Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO)

频道: Lenny's Podcast
视频: https://www.youtube.com/watch?v=t0GiTyz4syY
原文语言: en
统计: 共 142 轮 · Lenny 71 · Elizabeth 67

[0:00] Lenny

Everyone can be everything now. PMs can ship code, designers can write PRDs, engineers can product, and there's this confusion and frustration of what is my job anymore.

现在每个人好像都能干所有事了。PM 可以写代码上线，设计师可以写 PRD，工程师可以做产品，于是就冒出一种困惑和挫败感——我这份工作到底还算是干什么的？

[0:08] Elizabeth

Anytime a new technology comes along, you go through a storming phase before you go through the forming phase of things. We are in the middle of that right now. [music] I don't think that means we should put AI back into the box and say let's not use it.

每次有新技术出现，你都得先经历一段 storming（震荡期），然后才进入 forming（成型期）。我们现在正处在震荡期的正中间。[音乐] 但我不认为这意味着我们该把 AI 塞回盒子里，说算了别用了。

[0:23] Lenny

If we all become builders, will we still need separate functions?

如果我们都变成了 builder，还需要区分职能吗？

[0:26] Elizabeth

I still see a craft excellence that's really important that [music] I don't think is going away anytime soon. I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce.

我依然认为手艺上的卓越（craft excellence）非常重要，[音乐] 而且短期内不会消失。在我看来，顶尖的工程能力依然稀缺，顶尖的数据科学依然稀缺，顶尖的创造力也依然稀缺。

[0:38] Lenny

If you look at the early culture deck of Netflix, high agency, autonomy, paying top of market, this is what I hear constantly now from how the top AI labs operate.

你去看 Netflix 早年那份 culture deck——高自主权、高授权、薪酬给到市场顶格——这恰恰是我现在不断从顶级 AI 实验室的运作方式里听到的东西。

[0:47] Elizabeth

Netflix's culture has always been excellence as an operating system. It's a resistance [music] to do the thing that a lot of bigger companies would do and to feel comfortable in that discomfort very often.

Netflix 的文化一直是把「卓越」当成一套操作系统来跑。它是一种抵抗 [音乐]——抵抗那些更大的公司通常会做的事，并且要能长期安然待在那种不适感里。

[0:58] Lenny

What are the ingredients to make this happen?

那要做到这一点，需要哪些要素？

[1:00] Elizabeth

Talent density is the non-negotiable, being very comfortable with risk-taking in cases where things are not going well, not assume that process is going to fix it.

人才密度是不可妥协的底线。还有就是，即便在事情进展不顺的时候也能坦然承担风险，而不是想当然地觉得加一道流程就能把问题解决掉。

[1:09] Lenny

What have you added to the career ladders within this AI world?

在 AI 这个新世界里，你们往职级阶梯（career ladder）里加了些什么？

[1:13] Elizabeth

more systems thinkers, people who can look across all the business domains and abstract that [music] to here's the building blocks we're going to need.

更多的系统思考者——那种能横跨所有业务领域去看问题、并把它抽象成 [音乐]「我们需要的是这样几块积木」的人。

[1:22] Lenny

How do people learn this?

这种能力要怎么练出来？

[1:23] Elizabeth

Small trick, each problem you're trying to solve, step out one [music] click to the what am I assuming is true about the broader space.

有个小技巧：对你要解决的每一个问题，都往外退一格 [音乐]，问自己「关于这个更大的领域，我默认哪些事情是成立的？」

[1:34] Lenny

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. [music] And as a long time head of engineering and product and data science, Elizabeth has such a unique perspective on where things [music] are heading and what's worth paying attention to. Prior to Netflix, Elizabeth was VP of Science at Lyft, Chief Operating Officer at Nuna, [music] 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.

今天的嘉宾是 Elizabeth Stone，Netflix 的首席产品与技术官。这是 Elizabeth 第二次做客本节目。她第一次来的时候还只是 CTO，那一期在很长一段时间里都是本播客最受欢迎的节目之一。你很快就会明白这次对话为什么同样精彩——因为两年半前我们聊的时候，AI 才刚刚冒头。[音乐] 而作为长期同时统管工程、产品和数据科学的负责人，Elizabeth 对事情正在往哪儿走、[音乐] 什么才真正值得关注，有着非常独特的视角。加入 Netflix 之前，Elizabeth 是 Lyft 的科学副总裁、Nuna 的首席运营官、[音乐] Analysis Group 的经济学家，以及美林（Merrill Lynch）的交易员。进入正题之前，别忘了去 LennysProductPass.com 看看——那里有全世界最热门、打磨最精良的一批 AI 产品，整整一年免费，只对 Lenny's Newsletter 的订阅者开放。好，有请 Elizabeth Stone。Elizabeth，非常感谢你来，欢迎再次做客本播客。

[2:33] Elizabeth

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

谢谢，很荣幸能来。一次，现在是第二次了。

[2:36] Lenny

That's right. That's a rare 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.

没错，这对我来说也是难得的待遇。不知道你知不知道，你上次那期最后成了我播客里第二受欢迎的一期。有很长一段时间，你就紧跟在 Brian Chesky 后面。

[2:50] Elizabeth

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

哇，我既惊喜，又忍不住有点好胜心——我要怎么才能爬到第一？[清嗓] 不过这事儿先放一边吧。

[2:59] Lenny

That's This is our This is our shot.

这个……这就是我们的机会啊。

[3:01] Elizabeth

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

Brian 太厉害了，这一个我就认了。

[3:04] Lenny

Yeah, he is uh and then there's just like all these fancy AI people that are just coming, you know, coming in hot.

是啊，他确实厉害。而且现在还有一堆特别火的 AI 圈的人也在往上冲呢。

[3:09] Elizabeth

[laughter]

[笑]

[3:10] Lenny

Um so, it's been 2 and 1/2 years at this point. A lot's changed. Uh obviously AI, something AI is allowing uh 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?

那么，到现在已经过去两年半了，变化很大。很明显，AI 让人们能做到的一件事，就是现在每个人好像都能干所有事了。就是那种 PM 可以写代码上线、设计师可以写 PRD、工程师可以做产品，人人皆可万能。这个话题里有好几层。其中一层是，我听到有人说，随之而来的还有一种困惑和挫败感——我这份工作到底还算什么？作为 PM、作为设计师，我到底该对什么负责？这种情况你自己遇到过吗？

[3:43] Elizabeth

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 cuz this is kind of 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 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.

在 Netflix 内部，我确实听到过这种声音。我觉得，每次有新技术出现，尤其是像 GenAI 这种颠覆性这么强的，都会先经历一段 storming（震荡期），然后才进入 forming（成型期）。我认为我们现在正处在震荡期的正中间。但这并不代表我们就该把 AI 塞回盒子里，说「算了别用了，它把我们对自己角色的既有认知全搅乱了」。我觉得这只意味着，我们必须更审慎地去想：怎么拿到它的好处，同时把代价压下来。

我觉得大家去实验「我怎么能更快地把一个想法推进、做出原型、写出一版能拿去验证的初始代码」，这是件好事。但我是不是认为这意味着任何人都可以直接把代码发上生产？每个人都真的该什么都干？大概不是。不过让人们去探索什么是可能的，我认为是好事。

另外，就像我前面提到的，把产品和技术团队放在一起的好处是：只要业务问题足够清晰，我认为角色之间有一定的流动性是没问题的，甚至是健康的。因为这样一来，产品和设计就不用干等着工程团队腾出手来才能做原型，他们自己就能先往前推。但他们依然要和工程伙伴一起去想：这东西该怎么产品化？怎么规模化？护栏（guardrails）在哪里？所以我不觉得这让职能上的专业能力过时了。我觉得它意味着团队要更能接受这种状态：也许这能帮我们在某个方向上跑得更快。

从组织的角度看，我会去想的是——要让这件事更有条理、少一些挫败感，为了拿到好处而不是代价，有哪些东西必须先到位。这包括：可信数据源要清晰；上生产、或者做大改动之前的测试要有护栏；要想清楚哪些场景我们可以信任 AI 的输出，哪些地方需要有流程或评审来确认产出质量足够高。还有很重要的一点是反复强调：最终为结果负责的仍然是人。可以是某个 agent 写的代码，也可以是我在自己并不擅长的领域借它做了个分析，但这并不会免除你对自己产出的那份责任。所以我认为，把资源投进这些核心基础设施和工作习惯里，同时反复强调对结果的当责，能帮我们在「技术上能做什么」和「我们实际该做什么」之间找到平衡。

[6:27] Lenny

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. 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 [music] required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS.

本期节目由我们本季的冠名赞助商 WorkOS 支持。OpenAI、Vercel、Replit、Sierra、Clay，以及其他数百家跑出来的公司，有什么共同点？它们背后都跑着 WorkOS。如果你在做面向企业的产品，你一定体会过接单点登录（SSO）、SCIM、RBAC、审计日志这些大公司必备能力的痛苦。[音乐] WorkOS 把这些卡单的拦路虎变成了开箱即用的 API，背后是一套专为 B2B SaaS 打造的现代开发者平台。

[6:59]

[music]

[音乐]

[6:59] Lenny

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.

我投的每一家创业公司，只要开始往上打企业市场，最后几乎无一例外都用上了 WorkOS。因为他们就是最好的。不管你是刚拿种子轮、想拿下第一个企业客户，还是已经在全球扩张的独角兽，WorkOS 都是让你最快具备企业级能力、打开增长的那条路。它本质上就是企业级功能里的 Stripe。去 workos.com 就能上手，或者直接去他们的 Slack——那儿有真的工程师在等着回答你的问题。WorkOS 让你能靠好用的 API、完备的文档和顺滑的开发体验更快地把东西搭出来。今天就去 workos.com，让你的产品具备企业级能力。

[7:37] Lenny

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.

你这次回来做客特别棒的一点是，我们上次聊的时候，AI 还没变成席卷世界的巨变。所以这次我们可以顺着这条弧线往下聊——聊聊我们所有人都经历的这场转变。

[7:50] Elizabeth

Mhm.

嗯嗯。

[7:51] Lenny

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, uh design, data science, user research, which roles have changed most? Which roles have changed least? Like, what's most different in the last two since two and a half years ago?

回到产品和工程团队的角色上，我很好奇这些角色在过去两年半里到底变了多少。如果看产品、工程、设计、数据科学、用户研究——哪个角色变化最大？哪个变化最小？跟两年半前比，最大的不同是什么？

[8:12] Elizabeth

So, you've mentioned some of the things, so I'll 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 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 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.

你已经提到了一些，那我就先重复一遍，再往上叠。我发现，跟几年前比，PM、设计师、数据科学家现在能在产品开发生命周期里走得更远，才需要工程真正站到最前面来解锁下一步。

我这么说是带着一点谨慎的——就像我们刚才聊的，我不觉得突然冒出几千个原型是好事，如果它们并没有对准「这是一个对业务很重要的问题」，如果工程伙伴并不知道我们在解这个问题、也不知道设计师和产品经理会牵头先把这个想法捏出形状的话。它不能是在真空里做，也不能是往墙上乱扔意大利面看哪根能粘住。但如果问题是对的、方法是审慎的、大家也对齐了，我确实看到产品、设计、数据科学能更快地朝「我们先做出一个能验证这个假设的东西」推进。这里面既有做原型，也有写代码。

另一件我觉得非常有价值的事是：Netflix 这堵「虚拟的墙」里跑着海量的信息。我们有几十年积累下来的实验，有来自用户的洞察，有来自全公司各个业务方的输入。这以前一直是个挺棘手的问题——我们怎么才能把这段漫长的知识和经验积累用到极致，说「把它套到我们现在这个问题上」，从而更快判断出这条路有戏，或者这件事我们以前学到过什么、这里可以复用。

而 AI 在这方面非常强：提炼信息、横跨一大堆材料去看、围绕它做分析、抓出一些可以当起点的洞察。我不建议完全依赖它，但它确实是个不错的起跑优势。我自己日常工作里也很有感——以前我得发封邮件去打扰别人：「提醒我一下，我们哪一年做过什么研究？当时的问题是什么？跑的是什么实验？」现在我几乎瞬间就能找到。然后我可以形成自己的判断：「这里面我觉得有意思的是什么」，一下就跳过了好几步，直奔「这里有没有可以动手做的东西」。

所以这就是数据分析、建模、信息提炼这些事。回到你最初的问题——我看到越来越多的人在做这些。以前这只能是那些在这儿待了 20 年、见过每一个实验、或者知道去哪儿翻的老专家才干得了的事；现在产品和技术下面的各个职能都能更快地做到。而对我们来说一个巨大的解锁是：坐在财务、内容、广告那边的业务伙伴也能这么干，然后带着一个初步假设回来，再和数据科学家、工程师深入合作。

所以在假设生成、做原型、深度思考问题这几件事上，确实有一种在加速的感觉，而且各职能都能以更流动的方式去做。但我依然看到比较优势的存在。数据科学家仍然会是这些问题上的专家：这份数据能不能信？我们的解读方式对不对？哪些该看数据、哪些该靠判断？产品经理仍然会格外擅长问：我们真的把「是什么」框对了吗？我们要解的这个问题定义对了吗？工程师在「怎么做」上仍然有自己的手艺：这东西怎么扩展？高质量长什么样？按我们现在的构建和部署方式，这会给我们埋下什么坑？所以那些比较优势的内核我依然看得见，只是我们在很多原本会卡住的环节上，能更流畅地往前走了。

[11:55] Lenny

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.

这里面有太多有意思的东西了。第一点，是你最后说的那个，也是我一直在琢磨的：如果我们都变成了 builder，还需要区分职能吗？现在有个「member of technical staff」的趋势，就是——行，我们不设 title，你可以是任何角色，你不必被塞进某个格子里。而你这里在说的是，你相信我们还会继续有专业分工：做产品的人、工程师、数据科学、设计师。虽然他们会更多地去做别的职能的事，但——你看我理解得对不对——拥有某个具体的学科、技能和背景，依然有很大的价值。

[12:26] Elizabeth

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. So I yes, some things are easier, but that hasn't dissolved in my mind.

我还是认为，各个职能里的那种「手艺水准」非常重要，短期内不会消失。哪怕工作在职能边界上开始流动、变得模糊，这一点也不变。这又回到我前面说的：终归还是要有人来确保我们做的事情说得通——我们解的是对的问题，而且解法对 Netflix 会员或业务方来说是最优的。我去跟工程师、数据科学家、设计师聊，是的，他们现在会说的「语言」比过去多了，因为有这些 AI 工具帮忙。但当我去看他们的手艺、看他们怎么定义「什么才算好」的时候，那里面还是有不可替代的东西。这一点在所有层级都成立。而且到今天我仍然觉得，顶级的工程能力是稀缺的，顶级的数据科学是稀缺的，顶级的创造力也是稀缺的。所以，是的，有些事变容易了，但在我看来那种稀缺性并没有被稀释掉。

[13:27] Lenny

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.

随着 AI 工具和 LLM 起来，有没有哪些职能你们招得更多了——比如工程、PM 或者设计，这块的「饼」在变大；又有哪些职能的需求在变少？

[13:43] Elizabeth

Not sure that it matches exactly to functions, but I can tell you what we're having 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 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 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. It that's true for people who are already at Netflix and people who we are adding to the team that that curiosity innovation mindset has not it's not been more important, at least in the time that I've been working in this field.

不一定能精确对应到某个职能，但我可以说说我们看到的、需要更多的是什么。在有 AI 的世界里，我们需要更多的 systems thinker（系统思考者）。这在不同职能里长得不太一样，我举几个例子。

比如 Netflix 中央工程下面的核心基础设施团队。过去让 Netflix 成功的一大原因是：各个业务团队面对具体的业务问题，可以自己跑得很快、直接交付。他们常常并不觉得自己非得走一条中央铺好的路（paved path），而是自己搭一套需要的技术栈，把问题解决掉、把影响力做出来。但在 AI 的世界里，agent 会跨多个系统运行、需要 source of truth 数据，这时候「有几条首选的铺装路径」就重要多了——它能把收益最大化，同时立起一些护栏，保证我们做出来的东西是靠谱的。共同的基础设施、共同的铺装路径、用一套核心能力把问题一次性解决掉，这件事的分量上升了。所以我们在招更多这样的人：能横着看完所有业务领域，然后抽象出「在 AI 世界里我们会需要哪些积木」。

这是一个视角。另一个视角是：把 Netflix 带到今天的那套东西，带不了 Netflix 去下一站。未来要跑得快，我们必须有一套更强的基础设施。所以现在工程岗的画像更偏分布式系统、更偏基础设施、更偏这种系统思维，而不是某个局部业务的专精。当然我们还是有很多深耕个性化推荐、广告、内容分发的人。所以核心基础设施和系统思考对我们来说更像是叠加上去的一层。

再举个设计的例子。我们的体验设计团队去沉淀模板、去用系统思维定义「Netflix 的好设计长什么样」，这件事极其重要。因为这样才能让很多人——包括那些不是科班出身的设计师——也做得出连贯的、能嵌进端到端会员体验里的产品。最让我紧张的就是冒出好几套设计语言、好几种交互方式，最后拼出一堆「弗兰肯斯坦」。所以我们招设计师，招的也是能用设计系统思维思考的人：模板怎么定？品牌怎么表达？好的用户体验是什么样？Netflix 到底是什么、Netflix 的独门秘方是什么？现在我们设计团队里，需要这样思考的人比「我能不能为某个具体产品做某个具体功能的设计」的人更多了。

所以就是这种往后退一步看大图的能力，我觉得每一个职能里都在发生。这既要求现有团队做一些技能上的重新校准，也要求我们去招本来就有这类专长的人。

而贯穿所有这些的，是一次心态转变。对探索、对尝试新东西没有兴奋感的人，理解不了「很多事情正在变」、在这种模糊性里待不住的人，我们是不招的。这对已经在 Netflix 的人和新加进来的人都一样。至少在我做这一行的这些年里，好奇心和创新心态从来没有像现在这么重要过。

[17:22] Lenny

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 and design language and basically teach people to fish so they can not be blocked or is there is there other reasons?

关于系统思考这块，它变得更重要，是因为大家现在跑得太快，所以你必须投入去做平台、做框架、做设计语言，本质上是「授人以渔」，让他们不被卡住？还是说另有原因？

[17:38] Elizabeth

I think it's probably velocity. So platforms do have a benefit of leverage. So in general, that 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 how shipping high quality code and design and user experiences? And so I 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 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 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.

我觉得主要还是速度。平台本身就带杠杆——不管有没有 AI，平台都能把大多数团队送到 80% 的位置，剩下的他们就不用重造那些积木了。我们现在在业务上下的赌注更多、想造的东西更多，所以平台思维是好事。而且「平台是关键使能器」这个认知，对 Netflix 来说其实是比较近期才建立起来的。

但在 AI 的世界里还有另一层——脚手架。不只是速度更快，而是像我们刚才说的，更多人在做更多种类的、跟以前不一样或者全新的工作。这里面是有风险的：这种情况下权限和身份怎么管？安全怎么办？怎么保证交付出去的代码、设计和用户体验是高质量的？让每个动手做东西的人自己去查「能不能提醒我一下这里什么算好、有哪些边界和护栏」，我不觉得这种模式规模化得了。我认为这些必须编码进我们的铺装路径和工作方式里。

数据科学和分析这边也一样，要把这些编码下来：source of truth 数据在哪、怎么解读、怎么取用、能拿它做什么、不能做什么、哪类数据要格外小心。一个几千人的组织，不可能再靠口口相传的部落知识，或者靠「我去找那个唯一懂这事的人」来运转。这个挑战在 AI 之前就存在，只是有了 AI 之后更紧迫了。我其实挺喜欢拿 AI 或者任何新技术当由头的——这些事我们本来就知道该做，那没有比现在更好的时机，就在全团队更下重手地投进去。

[19:27] Lenny

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.

我在想，它变得更值钱可能还有一个原因：现在很多活是 agent 在干，给它们喂上下文、搭好脚手架、给它设计语言，整个过程就快多了。

[19:38] Elizabeth

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.

对。我们在 Netflix 的一个愿景就是：将来会有海量 agent 参与做事，多到你必须有办法在这中间做推理和判断。人是那个决定「我们要解决什么问题」的角色——我们产出的东西有影响力吗、质量够高吗？但活会由人和 agent 一起干。这既带来速度和收益，也带来风险。我觉得从工程视角看这一点特别重要：我们得想清楚怎么管住它，既让人跑得快，又不给公司留下不必要的隐患和风险。

[20:20] Lenny

This connects so directly with Jenny Wen was on the podcast. She was head of design for Cloud Code and Co-work and had this whole design process is dead kind of thesis and the pitch there is just there's no time for design, the design process. And instead as a designer, you're just kind of steering people and pointing them in the direction and adjusting and also thinking big picture is when you have the time. And it feels like that's kind of what you're describing here is like create the platform for people to move fast and then there's no time for like design process of a specific new feature.

这跟 Jenny Wen 上我们播客时讲的东西直接对上了。她是 Claude Code 和 Cowork 的设计负责人，提出了一整套「设计流程已死」的观点。她的说法是：现在根本没时间走设计流程了。作为设计师，你更多是在给大家掌舵、指方向、随时校正，然后在有余裕的时候去想大图。感觉你刚才描述的也是这个意思——先把平台搭好让大家跑得快，然后就没时间为某个具体的新功能走一遍完整的设计流程了。

[20:51] Elizabeth

I have mixed feelings about that because I we do want to enable with infrastructure and systems thinking more people to do great work with strong design as part of it. 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. We It can move faster. The 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 gets squeezed out just because we can write code faster. We can do data analysis faster. That feels like, at least for a large-scale consumer product like Netflix, I feel like we would lose one of the things that makes Netflix great, which is the product, technology, and design makes a lot of complexity invisible, and makes for a seamless customer experience. That That's a design mindset that has to be core to it. So, if the work itself might look different, but I don't think we lose the mindset.

我对这个说法感受挺复杂的。一方面，我们确实希望通过基础设施和系统思维，让更多人能做出好东西，而且里面自带扎实的设计——新技术给了这个机会，为什么不用呢。但另一方面，对我们最重要的那些优先级来说，设计是决定「能不能用对的方式解决问题」的关键。所以重要的设计工作，我们还是会留出时间。它可以做得更快——设计师自己的工具箱变丰富了，所以他们能以更快的速度做出很棒的东西，出更多方案，更快地学习、迭代、测试。

但如果说「因为代码写得更快了、数据分析做得更快了，所以设计、深度的设计功力和设计思考就被挤出去了」，我觉得那是个错误。至少对 Netflix 这种大规模消费级产品来说，我觉得那样我们会丢掉一件让 Netflix 之所以是 Netflix 的东西——产品、技术和设计一起，把大量复杂度藏到看不见的地方，做出一个无缝的用户体验。这是一种必须内核化的设计心态。所以工作形态可能会变，但我不觉得我们会丢掉这个心态。

[22:06] Lenny

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

这个反驳角度太棒了。所以我听下来，在往上走的技能和特质是：系统思考，以及那种面对变化、面对将要发生的事既自在又兴奋、不死守老一套的心态。那什么在往下走？有哪些是你以前更看重、现在没那么看重的？

[22:28] Elizabeth

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

那种非常窄、非常深的专精，我感觉空间在收窄。当然我也举得出还需要它的例子——某些行业或技术领域，全世界真正懂它怎么运作的就那么几个人。我们团队里就有这样的例子，比如编码（encoding）、我们的播放系统怎么工作，这些对 Netflix 来说是极具创新性和开创性的东西。我仍然相信这些领域需要专精的从业者。

但作为一般规律，跟五年、十年前比，我认为我们现在专才更少了，通才、或者说能往多个方向延展的人更多了。这个「延展」可以是跨职能的，也可以是跨工程内部门类的——我能不能同时在后端和前端系统里游走？我能不能比较有底气地接进基础设施？我觉得现在需要的心态是「这个我能很快学会」，这又回到系统思考上。

而且我觉得跟过去比，现在专才要拓宽自己的工具箱其实容易多了。所以也许我们需要的专才数量少了，因为人才本来就有能力往那个方向长。另外，死守一个很窄的专长，多少会触发我的一个担心：那这个人往不同方向生长、去探索的心态还在吗？我也不想把话说得太绝对，但我觉得关键是——即便是专才，也仍然要有那股「我想试试解这类问题的新办法」的劲，而不是「我们过去一直这么干」。

[24:12] Lenny

And when you say specialist, are you thinking like front end, I'm a front end engineer versus a back end, or are there other

你说的专才，指的是像「我是前端工程师」对「我是后端工程师」这种吗？还是还有别的——

[24:17] Elizabeth

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

对，也可能是某个领域的知识体系。对，就是「我是个很深的——」

[24:22] Lenny

expert.

——专家。

[24:22] Elizabeth

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

我是支付专家。我是广告交易市场设计专家。我是某个影视制作片场专用工具的专家。

[24:34] Lenny

Mhm.

嗯。

[24:34] Elizabeth

So, there 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? So, I think it's the layers of the stack from an engineering perspective that there's less specialty. And then tools that are unlikely to be static or like to have a lot of inertia around them. I would think like we would want people who are able to innovate and imagine like what's the future version of this? And so we want more talent like that.

所以在某个专业领域做专家是有优势的，前提是这个人愿意生长、愿意延展到「这真的还是对的工具吗？这真的还是思考这个问题的对的方式吗」。所以我觉得，从工程角度看，是技术栈的分层上专精变少了。而那些不太可能一成不变、或者本身惯性很大的工具，我会更想找那种能创新、能想象「这东西的未来版本长什么样」的人。这类人才我们想要更多。

[25:09] Lenny

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? Other Is it just do it for a long time? Work at a lot of complex projects? Like I think of this book that everyone always references with the slinky on the front, Thinking in Systems.

很棒。回到系统思考这块——听到这儿的人会想，好，那我得练一练系统思考了。这个能力具体怎么练？除了……就是干得久了自然会有？多做几个复杂项目？我想到大家老提的那本书，封面上有个彩虹圈的，《系统之美》（Thinking in Systems）。

[25:28] Elizabeth

[laughter]

（笑）

[25:28] Lenny

Yeah, how do people learn this?

是啊，大家到底该怎么学这个？

[25:31] Elizabeth

Small trick. Each problem you're trying to solve, step out one click. Do the like, 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 it could 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 that like you don't have to boil the whole ocean. You don't have to solve for Netflix's overall strategy and who are we 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 that are we solving the right problem in the right way that matters for the end consumer.

有个小技巧：对你要解的每一个问题，都往外退一格。问自己——在解这个问题的时候，我对更大的那个空间默认了哪些假设是成立的？

比如我被派去为 Netflix 的会员体验做一个新功能。我可以先停一拍，想想：我们真正要解决的那个更大的消费者问题是什么？这个功能要支撑的是哪类内容？我原本打算的这种做法，放到多种内容类型上还能规模化吗？还是说它其实可以做成一个能力，贡献进平台，让多个领域都能用？我用这个功能解决的这个消费者问题，在「娱乐世界不断扩张、我们又想让体验更个性化更沉浸」的背景下，会是 Netflix 接下来最需要解决的重要问题之一吗？

这些问题不需要你把整片海都烧干。你不用去解 Netflix 的整体战略，不用去想我们相对竞争对手是谁。你只要拿着你负责的那块，把你在解的问题往外拉一格，然后质疑一下。我也不建议在「质疑」这个状态里待太久，因为那样你就卡住了，不往前推进了。但我觉得这个动作能帮人开始用系统的方式思考，去追问：我们解的是不是对的问题？方式对不对？这对终端消费者真的重要吗？

[27:06] Lenny

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?

你这么一说，我脑子里冒出另一个角度：想象一下如果你是你的老板，他们会怎么看？他们的视角不只是你这一个团队、一个问题、一个 KPI，而是更大的那张图。

[27:18] Elizabeth

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

这些年我收到过类似的建议：有没有什么办法，我把我的工作做了，同时也帮我老板把他的工作做了？如果我不只盯着我直接负责的那些事，而是站在我老板的视角去看——不只是产品和技术，还有财务、内容和业务的其他部分——我自然就会拉远镜头，去想这些零件怎么拼到一起、整体怎么才能大于部分之和。我觉得这种思考很有用。

对工程师来说也一样：我怎么把这些系统留得比我接手时更好？我怎么做出一个高质量、别人也用得起来、能规模化的东西？所以这里既有「我怎么帮我老板」，也有「我怎么帮我同事」——后者正是我们工程原则的核心之一：做对整个组织正确的事，而不是只做对你局部最优的事。这也是系统思考。

所以它跟资历高低无关，它关乎我解这个问题、我造这个东西的方式有多大的覆盖面：它对我的同事有用吗？我有没有为将来我们想做的那些创新留下一个更强的底子？

[28:24] Lenny

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

这条建议特别实操。让你老板的日子更好过，永远是个好招。

[28:30] Elizabeth

Career-wise, several reasons. Yeah.

从职业发展的角度，理由可不止一条。是吧。

[28:32] Lenny

[laughter]

（笑）

[28:34] Lenny

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 or not. Like, did you not change your career ladders and performance you know, criteria?

顺着这条线再问一点。我知道你们最近才加了职级和晋升通道（career ladder），这对 Netflix 来说是个新东西，以前是没有的。那在 AI 这个新环境下，你们往职级体系里加了什么？有没有哪些是你们希望大家更多投入、更看重的，或者反过来不那么看重的？还是说你们其实并没有改职级体系和绩效标准？

[28:58] Elizabeth

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 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 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 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 I doubt it's a shift that's done versus we're right in the middle of it.

我们目前的做法是：不去逐级定义「AI 到底怎么改变了这一级的期待」，而是在 Netflix 所有人才之上——包括团队里的人和负责招人的人——盖一层通用的东西，讲对 AI 熟练度（AI fluency）的期待。

这个要求具体长什么样，会因职能而异，也会因你处在职业的哪个阶段而异——可能是你在哪一级，也可能是你在做什么类型的角色、什么形态的工作。但 AI fluency 这个期待本身其实挺难定义的：它是指我有实验心态吗？是指我知道 AI 在哪儿有用、在哪儿没用吗？还是指我真的用 AI 做出过东西？我感觉它在职级体系里的呈现方式、我们讨论它的方式，几乎是按季度在变，甚至按月、按天在变，因为技术本身跑得太快了。

所以最有用的做法不是把它做成分级或分角色的，而是把所有人都推向对 AI fluency 的期待。这不是说为了用技术而用技术，而是在它有用的地方用，并且对此有好的判断力，同时保持开放心态去探索、去试新东西。这一条对所有角色都没得商量，在 Netflix 最高层也一样——我们也在说，我们自己也需要对 AI 有很深的熟练度，哪怕日常工作里并不写代码。

这是变化的地方，然后它也体现到了我们的招聘实践里。我们开始习惯在面试里去探：候选人是怎么看 AI 和技术的？他们在日常、在现在的岗位上用什么？他们对变化和探索有多自在？甚至像编程面试这种，我们当然也允许候选人用 AI 工具，因为这本来就是现在这份工作要求的一部分。这些是我们已经做出的调整，但我觉得这事远没有做完——我们正处在过程中间。

[30:59] Lenny

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?

我再顺着这条线往下问。AI 对写代码显然是颠覆性的，对做原型也是一个巨大的解锁。除此之外，AI 在 Netflix 还有哪些特别有价值、但大家可能想不到或者没意识到的用法？

[31:16] Elizabeth

So, there's two that come to mind. So, the first is data analysis, distillation of information, modeling, which is, you know, get 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 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.

有两个我第一时间想到。

第一个是数据分析、信息萃取和建模。就是用这些工具把我们手上所有的洞察拢起来，跟我前面提到的类似：我们跑过哪些实验？针对某个问题我该看哪些指标？我们做过哪些消费者调研？这块的速度和质量都高了很多——前提是你要核对结果是否有效，要跟你身边的数据科学家一起确认「我用的是不是 source of truth 数据」。但这确实是个很好的用法，也是我个人用这些工具最多的场景。所以它已经超出了原型和写代码，进到了通用的分析思考，进到了把数据翻译成行动和洞察。

第二个是内容制作和创作这一侧的业务，应用场景非常多。其实在 GenAI 之前就是这样了——很多制作工具里早就深度用了 ML 和 AI。我们用它来规模化生产宣传物料，用它做字幕和配音的本地化。而 GenAI 在创意构思上带来的是一次阶跃。比如我们叫做「预演可视化」（pre-visualization）的东西，本质上是在你把人拉到片场、真正开拍之前，就先把创作者脑子里的画面呈现出来。

后期制作里的用例也很多。我们最近收购了一家由 Ben Affleck 创办的公司 InterPositive，他们做了一套模型和能力，让你在拍完之后还能重新布光、重新构图、重新取景、改台词，效果非常显著，能拿到质量更高的内容——而且整个过程仍然由导演和创作者主导，是他说「我想再试个别的路子，把这个想法呈现出来」。这块的影响力极有想象空间，我们看到很多剧组在用各种工具，有些是我们自研的，有些是我们通过外部供应商引进的，都用在这些内容创作场景上。

再往下，是内容怎么进到产品里——我提过本地化、字幕和配音，还有怎么规模化地做出高质量的预告片、图像和海报，用它们帮每部作品在全世界找到自己的观众。从 AI 的影响来看，这些都是巨大的杠杆。所以你看，它同样远远超出了原型和写代码，进到了创意类的用法。你可以想象，这些东西既然对影视片场制作有用，那对广告也有用，对营销、对站外投放的 campaign 也有用——这些都是我们正在探索的方向。

[34:01] Lenny

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本期节目由 Mercury 赞助——一家彻底不一样的银行服务，超过 30 万创业者在用，现在还上线了 Command。我自己做 Mercury 的客户已经六年多，一次都没动过换掉的念头。Mercury 基本上就是「银行由产品人来做、而不是由银行家来做」会长成的样子。开发票、转账、给团队成员开虚拟卡，都特别顺手，甚至可以说挺好玩。你的银行有 API 吗？有原生的终端 CLI 吗？有为 AI 准备好的 MCP server 吗？我看没有。而且他们最近刚发布了 Command——一个直接内建在 Mercury 里的对话式界面，相当于你的财务操盘手。我一直在用 Command 转账、看自己在哪些类目花钱最多、分析现金流。就在今天，我还用它查了过去一年某个赞助商总共给我付了多少钱。我就问一句「过去一年 X 给我付了多少？」十秒后答案就出来了。真的太酷了。去 mercury.com 了解更多，几分钟就能在线申请。Mercury 是一家金融科技公司，不是 FDIC 承保的银行。银行服务由 Choice Financial Group 和 Column NA 提供，二者均为 FDIC 成员。

[35:13] Lenny

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

你刚提到 Netflix 很早就在做 AI 和 ML 了。年轻一点的朋友可能没印象，但你们当年办过一个比赛来优化算法。对，Netflix Prize。这就是个特别好的例子，说明你们入场有多早。我记得奖金是一百万美元，谁能把 Netflix 的推荐排序算法优化得最多，钱就归谁。最后获胜者好像也就提升了几个百分点。但当时这是件天大的事，全世界一堆聪明绝顶的人都扑进来了。而且这个比赛办过好几届，对吧？

[35:49] Elizabeth

I mean, you said it on my behalf. Um 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, that 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 and easier, which is one of the challenges that Netflix has. And using AI and ML has been a way to do that. You want to personalize right title for the right person at the right moment, that problem gets harder. The 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?" And in 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 I love that it 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.

你都替我把话说完了。经常有人问 Netflix 到底怎么看 AI，这时候提醒大家这一点特别管用——这事对我们来说一点都不新鲜，尤其是个性化推荐，一直是我们给会员提供好体验的核心。我们的内容体量太大了，而且还在不断变多，这本身就是我们面对的挑战之一。怎么让「发现内容」这件事越来越容易，是 Netflix 一直要解的题，而 AI 和 ML 就是我们解题的手段。你要在对的时刻，把对的片子推给对的人，这个问题只会越来越难。片库越精彩、内容门类越宽——不只是电影和剧集，还有游戏、直播、播客——个性化在整个体验里就越重要。所以我们能把这些积累拿出来说：好，那现在这个问题该怎么解？因为技术更强了。但这段历史让我们起跑就领先一截——我们很清楚要解的是什么问题，也很清楚 Netflix 为会员解掉它有多重要。创作这一侧也一样，就像我刚说的，AI 和 ML 在视觉特效、语言本地化这些事上已经用了很久。现在我们要问的是：技术更强之后，下一个阶段会是什么样？这两条线最后都落回 Netflix 的一个强项——把娱乐和技术揉在一起，并确保我们一直跑在前面，做出更好的东西。所以我特别喜欢这一点：它是我们历史的一部分，现在仍然是我们的强项，而且未来必须是强项——毕竟挑战的规模就摆在那儿：娱乐内容的广度不断扩张，同时还得保住极好的体验。

[37:32] Lenny

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

是啊。我还特别喜欢一点：那时候大家管它叫机器学习，一说 AI 就是「不不不，这不是 AI，AI 永远不可能实现，这就是机器学习」。

[37:41] Elizabeth

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

结果突然之间，什么都叫 AI 了，其中一部分其实还是机器学习。

[37:45] Lenny

That's right.

没错。

[37:45] Elizabeth

So, I

所以我……

[37:46]

[laughter]

（笑）

[37:46] Elizabeth

I I tried to You know, it depends like the thing that is of the moment to describe. So, I think we bucket all of it as AI now.

我试着……你懂的，这取决于当下流行拿哪个词来描述。所以现在我们干脆把这些统统归到 AI 里。

[37:54] Lenny

Yeah, AI has become

对，AI 已经变成……

[37:55] Elizabeth

of 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, like I don't think it would surprise anyone that Netflix is using a broad array. And it 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 and it's good not to forget that 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.

……很多 AI 的用例其实并不是生成式的用例。真要一件件聊，能挖出一个无底洞。但总体上，Netflix 在大范围地用 AI，这应该不会让任何人意外。而且现在大家对「有什么可能」这么兴奋，对在 Netflix 工作的人来说好玩的地方在于：如果你真的热衷于把技术用在创意表达上、用在面向消费者的产品上、用在基础设施上，这些问题我们全都有，而 AI 就在它们的正中央。这一点值得记住——哪怕 Netflix 没被贴上「AI 公司」的标签，它也成立。AI 对我们来说就是一个用得很顺手的工具，用它做出好的娱乐和技术成果。

[38:37] Lenny

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 like 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. Uh this is all stuff that every AI like 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.

还有一件特别有意思的事，我继续夸一下 Netflix。如果你去看 Netflix 早期那份文化手册（culture deck），再回想我们上次的对话，冒出来的关键词是：高自主权（high agency）、自治、高人才密度、非常自下而上的思路、极快地做实验和上线、给市场最高的薪酬。这些东西，跟我现在天天从头部 AI 实验室那边听到的运作方式几乎一模一样。所以大家最后都殊途同归走到了这里，而 Netflix 从一开始就在这儿了。

[39:13] Elizabeth

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 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 it's not something that comes easily. So having culture is not a static thing. 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.

是啊，在「什么让人才变得出色」这件事上，确实有点先见之明。Netflix 文化里的那些要素，我一直把它们理解成——这么说可能有点书呆子气——「把卓越当成一套操作系统」。所有那些文化要素本身都不是终点。目标不是「让每个人拥有尽可能大的权限」，也不是「我们讨厌流程，所以干脆一点流程都别留」。它背后是一个非常坚定的判断：要达到卓越，就得给人足够的自主权和当责；把决策尽可能下放到组织的最深处；招那些你能放心信任其判断力和决策质量的人。最后这会带来极好的结果，外加更强的驱动力和责任感。它意味着团队里每个人都会觉得：我手上被交了一大把钥匙，也要为这里发生的事负责。我自己的体会是，当你知道自己扛着这种程度的信任和责任时，你会想拿出最好的表现。所以有件事感觉非常自洽——Netflix 的文化一直瞄准的就是卓越。当你有顶尖的人才，又给了他们做出最好工作的空间，不去微观管理、不用流程把它淹死，你实际拿到的结果会好得多。所以我确实觉得，新一代的公司正在捡起某种对我们来说非常熟悉的东西。而且这事一点都不容易。文化不是静态的，公司变大、要解的问题类型变了，文化也得跟着长、跟着演化。但「我们奔着卓越去，并且相信顶尖人才需要有空间做出最好的工作」这一条从没变过，我认为它会继续是我们的独门秘方。

[41:10] Lenny

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.

我太喜欢这个说法了——把卓越当成操作系统。在「怎么搭一家公司」这件事上，这非常「系统思维」，可以这么说吧。

[41:18] Elizabeth

Exactly, Lenny.

完全正确，Lenny。

[41:19]

[laughter]

（笑）

[41:20] Lenny

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.

所以，听这期节目的每个人应该都会想要「把卓越当成操作系统」——谁会不想要呢？那如果你能讲讲要做成这件事需要哪些配料，会特别有帮助。第一个显然是高人才密度，只招最好的人。第二个是当责。基本上就是输入端和输出端：输入端招最顶尖的人，然后让他们担起责任、给他们自主权。你觉得要搭起这套「卓越操作系统」，支柱大概有哪些？如果有创始人在听这期，我是真希望他们能照着做起来。

[41:51] Elizabeth

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.

人才密度是不可谈判的前提。你必须从这儿开始。没有它，你就不可能做到在组织的每一层都对决策有信心，也不可能让大家放手去冒险、去快速创新。而这正是 Netflix 文化里「卓越」很重要的一部分——对冒险非常坦然。我们不追求避免失败，而是在失败发生时尽快恢复。这方面有过很好的例子。我们进军直播就是绝佳案例：非常坦然地承担了巨大风险，明知道一开始不会完美，明知道我们会飞快地学到东西，也会因此变得更好。看着团队一路把它啃下来，我从没有比那时更为团队骄傲过。所以你需要人才密度，需要坦然接受大家会拿着你给的 context、凭强判断力去冒险，并且为「对业务最好的结果」去争。同时你必须非常清楚：你做的事是在为消费者和 Netflix 创造结果。所以是 Netflix 重要、Netflix 的会员重要，而不是我个人的成功或我个人的偏好。这套「卓越操作系统」里有一种无我的成分。另外我想说，有些事对人类来说真的很反本能。我可以举几个需要去适应的例子。确实有些日子，我看着某个决策发生，心里想「唔，换我会做不一样的决定，这真的是最好的选择吗？」但我的职责——尤其在 Netflix 的文化里——不是每一次都跳进去推翻、否决或者质问对方，特别是当这事没那么重大、不至于把房子烧了的时候。让他们自己做这个决定，并从中学习。事后再去要那份复盘：结果怎么样？也许是我错了；也许那个决定非常棒。这跟冒险是一体的：帮大家学会坦然地自己拿主意，尤其是当这些决定不可能全对、他们注定要从中吃点苦头的时候。我自己也从我的老板和同僚那里体会过这一点，他们会说：「这是你的决定。我可以给意见，可以帮你一起想，但最后是你的。」而当赌注很高、当我觉得自己要为整个组织的产出负责时，让别人去冒险这件事真的不会自然发生，它会让人不舒服。我觉得这还意味着——再举一个例子——当事情不顺的时候，别默认「加流程就能修好它」。这是我过去几年学到的：每次觉得规划很难的时候……我从来没听谁说过「我们找到了完美的规划方式」，或者「完美的反馈、定级、定薪方式」。但每一次我们一看到问题就往上加流程，结果都是花了更多时间，却没换来更好的结果。所以这是另一件反本能的事：所有人的本能都是，当事情又难又复杂时，你以为给它套上一堆约束就是在把问题简化，但这恰恰堵死了另一个方向——有没有更有创造性的方式来做规划、做人的决策、做优先级取舍，从而真正带来更好的结果？所以这需要你抵抗住「大公司都会那么干」的冲动，并且很多时候要能安于那种不适。这是我在自己岗位上真切的体会，我相信 Netflix 很多人也一样，因为你要努力不去做那个「标准动作」。

[45:39] Lenny

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.

这话说起来、听起来都很容易，但我太懂你的意思了——有人搞砸了，你就会想：好，是哪里出了问题？我们上个流程来防止它再发生。而你说的是，你得忍住这个冲动。因为它会拖慢一切，而且最优秀的人不想待在一个到处是清单、流程和关卡的地方。

[45:58] Elizabeth

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.

对。我觉得最优秀的人想要的是：会有一场不追责的复盘（blameless retro），而他们会强烈地觉得这是自己的责任，会去想「我怎么确保这事不再发生」——不是靠流程，而是「我怎么把这些经验分享出去？我下次换种做法，怎么拿到更好的结果？」当你信任大家去复盘、去学习和成长，我认为长期看你会得到好得多的结果，也会得到一个强韧得多的团队。这恰恰是我们作为领导者职责的一部分：你要养出一个有韧性、能持久、知道怎么做出巨大影响的团队，而不是想控制每一件事。

[46:41] Lenny

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?

而这正是打造高人才密度团队的关键。这里有两面我想简单聊聊：一面是招人，另一面是留人。你们最出名的就是 keeper test（留任测试），上次我们聊过。这又是一件对人来说很反本能的事。想了解它到底是什么的听众可以去听我们第一期对话，不过这几年它有什么演变吗？它现在还是文化的核心吗？

[47:05] Elizabeth

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. Because it 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 does someone pass 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 it's just it's 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. Like that that's an important part of making peo- 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.

大家引用 keeper test 的时候，常常把它想成「你决定让某人走、觉得他不适合这个岗位」的那个时刻，以及随之而来的那场对话。但它同样常被用来谈一个人有多出色、在岗位上做得多好。因为它的入口是我对我的直接下属说，或者他们来问我：「我在你的 keeper test 上表现怎么样？」而绝大多数时候我的回答是：「我会拼了命把你留下来。」然后我会一条条讲：我觉得你哪些地方做得特别棒、你的强项在哪、你在哪儿创造了很大影响，以及你还能在哪些地方做得更好。所以它是一场对人非常正向、很鼓舞人的对话的入口，只不过开场白是「我通过 keeper test 了吗？」当然也有更难的场景：我在评估某人是否通过，或者他们来问我，而我必须说出最难开口的那句话——老实讲，你现在没通过。有些情况我会说，我觉得你能达到，然后给出反馈和里程碑；也有些情况我会说，我们真的努力过了，但我看不到通向成功的路。所以它就是一个锚点、一个入口，之后可以往很多不同方向走。我喜欢它的一点是，它在反馈这件事上是很好的卫生习惯——定期确认进展如何，有时候逼你把难说的话说出口，而不是绕开。另外，要留住优秀的人，你确实需要告诉他们「你做得很棒」，这是让人感到被认可、被重视的重要一环。所以我不希望大家以为我们对它的看法很负面，它其实还有很正向的另一面。

[48:53] Lenny

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 is that roughly the way to understand it?

很棒。我试着简单解释一下这是什么，这样大家不用专门去听另一期播客。Netflix 文化里这条的大意是：当你手下有人向你汇报时，你应该一直问自己——如果今天重新来一次，以我现在对这个人的了解，我还会不会招他？如果不会，那我大概就该让他走。这么做是为了守住很高的标准，绝不将就——不能是「算了，人都在这儿了，就先留着吧」。这样理解大致对吗？

[49:20] Elizabeth

Yeah, and the way it 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 if my sense is relief of, oh yeah, it probably would be better to have someone else in this role, I should have taken action in having that conversation sooner.

对。还有一个推论式的问法：如果这个人今天来找我说他要走，我会不会拼命把他留下来？如果我心里冒出来的感觉是「松了一口气」——哦，其实换个人来做这个岗位可能更好——那说明我早就该采取行动，把那场对话提前说出口。

[49:39] Lenny

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

我很喜欢你刚才说的，为了维持这些，你得做太多让人不舒服的事……

[49:45] Elizabeth

Yeah, it's the Well, the keeper test is one, maintaining talent entity, 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 as 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. Cuz 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.

是的。keeper test 是一件；维持人才密度是一件；在领导层之间「给 context 而不是给控制」也是一件。我们常说要「高度对齐、松散耦合」（highly aligned but loosely coupled），也就是轻流程——只保留最低限度的流程，确保我们对优先级达成共识、并且能把它执行下去，这才是我们真正要的。所有这些事，都不是人性或者规模化的组织天然会去做的。所以要维持住让 Netflix 特别的那个东西，需要持续不断的自律。因为归根到底，是工作本身和文化在吸引人、留住人，而我们需要这个来把生意做成。

[50:22] Lenny

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

这正好是我要问的。要让这一切跑起来，你得吸引到最优秀的人。而吸引最优秀的人一直都很难，现在感觉难到离谱——这么多钱在飞，那些光鲜的 AI 实验室，竞争激烈得不行，简直疯了。你发现什么办法真正管用，能说服顶尖的人还是选择来 Netflix，而不是去那些更炫的地方？

[50:49] Elizabeth

Yeah, we've always had a lot of competition for talent. It might feel more pronounced right now, but we we have great talent on the team. Maybe that goes without saying, but I feel like I should say it out loud cuz 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 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 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 that 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.

我们在人才上一直面临激烈竞争。现在可能感觉更突出，但我们团队里的人才非常强。这话也许不用说，但我觉得该说出口，因为我真这么认为：Netflix 有非常出色的人才，无论是最近加入的还是待了很久的。团队做出的东西一直让我惊艳。所以我并不觉得我们受了什么损失，或者别的公司把好人都吸走了——因为在我看来，他们中有很多人就坐在 Netflix。不过我们确实需要更明确地讲清楚：什么样的人、什么样的才能会在 Netflix 如鱼得水，而不是更适合别的公司、比如某些前沿实验室。在 Netflix，你必须对「把技术用起来」这件事有热情，对用产品去解某一类问题有热情。你得热爱娱乐，得热爱大规模的消费级产品，得热爱它的全球属性。有非常多才华横溢的人恰恰爱这个交叉地带——我自己就是其中之一：科技、产品、娱乐，以及怎么把这些东西融合成某种了不起的样子。你用 AI 去做，也用别的技术和产品去做。但这必须是能让你对 Netflix 的很多岗位真正兴奋起来的东西。反过来，如果真正吸引你的是前沿模型公司在做的那些底层工作——那当然也很激动人心——那是另一种人格画像，是另一套「我想待的问题空间」。但我不觉得世界上缺少那种对「技术的应用」真正兴奋、并且能把它和自己每天喜欢、每天在用的东西（比如 Netflix）连起来的人。这就是让我早上愿意起床的动力，我想团队里很多人也一样。我们常聊到：有些事是只有 Netflix 的人才能做的，换成另一个深耕应用的行业也是同理。我觉得这挺鼓舞人的。

[52:54] Lenny

I want to kind of touch on a couple things that I've been thinking about in this world of AI that we're uh approaching. One is uh 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. Uh 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've you think about? Are you hiring junior people? How do you think about this if this what happens with junior people not necessarily learning or having a path to learn to become the senior person?

我想聊几件我一直在琢磨的、关于我们正走进的这个 AI 世界的事。第一件是初级人才。感觉现在所有人都在说……你们就是个很好的例子：招了很多已经证明过自己的资深高手，高人才密度、高门槛。同时 AI 又让做事变得太容易了，容易到大家可能压根学不会怎么做事——我说的是初级工程师、初级 PM、初级设计师。那新人到底怎么才能长成这些厉害的资深人？这方面你有什么思考吗？你们还招初级的人吗？如果初级的人未必还能学到东西、未必还有成长为资深人的路径，你怎么看这件事？

[53:39] Elizabeth

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 is a 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. 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 that 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 there's the part of the persona, which is who are you as a new grad who's an engineer, but there's also who are you 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. To the 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. So, going back to as the 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 it 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 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 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 cuz 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.

我们仍然在招初级的人，而且他们对我们的人才战略真的很重要。我们还有实习生项目，也还有应届生项目——应届生项目是几年前才开始的。在那之前，我们所有职能都只招更有经验的人。现在我们确实会直接从本科和研究生项目里招人，而且会继续这么做。所以哪怕在 AI 让一些事变简单的世界里……我们前面聊到心态、AI 熟练度。以我的经验，年轻人心态更开放，他们对这些新的工作方式往往更「原生」。而对 Netflix 这样的公司来说，他们对娱乐正在怎么变、消费者行为正在怎么变、产品和技术如何在他们日常使用的产品里影响这一切，也都门儿清。这些是团队里非常需要的东西。所以除了「作为一个应届工程师你是谁」这一层画像之外，还有「作为一个二十出头、对世界有自己视角的人，你是谁」——那种视角非常有价值，还有对世界正在如何变化的那份从容。所以我才说他们是我们人才战略里很关键的一部分。至于说，你刚上岗，手上就有五年前、十年前根本不存在的 AI 工具——我会说，对手艺的精通仍然非常重要。回到「作为团队成员」的视角：我要为自己提交到生产环境的代码质量负责，要为自己做的产品的质量负责——它是怎么设计的、用户和消费者的体验是什么样的。这些一样都不会消失。所以说到团队里更初级、职业生涯更早期的人，我们需要在辅导上投入同样多的精力：告诉他们「好」长什么样、这些工具该怎么用，但你依然要为结果、为产出的质量担责。我前面好像提过，我发现那种精通和手艺上的卓越现在仍然稀缺。所以我们要确保自己在教这个。我觉得「如果我不像以前那样必须亲自动手，我还怎么练出这些能力」是个很合理的担忧——但你依然要承担审代码、测代码、能诊断问题、知道什么是好产品的责任。我认为「这才是卓越——一个解决了重要问题的产品，以及它被设计出来的方式」，这种判断力是非常稀缺的能力。所以我不认为手艺上的精通会变得不重要。真正要变的，大概是我们培养和成长人才的方式，因为他们会用不一样的工具。而且我可以向你保证，职业生涯早期的人才也一定会教给我这种「老家伙」很多新东西。所以我觉得这是双向的。

[56:26] Lenny

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?

你觉得工程这个岗位未来——五年、十年吧——会走向哪儿？人还需不需要懂代码？还是说上面会盖一层抽象层，你连 C++、Java、Python 这些都不用学了？

[56:42] Elizabeth

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 is something Is it a good product? Is it a bad product? Is it 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 are these systems 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 I hope that doesn't go away cuz it you know, that that's like a 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.

我觉得「能用 Python 或 C++ 这种具体语言把代码写出来」和「理解代码、计算机系统、产品是怎么运作的」是两回事。后者我不认为会消失。因为如果我们把所有语言、所有代码都托付给 agent，我们就没法判断——这产品到底好不好？它有没有按预期跑？出问题的时候到底是为什么？就像我前面说的，我们承担了很多风险，快速失败、快速恢复，而这要求你理解这些系统是怎么运转的。我可能会用 agent 帮我理解这些东西、帮我更快发现异常或者故障、做初步定位，但我自己仍然得对「我们在造的这个东西是什么、它怎么工作」有一种熟练感。这样我才知道它好不好，才知道该怎么修。我真心希望这一点别消失，因为说到底——我们怎么通过自己造的东西让世界变得更好一点？这件事本身就需要你对自己造了什么有所理解。

[57:46] Lenny

What I'm hearing which it 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 just as a person not writing it to actually you know, have that instilled in you.

我听下来的意思是——代码你可以不亲手写，但你必须理解它、理解正在发生什么，这说得通。可难就难在，一个人不亲手写，这种理解很难真正长进自己身上。

[57:59] Elizabeth

I think that's one of the 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 that's 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.

这恰恰是眼下学习曲线特别陡的地方之一。你去看这些模型或者 agent 写出来的一些代码，非常难跟得上。感觉就是：我知道性能变好了，但完全不知道为什么；万一它挂了，我也完全不知道怎么修。这让我很不踏实。当然，也可能只是因为我自己还在那条学习曲线上——在那样的世界里我们该怎么干活？得有哪一套测试、哪一套解释和理解，人才能真正安心？但第一眼看上去，就是陌生，就是让人心里发慌。所以我觉得工程这个职业会慢慢演化到能适应这件事、对它形成熟练度，知道怎么去引导新技术、agent 和新能力，让我们对产出真正放心。

[58:48] Lenny

I wonder what the metaphor is for this where this like it's 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 PRs a day.

我一直在想这事该拿什么来比。工程在短短两年里变化之大，我到现在还是觉得震撼。日常完全变了个样：以前就是坐在那儿写代码，现在你是在跟 agent 对话、审代码，一天合一堆 PR。

[59:05] Elizabeth

It feels like it's a 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.

感觉这是工程变化速度的一次提速。但你把尺度拉到过去十年、二十年去看，你也会说同样的话。

[59:16] Lenny

Mhm.

嗯。

[59:17] Elizabeth

So it there'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 it's not totally unfamiliar that engineering or data science or product would have these big shifts, just like how filmmaking works. If you will 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.

所以只是节奏更快了，过去这几年变得这么快，确实让人一时消化不过来。但「工程、数据科学或者产品会经历大转变」这件事本身并不陌生，就像电影是怎么拍的一样。你回看过去一百年，因为技术和各种新工具的加入，拍电影的方式已经完全不同了。只是感觉这个周期在加速。

[59:46] Lenny

Okay, I want to talk about entertainment for for a brief moment. Just I'm curious just like how entertainment will change over time and say like 5, I don't know, 5, 10 just you know, 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. Uh I got TikTok, I got Instagram feeds of stuff. Like how much different do you think this will be in I don't know, 5 years? The way we entertain ourselves.

好，我想花一点时间聊聊娱乐。我挺好奇娱乐本身接下来会怎么变，比如五年、十年。今天我们打开 Netflix，翻翻剧、看看视频——这套体验其实好久没怎么变了：「行，我要追 The Pitt，一口气刷完」「我要看部电影」，另外我还有 TikTok、有 Instagram 的各种信息流。你觉得五年后，我们娱乐自己的方式会有多大不同？

[1:00:13] Elizabeth

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 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. 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 I think Netflix is very well positioned to understand how to solve that problem across entertainment, product, and tech.

在 Netflix，这件事已经在变了，因为未来的娱乐不会只有一种形态，其实现在就已经不止一种了。我们之所以把内容拓展到电影和剧集之外，很大一部分原因是消费者的期待变了——他们希望在不同的内容形态、不同的设备、一天里不同的时段都有丰富的选择，Netflix 得把这些都服务好，才谈得上满足他们的期待，并且希望往后还能超出期待。所以当我们加上手游和电视端/云游戏、直播内容、播客，和更广泛的一批创作者合作、把他们带上 Netflix，所有这些都在拓宽「娱乐」这个词的边界，而 Netflix 有能力去定义和扩展它。这同时也把另一件事的门槛抬高了：我们怎么帮一个 Netflix 会员把这些理出头绪来？也就是说，怎么给你一条特别顺的动线——从我听 Bill Simmons 的播客，到我去看 Quarterback（我很喜欢这类偏传统影视范畴的 Netflix 内容），再到我玩最新的 FIFA 云游戏。而且我希望在电视上能玩，在手机上也能玩，因为我人在路上；我希望在一天的不同时刻都能发现内容、进入内容。这条动线我们已经在往 Netflix 里搭了，我觉得它会越来越强。所以娱乐的未来不会是单一的一种东西，它必须更个性化、更沉浸、更互动，让你觉得「这是一个世界，我可以按此刻想要的东西，往很多不同方向去探索」。而 Netflix 面临的挑战是：我们必须让「发现内容」和「进入内容」比今天容易得多。我们的内容量非常大，再加上市面上那么多服务和产品，体验很容易变得很碎。我认为在娱乐、产品、技术这三块的交汇处，Netflix 是最有条件想明白怎么解这个问题的。

[1:02:19] Lenny

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

这里面另一个变量当然是 AI。作为一个局外的观察者，我觉得特别有意思的是：在科技圈，大家是「AI，我爱它，这就是未来，这是最棒的东西」；到了好莱坞，就变成「不行，关掉它」。这中间——

[1:02:33] Elizabeth

There's a mix. There's a very wide array. So, we 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 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 and 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 then 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.

其实是混杂的，光谱非常宽。Netflix 在这件事里的角色，是让创作者能用任何他们想用的工具，把自己的构想变成现实。会有一部分创作者、导演站在光谱的一端说：「绝对不行，不用 AI。我不是这么做制作的，这跟我的创作理念不合。」没问题，我们照样和这些创作者合作。也有另一批创作者——而且我觉得人数在变多——非常有兴趣去探索：「等一下，这些生成式 AI 工具是不是能做到以前做不到的事？我能不能用一种新方式讲故事？能不能把故事做得质量更高、让观众更有共鸣？在把一个故事变成作品这件事上，我能不能玩出更多创意？」这些人我们也支持，处在中间地带的所有人我们同样支持。这个位置对我们非常重要，还是那句话：娱乐不会只有一种形态，也不会只有一种格式。我觉得未来既会有感觉很传统的电影和剧集，也会有一批了不起的创作者带出来的全新形态，Netflix 想参与其中。这意味着我们提供的工具、我们建立的合作方式都得有弹性，真正站在「赋能创作者」的立场上，而不是规定「只能按这一种方式来」。

[1:03:54] Lenny

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 Do you think we'll get to a place where it's just like whole TV shows are AI and people love it?

我觉得 AI 生成的内容能好到什么程度，会让很多人吃一惊。比如 Spencer Pratt 发的那些视频，大家都在说「哇，还挺好看的」。一眼就知道是 AI，但就是很有意思。你觉得我们会走到那一步吗——整部剧都是 AI 做的，而观众还很买账？

[1:04:09] Elizabeth

I have a hard time picturing entertainment that doesn't have humans at the heart of it. So that 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 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.

我很难想象一种内核里没有人的娱乐。我说的「人」，是指创作和讲故事的人，我认为这是一种稀缺而宝贵的能力。是啊，讲故事和人性本来就是一回事，还包括知道什么才能真正打动人。所以我觉得人会一直是故事里的核心、是关键的那部分。而且对我来说，看屏幕上那些没有人味儿的角色，吸引力就是要弱一些。讲故事真正的力量在于——你看到的是另一个人，你在看他怎么演绎一个角色、怎么把一种情绪演活，这特别「人」。AI 会不会帮着把这些呈现出来？会不会在某些制作里、在如何做出某种质感和调性上起到实质作用？当然会。但我看不到那种把人从骨架里抽掉的版本。

[1:05:12] Lenny

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.

有一句话，我印象里常被误安到 Salman Rushdie 头上——大意是：孩子出生后，最先要的是食物、水和保护，紧接着要的就是「给我讲个故事」。

[1:05:27] Elizabeth

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.

从有人类开始就是这样，讲故事一直是社群、人际网络、人的情感和连接里很关键的一环。所以我特别喜欢「技术能放大这件事、能用全新的、让人兴奋的方式把它呈现出来」这个想法。但如果说讲故事的中心可以不再是人性，那总感觉少了点什么。

[1:05:56] Lenny

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

嗯。接下来这些年，我们肯定会看到一些很疯狂的东西冒出来。

[1:05:59] Elizabeth

Oh, I'm sure. There's no question about that. And a lot of it could be very entertaining. You know, I 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.

那当然，这点毫无疑问。而且里面很多会非常好看，这我也不否认。但我觉得整体会是一个很宽的谱系，而 Netflix 需要站在中心去塑造它、把它做出来——这也正是我们的计划。

[1:06:15] Lenny

Amazing. Well, we covered a lot of ground, Elizabeth. Uh 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?

太棒了。Elizabeth，我们今天聊了很多。在进入激动人心的闪电问答之前，还有什么你想分享的、想留给听众的，或者前面聊过的哪个点你想再强调一遍？

[1:06:27] Elizabeth

It probably came across throughout, but I 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. Um I also think that as an industry we spend a lot of time sometimes talking about the the pure tech or the 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 in 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?

前面应该已经透出来了，但我想再强调一次：现在是做娱乐产品特别令人兴奋的时候。我们聊的这一切——技术在变、消费者在变、连「娱乐」这个概念本身也在变——我们正处在一个创新速度快得不可思议的阶段。这也是我留在 Netflix 的原因，我觉得这地方待着很有意思。如果不把这点说透，我会觉得少讲了什么。另外我也觉得，我们这个行业有时候花太多时间在谈纯技术、谈能力本身，反而见木不见林。我们真正在做的，是做出用户喜欢的好消费产品，做出大家喜欢的好娱乐内容，成为他们的心头好。我不希望这个初衷被淹没——底下当然有很了不起的技术和产品工作，但归根到底，最能鼓舞人的问题是：我们给全世界的人带去了什么？

[1:07:23] Lenny

And on those lines, there's been such a uh 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. 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.

顺着这个说，这些年消费级新产品、新体验不是过剩，恰恰相反，是荒得厉害。成功的太少了，几乎没有哪个消费级创业公司真跑通。而 AI 让人觉得，终于又有机会跑出点新东西。我觉得 Netflix 是极少数还在持续交付出色消费产品和商业成绩的公司和品牌之一，这样的真的不多。

[1:07:46] Elizabeth

Yeah. We're going to keep that up.

是啊，我们会继续保持。

[1:07:49] Lenny

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

那么，我们就进入激动人心的闪电问答环节。有五个问题要问你，准备好了吗？

[1:07:54] Elizabeth

Okay, I'm ready.

好，准备好了。

[1:07:55] Lenny

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

好。有哪两三本书是你最常推荐给别人的？

[1:08:01] Elizabeth

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

我总得说几本和上次不一样的书吧。

[1:08:04] Lenny

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

不一定啊。不过这样挺好的。

[1:08:06] Elizabeth

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 I worked on Wall Street and I like reminding people what it was like in the way back time.

我还是挺爱翻老书的。现在想到两本：Jon Krakauer 的《Into Thin Air》（进入空气稀薄地带），还有 Michael Lewis 的《Liar's Poker》（说谎者的扑克牌）。我以前在华尔街工作过，喜欢提醒大家当年那个年代是什么样子。

[1:08:23] Lenny

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.

最近有哪部电影或者剧是你特别喜欢的？这个问题对在 Netflix 工作的人可能太难了，但我还是想看看你会说什么。

[1:08:29] Elizabeth

The The list is very long. Um 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.

清单可太长了。我最近看的一部是《Remarkably Bright Creatures》，我妈推荐的。特别催泪。说到讲故事里「人」的那部分，这就是最好的例子。

[1:08:41] Lenny

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

最近发现的、你特别喜欢的产品是什么？

[1:08:44] Elizabeth

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

对我的健康和状态特别关键的一个——Eight Sleep。

[1:08:48] Lenny

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

有没有什么人生座右铭，是你在工作或生活里经常回想起来的？

[1:08:53] Elizabeth

I often go back to the things that my parents instilled in me in very early times. So, the 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.

我经常回到父母从小灌输给我的那些东西。可能有点重复了，不过还是说吧。第一，每天都会有好事发生，你得留心去发现它——哪怕在压力最大的时候也一样。第二，最后那 5% 的努力，往往决定了成败。

[1:09:17] Lenny

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

这两句太棒了，真的戳到我了。最后一个问题。我对这事一无所知，但你提到你要去参加某种骑行活动？

[1:09:26] Elizabeth

Oh, yeah.

哦，对。

[1:09:27] Lenny

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

跟我们讲讲吧，到底是怎么回事？你要去干什么？

[1:09:30] Elizabeth

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.

我和我先生要去骑一趟——在环法自行车赛（Tour de France）最后一周跟着比赛一起骑。环法一共三周，最后一周有很多爬山赛段。我们每天早上骑一段当天的赛道，下午看比赛。这可不是给胆小的人准备的，所以我现在在练体能，好让自己能真的享受那几段骑行——毕竟名义上还是度假嘛。

[1:09:56]

[laughter]

[笑]

[1:09:57] Lenny

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

天呐，这度假方式我太喜欢了——「我们就是去看场比赛而已」。

[1:10:00] Elizabeth

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

我喜欢骑行，也喜欢职业体育。能亲身参与进去挺有意思的。

[1:10:06] Lenny

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

那这算是比赛吗？还是说你就轻轻松松把赛道骑完就行？

[1:10:11] Elizabeth

You go nonchalantly. But, still there are I think I mentioned Yeah, it is It's physically and mentally challenging. And I 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.

是轻松骑。不过——我前面好像也提到了——身体和心理上都挺有挑战的。虽然不是比赛，但我也不想垫底，所以得练到能跟得上大部队的程度。

[1:10:25] Lenny

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

哇。我特别喜欢这件事跟你的工作反差这么大，感觉完全是另一个世界。

[1:10:29] Elizabeth

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

这是很好的平衡，能让我到户外去，也给我一些不一样的视角。所以我挺期待的。

[1:10:36] Lenny

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?

Elizabeth，你太棒了。最后两个问题：如果大家想关注你，或者想就今天聊到的内容找你，网上哪里能找到你？另外，听众能怎么帮到你？

[1:10:45] Elizabeth

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. Um and then how listeners can be useful, try all the new stuff that we're putting out there. Um 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.

想找我、或者想了解我们在做的事，最好的地方其实是 Netflix 的技术博客（Netflix Tech Blog）——今天聊到的很多东西我们都会往上面放。我们在努力把手头这些好玩的事讲得更清楚，所以那通常是第一站。至于听众能怎么帮我们——去试试我们推出的所有新东西：看直播活动、玩游戏、体验我们手机端新上的竖屏视频流 Clips，然后给我们反馈。我们想把它们做得更好，而且这里面很多都是我们从 0 到 1 的新尝试，所以我们希望尽快做到「很好」甚至「卓越」。

[1:11:25] Lenny

I love that the homework is go watch Netflix and

我太喜欢这个作业了——去看 Netflix，然后……

[1:11:29] Elizabeth

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

你也可以看点别的，然后告诉我们怎样能做得更好——不过我最关心的当然还是 Netflix 怎样能做得更好。

[1:11:36] Lenny

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

太好了，我这就去看。Elizabeth，非常感谢你来，也谢谢你再次做客。

[1:11:41] Elizabeth

Thank you for having me. Always fun.

谢谢你邀请我，每次都很开心。

[1:11:44] Lenny

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.

非常感谢你的收听。如果觉得这期有价值，可以在 Apple Podcasts、Spotify 或者你常用的播客 App 上订阅本节目。也欢迎给我们打个分或留个评价，这能帮更多听众发现这档播客。所有往期节目和节目的更多信息，都可以在 lennyspodcast.com 上找到。下期见。