微软首席产品官：如果你没有用AI做原型设计，那你就做错了 | Aparna Chennapragada

我有一个很俗气的Chrome扩展。每次我打开新标签页时，它就会显示：如何利用AI来完成你即将要做的事情？

I have a cheesy Chrome extension. Literally, whenever I open a new tab, it just says, how can you use AI to do what you're going to do right now?

你认为未来的产品开发会有哪些不同？

How do you see the future of product development being different?

如果你不通过原型设计和构建来验证你想打造的产品，那你就做错了。将核心领域的专业性和审美判断力置于中心位置变得更为重要，否则你只会得到一个拼凑而成的怪物产品。

If you're not prototyping and building to see what you want to build, I think you're doing it wrong. It becomes even more important to have the territorial and taste making at the heart of it because otherwise, you just have a Frankenstein product.

你教过我一个缩写词NLX，那是什么意思？

There's this acronym that you taught me, NLX. What is that?

自然语言交互界面。NLX就是新的用户体验(UX)。我常听产品开发者说，有了AI后模型主导产品。但这并不意味着不需要设计。

Natural language interface. NLX is the new UX. Often I hear product builders say, oh, yeah. With AI, like, model leads the products. That doesn't mean it's not designed.

你我现在进行的对话是播客形式。我在微软还会有另一场对话，那就是会议。对话同样有语法规则，有其结构体系。

You and I are having a conversation. It's a podcast. I'll have another conversation at Microsoft, and that's a meeting. Conversations also have grammars. They have structures.

它们包含隐形的用户界面元素。作为交互媒介的自然语言，其新原则、新结构是什么？

They have UI elements. They're invisible. What are the new principles, new constructs in natural language as a interface?

我刚看到Cursor在两年内实现了3亿美元的年度经常性收入。有趣的是，你们在这个AI编程工具领域占据了非常有利的位置。你们曾说Copilot是世界上首个此类工具。遥遥领先于所有人，后来发生了什么？

I just saw that Cursor hit 300,000,000 ARR in two years. Interestingly, you guys were very well positioned to do really well in this AI coding tool space. You guys said Copilot, the first tool in the world at this stuff. So ahead of everyone, what happened?

我想说

I would say

今天我的嘉宾是Aparna Shinapraghada。Aparna现任微软首席产品官，负责生产力工具的AI产品战略及智能体研发工作。她曾担任Robinhood首席产品官、谷歌副总裁，主导过Google Lens、搜索、购物、增强现实AI助手等多个项目，更早时是Akamai的资深工程主管，并曾任eBay和Capital One董事。在我们的对话中，我们聊到：做B2B业务就像尚格·云顿在两辆移动卡车上劈叉、她如何让团队以未来视角运作来构建趋势所需、为何人们仍需学习编程、产品经理岗位不会消失的原因、自然语言交互如何成为新用户体验等等话题。

Today, my guest is Aparna Shinapraghada. Aparna is chief product officer at Microsoft, where she oversees AI product strategy for their productivity tools and their work on agents. Previously, she was chief product officer at Robinhood, vice president at Google, where she worked on Google Lens, search, shopping, augmented reality AI assistant, and a lot more. She was also a longtime engineering leader at Akamai and on the board of eBay and Capital One. In our conversation, we chat about how working in b two b is like being Jean Claude Van Damme doing the splits across two moving trucks, how she's operationalizing her team living in the future so that they're building towards where things are going, why people still need to learn to code, why the PM role isn't going anywhere, why NLX is the new UX, and so much more.

若喜欢本期播客，别忘了在您常用的播客应用或YouTube订阅关注。成为我年度通讯订阅用户，还可免费获得Linear、Superhuman、Notion、Perplexity和Granola等产品的一年使用权。访问lenny'snewsletter.com点击bundle查看详情。现在请欣赏与Shena Prajada的对话。本期节目由Epo赞助播出。

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 products, including Linear, Superhuman, Notion, Perplexity, and Granola. Check it out at lenny'snewsletter.com and click bundle. With that, I bring you a part of Shena Prajada. This episode is brought to you by Epo.

Epo是由Airbnb和Snowflake前团队打造的下一代AB测试与功能管理平台，服务于现代增长团队。Twitch、Miro、ClickUp和DraftKings等公司都依赖EPO进行实验。实验对推动增长和评估新功能表现愈发关键，而EPO能加速实验进程，并提供其他商业工具无法实现的深度分析。我在Airbnb时最爱的就是自主设置实验、排查问题、分析性能的一站式实验平台。

Epo is a next generation a b testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams. Companies like Twitch, Miro, ClickUp, and DraftKings rely on EPO to power their experiments. Experimentation is increasingly essential for driving growth and for understanding the performance of new features. And EPO helps you increase experimentation velocity while unlocking rigorous deep analysis in a way that no other commercial tool does. When I was at Airbnb, one of the things that I loved most was our experimentation platform, where I could set up experiments easily, troubleshoot issues, analyze and performance all on my own.

EPO不仅具备这些功能，还能通过先进统计方法缩短实验周期数周，提供直观的性能分析界面，以及开箱即用的报告系统，避免冗长的分析流程。它还能便捷地分享实验洞察，激发AB测试飞轮的新思路。EPO支持产品、增长、机器学习、变现和邮件营销等全场景实验。立即访问getepo.com/leni，提升十倍实验速度。网址是getepp0.com/leni。

EPO does all that and more with advanced statistical methods that can help you shave weeks off experiment time, an accessible UI for diving deeper into performance, and out of the box reporting that helps you avoid annoying, prolonged analytic cycles. Also makes it easy for you to share experiment insights with your team, sparking new ideas for the AB testing flywheel. Epo powers experimentation across every use case, including product, growth, machine learning, monetization, and email marketing. Check out EPO at getepo.com/leni and 10 x your experiment velocity. That's getepp0.com/leni.

本期节目由Pragmatic Institute赞助，他们是产品专业知识的权威机构。Pragmatic Institute通过专为实战设计的课程、研讨会和认证，帮助产品人士将想法转化为影响力。三十多年来，他们已为谷歌、微软和Salesforce等公司的25万多名产品领导者提供培训，传授构建规模化市场赢家的实用策略。Pragmatic的全职讲师均拥有25年以上实战领导经验，教授经证实能带来真实成效的策略。学习收获远不止于知识本身。

This episode is brought to you by Pragmatic Institute, the trusted leader in product expertise. Pragmatic Institute helps product professionals turn ideas into impact through proven courses, workshops, and certifications designed for real world success. For over thirty years, they've trained more than 250,000 product leaders at companies like Google, Microsoft, and Salesforce, equipping them with practical strategies to build and scale market winning products. Pragmatic's full time instructors each bring over twenty five years of hands on leadership experience, teaching strategies proven to deliver real world results. And it's not just about what you learn.

这也关乎你与谁共同学习。完成课程后，你将加入一个由40多位产品专业人士组成的活跃社群。你将参与有意义的对话，与同侪和导师合作，并直接获得导师指导以优化策略、紧跟趋势。使用优惠码Lenny20可在pragmaticinstitute.com/lenny享受8折优惠。Aparna，非常感谢你的到来，欢迎参加播客。

It's also about who you learn it with. Completing a course connects you to an active community of over 40 product professionals. You'll engage in meaningful conversations, collaborate with peers and mentors, and gain direct instructor access to refine your strategies and stay ahead of trends. Get 20% off with code Lenny 20 at pragmaticinstitute.com/lenny. Aparna, thank you so much for being here, and welcome to the podcast.

谢谢你，Lenny。感谢邀请我。

Thank you, Lenny. Thanks for having me.

当我向许多与你共事的人询问应该问你什么、了解你哪些方面时，一个反复被提及的点是——我想大多数人可能不知道——你对单口喜剧非常热衷，甚至带着半专业的态度。你究竟有多认真对待这件事？它在你生活中占多大比重？最重要的是，这对你打造更好的产品有何帮助？

When I asked a lot of people that work with you what I should ask you about and what's that what I should know about you, something that came up again and again is something that I think most people don't know about you, which is that you're you're big into stand up comedy, and you take it semi seriously. Just how serious are you about this? How how much of your life is this? And most importantly, how does this help you build better products?

很难说我对这种搞笑事业有多认真，但我确实会观看和表演单口喜剧。我参加开放麦活动，也做过几场演出。哇，我现在正在构思一个关于AI的段子——毫不意外地，是关于硅谷的AI和科技话题。

It's hard to say I'm serious about, like, a funny business, but I I do I do watch and do stand up comedy. I do open mics. I've done a few shows. Wow. I have one set brewing that is around AI, unsurprisingly AI and tech in Silicon Valley.

你知道，这对我来说是个意外发现。我一直是《周六夜现场》的粉丝，也喜欢喜剧。但我去开放麦是因为我儿子唱歌，他去参加歌唱开放麦时说：'妈妈，你也该试试'。我就想，好吧，去体验下。结果发现自己不仅乐在其中，还挺擅长的。

You know, it's really interesting for me. This was an accidental discovery like I'd always been an SNL fan and like just comedy fan. But I went to an open mic because, you know, my son sings and he went to the open mic for singing and he's like, mom, you should go do this. And I was like, oh, let me go give it a try. And I found that I enjoyed it and was good at it.

回到你关于打造更好产品的问题，我觉得两者都需要PMF——产品市场契合度，或者叫'笑点市场契合度'。但实际有几点确实很有用：在开放麦测试段子时，你会经历紧凑的迭代周期并获得实时反馈。开放麦就是真实的现场实验——抛出内容后立刻获得清晰的微观用户反馈，有时甚至是尖锐批评。

To your question though about building better products, I'd say both have PMF. I mean, market fit, punchline market fit. But actually there are a couple of things that I do find really powerful and useful because in open mics or even when you're testing these things, it's a very tight cycle of iteration and you get live. Open mics are the real live experiments. You put something out there, you get very clear micro feedback from users and then you get tough feedback sometimes.

我认为作为产品构建者，这其实是项重要技能。有时候你推出的初版产品虽有宏大愿景，但还不够完善。正如Reid Hoffman所说：'如果你不为初版产品感到尴尬，说明你动作太慢了。'缩短这个差距的过程，恰恰能培养韧性。

I think as product builders, that's actually one of the great skills to have, which is, yeah, you you sometimes launch stuff that you know have a fantastic vision, but the first version is not quite there. Right? And Reid Hoffman says this, hey, if you don't launch the first version and are not embarrassed, you're doing it too slow. Just that gap and closing that, it's good resilience.

是啊，我从未见过这两者之间的珊瑚区域。我没想到你实际上在做节目，还在片场工作。我本来没打算让你讲笑话，但既然你在做一个关于AI的完整项目，能分享一下片场的趣事吗？

Yeah. I never saw these coral areas between these two things. I didn't realize you actually did, like, shows and you're working on a set. I wasn't gonna ask you for a joke, but if you're working on on a whole thing about AI, is there something that you can share from that set?

或许可以分享一个笑话：人们总把AI聊天产品想象成女性，因为你看不透它的运作原理，像个黑匣子，猜不透它在想什么。我们围绕这个梗做了整套设计。不过反过来说，它们可能更像男性——毕竟经常产生幻觉。

One joke I'd maybe share is people think about these AI chat products as women because, you know, you don't know what's going on. It's a black box. And you don't know what it's what what they're thinking. There's like an entire set around that. But obviously, on the flip side too that, you know, they're probably more like men in the sense that they hallucinate a lot.

它们目前还不太可靠。我都不太敢笑这个...

They're they kind of are not yet reliable. I'm afraid to laugh at this a

有点意思。太棒了，好吧。

little bit. This is great. Okay.

而且即使不知道答案，它们也会编造内容，还表现得特别自信。

And they even when they don't know the answer, they make up stuff. They're very confident.

真不错。顺便问下，这个节目会在哪里播出？

This is good. Where are we gonna be seeing the show, by the way?

22台。

22.

好的，这太棒了。好，让我们重新严肃起来。你职业生涯的大部分时间都在许多消费互联网公司工作。

Okay. This is great. Okay. Let's get serious again. So you worked at most of your career at a lot of consumer Internet companies.

你在谷歌、Robinhood工作过，是eBay和Capital One的董事会成员，现在又在微软。我很好奇，在微软这样的公司工作与在消费互联网公司打造产品最大的不同是什么？

You worked at Google, Robinhood. You're on the board of eBay. You're on the board of Capital One. Now you're at Microsoft. I'm curious just what is most different about working at a company like Microsoft and building product at a company like Microsoft?

我认为，从理智上讲，我知道企业领域——尤其是微软我最关注的领域——专注于企业和生产力，通过AI改变公司。对我来说，有两点显得非常不同。其一，实际上我前几天刚发帖谈到，在消费领域，你可能会觉得‘哦，我们有一套让产品或功能运作并令人愉悦的固定模式’。但在企业领域，几乎每次你以为掌握了一个用例，实际上都需要确保功能运行良好且有治理机制。比如分享文档链接这样简单的事，既要便捷无阻，又要安全可靠，具备可审计性。

I think, intellectually, I knew that enterprise, particularly the area that I look at most at Microsoft is focused on enterprise and productivity and transforming companies through AI. To me, think two things really strike as very different. One, in fact, I just posted about this the other day saying, in consumer, you're kind of like, oh, we have a playbook for make the product work or make the feature work and make it delightful. But I think in the enterprise, almost have every time you think you have one use case, really do, which is how do you make sure that the feature works well and there's governance of the feature. If you think about even something as simple as sharing a link to a document, you want it to be easy, frictionless, but at the same time you want that to be secure and safe and being able to have auditability and all of those things.

我常发现，从消费领域转向企业领域时，容易陷入两种陷阱：要么忽视这些要求，只关注单方面；要么过度束缚用户体验。所以这其中需要艺术、科学、微妙平衡和新的方法论——这是我的一大领悟。

And often I find that when you go from consumer to enterprise, you fall into a trap of either disregarding that and say, oh, you know, we'll just focus on one side of the house or kind of overly crippling the user experience. Right? And kind of leaning on the other side. So I think the there's an art and science and nuance and playbook there too. So that's one big learning for me.

另一个领悟，尤其是在AI时代，让我想起二月份有个著名的范·达姆预告片——两辆公交车劈叉的名场面。对，就是那个劈叉镜头。

The other learning and especially in the AI era for me has been about this you know, I think there's a famous trailer from the February on Van Dam on these, like, two trailer two buses. Mhmm. Like, doing the splits. Yeah. Doing the splits.

没错。我感觉许多公司，包括科技公司，尤其是我接触的企业，都处于这两种模式：一方面，这是我们经历过的最压缩的技术周期，一切以周月而非年十年计（想想移动、云和互联网时代）；另一方面，人类的生产力习惯改变很难，企业变革管理同样艰难——不能操之过急。

Exactly. And I feel like a lot of the the companies, including the tech companies, but certainly the enterprises that I talked to, are in these two modes where on one hand, this is the most compressed tech cycle that we've ever experienced. Right? It's all in the order of weeks and months versus years and decades if you think about like mobile and cloud and Internet. And there's just like so much happening, the intelligence overhang.

另一方面，人类习惯和生产力变革的惰性也很强。改变很难，企业内部的变革管理同样具有挑战性。对吧？这方面不能太过冒进。

On the other hand, there's also like humans and habits that productivity habits change. It's hard to change. And change management through the company is also hard. Right? You don't want to kind of be rash on that.

所以，就像你知道的，未来分布不均，即便在公司内部也是如此。

So, it's like, you know, the future is unevenly distributed, but even within the companies.

关于范·达姆所面临的治理、采纳和改变行为等问题的第二个方面，你有没有学到什么方法能推动这些进程？

On the second bucket of this other this the the bust that Van Damme's riding on of governance and and I don't know, adoption and changing behavior and stuff. Is there something you've learned about how to get past that, help that along more?

关键是不该阻碍那些早期采用者。我认为这是另一个教训。实际上，这也是我最近与人合作的原因之一——我们采取双轨制：长期变革管理以可信方式推进，同时开展名为‘前沿计划’的项目，推出尖端实验性功能。我们刚打造出全球首个为工作设计的深度研究智能体，专为工作场景训练。当然，它还存在各种不完善之处。

The thing not to do is hold back folks who are early adopters. I think that's the other one learning. In fact, I think that's one of the reasons why recently have been working with folks to say, we have both, which is the longer term change management, being able to do it in a trusted way, at the same time do this program we're calling Frontier Program and roll out cutting edge experimental features. Just built this world's first agent, deep research agent made for work, right, post trained for work. And, of course, it has all sorts of edges, rough edges.

但如果企业内外有早期采用者，我们如何在不要求整个公司彻底转型、发展全新能力的前提下，将这些工具交到他们手中？

But if there are early adopters in an enterprise or outside, how can we kind of put that in the hands of those folks without kind of insisting that all of the all of the company be completely different developing different muscles.

你提到的这个‘前沿计划’，我想多了解些。核心理念是什么？是让人们在这种未来主义环境中工作吗？具体如何运作？

This program, Franzir, you're talking about, I wanted to spend a little time on it. So, what is the idea? The idea here is like people are working in this futuristic environment. How does that actually work?

没错，理念正是如此——我想将‘活在未来一年’的个人座右铭制度化、可操作化。想象成立一家‘前沿咨询集团’或‘前沿公司’，如果你真的生活在拥有所有AI工具和DAP高级深度研究智能的环境中，你会提出什么问题？从事什么工作？如何改变日常工作的方式？

Yeah, I think the idea is exactly this, which is like, I want to institutionalize and operationalize my personal motto of living one year in the future and say, what does this imagine a company or a setup Frontier Consulting Group or Frontier Inc, right? And if you did live in that environment where you had all the AI tools and really advanced deep research intelligence on DAP, what are the kinds of questions you'd be asking? What what's the kind of work you'd be doing? How would you change? How you're going about your workday.

这就是前提。你会问：这如何改变个人？但长远来看，我们还想思考前沿团队的模样。我们常讨论前沿实验室和模型，模型层非常关键——毕竟正是它赋能了所有这些产品构建。

So that's the premise. And you'd say, hey, how does it change an individual? But also down the lane, we wanna think about what does a frontier team look like. We talk a lot about frontier labs and models. I think models layer is amazing and obviously, like, you know, that's what empowers all these product building to happen.

但我想推动大家思考：前沿产品应该是什么样？更重要的是，前沿工作方式应该是怎样的？比如，一个只有三人却拥有海量计算资源和AI工具的团队会是什么形态？

But I wanna push us to think about what does a Frontier product look like? And more importantly, how does a Frontier way of working? Right? Like, what does a team with three people and tons of, like, compute and AI tools look like?

那具体如何运作呢？微软内部是否有个专门团队，职责就是运用我们所有最新工具来开发产品？

So how exactly does this work? There's, like, a team within Microsoft that's like, your job is to use all of our latest tools and build product using that.

目前架构就是这样。这个架构刚建立几周。但期间我们做了件事——实际上在外部成立了个模拟公司，邀请想接触尖端科研项目和深度研究智能体的人来参与。

Is that That how is the setup. We are just a few weeks into that setup. But meanwhile, what we've done is like we've actually set up in like a comp external, like, a fake company and said, hey. If you are somebody who wants to come play with some of the cutting edge science projects and deep research agents and, you know, agents at work, come party here.

哇，才几周时间。看来最终效果还有待观察。

Wow. Okay. And it's only a few weeks in. Okay. So TBD, how it all goes.

没错。关键是要看到，传统模式下我们总是习惯跨公司、跨行业以宏观方式推进产品发布。

Yeah. Yeah. And again, like these are micro Let's see. The meta point here, right, also is that, you know, in the traditional way, we've kind of always thought about across the companies, across industries, really thinking about rollouts in these macro ways. Right?

先开发产品再逐步推广，经过全面测试才正式上市。这很重要，毕竟医药、法律等行业都依赖这些产品。但在AI迭代加速的今天，如何让人们提前体验未来一年的技术？

You build something and you kind of like roll it out. You have a general availability for and then you take the time. And that's really important too because again, like we're talking about pharma companies, legal companies relying on this. So we do want to have that. But at the same time, given the compressed cycles of AI, how do we start to have people experience what's the one year in the future?

我们可以从几个维度展开讨论：产品链开发的变化、工程实践的变化，还有你重点研究的智能体领域。

Let's follow this thread in a few different directions. There's, like, how product chain development changes. There's how engineering changes. There's also just agents. I know you're spending a lot of time in agents.

感觉现在如果不是在做智能体相关的工作，都不好意思说自己是AI公司了

Feels like you're not an AI company these days if you're not working on agents

是啊。

Yeah.

或者正在开发一个智能体。

Or building an agent.

曼尼，我们搞错了。我们本应该强迫你在对话中尽量晚点提到智能体这个词的。

Manny, we're doing this wrong. We didn't force you didn't use the word agents, like, so far into the conversation.

我我尽力拖延这个话题了。嗯哼。在旧金山，每次聊天都差不多——我平均能坚持三分钟不谈AI，我敢打赌。

I I try hard to push it out as far as I can. Uh-huh. It's like it's like every conversation in San Francisco, it's just like how long until I start talking about AI? Yeah. It's like three minutes average, I bet.

天啊好吧。关于智能体，我知道你在微软主导这方面工作。很多人都在困惑这到底意味着什么？会带来哪些改变？

Oh, man. Okay. So so with agents, I know that you're leading a lot of this work at Microsoft. And a lot of people are wondering what the hell what the hell does this mean? What what is gonna change?

给我们稍微透露下，在你看来智能体普及后的世界会有何不同？

Give us just a glimpse into how you see the world being different in a world of agents being around more.

但事情有短期和长期之分，对吧？现在有很多关于未来终极形态的过度亢奋的讨论。而我更倾向于从实际产品构建的角度来看待这个问题，明白吗？

But there's a short term and there's a long term. Right? There's a lot of, you know, hyperventilated talk about kind of the the eventual future and all of that. I take a much more practical product building lens on this. Right?

归根结底，我认为这些都是工具。没错吧？底层逻辑上，存在随机模型与确定性编程模型的区别——能看出来我是计算机科学家吧——这种世界观确实塑造了我的思考方式。

And I think about these at the end of the day, they're tools. Right? Yes, underneath it, there's stochastic models versus very deterministic programming models. You can tell I'm a computer scientist. The way that that worldview definitely shapes how I think about this.

在我看来，短期来看存在一个演进过程。我们经历过应用时代，现在正式进入了辅助时代，就像人类驾驶的副驾驶概念。我认为人类仍处于主导地位，但获得AI大量辅助。所以我从自主性、委托度和智能水平这几个维度来思考。

To me, short term is there's an evolution. We had apps. And now I think we are formally in the assistance era where there's like human driving the that's what we think of as copilot, right? Like, I think the human driving kind of in the driver's seat, but having a lot of assistance from AI. So, I think of this as then you look at the dimension of almost like autonomy and delegation and intelligence.

以智能为例，当深度推理突破发生时，你自然可以委托更多任务给智能体。我认为存在这样一个维度：智能体是能独立运行任务的软件进程，不再需要手把手指导。你可以直接说：这是我的目标。

As the intelligence, for example, when deep reasoning unlock happened, of course, then you could say you can delegate more the agent. So, think to me, I think there's one dimension where you say, hey, agents are somewhat independent software processes that can run tasks. You're not just thinking about hand hold hand holding and fine motor stuff. You're saying, hey. Here's my goal.

去实现它。举个例子，我们正在开发一个研究型智能体。昨晚我说：

Go make this happen. Like, I'll give you an example. Right? So we're working on this researcher agent for work. And last night, I said, hey.

我即将与领导团队召开重要会议，需要展示这些框架和路线图。去查参会人员的背景，分析他们对这个话题的看法，然后帮我构思最佳说服策略。

You know, I'm really I I have an important meeting coming up with the leadership team. I really wanna present these frameworks here, and this is the road map here. Go back and look at all the people that are in the meeting. What are their views on this topic? And kind of come up with how I should be thinking about the right persuasion pitch here.

神奇之处不仅在于节省时间——通常人们认为AI只是总结文档或提升效率——而在于它能激活我原本没有的神经突触，提供全新见解，甚至赋予我超能力。这就是AI的自然演进方向。

And what's magical about this is not just that it's saving time. Typically, think about the SoFAR AI as summarizing a document or saving time. Right? This is like firing synapses that I didn't quite have and actually giving me new insights and giving me, dare I say, superpowers. So, that's a natural evolution of AI, I would say.

所以，当我思考智能体时，我想到三点。一是自主性和独立性程度的提升，你可以委托它执行越来越复杂的任务。第二点我认为是复杂性。对吧？这不是一次性的简单操作。

So, when I think about agents, I think about three things. One is an increasing level of autonomy and kind of independence that you can delegate higher and higher order task. Second thing I think of it is complexity. Right? So it's not just a one shot.

比如，'生成这张图片'或'完成这个任务'或'总结文档'这类简单指令。而是'根据我对增强现实应用的构想，帮我搭建这个原型'。对吧？这是个复杂任务。第三点我要说的是异步性。

Hey, create this image or do this thing or summarize the document. It's, you know, build me this prototype that expresses my idea of a an augmented reality app. Right? It's a complex task. And then the third thing I would say is asynchronous.

它能在你休息时持续工作。对吧？我认为这类工具的另一个重要特性就是——你不需要一直守在它面前。

It works when you're not working. Right? I think that's the other big thing about these things that you're not you don't have to sit in front of it.

这基本上回答了'什么是智能体'这个问题，就是这三个要点。能再重复一下是哪三点吗？

This is answers the question of what is an agent essentially, these three bullet points. So it's what are the three again?

当我思考智能体时，我始终围绕这三个维度。首先自主性，这是个光谱概念而非二元选择，关键在于我能委托它处理哪些事务。

When I think about agents, I think about these three things. Right? So one, it's autonomy, like being and it's a it's a spectrum. It's not a zero one. It's how do I actually delegate things that it can do.

第二点是复杂性。对吧？不是简单的'总结这份文档'、'生成这张图片'，而是'帮我开发这个原型'或'协助我完美完成这次会议'这样的需求。

Second, I think of as complexity. Right? It's not a one shot. Hey, summarize this document, generate this image, but it's, you know, build me this prototype or help me knock this meeting out of the park. Right?

第三点我认为是更自然的交互方式。这不仅指聊天界面，可能还包括直接与智能体进行会议沟通，口头讨论所有细节，或实时指出需要调整的部分。所以自主性、复杂性和自然交互这三个维度，至少会成为塑造优秀智能体的产品原则。

And then the third one I think of is, it's a much more natural interaction. That doesn't just mean chat, but it may be actually jumping on a meeting with the agent and being able to like talk through all of it or point it to things that I wanted done differently. So I think all three things, the autonomy, complexity, and the natural interaction are at least product principles that will shape really good ones, good agents.

这真的很有帮助。关于这类智能体，你在我们播客前聊天时教过我一个缩写词NLX。那是什么？它如何与智能体相关联？为什么人们对此思考得不够充分？

That is really helpful. Along this line of agents, there's this acronym that you taught me as we were chatting ahead of this podcast, NLX. What is that and how does that relate to agents and why are people not thinking about this enough?

哦，这是我最近痴迷的话题之一。自然语言界面。NLX就是新的用户体验。我认为关键在于：传统上我们非常刻意地考虑图形用户界面(GUI)，因为图形界面并非自然存在，必须被明确设计出来。

Oh, that's one of my Roman Empires these days. The natural language interface. NLX is the new UX. I think here's the deal. To me, I think traditionally we've thought very consciously about GUI because the graphical interfaces are not something natural and so they have had to be explicitly designed.

但它们是僵化的界面，对吧？而对话式界面和自然语言则更具弹性。这并不意味着不需要设计。我常听到产品构建者说'有了AI，模型直接决定产品形态'。

But they're rigid interfaces, right? What we have with conversational interface and natural language is it's a much more elastic, right? That doesn't mean it's not designed. People had often I hear product builders say, oh, yeah. With AI, like, model leads to the product.

就像我们现在进行播客对话，我在微软还有另一场会议对话。对话同样存在语法规则、结构框架和隐形界面元素。

So it's just you chat with it. You and I are having a conversation. It's a podcast. I'll have another conversation at Microsoft, and that's a meeting. So conversations also have grammars, they have structures, they have UI elements, they're invisible.

最让我兴奋的是探索自然语言作为界面的新原则和新构件。举例来说，提示词(prompt)本身就是新构件——就像下拉菜单曾是革命性元素。对于智能体，计划展示(editable plan)和过程可视化(showing the work)正在成为重要范式。

And so one of the things that I see and I'm really excited about is what are the new principles, new constructs in natural language as an interface? I'll give you a few examples. Right? And actually, like a lot of startups as well as big companies are really experimenting with this stuff. One is if you think about it, prompt itself is a new construct.

当智能体返回可编辑的执行计划时，这就是创新交互形式。另一个关键要素是工作进度可视化——你在Copilot、ChatGPT和DeepSeek等产品中都能看到这种'思维外显'设计。

And that's a new way, that's a new UI element, just like a dropdown was or a menu was. But others that are emerging, especially for agents, I think are plans. So when you give a high level goal, what we are seeing is that when the agent comes back with a plan, preferably an editable plan, that's a new construct. The other one that's that I think about a lot is showing the work. Right?

进度展示。你在不同产品中都能看到这点，对吧？无论是Copilot、ChatGPT还是DeepSeek，这种'出声思考'的设计理念随处可见。

Progress. You see this with different products. Right? You see with the Copilot. You see with ChatGPT, DeepSeek, this idea of thinking aloud.

这某种程度上展示了工作内容。但你具体做到什么程度呢？如果过于冗长，感觉就像在运行定时任务和脚本。但如果只有两次演示，我又不确定方向是否正确，目前还缺乏信心。所以这些都是需要考虑的新元素。

And it's kind of showing the work. But how much do you do it? If it's too verbose, it feels like I'm running some cron job and scripts. But if it's two tours, then I don't know if it's going in the right path, and I don't have the confidence yet. So there are all these new elements.

因此，如果你是一名产品构建者，这将成为产品设计中值得深入探索的有趣新领域。

So if you're a product builder, this is a fun new space to be digging in for product design.

这非常有趣，因为人们与各种聊天机器人对话时总觉得本就该如此。但实际上你设计了交互的每个细节，比如该透露多少思考过程——'这是我的计划，你觉得呢？'我认为这会令许多人惊讶，原来即便是看似简单的对话，背后也需要如此多的设计考量。

This is really interesting because I think people chat with all these chatbots and it just feels like this is just the way it is. But you actually are designing every element of the interaction, like how much to share about how much you're thinking. Here's the my plan. What do you think? So I think I think this this will surprise a lot of people just realizing there's so much that goes into just designing even these what seemingly are simple conversations.

没错。后续跟进就是个好例子。对吧？嗯。你可以说，听着——

Yeah. Another good example is follow ups. Right? Mhmm. You could say, look.

你向我提问后，我可以进行一系列跟进提问。而这些必须经过精心设计才能成功。比方说，如果我要求生成图片，结果它创建了黑白剪贴画风格的图，那么系统应该主动建议哪些后续操作？

You have a you you ask me a question, and then I I could ask a follow-up set of things. And that explicitly should be designed for success. Right? So, for example, if if I say, hey, create an image, and it created a black and white, you know, I don't know, like a a clip art version of something. What are the next obvious follow ups that it should be suggesting proactively?

建议太多会惹人烦，但建议太少又可能错失引导用户走向最佳体验的机会。

Now, too much and you're kind of annoying me, right? But too little and some sense, you've lost an opportunity to direct me or guide me into a happy path here.

这让我想起凯文·威尔上播客时的观点。他讨论过该展示多少思考过程的问题，有趣的是DeepSeek选择了完全透明的极端做法，用户反而很买账。这个现象确实耐人寻味。

This resonates a lot with when we had Kevin Wheel on the podcast. He talked about this question of just how much to show about what you're saying and, you know, and it's interesting that DeepSeek went to the extreme of just showing everything, and people liked it too. That I think that was interesting.

是的，Lenny，我认为这也是一个时间点的问题。因为在某种程度上，目前这些东西都是如此的黑箱操作。几乎就像偷看引擎盖下的任何东西一样。即使它很冗长，感觉像是，哦，我知道发生了什么，特别是因为计算推理时间，它需要很长时间来思考。所以，感觉就像如果你突然沉默，我会非常不舒服。

Yeah. And I think it's a point in time thing too, Lenny, because in some sense right now, these things are such black boxes. They're almost like peeking under the hood for anything. Even if it's verbose, feels like, oh, I know what's happening, especially because the compute inference time, it's taking long to think. So, it just feels like if you just went silent, I'd be very uncomfortable, I think.

没错。我确实觉得有那个时间点。但随着时间的推移，我也觉得这是一个非常适合个性化的领域。例如，就像人类一样，我的API会与其他人非常不同，我的界面可能也与众不同，我可能只想要直接的“给我一个简短的总结”，而不是“哦，我去了这里，然后又去了那里”这样的叙述。

Exactly. I do feel like there's that point in time. But over time, I also feel like this is an area ripe for personalization. For example, like again, in human, my API would be very different from somewhere, my interface is probably different from others and I might just want the direct, Hey, Give me the TLDR versus the, oh, so I went here and then I went there and it's like

稍微跟一下开头，我们在谈论未来将如何不同。比如，为这些聊天体验设计。有代理。稍微放大到一般的产品开发，感觉你们处于很多将改变我们构建产品方式的工具的前沿。而且你们的团队也在使用很多其他人无法接触到的工具。

Following the start a little bit, we're talking about just how the future is gonna be different. There's, like, designing for these chat experiences. There's agents. Kind of zooming out to just product development in general, it feels like you're at the forefront of a lot of the tools that are gonna change the way we build products. And also your teams are working with a lot of these tools that no one else has access to.

所以让我问一下，你认为未来的产品开发与今天最大的不同会是什么？你认为产品构建者应该做些什么准备来在那个未来中取得成功？

So let me just ask, how do you see the future of product development being different from today most? And what do you think product builders should be preparing for doing to kind of to succeed in that future?

我会从一个我在内部和外部都说的强烈声明开始，我正在努力实践它，那就是在这个时代，如果你不通过原型和构建来看到你想构建的东西，我认为你做错了。我称之为新的PRD（产品需求文档）的提示集。对吧？我坚持要求人们说，如果你在构建新项目、新功能，当然要带着原型和提示集来。我的意思不是说，嘿，现在每个人都像是软件工程师的最大版本，对吧？

I'll I'll start with one stark statement that I say internally and externally, and I am trying to live it, is that in this day and age, if you're not prototyping and building to see what you want to build, I think you're doing it wrong. I call it the prompt sets of the new PRDs. Right? Like, I really insist on folks saying, if you're building new projects, new features, of course come with prototypes and prompt sets. And I think the notion is not to say, hey, now like everybody is just, you know, like a biggest version of like a software engineer, right?

而是说，你知道，你有最快的路径来看到和体验你脑海中的东西，以便能够沟通。对吧？这是一种更高带宽的沟通方式。我认为这在产品构建中是一个真正的循环加速器。这是第一点。

It is to say, you know, you have the fastest path to kind of seeing and experiencing what's in your mind to to be able to communicate. Right? It's a much more high bandwidth way of communication. I think about that as a really a loop accelerator in terms of product building. That's number one.

当有疑问时，正如有人所说，演示优先于备忘录。对吧？我认为这真的是第一点。我会说第二点，这一点有点棘手，我看到的是，首次演示的时间要短得多。对吧？

When in doubt, as someone put it, demos before memos. Right? I think like that's that's really number one. I would say number two, this one is a little bit tricky, I'd say, that what I'm seeing is that the time to first demo is much shorter. Right?

但完成全面部署所需的时间会更长。因此我认为节奏会变得不均衡。过去通常是，你完成某个项目后，花几周时间迭代改进。而现在，原型设计、迭代乃至通过AI对话获取用户研究的内部循环都大大缩短了。但正因如此，规模化门槛反而大幅提高了，对吧？

But the time to like a full deployment is going to take longer. So I think that there's going to be an uneven cadence. So typically I think there was much more of a, hey, you've been this thing, you take a few weeks and then you can iterate and so on. Now, but that inner loop of like prototyping and iterating and getting even user research through AI conversations, all of that gets shortened. But I think the bar for scale therefore becomes much high, right?

从某种角度看，创意供给量将会激增。原型设计的创意供给会出现巨大增长，这很棒。

In some sense, you look at it, there's going to be a supply of ideas. Right? Like, there's massive increase in supply of ideas in prototypes. Right? And so, is great.

它抬高了基础水平，同时也抬高了天花板。关键在于，在这个时代如何脱颖而出——你必须确保自己的作品能突破噪音。所以我认为第二点是要学会克制，不要追逐每个创意。

It raises the floor. But it raises the ceiling as well. Right? In some sense, like how do you break out in these times that you have to you have to kind of make sure that this is this is something that rises above the noise. So I would say that it's simultaneously thinking about not chasing after every idea, like, right, I think is the second one.

第三点是关于全栈构建者的讨论。未来的产品团队会是什么形态？我认为在原型设计和早期创意探索阶段，角色界限必然变得模糊，会有少数人兼具多重能力。

I'd say the third thing is, you know, there's a lot of conversation around full stack builders. Right? What does the team of the future look like, the product building team? What I think about is, I think that is inevitable in terms of, like, there will be a few folks that are especially at the prototyping early idea discovery stage that the lines are blurred. Right?

同时仍需要少数品味决策者。虽然可以有大量人员参与实验，但核心必须存在具有编辑眼光和品味的决策者，否则只会产生弗兰肯斯坦式的畸形产品——这点永远不会改变。

There'll be a few taste makers at the same time. I think you can still have a lot of people experimenting. It becomes even more important to have that editorial and taste making fartier one or a few at the heart of it. Because otherwise you just have Frankenstein product, right? That definitely doesn't change.

还有个额外观点：很多人认为不必学习计算机科学，编码已死。我完全反对。编程语言的抽象层级一直在提升，我们早就不再用汇编语言，多数人甚至不用C语言，抽象层级越来越高。

I have one other additional bonus thing, which is a lot of folks think about, oh, you know, don't bother studying computer science or, you know, the coding is dead. I I just fundamentally disagree. If anything, I think you know, we've always had layer higher and higher layers of abstraction in programming. You know, like, we don't program in assembly anymore. Like most of us don't even program in C and like and then you're you're kind of, you know, higher and higher layers of abstraction.

在我看来，指挥计算机的方式永远存在，只是会站在更高的抽象层级上。这很棒，它实现了民主化，软件操作者的数量将呈数量级增长。

So to me, they will be ways that you will tell the computer what to do. Right? It'll just be at a much higher level of abstraction, which is great. It democratizes you. There'll be an order of magnitude more software operators.

比如，我们可能用SOLS替代SWISE，对吧？但这并不意味着你不懂计算机科学，这是一种思维方式，一种心智模型。所以我坚决不同意那种‘编程已死’的论调。

Like, instead of SWISE, maybe we'll have SOLS. Right? But that doesn't mean you don't understand computer science and it's a way of thinking and it's a mental model. So, I strongly disagree with the whole like coding is dead.

太棒了，我喜欢这个观点。那么软件操作员（software operator）是什么？这个缩写代表什么意思？

That's awesome. I love that. And so is software operator? What is that what that stands for?

刚编出来的词，不过没错。

Just made it out, but yes.

酷。如今原型设计已成为构建产品的核心理念。微软内部有没有什么机制来落实这一点，让它成为每个人都必须遵循的文化？比如‘展示前必须先给我看原型’这样的规定？

Cool. This idea of prototyping is being kind of core to building these days. Is there anything you do within Microsoft to operationalize that and make that just like a thing everyone has to culturally do it or is it like you must show me a prototype before you show me it?

要知道，我认为即使在微软，未来也是不均匀分布的。但确实存在强烈的文化势头和转变意愿，比如提倡‘让我们看看实时演示和原型’，甚至用这种方式沟通想法。当然，并非所有情况都适用——比如你要修改Excel底层功能时，可能产品本身已有足够深度让你明确该做什么，无需原型。但对于新事物、新产品、新功能，这绝对是必要的。

You know, I think it's again, like the future is here unevenly distributed even in Microsoft, I would say. But there is certainly a strong cultural momentum and shift and desire to say, hey, let's let's actually look at live demos, live prototypes, and to even, like, communicate the ideas. Right? And and to me, I mean, it's not always possible because, obviously, there are, like, things that are deeply like, you're trying to change something in like the bowels of Excel, probably don't there's even enough depth in the in the product that you know what you need to do and you don't need to prototype that. But if you're especially thinking about new things, new products, new features, absolutely.

好的。我们来谈谈产品管理。自从AI编程工具出现，就有种恐慌认为‘产品经理要失业了，我们不需要PM了，自己就能用工具构建东西，这些人还有什么用？’

Okay. Let's talk about product management. There's this fear that emerged as soon as all these AI coding tools came out of just like PMs are dead. We don't need PMs. We could just build things ourselves with what are these people hanging around for?

而我的发现恰恰相反：现在编程变简单了，核心问题反而变成‘我们该构建什么？为什么要构建？这个方案对吗？’以及后续的推广落地——这些正是产品经理最擅长的领域。

And what I found is it's actually the opposite that now that coding is easy, now the question is more and more, what should we be building? Why should we be building it? Is this right? Is this the right solution? Then getting adoption for it, which is what PMs are really good at.

所以我感觉恰恰相反。产品经理是最重要的角色，而且你知道，它也会发生变化。不过让我听听你的看法。你认为产品管理的未来会是什么样子？你觉得这个职位会消失吗？

And so I feel like it's the opposite. Like, PMs are the most important role and they're you know, it'll change too. But but let me get your take. Just what do you think the future of product management looks like? Do you think it's dead?

你认为它会蓬勃发展吗？还是会发生改变？

Do you think gonna thrive? Do think it's gonna change?

是的。我的意思是，如果你只是个填写TPS报告、主要处理流程的人，就像很多公司混淆了产品管理、流程管理和项目管理那样，那么你确实会质疑：这里的增值点在哪里？对吧？特别是当AI能读写五万份会议记录、跟踪事项、发送邮件等等的时候。

Yes. Meaning well, look. I mean, if you're a TPS report, mostly process person and, like, a lot of companies do get confused about product management and process and project management, I think then you do have a question of like, hey, what is the value add here? Right? Especially if like AI can read and write like 50,000 meeting notes and track things and send emails and so on.

但另一方面，我认为审美判断和编辑功能变得极其重要。在一个创意和原型数量呈指数级增长的世界里，你必须思考：这里的筛选机制是什么？这意味着对产品人员的标准提高了。不过我注意到一个有趣的现象，在我咨询的初创企业甚至大公司里，过去存在更多‘守门人’现象——比如‘我们应该先问问产品负责人的意见’。虽然编辑功能仍有其价值，但现在你需要靠实力赢得话语权。

But I think what I do think on the flip side is the taste making and kind of the editing function becomes really, really important, Right? In a world where the supply of ideas, supply of prototypes becomes even more, like an order of magnitude higher, you'd have to think about, like, what is the editing function here? So that does mean that the bar is higher for for you for product folks. But I think there's a there's an interesting side effect I am observing in, you know, startups that I'm advising companies and even within the companies that there's there used to be more gatekeeping, I would say, in terms of like, oh, this is, you know, we should ask the product leader what they're saying. And again, like there is a role for that editing function, but you have to earn it now.

你不能仅凭头衔获得这种权力。但这也释放了优秀工程师、用户研究员和设计师的潜在创意——他们现在口袋里就像装着个专家，能弥补他们通常不擅长的领域，让想法得以呈现。我认为这很棒。

You just don't get it because of this title. But there's also just like unlock of latent really good ideas from smart engineers, smart user researchers, smart designers who can now who now have like this expert in their pocket. Right? To kind of round out all the other things that they're not they're not typically skilled at to bring forth their ideas. And that's amazing, I think.

说到这个专家角色很有意思。我正在和一位工程师合作，他甚至用ChatGPT来更有效地与我沟通，比如‘把这个提案改成能说服Lenny的形式’。

And I think that expert it's interesting. I I'm working with an engineer on some stuff, and he uses ChatGPT to even communicate to me in a more effective ways. Like, turn this pitch into something that will convince Lenny this is a good idea.

顺便说，这其实是我的常用场景之一，我称之为‘WWXD’（X会怎么做）。以前我会问‘萨提亚会如何看待我们正在推销的这些对话或想法’。这就是深度推理加上相关性的力量——你提到的这位工程师恰好掌握了关于你的上下文。

By the way, that is actually one of my common use cases, which is the WWXD. I call it what would X do. I used to say, hey, what would Satya think about this particular set of conversations or ideas that we are pitching and so on. This is the power of deep reasoning plus relevant Right? This engineer you're talking about has that context about you.

所以这其实挺有意思的。

And so it's kind of very interesting.

要是人人都像萨提亚那么出名，有那么多公开信息就好了。不过我想你可以导入他们的邮件或利用现有工具，从与那个人的对话中理解他们。

If only everyone was as famous as Satya and had so much information out there, but I guess you can import all their emails or whatever tools exist to just, like, understand from the conversations you've had with that person.

是啊。我觉得这其实回到了你刚才说的观点——就像有个螺旋弹簧，我观察到普遍存在的智能过剩现象。产品开发某种程度上需要自我重塑，Shopify的托比称之为'条件反射式AI应用'，但这并不容易。

Yeah. And I think this is this goes back to actually what you were saying too, which is I think this idea of what is the there's like a coil spring. There's an intelligence overhang that I I just see across the board. And I think the part of product development has to almost rewire ourselves to I think Toby from Shopify calls it the reflexive AI usage. And that's not as easy.

我一直在思考原因。我装了个俗气的Chrome插件，每次打开新标签页就会问'现在这件事怎么用AI来做？'虽然老套，但能让人停下来思考当下目标。但即便对于沉浸在这个领域的人，难点在于更新认知基准实在太难——毕竟一年前模型还做不到某些事。

I've And been thinking about why. Basically, I mean, I have a cheesy Chrome extension literally whenever I open a new tab, it just says, how can you use AI to do what you're going to do right now? It's very cheesy, but it helps to pause and think, oh, what am I trying to do here? But the reason I find it hard and when I talk to even, like, people who are living and breathing in this space, they find it hard is that, you know, the updating of the priors is really hard. Like, the models couldn't do some things one year ago.

比如图像生成全是错字，推理能力薄弱，得不到深刻回答，也无法数据分析。几个月前的试用印象需要更新，但这很反直觉——就像要否定原有认知，告诉自己'不，这个婴儿一个月就长成15岁少年了'。

I mean, image generation was full of spellings or like reasoning, you just couldn't like, you know, have deeper and smarter answers. You couldn't do data analysis. So, like my impression of it from change, trying it a few months ago, that prior needs to be updated and it's hard to do that, right? You have to kind of do something almost counterintuitive and against the grain to say, No, no, ignore what you learned about what this can or cannot do. Like, the baby just grew up to be a 15 year old in a month.

最后这点太关键了。我们多年试用这些工具效果平平，突然它就突飞猛进，而人们可能早已放弃，却不知道情况已变。

I think that last point is so important that we've tried these tools over the years and it many like, so far, it hasn't been amazing. And then all of sudden, it is. And you kind of don't know that. And you've given up almost and things change.

这对产品建造者是个有趣的套利机会。若能逆势而为，不因几个月前的失败形成心理阴影，持续提高对AI的预期要求，就能解锁更多可能。

I think that's actually if you're a product builder listening to it, that's a really interesting arbitrage thing for you. Like if you can kind of cut against the grain and say, no, I won't have that scar tissue around like, you know, this didn't work a few months ago and keep setting high expectations and like demand more of the AI today. I think you can you can unlock more.

这么做很有男子气概。

There's a lot of alpha in in doing that.

没错。

That's right.

今天的节目由Coda赞助播出。我本人每天都在使用Coda管理我的播客和社区。我会把计划问每位嘉宾的问题放在这里，也会存放社区资源，以及管理工作流程。

Today's episode is brought to you by Coda. I personally use Coda every single day to manage my podcast and also to manage my community. It's where I put the questions that I plan to ask every guest that's coming on the podcast. It's where I put my community resources. How I manage my workflows.

Coda能这样帮助你：想象你在工作中启动一个项目，愿景清晰，明确知道谁负责什么、在哪里找到你所需的数据。实际上你无需浪费时间搜索任何内容，因为从项目追踪器、OKR到文档和电子表格，团队所需的一切都集中在一个标签页里——全在Coda中。通过Coda的协作一体化工作空间，你既能获得文档的灵活性、电子表格的结构性，又能拥有应用程序的强大功能和AI的智能，全部整合在易于组织的标签页里。如我之前所说，我每天都使用Coda，超过50,000个团队信赖Coda来保持协同与专注。

Here's how Coda can help you. Imagine starting a project at work, and your vision is clear, you know exactly who's doing what and where to find the data that you need to do your part. In fact, you don't have to waste time searching for anything, because everything your team needs from project trackers and OKRs to documents and spreadsheets lives in one tab, all in Coda. With Coda's collaborative all in one workspace, you get the flexibility of docs, the structure of spreadsheets, the power of applications, and the intelligence of AI, all in one easy to organize tab. Like I mentioned earlier, I use Coda every single day, and more than 50,000 teams trust Coda to keep them more aligned and focused.

如果你是初创团队，希望提升协同效率和敏捷性，Coda能帮助你在创纪录的时间内从规划过渡到执行。立即访问coda.i0/lenny，即可免费获得为期六个月的初创团队计划。输入c0da.i0/lenny即可免费开始，享受六个月团队计划。Coda.i0/lenny。稍后我会再提到这个有点俗气的插件。

If you're a startup team looking to increase alignment and agility, Coda can help you move from planning to execution in record time. To try it for yourself, go to coda.i0/lenny today and get six months free of the team plan for startups. That's c0da.i0/lenny to get started for free and get six months of the team plan. Coda.i0/lenny. I'm gonna come back to this cheesy plug in.

详细说说这个。这是个能让你在每个新标签页添加自定义信息的插件，它会显示：如何用AI实现这个？

Say more about this. So this is a plug in that just lets you put a custom message on every new tab and it just you have it say, how can you use AI to do this?

是的，就是这么俗气。有趣的是它确实有效——仅过去几周，我一直在做这个实验：看看我能多依赖AI？无论是工作还是生活中，每当要做手动操作时，我都会想：是否该要求AI来完成？

Yeah. It's as as cheesy as that. And it's interesting because it works in the last few weeks alone. I've been doing this, like, experiment to say, Hey, how much more AI pill can I get? Both at work and in a personal life to say, when I'm trying to do anything manual, should I be demanding the AI to do this?

太酷了。你知道这个Chrome扩展的名字吗？不知道的话就算了。

That's so cool. Do you know the name of this Chrome extension by any chance? Otherwise No.

不知道。这是我开发的。

No. I built it.

开发了这个Chrome扩展。太厉害了。那你用AI开发的吗？

Built the Chrome extension. That's so cool. Okay. Did you use AI to build it?

当然。

Of course.

哇。你用了什么工具？我猜是微软的某种工具吧。

Wow. Which tool did you use to do that? Some kind of Microsoft tool, I imagine.

对。呃不，其实就是在GitHub上用GitHub Copilot，我就想，好吧，这就是Windows版的...嗯...Chrome

Yes. Yeah. No, actually, it was just like I mean, live in GitHub and GitHub Copilot, so I just was like, okay, that's the Windows Yeah. Chrome

你会向公众发布这个吗？

Are you releasing this for the general public?

不，我是说，这正是有趣之处，令人惊叹的地方。我大概只花了十分钟就完成了这个。

No. I mean, this that's the fun that's the amazing thing. It took me like ten minutes to do this.

好的，我们把它链接出来，开源这个项目吧。好的。

Okay. Let's link to it. Let's get it out there. Open source this thing. Okay.

你提到了萨提亚，我有个问题。你是极少数同时与萨提亚和桑达尔在谷歌共事过的人之一。我想问你，他们的领导风格有何不同？

You mentioned Satya. I have a question about this. So you're one of the very few people that have worked very closely with both Satya and Sundar at Google. Let me ask you this. How do their leadership styles differ?

能不能分享一个关于他们每个人的有趣故事？

And is there just like a fun story you could share about each of them?

是的，我确实感到幸运，能够近距离观察这一代两位杰出的领导者。我想说，毫不意外，作为市值数万亿美元的科技公司CEO，他们在几乎所有你能想到的维度上都处于99.99%的顶尖水平。

Yeah. I do feel do feel lucky to have, you know, of have a window into these two amazing leaders of this generation. Would say, I mean, again, no surprise. They're as you'd expect from CEOs of multi trillion dollar market cap tech companies. They are 99.99 percentile in like almost every dimension you'd think of.

对吧？智力、同理心、领导力、产品战略等等。当然他们风格各有不同。我曾担任桑达尔的技术顾问，最初在谷歌协助建立了CEO办公室。这些差异也与时代背景有关，因为谷歌更侧重于消费者领域。

Right? Intellect, empathy, leadership, you know, be product strategy. There are of course flavors of differences. I was at the technical adviser for Sundar with the first at at Google and set up, you know, the office of the CEO there. And they are, again, a matter of, like, time and context because a lot there's a lot more consumer oriented focus there.

我发现他们最擅长的是保持冷静、审慎和深思熟虑，特别是在处理复杂生态系统时。无论是手机生态系统，还是搜索、出版商和广告商构成的复杂网络，桑达尔都是这方面的大师。而萨提亚让我惊叹的是他学习和不断修正思维模型的强烈求知欲。

So what I did find they're great at it as being really calm and measured and thoughtful in terms of, you know, taking making sure that things have dealing with the complex ecosystems. And if you think about the phone ecosystem or even like the search and publisher and advertiser ecosystem, it's a very complex ecosystem. He was a master at that. He's a master at that. And I think on Satya, I find it amazing the appetite he has for learning and fine tuning his mental models.

就像他能驾驭的不同视角层次一样，宏观的战略布局——游戏规则是什么，同时也能深入微观层面。嘿，为什么我们不喜欢这个？比如我在推特上看到的某个具体见解。而且你可以相信，在发现这些早期趋势方面，他几乎总是领先于其他人。所以这就像他们说的，像是从消防水带里学习知识。

And just like the the Zoom levels that he can operate at, the macro, the strategy, what's the game, but also the micro. Hey. What why are we don't like, here's, like, a specific insight that I saw on Twitter. And, you can count on the fact that he's ahead of pretty much everybody else in terms of spotting those early things too. So it's it's just been, like like, you know, learning from the fire hose as as they put it.

能与两位杰出人士共事真是绝佳机会。好了，我们换个完全不同的方向。让我问问这个我越来越常向人们提出的问题：关于产品构建，你学到的最反直觉、违背常见创业智慧或产品开发常识的经验是什么？

What a cool opportunity to work with two incredible folks. Okay. Let's go in a whole different direction. Let me just ask you this question that I've been asking people more and more. What's the most counterintuitive lesson that you've learned about building products that goes against common startup wisdom, common product building wisdom?

我不确定这是否算——虽然它本应成为常识——算是反直觉的发现。但我屡次体会到，当你从零到一创新时，人们总容易陷入类似《南方公园》桥段的思维：第一步，思考问题；第二步，内裤计划。

I don't know if it's I mean, as common as it should be, and it's like a counterintuitive thing. But I've repeatedly learned that when you're doing something new, zero to one, the temptation is to kind of think about, you know, it's like that South Park episode. Step one, think about the problem. Step two Underpants.

第三步。其实内裤才是第一步。

Step three. Underpants is step one.

内裤计划，没错吧？所以我总觉得人们容易急于求成，在解决问题前就想着扩张规模。因此我一直告诫团队：先解决问题，再考虑扩张。

Underpants. Exactly. Right? So I do feel like there's a temptation to rush and say to go to scale before solve. So I've always said to my teams, solve before scale.

明白吗？这意味着在解决问题阶段和扩展阶段需要完全不同的姿态——后者要么是产品已匹配市场，要么至少大致方向正确。举几个例子：在解决问题阶段，你需要大幅度试错调整，必须坦然接受这种情况——比如第一天你在构想植物识别工具...

Right? So what that what that does mean is there's a different posture and different mode when you're trying to solve a problem versus scaling something that's either post product market fit or even at least like in this roughly in the ballpark. So, to give you a couple of examples, right? I think when we when you look at the solve stage, there are wide lurches. You gotta be very comfortable with the fact that your day one thinking about, hey, a plant detection tool.

到了第十五天却可能发现：等等，这技术其实更适合外语翻译。顺便说，这不是假设，正是我们当年开发Google Lens时经历的真实情况——不断寻找技术的最佳应用场景。在外界看来，这过程简直像场混乱。

And then day 15, you're like, oh, actually, the tech is really good for translating in a foreign language. By the way, this is not hypothetical. This is what we kind of like looked at in Google Lens back back then and said, okay. Like, where what is the intersection and so on? So from the outside, it looks like chaos.

但实际上在创业过程中，你不仅需要非常包容，我认为你还应该对此抱有渴望。因为你最不希望的就是过早地固守某个局部高点，然后被困在那里攀登。初创公司、整个产品领域乃至大公司都会犯这个错误。三年后你才会醒悟：该怎么从这个困境中脱身？所以我认为这是一个重要的竞争策略。

But actually in the ins and you should be very com not only tolerant, I think you should be like should have an appetite for that. Because the last thing you want is prematurely like, you know, fix on one local hill and then you're climbing that. And startups and entire product areas and companies, big companies make that mistake. And three years later, you're like, oh, how do I get off this hill? So I'd say that's one big competitive thing.

比如当你思考当前所处阶段时——是解决问题模式？还是规模扩张模式？其中一个关键是要适应混乱状态。我学到的另一个教训是数据指标的危险性，对吧？

Like, when you're trying to think about what mode you're in, are you in the solve mode? Are you in the scale mode? One example is kind of making sure that you're comfortable with the chaos. I think the other lesson I've learned is the danger of metrics. Right?

我想再次强调，如果你曾参与过谷歌搜索或Office产品开发，你会对产品指标有极其精细的把握，包括输入指标等全套体系。但当从零开始时，过早确定指标首先会造成虚假精确。比如千名用户时的点击率毫无意义，留存率可能同样没有参考价值。

And I think, again, if you have worked on, you know, Google search or if you worked on, you know, office products, you really have like a very fine grained sense of what are the metrics for this product, you have the input metrics, have the whole shebang. But when you're looking at something zero to one, if you decide on a metric too prematurely, that's false precision first of all. Right? Like you kind of I mean, CTR when you have like thousand people doesn't mean anything. You know, retention also may not mean anything.

所以要对这些所谓的'成熟指标'保持高度警惕。你应该更关注定性反馈，比如点击音效这类感官信号。就像其他产品经理常说的：你的核心功能是设置定时器和播放音乐吗？

So really being very wary of like this big guy, big girl, grown up metrics as I call it. Right? You are looking for more qualitative, the sound of click. And what is your as the other kind of the handler uses, what is your set timer and play music? Right?

以Alexa、Siri和Google助手为例，它们都有看似万能的交互界面，但实际上只擅长一两项功能。比如设置定时器、播放音乐和问答游戏。你必须先完美实现这些核心功能，再考虑宣称'它能完成任何事'。

So if you look at like Alexa and like Siri and Google Assistant and all these things, they had a very promising broad interface. You could say anything, but I think there was one or two things that it was really good at. Right? Like, you could set a timer, you could play music, and you could play trivia. And so you've got to nail those things before you say, oh, yeah.

宣称'你可以用它做任何事'反而恰恰是个糟糕的策略

Here, you can do anything with it, which That's is not a good exactly

这就是我用谷歌家庭音箱的方式——非常基础。我还没试过问答游戏功能，或许该体验一下。

what I use my Google Home for. So basic. I don't do the trivia thing, Maybe I gotta give this a shot.

得试试这个。

Gotta try that.

是的。我还看到你讨论过类似的话题，就是如何从零到一实现某件事。有个小框架可以帮助判断当前时机是否适合这个想法。你对此怎么看？

Yeah. There's something along these lines that I've also seen you talk about, which is how to go zero to one with something. Just kinda little framework for helping you know if this is the right time for this idea. How do you think about that?

没错。你要考虑解决模式——这又回到我常说的'活在未来一年'的理念，我倾向于完全从零到一和解决模式的角度思考新产品类别。根据我的经验（可以说是吃过苦头才明白），要打造真正出色的产品，至少需要满足这三个关键转折点中的两个：第一，技术是否存在阶跃式进步？对吧？

Yeah. You think about the solve mode and this is again like sticking with my whole living in one year in the future, I gravitate towards the zero to one and solve mode products completely thinking about new category of products. And what I found, both the hard way I would say, is that you do want to look for at least two out of these three factors inflection points here if you want to make a really good product. Number one, is there a shift, is a step function in the tech? Right?

这个相对明显，比如深度学习对Google Lens就是转折点。当年语音识别对对话式搜索也是阶跃进步。Robinhood的案例中，世代更替非常明显——手机成为金融应用的主要载体。所以要抓住这种技术拐点。

That's somewhat obvious, I would say, like, you know, deep learning was one for Google Lens. Back then speech recognition was a step function for like conversational search. I would say for Robinhood, you know, the the generational shift was very clearly and the fact that phones were a a primary means for, you know, you could actually have an app or mobile app for finance that you could use. So look for that inflection. Right?

当前的技术拐点显然是理解和推理模型。但仅此不够。第二个关键因素是消费者行为转变。

What is the tech inflection? And right now, of course, and a lens and reasoning models are that step function. But that's not enough. I would say the second factor that we should look for is what is the consumer behavior shift? Right?

举例来说，我们开发Google Lens时发现：人们原本拍照主要是为了分享（自拍/风景等）。当存储几乎免费且手机无处不在时，大家开始拍摄万物。相机成了现实世界的键盘。这种量级跃迁意味着需要AI从海量照片中提取价值。

So, to give you an example, when we started working on Google Lens, what we said is look, people were taking mostly pictures for sharing, right? Selfies and sunsets and so on. Suddenly when storage became free and mostly free and everybody had phones everywhere all the time, you took pictures of everything. Then you had enough of pictures or you use the camera as the keyboard for your world, for the real world. So, how do you then say, Oh, this consumer shift is big and so therefore as you go order of magnitude more photos, then you want more to come out of them and you can apply AI to that.

第三个拐点（尤其在企业级市场）是商业模式革新。所有伟大产品都有这个特质——从搜索的二次竞价/CPC计费，到SaaS的企业服务变现方式。AI的变现模式更是全新领域：是按席位收费、用量计费、ONTAP模式，还是基于实际效果收费，都尚在探索初期。

I'd say the third inflection point, particularly I would say in enterprise but also in consumer is the business model shift. Is there an inflection point, natural inflection point in the business model? So any great products, if you think about all the way from search, again, the second price auction and the fact that you had CPCs. Same thing with SaaS and the fact that you could actually charge monetize enterprise products in a different way. And with AI, of course, the monetization is a whole different I mean, just barely scratched the surface of whether you do seat monetization, usage, ONTAP, and then of course outcome based stuff, outcome based monetization.

嘿，你帮我解决问题了吗？然后我会付你一些费用。对吧？所以，这三样对我来说都挺好的，但至少要有两样达标才能算个好产品。

Hey, have you solved the problem for me? And then I will pay you some fees. Right? So, all three, like to me are, you know, kind of like great, but at least two out of three for a good product.

本质上，当投资者评估初创公司时，他们总在问‘为什么是现在？为什么现在适合做这件事？’所以你的建议是应该从三个角度考量，其中至少两点要成立：技术上要有突破性变化，近期出现的新技术使之成为可能；消费者行为发生转变；或者你们发明了全新的商业模式。

So this is essentially when investors look at startups, they're always asking why now? Why is this the time to start this thing? And so your advice here is you should there's three ways to look at it, you should two two of these three should be true. There should be a a shift in technology, some new technology that has enabled this now recently. There's a shift in consumer behavior, and then there's maybe a new sort of or you've invented a new business model.

比如，任何能实现盈利的方式都能让你比同行更具优势

Like, any way to monetize something that it gives you an advantage over folks

没错。

Yep.

正在努力

Trying to do it

今天就搞定。绝对没问题。

today. Absolutely.

太棒了。你刚才的例子没提到Robinhood，我觉得那也是个好案例

Awesome. And you didn't mention Robinhood, think, in that example. That was another good example of

是啊，手机。对。我是说，再谈谈那种商业模式，比如不收取零费用，对吧？

Yeah. Phones. Yeah. Mean, talk about the business model of kind of again, like, not having a zero fees. Right?

再说一次，所有这些因素的结合才能真正解锁它。你不能光说，哦，我们只要有个更直观的界面，就指望人们会转用。

And again, like the combination of all of these things is what can unlock it. You can't just say, oh, we'll just have a much more better intuitive interface and hope that you know, people will switch to it.

好的。说到从零到一的产品，我要带大家进入本期播客的一个特别环节——‘热座角落’。我有个问题想问你，最近几期播客也提到过。像Cursor、vZero、Lovable、Bolt、Replit这些公司，简直是史上增长最快的企业。我刚看到Cursor两年内年收入就达到了3亿美元。

Okay. So speaking of zero to one products, I'm gonna take us to a occasional segment on this podcast that I call hot seat corner. And I have a question for you that is on my mind, and it's come up on a couple recent podcast actually. So there's these companies like Cursor, vZero, Lovable, Bolt, Replit that are, like, the fastest growing company's history. I just saw that Cursor hit 300,000,000 ARR in two years.

有趣的是，你们本可以在AI编程工具领域大展拳脚。你们推出了Copilot，全球首个此类工具，遥遥领先。你们开发了VS Code，这些公司都在基于它进行分支开发。你们拥有顶尖的AI基础设施和人才。

Interestingly, you guys were very well positioned to do really well in this space, this AI coding tool space. You guys hit Copilot, the first tool in the world at this stuff. So ahead of everyone. You build Versus Code, which is what all these companies are forking to build on. You have incredible AI infrastructure, incredible AI talent.

这本该是你们的市场。发生了什么？合作伙伴怎么了？

So this could have been your market. What happened? What happened to partner?

你知道吗，这个角度很有意思。我每天都会用GitHub Copilot。要我说，拆开来看，关键在于大语言模型解锁了代码生成这个神奇工具。对吧？所以现在代码生成带来的兴奋和行动力是真实的，它实现了我们讨论过的所有可能——比如从构思到原型，几分钟就能做出可点击的演示。

You know, it's interesting the framing so I'm a daily user of GitHub Copilot. And I would say, look, if you unpack, I think the thing, the beauty of this is that code generation has become an amazing tool that LLMs have unlocked. Right? So it is not so it is actually really good excitement and action that now code generation has just opened up all of these things that we've talked about the whole idea of like prototyping. Go go from idea to mocks and idea to kind of a clickable prototype in like in a few minutes.

这些正是代码生成应该实现的功能。关于我们的定位和GitHub的发展方向——它是个系统，而非单一产品或功能集合。对托管代码库的用户来说，GitHub提供自动补全辅助和聊天功能。

Those are the kinds of things that, of course, we should expect code generation to enable. The way I think about, you know, how we we are positioned and like what we what we do with GitHub is so it's a system, not just a product or a set of features. If I think about GitHub, it's for folks who are who have their repo there. Right? And you have kind of of course, you have the assistance in terms of auto complete and you can chat.

但现在我们有了代理面板。这是我们所见过的反馈循环最快的之一，真正强大的正向反馈。所以在某种意义上，当你拥有一个系统时，你在构建和设计过程中寻找的不仅仅是一个可以增长的单一产品，而是什么是知识库？什么是你的上下文？

But now we have the agent board. It's one one of the fastest of loops that we are seeing. Really strong positive feedback. So in some sense, when you have a system, what you are looking for in terms of building and designing it is not just a single product that can go grow, but it's the what is the repository? What is your context?

从你的专业知识中能生长出哪些功能集？对吧？如果你是一名真正的编码专家，你需要的是辅助工具，这个产品需要为此扩展。如果你是一名广泛的编码者，你仍然应该能够做到这一点，以此类推。所以我认为这就是GitHub定位并持续良好发展的方式。

What are the set of features that grow from your expertise? Right? If you're a really expert coder, you want the assistance, this product needs to scale for that. If you're a wide coder, you should still be able to do that and so on. So that I think is the way that GitHub is positioned to build on and growing honestly really well.

这太有趣了。核心在于无论使用什么工具，每个人最终都会来到GitHub，这某种程度上就是——

That's so interesting. The core of this is everyone ends up in GitHub anyway, no matter what tool they use, and that's kind of the

是的，我认为这个想法再次表明，代码生成作为一种工具将解锁更多产品。我的意思是，它们并非都是竞争对手，并非都在做同样的工作。我认为归根结底，当你为公司构建运行代码时，你需要一个系统，需要拥有像瑞士军刀那样的全套工具包。对吧？不仅仅是自动补全，不仅仅是聊天，也不仅仅是一个需要你手把手指导运行的软件代理。

Yeah, and I think the idea again is that you know, code generation as a tool will unlock lot more products. I mean, they're not all competitors to the fact of they're not all kind of, you know, doing the same job. I think when you're at the end of the day, like, you're building code for companies to run on, you need to have a system, you need to have kind of the ability an entire Swiss Army tool toolkit. Right? Not just the auto complete, not just a chat, not just like a software agent that runs and you kind of like handhold.

你需要所有这些协同工作。而这正是GitHub产品所追求的。

You need all of this to work together. And that's what the GitHub product is going after.

条条大路通GitHub。这个问题的另一面是，可能有5000家初创公司试图颠覆Excel，而你们只是静观其变。所以这里面有些东西运作得非常出色。

All roads lead to GitHub. On the flip side of this question, there have been probably 5,000 startups that have tried to disrupt Excel and you guys just keep waiting. So something there is working really well.

你这么说真有意思。当我来到微软时——我是Excel的粉丝——实际上我和一位Excel产品的元老级人物有过一次对话。我说，老兄，这个产品到底有什么魔力？他告诉了我几点让我印象深刻的有趣见解。其中之一是，我说，嘿，Excel证明了非程序员也需要编程。

That is so interesting you say that. So when I came to Microsoft and I'm an Excel fan, I actually had a conversation with one of the OG Excel product folks. I was like, Man, what is it about this product? He said a couple of things that were really interesting for me that just stuck with me. One is, I said, Hey, Excel is a proof that non coders also have to program.

编程确实非常强大，它是赋予所有非程序员强大编程能力的工具。我觉得这非常引人注目。其次，我发现另一件超酷的事——不知道你是否了解，但至少两年前我还不知道——竟然有这些精彩的Excel锦标赛，比如世界Excel锦标赛，你会看到人们施展魔法般的操作。对我来说，这里的启示还在于：有些工具学习曲线较陡。也许初期学习会有摩擦，但使用起来很棒，对吧？

Programming is really powerful and it's the tool that gives all of the non coders a really powerful programming ability. And I thought that was just really striking. And then the second thing that I found out super cool, I don't know if you know this, but I didn't know at least before two years ago, that there are these amazing Excel championships, like World Excel championships, where you see folks who can do just magic. And to me, I think the insight here is also that some tools are harder to learn. Perhaps in the beginning, there's friction in terms of learning, but great to use, right?

所以这是个典型案例：初始学习曲线，那段一次性的学习过程可能很棘手，但这是因为工具蕴含的巨大能量和深度。

So, it's a very good case of, hey, the learning curve initially, the one time learning curve might be tricky, but it is because there's so much power and depth in tool.

太有趣了。我从没把Excel当作编程语言，但这么想确实合理。而且我觉得一旦你习惯了它，认为这就是事物运作的方式，你就会困在这个模式里，其他所有工具都不得不复制这个模型，却很难做到同样出色。

That's so interesting. I never thought of Excel as a programming language, but it makes sense. And I feel like once you get used to it, and this is just the way things work, you're kind of stuck there and everything else has to basically copy that model, which is hard to be as good.

是的。我认为团队投入的深度和专注——这又是几十年如一日深耕的复利效应——来自那些每天依赖它生活工作的人们发出的深刻信号。

Yeah. And I think the the depth and the attention that the that the team has given, and again, that's the compounding effect over, you know, decades of working on, like, deep, deep signal, right, from people who live and who depend on it day in and day out.

明白了。为了收尾我们的对话，我想问个关于你职业生涯的问题。我发现多数人都有改变职业轨迹的关键时刻——可能是遇到某位经理，参与某个项目，或是获得某个职位。

Yeah. Okay. To kind of start to close out our conversation, I wanna ask this question around your career. And I find that most people have one moment in their career that changes the trajectory of their career. It could be a manager they had, it could be a project they worked on, it could be just a job they landed.

你认为职业生涯中最关键的转折点是什么？最终促使你成为微软首席产品官的那个时刻？

What would you say is the most pivotal moment in your career that eventually led you to becoming Chief Product Officer at Microsoft?

确实有个转折点。当时我在谷歌搜索部门，正在推进一个我认为本该成功的想法，但它失败了。就像...我当时说...

Actually, there is one moment where, you know, it was a turning point for me. I was in Google search. I was working on this idea that I thought should just work, and it didn't. Right? Like, I I I said, hey.

这些手机正成为一种趋势。个性化必须很重要。所以我大概花了一年左右的时间绞尽脑汁想让个性化功能奏效。结果发现，当你向谷歌搜索输入查询时，个性化其实没那么重要。于是，我们解散了这个团队。

These phones are becoming a thing. Personalization has to be important. So I I probably bang my head against the wall for a year or so trying to make personalization work. And it turns out when you have a, you know, query that you put into Google search, like, the personalization didn't matter as much. And so, you know, we disbanded the team.

但后来，我开始研发一个叫Google Now的产品，它是前者的变体，理念是：在手机上我们应该能主动推送内容。不是那种带个性化设置的搜索。比如你有航班要赶，系统应该能串联信息告诉你'鉴于路况和目的地距离，现在该出发了'。或者如果你痴迷于冷面喜剧演员，就该推荐你关注米奇·赫德伯格。智能手机本该在这些关键时刻更智能。

But then, think I started working on this product called Google Now, which was a twist on that, which said, hey, actually on the phone, we should be able to, like, push content. It's not about like, you know, searching with personalization. For example, if you have a flight coming up, it should we should be able to say, hey, connect the dots and say, you should leave now for the you know, given the traffic and where where you need to go and so on. Or if you're deeply interested in an old stand up comedy with deadpan artist, you should check out Mitch Hedberg. Like, these are kind of like these really moments that the smartphone should be smarter.

我带领这个产品从零到一完成了初期阶段，这是个关键转折点。它让我明白两件事：第一，我真正热爱的是预见未来趋势并打造相应产品，远胜过维护成熟产品；第二，残酷的是——过早行动等同于犯错。要知道那是在大语言模型和深度学习出现之前。

So I I let that product through the kind of the initial zero to one phase and that was a pivotal moment. It made me realize two things. One, I really love seeing around the corner and kind of seeing where where things go and building the product to rise to the occasion way more than, you know, the the scaling and sustaining products. Second, it's harsh, but being early is is the same as being wrong. You know, this is pre LLMs, pre deep learning.

关于下一个词预测等绝妙创意，我们早有构想，但当时缺乏算力支撑。界面很出色，智能程度却跟不上。第三点让我铭记的是：我有幸与一群极其聪明的人共事——现在人们常说的'人才密度'概念。

Lot of the really amazing ideas in terms of next token predictor, etcetera. We've been thinking of it, but, you know, didn't have the horsepower to go. The interface was great, the intelligence wasn't there. And I'd say the third thing that stuck with me is I got to work with some really smart, like, they talk about talent density now. Right?

这些后来都成就非凡的精英们，让我见识到小团队能创造的奇迹。

And I think really smart people who've gone on to do, like, amazing things. And so, kind of like, it gave me a taste of what a small group of people can do.

这故事很棒，因为它最终并未成功。Google Now后来消失了。顺便说，我对这个产品印象超级深刻，它非常酷，我记得当时研究过。

It's such a great story because because it didn't work out right in the end. Like, Google Now kinda went away. And by the way, I super remember that product. It was very cool. I remember looking at it.

它给人那种愉悦欢快的感觉。我的播客有个'失败角落'环节，专门分享失败经历及其带来的成长。你这个故事完美结合了两者。

It was very, like, delightful and happy. And so I also have this segment on the podcast called Failure Corner where people share a story of failure and how that helped them. I love this as a combination of those two.

是的。说实话，我认为当你这样做时确实很痛苦，因为你看到了可能实现与现状之间的差距。有时是硬性限制，比如在这个案例中需要五年或十年才能真正解锁其智能。

Yeah. I mean, I'm not gonna lie. I think it was it was it's painful when you do that because you you see the vision of what can be and what is. And sometimes it's hard limitations. Sometimes it takes like, you know, in this case it takes five years or ten years to kind of like really unlock the intelligence.

但有时距离产品成功只差一两个关键点击。而判断的关键在于认清你处于哪种情境。

But sometimes it's it's one or two key clicks click stops away from the product being great. And part of figuring out is knowing when you're in what situation.

从开始到决定放弃的这段时间有多长？就是当它明显行不通的时候。

How long was that period from starting out until just like moving on and it's not working?

是的。这个案例中值得庆幸的是，它奠定了Google Assistant的基础之一。当然，随着LLMs的发展，特别是Gemini带来的阶跃式进步，现在终于见效了。我认为这是普遍规律——有时你需要找出那些恒定的有效要素，对吧？

Yeah. I would say in that case, one of the good things is again, the it led the foundation of it was one of the foundations of the Google Assistant. And, of course, as the LLMs, you know, step function happen now with Gemini, it kind of like works out. And I think it's the same thing across the board, which is sometimes you wanna kind of figure out the invariants that do work. Right?

这些要素可以延续到产品的下一个版本。而有些时候你只能推倒重来。

That can then that then go on to the next version of the product. And other times you just have to start over.

Google现在是智能代理的先行者吗？给我的感觉就是这样。

Is Google now the first agent before agents? That's what it feels like.

这确实是最初的构想。但令我着迷的是界面设计——我们当时面临的是相反的问题。想想所有语音助手就知道了，对吧？

It was certainly the idea. Yeah. You know? But it is fascinating to me that the interface that there we had the opposite problem. Like, whether you think about all the voice assistants, right?

这个界面就像是，我们过度设计了，而智能性却没有跟上。如今，我感觉问题恰恰相反。我认为这些东西拥有惊人的智能，而我们现有的界面很大程度上就像是AOL拨号聊天机器人时代的产物。

The interface is like, we overshot and the intelligence wasn't there. Today, I feel like there's an opposite problem. I think these these things have amazing intelligence and the interface we have largely is like the AOL AOL dialogue modem chat bot.

我们已经探讨了很多内容。在进入激动人心的快问快答环节前，你还有什么想聊的，或者想留给听众的最后一点智慧箴言吗？

We've covered a lot of ground. Is there anything that you wanted to chat about or leave listeners with maybe a last nugget of wisdom before we get to a very exciting lightning round?

我想说的是，我真正感到兴奋的是探索人类与智能体如何协作这个理念。我认为有一系列伟大的产品和体验亟待重新构想。这是我的另一个执着点——如何真正创建一个人机协同的工作空间，让人类与智能体共同产出远超任何个体或少数人所能创造的成果。

I think I would say one thing that I'm really excited about is this idea of figuring out how we, as people and agents, collaborate together. Right? I think there's like some great set of products and experiences to be reimagined. That's my other Roman empire, which is how do we actually have this co working space where, you know, you have kind of like the the humans and agents and how do you actually kind of have an output that's much, much more significant than what any one of us or any few of us can produce.

这个想法很有意思，能详细说说吗？你设想中的人机协同工作空间是怎样的？是类似Microsoft Teams这样的平台，还是实体场所里配备小型机器人？

Well, I need to hear more about this. What do you when do you imagine a coworking space of humans and agents? What does this look like? Is this like Microsoft Teams or is this like a physical place with little robots?

噢，我还没考虑过实体场所，但我确实在深入思考——目前所有这些体验都是单机版的。我认为存在这样的机遇：假设我现在生活在未来一年，我们该如何实现人类之间、以及人类与智能体之间的协作？比如哪些任务可以委派？哪些需要监督？如何让信息在人类之间流动并由智能体协调？

Oh, I hadn't thought of the physical place, but I I I I am think I am thinking a lot about kind of, you know, right now all of these experiences are very single player, right? And I do think there's an opportunity to think about how do we, again, I'm living one year in the future, how do we actually have like, you know, collaborate with each other, but with also with agents and really figure out, for example, what tasks can we delegate? What can we kind of like inspect? How do we actually have information that flows between people that agents can mediate and so on?

好的，我很期待看到你们的成果。说到这里，我们已经来到激动人心的快问快答环节。准备好了吗？

Alright. I'm curious to see what you guys got cooking. With that, we've reached our very exciting lightning round. Are you ready?

开始吧。

Let's do it.

我们开始吧。第一个问题。你发现自己最常向别人推荐的两三本书是什么？

Let's do it. First question. What are two or three books that you find yourself recommending most to other people?

哦，我有近因偏差，但我最近在读一本叫《智能简史》的书。非常棒的书，你知道，我划了很多很多重点。我觉得它的前提是研究智能的进化，比如人类智能和大脑发展，并将这些与我们目前在人工智能领域看到的现象联系起来。

Oh, I have recency bias, but I've been reading this book called The Brief History of Intelligence. Phenomenal book and, you know, like lots of lots of underlining for me. I think it kind of the the premise is to it it looks at the evolution of intelligence, like human intelligence and kind of the the brain development and connects that to what we're what we're seeing with AI.

你最近有没有特别喜欢的电影或电视剧？

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

《哈克斯》。我一直在看这部剧。讲的是一个女人，她是个很棒的脱口秀喜剧演员，背景设定大概是七八十年代，她试图在一个传统上对女性不太友好的行业里闯出一片天。非常有趣又古怪。

Hacks. I've been watching this. It's about a woman who who's this, like, a great stand up comedian of I think it's set in kind of, like, the the fact that she she grew up, I think, in the seventies and eighties and kind of, like, really tried to break through in an industry that hasn't traditionally been, like, very friendly to women. So really fun and quirky.

你最近有没有发现什么特别喜欢的产品？可以是应用，也可以是实体物品。

Do you have a favorite product that you've recently discovered that you really love? Could be an app, could be some physical.

我确实用了很多微软的产品，GitHub Copilot是其中之一。但我想我可能会选Granola，应该是这个名字的应用。我觉得它非常有用，前几天刚试用了一下，就觉得，哦，这个在记录想法、笔记并结构化方面真的很实用。它给人一种恰到好处的感觉，就像我们之前聊到的某些东西那样。

I I do use a lot of Microsoft products, GitHub Copilot being one of them. But I think the one that I maybe I I'll pick is Granola, I think is the name of the app. I I found it really useful. I just gave it a spin the other day and I'm like, oh, this is really useful in terms of being able to, know, again, like, without being intrusive, just just capture the thoughts, notes, and structure it. Put some it it it felt like one of those things where, yep, the confidence of a few things like we were talking about.

对吧？比如实时转录技术已经变得非常好了。语音识别很棒。再加上足够的LLM魔法，让它变得结构化和有上下文关联。

Right? Like the transcription real time transcription tech has gotten really good. Voice recognition is great. And then enough of the LLM magic on top of it to kind of make it structured and contextual.

我是麦片的超级粉丝。简单介绍一下：如果你成为我通讯的年费订阅用户，你整个公司就能免费享用一年麦片。

I am a huge fan of granola. I'll give a quick picture. If you become an annual subscriber of my newsletter, you get a year free of granola for your entire company.

这我还真不知道。

Did not know that.

就是这样。你可以去Lenny'snewsletter.com看看，点击'捆绑套餐'这个词，就能看到具体操作方式。

There we go. So and then just check that out. Lenny'snewsletter.com and you click the word bundle, and you'll see how to do that.

非常酷。

Very cool.

确实很酷。还有两个问题：你有没有特别喜欢的人生格言？当你遇到困难时会反复想起的那种，或许也可以分享给大家，对工作或生活有所启发？

Very cool. Two more questions. Do you have a favorite life motto that you often come back to when you're dealing with something, maybe share with folks they find useful as well in work or in life?

我有一条。实际上，这句话作为我的邮件签名已经用了大概二十年。内容是'预测未来最好的方式就是创造它'，应该是艾伦·凯说的。我觉得这句话在两方面特别有用。

I have one. In fact, actually, this is my email signature for, I don't know, for the last twenty years or so. It says the best way to predict the future is to invent it. I think it's a quote by Alan Kay. I find it useful for two things.

首先，没人能预知一切。想想那些声称'事情一定会这样发展''过程必然如此这般'的人。我认为唯有亲身实践才是不可替代的。其次，如果你觉得某件事物应该存在，那就去亲手创造它。

One is, you know, no one knows anything. Like, when you think about, like, all the folks who are, you know, kind of think about, hey. This is the this is exactly how everything is going to look and this is exactly the sequence and so on. I think there is no substitute to experientially, like, building it. And and I think the second part is, you know, like, if you think there's something that is that should exist, go build it.

我很喜欢这个话题。最后一个问题。我们刚才聊了些单口喜剧，你觉得有没有哪位不太出名但值得大家去关注的单口喜剧演员？

I love that. Final question. We've talked about stand up comedy a bit. Is there a is there like a favorite under the radar stand up comedian that you think people should go check out?

哦，确实有几位。一个是印度裔美国——或者说英国印度裔的单口喜剧演员，叫Sindhu V，超级聪明，擅长妈妈类喜剧。另一位可能不算冷门，但我超爱他的表演风格，叫Nate Mergazi。

Oh, there's a there's a couple of them. So one, I think there's a there's an Indian American or I think I think a British Indian stand up comedian. Her name is Sindhu V. Super smart, you know, mom comedy. And I think the other one that he this is definitely not under the radar, but like, I've just like love his stick is Nate Mergazi.

他真的太棒了。

He's just so good.

Aparna，这次对话太精彩了。最后两个问题：大家如果想联系你或跟进你分享的内容，该去哪里找你？听众们怎样才能帮到你呢？

Aparna, this was amazing. Two final questions. Where can folks find you online if they want to reach out maybe and follow-up on anything you shared? And how can listeners be useful to you?

可以在LinkedIn和Twitter找到我，账号是Aperna c d。最近我在LinkedIn上更活跃些，非常欢迎大家在那边留言讨论。如果今天聊的内容——特别是关于小型团队如何运用AI工具，或是大家特别期待的新产品创意——能引发更多对话，随时联系我。

You can find me on LinkedIn and Twitter. Aperna c d is the handle. I do post stuff a lot more on LinkedIn these days. So, you know, would love would love to hear thoughts, comments, conversations there. I'd say one thing that would be super interesting is if any of this stuff sparked conversations, particularly around like kind of, you know, this what do what can a small team with a lot of AI tools do or new products that folks are really excited about saying that they should exist, hit me up.

太棒了。Perna，非常感谢你参加这次对话。

Amazing. Perna, thank you so much for being here.

谢谢大家，再见。

Thank you. Bye, everyone.

非常感谢您的收听。如果您觉得本期内容有价值，可以在苹果播客、Spotify或您喜欢的播客应用上订阅我们的节目。同时，请考虑给我们评分或留下评论，这将极大地帮助其他听众发现这个播客。您可以访问lenny'spodcast.com查看所有往期节目或了解更多关于本节目的信息。下期节目再见。

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