AI 市场趋势与传奇建造者的经验教训

你是 Meta 的 CTO，你是 Salesforce 的联席 CEO，你是 OpenAI 的董事会主席。你觉得 AI 市场会如何发展？

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

整个市场都会走向智能体。我认为整个市场都会走向基于结果的定价。这显然是构建和销售软件的正确方式。

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

这让我想到，我请过 Marc Benioff 上播客。你们曾是联席 CEO。他非常推崇智能体。

So, makes me think about it. I had Mark Benioff on the podcast. You guys were co-CEOs. He was extremely agentic.

销售生产力软件太难了，我深有体会。

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

想到你最大的错误，你会想起什么故事？

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

我当时是 Google Local 的产品经理。和 Marissa 和 Larry 进行了一次相当艰难的产品评审。在谷歌首页有链接的情况下表现不佳，有点尴尬。

I was the product manager for what was 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.

我觉得这对人们来说真的很鼓舞人心，听到尽管经历了这样巨大的失败，还是有可能成功。

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

他们算是给了我另一次机会，做了第二版，最终成就了 Google Maps。第一天就有大约 1000 万人使用。

They sort of gave me another shot to do a V2 of it that resulted in Google Maps. We got about 10 million people using it on the first day.

是什么样的心态让你在这么多不同的角色中取得成功？

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

每天早上醒来，问自己：今天我能做的最有影响力的事情是什么？

Waking up every morning. What is the most impactful thing I could do today?

今天我的嘉宾是 Bret Taylor。Bret 是一位绝对的传奇构建者和创始人。他在谷歌共同创建了 Google Maps。他共同创立了社交网络 FriendFeed，该网络发明了点赞按钮和实时信息流，并将其卖给了 Facebook。随后他成为 Facebook 的 CTO。然后他创办了一家名为 Quip 的生产力公司，以 7.5 亿美元卖给了 Salesforce。之后他成为 Salesforce 的联席 CEO。目前他还是 OpenAI 的董事会主席。他曾一度担任 Twitter 的董事会主席。如今，他是 Sierra 的联合创始人兼 CEO，这是一家 AI 初创公司，正在构建智能体，帮助公司处理客户服务、销售等事务。在我们的对话中，我们涵盖了很多内容，包括哪些技能和心态最帮助 Bret 在这么多角色中取得成功，为什么我们仍然低估了智能体对商业世界的影响，未来几年编程将如何变化，初创企业最大的机会在哪里，AI 定价和进入市场的经验教训，点赞按钮背后的故事，以及更多。这是一次与传奇构建者的真正史诗般的对话。如果你喜欢这个播客，别忘了在你最喜欢的播客应用或 YouTube 上订阅并关注。另外，如果你成为我新闻通讯的年度订阅者，你将免费获得一年一堆令人难以置信的产品，包括 Replit、Lovable、Bolt、Nad、Linear、Superhuman、Descript、WhisperFlow、Gamma、Perplexity、Warp、Granola、Magic Patterns、Raycast、JPRD、Mobin 等。请访问 lennysnewsletter.com 并点击 bundle。有了这些，我向你介绍 Bret Taylor。

Today my guest is Bret Taylor. Bret is an absolute legendary builder and founder. He co-created Google Maps at Google. He co-founded the social network FriendFeed, which invented the like button and the real-time newsfeed, which he sold to Facebook. He then became CTO at Facebook. He then started a productivity company called Quip, which he sold to Salesforce for $750 million. 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. In our conversation, we cover so much ground, including what skills and mindsets have most helped Bret be so successful in so many roles, why we're all still sleeping on the impact that agents are going to 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 products, including Replit, Lovable, Bolt, Nad, Linear, Superhuman, Descript, WhisperFlow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, JPRD, Mobin, and more. Check it out at lennysnewsletter.com and click bundle. With that, I bring you Bret Taylor.

本期节目由 CodeRabbit 赞助，这是一个 AI 代码审查平台，正在改变工程团队使用 AI 更快交付而不牺牲代码质量的方式。代码审查至关重要但耗时。CodeRabbit 充当你的 AI 副驾驶，为每个拉取请求提供即时代码审查评论和潜在影响。除了标记问题，CodeRabbit 还提供一键修复建议，并允许你使用 AI 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细微问题。CodeRabbit 还直接在 IDE 中提供免费的 AI 代码审查。它可在 VS Code、Cursor 和 Windsurf 中使用。CodeRabbit 迄今已审查超过 1000 万个 PR，安装在 100 万个仓库中，并被超过 7 万个开源项目使用。在 coderabbit.ai 使用代码 LENNY 即可免费获得一整年的 CodeRabbit。网址是 coderabbit.ai。

This episode is brought to you by CodeRabbit, the AI code review platform transforming how engineering teams ship faster with AI without sacrificing code quality. Code reviews are critical but time-consuming. CodeRabbit acts as your AI co-pilot, 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 AI patterns, catching subtle issues that traditional static analysis tools might miss. CodeRabbit also provides free AI code reviews directly in the IDE. It's available in VS Code, Cursor, and Windsurf. CodeRabbit has so far reviewed more than 10 million PRs, installed on 1 million repositories, and is used by over 70,000 open-source projects. Get CodeRabbit for free for an entire year at coderabbit.ai using code LENNY. That's coderabbit.ai.

本期节目由 Basecamp 赞助，这是 37signals 出品的著名直截了当的项目管理系统。大多数项目管理系统要么功能不足，要么复杂得令人沮丧。但 Basecamp 却异常清晰。它上手简单，易于组织，Basecamp 的可视化工具帮助你准确看到每个人在做什么以及所有工作的进展。将所有关于项目的文件和对话直接连接到项目本身，这样你总是知道东西在哪里，不必不断切换上下文。经营企业很难。管理你的项目应该很容易。在 basecamp.com/lenny 注册免费账户。用 Basecamp 取得进展。

This episode is brought to you by Basecamp. Basecamp is the famously straightforward project management system from 37signals. 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. Running a business is hard. Managing your projects should be easy. I've been a longtime fan of what 37signals 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.

Bret，非常感谢你来到这里。欢迎来到播客。

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

谢谢邀请我。

Thanks for having me.

我的荣幸。我想谈的太多了。你在职业生涯中做了很多令人难以置信的事情。你所做的事情令人难以置信，我们会谈论很多这类事情。但我想从相反的开始。我想谈谈你搞砸的时候，你大错特错的时候。我们在播客中有一个常设环节，我称之为“失败角落”，所以我觉得在我们进入你做的所有伟大事情之前，从这里开始会很有趣。当你想到你在构建产品时最大的错误，你会想起什么故事？

My pleasure. There's so much that I want to 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 going to talk about a lot of that sort of stuff. But I want to actually start with the opposite. I want to 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 I call 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?

这可能不是最大的，但这是我在谷歌担任产品经理时第一个显著的错误。所以对我来说，这感觉很重大，因为这对作为产品设计师的我来说非常具有塑造性。我在 2002 年底或 2003 年初加入谷歌，我是公司最早的一批助理产品经理之一，最初从事搜索系统的工作，基本上是将我们的索引从 10 亿网页扩展到 100 亿，这在当时是件大事。现在看起来有点过时了。然后我做得不错，所以我的老板 Marissa Mayer 给了我领导一个新产品计划的机会，这是对我的一次重大押注，你知道，这既是为谷歌做事的机会，但我也作为一个年轻的新产品经理受到相当多的审视，给我的前提是研究本地搜索，当时黄页仍然占主导地位，虽然谷歌在搜索网页方面非常擅长，但它并不擅长找水管工或餐馆，因为当时这还不是互联网的主要部分。所以这些内容不一定在互联网上，即使有，你确实需要不同的，你不想找曼哈顿的水管工，如果你想找旧金山的水管工，如果你是我，所以这既是技术问题，也是产品问题，也是内容问题。

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 product designer. So I joined Google in late 2002 early 2003 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 billion web pages to 10 billion which was a big deal at the time. It sort of seems quaint now. And then I did a decent job and so my boss Marissa Mayer 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 to 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 a different you didn't really want to find you know plumbers in Manhattan you want to find plumbers in San Francisco if you're me and so it was a kind of a both a technical problem and a product problem and a content problem.

我们推出了那个产品的第一个版本，我是那个产品的产品经理，叫 Google Local。现在回想起来，我会比当时更挑剔一些，但它有点像雅虎黄页的跟风版。本质上，就是把黄页搜索嫁接在 Google 搜索之上。只要查询词构造得当，你就能在搜索结果顶部看到那些列表。它还有一个独立网站，在 local.google.com。这个项目重要到在 Google 首页上，除了网页和图片，本地搜索也占了一席之地。所以它获得了顶级曝光。你几乎可以把任何链接放在 Google 首页上，就能获得大量流量。尽管如此，它表现并不好。在 Google 首页有链接却表现不佳，这有点尴尬。除了那种流量，没有更多能给你的产品一个机会的了。作为产品领导者和产品经理，产品本身没问题——它能用——但确实没有差异化。在很多方面，我是后来才反思这些的，比当时更多，虽然当时也有一些。为什么要用这个而不是雅虎黄页？更重要的是，为什么要用这个而不是黄页？它有点像之前存在的东西的数字版。

We launched the first version of that product that I was the product manager for, called Google Local. It was, I'll be a little bit more critical now than I might have been at the time, but it was a bit of a me-too version of Yahoo Yellow Pages. Essentially, it was grafting Yellow Pages search on top of Google search. With a properly crafted query, you could see those listings at the top of your search results. It also had a standalone site at local.google.com. It was an important enough initiative that on the Google homepage, alongside web and images, local was up there as well. So it got top billing. You could put almost any link on the Google homepage and get a lot of traffic to it. Despite that, it didn't do that well. To not do well with a link from the Google homepage is kind of embarrassing. There's not much more you can do to give a product an at-bat than that kind of traffic. As a product leader and product manager, the product was fine—it worked—but it really wasn't differentiated. In many ways, I've had these reflections more since than at the time, though I had some then. Why use this instead of Yahoo Yellow Pages? More than anything else, why use this instead of Yellow Pages? It was sort of a digital version of something that had come before.

我和玛丽莎、拉里等人进行了一次相当艰难的产品评审。还好——我不会被解雇什么的——但我名声上的光环有点褪色了。他们算是给了我另一次机会，让我做它的 V2 版本。我得到的印象是这不是我的最后一次机会，但我确实感到有点沮丧，从一个炙手可热的新产品经理变成了做这个新东西。

I had a pretty tough product review with Marissa and Larry and others. It was fine—I wasn't about to get fired or anything—but the shine on my reputation was waning a little bit. They sort of gave me another shot to do a V2 of it. I got the impression it wasn't my last shot, but I was certainly feeling a little dejected, going from a hotshot new PM to this new thing.

所以我们花了很多时间思考如何做出更有吸引力的东西，不仅仅是黄页的数字版，也不仅仅是和市面上其他产品相似的东西。那最终成了我们抽出的线头，最终导致了 Google 地图的诞生。我们从 MapQuest 获得了许可，可以在搜索结果旁边放一个小地图。它一直是产品中最难看的部分，我们内部总是对它冷嘲热讽。我们花了很多时间说，如果我们颠倒这里的层级，把地图作为画布会怎样？我们最终找到了拉斯和延斯·拉斯穆森，他们一直在做 Windows 地图产品，我们把他们招进了公司。通过那次探索，我们最终整合了很多不同的产品：地图、本地搜索、驾车路线。当时这些都是独立的产品类别，我们最终得到了一个重新定义了行业、也重新定义了我职业生涯的东西。

So we spent a lot of time thinking about how to make something much more compelling, not just a digital version of the yellow pages, and not just similar to other products out there. That ended up being the thread we pulled that resulted in Google Maps. We had licensed from MapQuest the ability to put a little map next to the search results. It was always the ugliest part of the product, and we always made backhanded comments about it internally. We spent a lot of time saying, what if we inverted the hierarchy here and made the map the canvas? We ended up finding Lars and Jens Rasmussen, who had been working on a Windows mapping product, and we got them into the company. Through that exploration, we ended up integrating a lot of different products: mapping, local search, driving directions. All of these were separate product categories at the time, and we ended up with something that redefined the industry and certainly my career.

对我来说，作为产品领导者，它改变了我对产品的思考方式，因为除了特性和功能，还有“我为什么要用这个东西”的问题。有几个有趣的时刻。当我们推出 Google 地图时，第一天就有大约 1000 万人使用，在当时互联网的规模下，这是巨大的。然后在 2005 年 8 月，我们整合了来自最近收购的 Keyhole 的卫星图像，它后来成为 Google 地球，同一天我们有 9000 万人使用。图像出来时，每个人都想看看自己房子的屋顶。

For me as a product leader, it changed the way I think about product, just because there's feature and functionality, and then there's why should I use this thing in the first place? There were a couple of interesting moments. When we launched Google Maps, we got about 10 million people using it on the first day, which at that scale of the internet at the time was huge. Then in August 2005, we integrated satellite imagery from a recent acquisition called Keyhole, which became Google Earth, and we got 90 million people using it on the same day. Everyone wanted to look at the top of their house when the imagery came out.

这里面有很多微妙的产品教训。首先，当你拥有这些新技术时，与其字面意义上数字化以前的东西，如果你能创造一种全新的体验，它就回答了一个新客户的问题：我为什么要理睬这个？所以真正拆开乐高积木，重新组装成新的东西，而不是仅仅数字化以前的东西。这就是 Google 地图的教训。它真正是平台原生的，这是纸质地图无法做到的，那是一个有意义的突破。

There are so many subtle product lessons in there. First, as you have these new technologies, rather than literally digitizing what came before, if you can create an entirely new experience, it answers the question for a new customer: why should I give this a time of day? So really disassembling the Lego set and reassembling it into something new, rather than just digitizing what was there before. That was the lesson in Google Maps. It really was native to the platform in a way that a paper map couldn't be, and that was a meaningful breakthrough.

至于卫星图像，说实话它并不是 Google 地图最重要的部分，但它是牛排上的滋滋声。它创造了一个病毒式传播的时刻——我不认为当时人们会说“病毒式”这个词，但它确实创造了。我们上了《周六夜现场》，那是最酷的事情。安迪·萨姆伯格，我记得那期叫《懒散的星期天》，用说唱提到了 Google 地图，拉斯和我互相发短信：我们做到了，我们上《周六夜现场》了，任务完成。这也表明，当你思考产品时，有“你为什么决定使用一个产品”，然后有“什么是持久价值”。这两者密切相关，但并不相同。我学到了很多教训，并带到了我之后做的每一个产品中。

With satellite imagery, it honestly wasn't the most important part of Google Maps, but it was the sizzle to the steak. It created a viral moment—I don't think the term viral was a thing people said back then, but it created one. We ran Saturday Night Live, which was the coolest thing. Andy Samberg, in I think it was called Lazy Sunday, rapped about Google Maps, and Lars and I were texting each other: we did it, we're on Saturday Night Live, mission accomplished. It also showed that as you're thinking about products, there's why you decide to use a product, and then what is the enduring value. Those are deeply related but not the same thing. I learned so many lessons I took with me for every subsequent product I worked on.

那是个很棒的故事。我认为这对人们来说真的很鼓舞人心，即使是您，布雷特，我即将分享您所有成功经历的人，也曾有过一次巨大的失败，Google 的 CEO 梅尔先生就像在说，布雷特，你搞砸了。那是一个如此大的赌注。所以第一，即使经历了这样巨大的失败，您也有可能像您已经做到的那样成功。还有您分享的一些产品教训，我只想强调几点：如果你只是做出一个比别人更好的复制品，你往往不会赢。你要寻找的是全新的体验、有差异化的东西、更有吸引力的东西。

That is an awesome story. I think it's really empowering for people to hear that even you, Bret, who I'm going to share all the successes you've had, have had a massive failure with the CEO of Google, Mr. Meer, just like, Bret, you screwed up. It was such a big bet. So one, it's possible to succeed as you have succeeded in spite of a massive failure like this. And some of the product lessons you share, just to highlight a few, is 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.

让我们换个话题，谈谈您从在很多事情上非常成功中学到了什么。我看了您的简历，您基本上在职业阶梯的每个层级都非常成功，而且担任过各种各样的角色。让我为那些不太了解您背景的人读几项。您曾是 Meta 的首席技术官。您曾是 Salesforce 的联合首席执行官。您还担任过 Salesforce 的首席产品官和首席运营官。在 Google，您以助理产品经理的身份加入，您在那里有个著名的故事——您没有提到——但您在一个周末内重建了 Google 地图。我们就不谈那个了。您曾是 OpenAI 的董事会主席。

Let's flip to talk about what you've learned from actually being very successful at a lot of things. 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. Let me just read a few of these things for folks that aren't super familiar with your background. You were CTO of Meta. You were co-CEO of Salesforce. You're also CPO and COO at Salesforce. At Google, you joined as an associate product manager, where you famously—you didn't mention this—but you rebuilt Google Maps in a weekend. We're not going to talk about that. You were chairman of the board at OpenAI.

你曾是 Twitter 的董事会主席。你还创立了三家不同的公司：一家社交网络、一家名为 Quip 的效率文档公司，以及现在的 Sierra。有个趣事：在 FriendFeed，你发明了“点赞”按钮，我不知道大家是否知道这个。还有信息流。我就顺带提一下，给你点功劳。所以，你基本上做过助理产品经理、独立贡献者产品经理、工程师、CPO、COO、CTO、CEO，横跨三家不同的公司，包括一家上市公司。很少有人能在所有这些角色和层级上都取得成功。所以我就直接问你这个问题：你在自己身上培养了哪些心态、习惯或工作方式，你认为它们对你胜任如此多样的角色和层级贡献最大？

You were chairman of the board at Twitter. You 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. 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?

是的，这其实是我引以为傲的事情。我喜欢自己戴过不同帽子的这个事实。当我遇到从某份工作中认识的老同事时，这其实挺有趣的。他们往往会通过那份工作的视角来看我。比如，我去见 Facebook 的人，他们主要把我当成工程师；见 Google 的人，他们主要把我当成产品人；在 Salesforce，很多和我打交道的人把我当成，找不到更好的词，就是“西装革履”的那种，比如老板。我不确定他们是否还把我当工程师，尽管我周末可能还在为了乐趣写代码。对我来说，一个原则就是对自己的身份保持非常灵活的看法。我确实认为自己，我可能会自称工程师，但更广泛地说，我认为自己是一个建造者。我喜欢构建产品，而我认为公司是构建产品最有效的方式之一。还有开源之类的东西，但我非常相信技术与资本主义的结合能为客户带来惊人的成果。因此，要真正构建有意义的东西，要成为伟大的创始人，你真的需要能够不让自己的身份过于僵化，以至于无法转变为公司当时需要你成为的样子。

Yeah, it's actually something I am proud of. 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. So, 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 of me largely as a product person. At Salesforce, a lot of the folks there interacted with me as like a, for lack of a better word, a suit, like the boss. And I'm not sure they think of me as an engineer at all, even though 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. I really think of myself, I probably would self-describe as an engineer, but more broadly, I think of myself as a builder. And 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'm a huge believer in the confluence of technology and capitalism to produce incredible outcomes for customers. And as a consequence, to really build something of significance, to be a great founder, you really need to be able to not have such a rigidified view of your identity that you can't transform into what the company needs you to be at that point.

而且你接触的每一位创始人，总有一天，我认为销售是创始人角色中很大的一部分。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加入你的公司，你必须说服客户愿意使用你公司生产的产品。你还要有好的设计品味，不仅针对产品，还包括营销和吸引新客户。你还要有扎实的工程能力。我的意思是，如果你在建设一家科技公司，技术是第一位的。这就是为什么这个行业如此具有变革性。

And every founder you'll talk to, one day, I think selling is a big part of being a founder. 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 company produces. You have to have good design taste, not just for your product but for your marketing and essentially soliciting new customers. You have to have good engineering. I mean, if you're building a technology company, the technology comes first. It's why this industry is so transformative.

我大概要归功于，我以前讲过这个故事，但我非常感谢她，我大概要归功于雪莉·桑德伯格，她真正改变了我对待新工作的方式。这个故事，我可能有点美化，但我觉得大体上是准确的。当时我刚成为 Facebook 的首席技术官。刚得到这份工作时，那是一种 CTO 的风格，我手下有相对较小的团队，但我几乎像一位非常资深的总架构师一样参与多个项目。后来某个时候，马克·扎克伯格重组了公司，把它分成了几个不同的部门。结果我手下有了一个非常大的团队，我基本上在管理我们的平台和移动部门，包括产品、设计和工程。所以我的直接下属从几个人变成了，我不知道，一千多人。那是一个庞大的团队，也是我做过的最大的管理岗位。我在 Google 时也当过经理，但团队规模不大。所以我当时做得还行，但不算出色。

I probably credit, and I've told this story before, but 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. 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 a very senior kind of architect on a number of projects. And then at some point, Mark Zuckerberg reorganized the company and kind of split it into a bunch of different groups. So I ended up with a very large group under me and I was essentially running our platform and mobile groups, products, design, engineering. So I went from a handful of reports to like, I don't know, over a thousand or something. It was a big group and it was the largest management job I'd had. I'd become a manager at Google but with a modest team. And so I was doing okay but not great.

然后有一次，雪莉看到了我。我当时，我记得，正在为一位合作伙伴编辑演示文稿，因为我拿到的演示文稿没有达到我的质量标准。我一边编辑一边抱怨。她把我拉进一个房间，跟我谈了一番，大意是要我像要求自己一样高标准地要求团队。如果某个人没有达到我的期望，我有什么计划把他们管理出公司，或者就是给我上了一堂管理入门课。她是一位了不起的导师，因为她能给你非常直接、常常让人有点不舒服的反馈，但你知道她关心你，所以这种反馈你会听进去。

And I had this moment where Sheryl saw me. I was, I think, editing a presentation for a partner just because 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 gave me a talking to, 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 manage them out of the company, or just kind of giving me management 101. And she's a remarkable mentor in the sense she can give you feedback that's very direct and often a bit uncomfortable, but you know she cares about you, so it's the type of feedback you listen to.

那天晚上我回到家，心里一直在琢磨这件事，不太高兴。我当时想，你知道，人在那种时刻自然会有点防御心理，比如：这真的是这样吗？我真的搞砸了吗？还是她反应过度了？然后第二天醒来，我想：不，她是对的。我意识到有一个潜意识的限制因素在阻碍我在工作中的成功，那就是我试图让工作去适应我认为自己喜欢做的事情。所以我花了很多时间在我热衷的一些产品和技术事务上，心想：我是老板，我应该专注于我想专注的事情，而不是去想：好，我在管理 Facebook 的移动和平台团队，今天最重要的事情是什么，才能让我们的移动和开发者平台成功？当我这样重新定义工作时，我做了不同的事情。

I sort of went home that night and I was kind of stewing on it and not very happy. I was like, you know, you get naturally a little defensive in those moments, like, is that really true? Am I really messing up or is she overreacting? And then I woke up the next day, I was like, no, she's right. And I had realized this subconscious limiter 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. So I was spending a lot of my time on some product and technology things that I was passionate about, thinking, you know, I'm the boss, I should focus on what I want to 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.

而最让我惊喜的是，我喜欢上了它。我原以为我喜欢工程和产品，但事实上，当我改变了组织并且它变得更成功时，我从看到这种成功中获得了巨大的快乐。我们的开发者平台有很多合作伙伴，当那里出现问题时，我会花时间处理合作关系，而且奏效了，我们的平台变得更健康，合作伙伴也更成功，我为这种成功感到自豪。然后我就开始在工作中表现得更好。我意识到，实际的工程或产品设计行为，以及所有我以为自己喜欢的事情。

And the thing that was the biggest pleasant surprise to me was I liked it. I thought I liked engineering and product, but in fact, when I changed the organization and it turned out to be more successful, I derived a great deal of joy from seeing that success. Our developer platform had a lot of partners, and when there was an issue there and I'd spend time on partnerships and it worked, 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.

我真正喜欢的是影响力。那次对话让我每天早上醒来，有时是字面意义上的，但肯定是在最广泛的意义上，问自己：今天我能做的最有影响力的事情是什么？并且真的像有一个外部顾问委员会在告诉你，哪些事情是你专注于它们就能最大化实现目标可能性的？有时是招聘，有时是产品，有时是工程，有时是销售。我变得更加自省，思考什么才是真正重要的工作。我也变得更加愿意去做那些我以前会说不是我最喜欢的事情，因为从影响力中获得的快乐如此之大，以至于我现在享受更多的事情。所以我真的归功于谢丽尔。我非常感激。实际上，有趣的是，我现在给别人反馈时经常想到这一点，就像那些能改变你职业轨迹的时刻。我把所有功劳都归于她。

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, if you had an external board of advisers telling you, what are the things where if you focus on them, you can maximize the likelihood that what you're trying to achieve will happen? And sometimes it's recruiting, sometimes it's product, 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 Cheryl. 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 kind of change the trajectory of your career. I give her all the credit for it.

有太多人分享谢丽尔·桑德伯格给他们建议并改变他们生活的故事。

There's so many people that share stories of Cheryl Sanberg giving them advice and that changing their life.

真是绝配。

What a match.

是的。我从这里得到的最大的收获，就是“今天我能做的最有影响力的事情是什么”这个问题。这是一个非常强大的启发式方法，值得牢记。正如你所说，你可能会意识到你不想做销售或招聘，但如果那是最有影响力的事情，而你最终去做了，你可能会发现，我喜欢这个，而且我擅长这个。

Yeah. My biggest takeaway from this, which is this question of what is the most impactful thing I could do today. 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.

我能稍微深入探讨一下吗？

Can I double click on that though for a sec?

当然可以。

Absolutely.

我认为这真的很难。创始人和产品经理面临的一个危险，尤其是创始人，是错误的叙事。人们不喜欢我的产品是因为 X。如果你这样告诉自己，也这样告诉你的团队，它突然就从直觉变成了事实。你最好希望你是对的，因为如果你围绕解决那个问题来制定战略，而你是错的，你的公司就会失败。所以，你知道，你为什么丢了一笔交易？你可以和负责那个客户的销售谈谈，或者也许产品经理参与了对话。在这些时刻保持智识上的诚实非常重要，因为你可能会说：“哦，他们没买是因为平台太贵了。”这是销售可能说的话。也许真正的原因是他们对你的平台没有看到太多价值。所以传达给销售的是太贵了。但实际上，问题是产品差异化。你可能会陷入关于定价的讨论，而实际上那里有一个更深、更难解决的问题。但这不是，你知道，就像你和某人分手时，你不会说：“因为我不再喜欢你了。”你会说：“不是你，是我。”你知道，你会说所有这些客套话，因为我们都是社会性动物，你想对周围的人友善。所以，你知道，字面上把客户在焦点小组或可用性研究中说的话当作事实，很少是正确的。它通常与真相相关，但弄清楚真相非常重要。所以，我认为我特别观察到首次创始人的一件事是，你往往基于自己的技能组合成为单一议题选民。如果你是一个伟大的工程师，你业务中几乎所有问题的答案都是工程。如果你是产品设计师，答案几乎都是那个众所周知的重新设计，我开玩笑说这是消费产品的死猫反弹，比如这次重新设计会解决我们所有的问题。我不知道它是否曾经奏效过。然后如果你，我遇到很多来自业务拓展背景的企业家。他们总是想着合作伙伴关系，你知道，哦，我们只要完成这个分销渠道的合作伙伴关系，一切都会改变。我认为作为创始人，重要的是要自我意识到，你会自然而然地、下意识地选择你的优势、你的超能力作为更多问题的解决方案。事实上，如果你认为那是你问题的解决方案，它可能是对的，但你很可能默认应该质疑它。就像如果你认为你整个职业生涯一直在做的事情是解决问题的方法，那么至少有 30% 的可能性是因为舒适和熟悉而选择的，而不是因为真相。所以我认为这就像那些技能之一。我认为这真的归结为，你有一个好的联合创始人吗？你有一个好的领导团队吗？如果你是产品经理，你的工程伙伴、你的营销伙伴，你真的想要非常真实的对话，以确保你实际上在做正确的事情。我认为说“今天最有影响力的事情是什么”很容易。我猜如果很多人尝试这样做，他们往往会对自己撒谎。这是一个非常难回答的问题。问题很有趣。能够准确回答它实际上是困难的部分。

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, because if you orient your strategy around fixing that problem, and you're wrong, your company's going to fail. So, you know, why did you lose a deal? You could talk to the salesperson who was 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 costs too much." That's something a salesperson might say. 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." You say, "It's not you, it's me." You know, you say all these sort of pleasantries because we're all social animals and you want to be pleasant with the people around you. So, you know, 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. If you're a product designer, the answer almost to, you know, the proverbial redesign, I joke like the dead cat bounce of a consumer product, like this next redesign will fix all of our problems. I don't know if it's ever worked. And then if you, I met a lot of entrepreneurs who 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 going to 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, your superpower, as a solution to more problems. And in fact, if 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 those skills. I think it really goes around to, 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 want to have very real conversations to ensure that you're actually working on the right, the actual correct thing. And I think it's easy to say what's the most impactful thing to do today. My guess 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.

这感觉像是你学到的一个非常重要的教训。有没有一个例子让你想起你是如何艰难地学到这一点的，或者你最终……

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 where you actually ended up...

你就想把这整个时间都花在我的失败上，但我对此没意见。

You just want to spend this whole thing on my failures, but I'm fine with that.

你曾经……

You've had...

FriendFeed 是我的第一家公司。在我们的巅峰时期，我们有 12 名员工，12 个我共事过的最优秀的人。我和吉姆·诺里斯一起创办了这家公司，他是我在斯坦福就认识的工程师，还有保罗·布赫海特和桑吉夫·辛格。保罗创办了 Gmail，桑吉夫是 Gmail 的第一位工程师。所以我们有谷歌地图的人和 Gmail 的人，这是一个非常棒的创始团队。我们做了一个社交网络。正如你所说，我们发明了很多后来在信息流中流行的概念。我们发明了点赞按钮。那真的很棒。那是一段有趣的时光。我们只在土耳其、意大利和伊朗真正受欢迎。有一次我们在伊朗被封锁了。所以我们只在土耳其、意大利和硅谷受欢迎。直到今天，实际上，很多硅谷的人都说：“我爱 FriendFeed。”我说：“那太好了。”但它并不是一个真正成功的业务。

FriendFeed 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 Buchheit and Sanjeev Singh. Paul started Gmail, Sanjeev was the first engineer on Gmail. So we had the Google Maps people and Gmail people, it was a pretty awesome founding team. We made a social network. As you said, we sort of invented a lot of concepts that became popular in the news feed. We invented the like button. It was really neat. It was a fun time. We were only really popular in Turkey, Italy, and Iran. And at one point we were blocked in Iran. So we're only popular in Turkey and Italy and Silicon Valley. To this day, actually, a lot of folks in Silicon Valley are like, "I love FriendFeed." I'm like, "That's awesome." It wasn't really a successful business.

我们是一个以关注为导向的社交网络，而不是以友谊为导向的，这意味着我们的很多内容更像 X 或 Twitter，而不是 Facebook。我们分享报纸文章、兴趣、科学社区之类的东西。有一段时间，Twitter，我们当时的竞争对手之一，尽管当时社交网络更多，我可能有点搞混了，但我想奥巴马、阿什顿·库彻和奥普拉·温弗瑞都在一个夏天加入了 Twitter，然后我们就被打得落花流水。这是一个很好的例子，说明我们只是在做产品。我想那 12 个人中有 11 个是工程师。我想是比兹·斯通——如果你和 Twitter 的人聊聊，他们能给你讲这段历史——但我觉得比兹真的专注于让名人和公众人物入驻 Twitter，这完全合理。如果你有一个以关注为导向的社交服务，那就放一些值得关注的人上去。相反，我们只专注于打磨产品。在我们人气巅峰时，我们非常自信。Twitter 有“失败鲸”，一半时间都在宕机，人们甚至无法使用。我们的产品创新更快，功能更多，人们喜欢它，而且我们 100% 的时间都在线。然而我们却彻底输了，而且和产品毫无关系。这是一个例子，说明为什么，众所周知，谷歌没有走出多少伟大的企业家。一旦谷歌如此成功，作为产品经理就很难看到分销、产品设计甚至商业模式，当你拥有 AdWords 并且钱从天而降时，就没有那么多审视了。像 PayPal 黑帮这样的人比典型的谷歌产品经理学到了更多关于创业精神的东西。所以我们只是被现实狠狠打脸，艰难地学习这些。那可能是最突出的例子。我可以告诉你那个产品的所有缺陷，但我不认为那是我们失败的原因。原因有很多，包括产品缺陷，但也有很多其他因素。所以随着时间的推移，我积累了这些技能。

We were a follower-oriented social network, not a friendship-oriented one, which meant a lot of our content was more like X or Twitter than Facebook. We shared newspaper articles, interests, scientific communities, things like that. There was a period when Twitter, one of our competitors at the time, though there were many more social networks then, I'm probably messing this up a bit, but I think Obama, Ashton Kutcher, and Oprah Winfrey all joined Twitter in a single summer, and we just got our ass kicked. It was a great example of how we were just making product. I think 11 of those 12 people were engineers. And I think it was Biz Stone—if you talk to the Twitter folks, they could give you the history—but I think Biz was really focused on getting celebrities and public figures onto Twitter, which is totally obvious. If you have a social service oriented towards following people, put some people on there worth following. Instead, we were exclusively focused on polishing the product. At our peak of popularity, we were very confident. Twitter had the fail whale and was down half the time; people couldn't even use it. Our product was innovating faster, had more features, people liked it, and we were up 100% of the time. Yet we totally lost for no reason related to product at all. It was an example of how, famously, not many great entrepreneurs have come out of Google. Once Google was so successful, it's hard as a product manager to see distribution, product design, and even business model when you have AdWords and money's raining from the sky. There wasn't as much scrutiny. Folks like the PayPal mafia learned a lot more about entrepreneurialism than a typical PM at Google. So we were just getting punched in the face, learning this the hard way. That was probably the most prominent example. I can tell you all the flaws of that product, but I don't think that was the reason we lost. There were many reasons, including product flaws, but also a lot of other stuff. So I've accumulated these skills over time.

对于人们如何知道该听谁的建议，你有什么启发式方法或建议吗？当你觉得“好吧，忽略这个人，但听这个人的”时，你会注意什么？

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, ignore this person, but listen to this person."

是的，这个问题很难。这确实归结为良好的判断力和对人品的判断力。特别难的一点是，一个人表达观点的自信程度与观点的质量之间没有很强的相关性。我不想说它们是负相关的，但有趣的是，现在有这么多播客。如果是我非常了解的话题，有时关于我熟悉领域的最雄辩、最自信的陈述反而最不准确，而且听起来极具说服力。所以这确实需要非常好的判断力。有一点是，我认为，不只是寻求建议，还要问“我应该向谁请教以获得好建议”，你会得到一些共同的答案，这往往是良好判断力的一个非常强的信号。还有一点我发现的是，当你寻求建议时，不要只问该做什么，还要问为什么。像个讨厌的两岁小孩一样，为什么为什么为什么？真正去理解别人给你建议时所使用的框架。关于建议的有趣之处在于，人们往往是从相对较少的经验中推断出来的。所以他们会说“永远不要这样做”或“总是那样做”，那是因为他们有过一次经历，某件事适得其反，或者如果他们做了某件事，结果可能会更好。所以这是一个有用的轶事，但如果你不问为什么，不理解他们只有一次经历以及发生了什么，它可能会被当作一条规则，而实际上它只是轶事数据。如果你向三个人征求意见，而他们都有非常相似的互动，你就可以创建一个第一性原理的框架，从这个框架中得出建议。当你开始应用它时，你是在以一种如果你只是遵循规则就无法达到的细微程度来应用它。所以我认为一是归结为良好的判断力。我不知道如何教授这一点。我认为这可能是非常——我非常相信良好的判断力。这是我招聘时看重的东西之一。我只是觉得这可能是来自自我反思的混合。作为企业家、产品经理，你真的需要对自己负责。如果你做了一个糟糕的决定，花时间反思它，真正理解为什么，并努力不断提高你的判断力。我认为归根结底，这就是为什么你是一个好的企业家、一个好的产品经理。

Yeah, that one's tough. It definitely comes down to good judgment and being a 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 want to say it's inversely correlated, but it's funny with all the podcasts out now. If there are topics I know a lot about, sometimes the most eloquent, confident statements about things I know a lot about are the least accurate, and they sound extremely persuasive. 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 one thing I found is when you ask for advice, don't just ask what to do, but why. Be like an obnoxious 2-year-old kid, you know, why why why? And really try 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 they'll say never do this or always do that, and it's because they had one experience where something backfired or something could have gone better if they had done it. So it's a useful anecdote, but 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 anecdata. And if you ask advice of three people and they all have very similar interactions, you can create a kind of 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. I don't know how to teach that. I think it's probably a very—I'm a huge believer in good judgment. It's one of the things I hire for. I just think that that's something that probably comes from a mix of self-reflection. You really need to hold yourself accountable as an entrepreneur, as a product manager. If you made a bad decision, spend time reflecting on it, really try to understand why, and try to always improve your judgment. I think at the end of the day, that is why you are a good entrepreneur, a good product manager.

第二点，当你得到建议时，要真正理解它的来源和原因，这样你才能形成自己对建议来源的独立看法，并认识到没有谁的建议具有统计显著性，或者说极少有。我的意思是，如果你从沃伦·巴菲特那里得到投资建议，那好吧，这确实具有统计显著性，但大多数建议并非如此。大多数建议就像是某件事在你身上发生过一次，然后你就有了遗憾。

And number two, when you get advice, really understand where it's coming from and why, so that you can create your own independent view of where that advice came from, and recognize that no one's advice is statistically significant, or very rarely is it. I mean, if you're getting advice on investing from Warren Buffett, yeah, okay, it's statistically significant, but that's not most advice. Most advice is like something happened to you once and you have regrets.

我喜欢你那种“我不知道我有没有好答案”的态度，然后你就给了我们一个令人难以置信的回答。我想换个方向。你提到你把自己描述为工程师。我知道我听说你仍然通过写代码来放松。让我问你这个问题。很多大学生都在思考的事情。你认为学习编程还有意义吗？你认为这在未来几年会发生重大变化吗？

I love that you're like, I don't know if I have a great answer, and then you just give us an incredible answer to this question. I want to go in a kind of a different direction. You mentioned that you describe yourself as an engineer. I know I heard you code to relax still. 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与学习编程是不同的答案。但我要说，我仍然认为学习计算机科学非常有价值。我这么说是因为我认为计算机科学不仅仅是编程。如果你理解像大 O 表示法或复杂性理论这样的东西，或者学习算法，知道为什么随机化算法有效，为什么两个具有相同大 O 复杂度的算法在实践中一个比另一个表现更好，为什么缓存未命中很重要，以及所有这些小细节——编程远不止写代码。我这么认为的原因是，我确实认为创建软件的行为将从在终端或 Visual Studio Code 中打字转变为操作代码生成机器。我认为这就是创建软件的未来。但我认为操作代码生成机器需要系统思维，我认为计算机科学——还有其他学科——但计算机科学是学习系统思维的绝佳专业。归根结底，AI 将促进软件的创建。在未来几年，我们可能会做更多我们甚至无法想象的事情，但作为代码生成机器的操作者，你的工作是制造产品或解决问题。你确实需要出色的系统思维，你将管理这台机器，它正在做很多繁琐的工作，比如制作按钮或连接网络。但是当你思考技术与业务问题的交汇点时，你试图影响一个系统，该系统将大规模地为你的客户解决这个问题。而系统思维始终是创建产品中最困难的部分。

I do still think 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 big-O notation or complexity theory, or study algorithms and know why a randomized algorithm works, and why two algorithms with the same big-O complexity can in practice perform better than others, and why a cache miss matters, and just all these little things—there's a lot more to coding than writing the code. The reason I think that is I 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. 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 creating this 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 generating machine is to make a product or to solve a problem. And you really need to have great systems thinking, and you're going to be managing this machine that's doing a lot of the tedious work of making the button or 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 creating products.

我给你一个老套但简单的例子，但我认为它很有代表性。在 Facebook，我们总是花很多时间设计信息流。如果你有一个非常好的设计师，他们给你看信息流的 Photoshop 模型，那总是很漂亮。照片里家庭幸福，照片是完美的照片，帖子语法完全正确，长度完全正常，评论和点赞按钮，一切都完美。然后你实现那个设计，看看你自己的信息流，它看起来——因为事实证明不是每个人的照片都是由专业摄影师拍摄的。帖子有各种不同的长度。评论就像“你太烂了”之类的东西。然后你突然意识到，在 Photoshop 中设计信息流是容易的部分。你需要真正设计一个系统，在输入不受你控制的情况下，产生一个在内容和视觉设计上都令人愉悦的体验。那是一个系统——它有点像是设计，但它是系统。我们实际做的——我确信自从我 2012 年离开后已经改变了很多——但我们做了一个系统，让设计师必须用真实的信息流数据来展示他们的设计，这些数据是杂乱的，而不是任何人工的东西，因为我认为这迫使过程更加现实。

I'll just give you a cheesy simple example, but I think it's representative. At Facebook, we would always spend a lot of time designing the newsfeed. And if you ever had a really good designer and they showed you a Photoshop mockup 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 all perfectly grammatically correct and of a completely normal length, and the comments and the like button, everything was just perfect. And then you'd implement that design and you'd look at your own newsfeed and it looked like—because it turns out not everyone's photos were made by a professional photographer. The posts were all these different lengths. The comments were like, you know, 'you suck' and all that stuff. And then all of a sudden you realize that designing a newsfeed in Photoshop is the easy part. You need to actually design a system that produces a delightful experience, both in content and visual design, given input you don't control. And that's a system—it's sort of a design, and it's a system. What we did practically—I'm sure it's changed a lot since I left in 2012—but we made a system so designers had to show their newsfeed designs with real newsfeed data that was messy, rather than anything artificial, because I think it forced the process to be more realistic.

但我这么说是因为我认为无论 AI 是写代码、做设计还是做其他所有事情，你都需要学会在头脑中有一个系统。你需要理解什么是困难的、什么是容易的、什么是可能的、什么是不可能的。顺便说一句，AI 也可以帮助你做到这一点。但我确实认为这是一项非常有用的技能。我认为总的来说，随着智能体式 AI 的出现以及 AI 在某些领域接近超级智能，我们完成工作的工具将发生很大变化。我认为对我们工作方式保持非常松散的联系非常重要。你知道，那个我们不会谈论的故事，当我重写 Google 地图时——每个人都谈论那个故事，因为我认为这是因为 Paul Buchheit 在某个播客上讲了它，然后传开了。我认为那最终会成为过去的遗迹，就像计算机发明之前 NASA 的人类计算员一样。“哇，一个人是计算员。哇，真有趣，给我讲讲那个故事。”我认为就像我擅长的东西在未来将不再有用，或者肯定不再有价值，这没关系。所以我认为我们需要对它保持非常松散的观点，但你不应该学习这些学科的想法——这有点像人们说“我不想学数学，因为我不会在我的职业生涯中使用它”。嗯，学习数学非常重要；它教你如何思考。它教你世界如何运作——物理、数学。我认为计算机科学，尤其是至少它的基础，将继续成为我们构建软件的基础。当你与比你更聪明的东西交互时，它产生你可能不完全理解的代码，你如何约束它，如何让它产生这些结果——我认为这实际上需要很多复杂性。

But I say that because I think that whether AI is writing code or doing the design or doing all these other things, 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. But I do think that's a really useful skill. I think in general with the advent of AI agents and AI approaching superintelligence 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 rewrote Google Maps—everyone talks about that story because I think it's because of Paul Buchheit who told it on some podcast and it made the rounds. I think that's going to end up sort of a vestige of the past, like the human calculators at NASA before computers were invented. 'Wow, a person was a calculator. Wow, that's fun, tell me that story.' I think just like what I was good at will no longer be useful in the future, or certainly not valuable in the future, and that's okay. So I think we need to have a really loose view of it, but the idea that you shouldn't study these disciplines—it's sort of like people say 'I don't want to study math because I'm not going to use it in my career for X.' Well, studying math is quite important; it teaches you how to think. It teaches you how the world works—physics, math. And I think computer science, especially at least 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 might 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.

一直存在这种二元对立：我到底该不该学编程？而你的观点是，要理解工程是如何运作的、系统是如何运作的、你的代码做了什么、以及它们如何相互关联，但你在办公桌上实际写代码的方式将发生巨大变化。

There's this always sense of this binary: should I learn to code 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.

这让我想起你最近在播客中提到的一个观点：你认为将会出现，或者说应该出现一种新的编程语言，它更多是为大语言模型（LLM）而非人类设计的。你能谈谈这个吗？因为我觉得很多人没有考虑到这一点。

This reminds me of something you mentioned on a podcast recently: this idea that you think there's going to be, or there should be, a new programming language that is more designed for LLMs versus humans. Can you just talk about that? Because I think a lot of people aren't thinking about that.

我不确定它是否算一种语言；我更愿意称之为一个编程系统，因为我觉得“语言”这个词可能太局限了。

I don't know if it's a language; I would call it a programming system because I think language might be too limited.

我对过去 40 年或更久计算机历史的简化概括是：我们先创造了计算机硬件，然后创造了打孔卡，那是 70 年代末期你告诉计算机该做什么的方式。接着我们发明了早期的操作系统和分时系统。从贝尔实验室和伯克利发明 Unix 这类东西开始，我们得到了 C 语言、Fortran 以及许多更高级的编程语言。我们逐渐提升了抽象层次，所以显然没人再用打孔卡了。很少有人写汇编语言，有些人写 C，有些人写 Rust，但很多人写 Python 和 TypeScript 之类的语言。随着我们发明越来越多的抽象，我们更容易做高杠杆的事情。

My reductive version of the past 40 years of computers, maybe more, is: we created the hardware for computers, then we created punch cards, which was the way in the late '70s you would tell a computer what to do. Then we invented early operating systems and time-sharing systems. 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 higher-level programming languages. We've sort of moved up the layers of abstraction, so no one does punch cards anymore. Few people write assembly language, some write C, some write Rust, but a lot of people write Python and TypeScript and things like that. As we've invented more and more abstractions, we've made it easier to do high-leverage things.

我总爱看当年 Google 或 Google Maps 有多了不起。现在你大概可以给很多 React 程序员一个任务，让他们做一个可拖拽的地图，我觉得很多人能做到。但在当年，那是真正的研发。1998 年 Salesforce 创立时，仅仅是把数据库放到云端就很困难。仅此一项就是技术护城河，而现在有了亚马逊云服务（AWS），这变得微不足道。那条技术护城河可笑地狭窄，但产品护城河却相当宽广。

I always 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 making a draggable map now, and I think a lot of people could do it. That was true R&D back in the day. When Salesforce was created in 1998, just putting a database in the cloud was hard. That alone was a technical moat that is now trivial with Amazon Web Services. That technical moat is comically narrow, but the product moat is quite large.

我认为，如果写代码的行为从非常昂贵变成边际成本趋近于零，那么我们构建的抽象中有多少是基于人类程序员的生产力呢？我觉得非常多。我总笑说，我猜 Python 可能是最常见的生成代码，因为它在训练数据中出现的频率太高了，数据科学家喜欢 Python，我也喜欢 Python。但让 AI 生成 Python 简直是滑稽的糟糕选择，因为它是有史以来效率最低的编程语言之一。由于全局解释器锁，它很慢，我写过很多高水平的网络服务，它确实很慢，而且很难验证。它不像 Perl 那么糟，但如果你有一个大型 Python 程序，你在运行时发现多少错误，而在发布前又能发现多少呢？Python 的设计初衷是高度人性化，几乎像人类的伪代码，让我能以愉快的方式写代码。这就是数据科学家如此喜欢它的原因。

I think that if the act of writing code goes from something that is very costly to the marginal cost of that going to zero, how many of the abstractions that we've built are based on human programmer productivity? I think a ton. 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. With the global interpreter lock, it's just slow, and I've written a lot of high-skill web services, and it's just quite slow and very hard to verify. It's not as bad as Perl, but if you have a big Python program, how many errors will you find at runtime versus before releasing it? Python was designed to be very ergonomic, almost like pseudo-code for humans, for me to write code in a delightful way. That's why data scientists love it so much.

当我们走向一个世界——让我们假设，虽然我不确定这会完全成真——人们不再大量编写代码，而是操作这些代码生成机器。我们可能不在乎编程语言有多人性化。我们在乎的是，当机器生成代码时，我们能否知道它是否按我们的意图做了？如果它没有按我们的意图做，我们能否轻松修改它？我认为编程语言中有很多见解可以服务于这一点。所以，我觉得 Rust 很有意思，因为如果我让你看一个 C 程序，并判断它是否泄漏内存，你可能做不好，因为这真的很难。如果是一个百万行的 C 程序，那会非常非常难。如果我让你验证一个 Rust 程序不泄漏内存，你只需要编译它。因为它具有编译时内存安全，仅仅成功编译这一行为就告诉你这是真的。

As we move to a world where, let's just postulate—and I'm not sure this will be completely true—that we're not going to write a lot of code as people. We're going to be operating these code-generating machines. We probably don't care how ergonomic the programming language is. 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 what 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, Rust, I think, is interesting because if 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. And if it's a million-line C program, that would 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. Because it has compile-time memory safety, just the act of compiling successfully tells you that's true.

我认为我们需要更多这样的东西，因为如果 AI 在生成代码，那么按定义，如果你必须逐行阅读，那将成为生成代码的瓶颈。或者更糟，你根本不会逐行阅读，而是把一堆不安全、未经验证的代码发布到现实世界中。所以问题是，如何让人类获得尽可能大的杠杆，这意味着让计算机替你完成工作？显然，最简单的形式是 AI 监督 AI 并进行代码审查，这很棒。当然，自我反思是提高 AI 系统鲁棒性的非常有效的方法。

I think we need more things like that because if an AI is generating this code, by definition, if you have to read every line, that is going to be the limiting factor for producing the code. Or worse, you're just not going to read every line and you're going to emit a bunch of unsafe, unverified code into the wild. 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? 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.

但我确实认为，如果写代码的繁琐程度不再重要，你或许可以叠加一些有些过时的技术，比如形式化验证、单元测试等等。如果你把这些都叠加起来，我有点像《黑客帝国》里那个看着绿色字符落下的家伙——我该如何创造一种东西，让我作为代码生成机器的操作者，能够极其快速地生成极其复杂、规模庞大的软件，并且知道它能正常工作？

But I do think 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, and other things. And if you layer all these on, I'm sort of thinking about it as the guy in the Matrix with the green letters coming down—how can I make something so I, as the operator of the code-generating machine, can produce incredibly complex, scale software incredibly quickly and know that it works?

如果你以此为设计中心，我认为你可能会改变语言，改变系统，改变所有这些，而且你可能会动用很多东西。真正有趣的是，你可以放宽很多约束，比如编码是免费的。好吧，那很酷。考虑到这一点，你想做什么？什么最适合语言、编译器、测试、自我反思、监督模型等等？我认为这更像是一个编程系统，而不是一种语言。

And if you start with that as your design center, I think you probably change the languages, you probably change the systems, you probably change all these things, and you're probably going to bring to bear a lot of things. And what's really fun about this is you can loosen a lot of constraints, like coding is free. Okay, so that's neat. With that in mind, what do you want to do? What would be best suited for the language, the compiler, for testing, for self-reflection, 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东西时，它确实能让创造者和构建者创造出极其健壮、极其复杂的系统。我对“氛围编程”感到非常兴奋，但我不确定生成原型是否一直是软件的瓶颈。真正的瓶颈其实是构建越来越复杂的系统，并敏捷地修改它们。

But I think when we create something like that, it can really enable creators and builders to create incredibly robust, incredibly complex systems. And I'm super excited about vibe coding, but I don't know if generating a prototype has been the limiting factor in software ever. It's actually building increasingly complex systems and changing them with agility.

你知道，如果你看看著名的 Netscape 1 到 Netscape 2 的重写，很多人把他们在与 Internet Explorer 竞争中的失败部分归因于此。做这些东西并不难，难的是维护它们，难的是确保健壮性。我认为我们正处于定义这种软件开发新体系的非常早期阶段，我非常期待看到接下来会出现什么。

You know, if you look at the famous Netscape 1 to Netscape 2 rewrite, a lot of people attribute that to part of their failure against Internet Explorer. It's like making these things is not hard; maintaining them is hard, and ensuring robustness is hard. I think we're in the very early phases of defining what this new system for developing software looks like, and I'm very excited to see what emerges.

我觉得我们绝对生活在未来，当像你这样的人建议我们构建一种《黑客帝国》式的体验时，这可能会成为编码和构建的未来。我等不及了。感觉这是一个绝佳的机会，也是一个有趣的项目。

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. Feels like a great opportunity and a fun project.

本期节目由 Vanta 赞助，我非常高兴邀请到 Vanta 的 CEO 兼联合创始人 Christina Cassiopo 来参加这次简短的对话。

This episode is brought to you by Vanta, and I am very excited to have Christina Cassiopo, CEO and co-founder of Vanta, joining me for this very short conversation.

很高兴来到这里。我是这个播客和新闻通讯的忠实粉丝。

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

Vanta 是节目的长期赞助商，但对于一些新听众来说，Vanta 是做什么的？面向哪些用户？

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。我们在 2018 年创立了 Vanta，专注于创始人，帮助他们开始建立安全计划，并通过 SOC 2 或 ISO 27001 等合规认证来为所有那些艰难的安全工作获得认可。如今，我们帮助超过 9,000 家公司，包括一些初创公司中的知名品牌，如 Atlassian、Ramp 和 LangChain，启动和扩展他们的安全计划，最终通过自动化合规、集中化 GRC 和加速安全审查来建立信任。

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 2 or ISO 27001. Today, we currently help over 9,000 companies, including some startup household names like Atlassian, Ramp, and LangChain, start and scale their security programs, and ultimately build trust by automating compliance, centralizing GRC, and accelerating security reviews.

太棒了。我从经验中知道这些事情需要大量时间和资源，没有人愿意花时间做这个。

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.

这非常符合我们的经验，无论是在公司成立之前还是在某种程度上公司运营期间。但我们的理念是，通过自动化、AI 和软件，我们帮助客户高效地与潜在客户和客户建立信任。你知道我们的玩笑话，我们创办这家合规公司就是为了让你不必亲自做这些。

That is very much our experience, both before the company 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.

我们感谢你这样做。而且你为听众提供了特别折扣。他们可以在 vanta.com/lenny 获得 Vanta 的 1,000 美元优惠。那就是 vanta.com/lenny，立减 1,000 美元。谢谢，Christina。

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

谢谢。

Thank you.

好的，沿着这个思路再问一个问题，然后我想把视角拉远，看看 AI 的发展方向。我喜欢问像你这样处于 AI 前沿的人的一个问题是，你在教你的孩子什么。我知道你有孩子。我觉得当他们长大时，世界会变得非常不同。你鼓励他们学习什么，你认为这可能与前辈们不同，以帮助他们在 AI 丰富的世界中取得成功？

Okay, one more question along these lines and 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 going to be very different when they grow up. What are you encouraging them to learn that you think might be different maybe from previous generations to help them be successful in a world of AI abundance?

我不知道我是否在教他们不同的东西，但我真的在努力鼓励他们把 AI 融入他们的生活。我实际上在回想，当我在 1997-98 年参加 AP 微积分考试时，AB 和 BC，我可以使用图形计算器。我还没有做过这个研究。我本来打算在我们的对话之前把这个输入 ChatGPT，但我稍后会做。在允许计算器进入考试之前和之后，微积分考试有变化吗？我猜是有的。但本质上，当你在考试中允许使用计算器时，你需要确保没有任何问题会因为学生有没有计算器而受益，这实际上迫使你重新思考问题，以测试不依赖于死记硬背的算术或你在图形计算器上能做的其他事情的微积分知识。我认为很多教育并没有假设你口袋里有一个超级智能，所以如果你让某人写一篇关于他们读过的书的文章，你可能很容易从像 ChatGPT 这样的大提供商那里幻觉出一篇，也许如果你在提示方面足够熟练，甚至你的老师也不会知道它是 AI 写的。那么你该怎么办？你如何以不同的方式教孩子？现在对老师来说真的很难，因为我认为我们还没有经历将计算器加入考试的过渡。所以我认为我们评估学生的许多机制都被 ChatGPT 之类的存在打破了。所以我认为我们处于一个非常尴尬的阶段。但我认为我们仍然可以既教孩子如何思考，也教孩子如何学习，我认为我们的教育系统可以赶上。而且我实际上认为这些模型可以成为历史上最有效的教育工具之一。我不知道你是视觉学习者还是阅读学习者。我喜欢阅读。我不喜欢去听讲座。我从中学得不好。我喜欢读书。如果你有一个不以你的风格教学的老师，你现在可以回家让 ChatGPT 用另一种机制教你。我的孩子用 ChatGPT 在考试前测验他们。你可以使用音频模式或聊天模式。它比抽认卡更好。我女儿带回家一本莎士比亚的书。她拍了一张她不理解的页面的照片，ChatGPT 向她解释得比我还好。我认为这个世界上的每个孩子都有一个个性化的导师，可以用他们最适合的方式教他们，无论是视觉、音频还是阅读。我们有一个平台可以测试你，可以测验你。我认为它真的是能动性的放大器。我认为那些有能动性的人，有学习愿望的孩子，你拥有的是你所有老师的最佳组合加上这些模型，你可以使用它。所以对我的孩子来说，我的大女儿学会了编程，她在做一个网站，每次她问我问题，我都会让她用 ChatGPT。不是因为我想当一个讨厌的父亲，而是我想，她需要学会使用这个工具，因为它太棒了。所以我真的在努力让他们学会如何在生活中建设性地使用它。但尽管如此，我现在对公立学校的老师感到非常同情。这很难，因为技术比我们的教育系统发展得快。而且我认为特别是在评估方面，现在对老师来说真的很有挑战性。我担心，因为这些技术放大了能动性，反之亦然。如果你是一个试图不学习的学生，我认为这些工具可能也提供了很多逃避的机制。所以我认为对家长和老师来说这是一个挑战。我认为我们将会经历几年坎坷。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。

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 1997-98, AB and BC, I could use a graphing calculator. And I haven't done this research. I was 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? I assume it did. But essentially, when you allow the calculator in the exam, you need to make sure that none of the questions benefit people for having a calculator or not, which actually forces you to sort of rethink the problems to test calculus knowledge that don't benefit from like rote arithmetic or the other things you can do on a graphing calculator. I think that a lot of education sort of doesn't presume you have a superintelligence in your pocket, and so 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 providers like ChatGPT, and maybe if you are skilled enough at prompting, maybe even your teacher won't know it's written by an AI. So what do you do? 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 ChatGPT and the like. 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 reading learner. I like to read. I didn't love going to lectures. I don't learn that well from them. I like to read the book. And 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. My kids use ChatGPT to quiz them before a test. You can use audio mode or chat mode. It's like better than flashcards. 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 a platform that can test you, that can quiz you. I think it's really an amplifier of agency. I think the folks who have agency, kids who have agency, who have aspirations to learn something, you have what is the best combination of every teacher you've ever had and these models, and you can use it. So with my kids, 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 needs to learn to use this tool because it's amazing. And so I really am trying to have them learn how to use it constructively in their lives. But all that said, I just feel a ton of empathy for public school teachers right now. It's very hard because the technology is 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, because these technologies amplify agency, the opposite can also be true. If you are a student trying to not learn something, I think these tools probably provide a lot of mechanisms to avoid it as well. And so I think there's a challenge for parents and teachers. And I think we're going to end up with kind of a bumpy handful of years here. But I brought up the calculus AP exam because obviously a graphing calculator is not ChatGPT, don't get me wrong.

但我认为，我们已经能够设法让作业、课堂学习和考试适应现有的技术，到目前为止还算成功。而且我相当有信心我们会解决这个问题。从更积极的一面看，我上的是公立学校，不知道你是不是，但有时候你会遇到一些很差的老师。而现在你有了一个出口。你不再需要是有钱人家的孩子才能请得起家教。如果你是个数学很好的孩子，而你的学校没有高级统计课，那么现在你有了。所以我认为，对于有自主性的孩子来说，这简直是一股巨大的民主化力量。我觉得这非常令人兴奋。我希望现在有一个 11 岁的孩子，10 年后会创办一家非常棒的公司，而 ChatGPT 就像是他们的主要导师，促成了那个结果。我觉得那太酷了。

But I think we've been able to figure out a way to conform homework, 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. And on the much more positive side, I went to public schools. I don't know if you did too, but you end up with some pretty bad teachers at times. And now you have an outlet. You don't need to be the rich kid who can afford a 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. So I think this is just an incredibly democratizing force for kids who have agency. And I think that's very exciting. I'm hopeful that there's an 11-year-old right now who's going to start a really amazing company 10 years from now, and ChatGPT is going to be like their primary tutor that led to that outcome. And I think that's pretty cool.

我有一个两岁的孩子，感觉就像有了一个新的里程碑：什么时候给他们手机，什么时候给他们 Snapchat 或现在孩子们用的什么，然后就是什么时候给他们第一个 ChatGPT 账号。哦不。我想知道这应该多快发生。

I have a 2-year-old, and it feels like there's a new milestone: when to give them a phone, when to give them Snapchat or whatever kids use these days, and then it's like when to give them their first ChatGPT account. Oh no. I wonder how soon that's supposed to happen.

我认为 ChatGPT 应该——我个人的看法是，它和前两者不同。我不认为手机在学校里对孩子有好处。我个人主张等很久再给。但我认为 ChatGPT 更像 Google 搜索。口袋里有一个让人上瘾、有推送通知的设备是一回事，但用 AI 来学习是另一回事。所以我认为这两者是不同的。我基本上把 AI 看作一种公用事业。而且我觉得，在 ChatGPT 之前，很少有家长会说：“我该什么时候让孩子用 Google 搜索？”那是一种不同类型的工具。我就是这样看待这些技术的。

I think ChatGPT should be—my personal take is it's different than the former two. I don't think mobile phones are great in school or great for kids. And I personally advocate for waiting a long time. But I think that ChatGPT is more like Google search. It's one thing to have a device in your pocket that's addictive and has push notifications, but 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 ChatGPT said, "When should I let my kid use Google search?" That's a different type of tool. And thinking of it like that is the way I think about these technologies.

那么，你给孩子们用的设备形态是 iPad 还是笔记本电脑之类的？

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

是的。他们用的是桌上的电脑。

Yeah. They use like the computer on the desk.

好的。好建议。这对我很有用，随着孩子们长大，我得学这些。好了，我要拉远镜头，谈谈商业战略和 AI。现在很多创始人思考的最大问题之一就是：我该在哪里构建？哪些是基础模型公司不会碾压、不会自己做的？作为一个正在打造非常成功的 AI 业务、同时也是 OpenAI 董事会成员的人，我觉得你对什么可能是好主意、什么可能不是好主意，有着非常独特的视角。你认为 AI 市场会如何发展？你认为创始人应该专注于哪里，又应该避免什么？

All right. Good tips. This is good for me to learn all these things as my kids age. Okay. I'm going to zoom out and let's talk about business strategy and 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, I feel like you have a really unique perspective on what is probably a good idea and what's probably not a good idea. Why do you think the AI market is going to play out, and where do you think founders should focus and also just try to avoid?

我认为 AI 市场会有三个细分市场，最终都会成为相当可观的市场，然后我会以我对它如何发展的看法作为结尾。首先是前沿模型市场，或者说基础模型市场。我认为这最终会由少数几家超大规模云服务商和真正的大型实验室主导，就像云基础设施即服务市场一样。原因在于，创建前沿模型完全取决于资本支出，你需要一家拥有巨额资本支出能力的公司来构建这样的模型。所有试图这样做的初创公司都已经被整合了，或者几乎所有的——Inflection、Adept、Character 等等。而且我认为，由于所需的资本支出规模，初创公司似乎没有可行的商业模式，而且你无法筹集到足够的资金来达到逃逸速度。此外，模型作为一种资产类别，其价值贬值相当快，所以你需要很大的规模才能从这种贬值如此之快的模型投资中获得回报。所以我认为，最终可能没有企业家应该去构建前沿模型。这是我的看法。

I think there's three segments of the AI market that will end up fairly meaningful markets, and then I'll end with how I think it's going to play out. So first is the frontier model market or foundation model market. I think this will end up a small handful of hyperscalers and really big labs, just like the cloud infrastructure as a service market. 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 one of these models. All of the companies that were startups that tried to do this have already been consolidated, or almost all of them—Inflection, Adept, Character, and others. And I think it's just not—there 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 fundraise to get to escape velocity. Also, the models deteriorate in value fairly quickly as an asset class, so you need 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 going to end up—probably no entrepreneur should build a frontier model. That's my take.

除非你是埃隆。

Unless you're Elon.

是的。哦，对。他不一样，对吧？而且他有能力筹集数十亿美元的资金。我猜你们大多数其他听众没有。而且他是有史以来最伟大的，这是有原因的。他不一样。你不要拿自己和他比。市场的另一部分是工具。我认为有很多人在淘金热中卖铲子。这包括数据标注服务、数据平台、评估工具。更专业的模型，比如 ElevenLabs 有一系列很棒的声音模型，很多公司都在用，质量非常高。这有点像，如果你想在 AI 领域取得成功，你需要哪些不同的工具和服务？工具市场有一些风险，因为它离太阳太近了。如果你看看基础设施即服务市场和云工具市场，比如 Confluent、Databricks 和 Snowflake，很多亚马逊、Azure 等公司在这些领域都有竞争产品，因为它们与基础设施本身非常接近，而且每个基础设施提供商都试图通过向上移动技术栈来实现差异化，而你就在那里。所以有一些真正重要的公司，就像我提到的 Snowflake、Databricks、Confluent 等等，但还有很多其他公司被基础设施提供商自己的技术所取代了。所以这些公司可能面临最大的风险，就是某一天，这些大型基础模型公司中的一个在开发者日发布与他们完全相同的产品。所以可能有很多人需要你的工具，但问题将是“是否”或“何时”，这可能是正确的思考方式。这些大型基础设施提供商之一推出了竞争产品——为什么人们还会继续选择你？所以这是一个好市场，但正如我所说，它有点离太阳太近。然后是应用 AI 市场。我认为这将为那些构建智能体的公司带来机会。我认为智能体就是新的应用。所以我认为这将是产品形态。所以有像 Sierra 这样的公司。我们帮助公司构建智能体来接听电话或处理聊天，用于客户体验和客户服务。还有像 Harvey 这样的公司，为法律和律师助理行业、反垄断审查、合同审查等制作智能体。还有做内容营销的公司。还有做供应链分析的公司。我认为这有点像软件即服务市场。

Yeah. Oh, yeah. He's different, right? And he has the capacity to raise billions in capital. And my guess is most of your other listeners don't. And he's the greatest of all time for a reason. And he's different. You don't compare yourself to him. The other part of the market is the tooling. And I think there's a lot of folks selling pickaxes in the gold rush. This is data labeling services, data platforms, eval tools. More specialized models like ElevenLabs 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 pretty close to the sun. If you look at the infrastructure as a service market and the cloud tooling market like Confluent, Databricks, and Snowflake, a lot of the Amazon, 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. So there's some real meaningful companies as I mentioned like Snowflake, Databricks, Confluent, and others, but there's a lot of others that were sort of obviated by technology from the infrastructure providers themselves. So those companies probably are the most at risk for a developer day from one of these big foundation model companies releasing exactly what they do. So 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? 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 going to be sort of the product form factor. 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 and paralegal profession, antitrust reviews, reviewing contracts, etc. 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.

它们很可能会成为利润率更高的公司，因为你销售的是实现业务成果的东西，而不是模型本身的副产品。它们几乎肯定会向模型提供商缴纳“税”，这就是为什么那些模型提供商最终会规模极大，但利润率可能略低。而且我认为它们的市场可能更不技术化。我的意思是，如果你想想最纯粹的软件即服务形式，你不会问用的是什么数据库，对吧？真正重要的是功能和特性。我认为智能体就会走向这个方向。我认为随着时间的推移，它会更关乎产品而非技术。

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. 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 the market for them will be probably less technical. I mean, if you think about the purest form of software as a service, it's not like you ask what database you use, right? It's really about the feature and function. I think that's where agents will go. I think it's going to be more about product than it is about technology over time.

回到我的比喻，1998 年马克和帕克创立 Salesforce 时，让数据库在云端运行本身就是一项技术成就。如今没人会问这个，因为你可以轻松地在 AWS 或 Azure 上启动一个数据库，毫无问题。我认为今天，在模型之上编排智能体流程听起来很花哨，也确实很难。但我很确定，随着技术改进，三四年后这就会变得稀松平常。所以随着时间推移，你会问：什么是智能体公司？嗯，它看起来更像软件即服务。你谈论如何处理模型的成分会少一些。就像现代 SaaS 一样，很少有人问你用什么数据库，但你可能会问很多关于工作流程和你在推动什么业务成果的问题。你是在为销售团队生成线索吗？你是在最小化采购支出吗？无论你提供什么价值，它都会慢慢朝那个方向演变。

Just going back to my metaphor, in 1998 when Marc and Parker started Salesforce, just getting that database running in the cloud was a technical achievement. Nowadays, no one asks about that because you can just spin up a database in AWS or Azure and it's no problem. I think today, getting an orchestration, orchestrating an agentic process on top of the models sounds really fancy and it's really hard and all that stuff. I'm pretty sure that's going to be in three or four years just as the technology improves. So over time you say, what is an agent company? Well, it looks a little bit more like software as a service. You 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 you're driving. 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.

我认为初创公司可能不应该构建基础模型。我的意思是，如果你对未来有愿景，你可以放手一搏。但我认为那可能是一个已经有些整合的、充满挑战的市场。我对另外两个市场非常兴奋。随着构建智能体变得越来越容易，我特别兴奋能看到大量长尾智能体公司涌现。

I don't think startups should probably build foundation models. I mean, you can shoot your shot 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 longtail agent companies come out.

我看过一个网站，列出了股市上排名前 50 的软件公司。显然前五名是像微软、亚马逊、谷歌这样的大公司，但接下来的 50 名都是 SaaS 公司。其中一些非常令人兴奋，一些则超级无聊，但这就是软件市场的演变方式。我认为智能体领域也会出现类似的情况。它不会只局限于像客户服务和软件工程这样的大市场。会有很多人们花费大量时间和资源的事情，智能体就能解决，但这需要一位真正深刻理解该业务问题的创业者。我认为这就是 AI 市场中将释放大量价值的地方。

I was looking at a website for the top 50 software companies in the stock market. Obviously the top five are the big ones like Microsoft, Amazon, Google, all that, but the next 50 are all SaaS companies. Some of them are very exciting, some of them are super boring, but this is how the software market has evolved. I think we're going to see something kind of similar with agents. It's not just going to be these huge markets like customer service and software engineering. It's going to be a lot of 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 deeply. And I think that's where a lot of the value is going to be unlocked in the AI market.

这非常有帮助。这让我想到，我请过马克·贝尼奥夫上播客，你们曾是联合 CEO，他非常“智能体上头”。他只想谈 Agentforce。显然你也是“智能体上头”。你认为人们忽略了什么，为什么这是软件工作方式的如此关键的变化？人们没看到什么？

That is incredibly helpful. This makes me think about I had Marc Benioff on the podcast, you guys were co-CEOs, and he was extremely agent-pilled. All he wanted to talk about was Agentforce. Clearly you are also very agent-pilled. What is it that you think people are missing about just why this is such a critical change in the way software is going to work? What are people not seeing?

我从没听过“智能体上头”这个词。我要用这个词。

I've never heard the term agent-pilled. I'm going to use that one.

如果你和像拉里·萨默斯这样的经济学家谈谈，他在 OpenAI 董事会和我共事，他们会说技术的价值是什么？嗯，它有助于推动经济中的生产力。如果你看看经济中生产力的重大飞跃之一，那是在 90 年代。我认为我交谈过的很多人认为那实际上是第一波计算浪潮，人们制造了 ERP 系统，把会计工作放进计算机和数据库，甚至大型机。我们说的是个人电脑时代，因为那是一个巨大的进步。想象一下以前一家大型跨国公司会有的数字账本，它确实彻底改变了各个部门。

If you talk to an economist like Larry Summers, who's on the OpenAI board with me, they'll talk about what is the value of technology? Well, it helps drive productivity in the economy. And if you look at one of the big jumps in productivity in the economy, it was in the '90s. And I think a lot of folks I talk to think it was actually that very first wave of computing where people made ERP systems and just put accounting into computers and databases, even mainframes. We're talking like the PC era because it was such a huge step up. Just imagine the ledgers of numbers you'd have for a large multinational company before, and it truly just transformed departments.

我给你举个简单的小例子。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，你走进一家机械工程公司，公司里大多数人是制图员。基本上你有一个工程设计，但你需要为所有不同的楼层和视角绘制图纸，然后交给承包商去施工。现在他的公司里制图员为零。你只需做设计，先是 AutoCAD，现在是 Revit，它是一个 3D 模型，制图实际上已经被淘汰了。它不再是需要做的事情。实际的设计和制图已经不存在了，它只是一个设计。这是真正的生产力提升，对吧？机械工程公司的工作是做设计。制图对承包商来说是一种必要的输出，但它并没有真正增加价值。它只是供应链的变化。

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. You went into a mechanical engineering firm, the majority of the firm were draftspeople. Basically you take an engineering design but you needed to do all the different vantage points and for all the different floors and give it to the contractor to do the thing. Now there are zero draft people at his company. You just make the design, first AutoCAD and now Revit, and it's a 3D model, and 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 a design. That's true productivity gains, right? The job of the mechanical engineering firm was to do a design. The drafting was 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.

如果你看看从个人电脑时代开始的软件行业历史，确实有显著的生产力提升，但远没有第一次巨大飞跃那么显著。我不够聪明，无法确切知道为什么。但有趣的是，技术带来的生产力提升的承诺，我认为并没有像一些人想象的那样实现。我认为智能体将真正开始再次扭转曲线，就像我们在计算早期所做的那样，因为软件正在从帮助个人稍微提高生产力，转变为自主完成一项工作。因此，就像机械工程公司不再需要制图员一样，你就不再需要有人做那件事了。这意味着他们可以做其他更高杠杆、更有生产力的事情，而且你实际上可以让更少的人完成更多工作，并真正推动经济中的生产力提升。

If you look at the history of the software industry from the PC on, there've 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 that the promise of productivity gains from technology hasn't been as realized, I think, as some people thought. I think agents will truly 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 to actually accomplishing a job autonomously. And as a consequence, just like you don't need draftspeople in a mechanical engineering firm, you just won't need someone doing that thing anymore. It means they can do something else that's higher leverage and more productive, and you can actually have a smaller group of people accomplish more, and truly drive productivity gains in the economy.

而且你知道，如果你卖过企业软件，你会作为供应商与客户进行这些讨论，你会进行价值讨论，你会做这些有点复杂的事情。比如，好吧，就像你在卖一个销售的东西。

And you know, if you've ever sold enterprise software, you end up in these discussions as a vendor with the customer where you'll have a value discussion and you'll do these somewhat convoluted things. Like, okay, it's like you're selling a sales thing.

嗯，如果每个销售员多卖 5%，诸如此类，你就该付我们一百万美元。你知道，差不多就是这么个对话。而且这很难归因，尤其是，这也是为什么销售生产力软件这么难，我在很远的地方就学到了，你知道，很难知道让每个人生产力提高 10% 的价值是什么。你真的让他们生产力提高 10% 了吗，还是别的什么变了？你并不真正知道这些，但现在有了智能体真正完成一项工作，不仅真正以非常实际的方式推动生产力，而且也是可衡量的。所以所有这些加在一起，我认为这实际上是我们在思考软件方式上的一个阶梯式变化，因为它自主地完成一项工作，这更像是显而易见的生产力驱动力。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。所有这些加在一起，对我来说感觉和云一样重要，或者我认为在技术上更重要，但就它如何改变软件行业的商业模式而言，会有一种前后之分。我不知道还有多少人还在卖永久授权的本地部署软件，但这一点上它正在减少。我认为我们将经历类似的转变。整个市场将走向智能体。我认为整个市场将走向基于结果的定价。不是因为这是唯一的方式，而是市场会把所有人拉向那里，因为这是构建和销售软件显然正确的方式。

Well, if every salesperson sells, you know, 5% more, da da da da, and you should pay us a million dollars. Like, you know, and it's roughly that conversation. And it's so unattributable, you know, especially and it's why it's so hard to sell productivity software, which I learned far away, is, 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. 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 will 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 transforms the business model of the software industry, where there's going to be like a before and after. Like, I don't know how many people still sell perpetually licensed on-premises software, but it's diminishing at this point. I think we're going to go through a similar transition. The whole market is going to 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 going to be like the market is going to pull everyone there because it's just so obviously the correct way to build and sell software.

让我接着最后一点说。我们最近请了 Mavon 上播客，他是定价专家、传奇人物、《货币化创新》的作者，他谈到了 AI 公司的定价策略，他非常赞同你的观点，即如果可以的话，你需要将产品定价为基于结果的产品。而关键点正是你分享的，即如果你能归因影响，并且它是自主的、自行运行的，就可以这样做。也许只是简单提一下，他实际上把 Sierra 作为成功案例的典范之一。你能简单解释一下，对于没听过这个术语的人，什么是基于结果的定价，然后 Sierra 是如何运作的，举个例子？

Let me pull on that last thread. So we had Mavon 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. And the axis 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 ch, and he actually used Sierra as one of the shining examples of this being successful. Can you just briefly 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?

是的，我先从例子开始，然后再扩展。在 Sierra，我们帮助公司打造面向客户的 AI 智能体，主要用于客户服务，但更广泛地说是客户体验。所以如果你有 Sirius XM 收音机的问题，你会打电话或与我们的 AI 智能体 Harmony 聊天。如果你有 ADT 家庭安防，你的警报不工作，你可以和他们的 AI 智能体聊天。Sonos、音箱，很多不同的消费品牌。你知道，如果你考虑运营一个呼叫中心，每接一个电话都有成本。大部分是劳动力成本。但如果你有，比如说，一个典型的电话在 10 到 20 美元之间。大部分是，有些是软件，有些是电话费，但很多只是接电话人的时薪。所以如果 AI 智能体能接那个电话并解决它，你知道，这在行业里通常被称为呼叫转移或遏制。这基本上意味着你节省了，比如说，15 美元，因为你不需要有人接电话。所以在我们行业，基本上我们说，嘿，如果 AI 智能体解决了客户的问题，他们满意了，你不需要接电话，那就有一个预先协商好的费率。这就是我们所说的基于解决。还有其他结果。我们有一些销售智能体按销售佣金支付，信不信由你。你知道，我们真的把我们的智能体视为真正的客户体验，就像你品牌的礼宾，我们想确保我们的商业模式与客户的商业模式一致。正如你所说，这些智能体需要自主，结果必须可衡量。这并不总是可能的，但我认为大致上是可能的。真正巧妙的是，如果你和任何 CFO 或采购主管谈，你知道，和他们的主要供应商，他们看物料清单，那是压倒性的，不可能知道你从那份合同中是否得到了你希望的价值。我认为基于消费的，特别是在基础设施领域流行的，更接近，但我不确定 token 是否真的是 AI 价值的良好衡量标准。我总是用这个类比，现在大多数编码智能体按 token 或按使用量定价，但有一个著名的故事，一个苹果工程师有一个糟糕的经理，让他每天报告写了多少行代码，这世界上每个工程师都知道是衡量生产力的愚蠢方式。他著名地带着一个负数的报告进去，因为我认为他做了一次大的重构，删掉了一堆，这是他向体制竖中指的方式。我认为 token 类似，你知道，是的，你用了很多 token，那很好。它是否产生了一个拉取请求，你知道，那很好？我认为这就是这一切的重点。我认为基于结果的定价和基于使用的定价之间有很大区别，因为特别是在 AI 中，它们甚至不一定相关。你可能有一个很长的电话，没有解决客户的问题，他们在网上给你负面评价，然后再次打电话给呼叫中心。所有这些努力都白费了。事实上，你可能增加了负面价值。所以我非常相信这一点。有趣的是它真的只是对齐。我认为每个科技公司都渴望成为合作伙伴，而不是供应商。我认为在 Sierra，我们真正是每个客户的合作伙伴，因为我们都对我们想要实现的目标一致。我认为这确实是软件行业应该去的方向。它需要公司有非常不同的形态。你必须能够帮助你的客户实现那些结果。你知道，你不能只是把软件扔到墙上，因为你永远不会得到报酬。如果它不，你必须，你知道，当你以正确的方式做这件事时，你的导向变得极其以客户为中心。我认为这只是软件行业的一个更好的版本。所以我认为从第一性原理来看是正确的。对采购合作伙伴是正确的，我认为对世界也是正确的。

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 Sirius XM 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, you 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. 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. Most of it, some of it software, some of it 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 in the industry is often called a call deflection or a containment. And that essentially means you saved, you know, call it $15, because you didn't have to have someone pick up the phone. So in our industry, basically we say, hey, if the AI agent 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 what we call resolution-based. There are other outcomes as well. We have some sales agents being paid a sales commission, believe it or not. You know, we really think of our agents as truly customer experience, like the concierge for your brand, and we want to make sure that our business model is aligned with our customer's business model. 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 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 an Apple engineer who had a bad manager who had 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. He famously went in with a report that had a negative number because I think he did a big refactor and deleted a bunch, and it was his way of saying like, you to the man. I think tokens are similar, you know, like, yeah, you used a lot of tokens, like good for you. Did it produce a pull request, you know, that was good? And I think that's the whole point of all this. 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. 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 want to achieve. 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 be able to help your customers achieve those outcomes. You know, you can't just throw software at the wall because you'll never get paid. If it doesn't, you have to, you know, really just your orientation becomes so extremely customer-centric when you do this the right way. 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.

如今头条上有很多质疑，比如 AI 到底在做什么？它真的在帮人们提高生产力吗？最近有一项研究，不知道你看到没有，显示工程师用 AI 后生产力反而下降了，因为 AI 把他们引向不同方向，他们还得去研究哪里出了问题。所以我觉得客户体验（CX）是一个很好的例子，你明显能看到收益。你在你的公司或你合作的其他公司，除了客户体验之外，是否看到了实实在在的生产力提升，那种明确是“这有效，而且影响巨大”的？

There's a lot of skepticism in the headlines these days about what AI is actually doing. Is it actually helping people be more productive? There was a recent study, I don't know if you saw, that showed engineers were less productive with AI because it was putting them in different directions, and they had to research what's going wrong. So I think CX is a really good example where you clearly see 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 clearly yes, this is working and a huge deal?

我对 AI 带来的生产力提升极为乐观，但我确实认为目前的工具和产品还不够成熟，而且这很反直觉。比如，我认识的几乎每家软件工程公司都在用类似 Cursor 的工具来辅助他们的软件工程师。目前大多数人把 Cursor 当作一种代码自动补全工具，尽管他们也有很多智能体式解决方案，而且还有很多智能体正在涌现。有趣的是，因为技术还不够成熟，它生成的代码经常有问题。所以很多人正在努力真正实现这些生产力提升。因为任何写过大量代码的工程师都会告诉你，查看、编辑和修复自己写的代码很容易。但审查别人的代码，尤其是找出别人代码中微妙的逻辑错误，其实非常难。这比自己编辑代码要难得多。所以如果编码智能体生成的代码经常出错，修复它可能需要大量的认知负担和时间。事实上，如果你最终给客户带来很多问题，你可能会生成很多功能，但实际上把机器搞乱了一点，得到的东西并不理想。

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's quite counterintuitive. For example, almost every software engineering firm I know uses something like Cursor to help 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 agentic agents coming as well. One of the interesting things is because the technology is sort of immature, the code it produces often has problems. So there are a lot of people approaching this to actually realize those productivity gains. Because as any engineer who's written a lot of code will tell you, it's pretty easy to look at, 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 much harder than editing code that you wrote yourself. So if the code produced by a coding agent is often incorrect, it can take a lot of 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 messing up the machine a little bit and having something that's not ideal.

我觉得有几项技术很有意思。首先，我认为现在有很多 AI 初创公司在做代码审查之类的事情。我认为智能体中的自我反思这个想法非常重要。让 AI 监督 AI 实际上非常有效。这样想：如果你制造一个 AI 智能体，它有 90% 的时间是正确的，那并不算太好。但制造另一个 AI 智能体来找出另外 10% 的错误有多难呢？那可能是一个可处理的问题。如果那个东西有 90% 的时间是正确的，为了论证起见，你可以把这些东西串联起来，得到 99% 时间正确的东西。所以这只是一个数学问题。事实证明，你可以制造一个生成代码的东西，再制造一个审查代码的东西，你本质上是在用算力来换取认知能力。你可以叠加更多层的认知、思考和推理，产生越来越稳健的结果。所以我对此非常兴奋。

There are a couple of techniques I think are interesting. First, I think there are 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. Think about it this way: if you produce an AI agent that's right 90% of the time, that's not that great. 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. It turns out you can make something to generate code, you can make something to review code, and you're essentially using compute for cognitive capacity. You can layer on more layers of cognition, thinking, and reasoning, and produce things increasingly robust. So I'm very excited about that.

但另一件事是根本原因分析。我们在 Sierra 有一位工程师，专门负责为我们的 Cursor 实例提供服务的模型上下文协议（MCP）服务器。我们的整个理念是，如果 Cursor 生成了错误的内容，与其直接修复，不如尝试找到根本原因，努力让 Cursor 下次能生成正确的代码。这本质上就是上下文工程：Cursor 缺少了哪些产生正确结果所必需的上下文？所以我认为，那些想在软件工程等部门获得生产力提升的人，如果现在就想看到收益，就需要停止等待模型神奇地自动工作。你真的必须建立系统性的根本原因分析，并说：我们如何对每一行糟糕的代码进行根本原因分析，并真正提供正确的上下文，构建正确的系统，让模型今天就能做到？随着时间的推移，这可能就不那么必要了，你需要的上下文工程也会减少。但你真的必须把这看作一个系统。我认为人们有点在等待模型神奇地变得更好。而我想说，那最终会发生。但如果你现在就想获得收益，你就得付出努力。我的意思是，这基本上就是应用型 AI 公司存在的原因。这项工作并不简单，但你可以做到。所以，对于使用像 Sierra 这样的平台的客户来说，是的，AI 智能体并不完美，但我们正在创建一个系统，让客户能够建立一个良性改进循环。如果你想从 65% 的自动化解决率提高到 75%，我们有大量的工具让 AI 帮你做到。识别改进的机会。找出人们为什么感到沮丧。我们可以为我们的智能体添加哪些新功能来提高解决率？你让 AI 替你找到大海捞针中的针。我认为这才是优化这些系统的真正方法。

The other thing though is root cause analysis. So we have an engineer at Sierra who exclusively focuses on the model context protocol server serving our Cursor instance. 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 that next time Cursor will produce the correct code. Essentially it's context engineering: what context did Cursor not have that would have been necessary to produce the right outcome? So I think people who are trying to get productivity gains in departments like software engineering need to stop waiting for the models to magically work if they want to see the gains now. You really have to create root cause analysis in systems and say, how do we 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 becomes less necessary, and you'll have less context engineering needed. But you really have to think of this as a system. And I think people are sort of waiting for the models to just magically get better. And I'm like, well, that will happen eventually. But if you want the gains now, you got to put in the work. I mean, that's essentially why applied AI companies exist. And the work is non-trivial, but you can do it. So, for customers using platforms like Sierra, yeah, AI agents aren't perfect, but we're creating a system that lets customers create a virtuous cycle of improvement. If you want to 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 at the top of the haystack on your behalf. And I think that's really the way to optimize these systems.

我从未听说过这种通过添加额外上下文来改进 Cursor 的技术。实际做法是什么？你是构建一个所有东西都通过它运行的 MCP 服务器，还是像添加 Cursor 规则那样？实际的方法是什么？

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 add Cursor rules? What's the actual approach there?

我可能有点超出我的专业范围了，但本质上就是 MCP，因为那是你向 Cursor 提供上下文的方式。我认为几乎总是这样，当一个模型做出糟糕的决策时，如果它是一个好模型，那就是缺乏上下文。所以你真的想找到你的特定产品和代码库与这些编码智能体和系统可用上下文之间的交集，并从根源上修复。这大概就是这里的原则。

I'm probably out of my depth here, but it's essentially MCP, because that's how you provide context to Cursor. And I think almost always when you have a model making a poor decision, if it's a good model, it's lack of context. So you really want to find the intersection of your particular product and codebase with the context available to these coding agents and systems, and fix it at the root. That's sort of the principle here.

明白了。这太酷了。我没听说过有人这么做。模型上下文协议（MCP）说得通。我们已经讨论了客户体验之外的生产力提升。为了给你一个机会分享你所构建的东西有多棒，你看到人们使用 Sierra 获得了哪些收益？

Got it. That is very cool. I hadn't heard of 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，我们的客户看到 50% 到 90% 的客户服务互动完全自动化，我认为这非常令人兴奋。我们服务非常广泛的客户。我们服务健康保险行业、医疗保健提供商领域、银行——你实际上可以通过我们一个客户在我们平台上构建的智能体来为你的房屋再融资——还有电信行业，如 DirecTV、SiriusXM，以及很多零售商，这真的很有趣。从 Wayfair 到像 Olkai 和 Chubby Shorts 这样的服装零售商。

Yeah, our customers see anywhere between 50 and 90% of their customer service interactions completely automated, which I think is really exciting. And we serve a 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. Everyone from Wayfair to clothing retailers like Olkai and Chubby Shorts.

真正了不起的是，我们的应用场景非常多样，从帮你注册——我们有一个智能体为大型约会应用提供客户支持——到帮你升级或降级 Sirius XM 套餐。其实挺有意思的，我们为从家庭报警系统到 Sonos 音箱，再到最近的 CT 扫描仪，提供技术支持，我觉得这太棒了。所以技术人员去修 CT 扫描仪时，可以和 AI 智能体聊天，让它指导整个流程。我们是这个领域的领导者。我们试图让世界上每家公司都能创建自己的智能体，并打上自己的品牌。我认为这会成为像网站或移动应用一样重要的数字触点。短期内，它确实能大幅降低运营客服团队的成本。而且了不起的是，客户满意度还很高。比如，那个 Weight Watchers 智能体的客户满意度是 4.6 分（满分 5 分），相当惊人。服务的有趣之处在于，人们往往是带着问题来的。所以当你有一个清晰的——我不知道你在机场有没有用过——那个智能体的满意度是 4.7 分（满分 5 分）。人们带着问题来，最后却感到满意。我认为这才是真正的机会所在。我们的整体愿景是走向这样一个世界：与客户的每一次互动都能即时、多语言、通过音频、通过聊天、数字化、通过电话，而且高度个性化。我觉得这非常令人兴奋。想想你和品牌有过的最美好时刻，就像那个认识你的店员。对我来说，就像杂货店的肉贩。我喜欢做饭，他认识我，我们会聊天。你能为一家拥有 1 亿客户的公司大规模实现这种体验吗？而且是以非常个性化的方式？我认为我们正处于实现这一目标的前沿。

What's really neat about it is a pretty diverse range of use cases, from helping you sign up for—we have an agent that helps with customer support in one of the big dating applications—to helping you upgrade or downgrade your Sirius XM plan. Actually, it's really funny. We do technical support for everything from home alarm systems to Sonos speakers, and more recently, CT scan machines, which I think is amazing. So technicians going in to fix a CT scan machine can chat with an AI agent to guide them through that process. We're the leader in the space. We're trying to enable every company in the world to create their agent with their brand at the top. I think that will become as meaningful a digital touchpoint as their website or mobile app. In the short term, it can really transform the costs of running a customer service team. And what's remarkable is doing so with really high customer satisfaction scores. For example, that Weight Watchers agent has a customer satisfaction score of 4.6 out of 5, which is pretty amazing. What's interesting about service is that often people are having a problem. So when you have a clear—I don't know if you use them at the airport—that agent has a satisfaction score of 4.7 out of 5. People come in with a problem and end up delighted. And I think that's really the opportunity here. Our whole vision is to move towards a world where every interaction with your customers can be instant, multilingual, over audio, over chat, digital, over the phone, and very personalized. And I think that's really exciting. If you think about the best moments you've had with a brand, it's like that store associate who knows you. For me, it's like the butcher at the grocery store. I love to cook. He knows me. We talk. Can you actually produce that at scale for a company with 100 million customers? And can you do it in a really personal way? I think we're really on the cusp of enabling that.

在我们进入激动人心的快问快答环节之前，我再问你一个问题。很多创始人在 AI 应用的市场推广上遇到困难。如今应用太多，产品太多，大型 B2B 公司的买家面临太多选择。显然，你们已经摸索出了一些门道。我想你的名气有帮助，投资者也有帮助。但关于如何成功推广 AI 产品，比如智能体类产品，你学到了什么？你觉得哪些经验对想做得更好的人有帮助？

Let me ask you one more question before we get to our very exciting lightning round. There's a lot of founders struggling with go-to-market in AI with their AI apps. There are 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 imagine your name helps, investors help. But what have you learned about 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?

我认为有一小部分经过验证有效的市场推广模式，关键是要为你的产品类别选择正确的模式。第一种是开发者主导型。Stripe 和 Twilio 是最早做到极致的典型代表。这种推广方式本质上是吸引个体工程师，通常在 CTO 的部门里，他们有责任也有相当大的自主权来选择解决方案。如果你的产品是平台型产品，这种方式就有效。但如果你的产品是帮助业务部门的，那就不适用，因为业务部门通常没有专门的工程团队，更不用说有自主权去下载新库或开始使用这样的网络服务了。如果你面向初创公司销售，这种方式尤其有效，因为初创公司的工程团队往往有相当大的自主权来选择服务，以解决创始人给出的问题。然后是产品驱动增长。这是一个宽泛的术语，显然每家公司的产品都很重要，但产品驱动增长更具体地指用户可以从网站注册，通常进入试用，通常可以用信用卡购买几个席位。这种方式适用于用户和买家是同一个人的情况。所以它几乎总是适用于小企业软件，因为个体经营者什么都自己做。所以如果你卖的是小企业软件，比如早期的 Shopify，还有很多类似的产品，你试图卖给小商户，那很好。但当买家和软件用户不同时，这种方式就不太奏效。所以我总是用费用报销软件这样的例子。这个软件的用户是单个员工，但买家通常是财务部门。所以让用户用信用卡注册购买没有意义，因为使用的人不是持卡人，这根本行不通。然后是直接销售。直接销售曾经——我不是说过时了——但如果我想想最好的直接销售公司，很多都源自 Oracle，但你可以想到 SAP、Oracle、ServiceNow、Salesforce，也许还有 Adobe，以及其他公司。这些公司以相对传统的销售方式向大型业务部门销售。我认为因为产品驱动增长变得非常流行，很多公司都采用这种方式，这很好。这种方式能产生优秀的产品。但如果产品驱动增长意味着你没有真正与软件的买家互动，你就不会增长。所以我最近看到很多 AI 公司，直接销售有点重新流行起来，因为我认为 AI 领域的很多机会恰好符合买家和用户不一定是同一个人的情况，这确实需要那种市场推广模式。我看到创业者容易犯错的地方是，他们选择市场推广模式时，没有想清楚购买这个软件的过程是什么，评估这个软件价值的过程是什么。我认为人们需要更多地从第一性原理出发，更深入地思考。坦率地说，我认为很多公司应该比现在更多地利用直接销售。尽管由于一些直接销售公司产品质量的名声有时是合理的，很多这种模式名声不太好。但我很高兴看到它在很多 AI 市场重新流行起来。

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 where famously Stripe and Twilio were probably two of the original ones that did this exceptionally. 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. 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, let alone the latitude to just 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, 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. So if you're selling small business software like Shopify in the early days, and there are a lot of other products like that, where you're trying to sell to small merchants, that's great. It doesn't work well when your buyer and the user of the software are different. So I always use the example of something like expense reporting software. The user of that software is an individual employee, but the buyer is often a finance department. So 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. Direct sales had gone—I don't say out of fashion—but if I think of the best direct sales companies, there's a lot of lineage from Oracle, but you think SAP, Oracle, ServiceNow, Salesforce, Adobe perhaps, and others as well. These were companies that sold into large lines of business in a relatively traditional sales motion. I think because product-led growth became very popular, 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, you're not going to grow. So I've actually seen more recently with a lot of AI companies, direct sales come a little bit back into fashion, because I think so many of the opportunities in AI 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 this software, what is the process of evaluating the value of this software. I think people just need to be much more first-principles about it and much more thoughtful about it. Candidly, I think a lot of companies should leverage direct sales more than they do. Even though, because of the sometimes justified reputation of the quality of products of some of these direct sales companies, a lot of it had gotten a bad name. I'm sort of thankful to see it coming back in a lot of the AI market.

我觉得这是很多创始人需要听到的信息，尤其是那些没有商业背景、对销售感到排斥的创始人。

I feel like this message is something a lot of founders need to hear, especially founders that aren't from a business background, that sales turns them off.

他们不认为自己会擅长销售。就是这种推动，嗯，这可能是你必须真正擅长的，这就是你获胜的方式，你不能只依赖产品，比如 Ruth。

They don't think they're going to be great at sales. Just this push of, uh, 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 Ruth.

是的。

Yeah.

Bret，你还有什么想分享的吗？最后的智慧结晶？在我们进入非常激动人心的快问快答环节之前，有什么想深入探讨的吗？

Bret, 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?

没有了，开始吧。

No. Go ahead.

好的，那我们开始吧。欢迎来到非常激动人心的快问快答环节。我有五个问题要问你。准备好了吗？

Okay, let's do it. Here we go. Welcome to our very exciting lightning round. I've got five questions for you. Are you ready?

准备好了，开始吧。

Yeah. Go ahead.

你发现自己最常推荐给别人的两三本书是什么？

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

我读的非虚构类书籍不多。但如果非要选一本和我们今天讨论话题相关的，那就是《与运气竞争》，这本书提出了“待完成工作”框架，我非常相信这个框架。我唯一的批评是，我觉得大多数这类商业书籍应该写成文章。所以也许买书，然后扔给 ChatGPT 让它给你总结。但还是要买书。这是克莱顿·克里斯坦森谈到的，但它确实是一个思考如何用产品传递价值的绝佳框架。而且我认为它确实影响了我……实际上，我确实推荐的一本书是《耐力》，讲的是沙克尔顿去南极的故事。书里有一半是他和船员们冻在船上，饿得要死，吃海豹肉。我这辈子没见过比这更好的关于坚韧的故事。这居然是真事，太了不起了。如果你是个正在经历困难的创业者，读读这本书。你会想：“好吧，情况可能更糟。”这也是一本很棒的书。这居然是真事，太了不起了。

I don't read a lot of non-fiction. 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 book that produced jobs to be done, which is a framework I really believe in. My only critique is I think most these sort of like business books should be like an article. So maybe buy the book and punch into ChatGPT 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 definitely influenced me 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 eating seal 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 kind of remarkable that it's a true story. And you know, if you're an entrepreneur going through a hard time, read that. You'll be like, 'Okay, it could be worse.' It's a great book, too. It's just remarkable that it's a true story.

而且他做得很好的一点是为加入的人设定期望，那个著名的……我不知道这是不是真的。这居然是真的，太了不起了。

And one thing he did a great job at is setting expectations for folks that joined that that famous. I don't know if that's true. It's like remarkable that's true.

哦，可能不是真的。

Oh, it might not be true.

我不知道，互联网谁知道呢？该死的深度伪造，甚至在那时候就有。好吧。

I don't know. The internet, who knows? God damn deep fakes even back then. Okay.

你最近有没有特别喜欢看的电影或电视剧？

Do you have a favorite recent movie or TV show that you've really enjoyed?

有，我最近没看什么新剧。我们刚和孩子们一起看了《盗梦空间》，他们很喜欢，也让我更加欣赏克里斯托弗·诺兰。所以，这电影太酷了。我喜欢这种电影，看完之后你能讨论两天。所以就是一部很棒的电影。我看到有人用，我想是 V3，制作自己的盗梦空间视频，世界互相折叠。

Yeah, I haven't gotten to 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. I love it's the type of movie when you watch your film and you can have conversations for two days afterwards about it. So just a great film. I saw someone using I think V3 to create their own inception videos where the world's wrapping on each other.

天哪，好吧。

Oh man. Okay.

你有没有最近发现并喜欢上的产品，或者你长期喜欢的产品？

Do you have a favorite product that you have recently discovered that you love or one you've loved for a long time?

我真的很喜欢 Cursor。我觉得它改变了……我喜欢创建软件，但我也对智能体感到兴奋。你知道，我一直很兴奋。我很高兴看到 OpenAI 的 Codex 和其他产品。所以我认为 Cursor 目前的形式会是一个过渡产品。我知道他们也在开发智能体。但我真的很享受把我热爱的东西，我毕生的热情，投入到这个 AI 工具中，看看它如何改变我创建软件的方式。所以我花了很多时间在这个产品上，因为它对我来说太核心了，而且它确实是一个精心打造的产品。

I'm really a big fan of Cursor. I think it's like changed. 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 OpenAI and others. So I think Cursor will be in its current form as a transition product. And I know that they're working on agents as well. But I've really enjoyed taking something I love and I've been my life's passion and really diving into this AI tool and seeing how it transforms how I create software. So I've just been 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.

我想这是第一次有人在这个回答中提到 Cursor，所以这可能是一个趋势的开始。Michael Trell 上过这个播客，他实际上在节目开头传达了和你非常相似的信息，关于代码的未来，代码之后是什么，以及这个在代码之上会有额外伪代码层的概念。

I think it's the first time someone's actually mentioned Cursor in this answer, so might be the beginning of a trend. Michael Trell 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 going to be this additional pseudo code layer on top of code.

是的。

Yeah.

和你的想法非常一致。

Very aligned with your thinking.

你有没有一个经常想起并觉得在工作和生活中很有用的座右铭？

Do you have a favorite life motto that you often come back to and find useful in work or in life?

预测未来的最好方式就是创造未来。我想这归功于施乐帕克研究中心的艾伦·凯，他发明了今天我们计算中使用的许多核心抽象。这就是为什么我热爱……我是一名创业者。这就是为什么我喜欢创造东西。所以这绝对是我的座右铭。

The best way to predict the future is to invent it. Which I think I attribute to Alan Kay of Xerox PARC, who 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's definitely like a life motto for me.

我觉得很多人这么说。我觉得你实际上已经做了很多次。你正在践行这个座右铭。

I feel like many people say this. I feel like you've actually done this so many times. You're living this motto.

最后一个问题。我们谈到你发明了点赞按钮、添加好友信息流。除了“赞”之外，有没有想过其他叫法？是显然就叫“赞”还是有其他想法？

Final question. We talked about you inventing the like button, add friend feed. Were there other thoughts of what they would call it other than like? Was it just like obviously like or was there other thinking there?

背景是这样的。那是在表情符号出现之前。如果你看 FriendFeed 帖子下的评论，至少 70% 是“酷”或“哇”或“耶”或“不错”。FriendFeed 的主要用途之一就是讨论事情。所以你会有一个帖子，下面有相当健康的讨论。与 Twitter 等相比，这是一个进行这些讨论的好地方。所以我们试图解决的产品问题是去掉所有单词回答，让讨论实际上是真正的评论，而不是你读过东西的确认。所以最初的框架是“一键评论”。我们就是这样想的。所以我做的第一个版本有一个心形。虽然她否认记得这件事，但有一位 Anna Yang，现在叫 Anna Mohler，她曾在这家公司工作，她讨厌这个设计。她说如果我看到每个帖子都有心形，我会吐的。这太过分了，你知道吗？而且有趣的是，我们模拟了如果是一篇关于悲剧的文章之类的情况。心形就不合适了，这实际上后来被证明很难翻译。“赞”是一个更中性的情感，这就是为什么它很难翻译，因为它很微妙。所以我们最终就这样了。我们从心形开始，我不知道我们是否听过“爱”这个词，但我们肯定是从图标开始的，然后“赞”感觉既积极又在积极范围内尽可能中性，这样它就能适用于更复杂的故事，但这一切都是因为我们需要一个一键评论。这就是这个概念来源。

The context. So this was before emoji. So if you read the comments on FriendFeed posts, at least 70% of them were 'cool' or 'wow' or 'yeah' or 'neat'. And one of the principal uses of FriendFeed was to have discussions about things. So you'd have a post and then a pretty wholesome 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. So the original framing was one-click comment. That was how we thought about it. And so the first version that I made had a heart. And though she denies remembering this, but there's an Anna Yang, now Anna Mohler, who has worked at the company, she hated it. She said like if I look at a like hearts on every post, I'm going to vomit. Like it's just too much, you know? And it also was interesting like we were simulating if it was like an article about a tragedy or something. A heart was just not the right thing, which actually turned out to be really hard to translate. 'Like' was just a much more neutral sentiment and that's why it was hard to translate because it was subtle. And so that's how we ended up with this. We started with a heart and I don't know if we ever heard the word 'love', but we definitely start off with the iconography and 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，我以前从没听过这个故事。这让我现在想到了 LinkedIn。他们基本上也在试图解决同样的问题。他们有所有这些自动回复式的胶囊标签。我觉得人们不喜欢……

Wow. I've never heard the 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 kind of pill tag things. I don't think people like...

有很多功能。

Have a lot of features.

这么多，这么多 AI 功能。Bret，这太棒了。这是我的荣幸。非常感谢你来到这个播客。最后两个问题。

So many so many AI features. Bret, this was incredible. This was an honor. I so appreciate you coming on this podcast. Two final questions.

如果大家想联系你，或者想看看是否愿意来 Sierra 工作，在网上哪里可以找到你？另外，听众怎样才能帮到你？

Where can folks find you online if they want to reach out? Maybe go see if they want to work at Sierra. And how can listeners be useful to you?

如果你想要一个 AI 智能体来帮忙做客服，可以去 s.ai。如果你想申请加入我们，可以访问 sierra.ai/careers。我们在旧金山、纽约、亚特兰大和伦敦都有办公室，而且每个部门都在大力招人。所以如果你感兴趣，欢迎联系我们。

If you want an AI agent to help with customer service, go to s.ai. If you want to apply here, sierra.ai/careers. 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.

那听众怎样才能帮到你？是试用 Sierra 吗？还有其他方面吗？

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

是的，试用 Sierra。我是个单一议题选民。

Yeah, try out Sierra. I'm a single issue voter.

紧扣主题，我喜欢。

Stay on message. I love it.

是的。

Yeah.

Brad，非常感谢你来做客。

Brad, 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。如果你觉得这期节目有价值，可以在 Apple Podcasts、Spotify 或你喜欢的播客应用上订阅本节目。另外，也请考虑给我们打个分或留个评论，这真的能帮助其他听众发现这个播客。你可以在 lennispodcast.com 找到所有往期节目或了解更多节目信息。下期见。

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 lennispodcast.com. See you in the next episode.