他拯救了OpenAI，发明了“点赞”按钮，还打造了谷歌地图：布雷特·泰勒谈职业未来、编程、智能代理及其他

你是Meta的首席技术官，Salesforce的联席CEO，OpenAI的董事会主席。你认为AI市场将如何发展？

You're CTO of Meta. You're co CEO of Salesforce. You're chairman of the board at OpenAI. How do you think the AI market is gonna play out?

整个市场将向智能代理方向发展。我认为整个市场将转向基于结果定价的模式。这显然是构建和销售软件最正确的方式。

The whole market is gonna go towards agents. I think the whole market is going to go towards outcomes based pricing. It's just so obviously the correct way to build and sell software.

这让我想起一件事。我曾邀请马克·贝尼奥夫上播客节目。你们曾是联席CEO。他对智能代理充满热情。

So makes me think about it. I had Marc Benioff on the podcast. You guys were co CEOs. He was extremely agent filled.

销售生产力软件非常困难，这是我们摸索出来的经验。

It's so hard to sell productivity software, which I learned our way.

当你回想自己犯过的最大错误时，脑海中会浮现什么故事？

What's a story that comes to mind when you think about your biggest mistake?

我曾是Google Local的产品经理。与玛丽莎和拉里进行了一次相当艰难的产品评审，没能充分利用谷歌首页的链接资源，现在想来有点尴尬。

I was the product manager for what's called Google Local. Had a pretty tough product review with Marissa and Larry, and to not do that well with a link from the Google homepage is, like, kind of embarrassing.

我认为让人们听到即使遭遇如此重大失败仍有可能成功，这非常鼓舞人心。

I think it's really empowering for people to hear it's possible to succeed in spite of a massive failure like this.

他们给了我第二次机会开发第二代产品，最终诞生了谷歌地图。上线第一天就获得了约1000万用户。

They sort of gave me another shot to do the v two of it that resulted in Google Maps. We got about 10,000,000 people using it on the first day.

是什么样的心态让你能在如此多样化的角色中都取得成功？

What mindset contributed to you being successful in such a variety of roles?

每天早晨醒来思考：今天我能做的最有影响力的事是什么？

Waking up every morning, what is the most impactful thing I could do today?

今天，我的嘉宾是布雷特·泰勒。布雷特是一位绝对的传奇建设者和创始人。他在谷歌共同创建了谷歌地图。他联合创立了社交网络FriendFeed，发明了点赞按钮和实时新闻推送功能，并将其出售给Facebook。之后他成为Facebook的首席技术官。

Today, my guest is Brett Taylor. Brett is an absolute legendary builder and founder. He cocreated Google Maps at Google. He cofounded the social network FriendFeed, which invented the like button and the real time news feed, which he sold to Facebook. He then became CTO at Facebook.

随后他创立了名为Quip的生产力公司，以7.5亿美元的价格出售给Salesforce。之后他成为Salesforce的联合首席执行官。他目前还担任OpenAI的董事会主席，曾一度担任Twitter的董事会主席。如今他是人工智能初创公司Sierra的联合创始人兼首席执行官，该公司致力于构建智能代理来帮助企业处理客户服务、销售等业务。

He then started a productivity company called Quip, which he sold to Salesforce for $750,000,000. He then became co CEO of Salesforce. He's also currently chairman of the board at OpenAI. At one point he was chairman of the board at Twitter. Today he's co founder and CEO of Sierra, an AI startup building agents to help companies with customer service, sales, and more.

在我们的对话中，我们涵盖了诸多话题，包括哪些技能和思维方式最帮助布雷特在众多角色中取得成功，为什么我们仍低估了智能代理对商业世界的影响，未来几年编程将如何变化，初创企业最大的机会在哪里，关于AI产品定价和市场进入的经验教训，点赞按钮背后的故事等等。这是一次与传奇建设者的真正史诗级对话。如果你喜欢这期播客，别忘了在你喜欢的播客应用或YouTube上订阅关注。此外，如果你成为我新闻通讯的年度订阅用户，你将免费获得包括Replit、Lovable、Bolt、N8N、Linear、Superhuman、Descript、Whisper Flow、Gamba、Perplexity、Warp、Granolah、Magic Patterns、Raycast、JetPr、Demobin等在内的多项超值服务。访问lenny'snewsletter.com点击bundle查看详情。

In our conversation we cover so much ground, including what skills and mindsets have most helped Brett be so successful in so many roles, why we're all still sleeping on the impact that agents are gonna have on the business world, how coding is going to change in the coming years, where the biggest opportunities remain for startups, lessons on pricing and go to market in AI, the story behind the like button, and so much more. This is a truly epic conversation with a legendary builder. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of incredible including Replit, Lovable, Bolt, N8N, Linear, Superhuman, Descript, Whisper Flow, Gamba, Perplexity, Warp, Granolah, Magic Patterns, Raycast, JetPr, Demobin, and more. Check it out at lenny'snewsletter.com and click bundle.

接下来，有请布雷特·泰勒。本期节目由Coderabbit赞助播出，这个AI代码审查平台正在改变工程团队如何在不牺牲代码质量的前提下利用AI加速开发。代码审查至关重要但耗时费力。Coderabbit作为你的AI副驾驶，即时提供代码审查意见和每个拉取请求的潜在影响。除了标记问题，Coderabbit还提供一键修复建议，并允许你使用AST grep模式定义自定义代码质量规则，捕捉传统静态分析工具可能遗漏的微妙问题。

With that, I bring you Brett Taylor. This episode is brought to you by Coderabbit, the AI code review platform transforming how engineering teams shift 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还直接在IDE中提供免费的AI代码审查服务，支持Versus Code、Cursor和Windsurf。Codebabbit目前已审查超过1000万次PR，安装在100万个代码库上，被7万多个开源项目使用。使用代码Lenny可在coderabbit.ai免费获取全年服务，访问coderabbit.ai即可。

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 coderabbit.ai using code Lenny. That's coderabbit.ai.

本期节目由Basecamp赞助播出。Basecamp是37 signals公司出品的著名简洁项目管理系统。大多数项目管理软件要么功能不足，要么复杂得令人沮丧，但Basecamp却清新明了。它上手简单，组织方便，Basecamp的可视化工具帮助你清晰了解每个人的工作内容及整体进展。将所有项目相关的文件和讨论直接关联到项目本身，让你随时掌握信息，无需频繁切换上下文。

This episode is brought to you by Basecamp. Basecamp is the famously straightforward project management system from 37 signals. Most project management systems are either inadequate or frustratingly complex, But Basecamp is refreshingly clear. It's simple to get started, easy to organize, and Basecamp's visual tools help you see exactly what everyone is working on and how all work is progressing. Keep all your files and conversations about projects directly connected to the projects themselves so that you always know where stuff is and you're not constantly switching contexts.

经营企业本就不易，管理项目应该简单。我长期关注37 signals公司的创新成果，非常高兴能与大家分享这个工具。前往basecamp.com/lenny注册免费账户，让Basecamp助你事半功倍。

Running a business is hard. Managing your projects should be easy. I've been a longtime fan of what thirty seven signals has been up to, and I'm really excited to be sharing this with you. Sign up for a free account at basecamp.com/lenny. Get somewhere with Basecamp.

布雷特，非常感谢你能来参加。欢迎来到播客节目。

Brett, thank you so much for being here. Welcome to the podcast.

谢谢邀请。

Thanks for having me.

我的荣幸。有太多话题想和你探讨。你职业生涯中完成了许多令人难以置信的成就，光是列举这些成就就让人惊叹不已，我们会重点讨论其中的许多内容。但我想先从相反的角度开始。

My pleasure. There's so much that I wanna talk about. You've done so many incredible things over the course of your career. It just boggles the mind, the things that you've done, and we're gonna talk about a lot of that sort of stuff. But I wanna actually start with the opposite.

我想聊聊你搞砸的一次经历，一个你犯下重大失误的时刻。我们播客有个固定环节叫'失败角落'。所以在探讨你的辉煌成就前，我觉得从这里开始会很有趣。在打造产品过程中，你想到的最大错误是什么故事？

I wanna talk about a time that you messed up, a time that you screwed up in a big way. We have this recurring segment on the podcast like I'll fail corner. And so I thought it'd be fun to just start there before we get into all the great stuff you've done. What's a story that comes to mind when you think about maybe your biggest mistake in building a product?

这可能不算最严重的，但是我作为谷歌产品经理首次重大失误。对我而言它意义重大，因为塑造了我作为产品设计师的成长轨迹。2002年末2003年初加入谷歌时，我是公司最早期的助理产品经理之一。最初负责扩展搜索系统索引，从10亿网页扩容到100亿——这在当时可是大事，现在回想起来有点老派了。

It may not be the biggest, but it was my first prominent mistake as a product manager at Google. So it's for me, it feels big because it was very formative for me as a a product designer. So I joined Google in late two thousand two, early two thousand three, and I was one of the earliest associate product managers at the company. And first was working on the search system, essentially expanding our index from 1,000,000,000 web pages to 10,000,000,000, which was a big deal at the time. It sort of seems quaint now.

由于表现尚可，我的上司玛丽莎·梅耶尔给了我领导新产品的机会，这对我而言是重大信任投票。作为年轻的产品经理，这既是服务谷歌的机会，也让我备受审视。项目定位是做本地搜索——当时黄页仍占主导地位，谷歌虽擅长网页搜索，却难找水管工或餐馆，因为这些内容当时在互联网占比很小。即使存在，也需要完全不同的检索方式。

And then I did a decent job and so my boss, Marissa Meyer, gave me the opportunity to lead a new product initiative, which was a big bet on me. And I was, you know, it was both an opportunity do something for Google, but I was also being pretty scrutinized just as a young new product manager. And the premise given to me was work on local search. At the time, the yellow pages was still dominant and while Google was really good at searching the web, it wasn't really good for finding a plumber or a restaurant just because it wasn't really a huge part of the Internet at the time. So this content wasn't necessarily on the Internet and even if it was, it was you really needed it different.

用户不需要找曼哈顿的水管工，而是要定位旧金山的服务商（比如我）。这既是技术难题，也是产品和内容难题。我们推出的首版产品叫Google Local，现在回想起来——可能比当时更苛刻些——它像是雅虎黄页的跟风版。基本就是在谷歌搜索上嫁接黄页功能，通过特定查询能在结果顶部显示商家列表。我们还有个独立站点local.google.com。

You didn't really wanna find, you know, plumbers in Manhattan, you wanna find plumbers in San Francisco, you're me. And so it was a kind of a both a technical problem and a product problem and a content problem. We launched the first version of that product that I was the product manager for was called Google Local and it was, you know, I'll be a little bit more critical now than I might have been at the time, but it was a little bit of a Me Too version of Yahoo Yellow Pages. You know, sort of essentially grafting on Yellow Pages search on top of Google search and with a properly crafted query, you could see those listings at the top of your search results. We had a standalone site @local.google.com.

这个项目重要到谷歌首页除了网页、图片搜索，还给了本地搜索入口。要知道在谷歌首页放任何链接都能带来巨大流量，即便如此它表现平平——在首页导流下仍成绩不佳，实在难堪。

And it was actually, it was an important enough initiative that actually there was a on the Google homepage, it had, you know, web images and and local was up there as well. So, you know, it's got top billing. I mean, you could put almost any link on the Google homepage and get a lot of traffic to it. And despite that, it didn't do that well. And to not do that well with a link from the Google homepage is like kind of embarrassing, you know.

作为产品负责人，获得这种量级的流量已是极致。产品本身没问题，但缺乏差异化。后来我不断反思：用户凭什么选这个而非雅虎黄页？更根本的是，凭什么不用纸质黄页？它只是旧事物的数字化翻版。

It's it's I mean, there's not there's not much one can do other than like more than giving you that kind of traffic to give you an ad that as a as a product leader or product manager. And the product was fine, I like it worked, but it really wasn't differentiated. And and I think in many ways, I think, again, I think I've had these reflections more sense than at the time that I had some of the time, but why use this instead of Yahoo Yellow Pages? But more than anything else, like, why use this instead of Yellow Pages? You know, it was sort of a digital version of of something that had come before.

玛丽莎和拉里等人的产品评审会相当严苛，虽然不至于被开除，但我头顶的光环确实黯淡了。他们给了我开发第二版的机会，虽非最后通牒，但从明星新人到受挫者的心理落差很大。我苦思如何打造真正有吸引力的产品，而非又一个数字黄页。

And a pretty tough product review with Marissa and Larry and others and it was fine. I wasn't like about to get fired or something, but it was like, you know, the shine on the on my reputation was sort of waning a little bit and they sort of gave me another shot to do like a v two of it. And and I sort of got the impression. It wasn't like my last shot, but it was sort of, you know, I certainly was feeling a little dejected from going from sort of a hotshot new PM to a new thing. So I spent a lot of time thinking about how can you make something that's just much more compelling and not just sort of a digital version of the Yellow Pages and not just so similar to some of the other products out there.

这个思考脉络最终催生了谷歌地图。当时我们向MapQuest授权了在搜索结果旁显示小地图的功能——这始终是产品最丑陋的部分，内部常调侃它。后来我们颠覆思路：何不以地图为画布？于是招揽了开发Windows地图产品的拉斯和延斯·拉斯穆森兄弟，开始探索新方向。

And that's ended up being the thread that we pulled that resulted in Google Maps. We had licensed from MapQuest the ability to put like this little map next to the search results. It was always the ugliest part of the product and we always, you know, made sort of these like backhanded comments about it internally. And we spent a lot of time saying like, what have we sort of inverted the hierarchy here and made the map the canvas? We ended up finding Lars and Jens Rasmussen who had been working on this Windows mapping product and we sort of got them into the company and started exploring this space.

通过整合地图、本地搜索、行车路线等当时各自独立的功能，最终我们重新定义了行业格局——也重塑了我的职业生涯。这次经历彻底改变我的产品思维：功能之外，更要解决'用户为何需要它'的根本问题。有几个标志性时刻：谷歌地图上线首日用户破1000万（当时互联网规模下很惊人）；2005年8月整合Keyhole卫星图像（后成谷歌地球）当天，用户激增至9000万。

It ended up where through that exploration we ended up integrating a lot of different products, we ended up integrating mapping local search, driving directions like all of these products at the time were actually separate product categories and ended up with something that kind of redefined the industry and certainly my career. But it took kind of, I think for me as a product leader, it changed the way I think about product just because there's sort of feature and functionality and then there's like, why should I use this thing in the first place? And it was notable there's a couple of interesting moments. I mean, when we launched Google Maps, we got about 10,000,000 people using it on the first day, which at that scale of the Internet at the time was huge. And then in August 2005, we integrated satellite imagery from a recent acquisition called Keyhole, which became Google Earth, and we got 90,000,000 people using it on the same day.

卫星图像发布时，所有人都想俯瞰自家屋顶。这个案例蕴含深刻的产品启示：面对新技术时，与其简单数字化旧事物，不如创造全新体验——这能天然解答用户'为何尝试'的疑问。就像拆解乐高后重组新形态，谷歌地图的突破在于它实现了纸质地图永远无法企及的、真正属于数字平台的体验。

Everyone wanted to look at the top of their house, you know, when the imagery came out. And it was really interesting because there's so many subtle product lessons in there. You know, first is I think as you have these new technologies rather than literally digitizing what came before, if you can create an entirely new experience, it it creates it sort of answers the question for a new customer like, why should I give this a time of day, you know? And so really disassembling the Lego set and reassembling something new rather than just digitizing what was there before, Certainly that was the lesson I think in Google Maps. Really was native to the platform in a way that like a paper map couldn't be, you know, and that was like a really meaningful breakthrough.

说实话，卫星图像并非谷歌地图最重要的部分，但它就像是牛排上的滋滋声——锦上添花。它创造了一个，虽然那时‘病毒式传播’这个词还不流行，但确实引发了病毒效应。我们登上了《周六夜现场》，这简直太酷了。安迪·桑伯格在《慵懒星期天》里用说唱提到谷歌地图，我和拉斯当时互发短信庆祝：我们做到了。

And then with satellite imagery, it honestly wasn't the most important part of Google Maps, but it was sort of the sizzle to the stake. And it created, you know, I don't think the term viral was a thing people said back then, but it created a viral moment. We run Saturday Night Live, which is the coolest thing. Andy Samberg in I think it's called Lazy Sunday, you know, rapped about Google Maps and Lars and I were texting each other. We did it.

我们亮相《周六夜现场》，使命达成。这也展示了微软Azure对产品的思考——选择产品的理由与持久价值密切相关但又不尽相同。这些经验教训伴随我后续开发的每一个产品。

We're on Saturday Live. Mission accomplished. And it was also showing that, you know, Azure is there thinking about products. There's the why you decide to use a product and then what is the enduring value and those are deeply related, but not all the same thing. And I just learned so many lessons I took with me for like every subsequent product that I worked on.

这故事太棒了。首先，它能激励人们——连你布雷特这样成功的人，也曾被谷歌CEO玛丽莎·梅耶尔当面指责搞砸了重大赌注。这说明即便经历重大失败，仍有可能像你这样取得成功。

That's that's an awesome story. One, think it's really empowering for people to hear. Even you, Brett, who I'm gonna share all the successes you've had, have had a massive failure with, the CEO of Google, Marissa Meyer, just like, Brett, you screwed up. This is and it was like such a big bet. So one just like it's possible to succeed as you have succeeded in spite of a massive failure like this.

你分享的产品经验中，我想强调：单纯模仿改进很难胜出，必须创造全新体验和差异化优势。现在聊聊你众多成功经历——我看了你的简历，你在每个职业阶段、各种角色中都表现出色。

And then some of the product lessons you shared just to highlight a few of these things, because I think this is great is just you will often not win if you just make something that's kind of a better copy of something else. What you want to look for is something that is an entirely new experience, something that's differentiated, something that's a lot more compelling. Let's flip to talk about what you've learned from actually being very successful at a lot of things. So I was looking at your resume and you basically have been very successful at every level of the career ladder and in such a huge variety of roles. So let me just read a few of these things for folks that aren't super familiar with your background.

你曾任Meta CTO、Salesforce联席CEO兼CPO/COO。在谷歌从产品经理助理起步——虽然你没提，但著名的谷歌地图是你周末开发的（这个我们就不深聊了）。

You were CTO of Meta. You're co CEO of Salesforce. You're also CPO at COO at Salesforce. At Google, you joined as an associate product manager where you famously you didn't mention this, but you you built Google Maps on the weekend. We're not gonna talk about that.

现任OpenAI董事会主席，曾任Twitter董事会主席。创立过三家公司：社交网络、文档工具Quip和现在的Sierra。冷知识：你在FriendFeed发明了点赞按钮和新闻流。

You're chairman of the board at OpenAI. You were chairman of the board at Twitter. You've also founded three different companies, one social network, one productivity docs company called Quip, and now Sierra. Fun fact, at FriendFeed, you invented the like button. I don't know if people know that, and also just the newsfeed.

这些成就值得被铭记。你从基层工程师、产品经理，到CPO/COO/CTO/CEO，还领导过上市公司。能在如此多元的岗位层级都取得成功实属罕见。请问：哪些思维模式或工作习惯让你在不同角色中都游刃有余？

I'll just throw that out there to give you some credit. So you're basically an associate product manager, an IC product manager, an engineer, CPO, COO, CTO, CEO of three different companies, including a public company. Very rare that somebody is successful at all these types of roles and all these levels. So let me just ask you this question. What mindsets or habits or just ways of working have you worked on building in yourself that you think have most contributed to you being successful in such a variety of roles and levels?

确实，我为自己多元身份自豪。有趣的是，不同时期的同事对我的认知截然不同——

Yeah. It's actually something I am proud of. I I like the fact I've worn different hats. It's actually amusing when I meet colleagues that I've known from one of those jobs. They'll often think of me through the lens of that job, you know.

Facebook同事视我为工程师，谷歌同事认为我是产品人，Salesforce员工则把我看作穿西装的老板（尽管我周末仍会写代码）。我的原则是保持身份认知的灵活性。

And so, you know, I'll go to meet folks from Facebook and they think of me largely as an engineer. They'll meet folks from Google. They think me largely as a product, you know, person. At Salesforce, you know, a lot of the folks there interact with me as like a for lack of a better word, a suit, you know, like the boss, and I I'm not sure they think of me as a as an engineer at all even though, you know, I was still probably coding on the weekends for fun. And one of the things that is a principle for me is to have a really flexible view of my own identity.

我自认是建造者，喜欢通过公司这种形式打造产品。坚信科技与资本结合能创造巨大价值。要成为杰出创始人，必须打破固化身份认知，随时转型适应公司需求——所有创始人都会告诉你，销售能力至关重要。

I really think of myself I probably would self describe as an engineer, but more broadly, I think of myself as a builder. I like to build products and I think companies are one of the most effective ways to build products. There's also things like open source, but I think I'm a huge believer in the confluence of technology and capitalism to produce just incredible outcomes for customers. And as a consequence, I think to really build something of significance, I think to be a great founder, really need to be able to not have such a ossified view of your identity that you can't transform into what the company needs you to be at that point. And every founder you'll talk to, one day I think selling is a big part of being a founder.

你必须说服投资者愿意投资你的公司。你必须说服候选人愿意为你的公司工作。你必须说服客户愿意使用你的客户生产的产品。你不仅需要对产品有良好的设计品味，还需要对营销和吸引新客户有独到见解。你必须拥有优秀的工程技术。

You have to sell investors on wanting to invest in your company. You have to sell candidates on wanting to work at your company. You have to sell customers to want to use the product that your customer produces. You have to have good design tastes not just for your product but for your your marketing and, you know, essentially soliciting new customers. You have to have good engineering.

我的意思是，你在建立一家科技公司，技术是第一位的。这就是为什么这个行业如此具有变革性。我可能之前讲过这个故事，但我非常感谢她，我大概要把这个转变归功于雪莉·桑德伯格，她真正改变了我对待新工作的方式。这个故事我可能有点添油加醋，但大体上是准确的。那时我刚成为Facebook的首席技术官。

I mean, you're building a technology company, the technology comes first. It's, you know, why this industry is so transformative. I probably credit I've and told this story before, but I'm I'm very grateful for her, but I probably credit Sheryl Sandberg for really changing the way I approach new jobs. The story and I might be embellishing a little bit, but I think it's broadly accurate. So I had just become the chief technology officer of Facebook.

当我刚得到这份工作时，CTO的角色更像是带领一个相对较小的团队，但我在多个项目中充当着类似高级架构师的角色。后来马克·扎克伯格重组了公司，将其划分为多个不同部门。最终我负责一个非常大的团队，基本上掌管着我们的平台和移动部门，包括产品、设计和工程。我从管理几个人突然变成了管理上千人，这是一个巨大的团队。这是我当时最大的管理挑战，虽然在谷歌也带过团队，但规模要小得多，当时我做得还行但不算出色。

And when I first got the job, it was sort of the flavor of CTO where I had relatively small group reporting into me, but contributed almost as like a very senior kind of architect, you know, on on a number of projects. And then at some point, Mark Zuckerberg reorganized the company and kinda split it into a bunch of different groups. I ended up with a very large group owner and me and I was essentially running our platform and mobile groups, products, design, engineering. So I went from you know a handful of reports to like over a thousand or something, it was big group. And it was the largest management job, had become a manager at Google but a modest team and so and I was doing okay but not great.

有一次雪莉看到我时——我记得当时正在为合作伙伴修改演示文稿，因为收到的初稿达不到我的质量标准——她把我拉进房间，给我上了一堂管理基础课。她教导我应该要求团队达到和我一样的高标准，如果有人达不到我的期望，我是否有计划将他们调离岗位。她是个非凡的导师，能给出非常直接、有时甚至让人不太舒服的反馈，但你知道她是真心为你好，所以这种建议你会认真听取。

And I had this moment where Cheryl saw me, I think I was editing a presentation for a partner just because the the presentation I got didn't meet my quality bar and I was editing it and sort of griping about it. She sort of pulled me into a room and kinda gave me a talking to like a little bit about holding my team to as high of a standard as I have. If someone wasn't meeting my expectations, what was my plan to like manage them out of the company? Or just like kind of giving me management 101. She's a remarkable mentor in the sense she can kind of give you feedback that's very direct and like often a bit uncomfortable and you know she cares about you, you know, and so it's the type of feedback you listen to.

那天晚上回家后我一直在琢磨这件事，心情不太好。人在这时候自然会有点防御心理：这真的对吗？我真的搞砸了吗？还是她反应过度了？但第二天醒来时，我意识到她是对的。我发现自己潜意识里有个限制器在阻碍工作表现——我总试图让工作内容符合自己原本喜欢做的事情。

I sort of went home that night and I was kinda stewing on it and like not very happy. Was like, you know, you get sort of naturally a little defensive in those moments like, that really true? Am I really fucking it up or is it, you know, is she overreacting? And then I woke up the next day, I was like, no, she's right. And I had realized sort of this subconscious, like, limiter that I that was limiting my success in the job, which is I was trying to conform the job to the things I thought I liked to do.

所以我过去花大量时间沉迷于某些产品和技术的思考——毕竟我是老板嘛，觉得就该专注自己感兴趣的事。后来我转变思路：既然负责Facebook移动和平台团队，那么每天最重要的事就是思考如何让移动开发者平台成功？当我这样重新定义工作后，行为模式就变了。最让我惊喜的是，我竟然爱上了这种状态。原以为我只喜欢工程和产品，但实际当组织因我的调整而更成功时，这种成功带来的喜悦远超想象。

So I was spending a lot of my time on some product and technology things that were I was passionate about thinking, you know, I'm the boss, know, I should, you know, focus on what I wanna focus on. Instead of thinking about, okay, I'm running the mobile and platform teams at Facebook, what's the most important thing to do today to make our mobile and developer platform successful? And when I reframed the job that way, I did different things. And the thing that was the biggest pleasant surprise to me was I liked it. You know, I thought I liked engineering and product, but in fact, when I, you know, changed an organization and it turned out to be more successful, I derived a great deal of joy from seeing that success.

我们的开发者平台有很多合作伙伴。当出现问题时，我投入时间处理伙伴关系，最终平台更健康、伙伴更成功——这种成就让我倍感自豪。渐渐地我变得更称职，也意识到我真正热爱的并非工程或产品设计本身，而是创造影响力。这个认知让我每天清晨（有时是字面意义的）都在思考：今天最能创造价值的事是什么？就像有个顾问团在指引你聚焦关键事项，最大化目标实现概率。

You know, our developer platform had a lot of partners and you know, when there was an issue there and I'd spend time on partnerships and it worked and you know, our platform became healthier, the partner became more successful, I took pride in that success. And then I just started being better at my job. And I realized that the actual act of engineering or product design or all the things I thought I liked, what I really liked is impact. And so that conversation led to my sort of waking up every morning, sometimes literally, but certainly in the broadest sense of the word saying, what is the most impactful thing I can do today? And really thinking almost like a if you had an external board of advisors, you know, telling you like, what are the things where if you focus on them, you can maximize the likelihood that what you're trying to achieve will happen.

有时是招聘，有时是产品，有时是工程，有时是销售。我变得更善于反思工作重点，也更愿意做曾经不喜欢的事——因为影响力带来的喜悦让一切都变得有趣。我真心感谢雪莉，现在给别人反馈时也常思考这点：那些改变职业轨迹的瞬间，这一切都要归功于她。

And sometimes it's recruiting, sometimes it's products, sometimes it's engineering, sometimes it's sales. And I've become much more self reflective just about what is important to work on. And I have become much more receptive to doing things that I previously would have said aren't my favorite things to do because I derive so much joy from having an impact that I enjoy a lot more things now. And so I really credit Sheryl, I'm so grateful and actually it's interesting, I think a lot about this when I give feedback to people now just like, those moments that can kinda like change the trajectory of your career, I mean, I give her all the credit for it.

好多人分享过雪莉·桑德伯格给建议改变人生的故事。真是楷模啊！我最大的收获就是这个核心问题：今天我能做的最有影响力的事是什么？

There's so many people that share stories of Sheryl Sandberg giving them advice and that changing their life. Yeah. What a a mensch. Yeah. My biggest takeaway from this, which is this question of what is the most impactful thing I could do today?

这个思维模型太有力量了。就像你说的，可能原本不喜欢销售或招聘，但如果这最能创造价值，实际做起来反而会发现：我既擅长又享受这个...对了能详细说说

Such a powerful heuristic just to kind of keep in mind. To your point, you may realize you don't want to be doing sales or hiring, but if that's the most impactful thing and you end up doing it, you may realize I like this and I'm good at this and and Can I double click on

尽管为了一个

that though for a

秒？绝对可以。

sec? Absolutely.

我认为这非常困难。对创始人和产品经理来说，尤其是创始人，一个危险在于错误的叙事。人们不喜欢我的产品是因为X。如果你对自己和团队这样讲，它就会从直觉变成事实。那你最好祈祷自己是对的。

I think it's really hard. One of the dangers for founders and product managers, but I think particularly for founders is incorrect storytelling. People don't like my product because of x. And if you tell that to yourself and you tell it to your team, all of a sudden it goes from being an intuition to being a fact. Well, you better hope you're right.

因为如果你的战略围绕解决那个问题而展开，而你又错了，公司就会失败。比如丢单的原因？你可以询问负责该客户的销售，或者参与对话的产品经理。在这些时刻保持理智诚实至关重要，因为可能会归咎于‘平台太贵’——这是销售常说的借口。

Because if you orient your strategy around fixing that problem and you're wrong, your company is gonna fail. So you know, why did you lose a deal? You know, you could talk to the salesperson who is on the account or perhaps maybe a product manager was involved in the conversation. It's very important to have intellectual honesty in those moments because you could say something like, oh, they didn't buy it because the platform cost too much. That and that's something a salesperson might say.

也许真实原因是他们并未看到平台价值，所以向销售传达为价格问题。实际上症结在于产品差异化。结果你可能陷入价格讨论，而真正需要解决的是更本质的难题。就像分手时不会说‘我不喜欢你了’，而是说‘不是你的问题’——人们总用社交辞令来维持体面。

Maybe the real reason is they didn't actually see much value in your platform, so it was communicated to the salesperson as it was too expensive. But in fact, the problem was product differentiation. And you could end up going into a discussion on pricing when in fact there was a much deeper, much harder problem to solve there. But it's not, you know, just like when you break up with someone you don't say it's because I don't like you anymore. Say it's not you, it's me.

客户在焦点小组或可用性测试中的反馈往往不准确。这些言论通常与真相相关，但需要深度辨析。我观察到初次创业者常受技能限制成为‘单一议题决策者’：优秀工程师认为所有问题都能用工程解决，产品设计师总寄望于‘下一次改版能扭转乾坤’——这就像消费品界的‘死猫反弹’理论。

Know, you say all these sort of pleasantries because we're all social animals and you wanna be pleasant with the people that you around you. So literally taking what a customer says or what a user says in like a focus group or a usability study is rarely correct. It often is related to what the truth is, but it's very important to get right. And so I think one of the things I've observed with first time founders in particular is you're often a single issue voter based on your skill set. So if you're a great engineer, the answer to almost every problem in your business is engineering.

商务背景出身的创业者则总想着‘只要达成某个分销合作就能改变一切’。创始人必须清醒意识到：你会下意识用自己最擅长的领域作为解决方案。但若你认为毕生所学能解决当前问题，至少有30%可能性是出于舒适区惯性而非真相。

If you're a product designer, the answer almost to you know, the proverbial redesign, I joke it's like the dead cat balance of a consumer product. This next redesign will fix all of our problems. Don't know if it's ever ever worked. And then you I've met a lot of entrepreneurs who like come from sort of a business development background, they're always thinking about partnerships and you know, oh, we just get this partnership done for this distribution channel, everything's gonna change. And I think it's really important when you're a founder to be self aware that you will naturally subconsciously pick the thing that is your strength, superpower as a solution to more problems.

这考验你是否拥有优秀的联合创始人或领导团队。产品经理需要与工程师、营销伙伴进行坦诚对话，确保在解决正确的问题。‘今天最具影响力的事是什么’这个问题看似简单，但多数人都在自欺欺人。准确回答才是真正的难点。

And in fact, if that you think that's a solution to your problem, it may be right, but you probably by default should question it. Like, if you think the thing that you've been doing your whole career is the way to fix your problem, it's at least 30% likely that you've chosen that because of comfort and familiarity, not truth. And so I think it's like one of the skills I think is it really goes around to like, do you have a good co founder? Do you have a good leadership team? If you're a product manager, your partner in engineering, your partner in marketing, you really wanna have very real conversations to ensure that you're actually working on the right, the actual correct thing.

我认为这是个极具挑战性的问题。问题本身有趣，但能精准作答才是关键。

And I think it's easy to say what's the most impactful thing to do today. My guess is if a lot of people try that, they'll lie to themselves more often than not. And it's a very challenging question to answer. The question is interesting. Being able to answer it accurately is actually the hard part.

这像是个血泪教训。能举个你亲身经历的例子吗？

This feels like such an important lesson you've learned. Is there an example that comes to mind where you learned this the hard way or you actually ended up

哦，你这是在拿我的失败经历大做文章，不过我无所谓。

Oh, taking You're a long just worse for this whole thing on my failures, but I'm fine with that.

你已经有过太多成功了。

You've had too many success.

Friendly是我的第一家公司。巅峰时期我们有12名员工，都是共事过最优秀的伙伴。创始团队包括斯坦福时期认识的工程师吉姆·诺里斯，还有保罗·吴赫特和桑吉夫·辛格——保罗是Gmail创始人，桑吉夫是Gmail首位工程师。我们既有谷歌地图团队又有Gmail团队，这个创始阵容简直梦幻。

Friendly was my first company. At our peak, we had 12 employees, 12 of the best people I've ever worked with. Started the company with Jim Norris, who's an engineer I've known since Stanford, and Paul Wuheit and Sanjeev Singh who Paul started Gmail, Sanjeev was the first engineer on Gmail. So we had the Google Maps people and the Gmail people. It was like pretty awesome founding team.

我们做了个社交网络，就像你说的，我们首创了很多后来流行的功能，比如信息流和点赞按钮，那段时光很有趣。但主要用户只在土耳其、意大利和伊朗。后来伊朗封杀了我们，就只剩土耳其、意大利和硅谷用户了。直到现在硅谷还有人会说'我爱死Fenfeed了'，这感觉挺棒的。

We made a social network, as you said, we sort of invented a lot of concepts that became popular in the newsfeed, we invented the like button, it was really neat, it was a fun time. We were only really popular in Turkey, Italy, and Iran. At one point, were blocked in Iran, so we're only popular in Turkey and Italy and Silicon Valley. To this day, actually, lot of folks in Silicon Valley are like, I love love Fenfeed. I'm like, that's awesome.

但这算不上成功的生意。我们是关注制社交网络而非好友制，内容更像推特而非脸书，主打新闻分享、兴趣社群和科学社区。当时推特还是竞争对手之一——那年头社交平台多如牛毛。奥巴马、艾什顿·库彻和奥普拉·温弗瑞那个夏天集体入驻推特，我们就被打得落花流水。

But it wasn't really a successful business. There was a we were a follower oriented social network, not a friendship oriented social network, which meant a lot of our content was more like X or Twitter than it is Facebook in that respect, and a lot of sharing newspaper articles, interests, scientific communities, things like that. And there was a period when Twitter, which was one of our competitors at the time, there was a lot more social networks at the time. I probably screwed us a little bit. I think Obama, Ashton Kutcher, and like Oprah Winfrey all went on Twitter like in a summer and we just got our ass kicked.

这是个典型教训。12人团队里11个是工程师，光顾着打磨产品。推特联合创始人比兹·斯通当时全力邀请名人入驻——现在回头看多么显而易见：关注制平台就该找值得关注的人。而我们却只专注产品优化。

Know, it's like and it was a great example of you. I think 11 of those 12 people were engineers and we're just making product. And I think it was BizStone. I mean, you talk to the Twitter folks, they could give you the history on this. But I think Biz was really focused on, like, getting celebrities and public figures onto Twitter, which is totally obvious.

推特当年服务器总崩溃（记得'故障鲸鱼'吗？）我们产品更稳定、功能更创新，用户也喜欢。但最终却输得一塌糊涂——和产品本身毫无关系。

Like, if you have a a social service that's oriented towards following people, put some people on there worth following. You know, like and instead, you know, we were exclusively focused on polishing the product. And we actually, I think, you know, at our sort of peak of popularity, we were very confident just, you know, I think it was a time when like Twitter had the fail whale and it was down half the time and people couldn't even use it. And, you know, we our product, we were innovating faster, we had more features, people liked it, we could and and we were up a 100% of the time. And we totally lost for no reason related to product at all.

这暴露出谷歌系创业者的通病：当AdWords印钞机日夜运转时，产品经理很难真正理解分销、商业模式这些要素。PayPal黑帮学到的创业经验可比谷歌PM多多了。我们这次就是被现实狠狠教育了——产品缺陷固然存在，但失败主因绝非于此。

And and it was an example of, you know, I think somewhat famously, not a like a lot of great entrepreneurs have come out of Google because once you like, Google was so successful. I think it's hard as a product manager to sort of see like distribution and all the product design and even business model when you have AdWords and money's raining from the sky, it's hard to, you know, there wasn't as much sort of scrutiny. And I think like it's folks like the PayPal mafia, I think learned a lot more about entrepreneurialism than like a typical PM at Google. So I, we're just getting punched in the face, know, and learning this the hard way. And so that was probably the most prominent example of it, know, and I think we probably did have a, I can tell you all the flaws of that product, but don't think that was like the reason why we lost.

问题在于缺乏经验就难有正确直觉。就算知道该找艾什顿·库彻代言，我通讯录里也没这号人物啊。更关键的是当时没找对人请教——科技行业最不缺的就是各种建议。

There's a lot of reasons. I think there was a lot of flaws with the product, but it was a lot of other stuff. And so I've learned like accumulated these skills over time. But when I say the hard part of that question is answering it correctly is, it's hard when you don't have experience in something to have intuition in it. So I think if there's probably a structural flaw, it wasn't that I I don't know if I could have figured out how to reach out to Ashton Kutcherman wanted to.

对吧？我又没有他的人脉。但当时确实没向合适的人寻求建议。不过这也是科技行业的好处——永远不缺建议来源。

Right? Yeah. It's not like he's on my, you know, on my Rolodex. But I probably wasn't soliciting advice from the right people. You know, I think that's what's great about the technology industry is there's a lot of advice.

选择听取谁的意见其实相当困难。但我觉得我们有些短视，你知道，我们沉浸在自己的小世界里打造产品，没有邀请外界人士来指出：你们看到了哪些潜在问题？哪些做法可能奏效？行业中有哪些我们尚未尝试但值得考虑的方向？这就是董事会存在的意义，也是寻找合适顾问的重要性——那些不总说你想听、但会告诉你需要听的内容的顾问。

Choosing whom you listen to is actually quite difficult. But I think we're somewhat myopic, know, we're kind of in our own little world creating this product and we weren't asking people to like from the outside end to say like, what are you seeing that could go wrong? What are you seeing that could go right? What are you seeing in the industry that we're not doing that you think we might wanna do? And this is why boards are important, this is why finding the right advisors, the advisors who actually tell you what you not necessarily wanna hear but you need to hear.

我认为这可能是当时缺失的一环。虽然不确定自己当时是否擅长营销，但如果我能征询到正确建议，本可以意识到这个短板。这是我从中汲取的深刻教训——我极度信赖董事会机制和优质建议的价值。

I think that was probably the missing part. I'm not sure I was great at marketing at the time, but if I had solicited the right advice, I could have learned that that was a shortcoming. And I think that was a deep lesson I took from that. I'm a huge believer in boards and getting good advice.

有没有什么经验法则或建议能帮助人们判断该听谁的意见？当你面对不同建议时，会特别关注哪些方面来决定最终采纳谁的？

Any kind of heuristics or advice for people to know whose advice to listen to? What do you pay attention to when you're like, okay. You know this person, but listen to this person.

这个问题确实棘手。归根结底还是取决于个人判断力和对他人品格的评估。最困难的是，人们表达观点时的自信程度与其观点质量往往不成正比——倒不一定是负相关，但想想现在满天飞的播客内容就很有意思。

Yeah. That one's tough. It is definitely it does come down to good judgment and being judge of people's character. One thing that is particularly hard is there's not a strong correlation between the confidence with which someone expresses an opinion and the quality of that opinion. I don't wanna say it's inversely correlated, but, you know, that's funny with all the podcasts out now.

在我熟悉的领域里，那些最雄辩自信的论断往往错得最离谱，偏偏听起来极具说服力。所以这真的需要极强的判断力。我的建议是：不仅要寻求建议，更要询问'我该向谁寻求好建议'——重复出现的名字就是可靠信号。另外，请教时别只问该怎么做，要像烦人的两岁小孩那样不断追问'为什么'。

If there's topics I know a lot about, you know, sometimes the most eloquent eloquent, confident statements about things I know a lot about are are the least accurate, and it sounds extremely persuasive. And and the so it does require very good judgment. One thing is I think not just asking for advice, but asking people who should I talk to to get good advice and you'll find some common answers there and that's often a really strong signal of good judgment. And then one thing I found is when you ask for advice, don't just ask what to do, but why? Like be it like an obnoxious two year old kid, you know, why why why why why?

关键要理解对方给出建议的思维框架。人们提建议时往往基于有限经验进行外推——他们会说'永远别这样做'或'总要那样做'，其实只是因为某次失败或成功的经历。如果不追问'为什么'，这些个案经验容易被误认为普适法则，而实际上只是孤立数据点。

And really trying to understand the framework that someone is using to give you advice. The interesting thing about advice is people are often extrapolating from relatively few experiences. So, you know, they'll say never do this or always do that. And it's because they had one experience where that something backfired or something could have gone better if they had done it. So it's a useful anecdote, if you don't ask why and understand they had one experience and here's what happened, it can come across as a rule when in fact it's it's anic data.

如果你向三个人请教并得到相似反馈，就能提炼出底层逻辑框架。这样应用建议时就能把握微妙差异，而非机械遵循规则。说到底这依赖个人判断力——虽然我不知道该如何传授这种能力。我个人极其推崇良好判断力。

And if you ask advice of three people and they all have very similar interactions, you can create kind of like a first principles framework from which that advice emerges. And when you start applying it, you're applying it with a degree of nuance that you couldn't if you're just following a rule. So I think one is it does come down to good judgment. Think, you know, I don't know how to teach that. I think it is probably a very I'm a huge believer in good judgment.

这正是我招聘时看重的特质。我认为这种能力源于自我反思——作为创业者或产品经理，你要诚实地复盘错误决策，持续精进判断力。归根结底，这才是成为优秀从业者的核心。其次，接受建议时要理解其来源和依据，从而形成自己的独立见解。

It's one of the things I hire for. I just think that that's something that, you know, probably comes from a mix of self reflection, you know, like you really need to hold yourselves accountable like as an entrepreneur, as a product manager. Like if you made a bad decision, spend time reflecting on it, like number one, and really try to understand why and try to like always improve your judgment. I think at the end of the day that is why you are a good entrepreneur, a good product manager. And number two, when you get advice, understand where it's coming from and why so that you can create sort of your own independent view of where that advice came from.

要明白绝大多数建议都不具备统计显著性——除非是沃伦·巴菲特的投资建议。大多数建议不过是某人基于个人遗憾给出的经验之谈。

And recognize that no one's advice is statistically significant or very rarely is it? I mean, you're giving like advice on investing, know, for Warren Buffett, yeah, okay, it's statistically significant. But that's not most advice is like something happened to you once and you have regrets.

太精彩了！你刚还说不知道有没有好答案，结果给出了完美解答。我想换个方向——你提到自认是工程师出身，听说你现在仍通过编程来放松？

I love that you're like, I don't I don't know if I have a great answer. Then you just give us an incredible answer to this question. I want go in a kind of a different direction. You mentioned that you described yourself as an engineer. I know I heard you code to relax still.

让我问你这个问题，很多大学生都在思考。你认为现在学习编程还有意义吗？你觉得未来几年这种情况会显著改变吗？

Let me just ask you this question, something a lot of people in college are thinking about. Do you think it still makes sense to learn to code? Do you think this will significantly change in the next few years?

我仍然认为学习计算机科学和学编程是两个不同的问题，但我认为学习计算机科学依然极具价值。因为计算机科学不仅仅是编码。如果你理解大O符号、复杂性理论、算法原理，明白为什么随机算法有效，为什么两个大O复杂度相同的算法实际表现不同，或是缓存未命中为何重要——编码远不止写代码本身。我认为软件创造将从终端或VS Code输入转变为操作代码生成机器。

I do still think it's studying computer science is a different answer than learning to code, but I would say I still think it's extremely valuable to study computer science. I say that because I think computer science is more than coding. If you understand things like, you know, big o notation or complexity theory or, you know, study algorithms and, you know, why why a randomized algorithm works and and, you know, why two algorithms with like the same sort of Big O complexity, one can in practice perform better than others and why a cache miss matters. And just all these little, there's a lot more to coding than writing the code. The reason I think that is, do think the act of creating software is going to transform from typing into a terminal or typing into Visual Studio Code to operating a code generating machine.

我认为这就是软件开发的未来。但操作代码生成机器需要系统思维。计算机科学（当然还有其他学科）是培养系统思维的绝佳专业。最终AI会协助软件开发，未来几年可能出现超乎想象的进步，但作为代码生成机器的操作者，你的职责是解决问题或创造产品，这需要强大的系统思维能力。

I think that is the future of creating software. But I think operating a code generating machine requires systems thinking. And I think that computer science, there are other disciplines as well, but computer science is a wonderful major to learn systems thinking. And at the end of the day, AI will facilitate, you know, creating the software. We may do a lot more in the next few years we can't even imagine, but your job as the operator of that code manager generating machine is to make a product or to solve a problem and you really need to have great systems thinking.

你将管理这台处理按钮制作、网络连接等繁琐工作的机器。但当你思考技术与商业问题的交汇点时，你需要构建能规模化解决客户问题的系统。这种系统思维始终是产品创造中最困难的部分。举个简单但典型的例子：在Facebook我们总花大量时间设计信息流。

And you're gonna be managing this machine that's doing a lot of the tedious work of making the button or, you know, connecting to the network. But as you're thinking of the intersection of a technology and a business problem, you're trying to affect a system that will solve that problem at scale for your customers. And that systems thinking is always the hardest part of of creating products. I'll just give you like it's it's a cheesy simple example, but I think it's representative. At Facebook, would always, you know, we spend a lot of time designing the newsfeed.

优秀设计师用Photoshop做出的信息流原型总是完美的——照片里家庭幸福、构图专业，贴文语法无误长度适中，评论区和按钮都无可挑剔。但实际实现后，你的信息流可能一团糟，因为真实用户的照片不专业，贴文长短不一，评论可能是'你太烂了'之类。这时你才意识到：用Photoshop设计是简单的，难的是构建一个能从不完美输入中产出愉悦体验的系统。

And have you ever had like a really, really good designer and they showed you at the time a Photoshop mock up of of the newsfeed, it was just always beautiful. The photos, the family was happy, and the photo was like a perfect photo, and the posts were like all perfectly grammatically correct and of a completely normal length, and the comments and the, you know, there was like button, everything was just perfect. And then you'd like implement that design and you'd look at your own newsfeed and it looked like shit because it turns out like not everyone's photos were made by like a professional photographer, the posts were all these different lengths, the comments were like, you you suck and like all that stuff. And then all of a sudden you realize that like designing a newsfeed like Photoshop is the easy part. You need to actually design a system that produces a like both in content and visual design, like a delightful experience given input you don't control.

这就是系统设计。我们后来要求设计师必须用真实混乱的数据展示设计，这迫使过程更贴近现实。我想说的是：无论AI写代码还是做设计，你都需要在脑中构建系统，理解难易与可能性的边界。顺便说，AI也能帮你培养这种能力。

And that's a system, that's not I mean, it's sort of a design. It's just what we did practically, I'm sure it's changed a lot since, you know, I left in 2012, but we made a system so, you know, designers had to show their newsfeed designs with real newsfeed data that was messy rather than, you know, anything artificial because I think it forced the process to be more realistic. But I say that because I think that like whether AI is writing code or doing the design or doing all these other things, like you need to learn how to have a system in your head. You need to understand the basics of what's hard and what's easy and what's possible and what's impossible. And AI can help you do that too by the way.

这确实是关键技能。随着AI智能体出现和某些领域接近超智能，我们的工作方式将巨变。重要的是保持工作方法的灵活性。就像我重写Google Maps的故事会成为历史遗迹——就像NASA人肉计算器的轶事那样。我擅长的技能未来可能不再有用，这很正常。

But I do think that's a really useful skill. I think in general with the advent of AI agents and, you know, AI approaching super intelligence in certain domains, I think the tools with which we do our job will change a lot. I think it's very important to have a very loose attachment to the way we do our jobs. And, you know, that story that we won't talk about when I like rewrote Google Maps, like everyone talks about that story because it's like and it's I think it's because of Paul Puhay who told it on some podcasts and they'll sort of made the rounds. I think that's gonna end up sort of this vestige of the past.

就像有人说'不学数学因为工作中用不到'——数学教会你思考方式和世界运行原理。计算机科学基础仍将是软件构建的基石。特别是当你与比你更智能的、产出你可能不完全理解的代码的系统交互时，如何约束它达成目标，这实际上需要极高的专业素养。

Like, I almost like the human calculators at NASA before the computers were invented like, wow, a person was a calculator? Wow, that's fun. Like tell me that story. I think just like what I was good at will no longer be useful in the future, certainly not like valuable in the future and that's okay. So I think we need to have a really loose view of it.

但认为不该学习这些学科是错误的。物理、数学等基础学科能培养关键思维能力。计算机科学基础将继续支撑软件开发。当与智能系统协作时，理解如何引导它产出符合要求的代码，这需要相当精深的专业知识。

But the idea that you shouldn't study these disciplines and sort of like people say, don't wanna study math because I'm not gonna use it in my career for X. Well, study math is quite important. Like it teaches you how to think, it teaches you like how the world works, physics, math. And I think computer science, especially at least sort of the foundations of it, will continue to be the foundations of how we build software and understanding that when you're interacting, particularly with something that's smarter than you, producing code you may not completely understand, how you constrain it and how you get it to produce these outcomes, I think it will require a lot of sophistication actually.

精彩回答。人们总纠结'要不要学编程'这个二元问题，而你的观点是：应该学习工程原理和系统运作方式，理解代码如何互联。但实际编码方式将发生巨变。这让我想起你最近播客提到的观点：你认为应该出现一种更适配大语言模型而非人类的新编程语言。

That's such a great answer. There's this always sense of this binary should I learn decode or not? And your point here is learn to understand how engineering works and how systems work and how what your code does and how it all interconnects. But the way you actually do the coding at your desk will change significantly. This reminds me of something you mentioned on a podcast recently, this idea that you think there's gonna or there should be a new programming language that is more designed for LLMs versus humans.

你能详细谈谈这个吗？因为我觉得很多人可能没考虑到这一点。

Can you just talk about that? Because I think a lot of people aren't thinking about that.

我不确定这是否算一种语言。我更倾向于称之为编程系统，因为'语言'这个概念可能太过局限。嗯。我对过去四十多年计算机发展的简化理解是：我们先创造了计算机硬件，接着发明了打孔卡——在七十年代末期，这是你给计算机下达指令的方式，大概七十年代中期到末期。然后我们开发了早期操作系统和分时系统。

I don't know if it's a language. I would call it a programming system because I think language might be too limited. Mhmm. My reductive version of the past, you know, what, forty years of of computers, maybe more, is, you know, you we created the hardware for computers, then we created punch cards, which is the way, you know, in, the late seventies, you would tell a computer what to do, or maybe mid to late seventies. Then we invented early operating systems and time sharing systems.

随着贝尔实验室和伯克利分校发明Unix这类系统，逐渐衍生出C语言、Fortran等高级编程语言（我认为先是Fortran然后是C）。我们不断向上构建抽象层，所以现在没人再用打孔卡了，也很少有人写汇编语言。有人用C，有人用Rust，但更多人使用Python和TypeScript这类语言。随着抽象层级越来越高，我们实现高杠杆效应变得越来越容易。

And from the invention of things like Unix at Bell Labs and Berkeley, you ended up with the C programming language, Fortran, and a lot of sort of higher level programming languages, I think Fortran and then then C. And you we've sort of moved up the layers of abstraction, so no one does punch cards anymore, obviously. Few people write assembly language. Some people write C, some people write Rust, but a lot of people write Python and TypeScript and things like that. And as we've invented more and more abstractions, we've made it easier to do high leverage things.

比如想想早期谷歌或谷歌地图有多惊艳——现在你让React程序员实现可拖拽地图，很多人应该都能做到。但在1998年Salesforce刚创立时，仅仅把数据库放到云端就是重大技术突破，这种技术护城河如今通过AWS变得轻而易举。虽然技术护城河已变得可笑地狭窄，但产品护城河依然宽广。如果编写代码的成本正趋向于零，那么我们构建的抽象层有多少是建立在人类程序员生产力基础上的？

So, you know, I always look if you look at how remarkable Google was back in the day or Google Maps, you could probably give a lot of React programmers the task of make a draggable map now and I think a lot of people could do it. That was true R and D, you know, in the day when Salesforce was created in 1998, just putting a database in the cloud was hard. And that was just like that alone was a technical moat that is now trivial with Amazon Web Services. And that technical moat is comically narrow, but the product mode is quite large. I think that if the act of writing code is going from something that is very costly to like the marginal cost of that going to zero, how many of the abstractions that we've built are based on, you know, human programmer productivity?

我认为非常多。我常开玩笑说Python可能是AI生成最多的代码，因为训练数据里Python含量太高，数据科学家又特别爱用——虽然它堪称AI生成最糟糕的选择之一，毕竟这是史上效率最低的语言之一。全球解释器锁导致其运行缓慢，我写过很多高性能Web服务，Python实在太慢了，而且极难验证。

I think a ton. You know, like I always laugh that I assume Python is probably the most common generated code just because how much it's in the training data and data scientists love Python and I love Python too. It's such a comically bad thing for AI to generate just because it's one of the most inefficient programming languages of all time. If you know the global interpreter lock and just slow, and I've written a lot of high skill web services and it's just quite slow. And it's very hard to verify.

虽然比Perl强点，但大型Python程序有多少错误会等到运行时才暴露？Python设计初衷就是人性化，让代码看起来像伪代码，让编程过程愉悦——这正是数据科学家钟爱它的原因。如果我们进入人类不直接写代码，而是操作代码生成机器的时代，编程语言的易用性可能就不再重要了。

Like it's not as bad as Perl, but like, you if you have a big Python program, how many errors will you find at runtime versus, you know, before releasing it? So Python was designed to be very ergonomic, almost look like pseudo code for humans, for me to write code in a delightful way. That's why data scientists love it so much. So as we move to a world where like, let's just postulate and I'm not sure this will be completely true that like we're not gonna write a lot of code as people, we're gonna be operating these code generating machines. We probably don't care how ergonomic the programming language is.

我们真正需要的是：当机器生成代码时，能否确认它实现了预期功能？如果没实现，能否轻松修改？我认为编程语言中有很多洞见可供借鉴。比如Rust就很有趣——要你判断C程序是否内存泄漏非常困难，但验证Rust程序只需成功编译即可，因为其编译期内存安全机制本身就是保证。

What we care about is when this machine generates code, do we know that it did what we wanted it to do? And if it doesn't, do we want it to do, can we change it easily? I think there's a lot of insights in programming languages that could serve this. So, know, Rust I think is interesting because I asked you to look at a C program and say, does it leak memory? You probably couldn't do it that well just because it's really hard.

如果是百万行级的C程序，验证几乎不可能。但Rust程序只要编译通过就证明没有内存泄漏。我们需要更多这类机制，因为如果必须逐行审查AI生成的代码，这将成为生产力瓶颈；更糟的是人们可能根本不审查，直接发布未经验证的不安全代码。核心问题是如何最大化人类杠杆效应——即让计算机代劳更多工作。

And if it's a very like a million line C program, that's be very, very hard. If I asked you to verify that a Rust program doesn't leak memory, you would just have to compile it. And, you know, because it has compile time, memory safety, just the act of compiling successfully tells you that's true. I think we need more things like that because if AI is generating this code by definition, if you have to read every line that is gonna be the limiting factor for producing the code or worse, you're just not gonna read every line and you're gonna emit a bunch of unsafe, unverified code into the wild. And so the question is, how do you enable humans to have as much leverage as possible, which means using computers to do the work on your behalf.

最直接的方式是让AI监督AI进行代码审查，自我反思确实是提升AI系统健壮性的有效手段。既然代码编写过程不再需要人类参与，我们可以重新启用那些'过时'的技术：形式化验证、单元测试等。想象自己像《黑客帝国》里看绿色代码雨的人——如何设计系统才能让我作为代码生成机器的操作者，快速产出复杂可靠的大规模软件？以此为设计核心，可能会彻底改变编程语言和系统架构。

You could have obviously the simplest form of this is AI, supervising AI and doing code reviews and that's great. Certainly, self reflection is a really effective way of improving the robustness of an AI system. But I do think if you, you know, if it doesn't matter how tedious it is to write the code, you could probably layer on some techniques that are sort of out of fashion like formal verification, unit testing, other things. And if you layer all these on, I'm sort of thinking about it as I as a, like the guy in the matrix with the green letters coming down like, how can I make something so I as a operator of the code generating machine can produce like incredibly complex scale software and incredibly quickly and know that it works? And if you start with that as your design center, think you probably change the languages, you probably change the systems, you probably change all these things and you're probably gonna bring to bear a lot of things.

最有趣的是可以打破许多限制：既然编码零成本，那语言设计、编译器、测试框架、自我反思机制、监督模型等环节该如何优化？这已经超越语言范畴，更像是一套完整的编程系统。

And what's really fun about is you can loosen a lot of constraints like coding is free. Okay, so that's neat. With that in mind, what do you wanna do? What would be best suited for the language, the compiler, for testing, for self reflection, you know, for supervisor models, all these things. I think that's more of a programming system than a language.

但我认为当我们创造出这样的工具时，它确实能让创作者、建设者们构建出极其强大且复杂的系统。我对Vibe编程感到非常兴奋，但生成原型从来不是软件开发中的限制因素。真正的挑战在于构建日益复杂的系统并灵活地进行修改。比如著名的网景浏览器从第一版到第二版的重写，很多人认为这某种程度上导致了他们在与IE竞争中的失败。构建这些系统并不难，难的是维护它们并确保其稳健性。

But I think when we create something like that, it can really enable creators, builders to create incredibly robust, incredibly complex systems. And I'm super excited about Vibe coding, but I don't know like generating a prototype has been the limiting factor in software ever. It's actually like building increasingly complex systems and actually changing them with agility. You know, you look at the famous like Netscape one to Netscape two rewrite, sort like somewhat a lot of people attribute that to part of their failure against Internet Explorer. It's like making these things is not hard, like maintaining them is hard and ensuring the robust is hard.

我认为我们正处于定义这种新型软件开发体系的早期阶段。我非常期待看到未来会涌现出怎样的成果。

And I think we've just sort of we're in the very early phases of defining what this new system for developing software looks like. I'm very excited to see what emerges.

当像你这样的人提议构建类似《黑客帝国》般的体验，并认为这可能是未来编程和开发的方向时，我感觉我们确实生活在未来。我迫不及待想看到那一天，这既是绝佳机遇又是趣味项目。本期节目由Vanta赞助，我非常荣幸邀请到Vanta的CEO兼联合创始人Christina Casiopo进行简短对话。

I feel like we're definitely living in the future when someone like you is suggesting that we build a matrix like experience and that's going to be potentially the future of coding and building. I can't wait for that. It feels like a great opportunity and a fun project. This episode is brought to you by Vanta, and I am very excited to have Christina Casiopo, CEO and cofounder of Vanta joining me for this very short conversation.

很荣幸参与。我是你们播客和新闻简报的忠实粉丝。

Great to be here. Big fan of the podcast and the newsletter.

Vanta是我们节目的长期赞助商。但对于新听众，能否介绍下Vanta的业务及服务对象？

Vanta is a longtime sponsor of the show. But for some of our newer listeners, what does Vanta do, and who is it for?

当然。我们2018年创立Vanta时专注于帮助创业者建立安全体系，并通过SOC2或ISO27001等合规认证获得认可。如今我们服务超过9,000家企业，包括Atlassian、Ramp和LingChain等知名初创公司，通过自动化合规、集中化治理风险与合规(GRC)、加速安全审查来帮助他们启动和扩展安全项目，最终建立信任。

Sure. So we started Vanta in 2018 focused on founders, helping them start to build out their security programs and get credit for all of that hard security work with compliance certifications like SOC two or ISO twenty seven zero one. Today, we currently help over 9,000 companies, including some startup household names like Atlassian, Ramp, and LingChain, start and scale their security programs, and ultimately build trust by automating compliance, centralizing GRC, and accelerating security reviews.

太棒了。根据经验，这些工作既耗时又耗资源，没人愿意亲自处理。

That is awesome. I know from experience that these things take a lot of time and a lot of resources, and nobody wants to spend time doing this.

这正是我们创业前后都深刻体会到的。但通过自动化、AI和软件，我们正帮助客户高效地与潜在客户建立信任。就像我们常说的玩笑话：我们成立这家合规公司，就是为了让你们不必亲自做这些。

That is very much our experience, both before the company and to some extent during it. But the idea is with automation, with AI, with software, we are helping customers build trust with prospects and customers in an efficient way. And, you know, our joke, we started this compliance company, so you don't have to.

感谢你们的付出。听众可通过vanta.com/leni专属链接享受1,000美元优惠。再次感谢Christina。

We appreciate you for doing that, and you have a special discount for listeners. They can get a thousand dollars off Vanta at vanta.com/leni. That's vanta.com/leni for $1,000 off Vanta. Thanks for that, Christina.

谢谢。

Thank you.

好的。沿着这个思路再问一个问题，然后我想放大视角谈谈AI的整体发展方向。作为身处AI前沿的人，我特别喜欢问像你这样的人一个问题：你在教孩子什么？我知道你有孩子。我觉得等他们长大时，世界会变得非常不同。

Okay. One more question along these lines, then I want to zoom out on just kind of where AI is heading. And something I love to ask folks like you that are at the cutting edge of AI is what you're teaching your kids. I know you have kids. I feel like the world is gonna be very different when they grow up.

你鼓励他们学习哪些你认为可能与前几代人不同的东西，以帮助他们在AI富足的世界里取得成功？

What are you encouraging them to learn that you think might is different maybe from previous generations to help them be successful in a world of AI abundance?

我不确定我教他们的方式是否不同，但我确实在努力鼓励他们将AI融入生活。其实我在回想97、98年参加AP微积分考试时，A、B和B、C部分都可以使用图形计算器。我还没做过这方面的研究——本该在对话前用ChatGPT查证的，不过之后我会补上——在允许使用计算器前后，微积分考试内容有变化吗？

I don't know if I'm teaching them differently, but I'm really trying to encourage them to make AI a part of their lives. I was reflecting actually when I took the AP calculus exams in ninety seven, ninety eight, a, b, and b, c, I could use a graphing calculator. And I haven't done this research. I I should be meaning to plug this into ChatGPT before our conversation, but I'll do it after. Did the calculus exam change before and after they allowed the calculator in the exam?

我猜是有的。但本质上，当考试允许使用计算器时，你必须确保所有题目都不会因为是否使用计算器而产生答题优势。这实际上迫使人们重新设计题目，以测试那些不依赖于机械算术或图形计算器功能的微积分知识。我认为当前很多教育体系都没有预设你口袋里装着超级智能。比如让学生写一篇读书报告，他们很可能直接用ChatGPT这类工具轻松生成。

I assume it did. But essentially to when you allow the calculator in the exam, you need to make sure that none of the questions, you know, benefit people for having a calculator or not. And which actually forces you to sort of rethink the problems to test calculus knowledge that don't benefit from like rote arithmetic or, you know, the other things you can do on a graphing calculator. I think that a lot of education is sort of doesn't presume you have a super intelligence in your pocket. And so, you know, if you ask someone to write an essay on a book that they read, you could probably hallucinate one pretty easily from one of the big, you know, providers like ChatGPT.

如果你的提示词技巧足够高超，可能连老师都分辨不出是不是AI写的。那么该怎么办？如何调整教学方式？这对教师来说非常困难，因为我们还没经历过像当年计算器进入考场那样的转型期。我认为现有的很多学生评估机制都被ChatGPT这类工具的存在打破了。

And maybe if you are skilled enough that prompting, maybe even your teacher won't know it's written by an AI. So what do you do? Like, how do you teach kids differently? It's really hard for teachers right now because I think we haven't gone through the transition of adding calculators to the exams. So I think a lot of the mechanisms we have to evaluate students are broken by the existence of ChatGePTA and the like.

所以我认为我们正处于一个非常尴尬的阶段。但我们依然可以既教会孩子如何思考，也教会他们如何学习。我相信我们的教育体系能够迎头赶上。实际上，我认为这些模型可能会成为历史上最有效的教育工具之一。不知道你是视觉型学习者还是阅读型，我个人喜欢阅读。

So I think we're in a very awkward phase. But I think we can still both teach kids how to think and teach kids how to learn. And I think our education system can catch up. And I actually think these models can be one of the most effective educational tools in history. I don't know if you're a visual learner or a reader, I like to read.

我不太喜欢听讲座，那种方式我学不好，我更喜欢自己看书。现在如果你遇到教学风格不适合的老师，回家后可以让ChatGPT用其他方式教你。我的孩子们考试前就用ChatGPT来测验自己，可以用语音模式或聊天模式，这比抽认卡好用多了。

I didn't love going to lectures, I don't learn that well from them, I like to like read the book. And, you know, if you have a teacher who doesn't teach in your style, you can now go home and ask ChatGPT to teach you in another mechanism. I my kids use ChatGPT to quiz them before a test. You can use audio mode or chat mode. It's like better than cue cards.

我女儿带了本莎士比亚的书回家，她把看不懂的那页拍下来，ChatGPT给她的解释比我讲得清楚多了。我觉得现在世界上每个孩子都能拥有一个个性化导师，用最适合他们的视觉、听觉或阅读方式来教学。这个平台既能测试你，也能考查你，真是个人学习能力的超级放大器。

You my daughter took home a Shakespeare book. She took a picture of a page she didn't understand and ChatGPT explained it to her way better than I would have as well. I think every child in this world has a personalized tutor that can teach them in the way that they best learn visually over audio, reading. We have platform that can test you, that can quiz you. I think it's really an amplifier of agency.

对于那些有自主学习意愿的孩子来说，这些模型就像是集合了你所有老师优点的最佳组合工具。比如我大女儿学编程做网站时，每次问我问题，我都让她去问ChatGPT。不是我想当个烦人的老爸，而是想让她学会使用这个神奇工具。我真心希望他们能学会在生活中有建设性地运用它。不过话说回来，我现在特别能体谅公立学校的老师们。

I think, you know, the folks who like kids who have agency, who have aspirations to learn something, I think you have what is the best combination of every teacher you've ever had in these these models and you can use it. So with my kids, you know, my oldest daughter learned how to code and she was making a website and every time she had a question for me, I would just make her use ChatGPT. Not because I was trying to be an obnoxious father, but I'm like, she used to learn that, like, to to use this tool because it's it's amazing. And I so I really am trying to have them learn how to use it constructively in their in their lives. But that all that said, I just feel a ton of empathy for public school teachers right now.

现在真的很困难，因为技术发展速度远超教育体系的适应能力。特别是在学习评估方面，对老师们来说极具挑战性。我担心的是，既然这些技术能放大学习主动性，反过来也可能被不想学习的学生利用——这些工具同样提供了很多逃避学习的途径。所以家长和教师都面临挑战，未来几年我们可能得经历一段颠簸的适应期。

It's very hard because we're just with the technologies moving faster than our educational system. And I think particularly as it relates to evaluation, it's just really challenging for teachers right now. And I worry, you know, because these technologies amplify agency, the opposite can also be true if you are a student trying to like not learn something. I think these tools probably provide a lot of mechanisms to avoid it as well. So I think there's a challenge for parents and teachers and I think we're gonna end up with kind of like a bumpy handful of years here.

但我提到AP微积分考试是因为显然图形计算器不是ChatGPT，别误会。我认为迄今为止我们已成功找到方法，让作业、课堂学习和测试适应现有技术。我相当有信心我们会解决这个问题，而且我持更乐观态度——我上的是公立学校，不知道你是否也是，有时会遇到一些糟糕的老师，但现在你有了出路。你不再需要是有钱请家教的孩子才能获得辅导。如果你是个数学优异但学校没有高级统计课程的孩子，现在你有了。

But I brought up the calculus AP exam because obviously a graphing calculator is not chat GPT, don't get me wrong. But I think we've been able to figure out a way to conform, you know, homework and in class learning and tests around the technologies available to us fairly successfully to date. And I'm fairly confident we'll figure it out, you know, and I like think it's gonna and I on the much more positive side, I mean I went to public schools, don't know if you did too, you end up with some pretty bad teachers, you know, at times and now you have an outlet. You don't need to be the, you know, rich kid who can afford to tutor anymore to get tutoring. If you are a kid who excels in math and your school doesn't have advanced statistics classes, well now you do.

所以我认为这对有自主权的孩子来说是极大的民主化力量，这非常令人兴奋。我期待现在有个11岁孩子，十年后会创立一家了不起的公司，而ChatGPT将成为促成这一结果的主要导师，这很酷。

So I think this is just an incredibly democratizing force with kids who have agency and I think that's very exciting. I'm hopeful that there's a 11 year old right now who's gonna start a really amazing company, you know, ten years from now who's like ChatGPT is gonna be like their primary tutor that like led to that outcome and I think that's pretty cool.

我有个两岁孩子，感觉每个新阶段都要做决定：什么时候给手机、什么时候给Snapchat（现在孩子都用什么？），然后是什么时候给第一个ChatGPT账号。不知道这该多早开始。

I have a two year old and it feels like there's like a new milestone of there's like when to give them a phone, when to give them, I don't know, Snapchat, whatever kids use these days, and then it's like when to give them their first ChatGPT account. I don't know. I wonder I wonder how soon that's supposed to happen.

我个人观点是ChatGPT更像改革者。我不认为手机适合学校或孩子，我主张尽量延后使用。但ChatGPT更像谷歌搜索——口袋里有让人上瘾的推送通知设备是一回事，用AI学习是另一回事。我认为AI本质上是工具，以前很少有家长问‘该什么时候让孩子用谷歌搜索’？

I think ChatGPT my personal take is from the reformer too. I I I don't think mobile phones are great in school or great for kids, and I I I personally advocate for waiting a long time. But I think that chat GBT is more like Google search and, you know, it's one thing to have a device in your pocket that's addictive and has push notifications, it's another thing to use AI to learn. And so I think the two are different and I really think of AI fundamentally as a utility. And I don't think a lot of parents before Chattypedia said, when should I let my kid use Google search?

这是不同类型的工具。我认为这样看待技术才是正确方式。

You know, that's like a different type of tool. I think thinking it like that is the way I think about these technologies.

那你给孩子用的设备是iPad还是笔记本电脑？

And so is the form factor for your kids like an iPad or a laptop or something?

他们用书桌上的电脑。

Yeah. They use like the computer on the desk.

明白了。这些建议对我很有用，等孩子长大时可以参考。

Got it. All right. Good tips. This is good for me to learn all these things as my kid ages. Okay.

现在放大视角聊聊AI商业策略。如今创始人最关心的问题是：我该在哪里构建？基础模型公司不会碾压哪些领域？作为成功AI企业创始人和OpenAI董事，你对哪些方向可行应该有独特见解。你认为AI市场会如何发展？

I'm gonna zoom out and let's talk about business strategy AI. One of the biggest questions a lot of founders think about these days is just where should I build? What will foundational model companies not squash and do themselves? Being someone building a very successful AI business and also being on the board of OpenAI, feel like you have a really unique perspective on what is probably a good idea and it's probably not a good idea. Why do you think the AI market is gonna play out?

你觉得创始人应该聚焦哪些领域？又该避开哪些？

And where do you think founders should focus and also just try to avoid?

我认为AI市场最终会形成三个具有相当规模的细分领域，最后我会谈谈我对市场走向的看法。首先是前沿模型市场或基础模型市场。我认为这将由少数几家超大规模云服务商和大型实验室主导，就像云基础设施即服务市场一样。原因在于打造前沿模型完全取决于资本支出，需要具备巨额资本支出能力的公司来构建这些模型。所有试图进入这一领域的初创企业几乎都已整合完毕——Inflection、Adapt、Character等公司就是例证。

I think there's three segments of the AI market that will end up fairly meaningful markets, and then I'll I'll end with how I think it's gonna play out. So first is the frontier model market or foundation model market. I think this will end up the small handful of hyperscalers and really big labs, just like the cloud infrastructure as a service market. And the reason for that is that creating a frontier model is entirely a function of CapEx and you need a company with huge amounts of CapEx capacity to build on these models. All of the companies that were startups that tried to do this have already been consolidated or almost all of them inflection, adapt, character and others.

我认为这根本不是一个可行的初创企业商业模式，因为所需资本支出过于庞大，融资跑道根本不足以支撑企业达到逃逸速度。而且作为资产类别，这些模型的价值衰减速度极快。因此需要极大的规模才能在价值快速衰减的模型上实现投资回报。所以我的结论是：创业者不该涉足前沿模型开发。这是我的观点，除非...

I think it's just not, that doesn't appear to be a viable business model for a startup because of the amount of CapEx required and there's just not enough runway you can get fundraising runway to get to escape velocity and also the models deteriorate in value fairly quickly as an asset class. So you need just a lot of scale to make a return on the investment for a model that deteriorates in value so quickly. So I think that's gonna end up probably no entrepreneurs should build a frontier model. That's my my Unless

你是埃隆·马斯克。

you're Elon.

没错。哦对，他...他是特例对吧？而且他确实有能力募集数十亿资金。

Yeah. Oh, yeah. He's he's not he's he's different. Right? And and he has the capacity to raise billions in capital.

我猜在座其他听众都没这个本事。他成为史上最伟大企业家是有原因的，他是不可复制的。市场另一部分是工具链——就像淘金热时期卖镐头的商贩，现在也有大批人在做AI开发工具。

And my guess is most of your other listeners don't. And then he's the greatest of all time for a reason and he's different. You don't compare yourself to him, you know. The other part of the market is the tooling. You know, I think there's, you know, a lot of folks selling pickaxes in the gold rush.

这是数据标注服务，也就是数据平台。它包括评估工具、更专业的模型，比如Eleven Labs拥有一套优质的语音模型，被许多公司广泛采用。如果你想在AI领域取得成功，需要哪些不同的工具和服务呢？工具市场存在一定风险，因为它可能过于接近核心领域。

This is data labeling services. This is, you know, data platforms. It's eval tools, more specialized models like eleven Labs has a great set of voice models that a lot of companies use that are really high quality. And it's sort of like if you're trying to be successful in AI, what are the different tools and services that you need? There is some risk to the tooling market because it's probably it's pretty close to the sun.

观察基础设施即服务市场和云工具市场（如Confluent、Databricks和Snowflake），亚马逊、Azure等巨头都在这些领域推出了竞争产品，因为它们与基础设施本身紧密相邻。每个基础设施提供商都试图通过向上层堆栈延伸来实现差异化。正如我提到的Snowflake、Databricks、Confluent等公司，确实存在许多有实质意义的公司，但也有不少被基础设施提供商自身技术所取代。这些公司最可能面临的风险是，当某个大型基础模型公司发布与其功能完全相同的产品时。因此，虽然可能有很多人需要你的工具，但关键问题是：当这些大型基础设施提供商推出竞品时，人们为何还要继续选择你？

So if you look at the infrastructure as a service market and the cloud tooling market like the Confluent and Databricks and Snowflake, a lot of the Amazon and Azure and others have competing products in those areas because they're very adjacent to the infrastructure itself. And every infrastructure provider is trying to differentiate by moving up the stack and you're right there. And so, there's some real meaningful companies as I mentioned, like Snowflake, Databricks, Confluent and others, there's a lot of others that were sort of obviated by technology from the infrastructure providers themselves. Those companies probably are the most at risk for, you know, a developer day from one of these big foundation model companies releasing exactly what they do. So you have to, there's probably a lot of people who need your tool, but the question will be if or when is probably the right way to think about it, one of these large infrastructure providers introduces a competitor, why will people continue to choose you?

这是个不错的市场，但正如我所说，它有点过于接近核心领域。然后是应用AI市场。我认为这对于开发智能体的公司将大有可为。我认为智能体就是新的应用程序形态，这将成为主流的产品形式。

So it's a good market, but it's a little bit close to the sun, as I said. And then there's the applied AI market. I think this will play out for companies who build agents. I think agent is the new app. And so I think that's gonna be sort of the product form factor.

例如Sierra这样的公司，我们帮助企业构建智能体来处理电话或聊天，改善客户体验和服务。还有像Harvey这样的公司，为法律行业开发智能体，处理反垄断审查、合同审核等事务。此外还有专注内容营销、供应链分析的公司。这类似于软件即服务市场，利润率可能更高，因为你销售的是能直接带来商业成果的产品，而非模型的副产品。

So there's companies like Sierra, we help companies build agents to answer the phone or answer the chat for customer experience and customer service. There's companies like Harvey that make agents for both the legal, paralegal profession, antitrust reviews, reviewing contracts, etcetera, etcetera. There's companies that do content marketing, there's companies that do supply chain analysis. I think this is sort of like the software as a service market. They'll probably be higher margin companies because you're selling something that achieves a business outcome as opposed to being a byproduct of the models themselves.

这些公司几乎肯定要向模型提供商支付费用，这就是为什么模型提供商最终会形成极大规模，但利润率可能略低。我认为它们的市场面向的可能是技术性较低的客户。纯粹的软件即服务，你不会关心它使用什么数据库，真正重要的是功能和特性。我认为智能体也将朝这个方向发展。

They will almost certainly pay taxes down to the model providers, which is why those model providers will end up extremely large scale, but probably slightly lower margin. And I think, you know, the market for them will be probably less technical. I mean, you know, if you think about the purest form of software as a service, it's not like you ask like, what database do you use, right? It's really about the feature and function. I think that's where agents will go.

我认为随着时间的推移，重点将更多转向产品而非技术。回想我的比喻，1998年马克和帕克创立Salesforce时，仅让数据库在云端运行就是一项技术壮举。如今，没人会问这个问题，因为你能在AWS或Azure上轻松创建数据库。如今，在模型之上协调一个自主代理流程听起来很炫酷且困难，但我确信三四年后这会变得简单。

I think it's gonna be more about product than it is about technology over time. You know, just going back to my metaphor, you know, in 1998 when Mark and Parker started Salesforce, just getting that database running the cloud was like a technical achievement. You know, nowadays, like, you know, no one asks about that because you can just spin up a database in AWS or Azure and it's like, no problem. I think today, you know, getting an orchestrating an agentic process on top of the models is like, sounds really fancy and it's really hard and all that stuff. You know, I'm pretty sure that's gonna be easy in three or four years.

随着技术进步，情况就是如此。所以长远来看，你会问什么是代理公司？它有点像软件即服务（SaaS）。你会少谈论如何处理模型，就像现代SaaS很少被问及使用什么数据库。但你可能会大量询问工作流程及驱动的业务成果。

It's just like, just as the technology improves. And so over time you say like, what is an agent company? Well, it looks a little bit like software as a service. You're gonna talk a little bit less about how you deal with the models in the same way modern SaaS, few people ask what database you use. But you'll probably ask a lot about the workflows and what business outcomes that you're driving.

你是在为销售团队生成潜在客户？还是在最小化采购支出？无论提供什么价值，都会慢慢朝那个方向演变。我很兴奋。但我认为初创公司或许不该构建基础模型。

Are you generating leads for a sales team? Are you minimizing your procurement spend? Whatever value you're providing, it's going to sort of slowly evolve towards that. I'm very excited. I don't think startups should probably build foundation models, think.

当然，如果你有未来愿景，可以放手一搏。但我觉得这可能是个已经趋于整合的挑战性市场。我对另外两个市场非常兴奋。尤其期待随着构建代理变得更简单，会涌现大量长尾代理公司。我最近看了股市上前50大软件公司的网站。

But I mean, you can shoot your shot, you know, if you have a vision for the future, go for it. But I think it's probably a challenging market that's already sort of consolidated. I'm very excited about the other two markets. I'm particularly excited as building agents becomes easier to see a lot of long tail agent companies come out. I was looking at a website for the top 50 software companies in the stock market.

显然前五名是微软、亚马逊、谷歌这些巨头。但接下来的50家全是SaaS公司，其中有些非常激动人心，有些则超级无聊。这就是软件市场的演变方式。

And obviously, like the top five are the big big boy ones like Microsoft, Amazon, Google, all that. But like the next 50 are all SaaS companies. And they're like, some of them are very exciting. Some of them are like super boring. But this is like how the software market has evolved.

我认为代理领域会出现类似情况。不仅会有像客户服务和软件工程这样的大市场，还会出现许多针对耗时耗力问题的代理解决方案，这需要真正深度理解业务问题的创业者。我认为AI市场的价值将主要在这里释放。

I think we're gonna see something kind of similar with agents. Like, it's not just gonna be like these huge markets like we're in like customer service and software engineering. It's gonna be like a lot of like things where people are spending a lot of time and resources that an agent can just solve, but it requires an entrepreneur who actually understands that business problem like in deep deeply. And I think that's where like a lot of the value is gonna be unlocked, in the AI market.

这非常有启发性。让我想起曾邀请马克·贝尼奥夫上播客，你们曾是联席CEO，他满脑子都是代理，只想谈论Agent Force（代理力量）。

That is incredibly helpful. So makes me think about it. Had Marc Benioff on the podcast. You guys were co CEOs, and he was, extremely agent filled. All you wanted to talk about was Agent Force.

显然你也充满代理思维。是什么...

Clearly, you are also very agent filled. What is it that

你听到了'代理'这个词。等等。

you heard the term agent. Hold on.

我要...

I'm gonna

用那个。所以很明显，

use that one. So Clearly,

你们看到了一些东西，就像是，好吧，我们需要全力投入代理。这就是未来。你认为人们忽略了什么，为什么这是软件工作方式如此关键的变化？人们没有看到什么？

you guys saw something that was just like, okay, we need to go all in on agents. This is the future. What is it you think people are missing about just like why this is such a critical change in the way software is gonna work? What are what's what are people not seeing?

如果你和像拉里·萨默斯这样的经济学家交谈，他和我一起在OpenAI董事会，他们会谈论技术的价值是什么？它是否有助于推动经济生产力？如果你看看经济生产力的一个大跃升是在九十年代。我认为很多和我交谈过的人认为实际上是计算的第一波浪潮，人们建立了ERP系统，把会计放进了电脑和数据库，甚至是大型机，我们说的是PC时代，因为它是一个巨大的进步。就像，想象一下，你知道，像大型跨国公司的数字账本之前的样子，它真正改变了部门。

If you talk to an economist like Larry Summers, who are on the OpenAI board with me, they'll talk about like, what is the value of technology? Will it helps drive productivity in the economy? And if you look at the one of the big jumps in productivity in the economy was in the nineties. And I think a lot of folks I talked to think it was actually that very first wave of computing where people made like ERP systems and just like put accounting into computers and databases, even like mainframes, we're talking like the PC era, because it was such a huge step up. Like, you know, just imagine like the ledgers of, you know, numbers that you'd have for like a large multinational company before and it truly just transformed departments.

我给你一个小例子，我父亲刚退休，他是一名机械工程师，他谈到他在70年代末刚开始职业生涯时，进入了一家机械工程公司，公司的大部分人是绘图员。基本上，你拿到一个工程设计，需要做所有的不同视角和所有不同的楼层，然后交给承包商去做。现在他的公司没有绘图员了，你只需要做设计，先是AutoCAD，现在是Revit，你知道，它是一个三维模型，绘图实际上已经被淘汰了，不再需要做这件事了。实际的设计和绘图已经不存在了。就像你可以，它只是一个设计。

I'll give you a little toy example, my dad just retired, he was a mechanical engineer and he was talking about when he first started his career in the late 70s and he went into a mechanical engineering firm, the majority of the firm were drafts people. So basically you take an engineering design, you needed to do all the different vantage points and for all the different floors and to give to the contractor to do the thing. Now there are zero drafts people at his company, you just make the design and first AutoCAD and now Revit and it you know, it's a three d model and you know, the drafting has actually been eliminated, it's just not a thing one needs to do anymore. The actual design and drafting is not a thing that exists. It's just like you can, it's just a design.

这是真正的生产力提升，就像，你知道，机械工程公司的工作是做设计，绘图是承包商需要的输出，但它并没有真正增加价值，只是供应链的变化。如果你看看从PC开始的软件行业历史，有有意义的生产力提升，但远不如第一次巨大的跃升。我不够聪明，不知道确切的原因，但有趣的是，技术的生产力提升承诺并没有像一些人认为的那样实现。我认为代理将真正开始再次改变曲线，就像我们在计算的早期所做的那样，因为软件正在从帮助个人稍微提高生产力，你知道，到实际上自主完成一项工作。因此，就像你不需要机械工程公司的绘图员一样，你不再需要有人做那件事了。

That's true productivity gains, It's like, you know, the job of the mechanical engineering firm was to do a design, the drafting was like sort of this necessary output for the contractor, but it wasn't really adding value, it was just sort of like the supply chain change. If you look at the history of the software industry from the PC on, there's been meaningful productivity gains but just not nearly as meaningful as that first huge jump. And I'm not smart enough to know exactly why, but it is interesting like there has the promise of productivity gains from technology hasn't been as realized I think as some people thought. I think agents will truly like start to bend the curve again like we did in the very early days of computing because software is going from helping an individual be slightly more productive, you know, to actually accomplishing a job autonomously. And as a consequence, just like you don't need drafts people in a mechanical engineering firm, you just won't need someone doing that thing anymore.

这意味着他们可以做更高杠杆和更有生产力的事情，实际上，你知道，更少的人可以完成更多的事情，真正推动经济生产力的提升。而且，你知道，如果你曾经销售过企业软件，你最终会作为供应商与客户进行这些讨论，你会有一个价值讨论，你会做一些有点复杂的，你知道，事情，比如，你在销售一个销售工具。好吧，如果每个销售人员多卖5%，你应该付我们一百万美元。就像，你知道，大致是那样的对话。

It It means they can do something else that's higher leverage and more productive and you can actually, you know, a smaller group of people can accomplish more and, you know, truly drive productivity gains in the economy. And, you know, I think if you've ever sold enterprise software, you end up in these discussions as a vendor with the customer where you'll have like a value discussion and you'll do these like somewhat convoluted, you know, things like, okay, it's like, you're selling a sales thing. Okay. Well, if every salesperson sells, you know, 5% more, and you should pay us a million dollars. Like, you know, it's roughly that conversation.

这是如此难以归因，你知道，特别是这就是为什么销售生产力软件如此困难，我学到了我们的方式。你知道，很难知道，你知道，让每个人提高10%生产力的价值是什么？你真的让他们提高了10%的生产力吗？还是其他事情改变了？你并不真正知道所有这些事情。但现在有了一个代理实际完成一项工作，它不仅真正以非常真实的方式推动生产力，而且也是可衡量的。

And it's so unattributable, you know, especially and it's why it's so hard to sell productivity software, which I learned our ways. You know, it's just hard to know, you know, what's the value of making everyone 10% more productive? Did you actually make them 10% more productive or did something else change? You don't really know all these things. But now with an agent actually accomplishing a job, not only is it actually truly driving productivity in a very real way, but it's measurable as well.

所以所有这些结合起来意味着我认为这实际上是软件思维方式的一个阶跃变化，因为它自主完成一项工作，这更像是自我证明的生产力驱动因素。它是可衡量的，所以人们也会以不同的方式重视它，这也是为什么我也相信基于结果的软件定价。所有这些对我来说，感觉就像云一样重要，或者我认为在技术上更重要，但就像它如何改变软件行业的商业模式，将有一个前后之分。就像，我不知道还有多少人销售永久许可的本地软件，但现在已经是微乎其微了。我认为我们将经历一个类似的转变。

So all those things combined means I think this is actually like a step change in how we think about software because it does a job autonomously, which is like sort of more self evident, a productivity driver. It's measurable, so people value it differently as well, which is why I also believe in outcomes based pricing for software. And all of that combined to me, it feels like as significant as the cloud or I think more technologically, but just in terms of like how it like transforms the business model of the software industry where there's gonna be like a before and after. Like, I don't know how many people still sell perpetually licensed on premises software, but it's de minimis at this point. I think we're gonna go through a similar transition.

整个市场将转向代理。我认为整个市场将转向基于结果的定价。不是因为这是唯一的方式，而是市场将把每个人都拉向那里，因为它显然是构建和销售软件的正确方式。

Like the whole market is gonna go towards agents. I think the whole market is going to go towards outcomes based pricing. Not because it's the only way, but it's gonna be like the market is gonna pull everyone there because it's just so obviously the correct way to build and sell software.

让我拉一下最后那根线。所以我们最近在播客上邀请了马德哈万，定价专家，传奇人物，《创新变现》的作者。他谈到了AI公司的定价策略，他非常支持你的观点，如果你能，你需要把你的产品定价为基于结果的产品。访问使用正是你分享的，如果你能归因影响并且它是自主的，它自己运行。也许只是他实际上用了Sierra作为这个成功的闪亮例子之一。

Let me pull on that last thread. So we had Madhavan on the podcast recently, pricing expert, legend, monetizing innovation author. And he, talked about pricing strategy for AI companies and he was very much in your camp of, if you can, you need to price your product as an outcome based product. The access uses exactly what you shared, which is you can do that if you can attribute the impact and it's autonomous, it's running on its own. Maybe just and he actually used Sierra as one of the shining examples of this being successful.

能否简要解释一下什么是基于结果的定价？针对那些初次接触这个术语的人，再以Sierra为例说明它是如何运作的？

Can you just briefly just explain a little bit what is outcome based pricing for people that haven't heard this term before and then just how does it work for Sierra to give an example?

好的，我先举例再展开说明。在Sierra，我们主要帮助企业开发面向客户的AI代理，最初用于客服，但更广泛地应用于客户体验领域。比如您的SiriusXM收音机出问题时会联系我们的AI助手Harmony；ADT家庭安防系统报警失灵时可以咨询他们的AI代理；Sonos音响等众多消费品牌也是如此。想象运营一个呼叫中心，接听每通电话都有成本。

Yeah, I'll start with the example and then I'll broaden it. So at Sierra, we help companies make customer facing AI agents primarily for customer service, but more broadly for customer experience. So if you have a problem with your SiriusXM radio, you'll call or chat with Harmony who's our AI agent. If you have ADT home security and your alarm doesn't work, can chat with their AI agent, Sonos speakers, a lot of different consumer brands. And, you know, if you think about running a call center, there's a cost for every phone call that you take.

其中大部分是人力成本。假设一通典型电话成本在10到20美元之间，部分用于软件和通讯，但主要支付接线员的时薪。如果AI代理能接听并解决问题——业内常称为呼叫转移或问题拦截——就意味着节省了约15美元的人力成本。

Most of it is labor costs. But if you have, let's just say a typical phone call is anywhere between 10 and $20 US dollars. Most of it some of it's software, some of it's telephony, but a lot of it is just like the hourly wage of the person answering the phone. So if an AI agent can take that call and solve it, you know, that is in the industry often called a call deflection or a containment. And that essentially means you saved, you know, it $15 because you didn't have to have someone pick up the phone.

因此在我们行业，当AI代理成功解决客户问题且无需人工介入时，会按预先协商的费率收费，我们称之为基于解决的定价。还有其他模式，比如有些销售代理会按销售佣金结算。实际上，我们将这些代理视为品牌的数字礼宾员，确保商业模式与客户需求对齐。

So in our industry basically we say, hey, if the AI agent, you know, solves the customer's problem, they're happy with it and you didn't have to pick up the phone, there's a pre negotiated rate for that. And that's we call it like resolution based. There are other outcomes as well. We have some sales agents being paid a sales commission, believe it or not, you know, do. We really think of our agents as truly customer experience, like the concierge for your brand and we wanna make sure that, you know, our business models align with our customers' business model.

正如您所说，这些代理需要自主运作且结果可量化。虽然不总能实现，但大体可行。最妙的是，任何CFO或采购主管面对大型供应商时，面对冗长的物料清单都难以评估合同价值。基础设施领域流行的按用量计费更接近本质，但我不认为AI令牌数能真正体现价值——就像那个著名的苹果工程师故事：当愚蠢的经理要求日报代码行数时，他提交了负数报告抗议重构删减的代码。

As you said, these agents need to be autonomous and the outcome has to be measurable. That's not always possible, but I think it's broadly possible. And what's really neat about it is if you talk to any CFO or head of procurement, you know, with their big vendors, they look at the bill of materials and it's like overwhelming and it's impossible to know if you're getting the value that you hoped from that contract. I think consumption based, which was popular particularly in the infrastructure space is closer to it, but I'm not sure like a token is actually a good measure of value from AI either. I always use the analogy like right now, most of the coding agents are priced per token or per utilization, but there's this famous story of a Apple engineer who had a bad manager who's like how'd you report how many lines of code you wrote every day, which every engineer in the world knows is an idiotic way to measure productivity.

令牌数同理——用量大不代表产出优质合并请求。关键在于：基于结果的定价与按用量计费截然不同，尤其在AI领域可能毫无关联。一通长电话若未解决问题，反而招致差评和二次呼叫，所有努力就白费了，甚至产生负面价值。

He famously went in with a report that had a negative number because I think he did a big refactoring and deleted a bunch and it was his way of saying like fuck you to the man. I think tokens are similar, you know, like, yeah, you used a lot of tokens like good for you. Did the you know, did it produce a pull request, you know, that was good. And I think that's the whole point of all this. I don't think, I think there's a huge difference between outcomes based pricing and usage based pricing because especially in AI, they're not necessarily even correlated and you could have a long phone call not solve the customer's problem and they give you a negative review online and call the call center again.

我坚信这种模式的美妙之处在于真正的价值对齐。所有科技公司都渴望成为合作伙伴而非供应商，而Sierra通过这种模式与每个客户形成了共生关系——我们共同的目标成就了这种深度绑定。

All that effort was for nothing. In fact, you might have added negative value. And so I am a huge believer in this and what's fun about it is, it really just aligns. I think every technology company aspires to be a partner, not a vendor. And I think at Sierra, we are truly a partner to every single one of our customers because we're all aligned on what we wanna achieve.

这正是软件行业应有的方向。它要求企业具备全方位服务能力——不能只是交付软件，必须切实帮助客户达成目标。采用这种模式后，企业会自然转变为极度以客户为中心的组织形态。

And I think that is really where the software industry should go. It requires a lot of different shape of a company. You just have to have, you have to be able to help your customers achieve those outcomes. Can't just throw software at the wall because you'll never get paid. If it doesn't, you have to really just your orientation becomes so extremely customer centric when you do this the right way.

我认为这是软件产业的进阶形态。从第一性原理看，这对采购方、合作伙伴乃至整个生态都是更优解。

I think it's just a better version of the software industry. So I think it's right from first principles, it's right for procurement partners, and I think it's right for the world.

我们刚才谈到效率提升。近期头条新闻对AI实际效用充满质疑——比如有研究显示工程师使用AI后效率反而下降，因为需要额外纠错调试。不知您是否关注到这个现象？

We've been chatting a little bit about productivity gains. There's a lot of skepticism in the headlines these days of just like, what is AI actually doing? Like, is it actually helping people be more productive? There was a recent study actually, I don't know if you saw where they showed engineers were less productive with AI because it was just putting them in different directions. They had to research all what's going wrong here.

因此我认为CX是一个很好的例子，你们显然看到了收益。在CX之外的领域，比如生产力方面，你们公司或合作的其他公司是否也看到了实际收益？就是说，能明确肯定‘这确实有效且意义重大’的那种？

So I think CX is a really good example where you clearly are seeing gains. Are you seeing actual gains at your company or any other company you work with outside of CX in terms of productivity that is, like, clearly, yes, this is working and a huge deal?

我对AI带来的生产力提升极为乐观，但确实认为当前工具和产品尚不成熟，这有些反直觉。比如，几乎所有我认识的软件工程公司都在使用类似Cursor的工具辅助工程师。目前大多数人把Cursor当作代码自动补全工具，虽然它们也提供很多代理解决方案——OpenAI的Codex、Cloud（记不清Anthropic的具体产品名）等都有这类代理服务。由于技术尚未成熟，它生成的代码常存在问题。

I'm extremely bullish on the productivity gains from AI, but I do think the tools and products right now are somewhat immature and it and it's quite counterintuitive. So for example, I almost every software engineering firm I know uses something like Cursor to help their their software engineers. Most people use Cursor right now as a kind of coding autocomplete, though they have a lot of agentic solutions and there's a lot of OpenAI's codex and there's, you know, Cloud has, can't remember the Anthropic products. There's lots of agentic, you know, agents coming as well. One of the interesting things because the technology is sort of immature, the code it produces often has problems.

因此许多人正在尝试真正实现这些生产力提升。正如资深工程师所言，检查和修改自己写的代码很容易，但审查他人代码或发现其中微妙的逻辑错误却非常困难。如果代码代理生成的代码经常出错，修复它们反而会消耗大量认知资源和时间。更糟的是，若给客户交付了大量有问题的功能，可能会适得其反。

So there's a lot of people sort of approaching this to sort of actually realize those productivity gains because as any engineer who's written a lot of code will tell you, it's pretty easy to like look at and edit and fix code you wrote. Reviewing other people's code or particularly finding a subtle logical error in someone else's code is actually really hard. It's actually much harder than editing code that you wrote yourself. So if the code produced by a coding agent is often incorrect, it actually can take a lot of like cognitive load and time to fix it. And in fact, if you end up producing lots of issues with your customers, you could end up producing a lot of features but actually is like, you know, mucking up the machine a little bit and having something that's not ideal.

我认为有几个有趣的方法：首先，现在很多AI初创公司专注于代码审查。代理的自我反思能力非常重要——用AI监督AI非常有效。试想，如果一个AI代理的正确率是90%，这并不理想。

There's a couple of techniques I think are interesting. Like first, think there's a lot of AI startups now working on things like code reviews. I think this idea of self reflection in agents is really important. Having AI supervise the AI is actually very effective. Just think about it this way, if you produce an AI agent that's right 90% of the time, that's not that great.

但再开发一个AI代理来找出剩余10%的错误呢？这或许是可行的。假设第二个代理也有90%的正确率，将两者结合就能达到99%。这本质上是数学问题：通过叠加认知层（生成代码、审查代码），用算力换取智能，最终产出更可靠的结果。这让我非常兴奋。

But how hard would it be to make another AI agent to find the errors the other 10% of the time? That might be a tractable problem. And if that thing's right 90% of the time, just for argument's sake, you can wire those things together and have something that's right 99% of the time. So it's just a math problem like, you know, and it turns out that you can make something to generate code, can make something to review code and you're essentially using compute for cognitive capacity and you can layer on more layers of cognition and thinking and reasoning and produce things increasingly robust. So I'm very excited about that.

另一个关键是根因分析。我们CIRA有位工程师专门研究模型上下文协议服务器，为Cursor实例提供服务。我们的理念是：当Cursor生成错误代码时，不仅要修复，更要找出根源——Cursor缺少哪些必要上下文才导致错误？这就是上下文工程。

The other thing though is root cause analysis. So we have an engineer at CIRA who exclusively focuses on the model context protocol server serving our cursor instance. And our whole philosophy is rather than if cursor generated something incorrect, rather than just fixing it, try to root cause it. Try to get it so like the next time cursor will produce the correct code. So like, and essentially is context engineering, like what context did Cursor not have that would have been necessary to produce the right outcome?

想在软件工程等部门获得生产力提升，就不能坐等模型自动变完美。必须建立根因分析体系：如何追溯每行错误代码的根源？如何提供正确上下文？虽然未来可能不再需要这么多上下文工程，但现在必须将其视为系统工程。很多人却期待模型自己神奇地进步。

So I think people who are trying to get productivity gains in departments like software engineering need to stop sort of waiting for the models to magically work if they wanna see the gains now. And you really have to create like root cause analysis and systems and say like, how do we sort of go root cause every bad line of code and actually give the right context and produce the right system so the models can do it today. Over time that probably like less necessary and you'll have less context engineering necessary to do it. But you really have to think of this as a system. And I think people are sort of like waiting for the models to just magically get better.

模型最终会进步，但若想现在就获益，必须付出努力——这正是应用型AI公司存在的意义。这项工作并不简单，但可行。比如使用Sera平台的客户，虽然AI代理不完美，但我们构建的系统能形成良性改进循环。

I'm like, well, that will happen eventually. But if you want the gains now, you gotta put in the work. I mean, that's essentially why Applied AI companies exist. And the work is non trivial, but it's you can do it. And so, you know, for customers using platforms like Sera, yeah, AI agents aren't perfect but we're creating a system that lets customers create a virtuous cycle of improvement.

若想将自动化解决率从65%提升到75%，我们有海量工具协助：识别改进机会、分析用户痛点、为代理新增能力。AI能帮你从干草堆中找出针尖，这才是优化系统的正确方式。

If you wanna go from a 65% automated resolution rate to 75%, we have a billion tools to let AI help you do that. Identify opportunities for improvement, figure out why people are frustrated, what new capabilities can we add to our agent to improve the resolution rate. And you sort of let AI put the needles the haste at the top of the haystack on your behalf and I think that's really the way to optimize these systems.

我从未听过通过添加上下文来改进Cursor的方法。具体如何实现？是通过构建MCP服务器统一处理，还是制定Cursor规则？实际采用什么方案？

I've never heard of this technique of improving cursor by adding additional context. What's the actual way of doing that? You build an MCP server that everything runs through or is it like you had cursor rules? What's the actual approach there?

我可能有点力不从心，这本质上是MCP（模型上下文协议）。但核心在于，你知道，这就是为光标提供上下文的方式。我认为，当一个优秀模型做出糟糕决策时，几乎总是因为缺乏上下文。所以关键在于找到你的特定产品、代码库与这些编码代理系统可用上下文的交集，从根源解决问题——这就是这里的核心原则。

I'm probably out of my depth here, it's essentially MCP. But it's essentially, you know, because that's how you provide context to cursor. And I think that almost always when you have a model making a poor decision, if it's a good model, it's lack of context. And so you really wanna like, you know, find the intersection of your particular product and code base with the context available to these coding agents and systems and fix it at the root is sort of the principle here.

明白了，这非常酷。听说有人在实践这个模型上下文协议，很有道理。我们聊过CX（客户体验）之外的生产力提升，趁此机会分享一下你们的成果有多惊人——用户使用Sierra后取得了哪些显著成效？

Got it, that is very cool. And heard people doing that model context protocol, makes sense. We've talked about productivity gains outside CX, just to give you a chance to share how amazing what you've built is, what are some of the gains you see from people using Sierra?

是的。我们的客户实现了50%-90%的客服交互完全自动化，这非常令人振奋。我们的客户覆盖面极广——健康保险行业、医疗提供商、银行。你甚至可以通过我们平台上的代理重贷房贷（某客户开发的），还有电信行业如DIRECTV、SiriusXM，以及大量零售商，这很有趣。

Yeah. We have our customers see anywhere between 5090% of their customer service interactions completely automated, which I think is really exciting. And we serve just a really, really broad range of customers. We serve the health insurance industry, the healthcare provider space, banks. You can actually refinance your home using an agent, one of our customers built on our platform to the telecommunications industry, DIRECTV, SiriusXM, to a lot of retailers as well, which is really fun.

从Wayfair到Olakai和Chubby Shorts等服装零售商。最妙的是应用场景的多样性：有帮用户注册交友软件客服代理的，有协助调整SiriusXM套餐的。最神奇的是技术支援——从家庭安防系统、Sonos音响到最近的CAT扫描仪（想想技术人员能通过AI代理对话获得维修指导）。我们是这个领域的领导者。

Everyone from Wayfair to clothing retailers like Olakai and Chubby Shorts. What's really neat about it is a pretty diverse range of use cases and it's everything from helping you, you know, sign up for we have an agent that helps with customer support and one of the big dating applications to, you know, helping you upgrade or downgrade your SiriusXM plan. Actually, it's really funny, we do technical support from everything from home alarm systems to Sonos speakers to more recently CAT scan machines, which I think is amazing. So technicians going in and fixing the CAT scan machine can chat with an AI agent to help them, guide them through that process. We're the leader in this space.

我们的目标是让全球企业都能创建带有自身品牌标识的代理——这将成为与官网、APP同等重要的数字触点。短期内它能大幅降低客服团队成本，更惊人的是还能保持高满意度。比如Weight Watchers代理的客户满意度达4.6/5分。有趣的是服务场景往往发生在用户遇到问题时——比如机场应用（那个代理CSAT分数4.7/5），印度用户带着问题来却满意而归。

We're trying to enable every company in the world to create their agent with their brand at the top that I think will become as meaningful of a digital touchpoint as their website or their mobile app. In the short term, it can really transform the costs of running a customer service team, you know, and what's remarkable is do so with really high customer satisfaction scores. You know, that Weight Watchers agent I believe has a customer satisfaction score of 4.6 out of five, which is pretty amazing. Know, that and and what's interesting about service too, it's often people are having a problem. And so, you know, when you have a clear I don't know if you use them in the airport.

我们的愿景是打造这样的世界：每次客户交互都能即时、多语言、多渠道（音频/聊天/数字/电话）且高度个性化。想想你与品牌的最佳互动体验——就像我常去的肉铺，老板认识我还会聊天。我们能否为拥有1亿客户的企业规模化复制这种体验？能否保持这种人情味？我们正站在实现这个愿景的临界点上。

I think that agent has a CSAT score of 4.7 out of five. You know, people are coming in with a problem in India and delighted, and I think that's really the opportunity here. And our whole vision is that we're gonna move towards a world where every single one of the interactions with your customers can be instant, it can be multilingual, it can be over audio, it can be over chat, it can be digital, it can be over the phone, and it could be very personalized. And I think that's really, really exciting. And if you think about all the best moments you've had with a brand, it's like that store associate who you know, and you know, it's like for me, it's like the butcher at the grocery store, I love to cook.

（续前）这正是我们努力突破的方向。

He knows me, we talk. Like, can you actually produce that at scale for a company with a 100,000,000 customers and can you do it in a in a really personal way? And I think we're really at the on the cusp of enabling that.

进入激动人心的快问快答前再问一个：很多AI应用创始人在GTM（市场进入）策略上挣扎。现在AI产品泛滥，大企业采购应接不暇。你们显然找到了秘诀——虽然名气和投资人帮助很大，但...

Let me ask you one more question before we get to a very exciting lightning round. There's a lot of founders struggling with go to market in AI with their AI apps. There's so many apps these days, so many products, so many things coming at buyers at large B2B companies. Clearly you guys have figured something out. I imagined your name helps, investors help, but what

有

have

关于AI产品（特别是代理类产品）的成功GTM策略，你们有哪些经验值得其他创业者借鉴？

you learned about just how to successfully do go to market with an AI product, say an agent specific product that you think would be helpful for folks trying to do this better?

我认为市场上已被验证有效的营销模式屈指可数，选择适合目标产品类别的模式至关重要。其中一类是开发者主导型，Stripe和Twilio堪称这一模式的早期典范。其核心营销策略是吸引CTO部门中具有决策权的工程师个体，他们既有责任也有相当自主权来选择解决方案。

I think there's a small handful of go to market models that have been proven to work. And I think it's important to choose the right one for the product category you're going after. One category I would say is developer led. This is somewhere famously Stripe and Twilio were probably, like, two of the original that did this exceptionally. And essentially, the go to market motion there is to appeal to an individual engineer often within the department of the CTO who have accountability and a fair amount of latitude to choose a solution.

这种模式适用于平台型产品，但若产品面向业务部门则行不通——因为业务部门通常没有专职工程团队，更不用说自主下载新库或启用网络服务的权限。该模式尤其适合初创企业，其工程团队往往拥有较大自主权来选择创始人指定的问题解决方案。另一种是产品驱动增长（PLG），广义上所有公司都重视产品，但PLG特指用户可直接通过网站注册、试用或信用卡购买少量席位的模式。

This works if your product is sort of a platform product. It doesn't work, for example, if your product is trying to help a line of business because lines of business typically don't have dedicated engineering teams or let alone the latitude to just go, you know, download a new library or start using a web service like that. It particularly works well if you sell to startups, just because startups tend to have engineering teams with quite a bit of latitude to choose services to help them solve the problem given by the founder. Then there's product led growth. It's a broad term, obviously every company's product matters, but product led growth more specifically means users can sign up from the website, often get put on a trial, often you can buy a couple seats with a credit card.

当软件使用者与购买者为同一人时，PLG效果显著。比如面向个体商户的小型企业软件（早期Shopify等），因为经营者包办所有事务。但当使用者与购买者分离时就会失效，例如费用报销软件——使用者是普通员工，购买者却是财务部门，让员工用信用卡购买显然不合理。

And those work where your user and your buyer are the same person. So it works for small business software almost always because sole proprietors do everything. And so you're selling small business software like, you know, Shopify in the early days and there's a lot of other products like that where you're trying to sell to small merchants, you know, that's great. It doesn't work well when your buyer and the user of the software are different. So I'll use the example of something like expense reporting software.

然后是直销模式。虽然我不想说它过时，但顶尖直销企业如SAP、Oracle、ServiceNow、Salesforce和Adobe，主要面向大型业务部门采用传统销售流程。由于PLG模式近年盛行，许多公司盲目跟风——但若因此忽视实际购买者的需求，企业将难以壮大。

The user of that software is an individual employee, but the buyer is often a finance department. And so, you know, having sign up and buy with your credit card doesn't make sense because the person using it is not the person with the credit card and it just doesn't work. And then there's direct sales. And direct sales had gone, I don't wanna say out of fashion, but if I think of like the best direct sales companies, I probably there's a lot of lineage from Oracle, but you think SAP, Oracle, ServiceNow, Salesforce, Adobe perhaps, and there's others as well. And these were companies that sold into, you know, large lines of business in a relatively traditional sales motion.

我注意到AI领域正重拾直销模式，因为多数AI产品的购买者与使用者角色分离，必须采用对应营销策略。创业者常犯的错误是未深度思考软件采购流程和价值评估机制就选择营销模式。大家更需要回归第一性原理，进行更审慎的思考。

I think because product led growth became very popular, I think a lot of companies use that which is great. That motion produces great products, but if PLG means that you aren't actually engaging with the buyer of your software, like you're not gonna grow. And so I've actually seen more recently with a lot of AI companies, direct sales come a little bit more back into fashion because I think so many of the opportunities in AI are actually meet that qualification where the buyer and the user are not necessarily the same person and it really requires that go to market motion. Where I see entrepreneurs stumble is they'll sort of choose a go to market motion without thinking through what is the process of purchasing the software, What is the process of evaluating value of this software? And I think people just need to be much more like first principles about it and much more thoughtful about it.

坦白说，更多公司应该加大直销力度。尽管某些直销企业的产品质量曾给该模式带来污名，但我很高兴看到它在AI市场的复兴。

And candidly, think like a lot of companies should leverage direct sales more than they do. And even though it like because of the, you know, sometimes justified reputation of the quality of products of some of these direct sales companies, lot of it sort of had gotten a bad name. And I think I'm sort of thankful to see it coming back in a lot of the AI market.

这番话值得许多创始人倾听，尤其是非商业背景、对销售有抵触情绪的创始人。他们必须认清：要赢得市场就不能只依赖产品驱动增长，掌握销售技能可能才是制胜关键。

Feel like this message is something a lot of founders need to hear, especially founders that aren't from a business background of, that, you know, sales turned them off. They don't think they're gonna be great at sales, just this push of, this might be what you have to get really good at and this is how you win and you can't just rely on product like growth.

确实。

Yeah.

Brett，还有什么想分享的智慧结晶吗？在进入激动人心的快问快答环节前，有没有需要重点强调的内容？

Brett, is there anything else that you wanted to share? Any last nugget of wisdom? Anything you want to double click on before we get to our very exciting lightning round?

没有，请继续。

No. Go ahead.

好的，我们开始吧。欢迎来到我们激动人心的闪电问答环节。我有五个问题要问你。

Okay. Let's do it. Here we go. Welcome to our very exciting lightning round. I've got five questions for you.

准备好了吗？

Are you ready?

准备好了，开始吧。

Yeah. Go ahead.

你平时最常向别人推荐的两三本书是什么？

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

我不常读非虚构类书籍，但如果要选一本与我们讨论话题相关的，可能是《与运气竞争》，这本书提出了'待办任务'理论框架，我十分认同。唯一的批评是这类商业书籍其实更适合写成文章——建议买书后用ChatGPT生成摘要。这是克莱顿·克里斯坦森提出的理论，确实为思考产品价值交付提供了绝佳框架，对我影响深远。

I don't read a lot of nonfiction, but probably if I had to pick one sort of in the area of the topics we talked about, competing against luck, which was the the book that produced jobs to be done, which is a framework I really believe in. My only critique is I think most of these sort of like business books should be like an article. So maybe buy the book and punch into chat GPT and get the summary. But buy the book, it's Clayton Christensen talked about it, but it's a really good framework for thinking about delivering value with their products. And I think it's a I I they definitely influenced me.

实际上我常推荐的是《耐力》，讲述沙克尔顿南极探险的真实故事。半本书都在描写他们如何在冰封船舱里濒临饿死的绝境——这是我见过最震撼的坚韧故事。创业者遇到困境时读它会觉得：情况还能更糟呢。

On the actually, one book I do recommend is Endurance, which is the story of Shackleton's trip to go to the South Pole. Like half the book is him starving to death and he didn't see old meat with his crew of people frozen in their boat. I've never seen a better story of grit in my entire life. It's like kind of remarkable that it's a true story. And, you know, if you wanna like if you're an entrepreneur going through a hard time, that, you'd be like, okay, could be worse.

这本书本身就很棒，更惊人的是它基于真实事件。

It's a great book too. It's just remarkable that it's a true story.

沙克尔顿最了不起的是他为加入那次著名探险的队员设定了明确的预期

And one thing he did a great job at is setting expectations for folks that joined that famous

那个说法未必准确，但确实很惊人。

That app. Don't know that's true, it's like remarkable.

可能不是真的。

Might not be true.

我不知道，我是说，互联网嘛，谁知道呢？

I don't know, mean, internet, who knows?

天啊，那时候就有深度伪造了。好吧，你最近有没有特别喜欢的电影或电视剧？

Goddamn, deep fakes even back then. Okay, do you have a favorite recent movie or TV show that you've really enjoyed?

我最近没看什么新剧。刚和孩子们一起看了《盗梦空间》，他们很喜欢，这让我重新欣赏起克里斯托弗·诺兰。这电影太酷了——就是那种看完后能让你讨论两天的佳作。

I haven't gotten any new TV shows recently. We just watched, inception with the kids, and they loved it and, made me, appreciate Christopher Nolan. So, I and what a cool movie. Cool con I love it's a type of movie when you watch your film and you have conversations for two days afterwards about it, so just a great film.

我看到有人用Vio three（可能是V3？）制作自己的盗梦空间视频，画面会自我包裹。太厉害了。那你最近有没有特别喜爱的新产品，或是长期钟爱的老物件？

I saw someone using, I think, Vio three to create their own inception videos where the world's wrapping in on each other. Oh, man. Okay. Do you have a favorite product that you have recently discovered that you love or one you've loved for a long time?

我特别推崇Cursor。它改变了我的工作方式——我热爱软件开发，但对AI代理更期待。OpenAI的Codex等项目都让我非常兴奋。

I'm really a big fan of Cursor. I think it's like change. I'm I love creating software and I'm excited though for agents. You know, I've been really excited. I was very excited to see Codex from OpenAir and others.

我认为Cursor当前形态只是过渡产品，他们也在开发代理功能。但能深入体验这个AI工具如何变革我的编程方式，让我沉浸其中——毕竟这既是我的毕生热情，又是工艺精湛的产品。

So I think Cursor will be in its current form as a transition product. And I know they're working on agents as well. But I really enjoyed taking something I love and I'm like been my life's passion and really diving into this AI tool and like seeing how it transforms, I create software. So I've just been like spending a lot of time with the product just because it's so core to my like what I love to do and it's a really well crafted product.

这是节目里首次有人提到Cursor，可能预示新趋势。Michael Trull在播客中的观点和你很像——关于代码的未来，以及即将出现的伪代码层。你有经常用来指导工作/生活的人生格言吗？

I think that's the first time someone's actually mentioned cursor in this answer, so it might be the beginning of a trend. Michael Trull was on the podcast, and he actually had a very similar message as you had at the beginning of this chat about the future of code, what comes after code, and this concept that there's gonna be this additional pseudo code layer on top of code. Yeah. Very aligned with your thinking. Do you have a favorite life motto that you often come back to and find useful in work or in life?

「预测未来最好的方式就是创造它」——我认为这句话源自施乐帕克的艾伦·凯。他发明了当今计算机领域的核心抽象概念。这正是我成为创业者、热爱创造的原因，它确实是我的座右铭。

The best way to predict the future is to invent it, which I think I attribute to Alan Kay of Xerox PARC. He invented a lot of the core abstractions that we use in computing today. It's why I love I am an entrepreneur. It's why I love to build things. So it is definitely like a life motto for me.

很多人只是说说，而你践行了无数次。最后问题：在FriendFeed设计点赞按钮时，除了「Like」还考虑过其他命名方案吗？

I feel like many people like say this, I feel like you've actually done this so many times. You're living this motto. Final question. We talked about you inventing the like button at FriendFeed. Were there other, thoughts of what they would call it other than like?

当时「Like」是显而易见的选择吗？还是有过其他考量？

Was it just like obviously like? Or is there other thinking there?

这个背景是在表情符号出现之前。那时如果你看FriendFeed帖子下的评论，至少70%都是‘酷’、‘哇’、‘是啊’或‘不错’之类。FriendFeed的主要用途之一就是围绕事物展开讨论。你会有一个帖子，下面跟着相当丰富的讨论，与Twitter等其他平台相比，这里简直是进行这类讨论的绝佳场所。所以我们当时要解决的产品问题就是剔除所有这些单字回复，让讨论真正成为实质性的评论，而非仅仅表示‘已读’的敷衍。

The context of this was before emoji. So there if you read the comments on friend feed posts, at least 70% of them are cool or wow or yeah or neat. And one of the principal like uses of FriendFeed was to have discussions about things. So you'd have a post and then a pretty fulsome discussion underneath and it was a very compared to, you know, Twitter and others, it was like a great place to have those discussions. And so the product problem we were trying to solve is get all the one word answers out so that the discussion was actually like actual comments as opposed to acknowledgements that you read the thing.

最初我们将其定义为‘一键评论’功能。第一版设计我用了爱心图标——虽然她现在矢口否认——当时有位叫Anna Yang（现名Anna Muller）的同事极其厌恶这个设计。她说‘如果每个帖子都挂满爱心，我简直要吐了’，觉得这种表达太过甜腻直白。

So we the original framing was one click comment. That was how we thought about it. And so we the first version that I made had a heart and there she denies remembering this, but there's a Anna Yang, now Anna Muller, who has worked at the company, she hated it. She said like, if I look at a heart like hearts on every post, I'm gonna vomit. Like, it's just too it's like too too much, you know.

这还引发了个有趣现象：当我们模拟在悲剧新闻等严肃内容下使用时，爱心图标显得极不恰当。后来发现真正难翻译的是那种微妙的中性情感表达，正是这种细腻差异让我们最终调整了设计方向。

And and it also was interesting like, we were simulating and it was like an article about a tragedy or something. A heart was just not the right thing. Like, which actually turned out to be really hard to translate was just a much more neutral sentiment. And and then that's why it was hard to translate because it was subtle. And we so that's how we ended up with this.

我们确实从爱心图标起步（虽不确定是否直接关联‘爱’这个概念），最初选择的视觉符号必须保持积极但又尽可能中立，以适应复杂多元的内容场景。但核心诉求始终未变：我们需要一个‘一键评论’方案，这才是整个概念的起源。

We started with a heart and and I I don't know if we ever heard the word love, but we definitely start off the iconography. Then like, which just felt like this positive yet as neutral as possible within the realm of positive so that it could work for like a more complex story. But it was all because we needed a one click comment. That's where the concept came from.

哇，你从没听过这故事吗？让我联想到现在的LinkedIn——他们本质上在解决同样问题，那些自动回复功能...

Wow. Have you never heard this story before? Makes me think about LinkedIn now. They have they're basically trying to solve that same problem. They have all these auto reply.

你没法给内容打标签。我觉得用户并不喜欢...

You can't pill tag things. I don't think people like They

他们功能确实很多。

have a lot of features.

多到离谱，全是AI功能。

So many. So many AI features.

确实。

Yeah.

Brett，这太精彩了。能邀请你上播客是我的荣幸。最后两个问题：如果想联系你，大家该去哪里找你？

Brett, this was incredible. This was an honor. I so appreciate you coming on this podcast. Two final questions. Where can folks find you online if they wanna reach out?

或许去看看他们是否愿意在Sierra工作？听众们怎样才能对你们有所帮助呢？

Maybe go see if they wanna work at Sierra? And how can listeners be useful to you?

如果你需要AI助手处理客户服务，请访问sierra.ai。如果想申请职位，前往sierra.ai/careers。我们在旧金山、纽约、亚特兰大和伦敦设有办公室，正在各部门大力招聘。感兴趣的话请随时联系我们。

If you want an AI agent to help with customer service, go to sierra.ai. If you want to apply here, sierra.ai/careers. We're we have offices in San Francisco, New York, Atlanta, and London, and are hiring pretty aggressively in every department. So please reach out if you're interested.

听众们具体能如何帮到你们？是试用Sierra吗？还有其他方式吗？

And how can listeners be useful to you? Is it try out Sierra? Anything else there?

对，试用Sierra。我是个新加坡式英语者，你懂的。

Yeah. Try out Sierra. I'm a singlish you better.

紧扣主题不跑偏，我喜欢。布雷特，非常感谢你的到来。

Staying on the message. I love it. Yeah. Brett Brett, thank you so much for being here.

谢谢邀请。

Yeah. Thanks for having me.

大家再见，非常感谢收听。如果觉得有价值，可以在苹果播客、Spotify或你喜欢的播客平台订阅节目。也请考虑给我们评分或留言，这能帮助其他听众发现本节目。所有往期内容及节目详情请访问lenny'spodcast.com。

Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lenny'spodcast.com.

下期节目再见。

See you in the next episode.