8万家企业如何用AI构建：产品如有机体、组织架构图的消亡，以及为何到2026年智能体将超过员工数量 | 阿莎·夏尔马（微软AI平台公司副总裁）

你曾提到，我们才刚刚开始触及代理型社会真实面貌的表层。

You said that we're just starting to scratch the surface of what an agentic society actually looks like.

我们正迈向一个优质产出的边际成本趋近于零的世界。生产力与产出的需求将呈指数级增长，而实现规模化的途径就是通过代理。当这一切发生时，组织结构图将真正成为组织结构图——你不再需要那么多层级。

We're approaching this world in which the marginal cost of the good output is approaching zero. We're going to see exponential demand for productivity and output. The way that you scale to that is with agents. When all of that happens, the org chart starts to become the org chart. You just don't need as many layers.

我们之前聊过你提出的概念：产品正从静态成品转向有机生命体。

We were chatting about this concept you have that we're moving from product as artifact to product as organism.

由于当前这些模型已如此高效，你会希望针对特定结果对其进行调优。突然间，它们就成了会随着交互增多而不断进化的生命体。我认为这才是每家公司的新知识产权——会思考、会生存、会学习的产品。

Because these models are so effective at this point, you want to start to tune them to certain types of outcomes. All of a sudden, these are these living organisms that just get better with the more interactions that happen. I think this is the new IP of every single company, products that think and live and learn.

现在的规划简直疯狂。当GPT-5突然发布时，谁还能按部就班地制定路线图？

Planning right now is just crazy. How does anyone plan a road map when there's just like, okay. GPT five's out.

我们将其视为不同的发展阶段：第一季可能是AI原型期，接着是围绕模型与推理模型的时期，而现在正迎来代理的时代。

We think about it as what season are we in? Season one might have been prototyping of AI, and then it was all around models and reasoning models. And now it's the advent of agents.

今天我的嘉宾是阿莎·夏尔马。作为微软AI平台产品执行副总裁，她统管AI基础设施、基础模型和代理工具链，同时领导核心AI部门的应用工程、负责任AI及增长战略。她曾任Instacart首席运营官和Meta产品副总裁，主导Messenger、Instagram Direct、Messenger Kids及远程存在项目。她还任职家得宝和Coupang董事会，并拥有跆拳道黑带二段。阿莎的独特职位让她能比全球绝大多数人更清晰地洞察AI发展趋势，以及企业构建大规模AI产品的成败关键。

Today, my guest is Asha Sharma. Asha is chief vice president of product for Microsoft's AI platform, where she oversees their AI infrastructure, foundation models, and agent tool chains, while also leading applied engineering, responsible AI, and growth for the core AI division. She was previously COO at Instacart and VP of product at Meta, where she ran Messenger, Instagram Direct, Messenger Kids, and Remote Presence. She also sits on the boards of The Home Depot and Coupang, and she's a second degree black belt in Taekwondo. Asha is a really unique and rare role that allows her to see more than most anyone else in the world where things are heading with AI and what works and doesn't work for companies that are building large scale AI products.

对话中，阿莎分享了许多独家洞见：为何产品正从成品转向有机体、图形界面为何将被代码原生接口取代、为何后训练是新预训练、即将到来的代理型社会、当今及未来成功构建者的必备素质，以及她从密切合作的萨提亚身上学到的最重要领导力课程。若喜欢本期节目，请记得在您常用的播客平台或YouTube订阅。年度订阅我的通讯还可免费获赠15款卓越产品年费，包括Lovable、Replit等。详情访问lenny'snewsletter.com点击产品通行证。

In our conversation, Asha shares a bunch of trends and predictions that she's seeing that I haven't heard anyone else talk about. Why we're moving from a product as artifact to product as organism world, why GUIs are being replaced by code native interfaces, why post training is the new pre training, the coming agentic society, what it takes to be a successful builder today and going forward, and also her single biggest leadership lesson that she learned from Satya, who she works closely with. 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 15 incredible products, including Lovable, Replit, Bold, N8N, Linear, Superhuman, Descript, WhisperFlow, Gamma, Perplexity, Warp, Granolah, Magic Patterns, Raycast, Chappy RD, and Mobin. Check it out at lenny'snewsletter.com and click product pass.

节目由Interpret赞助。这款客户智能平台被Canva、Notion等顶尖产品团队用于整合实时客户对话（从Gong录音到Zendesk工单），并通过专属知识图谱将反馈关联至收入等核心指标。若2025年您计划升级客户之声系统，请访问interpret.com/leni。

With that, I bring you Asha Sharma. This episode is brought to you by Interpret. Interpret is a customer intelligence platform used by a leading CXN product orgs like Canva, Notion, Perplexity, Strava, Hinge, and Linear to leverage the voice of the customer and build best in class products. Interpret unifies all customer conversations in real time from Gong recordings to Zendesk tickets to Twitter threads and makes it available for your team for analysis and for action. What makes Interpret unique is its ability to build and update a customer specific knowledge graph that provides the most granular and accurate categorization of all customer feedback and connects that customer feedback to critical metrics like revenue and CSAT.

本期节目由开发者智能平台DX赞助。在AI时代，企业需要快速适应，但许多领导者难以回答关键问题：哪些工具真正有效？

If modernizing your voice of customer program to a generational upgrade is a 2025 priority, like customer centric industry leaders like Canva, Notion, Perplexity, and Linear, reach out to the team at interpret dot com slash leni. That's e n t e r p r e t dot com slash leni. Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers. To thrive in the AI era, organizations need to adapt quickly. But many organization leaders struggle to answer pressing questions like which tools are working?

它们是如何被使用的？真正驱动价值的是什么？DX提供了领导者应对这一转变所需的数据与洞察。通过DX，像Dropbox、booking.com、Adyen和Intercom这样的公司能深入理解AI如何为他们的开发者创造价值，以及AI对工程生产力产生了何种影响。欲了解更多，请访问DX官网getdx.com/lenny。

How are they being used? What's actually driving value? DX provides the data and insights that leaders need to navigate this shift. With DX, companies like Dropbox, booking.com, Adyen, and Intercom get a deep understanding of how AI is providing value to their developers and what impact AI is having on engineering productivity. To learn more, visit DX's website at getdx.com/lenny.

网址是getdx.com/lenny。Asha，非常感谢你能来参加，欢迎来到播客节目。

That's getdx.com/lenny. Asha, thank you so much for being here, and welcome to the podcast.

谢谢邀请。

Thanks for having me.

我想从我们录制前聊到的一个概念开始——这是我从未听说过的，但觉得对大家会很有启发——你提出的从‘产品作为制品’到‘产品作为有机体’的转变。谈谈这个理念的含义以及人们需要理解什么。

I want to start with something that we were chatting about before this that I've never heard about as a concept that I think is going to be really helpful for people to think about, which is this concept you have that we're moving from product as artifact to product as organism. Talk about what that means and what people need to understand here.

这个转变非常有趣，尤其是过去一年左右。当我加入微软时，正值OpenAI和大型基础模型兴起之后。紧接着就出现了模型爆炸——各种专有、开源的前沿模型不断突破边界线。它们既更高效，又逐渐在多个领域展现出专业能力。最近这些模型甚至能进行工具调用、函数调用并执行操作。我认为这正催生出一类新型产品，并已初见成效。

It's been a pretty interesting shift, especially over the last year or so, because when I got to Microsoft, it was kind of right after OpenAI and the large foundation models happened. And then immediately after there was this explosion of models, proprietary open frontier models that were pushing the frontier curve. And so they were both more efficient and then we're starting to see domain level expertise in a bunch of them. And then, you know, even more recently models now can, you know, tool call and they can function call and they can take action. And I think that's just giving way to a new type of products that are starting to see some success.

突然间产品不再只是我们发布的静态制品。不再是简单提出创意、解决问题、推向市场、稍作改进再加个仪表盘的模式。现在的核心指标变成了：产品团队如何像新陈代谢般吸收数据，消化奖励模型，最终产生某种成果。由于当前模型已如此高效，你需要开始针对特定结果（如价格、性能或质量）进行调优。这令人兴奋——这些产品就像活的有机体，交互越多就变得越强。

And so all of a sudden products aren't just like these static artifacts that we start to ship. That's not just like, hey, come up with an idea or an insight, go solve a problem, ship it into the world, maybe make it a little bit better and then have a dashboard. All of a sudden the whole KPI is what is the metabolism of a product team to be able to ingest data and then digest the rewards model and then create some sort of outcome. Because these models are so effective at this point, you want to start to tune them to certain types of outcomes, whether it's price or performance or quality. And so it's pretty exciting because all of a sudden these are these living organisms that just get better with the more interactions that happen.

从很多方面看，我认为这就是每家公司的新知识产权，这完全改变了产品构建方式，甚至改变了我们对会思考、会进化、会学习的产品的认知——这真的很激动人心。

And in many ways, I think this is the new IP of every single company and it's a completely different way to build product and even think about, you know, products that think and live and learn, which is kind of exciting.

听到这里，我想起之前采访Cursor CEO Michael Terrell时，他重点谈到他们的核心优势是收集用户使用Cursor时的数据——人们选择接受某些建议而拒绝其他建议。你指的是这种专有数据吗？还是说还有更深层次的东西？

So when I hear this, what I'm thinking about is when I had Michael Terrell on the podcast, the Cursor CEO, he talked a lot about how their big mode is the data that they capture from people using Cursor, selecting, accepting certain suggestions, not accepting other suggestions. Is that what you're talking about here? Just like the proprietary data that companies gather from people using their product or is there something beyond that even?

后训练兴起的原因在于模型本身已足够强大。今年Nathan Lambert做了项有趣的研究，分析了所有顶级排行榜。研究表明：当模型参数达到300亿后，投入数十亿token进行预训练的经济效益就开始不合理了。这时你可以转向优化训练循环。确实，使用自有数据是最佳方式，但也能通过合成数据生成。

I think why we're seeing like the rise of post training happen is just that the models themselves are so powerful. As of this year, Nathan Lambert did this study that I thought was pretty interesting of all the top leaderboards. And it showed that, you know, once a model hits 30,000,000,000 parameters, the CapEx to actually train a model and put, you know, billions of tokens into a kind of pre run kind of doesn't economically make sense. And you can kind of start to optimize on the loop. And so, yeah, in many ways, I think using your own data is the best way to do that, but you can synthetically generate data.

你需要设计奖励机制，实际部署，严格进行AB测试，找到最适合的待办事项或使用场景。然后没错——这些过程产生的数据就能供你学习。

You have to come up with a rewards design. You have to actually roll it out. You have to AB test it rigorously. You have to find the job to be done or the use case that it makes the most sense for. And then yes, like that generates data that you can learn from.

我从未见过任何产品采用单一循环模式。我认为更像是多条轨道并行运作，类似于流水线，可以这么说，它们共同产出成果。

I haven't ever seen it be one loop for any sort of product. I think it's multiple tracks running in parallel that are kind of like assembly lines, if you will, and kind of producing that.

那么这种'产品即有机体'的论点，主要是针对模型公司成立，还是同样适用于SaaS业务、工具类产品以及终端用户工具呢？

And so is this kind of thesis that we're moving towards product as organism, is this basically for model companies or is this also true for, I don't know, SaaS businesses and tools, end user tools?

我认为软件作为基础元素正在变革，其内部产物正演变为与软件组件并存的模型。因此在很多方面，所有软件产品终将成为模型化产品。

Look, I think that software as a primitive is changing and kind of the artifact inside of it is a model alongside the software components itself. And so in many ways I think that, you know, software products will all be model for products, if you will.

这让我想起为什么刚邀请Nick Turley上播客——就是我们录制前聊到的ChatGPT负责人。我问他GPT-5发布后ChatGPT会有多大变化，他说本质相同，是同一款产品，就像模型指导我们如何打造JAGGPT产品那样。

This reminds me why I just had Nick Turley on the podcast that we were talking about before we started recording head of ChatGPT. And I was asking just like, how much does ChatGPT change with GPT-five coming out? And he's just like, it's the same thing. They're the same product. It's just like the model tells us what to do in the product of JAGGPT.

这让我想到另一个问题：为什么GPT-5不能自主构建用户界面？就像随着使用自动进化，Canvas等功能正在这样做。当你谈论'产品即有机体'时，我认为产品UX可以根据使用情况自动调整演变，无需产品团队干预。

And and it makes me think about something else of just like you would think, why can't just GPT five build its own user interface Just like as you use it, it just evolved. It's sort of what it's doing with Canvas and all these things. But like, that's like another way I think about when you talk about this idea of product as organism is the product. The UX can shift based on how you're using it and evolve automatically without having product teams have to do anything.

我完全相信这是未来趋势。我的使用体验应该与你不同——虽然这类似管理员权限与个性化设置，但未来能实时实现。这将是个有趣的世界，对智能体、高级用户和新用户都会呈现不同形态。

I 100% believe that's where the world is going. And that my experience should look and feel different than yours. I mean, that's kind of been the admin and personalization, but now you can do it on the fly in the future. So I think that'll be a pretty fun world. I also think it will look different for agents and it will look different for kinda power users and new users and all of those things too.

让我们宏观些：你合作过众多基于你们或其他平台开发AI产品的公司，有的表现出色，有的举步维艰。你认为成功打造AI产品的公司有哪些共性模式？失败者又缺什么？

Let me kind of zoom out a little bit and ask you this question. You work with a bunch of companies that are building AI products on your platform, other platforms. I imagine some just do an awesome job and are killing it. Some are struggling. What do you find are kind of common patterns across the companies that do really well and have a lot of success building really successful AI products and ones that don't?

成功模式可分组织层面和建造者层面。宏观来看，成功企业首先全员拥抱AI并具备AI素养——日常使用Copilot等工具，理解如何提升上限降低门槛。

Yeah. So I think there's things that are kind of more broadly applying to the organization themselves. And then there's things that are applying to the people who are building the AI products too. So more broadly, I think there's a pattern that's starting to emerge for successful companies. Like one is they are embracing AI and everybody becomes AI fluent.

其次他们会思考：如何用AI优化现有流程？比如客户支持或将欺诈处理周期从15天缩短至10天。完整走过流程映射、AI应用、效果验证到财务收益的闭环。

So I think everybody's using some sort of Copilot or some sort of AI in their day to day workflows. Like job one, so everyone's not afraid of it, understands how we can raise the ceiling and kind of lower the floor for like all sorts of skills and tasks. Number two, from there, they start to say, okay, how can I take a process that already exists and apply AI to making it better? That might be something like customer support or taking fraud down from fifteen days to kinda cure to ten days. And like going through that entire loop of mapping out the process, applying AI to it, seeing some sort of impact, and then feeling the P and L or the kind of intrinsic benefits that looks like.

第三阶段是：见证成效后，如何驱动增长？可能是提升客户体验以改善LTV或留存率，也可能是共同创造新概念或品类。

The third thing then is like, okay, great. Now that you've seen impact, everybody is using it, how do you actually use it to inflect growth? And that can be something like improving the customer experience. So your LTV or retention improves. It could be co creating a new kind of set of concepts or categories.

可能是这样，从嵌入式代理发展到具身代理，然后能够处理指数级增长的任务。我认为企业失败的原因在于为AI而AI。他们同时启动大量项目，却没有蓝图来理解技术如何从现有架构中真正运作。他们并未将其视为真正的投资，因此缺乏完善的衡量、可观测性和评估体系。

It could be, you know, going from agents that are embedded to agents that are embodied and then being able to take you know, exponential number of tasks. I think that where companies fail is that they're doing AI for AI's sake. They have a ton of projects that they're kicking off at the same time without a blueprint to understand how it actually worked from what their stack looks like. And they aren't treating it like a real investment. And so they don't have the measurement and the observability and the evals all kind of set up.

它将实现端到端的运作。我认为企业的难点在于技术日新月异——去年AI领域就推出了约7万种企业工具，很难判断该用哪种工具达成什么目标。因此必须押注某个平台或应用服务器层，使其能灵活替换组件，而不受限于任何单一技术或工具。

It's gonna do that end to end. I think the tricky thing is for enterprises is the technology is changing. There's something like 70,000 enterprise tools, like in the AI space launched last year. It's really hard to know which one you should use for what outcome. And so you really need to bet on a platform or some sort of app server type layer that allows you to swap things in and out, and not really be beholden to anything, any one technology or any one tool.

因为现实是整体格局都将改变。必须为趋势而非现状构建系统。这是我在企业层面观察到的现象。实际上建设者本身也在经历根本性变革，对吧？

Because the reality is is the whole thing is going to change. Feel like you have to actually build for the slope instead of the snapshot of where you are. So that's kind of what I see at the enterprise level. I think the builders themselves are actually changing pretty fundamentally too. Right?

每次技术革新都会催生新角色——从大型机到个人电脑诞生了车库工程师，服务器转向云和移动时代出现了SEO专家、CDN、增长产品经理、用户体验研究员、前后端工程师等等。现在我认为通才时代正在来临，全栈建设者迎来复兴。普通企业推出产品需要约10个步骤：安全审查、规格制定、用户研究等等。

Every single advent, like change of technology has invented like a changing set of roles. Like mainframes to PCs, like the whole garage engineers. And then when we went from, you know, server to cloud and mobile, there was like SEO specialists and CDNs and, you know, growth PMs and UXR and front end, back end and yada yada. And now I think we're seeing this advent of the polymath and where I think that full stack builders are kind of having their renaissance where if you take like an average organization, it takes probably 10 steps to launch a product. It could be security review, it could be spec, it could be, you know, user research.

常规企业涉及五六个甚至七八个职能部门，加上六七层架构，突然就产生500个产品交付触点。当每周涌现500个新模型或技术时，这种模式根本行不通。因此我坚信全栈建设者的价值。

And there's what, five plus functions, maybe six or seven, I'm being generous for a normal organization. And then you have like six or seven layers. So all of a sudden you have 500 different touch points that have to happen to get a product out. And when there are 500 models available a week or 500 new technologies, that is just insufficient. And so I really believe in the concept of a full stack builder.

新兴AI原生公司正在实践这点，甚至有五十年历史的企业也开始这样运作。这种方式能提升速度和吞吐量，形成完整闭环以加速技术消化。

You're seeing it with a bunch of the AI native companies that are coming up. I'm even seeing it in enterprises that have been around for fifty years starting to operate in that way. And I think that gives you velocity and throughput and then gives you the whole loop to start to actually metabolize and go through that much faster.

这确实是反复出现的主题——产品、工程、设计的职能维恩图正在融合，角色边界不断扩展。产品经理需要提升设计或工程能力。

That's definitely a recurring theme in these conversations is just kind of the Venn diagrams of PM engineering design are starting to converge and more and more of other disciplines within your role. So PM needs to level up on design and or engineering.

完全同意。关键是闭环而非单线作战。无论身处什么职能，都必须痴迷于理解产品效率、成本收益、系统设计目标、UI/UX在代理或用户端的呈现方式，并快速掌握这些能力。

Yeah, I completely agree. I think it's all about the loop, not the lane here. And so I think that whatever function you are, you have to be obsessed with trying to understand like the efficiency or the cost of the product, the actual rewards, you like, you know, system design that you're going after the actual UI, UX, how that actually manifests for agents or people. You have to start to get really good at that really quickly.

你刚说的'闭环而非单线'这个说法很有趣，能详细阐述吗？

I like this phrase you just use the loop and not the lane. Can you say more about that?

这呼应了我们之前关于信号闭环、产品进化为生命体而非静态产物的讨论。精通这个闭环就是产品本身，就是知识产权，就是每个组织的未来。反馈将变得持续不断，可观测性将成为文化基因。

Oh, it's just going back to our previous discussion on, you know, the signals loop and products evolving and becoming these living organisms and not these artifacts. And if you think about getting really good at that loop, I think that is the product. That is the IP. That is the future of every organization. And I think feedback becomes continuous and observability becomes the culture.

我认为在未来劳动力中，职能界限会逐渐模糊。

And I think that functions start to blur in future workforces.

为了让这个更具体，能否举个产品或公司的优秀案例，真正体现了这种循环运作模式？

To make this even more real, is there an example of a product or a company that is a really good example of doing this well, living this kind of loop life?

从AI领域来看，我们观察到大多数公司都在这样做。比如编程领域，你提到的Cursor，GitHub也有类似功能——我们使用由30个国家语言数据微调的模型组合，通过循环迭代生成后续建议或代码补全。我们还有款叫Dragon的医疗AI产品，当我们将专家标注的60万条医患对话数据（而非合成数据）输入模型持续优化后，接受率从30-60%跃升至83%左右。

I think most companies that we're seeing in the space from an AI perspective are doing this. I can tell you about a couple that we're working on. Obviously in the coding space, you mentioned cursor, GitHub has very similar features that we're using kind of an ensemble of models that have been fine tuned across 30 different countries, all of the languages to actually then go iterate in a loop for next set of suggestions or code completions and things like that. We've got an AI product called Dragon that's for physicians. And we saw a massive difference from when we used synthetic fine tuning to when we annotated 600,000 patient physician interactions by experts and actually fed that into the model and continuously optimized it to then produce like, you know, I think we're sitting between thirty and sixty character acceptance rate depending on the run to something like 83%.

这只需要一个小型团队（而非庞大组织）就能实现跨职能的循环迭代，各种界限都在消融。

And so that required a small group of individuals, not a large organization that were able to actually iterate in this loop across functions and kind of all of those lines dissolving.

非常有意思。我理解的核心优势在于：收集运营数据后，投入大量时间进行高质量标注来微调模型就是制胜关键。对了，你之前提到从图形界面到代码原生接口的转变，能详细说说这对产品构建者意味着什么吗？

That's super interesting. So kind of what I'm hearing here is if you can gather data on how things are going and then spend a lot of time creating high quality labeling to feed back into it, to fine tune it is basically the big advantage is how you win in a lot of this stuff. Okay. Along these lines, something else that you told me that you've been noticing that I want to hear more about is the shift from GUIs and you kind of referenced this from GUIs to code native interfaces. Yeah.

请具体解释这个概念的内涵、表现形式及其对产品开发者的影响。

Talk about what that means, what that looks like and what this means for folks building products.

这本质上是关于未来产品制造的定义。虽然人们本能会想到图形界面，但回顾历史：数据库从桌面端转向SQL，云计算从控制台转向Terraform。我们现在正目睹同样的模式在AI领域重演。

I think it kind of goes back to what does it mean to kind of be a product maker in the future? I think that everybody's instinct is like, is GUI. But if you kind of think back in history, like databases kind of went from the desktop kind of down into SQL. I think cloud was all about consoles and now it's about Terraform. And so I think we're literally just seeing the same pattern that's played out in history start to play out in AI.

就像AI领域的其他事物遵循摩尔定律加速发展，文本流与大语言模型的契合度更高。未来产品的趋势将更注重可组合性而非界面设计——产品人需要彻底转变思维，减少对UI的过度关注，更多思考：智能体如何解析内容？如何实现无限扩展？协作机制如何运作？

And like everything else in AI, it's like Moore's law and it's getting faster. So I think that's just accelerating. And if you think about like a stream of text just connects better with LLMs. And so I think that there's a bunch of trends that are kind of working in the favor for like the future of products being about composability and not the canvas. And I think that product makers really need to rewire their mindset around this, because I think we spend an inordinate amount of time thinking about the UI of something rather than how something composes, how an agent's going to be able to read something, how do you actually get infinite scale, how does that collaboration start to work?

尽管这是长期存在的趋势，但这确实代表着全新的思维方式。

And so, I think it's just a new way of thinking, even though it's long been a trend that's happened in these changes.

所以你的预测是终端化（如Claude代码模式）？还是智能体主导？或是两者兼有？你指的是...

So, is the prediction here that it's terminals, like Claude code sort of experiences, or is it that it's agents that are taking Or is it both? Is that kind of what you're

不，只是...是的，我是说，听着，如果我们中有人知道那会很棒。我认为终端之所以出色，以及编码时感觉如此良好的原因，在于它能以文本流的方式与LLM交互。我认为两者可以并行不悖——人类将继续提交代码，我们会找到新的实现方式，无论是在IDE中、GitHub Copilot里，还是某个新的开发环境。我们将与智能体协作完成这些，智能体之间也会相互协作，并由此持续进化。

No, just it's Yeah, it's I mean, look, if any of us knew, that would be amazing. I just think that the reason why terminals are great and it feels really great when you code is because of the way it can interact with an LLM with the text stream. And I think that both can be true that humans will continue to commit code and we'll find, you know, new ways to actually do that, whether it's in the IDE, whether it's in GitHub Copilot, whether it's in, you know, some new development environment. And I think that we'll do that with agents and agents will do that with each other and we'll continue to kind of evolve from there.

我们播客曾邀请过Sierra创始人Brett Taylor，他也有类似预测：所有软件公司都将转型为智能体公司。你刚才说的本质上是软件将变成后台运行的存在，GUI界面会大幅减少。你认为交互方式会保持我们现在习惯的聊天界面吗？这会是智能体的主要交互方式，还是会出现其他形式？

We had a Brett Taylor in the podcast, founder of Sierra, and he had a similar prediction that all software companies are going to become agent companies. And it's essentially what you're saying here is that like your software will just be this thing that's running in the background and there's much less of a GUI. Do you think it still becomes like this chat interface the way we're kind of getting used to? Is that like the primary interface with agents or is there anything something else happening?

我认为对话是非常强大的交互形式。我曾从事即时通讯领域的工作，它确实适合多种沟通场景，但并非唯一方式。比如我们现在仍用邮件协作，使用Word和PPT等文档工具。

Like, I think that conversation is a really powerful interface. I worked on messaging. I think it's great for lots of forms of communication, but it's not the only form of communication. I mean, we use email today to collaborate with each other. We use docs, like everybody uses Word and PowerPoint.

全球有十亿人生活在各类数字工具构成的环境中，我认为这些都可以成为未来图景中重要的可组合模块。它们理应如此，这让我感到兴奋。聊天交互会很重要，但显然不够全面。

You know, there's a billion people living in places of artifacts that I think can become really important composable pieces of the picture. And I think they should be. So I'm excited about that. I think that chat will be important, but certainly not sufficient.

有趣的是，ChatGPT作为有史以来增长最快的产品——或许也是最具影响力的产品——其核心形式正是聊天。

What's interesting is ChatGPT, the number one fastest growing product of all time, maybe the most important consequential product of all time is chat.

是的，这很棒。

Yeah, it's great.

我

I

认为我们需要自问的是：未来会永远只有聊天这一种形式吗？

think the question we have to ask ourselves is, will it only always be chat?

对，对。Nick的描述很有趣，他说我们正处于ChatGPT的MS DOS时代——这与你说的形成反向类比。就像先经历命令行阶段，再发展到GUI，最后可能又回归本源。

Yeah. Yeah. The way Nick described it is we're in the MS DOS era of ChatGPT and there's a which is interesting. It's like the reverse of what you're saying. So it's like maybe if you start as that and then you have to move to GUI and then maybe it'll go back.

但他预测会出现类似Windows的版本，让用户更容易理解到底发生了什么。

But he said there's gonna be like a Windows version where it's much easier to understand what the hell is going on.

是的，我认为这很明智。每家公司都应该将AI带到用户所在的地方。ChatGPT的所有用户都在使用聊天功能，这是一个非凡的产品。我们在全球有许多人以不同方式工作，我们应该思考如何利用AI来赋能这些工作方式。

Yeah, I mean, look, like I think that it's smart. Should, every company should be bringing AI to where their users are. And ChatGPT has all of their users using chat and it's a phenomenal product. And we've got lots of people around the world that do work in many different ways. And we should be thinking about how we use AI to enable that.

那么我们来谈谈智能体。你花了很多时间研究智能体、构建智能体、帮助企业开发智能体。你有一句我非常喜欢的精彩论述——你说我们才刚刚开始触及智能体社会的表面。我特别喜欢‘智能体社会’这个概念。

So let's talk about agents. You spent a lot of time working with agents, building agents, helping companies build agents. You have this really great quote that I love. You said that we're just starting to scratch the surface of what an agentic society actually looks like. I just love this idea of an agentic society.

未来这究竟会是什么样子？

What that actually look like in the future?

天啊，说起来很有趣。你刚才提到你两岁的孩子，而我儿子刚满一岁，我甚至无法想象两岁的生活，感觉那还很遥远，到时候会发展成什么样。我认为未来的工作形态将截然不同，我们正在接近一个优质产出的边际成本趋近于零的世界。

Oh gosh. I mean, it's funny. You were telling me about your two year old and I have my son where I'm just turned one and I can't even imagine life at two, because I'm just like, that is so far away and what will have then developed. Look like, I think that in the future, work will look really different. I think that we're approaching this world in which the marginal cost of a good output is approaching zero.

当那一刻到来时，我们将看到对生产力和产出的指数级需求。而实现规模化的方式就是通过智能体——那些嵌入工具和软件模块的智能体。未来这类智能体的数量将远超现今使用的软件，还会出现具身智能体，现在已初见端倪，对吧？

And I think when that happens, we're going to see exponential demand for productivity and outputs. And I think that the way that you scale to that is with agents and it's agents that are embedded and their tools and their pieces of software. And I think there's going to be a ton of those far more than the software that we use today. And then I think there could be a set of embodied agents that are developed. And we start to see that now, right?

你可以把代码审查任务分配给Copilot，可以创建具备自主性的软件开发代表，它能帮你完成部分潜在客户开发和数据挖掘。当这一切实现时，组织结构图将转变为工作流程图。任务和吞吐量会变得比以往更重要，管理层级也将大幅减少。

You can assign a pull request to copilot. You can create a software development rep that's agentic that can kind of do some of the lead generation and mining for you. And so I think that when all of that happens, the org chart starts to become the work chart. I think that tasks and throughput become more important than they have been before. I also think that you just don't need as many layers.

我认为几年后整个组织架构可能都会改头换面。对此我感到非常兴奋——会议仍会存在且可能依然古怪，但应该会有所改善。普通员工将能扩展技能树，因为他们可以带着专属的智能体工具栈上班，就像自带设备一样，获得前所未有的能力接入。

Like I think the whole kind of organizational construct might start to look different in a few years. And so I'm pretty excited about it. I think meetings will still be meetings and they'll be weird, but I think they will be a bit better. And I think there'll be lots of changes. I think that for the average employee, my hope and kind of my optimistic view is that they will be able to expand their skillset because now they have their own agent stack that they can bring with them to work, just like you can kind of bring your own device and you can start to have access to a set of skills that you never had before.

想象全美约两千万相关从业者技能提升20%，这对GDP的推动将是指数级的。这前景相当有趣。

And so if you think about, you know, the 20,000,000 people that maybe sit in that space across America and they get 20% more skilled, that's like pretty exponential for GDP. And so it's pretty fun.

你关于‘组织结构变成工作流程图’的论述非常深刻。不知你是否这个意思——当我设想组建团队时，会设定使命、目标和KPI，由人类执行。但听你描述后我突然意识到：如果用智能体执行，它们的指令就是‘去推动转化率’，这些智能体群就构成了组织架构中的‘转化入职团队’，就像一群自主工作的智能体。

This comment you made about the becomes work the org chart is such a profound concept because I don't know if this is what you meant, but what imagining is you build these teams and here's your mission and goal and KPIs and it's humans and like, oh, cool, go do this first. And what I'm recognizing as you're talking is like, okay, but if you have agents doing that, that is their prompt, go drive conversion. And then you have all these agents and that's the org part. This is the conversion onboarding team. And that's like a bunch of agents off doing their work.

你是这个意思吗？

Is that what you mean?

是的。我是说，如今我们思考问题的方式往往是：组织架构中谁向谁汇报，谁负责哪些领域。但归根结底，当你拥有一批能干的智能体时——人类本就具备多任务处理能力——你就不会执着于层级结构或向上沟通。你会开始探索基于任务的外向型协作机会。我认为在组织中，人类始终会决定AI的使用方式和应用场景。

Yeah. I mean, yeah, I think like today we think in terms of, hey, who reports to who in the org chart and who's responsible for these areas. And I think at the end of the day, you have a set of capable agents and people are capable of more things, you're not gonna start to think in hierarchy and communicating upward. You're gonna start to figure out like kind of outward task based type of opportunities. I think that humans will always decide in organizations how AI is used and what we want to apply it to.

但当新问题或新任务出现时，如何自动决定其分配路径？谁在负责该任务？具体如何执行？如何监督智能体行为是否正确？出现偏差时又如何微调？这些确实令人兴奋。

But yeah, it's kind of exciting when a new issue comes up or a new task comes up, how do you actually automatically decide where to route it? Who's working on that task? How do you actually go work on it? How do you observe if the agent's doing the right thing? How do you fine tune it if they're not?

所有这些环节。当然这只是我的推测。但这样的前景确实激动人心。更棒的是这意味着我们能成就更多。

Like all of those things. So I think that I'm just speculating, right? Yeah. That there's a world in which that could be pretty exciting. And I think that's great because we can just accomplish more.

你提到审核工作会越来越重要——如果有一千个智能体同时运作，天啊，那监督压力太大了。你认为审核机制会如何进化？如何扩展这种监督能力？

You touched on this point that reviewing the work is gonna be increasingly important if you have like a thousand agents off doing work. It's just like, holy moly, that's a lot to look at and make sure they're doing the right thing. How do you think that evolves? Just like being able to scale your ability to review the work that's being done.

我们之前讨论的闭环机制会愈发关键：微调、自我修复、可观测性、完善的评估体系等。好在现有系统已能管理数十亿级用户，无需 reinvent the wheel。当然如果实现智能体规模化，仍需学习新知识，但设备管理、权限控制这些基础问题已有成熟方案。

Yeah, I think that the same kind of loop that we talked about becomes increasingly important, like fine tuning and self healing, observability, really good evals, all of that. I mean, the good news is that there are systems that manage this for billions of people today that already exist. And so I think that, you know, we don't have to reinvent the wheel. There's certainly going to be a bunch of new things to learn if that world ever plays out. But I think, you know, managing devices and policies and group access, all those things are solved problems, which is good.

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人们正以各种方式使用智能体。除了编程这类主要应用外，你和团队是否发现其他高价值应用场景？有没有什么突破性案例？

People are using agents in all these different ways. Is there any way you and your team have found a value in working with agents of some kind other than coding, I imagine, is a big part of it? Just anything there that's like, wow, that's a big deal.

目前AI智能体已融入我们多数工作流。比如我最爱的应用场景：当工程团队不在线时，我会参与现场故障处理。通过智能体自动生成事件摘要特别实用——通常15人同时讨论时根本理不清故障始末。有了摘要我就能快速定位问题并跟进，这彻底改变了整个DevOps领域的工作方式。

At this point we have AI and agents in many of our workflows, like one of my favorite ones. So right now are my engineering partners out. So I jump on the live site bridges when something goes down and you know, as something as simple as like, you can automatically get a summary of everything that just happened because usually there's 15 people talking, you don't actually know where the incident started, where it's going to end and everything. And then all of a sudden I have that and I can kind of figure out and ask questions and get updates, like awesome. I think that kind of the entire kind of DevOps areas is changing.

我们使用Spark来创建原型。因此团队每个人都需编程，但有时仅通过闲聊或用自然语言交流反而能催生出更有趣、更具表现力且更能体现创造力的原型。我们就是这样实践的。我认为现在人人都在用AI写作，利用AI寻找效率提升的途径，比如生成文档之类的工作。

We use Spark to create prototypes. So everybody on the team is expected to code, but like, you know, sometimes just chatting in and like talking in real words actually gets you to a prototype that's more interesting and like more expressive and reflective of your creativity. So we use that. I mean, I think everybody's using AI to write. Everybody's using AI to kind of find ways to have efficiencies and like coming up with documentation and things like that.

所以我认为AI已无处不在，这很酷。但我觉得对于智能代理协作的可能性，我们才刚刚触及皮毛。

And so I think it's everywhere, which is cool. I think that we're just scratching the surface though for kind of like what's possible in terms of working with agents.

每当有人问我如何使用AI时，我都是这种感觉——它已经渗透到我做的每件小事里，现在根本不知道该怎么描述它。

That's how I always feel when people ask me how I use AI. I'm just like, it's just like everywhere. It's just like in every little sprinkled in everything I do now. I don't even know how to describe it.

是啊，很难想象没有AI的世界了。

Yeah. It's hard to remember a world where it didn't really exist.

确实。有位合作的产品经理Peter Yang说过，他现在根本不知道没有AI该怎么写战略文档——以前人们到底是怎么完成的？

Yeah. There's there's a product manager that I collab with, Peter Yang, who talks about how he just doesn't. I don't even know how to do a strategy doc anymore without AI. How did people do this without having someone

你觉得未来还会有战略文档这种东西吗？这个问题很有意思。

do think there will be strategy docs in the future? That's going to be interesting.

我曾写过一篇文章，探讨PM工作中哪些技能最容易被AI取代。关于战略能力争议最大——假设某个AI掌握了你所有的市场趋势认知、指标数据和产品现状，它制定的战略可能比人类更出色。但很多人认为这恰恰是AI长期难以胜任的领域，因为需要人类判断力。

I have this like I wrote this post once of like which skills of a PM job will be most replaced by AI. And strategy is the one that people are the most have the biggest debate on. Like you could argue, I don't know if like, let's get into it briefly. Would think if some AI had all of the information you had about where the market's going, your metrics, your product today, it would be so good at developing a strategy for you. Many people think that's the one thing AI will be really not good at for a long time because that's where we need all this human judgment stuff.

不知道你怎么看？

I don't know, do you have any thoughts?

我认为那些改变世界的产品，往往需要确定性逻辑输入与人类独有的创造力、想象力、判断力和愿景的结合。比如微软的软件工厂愿景并非必然，Instacart的成功源于不同于Webvan的思维方式，iPod的突破也是如此——这些都需要亲身实践才能获得的判断与迭代。

I think that some of the most consequential products in the world required a bunch of kind of deterministic, like logical sets of inputs and like sparks of creativity and imagination and judgment and vision that could not be achieved without humans. Like Microsoft is like the vision of a software factory and creating what Microsoft did wasn't inevitable. Instacart, you know, there was web bands and web bands didn't work, but Instacart did work because of a different way of thinking about it. That came through judgment and iteration and a bunch of things that you couldn't have learned unless you actually went through the process. You know, the iPod, like you go forward.

因此我认为文档本身会逐渐演变为生产力工具中的应用程序或其他产物，这本质上是工作方式的变革。

So I think it's there. I think docs themselves, like for every idea, for every, you know, need will just start to kind of fade into, you know, applications and different artifacts in the productivity suite, which, you know, is just a different way of working.

是的。就像你最初的问题，我并没有完全回答，但我认为很重要。你在问我们是否真的需要战略文档？我想某种程度上每个人都需对战略达成共识。也许形式并不重要。

Yeah. Like your original question, which I didn't quite answer, but I think is important. You're asking like, do we even need strategy docs? And I guess it's just like somehow everyone needs to be aligned on the strategy. Maybe it's not a different.

对，也可能是其他形式。

Yeah, it could be some other.

我的意思是，要正确构建一个能跟上AI发展的组织，你需要不同于传统工作方式的协调机制。

I mean, you architect an organization the right way to keep up with AI, you need a different alignment mechanisms than traditional ways of actually working.

那让我具体问问这个。现在的规划简直疯狂。当GPT-5突然发布时，人们该怎么制定路线图？你们团队是如何设定实际路线图和战略的？

So let me ask you actually about that. So planning right now is just crazy. How does anyone plan a roadmap when there's just like, okay, GPT-five's out. Okay, great. What works for you for setting an actual roadmap and a strategy for your team?

比如你们规划多远？多久需要重新评估一切？

Like how far out do you plan? How often do you have to rethink everything?

首先声明大家都在摸索，大组织比小团队更难调整——后者能自主运作，各有利弊。我们公司的产品团队传统上按学期规划，每六个月重新制定战略，回顾前瞻。这种方式很有价值。

I mean, I'll caveat this by saying like, everyone's just figuring it out and it's a lot harder to figure it out when you're a larger organization than when you're, you know, much smaller and you get to kind of, you know, run something yourself and there's pros and cons to both. So here's what we do. The company historically, at least in our product teams had kind of semesters that they planned again. So think of that as every six months, there's kind of a strategy with back, look forward, all of those things. I think that's very valuable.

但六个月周期要真正把握前沿变化极具挑战。我们将其视为季节更替——一个由行业根本性变革或客户需求定义的阶段。比如AI原型期、GPT早期是第一季，模型推理是第二季，现在进入智能体时代。

I think like the idea of six months though, and really understanding what's changing out in front is truly challenging to kind of have a overbaked situation. And so we kind of think about it as, you know, what season are we in? And so a season which is very uncomfortable can be denoted by a set of secular changes that are happening in the industry or that are happening from customers. And you can think about season one might've been like, you know, the prototyping of AI and kind of the early GPT work. And then it was all around models and reasoning models, and now it's the advent of agents.

这些季节可能持续一年、半年或三个月。关键是让全员理解：根本性变革是什么？要解决哪些客户问题？成功标准是什么？我们通过北极星指标来统一认知。

And so that can last a year, that can last six months, that can last three months, but like grounding everybody on the ethos of what are the secular changes? What are the customer problems we need to solve? What does winning look like? So everybody has that shared sense. What is the North Star metric is something that we do.

第二是设定宽松的季度OKR：基于当前认知，下季度该如何推进？然后各小队制定4-6周目标来攻克具体问题。作为公司AI平台部门，我们经常调整这些计划，必须保持开放心态——这就是我们的行业特性。

The second thing that we do is that we have kind of loose quarterly OKRs. So like, okay, if we believe that, what do we need to do next quarter to actually put ourselves on a path to that? And then from there, you know, teams are operating in squads and they're kind of setting out, you know, four to six week goals that they're trying to go after for problem areas to go ladder up to that, You know? Especially as the platform for the company and the platform for our adver customers with AI, I will say we go through lots of changes to that all the time. And I think we have to just have an openness that that is the business that we're in.

另外我们会预留弹性空间，不仅应对突发情况，更为主动变革。必须持续思考如何颠覆现有平台架构，并投资必要资源。所以我们采取双轨并行策略。

I think the other thing is just like, we try to leave slack in the system, not just for the unplanned, but for the slope. I think that we have to continuously be thinking about how we're going to disrupt the platform in our thinking and what we need to be investing in to make that possible. And so we try to do a little bit of both.

这太棒了。所以我听到的是，这里有一个季节的概念，而且大家都保持一致。好的。现在是代理的时代。这就是正在发生的事情。

This is awesome. So what I'm hearing here is there's this concept of seasons and everyone's aligned. Okay. This is time for agents. This is what's happening right now.

我们将围绕代理来制定我们的战略。然后有一些宽松的季度OKR，你大致规划三个月。然后在系统中留出一些灵活性以适应变化。是的。当前的季节是代理吗？

We're going to center around our strategy around agents. And then there's these loose quarterly OKRs you plan for three months roughly. And then you leave some slack in the system for things to change. Yes. Is the current season agents?

你会如何描述我们现在所处的季节？

How would you describe what season we're in right now?

是的，好的。是代理。代理的崛起。

Yeah. Okay. It's Agents. The Rise of Agents.

代理的崛起听起来像终结者电影。你对下一个季节有什么感觉吗？有没有什么像是，哦，这个可能会是下一个？

The Rise of Agents sounds like a Terminator movie. Do you have a sense of what the next season might be? Is there any like, oh, this might be coming next?

天哪，我不知道。但我想，你看，我们现在有超过15,000个代理部署在我们的服务上。至少在Azure服务上，公司还有其他一些平台。我想说的是，我认为我们应该真正专注于确保我们有所有的对齐、责任、可观察性和评估，以使这些代理变得出色。我认为Manus在这个领域的突破是他们可以进行这些工具调用循环，让代理执行更长时间的任务，这是其他平台无法做到的。

Gosh, I don't. But I think that look like we have, you know, more than 15,000 agents that are deployed on our service today. At least at the Azure service, there's a bunch of other platforms in the company. And I would just say that I think that we should really focus on making sure that we have all of the alignment, accountability, observability, evals to making those agents like great. I think that Manus' breakthrough in this space was that they could like do these tool calling loops and have agents kind of do longer running tasks that really no other platform was able to do.

我认为像这样的东西是至关重要的。记忆是至关重要的。仍然有一堆构建块，我认为在野外让代理不完整，我认为我们必须在继续前进之前真正关注这些细节。

I think stuff like that is critical. Memory is critical. Like there's still a bunch of building blocks that I think like are leaving agents incomplete in the wild that I think we have to really sweat the details on before we move on.

所以就像代理一直持续到超级智能出现。然后我们就可以在沙滩上放松了。

So it's just like agents till the end of time until super intelligence. And then we're just on beaches chilling.

是的。代理直到出现有趣的梗。听着，是的。我认为很酷的是，三个月后可能会有新的东西出现，十三个月后也可能会有新的东西出现。我认为我们对一组构建块有一种信念，我们想提供这些构建块以使这些代理能够持久并具有高耐力。

Yes. Agents until dank memes. Look, like, yeah. I think the cool thing is, is like something new could come in three months, something new could come in thirteen months. I think like we kind of have this conviction on a set of building blocks that we wanna provide to enable these agents to endure and have high endurance.

所以这就是我们即将要做的

And so that's what we're about to

当你说有15,000个代理时，这是什么意思？是指可以使用15,000种类型的代理，还是说数量达到了15,000个

When you said there's 15,000 agents, what does that mean? Is that 15,000 types of agents you can use or is it like that's how

不，那是，流程是，你知道的，客户有15,000个。我想我应该重新确认一下数字。15,000个客户已经创建了代理。我认为实际代理数量可能达到数百万。

many No, that's, processes are you know, customers 15,000. I think I should re reference the numbers. 15,000 customers who have produced agents. I think the number of agents is actually like millions.

15,000个客户在你们平台上构建特定类型的代理，并且正在运行，而代理数量已达数百万...好吧。这太疯狂了。这些数字令人难以置信。那么让我换个方向聊聊。

15,000 customers that are building a specific kind of agent on your platform and they're running and the number of agents is in the millions just running in Okay. The How's it's wild. Some crazy numbers here. Okay. So let me just kind of go in a slightly different direction.

你就像是处在AI风暴的中心，目睹着一切发展。在担任这个角色之前，有没有什么事情是你希望自己早知道，而现在发现'啊，这个我真没想到'的？

You're kind of in this, you're kind of in the center of the storm of a lot of AI, just like seeing everything that's going on. Is there something you wish you'd known before stepping into this role that you're just like, okay, I see, I didn't expect this.

当我最初接手这个角色时，它被描述为像是身处野兽腹中。我的职业生涯大部分时间都在机器学习应用或业务的核心领域打造产品。令我惊讶的是，很多经验其实可以迁移——打造优秀平台的要素与打造优秀产品是相通的。对我来说，关键往往在于那些看不见的工作，而非表面功夫。比如，我最早任职的公司之一叫Porch Group。

When I first took the role, it was kind of described as like the belly of the beast. And I had spent most of my career building products at the center of machine learning and applications or businesses. And I think that to my surprise, a lot of the learnings have translated in terms of what makes a great platform is what makes a great product. And like the thing for me is like, it's often in the invisible work or the like, not the pixels that actually drives that. So like, for example, one of the first companies that I worked at was a company called Porch Group.

我是第七号员工，当时我们就立志要帮助人们打理家园。我们发明了许多功能，比如家庭报告、家居管理工具，还有房屋风格灵感库——你可以浏览所有房屋并标注每个房间。而在我任职期间，我们做的最重要的事就是创建了一个匹配平台，将600万专业人士与1,300种服务类型、37,000个邮政编码以及北美所有业主连接起来，真正实现家居护理。这就像一场渐进式的优化游戏，通过不断改进这个引擎来生成更高质量的潜在客户——正是这个让我们达到了首轮5亿美元的估值。

I was employee seven and we knew we wanted to help people take care of their home. And I think we invented so many features like the home report or like a way to manage your home or like house style inspiration, where you could like see all of the houses and map every single room. And the single most important thing that we could have done and did during my time there was create a matching platform that matched the 6,000,000 professionals with the 1,300 service types, the 37,000 zip codes and all of the homeowners in North America to actually take care of their home. And that was just the game of inches and kind of optimizing that engine in order to create higher quality leads essentially. That's what got us to the first $500,000,000 valuation.

后来我们在此基础上拓展了其他垂直服务和软件平台，最终推动公司上市。即时通讯领域也是如此。我领悟到最关键的一点是：WhatsApp的成功并非靠贴纸、动态故事或深色模式——事实上它胜出时可能根本没有这些功能。它赢在几个基本要素上，其一是通讯录整合。

That's eventually what we built on to actually have other vertical services and software platforms that IPO ed the company. Same with messaging. The number one learning that I had was look like WhatsApp didn't win because it had stickers or stories or dark mode. In fact, I don't even think it had all of those things when it won. It won on a few premises because one was the phone book.

使用WhatsApp时，你知道能联系到通讯录里的每个人，因为这些是你真正在乎的联系人。其二是可靠性和速度——我可以随时给印度的祖母发消息，确信她一定能收到。其三则是隐私保护。

Like you knew that when you use WhatsApp, you could reach every single person because you had their phone number. And those are the people that you care about when you're using messaging. It was the reliability and how fast it was. Like I could text my grandmother in India and know that she would get my text message all the time. And then it was the privacy.

当你每天给最亲密的四个人发200条消息时，你绝不想让其他人看到内容。因此端到端加密至关重要。真正重要的不是数百个花哨功能，而是底层架构和平台基础。Instacart也是如此——虽然有很多受欢迎的功能，但本质是每分钟更新3,000次的十亿级商品库，确保用户从心仪的商店收到 groceries。

Like when you are sending 200 messages a day to the four people you care about most, you wanna make sure no one else can read the messages. And so the end to end encryption really mattered. And so it wasn't the hundreds of features, it was all in kind of the infrastructure and the platform. And same with Instacart, right? Like there are so many loved features of Instacart, but at the end of the day, it's a billion items that updates 3,000 times every single minute to get homeowners their groceries from the store that they love.

现在我终于明白：平台价值不在于功能堆砌，而在于数据驻留（比如德国医院可以放心地微调模型而数据不会出境）、可用性、可靠性，以及为企业提供合适的工具组合与知识检索方式。这正是我们打造的平台理念，只是当初未能完全预见这些经验的可迁移性。

And so I think I wish I had known that because I think it would've curtailed my learning curve to say that it's not all the features for the platform that matters, it's the data residency. So the hospital in Germany that's fine tuning a model can do so in confidence and the data isn't going to leave the region. It's the availability, it's the reliability, it's, you know, making sure you have the right selection of the tools that enterprises need and the right way to retrieve the knowledge. And that's kind of the platform that we've built, but just didn't fully have that picture that those learnings would translate.

这真的很有趣。所以我听到的是，人们往往低估了马斯洛需求层次中最基础的部分，这些基础要素能帮助你在平台上取得成功，尤其是在包括即时通讯在内的平台上。比如可靠性、隐私性，还有可用性等等。

That's really interesting. So what I'm hearing is people kind of undervalue just have the simple bottom of the Maslow hierarchy of that help you win in platforms, especially in messaging platforms, including. So it's like reliability, privacy, I don't know, availability.

没错。性能、可靠性、隐私性、安全性，所有这些要素。

Yeah. Performance, reliability, privacy, safety, all of those things.

让我问你一个完全不同的问题。之前我们准备录制时，你说‘哦，我要和萨提亚开个重要会议，得改期’。能和他共事的人可不多，他是一位非常成功的领导者。

Let me ask you kind of a totally different question. When we were going to record this previously and you're like, oh, I have a big meeting with Satya, I get to do instead. And so we moved to a different time. Very few people get to work with Satya. He's quite a successful leader.

你从他身上学到了什么关于领导力或产品打造的经验吗？

What's something you've learned from him about, I don't know, leadership or product building?

我学到乐观是一种可再生资源。这家公司五十年来有无数理由可能失败，但它成功了。即使在AI时代早期面临挑战又取得成就，这个领域发展如此迅猛，他那种能持续产生能量、用乐观精神重燃每个人对使命的奉献的能力令人难以置信。我认为这是公司文化中极其重要的一部分。大家都在谈论成长型思维。

I've learned that optimism is a renewable resource. Like this company for fifty years has had, you know, every reason not to succeed and it has. And even as it's had early success in the AI era and challenges and other successes, the space is developing so quickly, I think that his ability to generate energy and to use his optimism to kind of renew everybody's dedication to the mission is unbelievable. And I think it's such an important part of the culture. Everybody talks about the growth mindset.

这确实是文化的核心部分。但在我看来，在完全竞争的人才环境中，能够持续为每个人每天明确目标、注入能量，并用乐观精神巩固承诺的能力，实在令人惊叹。

That's real, huge part of the culture. But I think the ability to generate energy and clarity on what we need to go do and use optimism to renew the commitment every single day for every single person in an entirely competitive talent space is like, is pretty amazing.

你觉得这是他与生俱来的特质，还是他后天培养的这种为所有人传递乐观的能力？

Is that something you think that was just innate to him or it's something that he's worked on to just generate this optimism on behalf of everyone?

我不清楚。我们该问问他本人，但我对此深感钦佩。

I have no idea. We should ask him, but I am like deeply impressed by it.

有趣的是，这些很大程度上归结为一种氛围。就像他散发的这种能量——不仅仅是言辞，而是他周身洋溢的乐观与活力。

It's interesting that a lot of this comes down to just vibes. It's just like this vibe of, you know, like imagine it's not him, just the words he uses. It's just like this energy that he exudes optimism and energy.

想想看，我们每天选择关上门离开孩子去工作——刚有人这么对我说，我觉得很精辟。你必须投身于真正打动你的事业，深信它能让世界更美好。我想这就是为什么氛围如此重要。

I mean, think about it. We all choose to, you know, who just said this to me and I thought it was great. We all choose to close the door on our kids every single day to go work on something. And so you have to work on something that is like deeply moving to you and is like, you you have a deep belief that is going to make the world a better place. And like, I think that's why it's vibes.

我认为你必须追随并肩负起一项超越个人使命的责任感。

Like, I think you have to follow and have a sense of duty towards a mission that is bigger than yourself.

这让我想起我在这个播客中多次引用的一句话，它真的深深触动人心：唯一会记得你加班的人是你的孩子。

It makes me think of a line that I've referenced a couple of times on this podcast that's really hits people really hard. That the only people that'll remember you working late are your kids.

好吧，我不明白你这话的意图，但这听起来有点... 你知道，不太对劲

Okay, I don't know where we're going with that, but that was like, know, not Too much, your

过头了，我们扯太远了。天啊，好吧，那我问你，是什么在驱动着你？

too much, we've gone too far. Oh man, okay, well let me ask you this, what's We driving

可能会惹恼我们的客户。我们本可以采取不同的处理方式。

could upset our customers. We could have gone a different route on that one.

这才是重点。是什么驱动着你？是什么让你对正在做的工作保持热情？

This is the real stuff. What's driving you? What's driving you? What's keeping you excited about the work that you're doing?

AI将如何从劳动力角度帮助我们，如何从医疗健康角度改变现状。比如，我母亲患有癌症，我经常想——哇，我们或许能在我有生之年找到治愈她这类癌症的方法。三年前我还觉得这是天方夜谭。所有这些都意义深远。而我现在特别关注的是，既然我们知道自己正身处这个拥有强大技术的时代，该如何最大化它的效益，以及如何构建一个让人们能充分利用它的平台。

What AI will help us do from a workforce perspective, what it will help us do from a healthcare perspective. Like, my mom has cancer and I think a lot about how, wow, we might find a way to solve the form of cancer she has in my lifetime. And I never thought that was possible three years ago. Like all of that's deeply profound. And the thing that like, I personally think a lot about now that we know that we're living in this time working with such powerful technology is the effects of it and how I can best build a platform where people can make use of it.

我在微软工作的原因在于，这家公司的核心理念是'如何帮助个人和企业成就更多'。对我而言，深夜除了思考GPU之外，我还在想：未来我的儿子还会有同学吗？这并非因为AI代理会取代人类，而是生育率正在下降——我们成长的上世纪九十年代平均生育率是3，现在只有2.3。

So like the reason why I work at Microsoft is because like the whole ethos of the company is like, how do I help people and businesses achieve more? And like more for me and the thing like I think about at night outside of, you know, GPUs is, you know, I think about like, will my son have classmates in the future? And that's not because agents are going to replace them. It's because the fertility rates are declining, right? Like the average birth rate in the nineties when we were growing up was like three and now it's two point three.

据估计到2025年，生育率将跌破替代水平。我认为AI能对此产生巨大影响，事实上已经在发挥作用。我刚读到伦敦某家医院运用AI匹配卵子和精子，既提高了受孕率又降低了成本。昨天ChatGPT-5发布会上，就有它在医疗领域助力的精彩案例。斯坦福大学是我所建平台的重要客户，他们正在利用AI进行肿瘤复查。

And in 02/1950, it's estimated to be, you know, below replacement. And I think that AI can have such a big effect on it and already is. Like, I was just reading about a hospital in London that's, you know, able to improve pregnancy rates by using AI to match, eggs and sperms and they're cutting costs at the same time. You saw with the ChatGPT five launch yesterday, such an amazing story about how ChatGPT is helping in healthcare. Stanford's one of our big customers with the platform that I build and they're working on using AI for tumor reviews.

正是这些突破将推动人类进步，延长寿命，赋予我们解决百年难题的能力。这就是让我兴奋的原因，也是我投身于此的意义所在。

And it's just like, that is like, it is these sets of things that will like move humanity forward and expand our lifetime and give us the like privilege to solve hundred year problems. And so that's why I'm excited and that's why I do what I do.

确实。尤其是在你负责构建支撑这一切的平台的岗位上，我能看出这会产生多么深远的影响。Asha，在我们进入激动人心的快速问答环节前，关于我们之前讨论的内容，你还有什么想补充、分享或特别强调的吗？

Yeah. Especially in your role where you're building the platform that enables all of this, I could see how impactful that could be. Asha, is there anything else that you wanted to touch on or share or double down on of anything we've talked about before we get to our very exciting lightning round?

我们略有提及，但我认为随着具备思考、行动和推理能力的智能体和产品的出现，强化学习将迎来新一轮浪潮。我深信这将成为未来阶段——至少接下来几个阶段——最重要的产品技术之一。

We touched on it a little bit, but I think that with the advent of agents and products that think and can act and reason, there's going to be this kind of new wave around RL. And I have a deep belief that that will become one of the most important product techniques kind of the next season or at least the next few seasons.

在我们的领域中指的是强化学习。

In our role is reinforcement learning.

没错，正是如此。我相信未来用于后期训练的资金投入将不亚于预训练阶段，甚至更多。我们讨论过Nathan Lambert的研究，他的结论是当模型参数达到300亿时，对其进行微调和优化更有意义。调查显示目前50%的开发者都在进行微调。虽然微调效果不错，但完整走完整个优化闭环才能获得更佳结果。

Yes, yes, exactly. Like, I believe we will see just as much money spent on post training as we will on pre training and in the future more on post training. We talked a little bit about Nathan Lambert's study where his review was that, you know, when a model hits 30,000,000,000 parameters, it makes more sense to kind of fine tune and optimize that. You know, 50% of developers, according to surveys are now fine tuning. And we know fine tuning is good, but like if you actually go through the full loop, you can get better results.

因此我认为这个领域潜力巨大。围绕技术栈的这个环节，将会涌现出一整套新的基础设施、平台和初创公司。现在不仅是构建平台的好时机，也是创办企业和思考这些问题的黄金时期。

So I think there's a bunch there. And I think there's a whole new set of infrastructure and platforms and companies that will be created that are all around this part of the stack. And so I think it's an exciting time to be in the platform space, but it's also an exciting time to be starting companies and be thinking about those problems.

我想确保大家真正理解你的观点，因为并非所有人都清楚预训练和后期训练的区别。用最简单的方式来说，这两者差异何在？为什么投资转向后期训练如此重要？

I wanna make sure people truly understand what you're saying here because not everyone truly understands post training, pre training. What's the simplest way to understand the difference there and just why it's such a big deal that investment is moving to post training?

我的理解是：构建基础模型需要巨大的算力支持和顶尖的科研能力——正如我们所见，科学家薪酬和人才价值正在急剧攀升，而这种专业能力目前在全球范围内仍属稀缺。这本身就是笔巨额资本支出。结合我们最初讨论的模型爆发式增长现象，现在各领域都有大量优质模型可供选择。从经济杠杆角度看，通过强化学习或微调来优化现成模型，能更高效地实现价格、性能、质量等目标。

The way that I think about it is, to create a foundation model, it requires a tremendous amount of compute, a tremendous amount of science expertise as we're seeing, which the cost for scientists or the average value is raising dramatically. And I think, you know, an expertise that we've seen it like isn't everywhere in the world right now. And so it's just a big CapEx investment to do that. And with this explosion of models that we talked about in the beginning, there's a lot of good models to choose from for different domains. And so I think that you just get more leverage economically, you get more leverage from a taste perspective of how you actually want to steer a model, if you're actually doing reinforcement learning or some sort of fine tuning to actually start to optimize what's off the shelf for some outcome like price, performance, quality.

仔细想想这很合理。就像古老的排序优化问题——你不会直接使用现成方案，尽管存在React组件这样优秀的框架和UI模块。你仍然需要根据具体使用场景或用户群体定制体验。这本质上是相同的工业化逻辑。

If you think about that, that's not crazy, right? Like, you know, ranking is an age old optimization problem where you don't wanna just take what's off the shelf because there's like amazing frameworks and UI and kind of components that, you know, the world is React components that are out there. You still want to tailor the experience to a set of use cases or set of people. I think it's just the same kind of industrial logic.

那么实践中这意味着：假设有个GPT-5模型，你说存在更高效的资金利用方式——基于这样的模型，用额外定制数据（无论是自有数据、采购数据还是人工参与的强化学习）进行训练，使其符合你的特定目标。

So in practice, what this means is there's like a GPT-five model. You're saying there's a lot of opportunity and a much more efficient way to spend money, which is take something like that and then train it on additional custom data that you have, whether it's data or just reinforcement learning, maybe even with humans to align it with what you wanted to achieve.

没错。数据来源可以是自有的、购买的、合成的，或是其他类型。

Yep. And it could be your own data. It could be data that you buy. It could be synthetic data. It could be something else.

但我认为我们会逐渐看到越来越多的公司和组织开始思考如何适配一个模型，而不是直接采用现成的方案，或是投入大量资金自建模型。

But I think that we're kind of going to start to see more and more companies and organizations kind of start to think about how do I adapt a model rather than how do I take something off the shelf as is or invest a bunch of money in building my own models.

对，我记得Cursor在播客里提过，他们有一系列支撑用户体验的模型。最终他们会有自己的专属方案——现在Winsurf还是哪家就已经在用自研模型了，不再直接接入Claude。

Yeah, forget. I know Cursor, when he was on the podcast, he shared that they have a bunch of models that support your experience with Cursor. And over time, they're just going have their own thing. I forget who has WinSurf or one of those guys just uses their own model now. They don't just plug into Claude.

我更倾向于模型系统学派。我相信模型多样性——比如Claude的Sonnet四代在某些场景表现惊艳，而GPT-五则适用于其他场景。有些任务需要关注模型延迟：你或许能接受思考时间，但有些场景需要快速检索响应。

I'm much more in the model system camp. Like I believe in model diversity. I think that in experience like Claude, like Sonnet four is awesome for a set of use cases versus GPT-five is different for different use cases. I think that there's some tasks where you care about the latency of the model. You're like cool with the thinking time, or you kind of want a quick retrieval and things like that.

关键在于有众多模型可供选择来实现目标。所以我更推崇模型生态系统，而非追求某个万能模型。

Like, I think the beauty is there's a lot of models that can kind of help you achieve that. And so I'm much more in the like model system rather than one model to rule them all.

这个术语准确吗？我也听过'集成模型'或'模型集成'的说法。

Is that the right term? I've also heard ensemble model, ensemble of models.

我认为模型集成是指可以独立微调和部署的多个模型组合。不过现在大家都在创造新术语来描述这些快速演进的概念——毕竟行业发展太快，很多观点都还缺乏充分数据支撑。

I think about an ensemble of models as a set of multiple models that then you can, you know, fine tune and deploy independently. But you know, at this point, we're all making up different terminology to define things that we like have deep beliefs on that have like, you know, limited sets of data points because everything is moving so fast.

好，现在进入激动人心的快问快答环节，我——

Yeah. With that, we've reached our very exciting lightning round. I'm

我已经迫不及待要开始快问快答了，正在调暗灯光营造气氛。

very excited for our lightning round and I'm like turning down the lights.

灯光马上就会重新亮起...想象一秒就好。第一个问题：你最常推荐给别人的两三本书是什么？

And then it'll come back on, imagine in one second. Okay. First question. What are two or three books you find yourself recommending most to other people?

职场方面首推《思考的机器》，核心是治本而非治标。经典例子是：解决交通拥堵不该靠减速带或限速，而要改善步行便利性和出行方式。个人阅读方面，Instacart的CMO曾向我推荐《明天，明天，再明天》。

At work, it's probably Thinking Machine. So it's all about treating the cause, not the symptoms. The prototypical example is like, if you wanna solve traffic, you don't actually put up speed bumps or speed limits. You actually have to like solve walkability and mobility and kind of like why people actually use cars. Outside of that, I kind of personally, the CMO of Instacart recommended to me tomorrow and tomorrow and tomorrow.

我上个月、去年乃至前年都反复阅读它，因为我实在太喜欢了。这个美妙的故事跨越了十多年。

And I read it like last month and last year and the year before, because I love it so much. Like this like beautiful story over ten years.

你最近特别喜欢的电影或电视剧有哪些？

What are some favorite recent movie or TV shows you really enjoyed?

《极速求生》、《双面疑云》、《为全人类》。尤其喜欢《为全人类》第四季，那种演绎太空竞赛另类可能性的设定很吸引我。

Formula one, Saw Twice, For All Mankind. For All Mankind, I like season four. I don't know. I like kind of playing out alternative theories to kind of how the space race might've looked.

最近有没有发现让你爱不释手的新产品？可以是科技产品、小工具或服装。

Do you have a favorite product you recently discovered that you really love? Could be tech, could be gadgets, could be clothing.

我刚加入家得宝董事会，正在做个小改造项目。发现得伟新推出的动力包用了软包电池，重量减轻50%但动力十足，单手操作电钻这类重工具时特别顺手。我们还测试了Brilliance智能家居系统。

So I just joined the board of the Home Depot and we're doing a little renovation project. And so there's this new kind of new to me, DEWALT kind of power pack and they use pouch cells. And so it's like 50% like lighter, but with all the power and it's like awesome for drills and like things that, you know, I need to lift up with one hand that feel heavy. So I love that. We also are testing out this new Brilliance smart home kind of system.

这个四英寸的高清中间件能连接所有设备。现在智能家居技术爆炸式发展，使用门槛太高，这个中间件或许能成为最终解决方案，但还需观察。

So it's like kind of four inches of like high res middleware that allows you to kind of connect to everything. And I've like reached peak kind of this sat with like the explosion of all the technology required to actually use your home. So it just might be the middleware that like sticks, but we'll see.

你刚说DISSAT？是‘不满’的缩写吗？

Did you say DISSAT? Is that short for dissatisfaction? Yes.

抱歉，我总不自觉用缩写词。

Sorry, I'm speaking in acronyms.

哇，从没听过DISSAT这个说法，真有意思。顺便说，你在家得宝董事会这事太酷了，和你其他工作领域截然不同。

Woah, I've never heard that, DISSAT. It's like, I love that. By the way, love that you're on the board of the Home Depot. What a different part of the spectrum of work.

确实很棒。第一次董事会时，在公司工作几十年的慈善部门主管对我说：欢迎加入地球上最伟大的公司。那种感觉很特别。

Yeah, it's been awesome. The very first board meeting, the head of philanthropy has been at the company for decades. And she said, welcome to the greatest company on the planet. Wow. It's pretty special.

你就像是，不微软。有没有从与他们合作中学到的东西被你带到了微软？

You're like, no Microsoft. Is there something you've learned from working with them that you've brought to Microsoft?

虽然这是今年才有的新事物，但我长期从事的产品都具有类似的影响力。比如我在Porch时，Instacart的专业人员，我们有60万购物者。显然家得宝也有员工。从企业文化角度，我最喜欢的一点是他们采用倒金字塔结构——不是高管在顶端，而是基层员工在顶端。门店本身就是总部，而传统意义上的总部更像是支持部门。

Like it's new, it's this year, but I've long worked on products that kind of had that impact. So like when I was at Porch, it was pros at Instacart, we had 600,000 shoppers And obviously the Home Depot has associates. One of my favorite things about the company culturally is they have this inverted pyramid where instead of having like executives at the top, the associates are at the top. And the stores themselves are headquarters. And then the kind of traditional HQ is kind of support.

所以这完全是以客户为中心的。当我思考卓越执行力和创建这些持久长期机构时，以及文化、意识形态和领导力如何形成——我总想到归根结底，AI将影响每个人每份工作。能跳出我们的圈子与人们相处，真正了解他们的实际痛点、对AI和技术的看法，以及我们需要做什么，这非常棒。

And so it's just like, it's so customer centric. And when I think about amazing execution and creating these durable long term institutions and kind of how culture and ideology and kind of leadership is formed, like I think about that and I think about at the end of the day, you know, AI is going to have an impact on every single person and every single job. And it's like amazing to kind of just spend time with people outside of our bubble and kind of really try and learn what their real pain and problems and how they think about AI and how they think about technology and kind of what we need to do.

好的，还有两个问题。你有最喜欢的人生格言吗？会经常与亲友分享的那种？

Okay, two more questions. Do you have a favorite life motto that you find yourself coming back to sharing with friends or family?

我曾采用'最小化遗憾'的思维框架，这很棒且沿用多年。但成年后组建家庭时，我的世界观有所改变。现在更注重最大化选择价值，这自然让我更重视家庭、健康、信任和人际关系。

I used to use the kind of minimize regret framework and it's great. And I've used that for a long time. I think that probably once I got into my adult years and started to kind of have a family and things like that, my kind of just worldview changed a little bit. And it was all about maximizing kind of option value. And it just gave the things that I naturally cared about, like family and health and trust and relationships.

这些事物突然有了新的价值维度——因为周末充分休息会在未来产生复利效应，保持健康也是如此。不必用加班来交换这些，家庭的重要性亦然。所以我的世界观是：70岁时回望人生，重点不是计算遗憾数量，而是展望未来还能有多少冒险——因为已经积累了技能、信任、人脉、家庭和影响力等财富。

Like it was just kind of like a new level of like value associated with those, because all of a sudden learning rest on the weekend can like compound in the future or, you know, having good health can compound in the future. Don't know, you kept to trade that off of working extra hours or, you know, the importance of family and all of those things. And so I think that like my worldview is like, when I'm 70, it's not about what do I look back on in my life and count the number of regrets. It's really about like looking forward in the number of adventures I will still have, because I have like accumulated this wealth of skills and trust and, you know, people and family and impact and things like that.

说到技能，网上说你是TechWon道二段黑带。天啊，这是真的吗？我还有个相关问题。

Speaking of skills, the internet tells me that you're a second degree black belt in Techland. Why, oh gosh, is this true? And then I have a question about it.

是真的。

This is true.

太厉害了！为什么觉得尴尬？这明明超酷的。

Okay, that's incredible. Why is this embarrassing? That's an incredible thing.

任何关于我的讨论都会让我本能地感到尴尬。

I'm generally embarrassed anytime anything is discussed about me.

好的，太棒了，没问题。你从跆拳道中学到了哪些对生活或工作有帮助的东西？

Okay, great, no problem. What's something that you learned from Taekwondo that has helped you with life or work?

跆拳道的精神层面远胜于身体层面。我认为这和我们所有的工作及产品开发是相通的——比如思维清晰、勇气，以及坚持到底、毫不动摇的雄心。除了冥想之外（我可能花了整个学习过程才真正学会清空大脑），这些就是它教会我的。是的，我觉得这非常了不起。

Taekwondo is more mental than it is physical. And so I think that's the same with kind of like all of our jobs and making products. Like, I think it's like mental clarity, it's courage, it's kind of the ambition to kind of see things through and be unwavering. And so I think that's literally, you know, what it taught me outside of meditating, which probably took me the entire time to like actually learn to meditate and clear my head. But yeah, I think it's awesome.

就像，虽然大家会幻想飞檐走壁之类的酷炫动作（当然这些也能做到），但真正的价值在于对精神境界的追求，你明白吗？

Like, think everybody imagines like, you know, flying psychics or running up a wall and like you can do those things too, but the real value is like the mental pursuit of it all, you know?

而且你居然也能做到那些动作！哇，好吧，我服了。我得试试这个。阿莎，这次对话太精彩了。最后还有两个问题——

And you can do those things too. Wow, okay, I'm good. I gotta get into this. Asha, this was awesome. Is there, or actually two final questions.

如果听众想联系你跟进某些事情，他们该去哪里找你？你希望他们通过什么方式联系？另外，听众如何能帮到你？

Where can folks find you online if they want to maybe follow-up on anything, if you want people to reach up and how can listeners be useful to you?

可以通过LinkedIn、邮件或短信联系我，这些渠道都能找到我。关于如何帮助我——我认为我们都处于探索的早期阶段，优秀的平台需要建立在优质用例和客户基础上。所以如果你有反馈、创意，或者希望AI能帮你实现的目标，我很乐意倾听。当前所有变革的特点就是新产品和用例将在各处涌现。

You can hit me up on LinkedIn or email or text. I think all of those are traceable. Look, like how can you be helpful to me is I think like we're all early in this journey and great platforms that are built on great use cases and built on great customers. And so like, if you have feedback, you have ideas, you have like things you want AI to be able to do to help you achieve more, I'd love to hear it. I think the thing about all of these changes is that all of these new products and use cases will be developed everywhere.

因此我始终在思考：如何让我们的平台成为支撑这些创新的基石。

And so I'm always just thinking about how can we be the platform to support that.

太棒了。阿莎，非常感谢你的到来。

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

谢谢邀请。

Thanks for having me.

大家再见！非常感谢收听。如果觉得本期节目有价值，欢迎在苹果播客、Spotify或你常用的播客平台订阅。也请考虑给我们评分或留言，这能帮助其他听众发现本节目。所有往期内容及节目详情请访问lenny'spodcast.com。

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

下期节目再见。

See you in the next episode.