AI 时代角色流动性的导航：Netflix 的 Elizabeth Stone

今天的嘉宾是 Netflix 的产品与技术官 Elizabeth Stone。这是 Elizabeth 第二次来到本播客。她第一次来的时候还只是 CTO，那期节目在很长一段时间里都是本播客最受欢迎的几期之一。你很快就会明白为什么这是一场精彩的对话，因为当我们两年半前聊天时，AI 才刚刚兴起。作为长期负责工程、产品和数据科学的负责人，Elizabeth 对未来的走向以及什么值得关注有着独特的视角。在加入 Netflix 之前，Elizabeth 曾任 Lyft 的科学副总裁、Nuna 的首席运营官、Analysis Group 的经济学家以及美林证券的交易员。在开始之前，别忘了访问 Lenny's Product Pass dot com，可以免费获得一整年全球最热门、最精良的 AI 产品，仅限 Lenny 的新闻订阅用户。话不多说，有请 Elizabeth Stone。Elizabeth，非常感谢你来到这里，欢迎回到播客。

Today my guest is Elizabeth Stone, product and technology officer at Netflix. This is Elizabeth's second visit to the podcast. Her first visit, when she was just a CTO, was for the longest time one of the most popular episodes of this podcast. You'll soon see why this is such a killer conversation because when we chatted two and a half years ago, AI was only starting to emerge. And as a long time head of engineering and product and data science, Elizabeth has such a unique perspective on where things are heading and what's worth paying attention to. Prior to Netflix, Elizabeth was VP of Science at Lyft, Chief Operating Officer at Nuna, an economist at The Analysis Group, and a trader at Merrill Lynch. Before we get into it, don't forget to check out Lenny's Product Pass dot com for an entire year free of the hottest and best crafted AI products in the world available exclusively to Lenny's newsletter subscribers. With that, I bring you Elizabeth Stone. Elizabeth, thank you so much for being here and welcome back to the podcast.

谢谢。我很荣幸能来，一次，现在是第二次。

Thank you. I'm honored to be here. Once and now twice.

没错。这对我来说是难得的享受。不知道你是否知道，你第一次来的时候，那期节目成了我第二受欢迎的节目，很长一段时间里仅次于 Brian Chesky。

That's right. That's a rare treat for me. I don't know if you know this, but your first visit to the podcast, your episode ended up being my second most popular episode. You're right behind Brian Chesky for the longest time.

嗯，我既惊喜又有点好胜心，想着怎么才能冲到第一？不过我先把这个放一边。

Well, I'm pleasantly surprised and also mildly competitive of how do I get to the first spot? But I'll set that aside for now.

这是我们的机会。

This is our shot.

Brian 很厉害，所以我就算了。

Brian's amazing, so I'll let that one go.

是啊，他确实厉害。然后还有那些花哨的 AI 人士正风头正劲。

Yeah, he is. And then there's just all these fancy AI people that are coming in hot.

所以，现在已经过去两年半了。很多事情都变了。显然，AI 让人们几乎什么都能做。PM 可以交付代码，设计师可以写 PRD，工程师可以做产品，每个人都是全能。这场对话涉及很多方面。其中之一是我听到有人说，他们感到困惑和沮丧，比如我的工作到底是什么？作为 PM、作为设计师，我到底该负责什么？你有这样的体会吗？

So, it's been 2 and a half years at this point. A lot's changed. Obviously AI is allowing people to do is everyone can kind of be everything now. This idea of PMs can ship code, designers can write PRDs, and engineers can product, and everyone's everything. There's a bunch of elements to this conversation. One is that I've heard from people that there's also this kind of confusion and frustration of like what is my job anymore? Like what am I responsible for as a PM, as a designer? Is that something you've experienced?

我在 Netflix 内部确实听到了这种声音。我认为每当一项新技术出现，尤其是像生成式 AI 这样具有变革性的技术时，在进入形成阶段之前，总会经历一个风暴阶段。我们现在就处于这个阶段。我不认为这意味着我们应该把 AI 放回盒子里，说我们不要用它，因为它打乱了我们所有关于角色的先入之见。但我确实认为，这意味着我们必须更加深思熟虑，如何获得好处同时减少代价。我认为人们正在尝试如何更快地构思、制作原型、拼凑初始代码以便测试，这是一件好事。但我是否认为这意味着任何人都应该把代码发布到生产环境？每个人都应该做所有事情？可能不是。但我认为人们探索可能性是好的。而且，就像我之前提到的，产品和工程团队在一起的好处是，如果业务问题很清晰，我认为角色之间有一些流动性是健康且可接受的，因为不必等待工程团队准备好才能做原型，产品和设计可以更快地推进。但他们仍然应该与工程伙伴合作，思考如何产品化？如何规模化？有哪些护栏？所以，我不认为这会让职能专长过时。我认为这意味着团队必须更适应这一点——也许这能帮助我们在某个方向上更快前进。从组织角度来看，我思考如何让这一切更协调、减少挫败感的一些要素，是我们必须建立起来才能获得好处而非代价的。这包括明确数据源真相、代码发布到生产环境或大规模变更前的测试护栏、思考哪些场景我们可以信任 AI 输出、哪些场景我们需要流程或审查来确保高质量结果。以及重申人类仍然对结果负责的重要性。所以，即使是一个智能体写了代码，或者我帮忙做了分析——尽管那不是我的背景——也不能免除人们对自己所创造的东西的责任。所以，我认为投资于这些核心基础设施和实践，并重申对结果的问责和责任，有助于平衡可能性和我们应该实际做的事情。

I hear it within Netflix, for sure. I think anytime a new technology comes along, especially one that's as transformative as GenAI, you go through a storming phase before you go through the forming phase of things. And I think we are in the middle of that right now. I don't think that means we should put AI back into the box and say let's not use it because this is complicating all of our preconceived notions about our roles, but I do think it means we have to be much more thoughtful about how do we get the benefits while reducing the costs. I think it's a great thing that people are experimenting with how can I develop an idea faster, prototype an idea, put together an initial set of code that would allow us to test it. Do I believe that means anyone should be shipping code to production? That everyone should actually be doing everything? Probably not. But I think that it's good for people to be exploring what's possible. And then, like I mentioned earlier, the benefit of having product and tech teams together is that if the business problem is clear, I think it's okay and it's healthy for there to be some fluidity in the roles that people play because instead of having to wait for the engineering team to be ready to be able to prototype something, product and design can move faster on it. But they should still work with their engineering partner to think through how should we productize this? How do we scale it? What are the guardrails for it? So, I don't think it makes the functional expertise obsolete. I think it means that teams have to be more comfortable with maybe this helps us move faster in a certain direction. From an organizational perspective, things I think about to make this more coherent or less frustrating are some of the things that have to be in place for us to get the benefits rather than the costs. So, that includes clarity on source of truth data, guardrails on shipping code to production or testing before we make large changes, thinking about opportunities where we can trust the output of AI versus we should have a process or review that helps us check that we're getting high quality outcomes. And the importance of reiterating that humans are still responsible for what happens. So, it can be that an agent wrote the code or I helped to do an analysis when that's not really my background, but it doesn't make people not have the responsibility that comes with what they've created. So, I think the investing in some of those core infrastructure and practices and reiterating the accountability and responsibility for the outcomes helps to balance some of like what's possible with what we should actually be doing.

本期节目由本季的呈现赞助商 WorkOS 提供。OpenAI、Vercel、Replit、Sierra、Clay 以及数百家其他成功公司有什么共同点？它们都由 WorkOS 提供支持。

This episode is brought to you by our season's presenting sponsor WorkOS. What do OpenAI and Vercel, Replit, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by WorkOS.

如果你在为企业构建产品，你一定体会过集成单点登录、SCIM、RBAC、审计日志等大型公司所需功能的痛苦。WorkOS 将这些交易障碍转化为即插即用的 API，并提供了一个专为 B2B SaaS 打造的现代开发者平台。我投资的每一家开始向高端市场扩张的初创公司，最终都会与 WorkOS 合作。因为他们是最好的。无论你是种子期初创公司，试图拿下第一个企业客户，还是正在全球扩张的独角兽，WorkOS 都是实现企业就绪和释放增长的最快路径。它本质上就是企业级功能的 Stripe。访问 workos.com 开始使用，或者直接去他们的 Slack，那里有真正的工程师在等着回答你的问题。WorkOS 让你通过令人愉悦的 API、全面的文档和流畅的开发者体验更快地构建。今天就访问 workos.com，让你的应用做好企业就绪。

If you're building a product for the enterprise, you've felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand upmarket ends up working with WorkOS. And that's because they are the best. Whether you are seed-stage startup trying to land your first enterprise customer or unicorn expanding globally, WorkOS is the fastest path to becoming enterprise ready and unlocking growth. It's essentially Stripe for enterprise features. Visit workos.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to workos.com to make your app enterprise ready today.

很高兴再次邀请你上播客，上次我们聊天时，AI 还没有在全球范围内带来巨大变革。所以，我们可以探讨一个很酷的弧线——我们都经历的这种转变。

What's really awesome about having you back on the podcast is we chatted like before AI was a massive transformation in the world. So, it's a really cool arc that we can explore here. This the shift that we've all gone through.

嗯。

Mhm.

回到产品和工程团队的角色，我很好奇这些角色在过去两年半里发生了多大变化。想想产品工程、设计、数据科学、用户研究，哪些角色变化最大？哪些变化最小？过去两年半里，最大的不同是什么？

Coming back to the roles of the product and inch team, I'm curious how much these roles have changed in the last two and a half years. If you think about product engineering, design, data science, user research, which roles have changed most? Which roles have changed least? Like, what's most different in the last two and a half years?

你提到了一些方面，我重复一下，然后再补充。我发现，相比几年前，产品经理、设计师、数据科学家在产品开发生命周期中，在工程真正需要冲在前面解锁问题之前，能走得更远了。我这么说有些谨慎，因为就像我们讨论过的，我不认为突然搞出成千上万个原型是好事，除非它们瞄准的是对业务重要的问题，并且工程伙伴知道我们在解决那个问题，设计师和产品经理会带头开始塑造想法，但这并不是在真空中工作，也不是乱枪打鸟。但当问题选对了，以深思熟虑的方式推进，并且大家对此达成一致时，我看到产品、设计、数据科学能更快地朝着“让我们针对这个假设做出可测试的东西”这个方向前进。那就是原型设计，那就是写代码。我看到的另一个非常有价值的事情是，在 Netflix 的虚拟围墙内，我们有很多信息在流动。我们有几十年来运行的实验，有来自消费者的洞察，有来自业务各方的输入。这曾经是一个真正的挑战：我们如何充分利用那些长期积累的知识和经验，把它们应用到当前的问题上，从而更快地判断哪条路有前景，或者哪些东西我们已有了解、可以在这里利用？而 AI 在提炼信息、纵观全局、进行分析、直击核心洞察方面非常强大。我不倾向于完全依赖它，但我认为它是一个很好的起点。即使在我自己的日常工作中，我也不再需要发邮件打扰别人问“提醒我一下，我们在哪一年做了什么研究，问题是什么，我们跑了什么测试？”——我几乎可以立刻找到答案。然后我可以形成自己的看法：“我觉得这里有趣的是……”，并且我已经跳过了一些步骤，直接问“这里有什么可操作的吗？”所以这涉及数据分析、建模、信息提炼，我观察到越来越多的人在做这些事，回到你最初的问题。以前这只能由那些在公司待了 20 年、看过每个实验或知道去哪找的专家来做，但现在我们能在产品和技术的所有职能中更快地做到这一点。对我们来说一个很大的解锁是，财务、内容和广告等业务部门的人也能这样做，然后带回一个初步的假设，再与数据科学家和工程师等更深入地合作。所以，在假设生成、原型设计、深入思考问题方面，感觉正在加速，各个职能能够更流畅地做到这一点。但我仍然看到了比较优势。数据科学家仍然是专家，他们擅长判断“我们能相信这些数据吗？我们解读的方式对吗？这里应该用数据还是判断？”产品经理仍然非常擅长说“我们真的把‘做什么’这个问题框定对了吗？我们解决问题的方式正确吗？”工程师仍然有关于“怎么做”的技艺：这东西如何扩展？高质量是什么样的？基于我们构建和部署的方式，这会给我们带来什么问题？所以我仍然看到了那些比较优势的精华。只是我们能够在很多通常会有阻碍的步骤上更流畅地移动了。

So, you've mentioned some of the things, so I'll reiterate them and then maybe build. So, I have found that PMs, designers, data scientists are able to get farther in the product development life cycle before engineering really needs to be front of the line in unlocking things than was true a couple years ago. I say that with some caution because, like we were talking about, I don't think it's great to all of a sudden have thousands of prototypes if they're not aimed at this is an important problem to solve for the business and the engineering partners are aware that we're solving that problem and that designers and product managers are going to take the lead in starting to shape the idea, but it's not working in a vacuum and it's not throwing a bunch of spaghetti at the wall to see what sticks. But when it's the right problem, approached in a thoughtful way with some alignment on that, I've seen product design data science move faster in the direction of let's get to something that's testable on this hypothesis. So, that's prototyping, that's writing code. The other thing I've seen as being very valuable is we have a lot of information running around in the virtual walls of Netflix. We have experiments we've run over decades. We have insights from consumers. We have input from stakeholders across the business. And that was a problem that really presented a challenge of like, how do we get the most out of that long history of knowledge and learnings to say, let's apply that to the problem we've got now to move faster in this is a promising path or this is something that we've learned something about and we could leverage here. And AI is very powerful at distilling information, looking across a broad set of things, doing an analysis around it, getting to the core of here's some insights to start with. I would hesitate to rely on that exclusively, but I think it's a head start. And I find even in my own work day-to-day, instead of sending an email that disrupts someone of like, remind me what research did we do in what year and what was the question and what was the test we ran? I can find that almost instantly. Then I can form my own, here's what I find interesting about this and I've now skipped a couple steps towards is there something actionable here? So that's data analysis, it's modeling, it's distillation of information and I'm seeing more people do that to your original question. So instead of that needing to be only the experts who were here for 20 years and saw every experiment or know where to find it, we're now able to do that faster within product and tech across all functions and a big unlock for us is our business stakeholders sitting in finance and content and advertising can do that as well and then bring back an initial hypothesis where they want to work more deeply with the data scientists and engineer and so on. So there's something there about the hypothesis generation, prototyping, thinking deeply about problems that feels like it's accelerating and that functions are able to do that in a more fluid way. But I still see comparative strengths. So data scientists are still going to be experts at can we trust this data? Are we interpreting it the right way? What's the data versus judgment that we should be applying here? A product manager is still going to be exceptional at saying, have we really framed the what of this? Like the problem we're solving in the right way? An engineer still has a craft around the how. How does this scale? What does high quality look like? What problems is this going to create for us based on how we build and deploy something? So I still see the nuggets of that comparative advantage. It's just that we're able to move more fluidly in a lot of steps that normally we would have blockers on.

这里有很多有趣的东西。你最后提到的这一点正是我一直在思考的。如果我们都成为构建者，我们还需要独立的职能吗？过去有一种“技术成员”的趋势，意思是“好吧，我们没有头衔，你可以做任何事，你不必被归类。”你在这里说的是，你相信我们仍然会有专业分工：产品人员、工程师、数据科学家、设计师。虽然他们做更多其他职能的事情，但在拥有特定学科、技能和背景方面仍然有很大的价值——告诉我我理解得对不对。

There's so much interesting stuff here. One is this last point you made is something I've been thinking about. If we all become builders, will we still need separate functions? There's this like member of technical staff trend that is happening in the past where it's like, all right, we don't have a title, you could be anything. You don't have to be in a bucket. What you're saying here is you believe we will continue to have specialties, product person, engineer, data science, designer. While they do more of other functions, there's still a lot of value in Tell me if I'm hearing you correct in having the specific discipline and skill and background.

我仍然看到一种工艺卓越性，在各个学科中都非常重要，我认为它不会很快消失。即使工作跨职能边界变得流畅或模糊。这又回到了我之前提到的：你仍然需要人类来确保我们所做的事情是有意义的。我们正在以对 Netflix 会员或业务利益相关者最有利的方式解决正确的问题。如果我和工程师、数据科学家、设计师交谈，是的，他们现在比过去会更多种“语言”，因为他们有这些 AI 工具的帮助。但当我思考工艺以及他们如何定义“好”时，仍然有一些东西是不可替代的。这在各个层面都是如此，而且你知道，我仍然觉得优秀的工程人才稀缺，优秀的数据科学人才稀缺，优秀的创造力稀缺。

I still see a craft excellence that's really important in the disciplines that I don't think is going away anytime soon. Even if there's fluidity or blurring of the work across the functional lines. It goes back to what I mentioned earlier of you still have humans who have to make sure that what we're doing makes sense. We're solving the right problems in a way that is best for Netflix members or business stakeholders. And that if I talk to an engineer, a data scientist, a designer, yes, they speak more languages now than they used to because they have the benefit of these AI tools. But there's still something that is not replaceable when I think about the craft and how they think about what good looks like. And that feels true across all levels and you know, I still find great engineering to be scarce. Great data science to be scarce. Great creativity to be scarce.

你是否发现某些职能的招聘需求在增加？比如工程、产品管理或设计等领域的饼图在扩大，而随着 AI 工具和 LLM 的兴起，哪些职能的需求在减少？

Are there functions that you are finding you are hiring more of? Like the pie chart pie expanding say for engineering or PM or design or something and then functions you're need less of with AI tool and LLMs rising.

我不确定是否完全对应到具体职能，但我可以告诉你我们观察到什么需求在增长。在 AI 时代，我们需要更多系统思考者。这在各个职能中表现略有不同，但我可以举几个例子。在 Netflix 中央工程的核心基础设施团队中，Netflix 过去成功的一个重要因素是：拥有特定业务问题的本地团队能够快速交付。他们通常不觉得自己需要走中央铺好的路径，而是构建自己需要的技术栈来解决问题并产生影响。但在 AI 时代，智能体跨多个系统运行，需要真实数据源，拥有优先铺好的路径——既能最大化收益又能设置护栏确保工作质量——就变得更加重要。通用基础设施、通用铺好的路径、用核心能力一次性解决问题，这些变得更重要。因此，我们正在招聘更多能够跨业务领域审视并抽象出“在 AI 时代我们需要哪些构建模块”的人。这是其中一个视角，但同样重要的是：让 Netflix 走到今天的模式，未必能带 Netflix 走向未来。我们需要更强大的基础设施来在未来快速前进。这意味着工程人员的画像更偏向分布式系统、基础设施和系统思维，而非本地业务专长。当然，我们仍然有深耕个性化、广告和内容交付的团队。所以，对我们来说，核心基础设施和系统思维是增量需求。再举一个例子，比如设计。体验设计团队开发模板和系统思维至关重要——定义 Netflix 的优秀用户体验设计是什么样，以便赋能更多人（包括非设计背景的人）开发出连贯、符合端到端会员体验的产品。我非常担心出现不同的设计语言或不同类型的用户交互，最终拼凑出“弗兰肯斯坦”式的产品。因此，我们招聘设计师时同样看重设计系统思维：如何思考模板、品牌表达、优秀用户体验，以及 Netflix 的差异化特色。现在，设计团队中需要以这种方式思考的人比单纯为某个特定产品设计功能的人更多。所以，每个职能都在后退一步审视全局，这需要现有团队重新调整技能，也需要招聘具备这类专长的人。而贯穿这一切的是思维模式的转变。我们不会招聘那些不热衷于探索、尝试新事物、不理解变化并适应模糊性的人——他们需要适应工作方式和合作方式的模糊化。这对 Netflix 现有员工和新加入的团队成员都适用。至少在我从事这个领域的时间里，这种好奇心和创新思维从未像现在这样重要。

Not sure that it matches exactly to functions, but I can tell you what we're seeing more of, we need more of. We need more systems thinkers in a world with AI. That looks a little bit different across functions, but I could play out a couple examples. So, in our core infrastructure team at Netflix in central engineering, a lot of what made Netflix successful over time was that local teams with specific business problems could move fast to deliver. They very often were not feeling like they needed to be on a central paved path. They built the stack that they needed to solve the problem and have the impact. In a world of AI with agents operating across multiple systems, wanting source of truth data, the importance of having preferred paved paths that get the most of the benefits and produce some guardrails so we can make sure we're doing good work, common infrastructure, common paved paths, solving problems once with a core set of capabilities becomes more important. So, we are hiring more people who can look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI. So, that's one of the lenses, but also just with a lens of what got Netflix here doesn't get Netflix there. And we're going to have to have a stronger set of infrastructure to move quickly in this future. So, that means that engineering profiles are more distributed systems, more infrastructure, more of that system thinking mindset than a local business expertise. Though, of course, we still have people who are deep in personalization and advertising and content delivery. So, it's more something additive for us to have that core infrastructure and systems thinking. If I take another example, like design, it's extremely important that our experience design team is developing templates and again systems thinking for what does great user design look like at Netflix so that they can enable lots of people, including those who are not designers by training, to develop products that are coherent, that fit into the end-end member experience. I get really nervous about having different design languages or different types of user interactions and shipping Frankensteins, basically. So, designers need to then be the people we're hiring again for design systems thinking. How do we think about templates and expression of the brand and what a good user experience looks like and what is Netflix and like the Netflix differentiated special sauce. So, there's more people on our design team that have to think that way now than could I help to design a specific feature for a specific product. So, there's this stepping back to look at the big picture that I think is happening in every single function and that requires some, yeah, reorientation of skills among the existing team and also hiring people who've got that type of expertise. And across all of it, it's a mindset shift. So, we are not hiring people who are not excited to explore, try new things, understand lots is changing and feel comfortable with that ambiguity, be comfortable that there's a blurring of how we work and how we partner. That's true for people who are already at Netflix and people who we are adding to the team that this curiosity innovation mindset has not been more important, at least in the time that I've been working in this field.

关于系统思考，它之所以变得更重要，是因为人们行动太快，你需要投资平台、框架和设计语言，本质上就是“授人以渔”，让他们不被阻塞，还是有其他原因？

On the systems thinking piece, is the reason this is becoming more important that it is people are moving so fast that you need to invest in platforms and frameworks and design language and basically teach people to fish so they can not be blocked or is there other reasons?

我认为主要是速度。平台确实有杠杆效应。总的来说，无论有没有 AI，平台都能让大多数团队完成 80% 的工作，他们无需重新发明这些构建模块。我们在业务中下了更多赌注，尝试构建更多东西。所以平台思维是好的，而 Netflix 相对较晚才意识到这是一个真正的关键推动因素。此外，在 AI 时代还需要一种“脚手架”的概念。不仅仅是更高的速度，而是有更多人从事不同类型的新工作。随之而来的风险是：如何在这种情境下考虑访问和身份？如何考虑安全？如何考虑交付高质量的代码、设计和用户体验？我认为让每个构建者都自己去摸索“什么是好的做法？我应该记住哪些缓冲或护栏？”是不可扩展的。我们需要将这些编码到铺好的路径和工作方式中。对于数据科学或分析领域，需要编码：真实数据源在哪里？如何解读？如何访问？哪些可以做、哪些不可以做？对某些类型的数据要小心。一个拥有数千人的组织不能再依赖部落知识或“我去找那个懂的人”。这在 AI 之前就已经是挑战，而 AI 可能让它变得更紧迫。我喜欢利用 AI 或任何新技术来推动我们已知需要做的工作。现在正是加大投入的最佳时机。

I think it's probably velocity. So platforms do have a benefit of leverage. So in general, that's an opportunity with or without AI for a platform to get most teams 80% of the way there. And then they don't have to reinvent those building blocks. We have more bets that we're making across the business, more things we're trying to build. So platform mindsets are good and it's something that is relatively more recent for Netflix to think about that being a real critical enabler. There is also the sense of a scaffolding in a world of AI. So not just the higher velocity, but you have more people doing more types of work that are different or new like we were talking about. And there's risk that comes with how do you think about access and identity in that situation? How do you think about security in that situation? How do you think about shipping high quality code and design and user experiences? And so I don't think it scales well to have each person who's building something have to go figure out. Could you remind me what good looks like here and what are the bumpers or guardrails I should keep in mind? I think we need to encode that in our paved paths and our ways of working. And for a data science or analytical field to encode here's the source of truth data, here's how to interpret it, here's how to access it, here's what to do with it or not to do with it and to be careful with certain types of data. I don't think an organization that has thousands of people can no longer rely on tribal knowledge or I'm going to find the one person who knows this. So this was a challenge that was there before AI. It's probably a more urgent challenge with AI and I like the idea of using AI or any new tech to motivate like we knew this is work we needed to do. No time like the present to invest in that more heavily across the team.

我在想，另一个原因是智能体现在承担了大量工作，为它们提供上下文、脚手架和设计语言，能大大加速这一切。

I wonder if another reason for this becoming more valuable is because agents are now doing a lot of work and giving them the context, giving them the scaffolding, giving them the design language just speeds all that up.

是的，我们在 Netflix 的一个愿景是，将会有大量智能体参与工作，你需要能够贯穿始终地进行推理和合理化。人类负责引导：我们需要解决什么问题？我们产出的东西是否 impactful 且高质量？但工作将由人类和智能体共同完成。这带来了速度和收益，也带来了风险。我认为，从工程角度来看，重要的是我们要找到一种管理方式，既能让人快速行动，又不会给公司带来过度的负面影响或风险。

Yeah, and one of the visions we have at Netflix is we will have so many agents that are contributing to doing work that you need to be able to reason and rationalize throughout that. You know, the humans are the ones guiding what's the problem we need to solve. Do I feel like what we're producing is impactful and high-quality output? But the work will be done by both humans and agents. And that creates velocity and benefits and it creates risks. And I think that's important from especially from an engineering perspective that we figure out how to manage that in a way that lets people move quickly but doesn't create undue downside or risks for the company.

这正好与 Jenny Wen 在播客中提到的内容直接相关。

This connects so directly with Jenny Wen was on the podcast.

她是 Claude Code 和 Co-work 的设计负责人，提出了“设计流程已死”的论点。核心意思是没时间做设计了，设计师只能引导方向、调整，有空时再思考大局。这听起来就像你描述的：创建平台让人们快速行动，然后就没时间做具体新功能的设计流程了。

She was head of design for Claude Code and Co-work and had this whole 'design process is dead' kind of thesis. The pitch is there's no time for design, the design process. Instead, as a designer, you're just steering people, pointing them in the right direction, adjusting, and thinking big picture when you have the time. It feels like that's what you're describing here: create the platform for people to move fast, and then there's no time for the design process of a specific new feature.

我对此感受复杂。我们确实希望通过基础设施和系统思维，让更多人能做出包含优秀设计的好作品。为什么不利用新技术提供的机会呢？但对于最重要的优先事项，设计对于以正确方式解决问题至关重要。所以我们仍然会为重要的设计工作留出时间。它可以更快。设计师自己有了更多工具，能以更快的速度做出出色的工作，展示更多选项，更快地学习、迭代和测试。但如果说因为写代码更快、做数据分析更快，设计和深层设计专长与思维就被挤掉了，我认为这是个错误。至少对于像 Netflix 这样的大规模消费产品，我觉得我们会失去让 Netflix 伟大的东西之一：产品、技术和设计让大量复杂性变得不可见，创造无缝的客户体验。这种设计思维必须成为核心。所以工作本身可能看起来不同，但我认为我们不会失去这种思维。

I have mixed feelings about that. We do want to enable more people to do great work with strong design as part of it, through infrastructure and systems thinking. Why not take that opportunity that the new tech provides? But for our most important priorities, design is critical to solve things in the right way. So we do still make time for important design work. It can move faster. Designers themselves have more tools in their toolkit, so they can do incredible work at a faster velocity, show more options, learn, iterate, test more quickly. But I think it would be a mistake to say design and deep design expertise and thinking get squeezed out just because we can write code faster or do data analysis faster. At least for a large-scale consumer product like Netflix, I feel like we would lose one of the things that makes Netflix great: product, technology, and design make a lot of complexity invisible and create a seamless customer experience. That design mindset has to be core. So the work itself might look different, but I don't think we lose the mindset.

这是个很棒的反驳。所以我听到的是上升趋势：你寻找的技能和特质——系统思维，以及乐于拥抱变化、不固步自封的心态。那你发现什么在下降？你过去更看重、现在不那么需要的是什么？

That's an awesome counterpoint. So what I'm hearing is trending up: skills, attributes you look for—systems thinking, and this mindset of being comfortable and excited about change and what's coming, not being stuck in your own ways. What are you finding is trending down? What are you less looking for that you used to value more highly?

非常狭窄、深度专业化的时代对我来说更有限了。我能举出仍然需要它的例子——行业或技术专长，世界上只有少数人真正了解其运作方式。我们团队中就有编码或播放系统方面的例子，这些对 Netflix 来说极具创新性。我仍然相信我们在这些领域需要专业从业者。但总的来说，与 5 或 10 年前相比，我认为我们需要的专家更少，而需要更多通才或能适应多个方向的人。这种适应可以是跨职能专长，也可以是跨工程领域。我能同时驾驭后端和前端系统吗？我能以丰富的专业知识接入基础设施吗？现在的思维必须是“我能快速学会”，这又回到了系统思维。我认为专家现在比过去更容易学会更广泛的工具。所以我们可能需要的专家更少，因为人才能够朝那个方向发展。固守狭窄的专业领域会让我担心成长方向和探索的心态。即使在我自己的评估中，我也不想过于狭隘，但重要的是专家仍然要有尝试新方法解决问题的意愿，而不是沿用过去的方式。

The days of very narrow, deep specialization feel more limited to me. I can come up with examples where we still need it—industry or technology expertise where only a few people in the world really know how things work. We have examples on the team for encoding or how our playback systems work, things that have been incredibly innovative and novel for Netflix. I still believe we need specialized practitioners in those spaces. But as a general rule, compared to 5 or 10 years ago, I believe we have fewer specialists and more people who are generalists or adaptable in multiple directions. That could be adaptable across functional expertise or across flavors of engineering. Can I navigate both back end and front end systems? Can I hook into infrastructure with a lot of expertise? The mindset now needs to be 'I can learn that quickly,' and that goes back to systems thinking. I think specialists can learn to have a broader array of tools more easily than was true in the past. So we need fewer of them perhaps because talent is able to grow in that direction. Sticking to a narrow specialty triggers a concern for me about the mindset of growing in different directions and exploring. I don't want to be too narrow even in my own assessment, but it's important that specialists still have that sense of wanting to try a new way of solving problems versus the way we have in the past.

你说专家时，是指前端工程师对后端，还是其他方面？

When you say specialist, are you thinking like front end engineer versus back end, or are there other...

对，也可能是某个领域的知识。我是深度……

Yeah, or it could be a domain set of knowledge. I'm a deep...

专家。

Expert.

我是支付专家。我是广告市场设计专家。我是工作室制作使用的非常特定工具的专家。

I'm a payments expert. I'm an ads marketplace design expert. I'm an expert in this very specific tooling that studio productions use.

嗯。

Mhm.

所以，在主题专长方面成为专家是一种优势，前提是这个人愿意成长并拓展，思考“这真的是正确的工具或思考问题的方式吗？”从工程角度来看，技术栈各层的专业性降低了。而那些不太可能一成不变或惯性很大的工具——我们希望人们能够创新并想象其未来版本。所以我们更需要这样的人才。

So being a specialist in subject matter expertise is an advantage, provided that person is willing to grow and extend into 'Is this really still the right tool or the right way to think about the problem?' From an engineering perspective, there's less specialty in the layers of the stack. And tools that are unlikely to be static or have a lot of inertia—we would want people who are able to innovate and imagine the future version of this. So we want more talent like that.

太棒了。回到系统思维这部分，听到这里的人会说：“好吧，我得提升我的系统思维能力。”人们如何培养这项技能？是长期实践吗？参与很多复杂项目？我想到了那本人人都引用的、封面有弹簧玩具的书《系统思考》。人们怎么学习这个？

Awesome. So coming back to the systems thinking piece, people hearing this are like, 'Okay, I got to work on my systems thinking skill set.' How do people develop the skill? Is it just do it for a long time? Work at a lot of complex projects? I think of this book that everyone always references with the slinky on the front, Thinking in Systems. How do people learn this?

一个小技巧。对于你试图解决的每个问题，后退一步。问自己：“在解决这个问题的更广阔空间中，我假设了什么为真？”比如我接到一个任务，要为 Netflix 会员体验构建一个新功能。我花一点时间思考：我们试图解决的更大消费者问题是什么？这个功能将支持什么类型的内容？我计划构建的方式是否能在多种内容类型上扩展？或者它是否是一种能力，可以贡献给多个领域的平台产品？我用这个功能解决的消费者问题，是否会是 Netflix 在娱乐世界不断扩展、希望使其更加个性化和沉浸式时需要解决的最重要的消费者问题之一？这些都是问题。你不必煮沸整个海洋。你不必解决 Netflix 的整体战略以及我们相对于竞争对手的定位。但你拿你负责的事情，将问题放大一级，然后质疑它。我不会在质疑状态中停留太久，否则你会卡住，无法前进。但我认为这有助于人们以系统的方式思考，并质疑我们是否在以对最终消费者重要的正确方式解决正确的问题。

Small trick. For each problem you're trying to solve, step out one click. Ask yourself, 'What am I assuming is true about the broader space in solving this problem?' So I was given a task to build some new feature for the Netflix member experience. Let me take one beat and think about what is the bigger consumer problem we're trying to solve here? What's the type of content that this feature is going to be able to support? Do I think that the way I was planning to build this is going to make sense in a way that scales across multiple content types? Or could it be something that's a capability that then is contributed to a platform set of offerings for multiple areas? Is the consumer problem that I'm solving with this feature going to be one of the most important consumer problems that Netflix is going to need to solve as we have an expanding world of entertainment and we want to make it more personalized and immersive? Those are all questions. You don't have to boil the whole ocean. You don't have to solve for Netflix's overall strategy and who we are relative to competition. But you take the thing you're responsible for and you just do one zoom out of the problem you're solving and question that. I wouldn't spend too long in the questioning state because then you're stuck. Then you're not making forward progress. But I think that helps people to think in terms of systems and question whether we are solving the right problem in the right way that matters for the end consumer.

按照你的描述，我想到的另一种方式是：想象如果你是自己的经理，他们会如何看待——不仅是你一个团队、问题和 KPI，而是更大的图景？

Another way as you describe it, another way I'm thinking about it is like think if you were your manager, how would they—what's their broader perspective across not just your one team and problem and KPI, but the larger picture?

多年来我得到的建议与此类似：有没有办法让我做好自己的工作，同时帮助我的经理做好他们的工作？

I've got advice over years that is similar to that: are there ways that I can do my job that helps my manager do their job?

所以，如果我思考所有我直接负责的事情，但站在我经理的角度来看——不只是产品和工程，还有财务、内容以及业务的其他部分——我会自然地拉远视角，思考所有这些组件如何协同，整体如何大于部分之和。我认为这是很有用的思考方式。对于工程师来说，思考如何让这些系统变得更好？如何打造高质量且能扩展的东西供他人使用？这里既有“我如何帮助我的经理”，也有“我如何帮助我的同事”，这是我们一些工程原则的核心：做对整个组织正确的事，而不仅仅是对你个人局部正确的事。这也是系统思维。所以这不仅仅是资历问题，而是我解决问题和构建东西的广度：它对我的同事有用吗？我是否为我们未来想要实现的创新留下了更强的版本？

And so if I thought about all the things I'm directly responsible for, but I thought about it from the perspective of my manager. So not just product and tech, but finance and content and other parts of the business, I would naturally zoom out and think about how all these component pieces need to come together and how the whole could be greater than the sum of the parts. I think that's useful thinking. And for engineers to think about how do I leave a better version of these systems? How do I think about the thing that's going to be high quality and scale for others? There's both a how do I help my manager and there's how do I help my colleagues, which is a core part of some of our engineering principles of do the thing that is right for the broader organization instead of just what's right for you locally. That's systems thinking as well. So it's not just seniority, but it's breadth of the way I solve this problem and I build this, is it going to be useful to my colleagues and am I going to leave a stronger version of things for the future set of innovations that we want to make?

这真是很棒的战术建议。让经理的工作更轻松总是一个好策略。

That is awesome tactical advice. Making your manager's life easier is always a good tactic.

从职业发展角度看，有几个原因。是的。

Career-wise, several reasons. Yeah.

顺着这个话题，我知道你们最近增加了职业阶梯和级别。这以前是你们没有的东西。那么在这个 AI 领域，你们在职业阶梯中增加了什么？有没有什么你希望人们更多投入的，或者你正在寻找更多或更少的东西？比如，你们有没有改变职业阶梯和绩效标准？

Following the thread a little bit, I know you all added career ladders and levels recently. It was like a new thing you guys used to not have these things. So kind of all on that thread, what have you added to the career ladders within this AI world? If anything that you find you want people to lean into more, you're looking to more or not. Like, did you not change your career ladders and performance criteria?

到目前为止，我们的做法不是试图在每个级别上精确说明 AI 如何改变那些期望，而是在 Netflix 的所有人才、团队成员以及招聘人员中增加一个覆盖层，来讨论对 AI 素养的期望。具体表现会因职能而异，也会根据你职业生涯的阶段而不同——可能是你所在的级别，或者你从事的角色类型。但 AI 素养这个期望很难定义。它意味着我有实验心态吗？意味着我知道 AI 在哪些地方有用、哪些地方没用吗？意味着我实际上用 AI 构建过东西吗？我觉得它在职业阶梯中的体现以及我们谈论它的方式几乎每个季度都在演变，甚至每个月或每天，因为技术本身进步太快了。所以最有用的做法不是让它与级别或角色绑定，而是鼓励每个人都朝着 AI 素养的期望努力。这并不意味着为了技术而使用技术，而是在技术有用的地方使用它，要有良好的判断力，并且要有开放的心态去探索和尝试新事物。这是所有角色不可妥协的要求，在 Netflix 的最高层也是如此——我们谈到即使日常工作不写代码，也需要对 AI 有深入的理解。所以这已经发生了变化，并且也体现在我们的招聘实践中。在面试中，我们越来越习惯于探讨人们如何看待 AI 或技术？他们在日常或当前工作中使用什么？他们对变化和探索的适应程度如何？甚至在编程面试中，我们当然允许候选人使用 AI 工具，因为那已经是工作所需的一部分了。这些是我们已经做出的转变，但我怀疑这个转变是否已经完成——我们正处在这个过程中。

So, the way we've approached this so far is instead of trying to articulate at each level exactly how AI changes those expectations, to instead put an overlay across all of the talent at Netflix, people on the team, and those who are hiring to talk about an aspiration for AI fluency. And what that looks like is going to vary by function. It's going to vary based on where you are in your career. That could be what level you're in or what type of role or persona work you're doing. But the aspiration for AI fluency, which is a tough thing to define. So, does it mean that I have an experimentation mindset? Does it mean that I know where AI is useful and not useful? Does it mean that I've actually built things using AI? I feel like the way that has shown up in career ladders and how we talk about it evolves almost by the quarter, if not month or day, because the tech itself is advancing so much. So, the most useful thing is not to make it level specific or role specific, but to encourage everyone towards the expectation on AI fluency, which doesn't mean use it as a tech for the sake of tech. It's tech where it's useful, to have good judgment about that, and to have the mindset to be open-minded to explore and try new things. That's the non-negotiable for all roles, and that's true at the senior most levels of Netflix, where we talk about we too need to have deep fluency in AI, even if we're not writing code as part of our day jobs. So, that's changed, and then that's showing up in our hiring practices as well. Getting comfortable within interviews exploring how are people thinking about AI or technology? What are they using in their day-to-day or their current job? How comfortable are they with change and exploration? And even for things like coding interviews, allowing candidates, of course, to use AI tools because that's going to be part of what the work requires now. So, those have been shifts that we've made, but I doubt it's a shift that's done versus we're right in the middle of it.

她将继续顺着这个话题。显然，AI 对编程具有变革性，是原型开发的一大突破。Netflix 还有哪些人们可能没想过或没意识到的、真正有影响力的 AI 用例？

And she's going to keep following this thread. Obviously, AI is transformative for coding. It's a big unlock for prototyping. Are there other use cases of AI at Netflix that have been really impactful that people may not think about or not realize?

我想到两个。第一个是数据分析、信息提炼和建模——也就是利用工具来掌握我们所有的洞察，类似于我之前提到的。我们做过哪些实验？对于某个问题，我应该关注哪些指标？我们做过哪些消费者研究？这带来了更高的速度和更高的质量，前提是你验证了结果的有效性，与你的本地数据科学家合作，并且使用的是真实数据源。但这是一个很好的用例，我个人也最常用这些工具来做这件事。所以它超越了原型开发和编程，进入了通用的分析思维，以及将数据转化为行动和洞察。另一个是内容制作和创作业务，有很多应用。这在生成式 AI 之前就已经存在了。机器学习和 AI 被深度用于许多制作工具。我们用它们来思考如何大规模创建宣传素材，如何进行字幕和配音的本地化。生成式 AI 在创意构思方面带来了巨大的阶跃式影响。我们称之为“预可视化”，或者基本上是在你把人员带到片场、开始实际制作之前，就将创作者的愿景变为现实。后期制作也有很多用例。我们最近收购了 Ben Affleck 创立的公司 Inner Positive，它构建了一套模型和能力，让你在拍摄完成后可以重新布光、重新构图、重新拍摄、更改对话，这些方式对获得更高质量的内容非常有影响力，但仍然由电影制作人主导，他们会说：“你知道吗？我想尝试别的东西来实现这个愿景。”这种影响非常有前景，我们看到很多制作都在利用不同的工具，有些是内部构建的，有些是通过其他供应商实现的，用于这些内容创作用例。然后，当我们思考内容如何呈现到产品中时，我提到了本地化、字幕和配音，还有我们如何大规模创建高质量预告片、图片和艺术作品，然后用来帮助确保作品在全球找到它们的受众。这些都是 AI 影响中的巨大杠杆。所以这再次远远超出了原型开发或编程，进入了创意用例。你可以想象，就像它们适用于电影和电视的制片厂制作一样，它们也适用于广告、营销、站外推广活动——这些都是我们正在探索的领域。

So, there's two that come to mind. So, the first is data analysis, distillation of information, modeling, which is, you know, using the tools to get our arms around all the insights we have, similar to what I mentioned before. What experiments have we run? What are the metrics that I should be looking at for a certain problem? What's the consumer research that we've done? And that is much higher velocity and much higher quality, contingent on you check that the results are valid, you work with your local data scientist and am I using the source of truth data on this? But, that's been a great one and that's one personally that I would say I most use some of these tools for. So, that goes beyond prototyping and coding to general analytical thinking and translating data to action and insight. The other one is on the content production, creation part of the business, which has lots of applications. This was true before GenAI. So, ML and AI were deeply used in a lot of the production tools. We've used them to think about how to create promotional assets at scale, how to localize in subtitles and dubs. So, GenAI is a big step function in where the impact can be in creative ideation. We call those things like pre-visualization or basically bringing a creator's vision to life before you even get into the you bring people to a set and start to actually go through the production itself. There's lots of use cases in post-production. So we recently acquired a company Inner Positive that was started by Ben Affleck that built a set of models and capabilities that allow you after you've shot something to relight, reframe, reshoot, change dialogue in ways that are very impactful to get higher quality content are still led by the filmmaker creator saying, you know what? I would like to try something else to bring this vision to life. But that impact is extremely promising and we're seeing lots of productions leverage different tools, some of them built in-house, some of them that we enable through other vendors for those content creation use cases. And then as we think about how content comes to the product, I mentioned localization, subtitles and dubs, but also how we create high-quality trailers, images, artwork at scale that then we can use to help make sure that titles find their audiences around the world. Those all are huge levers when we think about the AI impact. So that again goes well beyond prototyping or coding to some of the creative use cases and you can imagine that just like they work for studio productions for film and TV, they work for advertising, they work for marketing, off-service campaigns and so those are all areas that we're exploring.

本期节目由 Mercury 赞助播出。Mercury 是一家与众不同的银行，深受超过 30 万企业家喜爱。现在还有 Command 功能。我是 Mercury 超过 6 年的客户，从未想过要离开。

This episode is brought to you by Mercury, radically different banking loved by over 300,000 entrepreneurs. And now with Command. I've been a customer of Mercury's for over 6 years. I have never once thought about leaving.

Mercury 就是那种由产品人而非银行家打造的银行服务。他们让发送发票、转账、为团队成员设置虚拟卡变得极其简单，甚至可以说有趣。你的银行有 API、终端原生 CLI 或支持 AI 的 MCB 服务器吗？我觉得没有。就在最近，他们推出了 Command，一个直接内置于 Mercury 的对话式界面，充当你的财务运营官。我一直在用 Command 转账、查看哪些类别支出最多、分析现金流。就在今天，我用它查了过去一年从某个特定赞助商那里赚了多少钱。我只是问：“过去一年我从 X 那里赚了多少？”10 秒后我就得到了答案。这太酷了。访问 mercury.com 了解更多，几分钟内即可在线申请。Mercury 是一家金融科技公司，非 FDIC 承保银行。银行服务由 Choice Financial Group 和 Column NA 提供，均为 FDIC 成员。

Mercury is basically what happens when banking is built by product people, not by bankers. They make it so easy, dare I say fun, to send invoices, move money around, set up virtual cards for folks on my team. Does your bank have an API, a terminal native CLI, or an AI-ready MCB server? I don't think so. And just recently they launched command, a conversational interface built directly into Mercury, which acts as your financial operator. I've been using command to transfer money around, to figure out what categories I've been spending the most money in, analyze my cash flows. And just today I used it to find out how much I've made from a specific sponsor over the past year. I just asked, "How much have I made from X over the past year?" 10 seconds later I have an answer. It is so freaking cool. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and Column NA, members FDIC.

你提到 Netflix 很早就开始使用 AI 和 ML，时间很长了。年轻人可能不记得了，但你们举办过一个优化比赛。对，Netflix Prize。这正好说明了你们在 AI 和 ML 方面起步有多早。当时有一百万美元的奖金，用来优化 Netflix 的排名算法，哪怕只优化一点点。谁能优化得最多，谁就拿走奖金。我记得获胜者优化了几个百分点，差不多这样。那是一件大事，全世界的超级聪明人都参与其中。而且这个比赛举办了好几次，对吧？

You mentioned how Netflix has been very early to AI and ML for a long time. Younger people may not remember this, but y'all had this contest to optimize things. Yeah, the Netflix prize. It showed an example of how early you were to AI and ML. There was a million-dollar prize to optimize the Netflix ranking algorithm a little bit. Whoever could optimize it the most. And I think the winner optimized it by a few percentage points, something like that. And it was a huge deal. All these super smart people got around the world. And it happened a few times, right?

你替我说了。每当有人问 Netflix 如何看待 AI 时，提醒大家这一点很好：这对我们来说并不新鲜。尤其是在个性化方面，它一直是为核心成员提供出色体验的关键。我们拥有海量内容，而且内容还在不断增加，这是挑战之一。让发现内容变得越来越容易，也是 Netflix 面临的挑战之一。使用 AI 和 ML 正是解决这个问题的方法。你想在正确的时间为正确的人推荐正确的内容，这个问题越来越难。我们的内容库越丰富，不仅有电影和电视，还有游戏、直播和播客，个性化在体验中就越重要。因此，我们可以借鉴历史，说：“好吧，现在我们如何解决这个问题？”因为技术更强大，但这也让我们在明确要解决的问题以及解决它对会员的重要性方面有了先发优势。同样，正如我提到的，在创意方面也是如此。AI 和 ML 长期以来一直用于视觉特效或语言本地化。现在我们要问：“当技术更强大时，下一个时代是什么？”在这两种情况下，最终都利用了 Netflix 的优势，即娱乐与技术的结合，并确保我们保持领先，提供更好的内容。所以，我很高兴这是我们历史的一部分。它仍然是一个优势，而且考虑到我们在保持出色体验的同时面临的内容广度挑战，它必须是一个优势。

I mean, you said it on my behalf. Often when there's questions about how is Netflix thinking about AI, it's great to remind people of exactly that point, that this is not new to us. Especially for personalization, it's been central to delivering a great experience to members. It's impossible to take the breadth of content that we have. There's ever more content. That's one of the challenges we face. And make discovery easier and easier, which is one of the challenges that Netflix has. Using AI and ML has been a way to do that. You want to personalize the right title for the right person at the right moment, that problem gets harder. The more exciting our catalog gets, the greater breadth of content we have, not just film and TV, but games and live and podcasts, personalization becomes even more important in what that experience is. So, we can take a lot of that history and say, "Okay, well, now how do we solve this problem?" Because the tech is even more powerful, but it gives us a running head start in being clear about the problem to solve, how important it is that Netflix solve that for our members. And then the same is true, as I was mentioning, on the creative side of the house. AI and ML have been in things like visual effects or in localizing language for a long time. Now we say, "What's the next era of that when the tech is more powerful?" In both cases, it ends up taking a strength that Netflix has, which is marrying entertainment and technology, and making sure we stay ahead of the game to deliver things that are even better. So, I love that it's part of our history. It still continues to be a strength, and it's going to have to be a strength, given the size of the challenges we're facing around the breadth of entertainment while keeping a great experience.

是的。而且我很喜欢那时候它被称为机器学习，而 AI 就像“不，不，这不是 AI。AI 永远不会发生。这只是机器学习。”

Yeah. And I love that back then it was called machine learning, and AI was like, "No, no, this is not AI. AI is never going to happen. It's just machine learning."

嗯，然后突然间我们把所有东西都叫做 AI，而其中有些只是机器学习。

Well, then all of a sudden we call everything AI, and some of it's machine learning.

没错。

That's right.

所以，我认为我们现在把所有东西都归为 AI。

So, I think we bucket all of it as AI now.

是的，AI 已经成为一个宽泛的术语。有很多 AI 用例并非生成式用例。所以，我们可以深入探讨所有具体细节。但总的来说，我认为 Netflix 使用广泛的 AI 技术不会让任何人感到惊讶。而且，由于对可能性的兴奋，Netflix 员工的有趣之处在于，如果你对技术在创意输出、消费产品、基础设施方面的应用充满热情，我们拥有所有这些问题，而 AI 是其中的核心。即使 Netflix 没有被标榜为 AI 公司，也不应忘记这一点。AI 是我们非常擅长使用的工具，用来实现这些伟大的娱乐和技术成果。

Yeah, AI has become a broad term. There are many AI use cases that are not generative use cases. So, we could go down a deep dark hole of all the specific things. But in general, I don't think it would surprise anyone that Netflix is using a broad array. And with so much excitement about what's possible, the fun thing at Netflix for the people who work here is that if you're really passionate about the applications of tech for creative outlets, for consumer products, for infrastructure, we have all of those problems and AI is at the center of them. It's good not to forget that that's true even if Netflix isn't branded as an AI company. AI is a tool that we're very comfortable using to get these great entertainment and technology outcomes.

另一件非常有趣的事情是，继续夸夸 Netflix。如果你看看 Netflix 早期的文化手册以及我们上次的对话，从中浮现出来的东西包括高主动性。这是 Netflix 从一开始就核心的东西。高主动性、自主性、高人才密度、非常自下而上的思维、超快速的实验和发布、支付市场最高薪酬。这些都是每个 AI 实验室——我现在经常听到顶级 AI 实验室就是这样运作的。所以我们都殊途同归，而 Netflix 一直就在这里。

The other really interesting thing just to kind of keep complimenting Netflix here. If you look at the early culture deck of Netflix and also our conversation last time, things that emerge from that are things like high agency. This is something core to Netflix in the beginning. High agency, autonomy, high talent density, very bottom-up thinking, super quick experiments and launching, paying top of market. This is all stuff that every AI lab—this is what I hear constantly now from how the top AI labs operate. So we're all ending here and this is where Netflix has been forever.

是的，这在理解是什么让人才变得卓越方面有点先见之明。我一直在思考 Netflix 文化的所有这些方面——这听起来有点书呆子气——但“卓越作为操作系统”。所以所有这些文化元素的目标本身并不是终点。不是“让我们确保人们承担尽可能多的责任”或“我们不喜欢流程，所以让我们确保没有任何流程”。相反，这是一个非常坚定的观点：通过给人们大量的主动权和问责制，通过将决策尽可能推向组织深处，雇佣可以被信任拥有良好判断力并做出正确决策的优秀人才，你就能实现卓越。这最终会带来不可思议的成果，以及更多的动力和责任感。这意味着团队中的每个人都能感觉到“我被赋予了很大的权限，也对这里发生的事情承担很大的责任”。我自己也感到，当你肩负着那种信任和责任时，你会想要做到最好。所以，Netflix 的文化一直以卓越为目标，这感觉非常直观。当你拥有优秀的人才，并让他们在没有微观管理或流程束缚的情况下发挥最佳水平时，你实际上会得到更好的结果。因此，我确实认为新时代的公司正在借鉴一些让我们感到非常熟悉的东西。而这并非易事。所以，拥有文化并不是一成不变的。

Yeah, it's a little prescient in understanding what makes talent incredible. I've thought about all those aspects of the culture at Netflix as—this is going to sound a little bit nerdy—but excellence as an operating system. So the goal of all those cultural elements wasn't the end goal in themselves. It wasn't "let's just make sure people have as much responsibility as possible" or "let's, you know, we don't like process, so let's make sure that we don't have any of that." It was instead a very strongly held opinion that you get to excellence by giving people a lot of agency and accountability. By pushing decisions as deep in the organization as possible, hiring great people who can be trusted to have good judgment and make good decisions. And that ends up driving incredible outcomes plus a lot more motivation and sense of responsibility. It means every person on the team can feel like "I'm being given a lot of keys and a lot of accountability for what happens here" and I myself feel like when you know you're carrying that level of trust and accountability, you want to do your best work. And so there's something that feels very intuitive about Netflix's culture has always been aiming at excellence. And when you have great talent and you give them the ability to do their best work without micromanaging it or drowning it in process, you actually get much better outcomes. And so I do think that the newer era companies are picking up on something that is feeling very familiar to us. And it's not something that comes easily. So having culture is not a static thing.

随着公司规模扩大，文化需要成长和演变，你解决的问题类型也在变化。但追求卓越、信任非凡人才能够做出最佳作品这一理念从未改变，我认为这仍然是我们的独特秘诀。

Culture needs to grow and evolve as a company gets bigger, the types of problems you're solving change. But the notion that like we're going for excellence and trusting that exceptional talent needs to be able to do their best work. That's unchanged and something that I think continues to be a special sauce for us.

我喜欢这个概念，把卓越当作操作系统。这非常系统化思维，他可能会说，用于如何建立一家公司。

I love this concept, excellence as an operating system. It's very uh systems thinking, he he might say, for how to set up a company.

正是如此，Lenny。

Exactly, Lenny.

所以对于听众来说，每个人都会想要把卓越当作操作系统。谁会不想要呢？如果能听听实现这一点的要素会很有帮助。一个是高人才密度，只招最优秀的人。二是责任感。基本上就是输入和输出。输入优秀的人、顶尖人才，让他们负责，给予自主权。你会说创建这种卓越操作系统的支柱是什么？如果创始人在听，他们想做到这一点。

So for people that like everyone listening to this will want excellence as an operating system. Like who would not want this? Uh it'd be helpful for people to hear what are kind of the ingredients to make this happen. One is obviously high talent density, just hiring only the best. Two is accountability. Kind of there's like the input and the output essentially. Uh input amazing people, top the top people, give them make them accountable, give them autonomy. What would you say kind of like the pillars of creating this uh excellence as an operating system if people if founders are listening to this like I want them to do that.

人才密度是没得商量的。你必须从那里开始。如果没有它，你就无法达到对组织各层级决策充满信心的状态，无法让人们快速冒险和创新。这是 Netflix 文化中卓越的重要组成部分，即非常适应冒险。我们不是要避免失败，而是要在失败时快速恢复。我认为有很多很好的例子。我们进军直播就是一个很好的例子，我们乐于承担大量风险，知道它不会完美，知道我们会快速学习，并因此变得更好。看到团队如何克服困难，我从未如此自豪。所以，你必须有人才密度，要放心让人们利用你提供的背景、强大的判断力和冒险精神，为业务争取最佳结果。你必须非常清楚，你所做的是为消费者和 Netflix 带来成果。所以，Netflix 很重要，Netflix 会员很重要。这不是关于我个人的成功或我的偏好。所以，这种卓越操作系统包含一种无私精神。另一件我想说的是，有些事情对人类来说真的很不自然。我可以举几个例子，让你习惯。比如，有些日子我看到决策正在发生，我会想，“嗯，我会做出不同的决定。”这真的是最好的吗？但我的工作，尤其是在 Netflix 文化中，并不是在每种情况下都介入、否决或质疑某人，尤其是当它不重要、不会把公司搞垮的时候。让人们做决定并从中学习。之后要求他们反思，比如“结果如何？也许我错了。也许这个决定很棒。”这与冒险有关，帮助人们学会自如地做出自己的决定，尤其是当这些决定并不都是正确的，他们会从中吸取教训。我自己也感受过，我的老板和同事说：“这是你的决定。我可以提供意见，帮你头脑风暴，但最终是你的。”当风险很高，当我为组织的行为负责时，让人们去冒险可能会让人不舒服。我认为这也意味着，在事情进展不顺利的情况下，不要假设流程能解决它。所以，过去几年我学到的一件事是，当计划困难时，我从未听人说过“哦，我们找到了完美的计划方式”，或者完美的反馈、评级和薪酬方式。但每次我们看到这一点并增加更多流程时，我们花了更多时间却没有得到更好的结果。所以，这是另一件不自然的事情：当事情困难复杂时，每个人的倾向是认为通过增加很多约束就能简化问题，但实际上这违背了——是否有更创造性的方式来计划、做人事决策或优先级决策，从而带来更好的结果？所以，要抵制许多大公司会做的事情，并经常在这种不适中感到自在。这就是我在我的角色中感受到的，我相信 Netflix 的很多人也感受到，因为你试图不做标准的事情。

Well, the talent density is the non-negotiable. Like you have to start with that. If you don't have that, you can't get to a place where you have confidence in decision-making at all levels of the organization, allowing people to take risks and innovate quickly. That's a big part of excellence in the Netflix culture, which is being very comfortable with risk-taking. We don't try to avoid failures, we try to recover quickly when we have them. I think there's been great examples of that. Our foray into live was a wonderful example of being comfortable taking a ton of risk, knowing it would be imperfect, knowing we would learn fast, and we would be better for it. I've never been prouder of the team seeing how we worked through that. So, you have to be talent density, comfortable that people are going to take the context that you give them, strong judgment and risk taking, and fight for the things that are the best outcomes for the business. You have to be very clear that what you're doing is driving outcomes for consumers and Netflix. So, it's Netflix matters, Netflix members matter. It's not about my own personal success or what I prefer. So, there's a selflessness that is part of this excellence operating system. And then the other thing I would say is some of the things that are they're really unnatural for humans to do. So, I could give a couple examples of things to get comfortable with, which is there are certainly days where I see decisions happening, and I think, "Hmm, I would make a different decision." Like, is that really going to be the best thing? But, my job, especially in the Netflix culture, is not to step in in every one of those cases and overrule or veto or question someone, especially if it's it's not material, it's not going to burn the place down. Let people make that decision and learn from it. And ask for those reflections afterwards of like, "How did it go? Maybe I was wrong. Maybe the decision was a great one." But, that it's related to the risk taking and the like help people learn how to feel comfortable making their own decisions, especially when they're not all going to be the right decisions, and they're going to learn something tough from it. I felt that myself from my boss and my peers saying, "This is your decision. You know, I can provide input. I can help you brainstorm. It's yours in the end." And I that it it just doesn't come naturally when the stakes are high, when I feel responsible for what the org's doing to let people lean into risk can be uncomfortable. And I think that also means in cases where things are not going well as another example to not assume that process is going to fix it. So, if or something I've learned over the past few years, that when planning is difficult, I've never heard someone say like, "Oh, we figured out the perfect way to plan." Or the perfect way to go through feedback and leveling and compensation. But every time we saw that and we added more process, we spent more time without getting better outcomes. And so, it's another unnatural thing that I think everyone's inclination when things are hard and complicated is you think you're simplifying the problem by putting a lot of constraints around it, but it actually goes against the like, is there a more creative way to plan or to make people decisions or to make prioritization decisions that actually get us to better outcomes. And so, it's a resistance to do the thing that a lot of bigger companies would do and to feel comfortable in that discomfort very often. So, that's something I feel in my role and I I would believe a lot of people at Netflix feel it because you try not to do the thing that is standard.

说起来容易，听上去也容易，但我非常明白你的意思。当有人搞砸了，你会想，“好吧，哪里出了问题？我们制定一个流程来避免这种情况再次发生。”而你说的是，你需要抵制这种冲动。因为这会拖慢速度，最优秀的人不想在一个充满清单、流程和关卡的地方工作。

It's easy to say that and hear that, but I so know what you mean, where somebody screws up and you're like, "Okay, what was the thing that went wrong? Let's put a process in place to avoid this from happening." And what you're saying is like, you need to resist that. Uh because that slows things down and the best people don't want to be working in a place with all these checklist and process and gates and things like that.

不，我认为最优秀的人想知道会有无责回顾，他们会感到个人责任重大，以至于他们会说：“我如何确保这不再发生？”不是通过流程，而是如何分享这些经验？如何以不同的方式工作，确保下次取得更好的结果？当你信任人们进行反思、学习和成长时，我认为随着时间的推移，你会得到更好的结果。你会得到一个更强大的团队，我认为这是我们作为领导者角色的一部分——你试图培养一个坚韧、持久、知道如何产生巨大影响力的团队。你不是要控制一切。

No, I think the best people want to know there's going to be a blameless retro and they're going to feel so individually responsible that they're going to say, "How do I make sure this doesn't happen again?" Not with process, but with like how could I share these learnings? How could I do work differently to make sure that I get to a better outcome next time? When you are trusting people to take those reflections and learn and grow I think you get much better outcomes over time. You get a much stronger team, which I think is part of our role as leaders of like you're you're trying to grow a team that is resilient and durable and knows how to have great impact. You're not trying to control everything.

这是建立高人才密度团队的关键。我想简单聊两个方面。一个是招聘，另一个是留住人才。你们以“留任测试”闻名。我们上次谈过这个。这对人们来说又是一件不自然的事。想了解这是什么的人可以去听第一次对话，但过去几年它有什么演变？这仍然是文化的核心部分吗，这个留任测试的理念？

Which is a key to building a team with high talent density. There's two sides to this that I want to chat about briefly. One is the hiring and the other is keeping the people. So you're famous for the keepers test. We talked about this last time. Another unnatural thing for people. People that want to understand what this is, they can listen to the first conversation, but has that How has that evolved over the last couple years? That's still core part of the culture, this idea of the keepers test?

它经常被引用，人们认为留任测试是决定让某人离开、他们不适合这个角色的时刻以及相关对话。但它同样常用于讨论某人有多么出色，他们在某个角色中表现有多好。

It's often cited in a way where you think of keepers test as that moment where you decide to let someone go, that they're not the right fit for the role and the conversation about that. But it's equally commonly used to have a conversation about how extraordinary someone is. How well they're doing in a role.

因为切入点是我对我的直接下属说，或者他们对我说：“我在你的 Keeper Test 上表现如何？”绝大多数时候我的回答是：“我会拼命留住你。让我说说你做得特别好的几件事，你的优势在哪里，你在哪些方面产生了很大影响。以及你可以如何变得更好。”所以这是一场非常积极、鼓舞人心的对话的入口，但框架是“我通过 Keeper Test 了吗？”当然，也有更困难的情况，我在评估某人是否通过 Keeper Test，或者他们问我，而这是最难说出口的话——老实说，“你现在没有通过。”我认为在某些情况下你可以达到，那需要反馈和里程碑；或者在某些情况下你会说：“我们真的努力过了，但我看不到成功的路径。”所以它只是一个锚点和对话的入口，可以走向很多不同的方向。我喜欢它的地方在于，它是反馈和检查进展的良好习惯，有时会迫使你进行艰难的对话，而不是回避它。或者为了留住优秀人才，你确实需要说“你做得很好”。这是让人们感到被认可和重视的重要部分。所以我不想让人觉得我们只看到它消极的一面。我认为也有积极的一面。

Because the entry point is for me to say to one of my direct reports or for them to say to me, 'How am I doing on your keeper test?' And the lion's share of the time my response is, 'I would fight so hard to keep you.' Let me go through a set of things that I think you're doing such a great job at, what your strengths are, where you're having a lot of impact. Here's how you could be even better.' So it's an entry into a conversation that is very positive and uplifting for people, but the framing is, 'Do I pass the keeper test?' And then, of course, there's the harder situations where I'm evaluating whether someone passes the keeper test or they're asking me, and it's—this is the toughest thing to say, to be honest, 'You're not passing that right now.' I think you could get there in some cases, and that comes with feedback and what are those milestones? Or in some cases you're saying, 'We've really tried and I don't see the path to success.' So it's just an anchor and an entry point for a conversation that can go lots of different directions. And the thing I like about it is it's good hygiene on feedback and checking in on how things are going and forcing a tough conversation sometimes instead of shying away from it. Or to keep great talent, you do need to say you're doing great. That's an important part of making people feel recognized and valued. So I don't want it to come across that we just have this very negative view of it. I think there's this positive side of the coin as well.

太棒了。我想给听众解释一下这是什么，这样他们就不用去听其他播客了，我试着简单说明一下。这个想法是 Netflix 文化的一部分：当你有人向你汇报时，你应该一直思考，如果我现在——我今天会雇用这个人吗？基于我对他们的了解，如果不会，那我可能应该让他们离开。这个想法是为了保持高标准，永远不要将就，比如“好吧，这个人已经在了，我们就留着他们吧”。大致可以这样理解吗？

Awesome. I guess just to explain to people what this is so they don't have to go listen to other podcasts, I'll try to briefly explain it. The idea here, a part of the Netflix culture, is that when you have people reporting to you, you should always be thinking, if I were to—would I hire this person today? Knowing what I know about them, and if not, then I should probably let them go. And the idea there is to keep the high bar, to not ever just like settle, okay, this person they're here, I guess we'll keep them around. Is that roughly the way to understand it?

是的，这可以说是它的推论：如果那个人今天来跟我说他们要离职，我会努力留住他们吗？或者我会说——如果我的感觉是松了一口气，比如“哦，是啊，可能换个人来做这个角色更好”，那我应该早就采取行动进行那场对话了。

Yeah, and the way it can—it's sort of a corollary to that: if that person came to me today to say they were leaving, would I fight to keep them or not? Or would I say—if my sense is relief, like 'Oh yeah, it probably would be better to have someone else in this role,' I should have taken action in having that conversation sooner.

我喜欢你说的，这真是——要维持这些，你必须做很多不舒服的事情——

I love, as you said, it's such—so many uncomfortable things you have to do to maintain—

是的，就是——嗯，Keeper Test 是其中之一。保持人才密度，领导者之间要“情境而非控制”。我们谈论“高度一致但松散耦合”，这意味着轻量级流程——你知道，确保我们明确优先级并能执行的最低限度——这就是我们要解决的问题。所有这些都不是人类或大型组织通常会做的事情。所以需要持续的努力来维持让 Netflix 变得特别的东西。因为最终，是工作和文化吸引并留住人才，我们需要这样才能成为一家成功的公司。

Yeah, it's the—well, the keeper test is one. Maintaining talent density, context not control among leaders. We talk about being highly aligned but loosely coupled, which is where light process—you know, the minimum to make sure we're clear on the priorities and we can execute them—is what we're solving for. All of these things are not things that human beings or organizations at scale tend to do. So it's constant diligence to try to maintain the thing that's made Netflix a special place. Because in the end, it's the work and the culture that attracts people and retains people, and we need that to be a successful business.

这正是我想问的。所以要让这个模式奏效，你需要吸引最优秀的人才。吸引最优秀的人才一直很难。现在感觉难上加难，因为到处是巨额资金、花哨的 AI 实验室，竞争如此激烈。每个人都在，你知道，太疯狂了。你发现什么方法能有效说服顶尖人才仍然选择加入 Netflix，而不是去其他那些花哨的地方？

So that's exactly where I was going to go. So to make this work, you need to attract the best people. It's always been very hard to attract the best people. It feels insanely hard these days with the amount of dollars flying around, the fancy AI labs, so much competition. Everyone's just, you know, it's crazy. What have you found to be effective in convincing the top people to still come to Netflix and join versus all the other fancy places they can go?

是的，我们在人才方面一直面临激烈竞争。现在可能感觉更明显，但我们的团队有很棒的人才。也许这不用说，但我认为我应该大声说出来，因为我坚信这一点。Netflix 有不可思议的人才，无论是新员工还是老员工。团队所做的工作总是让我印象深刻。所以我不觉得我们受到了影响，或者其他公司吸走了所有优秀人才，因为我认为很多优秀人才就在 Netflix。确实感觉我们必须更明确地说明，什么样的人和人才在 Netflix 比在其他公司（比如一些前沿实验室）更容易茁壮成长。Netflix 的人必须对技术的应用充满热情。以及应用或构建产品来解决特定问题。你必须热爱娱乐。你必须热爱大规模消费产品。你必须热爱它的全球性。有很多非常有才华的人热爱这个甜蜜点。我就是其中之一——在技术、产品和娱乐之间，如何让这些东西以卓越的方式结合在一起？你用 AI 来实现。你用其他技术和产品来实现。但这必须成为让你对 Netflix 的许多角色感到兴奋的动力。如果你反而被前沿模型公司所做的基础性工作所吸引——这本身也很令人兴奋——那就是另一种人格了。那是不同的——比如“我想在这个问题空间工作”。但我不认为缺少对技术应用感到兴奋、并看到它与他们每天热爱和使用的东西（比如 Netflix）之间联系的人。所以，你知道，这让我早上起床，我认为也让很多团队成员起床，我们讨论说，这是只有 Netflix 的人才才能做的特别事情——或者填入另一个深度应用技术的行业。我认为这很鼓舞人心。

Yeah, we've always had a lot of competition for talent. It might feel more pronounced right now, but we have great talent on the team. Maybe that goes without saying, but I feel like I should say it out loud because I believe it. We have incredible talent at Netflix, recent hires, long-tenured people. I'm always impressed by the work that the team is doing. So I don't feel like we've suffered or like other companies are vacuuming up all the good people because so many of them I do think sit at Netflix. It does feel like we have to be more explicit about the types of people and talent that tend to thrive at Netflix versus other companies like some of the frontier labs. So people at Netflix have to be passionate about the application of technology. And the application or building products to solve a certain set of problems. You have to love entertainment. You have to love consumer products at scale. You have to love the global nature of that. There are a lot of incredibly talented people who love that sweet spot. I am one of them—between tech and product and entertainment and how do you make those things come together in a way that's remarkable? And you use AI to do it. You use other technologies and products to do it. But that has to be something that drives you to be really excited about a lot of the roles at Netflix. If instead you're drawn by some of the foundational work that the frontier model companies are doing, which is exciting in its own way, it's a different persona. It's a different—like here's the problem space that I want to work in. But I don't think there's a shortage of people who get really excited about the applications of the technology and see the connection to things that they love and use every day like Netflix. And so that, you know, that gets me up in the morning and I think it gets a lot of the team members up, and we have this conversation about like that's something special that only talent at Netflix can do—or fill in the blank for another industry that's deep in the application of it. I think that's inspiring.

我想谈谈我在我们正在接近的 AI 世界中思考的几件事。一个是初级员工。感觉每个人都像——有个好例子。你招聘了很多很棒的资深员工，他们已经证明了自己很棒，你知道，高人才密度，高标准。而且 AI 让做事变得如此容易，以至于人们可能学不到任何东西。我在想初级工程师、初级产品经理、初级设计师。就像，新人如何成为这些优秀的资深员工？你有什么想法吗？你在招聘初级员工吗？如果初级员工不一定能学习或有路径学习成为资深员工，你怎么看？

I want to kind of touch on a couple things that I've been thinking about in this world of AI that we're approaching. One is junior people. It feels like everyone's like—there's a good example. You're hiring a lot of awesome senior people that have proven they're awesome and you know, high talent density, high bars. Also just AI makes it so easy to do stuff that people may not be learning how to do anything. They're like junior engineers I'm thinking or junior PMs, junior designers. Like there's just like how do new people become these awesome senior people? Is there anything you think about? Are you hiring junior people? How do you think about this if this happens with junior people not necessarily learning or having a path to learn to become the senior person?

我们仍在招聘初级员工，他们对我们的人才战略非常重要。所以我们仍然有实习生项目，我们仍然有应届毕业生项目，这是几年前才开始的。在几年前，我们只招聘所有职能领域更有经验的人才。现在我们直接从本科和研究生项目招聘，我们会继续这样做。

We are still hiring junior people and they're really important to our talent strategy. So we still have an intern program, we still have a new grad program, which was new for us as of a few years ago. So prior to a few years ago, we were only hiring more experienced talent across all the functions. Now we do hire people straight from undergrad and graduate programs and we'll continue to do that.

所以，即使在 AI 让某些事情变得更简单的世界里，我们之前聊过心态和 AI 素养。根据我的经验，年轻人思想更开放，他们往往更自然地适应这些新的工作方式。对于像 Netflix 这样的公司，他们也非常了解娱乐行业的变化、消费者行为的改变，以及产品和技术如何影响他们使用的产品。这对我们的团队来说非常重要。所以，一方面是作为应届毕业工程师的身份，另一方面是作为二十岁出头、对世界有独特视角、并且对世界变化感到自在的人。这就是为什么我说这是我们人才战略的关键部分。

So, even in a world of AI where some things are easier, we were talking earlier about mindset, AI fluency. From my experience, younger folks are more open-minded. They tend to be more native in some of these new ways of working. For a company like Netflix, they're also very fluent in how entertainment is changing, how consumer behaviors are changing, how product and tech is influencing that in the products that they're using. That's really important to have on our team. So, there's the part of the persona, which is who you are as a new grad who's an engineer, but there's also who you are as someone who's in their early 20s and has a perspective on the world that is highly valuable and a comfort with the way the world is changing. So, that's why I say it's a critical part of our talent strategy.

好的，那么你进入这个角色，拥有了 5 或 10 年前不存在的 AI 工具，我想说手艺的精通仍然非常重要。

Okay, so you step into the role and you have AI tools that didn't exist 5 or 10 years ago, I would say mastery of the craft is still very important.

所以，回到作为团队成员，我对自己提交到生产环境的代码质量负责，对我构建的产品质量、设计方式以及用户体验负责。这些都不会消失。所以，如果考虑团队中资历较浅或早期职业的人才，我们需要同样投入指导，告诉他们什么是好的标准，如何使用这些工具，但你仍然要对结果和输出质量负责。我想我之前提到过，我发现手艺的精通和卓越仍然稀缺。所以我们要确保传授这些。我认为一个合理的担忧是：如果我不像以前那样亲力亲为，我如何获得这些技能？但你仍然要负责审查代码、测试代码、诊断问题、知道什么是好产品。我认为这是一种非常稀缺的技能，能够判断‘这个产品在解决重要问题方面和设计上是否卓越’。所以我不认为手艺精通的重要性会消失。可能我们培养和成长人才的方式必须改变，因为他们会使用不同的工具，而且我可以保证，早期职业人才也会教像我这样的老家伙很多新东西。所以我认为这是双向的。

So, going back to as a team member, I'm responsible for the quality of code that I am submitting for production, I'm responsible for the quality of products that I'm building, how they are designed, what that user consumer experience is. None of that is going away. So, if I think about more junior or earlier career talent on the teams, we need to be investing just as much in the mentorship of this is what good looks like, this is how you use these tools, but you still take accountability for what the outcomes are, what the quality of the output is. And I think I mentioned this earlier, I find that mastery and that craft excellence scarce still. So, we want to make sure we're teaching that. I think it's a valid concern of like, how do I get that if I'm not as hands-on as I would have had to be, but you still carry responsibility for reviewing code, testing code, being able to diagnose problems, knowing what a good product looks like. Like, I think that's a very scarce skill to say, 'This is excellence in a product that solves a problem that matters and in how it's designed.' So, I don't think that craft mastery, the importance of it, is going away. Probably the way we train and grow talent has to change because they're going to use different tools, and I can guarantee you that earlier career talent is going to be teaching older folks like me many new things, too. So, I think it goes in both directions.

你认为工程领域在未来 5 到 10 年会走向何方？你觉得人们还需要理解代码吗？还是说会有一个抽象层，你甚至不需要学习 C++、Java、Python 之类的语言？

Where do you think engineering goes in the, I don't know, 5, 10 years? Do you think people need to still understand code? Or do you think there's this abstraction layer that sits on top where you don't even have to learn C++, Java, Python, whatever?

我认为能够用 Python 或 C++ 等特定语言编写代码，与理解代码、计算机系统和产品的工作原理之间是有区别的。我不认为后者会消失。因为如果我们信任智能体掌握所有语言并编写所有代码，我们就无法知道某个东西为什么好或坏，或者当它出问题时是否如预期那样工作。就像我之前提到的，我们承担很多风险。我们快速失败，快速恢复。这需要理解这些系统是如何工作的。我可能会用智能体来帮助我理解这些事情，帮助我更快地检测异常或故障并进行分类，但我仍然需要流利地理解：我们正在构建的东西是什么，它是如何工作的？这样我才能知道它好不好，以及如何修复它。我不知道，我希望这不会消失，因为，你知道，这就像我们如何通过构建的东西让世界变得更好？我认为需要对所构建的东西有一定的理解。

I think there's a difference between being able to write lines of code in a particular language like Python or C++ and understanding how code, computer systems, products work. And I don't think the latter is going away. Because if we trusted agents to know all the languages and write all the code, we're not going to know why something is good or bad, or if it's working as we expected when it doesn't. Like I mentioned earlier, we take a lot of risk. We fail fast, we recover fast. That requires an understanding of how these systems are working. I might use an agent to help me understand those things, help me detect an anomaly or something that's broken faster and triage it, but I still need to have a fluency of like, what is this thing that we're building and how does it work? So I know if it's good and I know how to fix it. I don't know, I hope that doesn't go away because, you know, that's like, how do we make the world a better place through the stuff that we're building? I think requires some understanding of what we've built.

我听到的是，你可能不需要写代码，但你必须理解代码和正在发生的事情。但作为一个不写代码的人，要真正内化这些知识要难得多。

What I'm hearing which makes sense is you may not have to write the code but you have to understand it and what's happening. But it's so much harder to, as a person not writing it, to actually have that instilled in you.

我认为这是目前学习曲线非常陡峭的事情之一。看看这些模型或智能体编写的一些代码，它们很难理解。就像我知道我从中获得了更好的性能，但我不知道为什么，如果这个东西坏了，我也不知道如何修复。这让我感到不安。你知道，也许是因为我还在学习曲线上，比如我们如何在这个世界中运作？我们需要什么样的测试、合理化理解和认知才能适应它？但乍一看，它看起来非常陌生，非常令人不安。所以我认为工程学随着时间的推移会进化到适应这一点，并具备流利性，知道如何引导新技术、智能体和新的能力，以确保我们对输出感到非常满意。

I think that's one of the things that the learning curve is very steep on right now. So looking at some of the code that some of these models or agents are writing, they're very hard to follow. It's like I know I'm getting better performance from this but I have no idea why, and if this thing breaks I'm going to have no idea how to fix it. That makes me uncomfortable. You know, maybe that's because I'm still on that learning curve of like, how do we operate in that world? Like what's the set of tests or rationalization and understanding that we need to have to get comfortable with it? But at first glance it looks very unfamiliar and very unsettling. So I think engineering over time will evolve to be comfortable with that and have fluency in it and know how to guide new tech and agents and new capabilities to make sure that we feel really good about what the output is.

我想知道这有什么比喻。我继续对工程学在短短两年内发生的巨大转变感到震惊。就像完全不同的世界。你习惯了坐在那里写代码，而现在你只是和智能体对话、审查代码、每天发布一堆 PR。

I wonder what the metaphor is for this. It's like, I continue to be astounded by how much engineering has transformed in like 2 years. It's like a completely different drop down. You're just used to sit there and then I would write code, and now you're just talking to agents and reviewing code and shipping a bunch of PRs a day.

感觉像是工程学变化速度的加速。但如果你看过去 10 年或 20 年，你也会说同样的话。

It feels like it's an acceleration of how much engineering has changed. But if you looked over the last 10 years or 20 years you would say the same thing.

嗯。

Mhm.

所以只是事情在加速发展，我们很难理解过去几年变化有多快，但工程学、数据科学或产品出现这些重大转变并不完全陌生，就像电影制作一样。如果你回顾过去 100 年，由于技术和我们带来的新工具，它已经变得难以置信地不同。只是感觉周期在加快。

So it's just something that's moving faster and it's hard to wrap our heads around how quickly it's moved in the past couple of years, but it's not totally unfamiliar that engineering or data science or product would have these big shifts, just like how filmmaking works. If you go over the last 100 years, it's unbelievably different because of technology and new tools that we brought to it. Just feels like the cycle is speeding up.

好的，我想简短地谈谈娱乐。我只是好奇娱乐会如何随时间变化，比如 5 到 10 年。今天我们打开 Netflix，看看一些节目，看一些视频。它已经有一段时间没有变化了，就是那种想法：酷，我要看这部剧，全部看完。我要看一部电影。我有 TikTok，有 Instagram 的信息流。你认为这在 5 年内会有多大不同？我们娱乐自己的方式。

Okay, I want to talk about entertainment for a brief moment. Just I'm curious how entertainment will change over time in, say, 5 or 10 years. Today we open up Netflix, check out some shows, watch some videos. It hasn't changed in a while, just that idea of like, cool, I'm going to watch the pit and watch it all. I'm going to watch a movie. I got TikTok, I got Instagram feeds of stuff. How much different do you think this will be in, I don't know, 5 years? The way we entertain ourselves.

在 Netflix，这已经在改变了，因为娱乐在未来不会只有一种形式，现在也已经不是单一形式了。所以，我们超越电影和电视提供内容的部分原因是，消费者期望在格式、设备和一天中的时刻上有更大的多样性，Netflix 需要能够很好地服务这些需求，以满足消费者的期望，并希望随着时间的推移超越它们。

It's already changing at Netflix because entertainment is not going to be one thing in the future and it's already not one thing now. So, part of the reason that we are going beyond film and TV in our offering is because there's an expectation that consumers have of much greater variety across formats, devices, moments of the day that Netflix needs to be able to serve well in order to meet consumer expectations and hopefully exceed them over time.

所以，当我们考虑加入移动端、电视端、云游戏、直播内容、播客，以及与更多现在在 Netflix 平台上合作的创作者时，所有这些都拓宽了娱乐的定义，Netflix 能够定义并扩展它。这也给 Netflix 会员带来了更高的期望：我们如何让这一切对他们有意义？因此，我们如何为你展示一个无缝的旅程——从我听 Bill Simmons 的播客，到我看《四分卫》因为我喜欢它作为 Netflix 在更传统影视领域的作品之一，再到我玩最新的 FIFA 云游戏。我希望能在电视和手机上都能做到这一点，因为我现在在移动中，我希望能在一天中的不同时刻发现和参与内容。这已经是我们在 Netflix 中构建的旅程，我认为它会随着时间的推移变得越来越强大。所以，娱乐的未来不会是单一的东西，它必须更加个性化、沉浸式、互动式，给人一种“这是一个我可以根据当下需求向不同方向探索的世界”的感觉。而 Netflix 面临的挑战是，我们必须让发现和参与变得比今天容易得多。我们有海量内容，可能会让人感到非常碎片化，尤其是考虑到市面上所有的服务或产品。我认为 Netflix 在理解如何跨娱乐、产品和技术解决这个问题方面处于非常有利的位置。

So, when we think about the addition of mobile and TV or cloud games, live content, podcasts, working with a broader set of creators who are now on the Netflix service, all of those things create a greater breadth of what entertainment is and Netflix is able to define and expand that. And it puts a higher bar expectation on how do we make sense of that for a Netflix member? So, how do we show you this very seamless journey from I listen to the Bill Simmons podcast to I watch Quarterback because I love that as one of the Netflix offerings in the more, you could say, traditional film or TV space to I play the most recent FIFA cloud game. And I want to be able to do that in both TV and on my mobile phone because now I'm on the move and I want to be able to discover and engage with the content at different moments of the day. That's already a journey that we're building into Netflix, which I think will become stronger and stronger over time. So, the future of entertainment isn't going to be one thing and it's going to have to be more personalized, more immersive, more interactive with this sense of this is a world that I can explore in lots of different directions depending on what I'm looking for in the moment. And that the challenge Netflix has is we've got to make discovery and engagement much easier than it feels today. We have tons of content and it can feel very fragmented, especially when you consider all the services or offerings out there. And I think Netflix is very well positioned to understand how to solve that problem across entertainment, product, and tech.

另一个因素显然是 AI。作为外部观察者，看到科技界对 AI 的态度——‘我爱它，它是未来，它是最好的’——而好莱坞则是‘不，关掉它’，这很有趣。存在混合态度，非常广泛。

The other element of this is AI, obviously. As an outside observer, it's like so interesting to see how in tech, it's like AI, I love it. It's the future. It's the best. In Hollywood, it's like, "No. Shut it down." There's a mix. There's a very wide array.

所以，Netflix 的角色是赋能创作者，让他们使用任何想要的工具来将愿景变为现实。有些创作者或电影制作人可能处于光谱的一端，说：‘绝对不行，不用 AI，这不是我制作的方式，不符合我的愿景。’这没问题，我们与这些创作者合作。还有其他创作者——我认为数量在增长——他们非常感兴趣探索：‘等等，这些生成式 AI 工具能否实现以前不可能的事情？我能否用新方式讲故事？能否让故事质量更高、更能引起观众共鸣？能否在构思故事呈现时做更多创意的事情？’我们也支持他们，也支持所有处于中间立场的人。这对我们来说又是一个非常重要的立场，因为娱乐不会是单一的东西，不会有单一格式。我认为会有感觉传统的影视类型，也会有全新的格式，由令人难以置信的创作者帮助实现，Netflix 希望参与其中。这意味着我们需要在提供的工具和合作类型上保持灵活性，真正持有赋能创作者的视角，而不是规定我们只做这一种方式。

So, Netflix's role in this is to enable creators with whatever tools they want to use to bring their vision to life. There are going to be some creators or filmmakers who are on the end of the spectrum that says, "Absolutely not. No AI. That is not how I do production. It's not consistent with my vision." That's fine. We work with those creators. There's other creators, a growing number of them, I would say, who are very interested in exploring, "Wait, can these gen AI tools make something possible that wasn't possible before? Can I tell a story in a new way? Can I make that story higher quality and more resonant for audiences? Can I do things that are extra creative in how I think about bringing a story to life?" And we support them as well, and we support all the folks who are in the in-between. And that's a really important position for us to be in again, because entertainment is not going to be one thing. There's not going to be one format. I think there's going to be types of film and TV that feel traditional, and then there's going to be entirely new formats that unbelievable creators help to bring to life, and Netflix wants to participate in that. Which means we need to have a flexibility in the tools that we provide and the types of partnerships we have, and to really have a creator enablement view rather than a prescriptive that we only do this one way.

我认为人们会对 AI 内容的质量感到惊讶。比如 Spencer Pratt 的视频，大家都说‘哇，这很有趣’。显然是 AI，但非常有趣。你认为我们会达到一个地步，整部电视剧都是 AI 制作，而人们喜欢它吗？

I think people are going to be surprised by just how good AI content is. Like Spencer Pratt's videos are just like everyone's like, "Wow, this is entertaining." Obviously AI, but it's so interesting. Do you think we'll get to a place where it's just like whole TV shows are AI and people love it?

我很难想象娱乐的核心没有人类。也就是说，人类在故事创作中——我认为这是一种稀缺且有价值的技能。是的，讲故事与人性是一体的，比如知道什么能与人产生共鸣。所以我认为人类将始终是故事的核心或关键部分。对我来说，观看屏幕上缺乏人性的角色不那么引人入胜。而讲故事的力量真正在于看到另一个人，观察他们如何演绎角色或赋予情感生命。这是如此人性化的元素。AI 会帮助实现这一点吗？会在某些制作中扮演重要角色，或者帮助我们让作品呈现出特定的外观和感觉吗？是的，当然。但我看不到没有人类作为支柱的版本。

I have a hard time picturing entertainment that doesn't have humans at the heart of it. So that's humans in the creation of the storytelling, which I think is a scarce and valuable skill. Yeah, storytelling is one and the same with humanity. And like knowing what connects with people. So I think humans will be part of the always be a core part or a critical part of the story. And I think watching characters on screen who don't have that humanity feels less compelling to me. And what the power of storytelling really is, to like see another human and to watch how they perform a role or like bring an emotion to life. That's such a human element. Will AI help to bring that to life? Will play a material part in some of those productions or how we get them to look and feel a certain way? Yeah, definitely. But I don't see the version of it that doesn't have the human as the backbone.

有一句话，我认为是误归于 Salman Rushdie 的：孩子出生时，首先要求食物、水和投影，然后要求‘给我讲个故事’。

There's a quote that I think is misattributed to Salman Rushdie, which is when a child is born, they first ask for food and water and projection, and then they ask for tell me a story.

从时间之初开始，讲故事就一直是社区、社交网络以及人类情感和联系的关键部分。所以，我喜欢技术可以放大这一点，并以全新、新颖、激动人心的方式将其变为现实的想法。但是，如果说讲故事没有以人性为中心，那感觉就像缺少了什么。

It's a thing going back since the beginning of time that storytelling has been a key part of community and social networks and human feeling and connection. So, I love the idea that technology can amplify that and can bring that to life in very new, novel, exciting ways. But, if to say storytelling wouldn't have that humanity at the center feels like something would be missing.

未来几年我们会看到一些疯狂的东西从这当中涌现出来。

We're going to see some wild over the years coming out of this.

哦，我确信，毫无疑问。其中很多可能会非常有趣。你知道，我也不否认这一点。但是，我认为会有广泛的范围，而 Netflix 需要处于塑造这一切并将其变为现实的核心，这就是我们的计划。

Oh, I'm sure. There's no question about that. And a lot of it could be very entertaining. You know, I don't debate that, either. But, I think there's going to be a broad range, and I think Netflix needs to be at the center of shaping that and bringing that to life, which is our plan.

太棒了。我们涵盖了很多内容，Elizabeth。在我们进入非常激动人心的快速问答环节之前，你还有什么想分享的，想留给听众的，或者想强调我们讨论过的内容吗？

Amazing. Well, we covered a lot of ground, Elizabeth. Before we get to our very exciting lightning round, is there anything else that you wanted to share, leave listeners with, maybe double down on from things we've talked about?

这可能贯穿始终，但我想强调，现在是构建娱乐产品的非常激动人心的时刻。我们讨论的所有内容——技术、消费者以及娱乐本身的变化——我们正处于一个令人难以置信的高速创新时期。这就是让我留在 Netflix 的原因。我认为这是一个有趣的地方。如果我不重申这一点，我会觉得少了什么。我还认为，作为一个行业，我们有时花太多时间谈论纯技术或能力，有点只见树木不见森林。我们试图构建人们喜爱的优秀消费产品，制作人们喜爱的优秀娱乐内容。这是他们最喜欢的东西，我不想失去这一点。当然，下面有惊人的技术和产品，但最终，最鼓舞人心的是我们为世界各地的人们带来了什么。

It probably came across throughout, but I would underscore that this is a really exciting time to be building products in entertainment. Everything we talked about of like what's changing in the tech and consumers and like what is entertainment, we're at this unbelievable high-velocity innovation period. So, it's what keeps me at Netflix. I think it's a fun place to be. I would be missing something if I didn't reinforce that I think that's true. I also think that as an industry we spend a lot of time sometimes talking about the pure tech or the capability and we sort of lose the forest for the trees. We're trying to build great consumer products that people love. We're trying to make great entertainment that people love. And it's their favorite thing that I don't want that to be lost. Of course, there's amazing tech and product stuff that sits underneath, but in the end, the thing that's most inspirational is what do we bring to people around the world?

沿着这些思路，出现了与过剩相反的情况——新的消费产品和消费体验的匮乏。很少有成功案例，几乎没有任何消费类初创公司能成功。

And on those lines, there's been such a the opposite of glut, a drought of consumer new consumer products, consumer experiences. Like there's very few success, like almost no consumer startup works.

嗯，AI 感觉像是另一个可以发挥作用的机会，我觉得 Netflix 是少数几个持续提供出色消费者产品和业务的公司和品牌之一。这样的公司并不多。

Uh and AI feels like an opportunity for something else to work and I feel like Netflix is one of the rare companies and brands that continues to deliver an awesome consumer product and business. There's just not that many of them.

是的。我们会继续保持。

Yeah. We're going to keep that up.

好了，至此我们进入了非常激动人心的快问快答环节。我们准备了五个问题。你准备好了吗？

Well, with that, we reached our very exciting lightning round. We've got five questions for you. Are you ready?

好的，我准备好了。

Okay, I'm ready.

好的。你经常向别人推荐哪两三本书？

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

嗯，我得想出和上次不同的书。

I mean, I have to come up with different books than I said last time.

我不知道。但我觉得那听起来很棒。

I don't know. But I think that sounds great.

我还是喜欢好的怀旧作品。所以，我想到的两本是乔恩·克拉考尔的《进入空气稀薄地带》和迈克尔·刘易斯的《说谎者的扑克牌》。我在华尔街工作过，我喜欢提醒人们过去是什么样子。

I still like a good throwback. So, two that are coming to my mind: Into Thin Air, Jon Krakauer, and Liar's Poker, Michael Lewis. So, I worked on Wall Street and I like reminding people what it was like in the way back time.

你最近最喜欢的电影或电视剧是什么？这对在 Netflix 工作的人来说可能太难回答了，但我还是想听听。

Favorite recent movie or TV show you really enjoyed, which is maybe too hard for someone working at Netflix, but I'm going to see what comes out.

名单很长。我最近看的是《非凡的明亮生物》，是我妈妈推荐的。很催泪。这就是讲故事中人性的一面。

The list is very long. The most recent I watched, Remarkably Bright Creatures, after a recommendation from my mom. It's a tearjerker. Talk about the human part of storytelling.

你最近发现并非常喜欢的产品是什么？

Favorite product you've recently discovered that you really love?

对我的健康和幸福至关重要，Eight Sleep。

Critical for my health and well-being, Eight Sleep.

你有没有一个在工作和生活中经常想起的人生格言？

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

我经常回想起父母在我很小的时候灌输给我的东西。所以，可能有点重复。第一，每天都有好事发生。留意它。即使在最紧张的时候。第二，最后 5% 的努力通常能带来天壤之别。

I often go back to the things that my parents instilled in me in very early times. So, at the risk of repeating, maybe. First, something good happens every day. Watch for it. Even in the most stressful times. And second, that the last 5% of effort usually makes all the difference.

这些太棒了。打动了我。最后一个问题。我对此一无所知，但你提到你在参加某种骑行活动。

These are awesome. They hit me. Final question. I don't know anything about this, but you mentioned you're doing some kind of cycling event.

哦，是的。

Oh, yeah.

跟我们说说吧。你在做什么？

Tell us what's going on. What are you doing here?

所以，我和我丈夫正在计划一次旅行，在环法自行车赛的最后一周沿着赛道骑行。比赛持续三周。最后一周有很多山地赛段。所以我们每天早上骑行部分路线，下午观看比赛。不适合胆小的人。所以我正在努力训练，以便能享受那些骑行。毕竟这应该是假期。

So, my husband and I are doing a trip where we ride alongside the Tour de France for the last week of the race. So, the tour is 3 weeks. The last week has a lot of mountain stages. So, we get to ride part of the route each morning and then watch the race in the afternoon. Not for the faint of heart. So, I'm trying to train up so I can enjoy those rides. It's supposed to be vacation after all.

天哪。我喜欢这个假期。我们只是去看比赛。

My god. I love this vacation. We're just going to a race.

我热爱骑行。我热爱职业体育。能够参与其中很有趣。

I love cycling. I love professional sports. It's fun to be able to participate in it.

所以，这像是比赛，还是你只是尽量悠闲地骑完全程？

So, is this like racing or you just kind of try to go as nonchalantly through the course?

是悠闲地骑。但依然，有……我想我提到过……是的，这很考验身心。而且，你知道，这不是比赛，但我不想落在队伍后面。所以我得足够适应，能跟上节奏。

You go nonchalantly. But still, there are... I think I mentioned... Yeah, it is physically and mentally challenging. And, you know, it's not a race, but I don't want to be at the back of the pack. So, I got to be comfortable enough to hold my own.

哇。我喜欢这和你的工作如此不同。感觉像是另一件事。

Wow. I love how different this is from your job. And it feels like something else to do.

这是一个很好的平衡，让我到户外，给我很好的视角。所以我很期待。

It's a good balance and it gets me outdoors and gives me some nice perspective. So, I'm looking forward to it.

Elizabeth，你太棒了。最后两个问题。如果人们想关注你、联系你，或者有任何问题，他们可以在网上哪里找到你？听众怎样才能帮到你？

Elizabeth, you are awesome. Two final questions. Where can folks find you online if they want to follow you, reach out for maybe anything that came up? And how can listeners be useful to you?

找到我和我们正在做的一些工作的最佳地点是 Netflix 技术博客，实际上，我们把很多我谈到的东西都放在那里。我们正在努力更好地沟通我们正在做的有趣事情。所以，那通常是一个很好的第一站。至于听众如何能帮到我：尝试我们推出的所有新东西。观看直播活动，玩游戏，享受我们在移动端推出的名为 Clips 的新竖屏视频流，给我们反馈。我们想让它变得更好，而且其中很多对我们来说是从零到一的新尝试。所以我们正在努力尽快做到出色和卓越。

The best place to find me and some of the work we're doing or reach out is the Netflix tech blog, actually, where we're putting a lot of things that I've been talking about up there. We're trying to do a better job communicating about the fun stuff we're working on. So, that's a good first stop, usually. And then how listeners can be useful: try all the new stuff that we're putting out there. Watch the live events, play the games, have fun with the new vertical video feed that we have on mobile called Clips, send us feedback. So, we want to make it better, and a lot of these things are new zero-to-one efforts for us. So, we're trying to get to great and excellent as quickly as possible.

我喜欢这个作业是去看 Netflix 和……

I love that the homework is go watch Netflix and...

你也可以看其他东西，告诉我们如何改进，但我当然对如何让 Netflix 变得更好感兴趣。

You can also watch other things, tell us how we can be better, but I'm definitely interested in how can we be better at Netflix.

我喜欢。我这就去做。Elizabeth，非常感谢你来到这里，再次做客。

I love it. I'm going to go do that. Elizabeth, thank you so much for being here and being here again.

谢谢你邀请我。总是很有趣。

Thank you for having me. Always fun.

非常感谢你的收听。如果你觉得本期内容有价值，可以在 Apple Podcasts、Spotify 或你最喜欢的播客应用上订阅本节目。同时，请考虑给我们评分或留下评论，这真的能帮助其他听众找到这个播客。你可以在 lennyspodcast.com 找到所有往期节目或了解更多信息。下期再见。

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 lennyspodcast.com. See you in the next episode.