会迫使你变得过于严肃。当然有很多公司能平衡好这点...

We want this to be like lasting and important, and it should just be fun. Like, let's play around. And I think having that tension is like really valuable. And so I've always been, like, sort of hesitant to raise a lot of money because I think it locks, like, locks you into, like, having to be that serious thing that's, like, totally going for it. And there's lots of companies that figure out that balance.

但作为创始人，我希望能保持灵活性和那种玩味感。部分原因是我掌握着决策权，可能也有些深层心理因素——有兴趣可以深入探讨。总之这就是我的本心。我们初创Every时只融了70万美元种子轮，那可是创作者经济最狂热的时期...

But just for me, like, personally as a founder, I'm like, I want to keep the optionality alive and I want to keep the kind of playful feeling alive. And I think part of that comes from I know, like, I have the control to do what I want more or less. There's probably also some, like, deeper psychological things going on there, which I'm happy to talk about if you want to get into it. But, you know, I think there's also it's just that that's that's kinda what I want. And so when we started every we raised, a very small 700 k pre seed round, and this is at the the height of the creator economy.

当时我们差不多同时开始做电子报，整个市场疯狂撒钱，氛围简直离谱...

So we both we both started our newsletters. You and I started our newsletters around the same time. It was, like, the hypeiest, craziest thing. People were throwing money around. It was, like, wild.

我们只融70万就是为了既能实验创新、又有缓冲余地，但又不至于被资本绑架。还特意给所有投资人（包括你这个小股东）发了邮件声明...

So but we raised 700 k because it was, like, I wanna raise enough for us to be able experiment, have a little cash cushion, but not so much that it locks us into anything. And we like sent an email to all of our investors being like, and you're one of our investors, so you've probably got this email Tiny,

虽然投资额不大，但我确实在列

it's a tiny investor, but I'm in there. I'm in there.

我们在邮件里直说：这很可能不是风投型生意，别期待后续融资。甚至使用了改良版SAFE协议，约定三年后无论是否融资都可转股。这样既保留了做大做强的可能，也保住了我们想要的发展方式——就算成不了巨头，只要热爱就足够美好。

We sent an email to everyone being like, this is probably not a venture business, you should not expect us to raise again. And we even raised on this slightly modified safe that gave everyone the option to convert to equity in three years even if we didn't raise more money. So we we did it in a way that allowed us the option to get really big and do the traditional thing and also the option to do the do it the way we wanna do it. Maybe it's not a huge business, but we love it. That's great.

我们在最近一轮融资中采用了同样的策略，从里德·霍夫曼和Starting Line VC那里筹集了高达200万美元。我将其称为“筛选种子轮”，本质上他们承诺了200万美元，但我们随时可以按需提取。我们仅通过SAFE协议以固定估值上限操作。对我而言，这种方式在心理上极具助益，因为它让我能承担更大风险——即便账户归零，我也能随时获得资金支持。

And we did the same thing for this recent round where we raised up to 2,000,000 from Reed Hoffman and Starting Line VC. And we did it as what I've been calling a sift seed round, which is basically, they've committed $2,000,000, but we can pull it down whenever we want. And it's we just do it on a safe, at a set cap. And for me, that was that's really helpful because it allows me psychologically to take a lot more risk. Like, I don't if we go to zero on the bank account, I can get more money.

这样太棒了，完全无需纠结。但更重要的是，我和团队不需要盯着银行账户里巨额数字产生‘反正钱多可以烧’的心态。

Great. I don't have to think about it. But what's also really helpful is I'm not and the rest of the team is not staring at a gigantic number in the bank account being like, cool. Like, we can burn this. Let's burn it.

对投资者来说也是如此。像里德显然希望我们成功，但我认为他并不在意企业规模大小——他在理念上更认同我们的事业本质。如果未来做成大生意他当然高兴，而我寻求的正是这种价值观契合。因为我想守护项目的核心创造力，虽然追求巨大影响很重要，但实现影响力的途径本就可以多元。

And also for our investors, like, I I think Reid very much wants us to succeed, but, like, I don't think he he cares, like, what what size of business this is. Like, I think he's more philosophically aligned with the thing that we're trying to do. And if it becomes a huge business, he's psyched for it. And I think that kind of alignment is what I was looking for, because I think there's this core creative spirit to the thing that I wanna maintain. And I really care about having a big impact, but I think there's a lot of ways to have an impact.

比如打造百亿市值企业是一种方式，但改变人们看待世界和自我认知的方式同样重要——这正是故事的魔力所在。有时确实需要通过大公司实现，但并非必然。许多最触动我们的故事反而来自清贫的创作者。

And one of them is building a $10,000,000,000 business. I think another way is like really changing how people see the world. See themselves in the world, and I think that's what stories do. And you you don't necessarily sometimes you do that by building a gigantic company, but you don't necessarily always have to do that. Like a lot of the stories that we care about most are from people who maybe they weren't rich at all.

所以我非常认同当前模式：既能建立优质商业项目，又始终坚守改变人们自我认知的核心使命。

And so I really like creating this place where we can make a really good business, and I care a lot about that. But also core, the soul of it is about, changing how people see themselves in the I

你们创新出的募资中间路线很惊艳——不是自举创业，也不是传统风投，而是‘筛选种子轮’。200万这个额度也很妙，要是融5000万我可能反而会说‘别全存账户里’，但200万对你们刚好。

love that you've kind of innovated a new, like a middle ground way of fundraising, not bootstrap, and not just regular VC, it's a sip seed. And I love that this two mil like, you know, if I raise 50,000,000, it'd be like, okay. Get it. Let's not put 50,000,000 in our bank account, but you do have 2,000,000. It's too much for us.

没错，我们可不想看到账户里有那么多钱。

We can't Yeah. We don't wanna see that in our account.

这是另一点考量。虽然未来难料——说不定两年后我会因资金不足后悔——但我坚信小额资金也能推动我们走很远。

That's another thing. And, you know, we'll see how this ages. Like, I might be back here in two years crying the blues because, like, we didn't raise enough money or whatever. Who knows? But that's the other thing is I do think we can get so much further with with very small amounts of money.

比如Quora，整个项目至今总投入可能才30万美元，这很不可思议...

Like, Quora, I think all in to build Quora, we've spent maybe 300 k, maybe. That's crazy because

确实

And that

包括薪资。包括薪资。是的。

includes salaries. Includes salaries. Yeah.

哇。

Wow.

这个产品在三年前即使你有数十亿美元也技术上无法实现。根本不可能。因为没有GBT就无法做到邮件摘要、自动回复这类功能。所以不仅完全不可能，现在我们只需两名工程师就能完成过去需要约20人团队的工作量。我认为这意味着我们需要的资金更少了。

This product was not even technically possible even if you had billions of dollars like three years ago. Not possible. Because you can't do email summarizing and like automatic responses and all that kind of stuff without GBT. So not only was it totally impossible, but now we can get with two engineers, like we can get, you know, the the amount done that would would have taken a team of, like, 20 people. And I think that's you know, that means that we need less money.

我认为风险投资机构尚未真正意识到这一点。现在有些公司开始采用'种子自举'模式，我也好奇这会如何改变VC模式。当然，我们有特定的孵化模式，与VC模式有所不同。

And I don't think that VC has really caught up to that yet. And I think there are other companies that are doing there's a term called seed strapping. So there are other companies that are kind of starting to wake up to this too. And I'm curious about how it changes the VC model. For sure, for us, we have a specific incubation model, which is a bit different from a VC model.

我觉得在与创始人合作方面我们有些独特优势，这很酷。目前我们正在探索适合自身的发展路径，这与其他机构不同，让我们拭目以待。

And I think there's some differentiation in in the stuff that that we can do with founders, which is kinda cool. But, yeah, we're we're I'm just trying to figure out, like, a shape that works for me and that's different from other people, and we'll see how this goes.

几年后再回头看。从外部看进展很棒。结束前我想再问两件事：一是关于你们的咨询业务分支。

We'll revisit in a couple years. Yeah. Seems like it's going great from the outside. I wanna ask about a couple other things before we wrap up. One is around this consulting arm that you have.

我觉得这非常有意思，因为这可能成为十亿美元级业务。现在每家公司都在琢磨：别人到底搞懂了什么我们还没做的？我收到无数产品总监的邮件，要求引荐在AI应用上有突破的同僚。你们实质上为很多企业解决了这个问题。

I think it's really interesting because like I said, I feel like this could be a billion dollar business. I feel like every company right now is trying to figure out what the hell has everyone else figured out that we're not doing. I've had so many emails from chief product officers at companies being like, can you introduce me to some chief product officers that have done cool things with AI that we should learn from? Like so many people, and I just introduced them to each other. And it's cool because you guys are basically solving that problem for a lot of companies.

首先能否分享一下这个业务板块？其次，你们应该见过AI应用成功与失败的企业案例，两者间的关键差异是什么？

So one is just maybe share a bit about that side of the business for folks. And then two, I imagine you've seen companies that have done this really well, have adopted AI things that worked really well, they found really good gains, and then you found companies that don't. What do you find is the difference between those two?

我很喜欢这个问题，有明确观点。咨询业务方面，我们所有时间都在研究新模型、撰写相关文章并进行开发。由于受众广泛，自然有公司找我们传授经验。这部分业务虽刚起步约6-9个月，但已颇具规模——去年约百万美元，今年可能翻倍。

I love this question, and I have a very specific opinion about this. So one, yeah, the consulting arm, basically we spend all of our time playing around with new models, writing about them, and building stuff with them. And we have a big audience, so naturally, like we've gotten companies over time being like, can you just come and teach us how to do this? And so we started to do that. This is, you know, pretty nascent.

去年做了约百万美元，今年可能会更多，我们拭目以待。

It's probably been over the last like six to nine months, but like, it's a pretty big business now. Like, it's our it's it'll probably double this year. Like, last year, did about a million. Maybe it'll be maybe it'll be more this year. We'll see.

这取决于我们手头的几个大合同，所以实际金额可能远超这个数。

It depends on a couple we have a couple big contracts out, so it might be way more than that.

十亿。我预测是十亿美元。不过，再过些...

A billion. I predict a billion dollars But, in a few

对，基本上人们会问：'能来教我们怎么做这个吗？'我们的工作流程是先去调研你们的组织架构。我们会实地了解各个团队的工作内容，梳理重复性任务——就像我们早先讨论的那些案例。

yeah, basically people are like, can you come help us learn how to do this? So what we do is we spend some time going and researching your organization. So we go in and try to understand like, what is what are all the different teams doing? What are the repetitive tasks? Some of the stuff we were talking about earlier.

接着我们会先提交一份分析报告，不仅列出所有发现，还提供一个聊天机器人界面。你可以调阅所有访谈记录并自主提取洞察。我们还有完整的数据看板，直观显示哪些团队积极投入、哪些团队消极应对，以及基于访谈和AI分析得出的各团队潜在增效空间——这整套系统其实是我一年前和Devin花了个周末突击开发的原型。

And then what we will do is, first we present a little report, tells you, here's everything that we found. Here's not only that, but you have a chatbot where you can chat with all the interviews that we did, and you can pull out your own insights. We have a whole dashboard where it shows you, like, here's here are the teams that are really into this, here are the teams that are not, here's, like, how much how much leverage you might be able to get on different teams based on the interviews and based on the AI analysis. It's pretty cool. And this is like that's an app that I vibe coded over a weekend with Devin a year ago.

现在由Alex负责咨询业务并持续优化系统。我们还会根据访谈结果定制培训课程，因为AI作为通用技术的特点很有趣：企业里约10%的人会主动探索，10%坚决抵触，剩下80%需要你明确告诉他们'具体怎么应用到我的工作'——所以我们设计的培训会精确到'这些提示词用在这些场景'，这种针对性教学极大提升了落地效果。通常每个团队进行为期四周、每周一小时的培训。

And then Alex runs the consulting, has helped upgrade it. Then what we do is we have a training curriculum. So we go in and train each team, and we customize it based on the interviews that we do. Because one of the interesting things about AI is it's such a general purpose technology. And I think people who work inside companies, 10 of them are like, I'm super curious about this.

反馈相当不错。培训后我们经常继续帮他们搭建自动化流程，实施之前提到的那些AI运维方案。

10% are like, I will never touch this. And 80% are like, if you tell me how to do it for my job, I'll do it. And so we customize the training to be like, here are the exact prompts you're gonna use, and here are the exact situations you're gonna use them. And that really, I think, helps drive the adoption. We spend four weeks with each team, an hour a week, that kind of thing.

提到的

It seems to be really cool. And then we'll often also, after this, go and build automations and do some of the AI operations stuff we were

企业反响很好。我们合作对象包括大型对冲基金、私募机构和跨国企业等。关于你第二个问题——区分企业能否成功落地的关键因素？

talking about

我认为首要指标是CEO是否亲自使用ChatGPT（或同类工具）。如果CEO整天热衷于此并宣称'这太棒了'，全员就会跟进；如果CEO态度是'这是别人的事'，那就...

earlier. Companies really like it. I think the and we work with a lot of like big hedge funds and PE firms and big companies, all that kind of stuff. To your other to your your second question, which is like, what separates the good companies from the bad? Or the companies that end up adopt adopting this?

I think the the number one predictor is, does the CEO use Chachibati? Or insert your own chatbot. If the CEO is in it all the time being like, this is the coolest thing, everybody else is gonna start doing it. If the CEO is like, I don't know. This is for someone else.

I think the the number one predictor is, does the CEO use Chachibati? Or insert your own chatbot. If the CEO is in it all the time being like, this is the coolest thing, everybody else is gonna start doing it. If the CEO is like, I don't know. This is for someone else.

就像，没人能领导这场变革。他们要么对此持消极态度，所以肯定没人会去做，要么就会抱有不切实际的期望，因为他们对可能实现的东西毫无直觉，最终只会大失所望。但那些经常使用AI的CEO们既能激发热情，又能为可实现的成果设定合理预期，因此这些举措最终效果极佳。做得特别好的案例，比如我们合作的对冲基金Walleye——几周前我邀请其创始人上了我的播客《AI与我》，这家千亿美元规模的巨型对冲基金。我认为他们的做法堪称典范。

Like, no one else is gonna be able to lead that charge. And they're either going to have either they're gonna be negative on it, so definitely no one's gonna do it, or they're going to have way unrealistic expectations because they have no intuition for what's possible, and they're just gonna get really disappointed. But the CEOs that are using it all the time are able to, like, both drive the excitement and set reasonable expectations for what can be achieved, and so those things end up working really well. And the people that do this really well so for example, we we work with a hedge fund called Walleye, which I had the founder on my podcast AI and I few weeks ago, a gigantic $10,000,000,000 hedge fund. Like one of the things that they do, which I think is, I think they're basically the model for like how to do this.

他做的第一件事（现在很多CEO都在做）就是群发《我们是一家AI优先企业》的邮件。所有人都收到了备忘录。关键是要真正落实。他在备忘录里有句话让我特别欣赏：'这封邮件是用ChachiPeetea写的，你们也该这样做'。就是说要在文件里体现这种态度。

First thing he did, which a lot of CEOs are doing, is send the we're an AI first company email. Everyone's got the memo. You just gotta really do it. And one of the things he said in his memo, which I love is, I wrote this email with ChachiPeetea and you should too. So like, you gotta like In the memo.

没错。必须这样以身作则。而他的做法——我认为很多优秀公司也这么干——是每周召开会议让大家分享提示词和使用案例。他们还会每周给全员发邮件，内容类似：'这是我们使用TreachYPT的数据统计'。

Yeah. You gotta like lead from the front in that way. And then what he does, and I think what a lot of other like really cool companies do is they're doing like weekly meetings where people share prompts and share use cases. They're doing they do, like, a weekly email to their entire company being, okay. Here's our here's our usage here are our usage stats for TreachYPT.

这里会列出那些想出新颖提示词并做出贡献的员工。通过这种方式营造认知和势头——回到我之前说的10%早期采用者理论，你需要发掘并表彰公司里这些人，因为他们会自发投入大量时间摸索有效方法。你只需将他们总结的经验推广到整个组织。如果创建让他们获得认可的渠道，就能自然将他们的知识传递给其他人，并激励更多此类行为。

Here are the here are the people that, like, here are the people that came up with a new prompt and contributed to it. Like, create this this sort of, like, awareness and momentum because what's going back to the point I made earlier about, you know, 10% of people are early adopters. Those are the people inside of a company that you need to find and highlight because they're gonna just go spend all this time, like, figuring out what works. And then all you have to do is, like, translate what they learn into the rest of the organization. And so if you create forums for them to be rewarded, you're going to automatically transfer a lot of their learnings to everybody else and encourage more of it.

我认为这差不多就是秘诀所在。

And I think that's kind of the secret.

太棒了。这个建议很棒。总结下你分享的几个企业策略：首先是发备忘录——不知道是不是该叫它'托比式备忘录'（Toby memo），应该是这类倡议的先行者。核心就是AI优先，这将成为绩效考核标准，做任何事之前先考虑用AI解决。最后注明'这封邮件是用ChatGPT写的'，真是绝妙的主意。

That is awesome. I love this advice. So just to reflect back what you just shared, a few kind of tactics you find that you encourage within companies. One is just send this memo, the Toby memo, I don't know if that's the right way to describe it. Who I think was first along these lines, just worry AI first, it's gonna be part of your performance review, it's gonna be asking can you do it in AI before you could talk to anyone else, All these things, and then just note, I wrote this using ChatGPT, it's a great idea.

每周会议的构想也很棒，通过线下或Zoom会议让大家分享AI使用心得。还有每周数据邮件，展示全公司ChatGPT使用情况和优秀案例。最让我欣赏的是这个简单法则：如果CEO每天都用ChatGPT或Claude等工具，这事准能成。

This idea of a weekly meeting. So it's like a live or Zoom meeting where people share, here's the thing I've learned about using AI. And then this weekly stats email of here's how much we're using ChatGPT across the org, here's some people that did some awesome work. Amazing. And I especially love this very simple heuristic of if your CEO uses ChatGPT or Claude or whatever daily, it's gonna work out.

确实超酷。虽然为时尚早，但你们观察到企业全面拥抱AI后产生了哪些具体影响？无论是案例还是数据？

Yep. That is super cool. I know it's early, but what kind of impact have you seen from a company kind of leaning into this and adopting AI widely? Any anything you've seen either anecdotally or numbers wise?

现在还早，很难量化。但普遍而言，做得好的人觉得现有团队就能完成远超从前的工作量，在同等预算下推进得更快。实际上我很少见到有人说要大规模裁员——我也不想接那种咨询项目。

It's early. It's really hard to say other than I think generally people who do this well now feel like they can do way more work than they used to without having to hire more people. And so they're just going further faster at the same budget. I actually don't see, you know, I don't see a lot of people being like, cool, we're gonna like fire a bunch of people. Like, also, I don't really wanna do consulting like that.

那太糟糕了。但我们从不需要拒绝客户，多数人想的是：'太好了，我可以用现有团队走得更远'。这也呼应我最初关于工作机会回流美国的观点。

Like that sucks. But we've never had to say no. Mostly people are like, cool. I'm just going to go further with the the people that I that I have. I think also back to kind of the first point I made about reshoring American jobs.

我见过一些公司——并非我们合作的，而是我朋友所在的企业——他们的情况是：'我们在某地设了个呼叫中心，但我觉得在美国雇两个员工，利用某种客服平台就能完成同等工作量。'这些平台还不算完全自动化。比如Klarna CEO那套说辞，我觉得纯属扯淡。但确实，你可以用略低于雇佣100名海外员工的成本，在美国雇几个人。当然，这种计算每个人都要自己权衡，但我确实见证了这种趋势。

I have seen some companies, not the ones that we work with, but I have seen some companies and people that I'm friends with where they're like, we have a call center somewhere, but I think I can get the same amount done with like two employees in The US that have that use, like, one of these, you know, customer service platforms. Like, they're still not totally automatic. Like, I think that Klarna CEO thing, that was bullshit. But, yeah, you can have a couple people in The US that maybe maybe you pay a little bit less to than you would for, like, a 100 people somewhere else. And, obviously, you know, those are that's a calculus that everyone has to make for themselves, but I've definitely seen that happen.

这就是所谓'用同等人力完成更多工作'的实质。

And yeah, think that's the get more done with the same amount of people.

或许作为对话收尾，我想回到你提到的'分配经济'概念——按我的理解，我们曾处于靠执行获取报酬的知识经济时代，而你的观点是正在转向管理者技能更重要的分配经济，未来我们会花更多时间在管理上。这美妙之处在于它揭示了哪些技能将更重要——正是当下很多人思考的问题。能否就此展开，分享你认为关键的观点？

Maybe to close out our conversation, I want to come back to this idea that you referenced, but I want to spend a little more time on this, which is this idea of the allocation economy. If I understand it correctly, we've been in this knowledge economy where people get paid to do a thing, and your thesis is that we're moving to this allocation economy where skills become, the manager skills become more important, and we're going to be spending more of our time managing. And I think what's amazing about this is it also tells you which skills will matter more in the future, which is something I think a lot of people are thinking about. So maybe just answer that question and share whatever you think is important to share to give people a sense of what you're thinking.

这个理念源自我两年半前写的文章，那时AI代理还没被视为可行方案。我试图总结日常使用中的感悟：哪些技能对我真正有用？因为我相信这能折射更普遍的规律。

Yeah. So this is, based on an article I wrote, like, two, two and half years ago. So this is back before, like, agents were even, like, thought of as viable. And I was, really trying to think about how do I express what in my experience using this every day, like, what skills are useful for me? Because I think that'll be the case for for a lot of other people.

我认为最好的预测方法就是持续亲身实践。当时用GPT-3或GPT-4时，我常思考：如何精准描述问题？如何收集恰当信息？如何格式化输入让模型理解？如何选择适用模型？如何根据优劣划分任务——这部分给A模型，那部分给B模型？如何提供反馈？如何建立预期标准和评估体系？——这些正是我使用工具的日常。然后我恍然大悟：这不就是管理嘛。

And I think that's that's the kind of the best method, I think, to do these sorts of predictions is you have to be doing it all the time yourself, and then that informs your opinion about this stuff. So what I noticed using, at the time, like, b d three or maybe g b d four, was that I was spending a lot of time, for example, thinking about how do I communicate the problem? How do I gather the right information for the problem? How do I put it in the right way so that the model that I'm working with gets it? How do I pick which model to give it to?

一旦想通这点，很多现象就豁然开朗。比如常见抱怨是：'AI干这个能行吗？信不过效果还不如自己动手'——这完全就是新晋管理者的心声。总陷入两难：委派他人达不到预期，亲力亲为又无法提升效能。

And how do I maybe divide up the task to be like, okay, this model does this, this model does this, based on what I know to be like what's good and what's bad. How do I give them feedback? How do I have like a vision for what I want, and a set of criteria for whether it's good? All that stuff is exactly how I found myself using these tools. And I was like, oh, that's just managing.

管理者正是通过这些学会领导艺术：何时介入微管理？何时授权？如何建立信任？如何拆分任务？这些都是必经之路。

And and once that, like, once that clicks for you, I think you'll start to see a lot of other things. So a a really good example is there's a big complaint that it's like, well, how

能

can

所以这就是管理者必须学习的成长路径：把握介入与放权的平衡，建立信任机制，掌握任务分解的艺术。

I have AI do this? Like, can't trust that they're going do it well, so I should just do it myself. And I'm just like, yeah, that's exactly what every first time manager says. You always have this problem where you're like, okay, well if I delegate it, it's not done in the way that I want it to be done. If I do it myself, I get no leverage.

And so that's how a manager has to learn how to be a manager is like, when do I lean in and and maybe micromanage a little bit? And when when can I delegate? And how can I trust it? And how do I divide out the task? And all that kind of stuff.

And so that's how a manager has to learn how to be a manager is like, when do I lean in and and maybe micromanage a little bit? And when when can I delegate? And how can I trust it? And how do I divide out the task? And all that kind of stuff.

因此我认为这些技能之间存在大量重叠，只是目前这些技能尚未广泛普及，但未来会改变，因为成为管理者的成本将大幅降低。

And so I think there's a lot of overlap in those skills, and it just, those skills are not broadly distributed right now, but they will be in the future because it will be so much cheaper to be a manager.

具体而言，我看了你写的文章，你强调的更有价值的技能包括评估人才、视野、品味，以及如你所言，何时该深入细节、何时适合投入精力。对，太棒了。你还提到一个相关观点，即通才在未来会越来越有价值。你说每个人本质上都是通才。

And specifically, I was looking at the article you wrote, the skills that you highlight will be more valuable is evaluating talent, vision, taste, and to your point, when to get into the details, when it makes sense to dive in. Yeah. Awesome. And then there's also kind of a connected point you made that you referenced, is that generalists will become more and more valuable in the future. You mentioned that everyone and every is a generalist.

对，请详细说说这个观点。

Yeah. Share a little bit about that.

好。我觉得——或许因为我自己就是通才，所以你们需要持保留态度看待这个观点——我也这么认为。AI让我如此兴奋的原因之一，就是它能让我涉猎不同领域。

Yeah. I find I mean, maybe it's because I'm a generalist, so you should take take this with a grain of salt. Same. Same. I think that's one of the things that has made AI so awesome for me is like, I love to dabble in different things.

比如一天之内，我可以编写应用程序、制作视频、创作图像、写作等等。而ChatGPT始终伴我左右。我认为，从古希腊至今的文明进程中，我们发现越是专业化，越能实现多人协作。就像亚当·斯密描述的制针工厂——分工带来效率提升，这种模式确实产生了巨大效益。

So it's like, in one day, I can be like coding an app and like making a video and like making images and writing and like all that kind of stuff. And Charjibouti is right there with me. And I think what we've basically what has happened as civilization has progressed from, like, ancient Greece to now is what we've discovered is the more that we specialize, the the better we can coordinate across many different people. And so it's sort of it's like the Adam Smith, you know, like, there's a pin factory and someone's making a pin or whatever his thing is is specialization he gains from trade. And there have been a lot of really good impacts of that.

我最喜欢的例子是古希腊雅典。雅典公民（暂且搁置其对待女性和奴隶的黑历史）都是通才，一个人可能同时是战士、法官、陪审员甚至将军，一生中需承担多种社会角色。

And I think you can like one of my favorite examples of this is is back to like ancient Greece and ancient Athens. Athens is was a civilization of generalists, at least for citizens. It was there's like they have some, you know, a bad history with women and people who are slaves, but, like, let's just put that to the side for a second. If you're a citizen, generalist, you could you could be expected to be a fighter, a judge, a juror, maybe a general. Like, there's you could expect it to have many different roles inside of your society in your lifetime.

但随着雅典成为帝国，当需要派遣将军远征西西里时，专业能力变得重要。社会分工开始细化，人们通过协作完成目标。这种模式推动了文明发展，但从某种角度看也减少了乐趣——成为全面发展的人其实非常酷。

That changed though because Athens became an empire. And as it became an empire, if you're gonna send, like, a general off to, like, go and invade Sicily or whatever, you you want that person to be, like, pretty skilled. And so it started to break the general kind of thing into people start to have specific roles, and they coordinate with each other and all that kind of stuff. And I think that that pattern has actually been really good for developing civilization, but it's also, in a lot of ways, it's not as fun. It's actually really cool to be a well rounded person.

AI的有趣之处在于，它就像随身携带一万个博士学位，通晓人类知识的每个分支、每种艺术形式、所有制造方法。它擅长处理那些原本需要十年专研的领域——比如某种蝉的繁殖习性——现在随时都能为你解答。

And I think the interesting thing about AI is that it's a little bit like, you can think of it like having 10,000 PhDs in your pocket. It's like, it knows so much about every little branch of human knowledge, and every art form, and every way of making things or building things, and you just have access to that. So it's doing a lot of the It's good for doing a lot of the specialized tasks that you might have had to spend, like, ten years getting good at, you know, learning about this particular species of cicada. So you know exactly how they, like, you know, reproduce. But now you've got this thing in your pocket that can tell you all about that in any given context at any given time.

这使人们能自由跨越多领域。例如创业者可以更久保持15人小团队规模，成员长期保持通才状态。这种模式可能辐射至整个经济体系：取代巨型企业中每人只做螺丝钉的现状，形成更多由通才组成的小型组织——我认为这将是非常积极的转变。

And so you're empowered to jump a lot more between all those different domains of skill. And and you can get more done as, for example, like a founder where I think we can stay at 15 people much longer than we would be able to. So the people inside of every can stay generalists for much longer. And I think that that may sort of ripple out into the rest of the economy where instead of gigantic massive corporations where each person is doing one little button turning, you have many more smaller organizations with more generalists. And I think that would actually be a really good thing.

这让我想起和私人健身教练的对话。她说自己善于制定宏观计划却不擅长执行管理，ChatGPT对她简直是天赐之物——只需描述大致目标，AI就能帮她落地实施。

This reminds me, I was talking to my personal trainer that I'm trying out for a little bit and she said that she's a very big vision kind of high level person and not good at executing, getting like we're staying organized. And ChatGPT is such a godsend for her because she's just like, here's what I wanna do roughly, just help me get it done.

太棒了，我非常喜欢。

That's great. I love that.

所以，是的，这真的让我思考这些东西将释放多大的价值。这太神奇了，完全符合我的期待。但说到这里，我们已经进入激动人心的闪电问答环节了。丹，你准备好了吗？

So, yeah, it really made me think about just how much value all this stuff is gonna unlock. This was amazing, it was everything I wanted it to be. But with that, we reached our very exciting lightning round. Dan, are you ready?

我准备好了。

I'm ready.

开始吧。你最常向别人推荐的两三本书是什么？

Here we go. What are two or three books that you find yourself recommending most to other people?

嗯，我已经推荐过一本了，就是《战争与和平》。这本书绝对要读。如果你想了解托尔斯泰的入门作品，我会推荐《伊凡·伊里奇之死》。还有一本很棒的是乔治·桑德斯的《雨中池塘游泳》，这是一部俄罗斯短篇小说集，同时也讲述了写作技巧。我特别喜欢俄罗斯作家，因为许多俄罗斯小说家都在探讨技术对传统俄罗斯生活方式的影响。

Well, I already recommended one, which is War and Peace. Definitely gotta read that. If you want, like, a Tulsa like primer, I would read The Death The Death of Ivan Ilyich. Another good one is a swim in a pond in the rain, which is by George Saunders, and that's a collection of Russian short stories that is also about writing. And in particular, I really like the Russians because they're a lot of the Russian novelists are dealing with the effects of technology on a traditional Russian way of life.

他们处于一个非常有趣的中间地带，介于浪漫主义世界观和更理性主义的进步观之间。在《安娜·卡列尼娜》中你会看到这一点，当列文和农民一起在田里挥镰刀时，托尔斯泰就在思考：与其当一个试图提高农场效率的贵族，不如像这样单纯地挥舞镰刀反而更快乐。

And they're very kind of in this really interesting middle ground between a sort of romantic outlook on the world and a more rationalist, like, we're we're progress we're making progress. And that's one of the things you'll find in Anna Karenina when god. What's the guys what Levin is out in the fields with the peasants, like, doing the scythe thing. Like, that's that's Tolstoy, like, kind of, like, thinking about, oh, what would it be like instead of being a nobleman who's like trying to make make farms way more efficient. I was just like with my scythe, that was like really happy.

总之，他们探讨的许多主题与AI有相似之处。《主人与使者》是另一本非常好的书，主要讲述大脑两个半球如何认知现实。这本书非常精彩，我认为也与很多AI议题相关。我想...大概就是这三四本吧。

Anyway, so they're dealing with a lot of similar stuff to I think AI. The Master and His Emissary is another really good one, and that's about basically how the different hemispheres of the brain view reality. It's really, really good, and I think it I think it relates to a lot of AI stuff too. I think yeah. Think I think those are my those are my three or four.

确实是很棒的书单。我觉得几乎没人提到过这些书，这通常是个好兆头。最近有特别喜欢的电影或电视剧吗？

Yeah. Excellent list. I think nobody's mentioned most any of these. So this is that's always a good sign. You have a favorite recent movie or TV show you really enjoyed?

有，我超爱《死木》。你看过吗？

Yes. I really love Deadwood. Have you seen it?

我简直爱死这部剧了。还记得当年它突然停播，好像是因为主创要去忙HBO的其他项目，太遗憾了。这部剧真的太精彩了。

I I absolutely love it. I remember when they stopped it for some reason. I think he had to go do something else at HBO. It so sad. It was it's amazing.

是啊。

Yeah.

大卫·米尔奇简直不可思议，是国宝级的杰出编剧。但我真正喜欢这部剧的原因（最近才补看），是他谈到《死木》讲述秩序如何从混沌中诞生。那是个边疆小镇，人们蜂拥而至，没有法律，没有规则。

David Melch is incredible, national treasure, incredible writer. But what I what I really think what I really love about it, and I only recently watched it, is he talks about Deadwood being about how order forms out of chaos. So it's this, like, frontier town. People are going to it, and, like, there's no law. There's no rules.

到了第三季，镇上有了市长，各种产业进驻，成了真正像样的城镇。我特别钟爱这种演变。而且我认为西部边疆与技术前沿有诸多相似之处，这部剧对这类动态的研究非常有趣。

And by, like, season three, there's, like, a mayor and, like, you know, there all the industry has come in, and it's, a real proper town. And I just love that. And I think there's a lot of there's a lot of parallels from the, like, the Western frontier to technology frontiers. And so I think that show is, like, a really interesting study in that kind of dynamic.

我超爱这种与科技运作、AI诞生过程的关联性。太精彩了！谢谢分享。最近有什么让你爱不释手的新产品吗？

I love how everything connects to how tech works and how AI came to be. I I love this. Thank you. Do you have a favorite product you've recently discovered that you really love?

这个问题我答不好，因为我主要在用内部产品。不过我的标准答案是Granola——我确实超爱它。唯一不满的是（希望他们听到这期节目），我特别想要导出所有笔记的API功能。除此之外堪称完美产品。

I don't have a good answer for that because I just spend a lot of time using our internal products, but my stock answer is granola. So I do I do really love granola. I my one gripe with them, and I hope they listen to this podcast, is I really wanna export all my notes. I want an API. But other than that, I think it's a fantastic product.

这绝对是最近几个月节目里被提及最多的产品。Granola加油啊！顺便说，订阅我的年度通讯就能免费获赠一年Granola使用权——天大的福利！

That is definitely the most mentioned product in this segment for the past couple months. So Yeah. Catch up granola. I can't help but mention, you get a year free of granola if you become an annual subscriber of my newsletter. What a freaking deal.

不仅是个人，整个公司都能免费用一年Granola。这优惠简直了！

And not just you, but your whole company gets free granola for a year. What the what a deal.

声明这不是有偿推广，纯粹个人感受。很高兴它被纳入福利包。

This is not a paid promotion by me. I just you know, that's just what how I feel. So I'm glad I'm glad it's part of the bundle.

太棒了。那么，有什么你反复受用的人生格言吗？

Yeah. Incredible. Okay. Do you have a favorite life motto that you often come back to find useful in work or in life?

其实我常用Chechio PE（带记忆功能），上节目前我问它该用什么格言。它说：'深刻见证，勇敢构建'——你崇尚缓慢专注的观察，无论是阅读托尔斯泰、追踪冥想主题，还是解析大卫·米尔奇的段落。

So basically, like, I use Chechio PE all the time and has memory. So I was like, you know, I'm going on Lenny's podcast. What would my life motto be? And it said, your life motto is a witness deeply build bravely. You you prize slow, attentive seeing, whether it's reading Tolstoy, tracking meditation themes, or X raying a David Milch paragraph.

所以我们正在触及我刚才提到的所有要点，这真的很有趣。

So we're we're it's hitting all the stuff I just mentioned, which is really funny.

然后

And

勇敢地构建。你将那些洞见转化为具体的事物，比如问答社区、长篇论文以及诸如此类的东西。所以我认为这其中确实有些道理。实际上，这让我想起了真正的格言——虽然不是我原创的。

then build bravely. You turn those insights into concrete things, like every and Quora and long form essays and and all that kind of stuff. So I think there's I think there's something about that. Actually, this reminds me. This actually reminds me of the actual motto, which is and I didn't come up with this.

记得小普林尼说过：做值得书写的事，写值得阅读的文字。这似乎是个相当精辟的总结。

Think it's like Pliny the Younger said, do things worth writing about and write things worth reading. Seems like a pretty good summation.

做值得书写的事，读值得阅读的文字。

Do things worth writing about and read things worth reading.

写值得阅读的文字。

Write things worth reading.

写值得阅读的文字。这应该成为我们两家新闻简报的共同座右铭。确实非常精妙。好的。

Write things worth reading. That's that's should be the motto of both of our newsletters. Yeah. That is really good. Okay.

顺便说，我很喜欢你问ChatGPT'我的人生格言是什么'这个举动。

And by the way, love that you asked ChatGPT, what's my life motto?

等等，有趣的是它没有直接给出答案，但启发了答案。是的，这其实正是我使用它的方式。

And wait. This is interesting. So it didn't give me the answer, but inspired the answer. Yeah. I think that's actually, like, exactly how I use it.

哇，它已经是我们大脑的延伸了。最后一个问题：我曾读到你说过一度停止写作，当时觉得需要做其他事、经营公司，后来意识到必须回归写作，因为事情开始偏离轨道。感觉这与你常说的'做让你快乐的事，保持与喜悦为伴'形成了耐人寻味的呼应。

Wow. It's an extension of our brains already. Yeah. Last question, I was reading somewhere where you wrote that you stopped writing, at one point you're just like, I need to do other things, I need to build this company, and then you'd realize I need to get back to writing because things started going sideways. And I feel like this is such an interesting corollary to a lot of this stuff you talked about, of do things that make you happy, stay close to joy.