Netflix CPTO：AI 时代，产品和技术岗位会怎样变？
Netflix CPTO Elizabeth Stone 解释 AI 如何重写产品、工程和设计的边界，以及为什么系统思维、AI 流畅度和责任意识会成为团队的底层能力。
先给结论
这是一期适合产品经理、工程师、设计师、数据科学家和创业者完整收听的访谈。Elizabeth Stone 没有把 AI 讲成某个岗位的替代率预测，而是把问题拆成三层：岗位边界会变得更流动；专业判断和 craft excellence 仍然稀缺；组织必须用系统、基础设施和责任边界，把 AI 带来的速度变成可控的产出。真正值得带走的不是「谁会被淘汰」，而是「在 agent 参与工作的团队里，什么能力会变贵」。
如果你只想找一份 AI 编程工具清单，这期并不对口；如果你正在重新定义团队分工、职业阶梯、设计流程或产品开发方式，它很值得花完约 72 分钟。节目页面将嘉宾标为 Netflix 的 Product and Technology Officer，并把本期重点概括为 systems thinking、AI output、AI fluency 和 excellence as an operating system。1
这期节目在回答什么
Lenny Rachitsky 和 Elizabeth Stone 展开的是一场观点访谈：当 PM 可以写代码、设计师可以写 PRD、工程师可以参与产品决策，原来的产品、工程、设计、数据科学边界还剩下什么？他们从岗位混淆一路谈到招聘、系统思维、Netflix 的 AI 历史、内容制作和娱乐形态的下一步。
读者可以把它当成一张组织变革地图，而不是一场技术发布会。前半段讨论「人如何协作」，中段讨论「组织怎样提供 paved paths、事实基准数据和护栏」，后半段再把这些原则放回 Netflix 的文化和娱乐产品里。
1. AI 先打散岗位边界，但没有打散责任
Stone 把当下称为新技术带来的 storming phase：旧的角色预设被打乱了，但新的协作方式还没有形成。PM 能够写一段代码、设计师能够先做出原型，并不等于每个人都应该独立把东西发到生产环境。她给出的区分很实用：
- 在业务问题已经明确时，产品和设计可以更早开始原型、写初始代码、形成可测试的假设。
- 是否能产品化、能否扩展、怎样设 guardrails，仍然要和工程伙伴一起判断。
- AI 可以代替一部分检索、分析和实现动作，但不能代替对结果负责的人。
这里的关键不是把 AI 用量压低，而是把「探索」和「交付」分开。探索阶段允许角色更流动，交付阶段则需要明确谁确认 source of truth data，谁判断结果质量，谁承担发布后的后果。Stone 特别强调，即使 agent 写了代码，或者一个人借助 AI 做了原本不擅长的分析，人仍然不能把责任转交给工具。
这也解释了为什么「人人都能做一点」并没有自动变成「人人都应该做全部」。如果团队只追求原型数量，很快会得到成千上万个没有对应业务问题的实验；如果团队先对齐重要问题，再让不同职能向前走得更远，速度才有意义。
2. 职能会流动，专业能力不会消失
Stone 观察到，PM、设计师和数据科学家如今可以在产品开发生命周期里走得更远，工程不必一开始就站在最前面解锁所有事情。AI 能帮助他们更快地检索 Netflix 多年来积累的实验、消费者研究和业务输入，再把这些材料蒸馏成一个值得验证的初始假设。
但她没有因此得出「专业职能过时」的结论。她把不同职能仍然不可替代的部分说得很具体：
- 数据科学家要判断数据是否可信，解释是否正确，以及哪里应该加入判断而不是只看数据。
- 产品经理要判断问题的「what」有没有被正确框定，团队是不是在解决真正重要的问题。
- 工程师要负责「how」：怎样扩展、什么叫高质量、当前构建方式会带来哪些系统问题。
- 设计师要让复杂性对用户不可见，维持从局部功能到端到端体验的一致性。
因此，AI 带来的不是专业性的消失，而是「会说更多语言」的专业人士。一个设计师可能更快做出可运行的原型，一个数据科学家可能更快完成资料蒸馏，但他们仍然需要知道什么是好结果、哪些结论不能信、哪些选择会在规模化后反噬团队。Stone 认为优秀工程、优秀数据科学和优秀创意依旧稀缺，这个判断比「所有人都变成 builder」更接近她对未来的看法。
3. 招聘重点从职能数量转向系统思维
当被问到哪些岗位会增加、哪些会减少时，Stone 没有给出一张 PM、工程师、设计师的增减饼图。她的回答是：Netflix 更需要 systems thinkers。
原因在于 agent 会跨多个系统工作。过去，一个熟悉局部业务的团队可以自己搭技术栈、快速解决问题；未来，如果 agent 需要访问跨团队的数据和能力，组织就必须提供：
- 作为事实基准的数据，以及清楚的访问和解释方式；
- common infrastructure 和 preferred paved paths，让团队不必重复造基础积木；
- 访问、身份、安全、代码质量和用户体验方面的护栏；
- 能让 agent 获得上下文、设计语言和工作边界的脚手架。
这不是要把所有工作重新集中到中央团队，而是要把反复出现的基础能力做成可复用的路径。Stone 用 Netflix 的中央工程和设计系统举例：本地团队仍然需要解决具体业务问题，但组织要避免每个团队都重新发明基础设施、数据解释方式或用户交互语言。
设计的例子尤其能说明问题。AI 让更多没有受过设计训练的人可以参与产品构建，设计团队因此更需要定义模板、品牌表达和端到端体验，避免不同团队各自做出互不相容的交互，最后拼出一个 Frankensteins。设计师的工作不是只画某个功能，而是维护能让更多人正确表达的系统。
4. 哪些能力在升值，哪些能力在变窄
Stone 认为，过于狭窄且不愿扩展的深度专门化空间会变小。她并没有否定专家：编码、播放系统、广告市场、支付，或影视制作使用的特殊工具，仍然可能需要世界级的 subject-matter expertise。变化在于，专家也要愿意追问「这还是正确的工具和解决方式吗」，并且能向相邻领域学习。
她期待更多能够跨前端与后端、工程与基础设施、职能与业务领域移动的人。这里的 generalist 不是每件事都只懂一点，而是掌握一项专业后，能够快速获得下一项所需的语言和判断框架。
Stone 给出的系统思维练习只有一步：在解决问题时向外 zoom out 一层。
假设你接到任务，要为 Netflix 会员体验做一个新功能，先不要把问题扩大到「解决整个 Netflix 战略」。只要问一层更大的问题：真正的消费者问题是什么？这个能力能否支持多种内容类型？它会不会成为多个领域都能复用的平台能力？它解决的是不是当下最重要的消费者问题之一？
她还提供了一个职业化的版本：想想怎样让自己的工作帮助经理完成他们的工作，也想想怎样给同事留下更强的系统。系统思维因此不只是资历更高，而是让自己负责的局部工作对组织的其他部分和未来的创新有用。
5. AI fluency 不是新岗位，而是所有职业阶梯的 overlay
Netflix 没有试图为每一个级别写一套固定的 AI 指标，而是在所有人才上加一层对 AI fluency 的期待。它会因职能、角色和职业阶段而不同，但有几件事是共同的：
- 知道 AI 什么时候有用，什么时候不值得用。
- 愿意实验，也能判断实验结果是否可靠。
- 理解工具变化对自己的工作和合作方式意味着什么。
- 在 AI 参与后仍然对质量、结果和风险负责。
这套要求也适用于最高层。即使日常工作不写代码，领导者也需要深度理解 AI，才能判断工作方式、资源分配和输出质量。招聘方面，Netflix 会在面试中了解候选人怎样使用技术、怎样面对变化和探索；coding interview 也允许候选人使用 AI，因为真实工作已经包含这一层协作。
这里有一个容易被忽略的含义：AI fluency 不是「会不会点某个工具」的操作能力，而是对工具边界、结果质量和组织后果的判断能力。它变化很快，所以 Stone 反而不建议把它冻结成某个级别的一次性标准。
6. Netflix 的 AI 不只用于写代码
Stone 提到的第一个非编码用例，是把数据转成行动。Netflix 内部有多年的实验、消费者研究、指标和跨业务输入。AI 可以快速帮助人们找到相关资料、进行初步建模、蒸馏信息和生成假设；但本地数据科学家仍要确认事实基准和结果有效性。
第二类用例在内容生产：
- 在前期用 pre-visualization 把创作者的想法先变成可讨论的形态；
- 在后期辅助 relight、reframe、reshoot 和 dialogue change；
- 大规模生成宣传素材、图片、artwork、预告片；
- 处理 subtitles、dubs 和其他本地化工作。
Stone 特别提到 Netflix 在 2026 年 3 月宣布收购由 Ben Affleck 创办的 InterPositive。Netflix 官方说明，这家公司开发的是由电影人参与、面向电影制作的 AI 工具；这一背景和 Stone 的说法一致：工具可以增强创作者把愿景带到现实中的能力，但创作者仍然掌握方向。2
她还提醒，AI 不等于生成式 AI。Netflix 很早就把机器学习用在推荐、搜索、视觉特效和语言本地化上。Lenny 提到的 Netflix Prize，是 Netflix 早期启动的推荐算法竞赛；在这期节目里，它被用来说明 Netflix 对「如何让正确的人在正确时刻找到正确内容」的长期积累，而不是把所有 AI 都包装成最新的生成式故事。
7. 「把卓越当作操作系统」到底是什么
Stone 用这句话总结 Netflix 的文化，但她并不是在说「不要流程」或「每个人都无限自治」。她的解释更接近一套组织设计：
- 人才密度是前提。 只有先相信团队成员的判断，决策才可能真正下沉。
- 自主权必须和问责绑定。 人们拥有很多钥匙，也必须对结果负责。
- 允许冒险，快速恢复。 目标不是避免每一次失败，而是从失败中恢复并学习。
- 提供上下文，而不是控制。 领导者给优先级、背景和边界，不替每个人做所有决定。
- 高度一致、松散耦合。 只保留保证方向清楚和能够执行的最少流程。
她反复反对一种常见的组织反射：一旦有人犯错，就增加审批、清单和流程。Netflix 更想做的是不追责复盘，让当事人回答「我怎样分享教训、怎样改变下一次的做法」。如果每个问题都用新流程封住，组织可能更慢，优秀人才也更难发挥。
Keeper test 在这套文化里也不只是裁员工具。它可以被问成「如果这个人今天要离开，我会不会努力挽留」，也可以成为一次正向反馈：明确告诉优秀员工他们做得好、产生了什么影响，以及还能怎样做得更好。它的价值在于迫使管理者持续谈绩效，而不是回避不舒服的谈话。
8. 初级人才与工程师的下一步
Stone 明确说 Netflix 仍然招聘实习生和应届毕业生。她认为年轻人通常更开放，更熟悉新的工作方式，也更了解娱乐和消费者行为如何变化；这些不是「资历不足」可以抵消的视角。
但 AI 不能成为跳过专业训练的理由。初级工程师仍需学习审查和测试代码、诊断故障、判断产品质量；初级产品和设计人才也要学习怎样识别一个真正解决重要问题的结果。培养方式会改变，但 craft mastery 不会因此不重要。
对工程师来说，Stone 区分了「用某种语言写出每一行代码」与「理解代码、计算机系统和产品如何运作」。前一种技能可能被更高层的抽象替代，后一种理解却仍然是判断好坏、发现异常、调试和恢复的基础。她承认，agent 生成的代码有时很难解释，这正是工程领域需要重新建立测试、推理和解释流畅度的地方。
9. 娱乐的未来仍需要人
Stone 认为娱乐已经不再只有电影和电视剧。游戏、直播、播客、手机上的 Clips 竖屏视频流，以及更多创作者，会让消费者在不同设备、时间和格式之间移动。Netflix 面临的产品问题，不只是增加内容，而是让会员更容易发现内容，在收听播客、观看节目、玩游戏之间形成顺畅的体验。
在内容生产上，她没有采取「AI 全面替代」或「AI 全面禁止」的立场。Netflix 想支持拒绝 AI 的创作者，也支持想用生成式工具尝试新叙事方式的人，以及处于中间位置的人。娱乐格式会变得更丰富，平台需要提供灵活工具和合作方式，而不是规定所有创作者只能用一种流程。
但她很难想象完全没有人性骨架的娱乐。故事之所以能连接人，依赖人对情绪、经验和关系的理解。AI 可以参与制作、放大想法、改变画面和声音，但在 Stone 看来，创作者和人类叙事仍是内容的核心。
值不值得完整收听
值得，尤其适合三类人。 第一类是正在和 AI 一起重做产品流程的团队负责人：节目给出了从岗位流动、护栏、平台到问责的完整链条。第二类是正在重新思考职业发展的从业者：systems thinking 和 AI fluency 都被拆成了可以练习和观察的行为。第三类是对 Netflix、娱乐产品或内容制作感兴趣的人：后半段把组织文化、推荐系统、创作工具和新娱乐格式放到了一张图里。
不必把它当成预测报告。 节目没有给出未来五到十年的岗位数量、薪资变化或 Netflix 内部采用率，也没有深入展示具体模型架构。它的价值在于提供一套判断框架：当 AI 让更多人能够动手时，真正需要被重新设计的，是上下文、基础设施、专业判断和责任分配。
节目元信息
- 节目： Lenny's Podcast
- 单集： Netflix CPTO on AI and the future of product and tech roles | Elizabeth Stone
- 嘉宾： Elizabeth Stone，Netflix 产品与技术负责人
- 时长： 约 72 分钟
- 发布时间： 2026 年 7 月 19 日
- 官方页面： 1
逐字稿说明
以下保留官方英文逐字稿的全部 133 个说话段落，包括节目广告、听不清标记和结尾信息；每段英文后紧跟中文翻译，时间轴按每 10 分钟设置。英文原文来自本期官方节目页面所提供的转录入口。1
完整中英对照逐字稿
以下逐字稿按官方转录整理，英文原文完整保留；中文翻译紧随每个说话段落。时间轴按约每 10 分钟分段。
00:00-10:00
00:00 | Lenny Rachitsky
英文原文：
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.
中文翻译：
现在每个人都可以做所有事情。产品经理可以交付代码，设计师可以写 PRD，工程师可以做产品，于是大家都在困惑和沮丧：我的工作到底是什么？
00:09 | Elizabeth Stone
英文原文：
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. I don't think that means we should put AI back into the box and say, "Let's not use it."
中文翻译：
每当一种新技术出现，人们都会先经历一个 storming 阶段，然后才进入 forming 阶段。我们现在正处在中间。我不认为这意味着我们应该把 AI 关回盒子里，说「别用了」。
00:23 | Lenny Rachitsky
英文原文：
If we all become builders, will we still need separate functions?
中文翻译：
如果我们都变成了 builder，还需要彼此分开的职能吗？
00:26 | Elizabeth Stone
英文原文：
I still see a craft excellence that's really important that 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.
中文翻译：
我仍然认为各个领域的 craft excellence 非常重要，而且短期内不会消失。优秀的工程能力、优秀的数据科学能力和优秀的创造力，仍然稀缺。
00:38 | Lenny Rachitsky
英文原文：
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.
中文翻译：
如果你看 Netflix 早期的文化手册，高度主动性、自主权、按市场顶端付薪，这正是我现在不断听到的顶尖 AI 实验室的运作方式。
00:47 | Elizabeth Stone
英文原文：
Netflix's culture has always been excellence as an operating system. 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.
中文翻译：
Netflix 的文化一直都是「把卓越当作操作系统」。它经常是在抵抗大公司通常会做的事情，并且要经常让自己适应那种不适感。
00:58 | Lenny Rachitsky
英文原文：
What are the ingredients to make this happen?
中文翻译：
要做到这一点，关键要素是什么？
01:00 | Elizabeth Stone
英文原文：
Talent density is the non-negotiable, being very comfortable with risk-taking in cases where things are not going well and not assume that process is going to fix it.
中文翻译：
人才密度是不可谈判的前提；还要能在事情进展不顺时自在地承担风险，而不是假设流程会解决问题。
01:09 | Lenny Rachitsky
英文原文：
What have you added to the career ladders within this AI world?
中文翻译：
在 AI 时代，你们给职业阶梯增加了什么？
01:13 | Elizabeth Stone
英文原文：
We need more systems thinkers, people who can look across all the business domains and abstract that to here's the building blocks we're going to need.
中文翻译：
我们需要更多系统思考者，也就是能横跨所有业务领域，并把它们抽象成「在 AI 世界里我们需要哪些基础积木」的人。
01:23 | Lenny Rachitsky
英文原文：
How do people learn this?
中文翻译：
人们怎么学会这种能力？
01:23 | Elizabeth Stone
英文原文：
Small trick. Each problem you're trying to solve, step out one click to the what am I assuming is true about the broader space.
中文翻译：
有个小技巧：面对你正在解决的每个问题，向外退一步，问自己「关于更大的空间，我假设哪些事情是真的？」
01:34 | Lenny Rachitsky
英文原文：
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 the CTO was for the longest time, one of the most popular episodes of this podcast. You'll soon see why. This is such a killer conversation, because when we chatted two and a half years ago, AI was only starting to emerge. And as a long time head of engineering and product and data science, Elizabeth has such a unique perspective on where things are heading and what's worth paying attention to. Prior to Netflix, Elizabeth was VP of Science at Lyft, Chief Operating Officer at Nuna, an economist at the Analysis Group and a trader at Merrill Lynch. Before we get into it, don't forget to check out lennysproductpass.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.
中文翻译：
今天的嘉宾是 Netflix 的产品与技术负责人 Elizabeth Stone。这是 Elizabeth 第二次来播客。她第一次来时还只是 CTO，那一期曾长期位居本播客最受欢迎节目第二名。你很快就会明白原因。两年半前我们聊天时，AI 才刚开始崭露头角；而 Elizabeth 长期负责工程、产品和数据科学，对事情会走向哪里、什么值得关注，有非常独特的视角。在加入 Netflix 之前，她曾任 Lyft 科学部门副总裁、Nuna 首席运营官、Analysis Group 经济学家和 Merrill Lynch 交易员。开始之前，别忘了访问 lennysproductpass.com：Lenny's Newsletter 订阅者可以独家免费获得一年期的全球热门、高质量 AI 产品服务。下面请听 Elizabeth Stone。Elizabeth，非常感谢你再次来到节目。
02:33 | Elizabeth Stone
英文原文：
Thank you. I'm honored to be here, once and now twice.
中文翻译：
谢谢。能来一次、现在又来第二次，我感到非常荣幸。
02:37 | Lenny Rachitsky
英文原文：
That's right. That's a rare treat for me. I don't know if you know this, but your first visit to the podcast, your episode ended up being my second most popular episode. You're right behind Brian Chesky for the longest time.
中文翻译：
没错。对我来说这很难得。我不知道你是否知道，你第一次来节目时，那一期最后成了我第二受欢迎的节目。有很长一段时间，你排在 Brian Chesky 后面。
02:50 | Elizabeth Stone
英文原文：
Wow. I am pleasantly surprised and also mildly competitive of how do I get to the first spot.
中文翻译：
哇，我既惊喜，又有一点竞争心：我要怎样才能排到第一？
02:58 | Lenny Rachitsky
英文原文：
[inaudible 00:02:58], I guess.
中文翻译：
我猜是 [听不清 00:02:58]。
02:58 | Elizabeth Stone
英文原文：
But I'll set that aside for now.
中文翻译：
不过我先把这件事放一边。
03:01 | Lenny Rachitsky
英文原文：
This is our shot.
中文翻译：
这是我们的机会。
03:02 | Elizabeth Stone
英文原文：
Brian's amazing, so I'll let that one go.
中文翻译：
Brian 很厉害，所以这次我就算了。
03:04 | Lenny Rachitsky
英文原文：
Yeah, he is. And then there's just all these fancy AI people that are just coming in hot. So, it's been two and a half years at this point. A lot's changed. Obviously, AI. Something AI is allowing people to do is everyone can 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 of this conversation. One is that I've heard from people that there's also this kind of confusion and frustration of what is my job anymore. What am I responsible for as a PM, as a designer? Is that something you've experienced?
中文翻译：
是的，他确实很厉害。现在还有一大批很会做 AI 的人不断涌入。两年半过去了，很多事情都变了，最明显的就是 AI。AI 让人们可以做的一件事是：每个人似乎都能做所有事情。产品经理可以写代码，设计师可以写 PRD，工程师可以做产品，所有人都可以做所有人的工作。这里面有几个层面。我听不少人说，大家也会困惑和沮丧：我的工作到底是什么？作为产品经理或设计师，我究竟负责什么？你有没有感受到这一点？
03:44 | Elizabeth Stone
英文原文：
I hear it within Netflix for sure. I think anytime a new technology comes along, especially one that's as transformative as GenAI, you go through a storming phase before you go through the forming phase of things. And I think we are in the middle of that right now. I don't think that means we should put AI back into the box and say, let's not use it, because this is complicating all of our preconceived notions about our roles. But I do think it means we have to be much more thoughtful about how do we get the benefits while reducing the costs. I think it's a great thing that people are experimenting with. How can I develop an idea faster, prototype an idea, put together an initial set of code that would allow us to test it? Do I believe that means anyone should be shipping code to production, that everyone should actually be doing everything? Probably not, but I think that it's good for people to be exploring what's possible. And then like I mentioned earlier, the benefit of having product and tech teams together, is that if the business problem is clear, I think it's okay and it's healthy for there to be some fluidity in the roles that people play. Because instead of having to wait for the engineering team to be ready, to be able to prototype something, product and design can move faster on it, but they should still work with their engineering partner to think through, how should we productize this? How do we scale it? What are the guardrails for it? So, I don't think it makes the functional expertise obsolete. I think it means that teams have to be more comfortable with maybe this helps us move faster in a certain direction. From an organizational perspective, things I think about to make this more coherent or less frustrating, are some of the things that have to be in place for us to get the benefits rather than the cost. So, that includes clarity on source of truth data, guardrails on shipping code to production or testing, before we make large changes. Thinking about opportunities where we can trust the output of AI versus we should have a process or review that helps us check that we're getting high quality outcomes. And the importance of reiterating that humans are still responsible for what happens. So, it can be that an agent wrote the code or I helped to do an analysis when that's not really my background, but it doesn't make people not have the responsibility that comes with what they've created. So, I think investing in some of those core infrastructure and practices, and reiterating the accountability and responsibility for the outcomes, helps to balance some of what's possible with what we should actually be doing.
中文翻译：
在 Netflix 内部当然能听到这种声音。我认为每当一种新技术出现，尤其是像生成式 AI 这样具有变革性的技术，人们都会先经历一个 storming 阶段，然后进入 forming 阶段。我们现在就在中间。我不认为这意味着我们应该把 AI 关回盒子里，说「别用了」，因为它让我们过去对岗位的预设都变复杂了。但我确实认为，我们必须更有意识地思考，怎样获得好处，同时降低成本。
我觉得大家探索这些可能性是好事：怎样更快形成一个想法、做出原型、写出一批最初的代码来测试它？但我是否认为这意味着每个人都该把代码发到生产环境，或者每个人真的都该做所有事情？可能不是。不过，探索可能性是好的。
正如我刚才提到的，产品和技术团队在一起的好处是，只要业务问题清晰，团队成员在工作角色上保持一定流动性是健康的。产品和设计不必等工程团队准备好，便可以更快推进原型；但他们仍应和工程伙伴一起思考：怎样把它产品化？怎样扩展？护栏是什么？所以我不认为职能专长会过时，而是团队要更习惯于：这或许能帮助我们先朝某个方向走快一点。
从组织角度看，我会思考怎样让这件事更一致、更少令人沮丧，哪些条件能让收益大于成本。这包括明确 source of truth data，也就是作为事实基准的数据；在代码进入生产或进行大规模变更前设置测试和发布护栏；判断哪些场景可以信任 AI 输出，哪些场景需要流程或复核来确认结果质量。
还要反复强调，人仍然要为结果负责。可能是 agent 写了代码，也可能是我借助 AI 做了原本并非自己专长的分析，但这并不意味着我可以逃避对成果的责任。我认为，投入核心基础设施和工作实践，并重申对结果的问责和责任，才能在可能做什么与实际上该做什么之间取得平衡。
06:27 | Lenny Rachitsky
英文原文：
This episode is brought to you by our season's presenting sponsor, WorkOS. What do OpenAI, Anthropic, Cursor, 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 felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in, that starts to expand upmarket, ends up working with WorkOS, and that's because they are the best. Whether you are a seed stage startup trying to land your first enterprise customer or unicorn expanding globally, WorkOS is the fastest path to becoming enterprise ready and unblocking 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. What's really awesome about having you back on the podcast is we chatted before AI was a massive transformation in the world. So, it's a really cool arc that we can explore here, the shift that we've all gone through. Coming back to the roles of the product and eng teams, I'm curious how much these roles have changed in the last two and a half years. If you think about product engineering, design, data science, user research, which roles have changed most? Which roles have changed least? What's most different since two and a half years ago?
中文翻译：
这里有很多有意思的地方。其中一个就是你刚才说的最后一点，也是我一直在思考的：如果我们都变成 builders，还需要分开的职能吗？AI 领域正在出现 member of technical staff 这样的趋势，意思是大家不再需要一个固定头衔，也不用被装进某个职能桶里。你的意思是，你认为产品、工程、数据科学、设计这些专业仍会继续存在？虽然人们会承担更多其他职能的工作，但拥有具体的学科、技能和背景仍然很有价值？
08:13 | Elizabeth Stone
英文原文：
So, you've mentioned some of the things, so I'll reiterate them and then maybe build. So, I have found that PMs, designers, data scientists, are able to get farther in the product development lifecycle 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, approach 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, has been 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, how do we get the most out of that long history of knowledge and learnings, to say, let's apply that to the problem we've got now to move faster in this is a promising path or this is something that we've learned something about and we could leverage here? And AI is very powerful at distilling information, looking across a broad set of things, doing an analysis around it, getting to the core of here's some insights to start with. I would hesitate to rely on that exclusively, but I think it's a head start. And I find even in my own work day-to-day, instead of sending an email that disrupts someone of like, remind me what research did we do in what year and what was the question and what was the test we ran? I can find that almost instantly. Then I can form my own, here's what I find interesting about this. And I've now skipped a couple steps towards, is there something actionable here? So, that's data analysis, it's modeling, it's distillation of information. And I'm seeing more people do that, to your original question. So, instead of that needing to be only the experts who were here for 20 years and saw every experiment or know where to find it, we're now able to do that faster within product and tech across all functions. And a big unlock for us is our business stakeholders, sitting in finance and content and advertising, can do that as well. And then bring back an initial hypothesis where they want to work more deeply with the data scientist and engineer and so on. So, there's something there about the hypothesis generation, prototyping, thinking deeply about problems, that feels like it's accelerating and that functions are able to do that in a more fluid way. But I still see comparative strengths. So, data scientists are still going to be experts at, can we trust this data? Are we interpreting it the right way? What's the data versus judgment that we should be applying here? A product manager is still going to be exceptional at saying, have we really framed the what of this, 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.
中文翻译：
即使工作在不同职能边界之间变得流动、模糊，我仍然认为各个学科中的 craft excellence 很重要，而且短期内不会消失。这又回到了我前面说的：仍然需要人来确认我们做的事情是否合理，确认我们在为 Netflix 会员或业务利益相关者解决正确的问题，并且方式是最合适的。
如果我和工程师、数据科学家或设计师交流，他们确实因为 AI 工具而比过去更会说「多种语言」了，但当我想到专业能力以及他们如何判断什么是好的工作时，仍然存在无法替代的东西，而且各个层级都如此。我仍然觉得优秀工程师稀缺，优秀数据科学家稀缺，优秀创意也稀缺。
10:00-20:00
11:55 | Lenny Rachitsky
英文原文：
There's so much interesting stuff here. One is this last point you made, something I've been thinking about. If we all become builders, will we still need separate functions? There's this member of technical staff trend that is happening across AI, 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, and tell me if I'm hearing you correct, in having this specific discipline and skill and background.
中文翻译：
这里还有很多有意思的地方。你刚才说的最后一点也是我一直在想的：如果我们都变成 builders，还需要独立的职能吗？AI 领域正在出现 member of technical staff 这样的趋势，似乎是在说：好吧，我们不需要一个固定头衔，你可以做任何事情，不必被装进某个职能桶里。你的意思是，你认为产品、工程、数据科学、设计这些专业仍会继续存在？虽然人们会做更多其他职能的工作，但拥有具体的学科、技能和背景，仍然很有价值？
12:26 | Elizabeth Stone
英文原文：
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 I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce. So, yes, some things are easier, but that hasn't dissolved in my mind.
中文翻译：
即使工作在不同职能边界之间变得流动、模糊，我仍然认为各个学科中的 craft excellence 很重要，而且短期内不会消失。这又回到了我前面说的：仍然需要人来确认我们做的事情是否合理，确认我们在为 Netflix 会员或业务利益相关者解决正确的问题，并且方式是最合适的。
如果我和工程师、数据科学家或设计师交流，他们确实因为 AI 工具而比过去更会说「多种语言」了，但当我想到专业能力以及他们如何判断什么是好的工作时，仍然存在无法替代的东西，而且各个层级都如此。我仍然觉得优秀工程师稀缺，优秀数据科学家稀缺，优秀创意也稀缺。所以，有些事情变容易了，但在我看来，这些稀缺性并没有消失。
13:27 | Lenny Rachitsky
英文原文：
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 tooling and LLMs rising?
中文翻译：
你发现哪些职能正在招更多人？比如工程、产品、设计的那块饼变大了；在 AI 工具和大语言模型不断发展之后，有没有哪些职能反而需要更少的人？
13:43 | Elizabeth Stone
英文原文：
I'm not sure that it matches exactly to functions, but I can tell you what we're seeing more of, we need more of. We need more systems thinkers in a world with AI. That looks a little bit different across functions, but I could play out a couple examples. So, in our core infrastructure team at Netflix in central engineering, a lot of what made Netflix successful over time was that local teams with specific business problems could move fast to deliver. They very often were not feeling like they needed to be on a central paved path. They built the stack that they needed to solve the problem and have the impact. In a world of AI with agents operating across multiple systems, wanting source of truth data, the importance of having preferred paved paths that get the most of the benefits and produce some guardrails so we can make sure we're doing good work. Common infrastructure, common paved paths, solving problems once with a core set of capabilities becomes more important. So, we are hiring more people who can look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI. So, that's one of the lenses, but also just with a lens of what got Netflix here doesn't get Netflix there. And we're going to have to have a stronger set of infrastructure to move quickly in this future. So, that means that engineering profiles are more distributed systems, more infrastructure, more of that system thinking mindset than a local business expertise. Though, of course, we still have people who are deep in personalization and advertising and content delivery. So, it's more something additive for us to have that core infrastructure and systems thinking. If I take another example, like design, it's extremely important that our experienced 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-to-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 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 reorientation of skills among the existing team and also hiring people who've got that type of expertise. And across all of it, it's a mindset shift. So, we are not hiring people who are not excited to explore, try new things, understand lots is changing and feel comfortable with that ambiguity, be comfortable that there's a blurring of how we work and how we partner. That's true for people who are already at Netflix and people who we are adding to the team, that that curiosity innovation mindset, it's not been more important, at least in the time that I've been working in this field.
中文翻译：
我不确定它是否能完全对应到某个职能，但我可以告诉你我们看到、也更需要什么：在 AI 世界里，我们需要更多系统思考者。这在不同职能上会有一点不同，我可以举几个例子。
Netflix 的核心基础设施团队，也就是中央工程团队，过去成功的一大原因是：面对具体业务问题的本地团队能够快速交付。它们通常不觉得自己必须遵循中央铺设的路径，而是构建解决问题、产生影响所需的技术栈。但在 AI 世界里，agent 会跨多个系统运行，也会需要事实基准数据；因此，拥有优先推荐的 paved paths，也就是把大部分收益保留下来、同时设置护栏的标准路径，变得更重要。共同基础设施、共同路径，以及用一套核心能力一次解决同类问题，都更加重要。
所以，我们会招聘更多能横跨业务领域、抽象出「AI 世界需要哪些基础积木」的人。另一个视角是：让 Netflix 走到今天的东西，不一定能让 Netflix 走到下一站。为了在未来快速前进，我们需要更强的基础设施。这意味着工程人才会更多偏向分布式系统、基础设施和系统思维，而不是只深耕某个本地业务。当然，我们仍需要在个性化、广告和内容分发方面有深厚经验的人；只是对我们来说，核心基础设施和系统思维是新增的能力。
再比如设计：有经验的设计团队必须开发模板，并以系统思维回答「在 Netflix，优秀的用户设计是什么样的」，这样他们才能帮助大量人，包括没有受过设计训练的人，做出彼此一致、符合会员端到端体验的产品。我非常担心出现不同的设计语言、不同的用户交互，然后发布出各种 Frankensteins，也就是拼凑出来的怪物。
因此，我们招聘设计师时，也要看他们能否进行设计系统层面的思考：怎样使用模板？怎样表达品牌？什么是好的用户体验？Netflix 的独特优势和 special sauce 是什么？现在设计团队里需要这样思考的人，比起只为某个产品设计某个具体功能的人更多。
我认为，每个职能都在发生这种退一步看全局的变化。这要求现有团队重新调整技能，也要求招聘具备这种专长的人。更重要的是心态转变：我们不会招聘那些不愿探索、尝试新事物、不愿理解变化很多、无法适应模糊性，或无法接受工作方式和合作方式正在模糊的人。至少在我从事这个领域的时间里，好奇和创新的心态从未如此重要。
17:22 | Lenny Rachitsky
英文原文：
On the systems thinking piece, is the reason this is becoming more important, that people are moving so fast that you need to invest in platforms and frameworks and design language, and basically teach people to fish so they can not be blocked or are there other reasons?
中文翻译：
关于系统思维，它变得更重要，是因为大家移动得太快，所以必须投资平台、框架和设计语言，教会人们如何「钓鱼」，让他们不被阻塞？还是还有其他原因？
17:38 | Elizabeth Stone
英文原文：
I think it's probably velocity. So, platforms do have a benefit of leverage. So, in general, that's an opportunity with or without AI, for a platform to get most teams 80% of the way there. And then they don't have to reinvent those building blocks. We have more bets that we're making across the business, more things we're trying to build. So, platform mindsets are good, and it's something that is relatively more recent for Netflix to think about that being a real critical enabler. There is also the sense of a scaffolding in a world of AI. So, not just the higher velocity, but you have more people doing more types of work that are different or new, like we were talking about. And there's risk that comes with, how do you think about access and identity in that situation? How do you think about security in that situation? How do you think about shipping high quality code and design and user experiences? And so, I don't think it scales well to have each person who's building something have to go figure out, could you remind me what good looks like here? And what are the bumpers or guardrails I should keep in mind? I think we need to encode that in our paved paths and our ways of working. And for a data science or analytical field to encode, here's the source of truth data, here's how to interpret it, here's how to access it, here's what to do with it or not to do with it and to be careful with certain types of data. 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... We knew this is work we needed to do. No time like the present to invest in that more heavily across the team.
中文翻译：
我觉得主要是速度。平台，无论有没有 AI，都有杠杆效应：通常可以让大多数团队走完 80% 的路，不必重新发明那些基础积木。现在我们在整个业务里下注更多、尝试构建更多东西，所以平台思维是有益的；对 Netflix 来说，把平台当作真正关键的赋能因素，也是相对较新的思考。
此外，在 AI 世界里，平台还像脚手架。不只是速度更快，而是更多人在做更多不同或全新的工作。这会带来风险：在这种情况下如何管理访问权限和身份？如何考虑安全？如何交付高质量的代码、设计和用户体验？如果每个构建者都要自己弄清楚「什么叫好」「我该记住哪些护栏」，规模就不会好看。我认为，我们需要把这些规则编码进 paved paths 和工作方式里。
对数据科学或分析领域来说，也要编码清楚：哪种数据是事实基准、如何解释、如何访问、哪些事情可以做或不能做，以及哪些数据类型要谨慎处理。一个拥有数千人的组织，再也不能只依赖 tribal knowledge，也不能总想着「我去找那个唯一懂这件事的人」。这在 AI 之前就是挑战，在 AI 时代只会更紧迫。我喜欢用 AI 或任何新技术来推动本来就知道需要做的工作；没有比现在更适合在团队范围内加大投入的时机了。
19:27 | Lenny Rachitsky
英文原文：
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?
中文翻译：
我在想，系统思维变得更有价值的另一个原因，是因为 agent 现在做了很多工作。给它们提供上下文、脚手架和设计语言，是不是就能把所有事情进一步加速？
19:38 | Elizabeth Stone
英文原文：
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. 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, 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.
中文翻译：
是的。我们在 Netflix 的一个愿景是，未来会有大量 agent 参与工作，因此人们需要能够持续地推理并解释整个过程。人负责指导：我们要解决的问题是什么？我们产出的东西是否有影响力，质量是否足够高？但具体工作会由人和 agent 一起完成。这会带来速度和收益，也会带来风险。尤其从工程角度，我们必须找到一种管理方式，让人们可以快速行动，
20:00-30:00
20:20 | Lenny Rachitsky
英文原文：
This connects so directly with... Jenny Wen was on the podcast. She was Head of Design for Claude Code and Cowork 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 pictures when you have the time. And it feels like that's what you're describing here is create the platform for people to move fast and then there's no time for design process of a specific new feature.
中文翻译：
这和 Jenny Wen 参加节目时说的观点直接相关。她当时是 Claude Code 和 Cowork 的设计负责人，提出过「design process is dead」的完整论点：没有时间再做设计和设计流程了。设计师更多是在引导大家、把大家指向某个方向，不断调整；如果有时间，再思考更大的图景。你现在描述的好像就是：搭好平台，让人们快速行动，于是具体新功能就没有时间走传统的设计流程了。
20:51 | Elizabeth Stone
英文原文：
I have mixed feelings about that, because 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. 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. 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's a design mindset that has to be core to it. So, the work itself might look different, but I don't think we lose the mindset.
中文翻译：
我对此有复杂的看法。一方面，我们确实希望借助基础设施和系统思维，让更多人把设计质量做进工作里。既然新技术提供了这个机会，为什么不用？但对于最重要的优先事项，设计对能否把问题解决正确仍然关键，所以我们仍会为重要的设计工作留出时间。
这类工作可以更快：设计师自己拥有更多工具，可以以更高速度做出很出色的成果，展示更多选项，更快学习、迭代和测试。但仅仅因为我们能更快写代码、更快做数据分析，就说设计、深度设计专长和设计思考会被挤出去，我认为这是错误的。至少对 Netflix 这样的大规模消费产品来说，这会让我们失去 Netflix 出色的一个原因：产品、技术和设计把很多复杂性变得不可见，带来无缝的客户体验。这种设计思维必须是核心。工作本身可能会变，但这种思维不会消失。
22:06 | Lenny Rachitsky
英文原文：
That's an awesome counterpoint. So, what I'm hearing is trending up skills, attributes you look for, systems thinking. And this mindset of being comfortable, and excited about change, and what's coming, and not being stuck in your own ways. What are you finding is trending down? What are you less looking for that you used to value more highly?
中文翻译：
这是一个很好的反驳。所以我听到的上升技能和特质包括：系统思维，以及对变化、即将到来的事情感到自在甚至兴奋，不要困在自己的旧方法里。那什么在下降？哪些东西是你现在不像过去那样看重的？
22:28 | Elizabeth Stone
英文原文：
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 still believe we need specialized practitioners in those spaces. But as a general rule, compared to five or 10 years ago, 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 backend and front end systems? Can I hook into infrastructure with a lot of expertise? I think 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, 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? And I don't want to be too narrow even in my own assessment of that, but 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.
中文翻译：
在我看来，非常狭窄、非常深入的专业化空间正在变小。我可以举出仍然需要它的例子，因为有些行业或技术领域，世界上真正懂其原理的人只有少数。比如编码，或者我们的播放系统，以及 Netflix 做过的一些极具创新性和独创性的东西，我们仍然需要这些领域的专业实践者。
但一般来说，与五到十年前相比，我认为我们需要更少的 specialist，更多能向多个方向适应的 generalist。这种适应可以跨职能，也可以跨工程类型：我能不能同时驾驭前端和后端系统？能不能连接到基础设施，并且有足够专业能力？现在更重要的心态是「我能很快学会」。这又回到了系统思维。
我认为，与过去相比，专业人士更容易学会更广泛的工具，所以我们或许需要更少的 specialist，因为人才可以向更广的方向成长。固守狭窄专长有时也会让我担心：你有没有向不同方向成长和探索的心态？不过我不想对它作过于狭窄的判断。重要的是，即使是 specialist，也要有「我想尝试新的解决方式，而不是沿用过去方式」的意识。
24:13 | Lenny Rachitsky
英文原文：
And when you say specialists, are you thinking front end, I'm a front end engineer versus a backend or are there other versions of that?
中文翻译：
你说 specialist 时，是指「我是前端工程师」而不是后端工程师，还是也包括其他类型？
24:18 | Elizabeth Stone
英文原文：
Yeah. Or it could be a domain set of knowledge of I'm a deep-
中文翻译：
可以是这样。也可以是一整套领域知识：我是某个领域的深度专家——
24:22 | Lenny Rachitsky
英文原文：
Like a payments expert?
中文翻译：
比如支付专家？
24:23 | Elizabeth Stone
英文原文：
I'm a payments expert. I'm an ads marketplace design expert. I'm an expert in this very specific tooling that studio productions use. So, their specialist and 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 to have a lot of inertia around them. I would think we would want people who are able to innovate and imagine what's the future version of this. And so, we want more talent like that.
中文翻译：
我是支付专家，是广告市场设计专家，是某种非常具体的、影视制作工作室使用的工具专家。这种专业和主题知识是优势，前提是这个人愿意继续成长和延伸，去追问「这真的仍然是合适的工具或解决问题的方式吗？」
所以，从工程角度说，技术栈的不同层次上，专门化会减少；而对于那些不太可能保持静态、也不太可能拥有很强惯性的工具，我们希望人才可以创新，并想象它未来会是什么样。我们需要更多这样的人才。
25:09 | Lenny Rachitsky
英文原文：
Awesome. So, coming back to the systems thinking piece, people hearing this are like, "Okay, I got to work on my systems thinking skillset." How do people develop the skill? Is it just do it for a long time, work at a lot of complex projects? I think of this book that everyone always references with the slinky on the front, Thinking in Systems.
中文翻译：
很好。回到系统思维，听到这里的人可能会想：「好，我得提升系统思维能力。」人们怎么培养这种能力？只是做很长时间、参与很多复杂项目吗？我想到一本大家总会提到、封面上有弹簧的书《系统之思》。
25:29 | Lenny Rachitsky
英文原文：
Yeah. How do people learn this?
中文翻译：
对，人们怎么学会它？
25:31 | Elizabeth Stone
英文原文：
Small trick. Each problem you're trying to solve, step out one click to 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 from 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 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?
中文翻译：
有个小技巧：面对你正在解决的每个问题，向外退一步，问「关于更大的空间，为了解决这个问题，我假设哪些事情是真的？」
比如有人交给我一个任务，要为 Netflix 会员体验构建新功能。我会先停一下，想想：我们真正要解决的更大的消费者问题是什么？这个功能将支持什么类型的内容？我计划的构建方式，能否在多种内容类型上扩展？还是说，它可能成为一个平台能力，由多个业务领域贡献进来？
我用这个功能解决的消费者问题，会不会是 Netflix 面对不断扩大的娱乐世界、希望让体验更个性化和沉浸式时，最重要的问题之一？这些问题并不要求你把整个海洋煮沸，也不要求你解决 Netflix 的整体战略或我们相对竞争者的位置。你只需要拿起自己负责的事情，把问题向外放大一层，然后对它提问。
我不会在提问状态停留太久，因为那样就会卡住，无法继续前进。但它能帮助人们用系统的方式思考：我们是否在用正确的方式解决真正影响终端消费者的正确问题？
27:07 | Lenny Rachitsky
英文原文：
As you describe it, another way I'm thinking about it is think... if you were your manager, what's their broader perspective across not just your one team and problem and KPI, but the larger picture?
中文翻译：
按你的描述，我想到另一种说法：如果你是自己的经理，你的经理不只看你所在团队、问题和 KPI，还会从更大的图景出发，你要以他们的视角思考？
27:18 | Elizabeth Stone
英文原文：
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 though 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?
中文翻译：
多年来我得到过类似的建议：有没有办法让我的工作帮助经理完成他们的工作？如果我不只考虑自己直接负责的事情，而是从经理的视角来想——不仅看产品和技术，还看财务、内容和业务的其他部分——我自然会退一步，思考这些组成部分怎样合在一起，以及整体如何大于部分之和。
我认为这很有用。工程师也可以思考：我怎样留下一个更好的系统版本？怎样构建一个对其他人来说质量更高、可扩展的东西？这里既有「我如何帮助经理」，也有「我如何帮助同事」。后者也是我们工程原则的核心：做对整个组织有利的事情，而不只是对本地团队有利的事情。
这同样是系统思维。它不只是关于资历，而是关于解决问题和构建东西时的视野宽度：它对同事有用吗？我是否会为未来要做的新创新留下一个更强的基础？
28:24 | Lenny Rachitsky
英文原文：
That is awesome tactical advice. Making your manager's life easier is always a good tactic career-wise.
中文翻译：
这是很棒的实用建议。从职业发展的角度，让经理的生活更轻松总是个好策略。
28:31 | Elizabeth Stone
英文原文：
Several reasons. Yeah.
中文翻译：
理由有好几个。
28:34 | Lenny Rachitsky
英文原文：
Following the thread a little bit, I know you all added career ladders and levels. Recently, it was a new thing. You used to not have these things. So, on that thread, what have you added to the career ladders within this AI world, if anything, that you find you want people to lean into more, you're looking to more or not? Did you not change your career ladders and performance criteria?
中文翻译：
我知道你们最近增加了职业阶梯和级别。以前没有这些东西。在 AI 世界里，你们在职业阶梯上增加了什么？有没有什么东西是你希望人们更多投入、你更重视的？还是你们没有改变职业阶梯和绩效标准？
28:58 | Elizabeth Stone
英文原文：
So, the way we've approached this so far is, instead of trying to articulate at each level exactly how AI changes those expectations, to instead put an overlay across all of the talent at Netflix, people on the team and those who are hiring, to talk about an aspiration for AI fluency. And what that looks like is going to vary by function. It's going to vary based on where you are in your career. That could be what level you're in or what type of role or persona work you're doing. But the aspiration for AI fluency, which is a tough thing to define. So, does it mean that I have an experimentation mindset? Does it mean that I know where AI is useful and not useful? Does it mean that I've actually built things using AI? I feel like the way that has shown up in career ladders and how we talk about it evolves almost by the quarter, if not month or day, because the tech itself is advancing so much. So, the most useful thing is not to make it level specific or role specific, but to encourage everyone towards the expectation on AI fluency, which doesn't mean use it as a tech for the sake of tech. It's tech where it's useful, to have good judgment about that and to have the mindset to be open-minded to explore and try new things, that's the non-negotiable for all roles. And that's true at the senior most levels of Netflix, where we talk about we too need to have deep fluency in AI, even if we're not writing code as part of our day jobs. So, that's changed. And then that's showing up in our hiring practices as well, getting comfortable within interviews, exploring how are people thinking about AI or technology. What are they using in their day-to-day or their current job? How comfortable are they with change and exploration? And even for things like coding interviews, allowing candidates, of course, to use AI tools, because that's going to be part of what the work requires now. So, those have been shifts that we've made, but I doubt it's a shift that's done versus we're right in the middle of it.
中文翻译：
到目前为止，我们的做法不是试图精确描述 AI 如何改变每一级的期望，而是在 Netflix 所有人身上加一个 overlay，讨论对 AI fluency 的期望。它会因职能而异，也会因职业阶段而异；可能取决于级别、角色类型，或者你承担的工作 persona。
AI fluency 很难定义。它是实验心态吗？是知道 AI 何时有用、何时没用吗？是实际用 AI 构建过东西吗？我觉得，职业阶梯和我们的讨论方式几乎按季度、甚至按月或按天变化，因为技术本身进步太快。
因此，最有用的不是把它做成某个级别或角色的要求，而是鼓励所有人朝 AI fluency 这个方向走。它不意味着为了使用技术而使用技术，而是在技术有用时使用它，具备良好的判断力，并且保持开放，愿意探索和尝试新事物。这对所有角色都不可妥协。
Netflix 最高层也是如此：即使日常工作不写代码，我们也需要对 AI 有深度流畅度。这已经改变了我们的做法，也体现在招聘上：面试时会了解候选人如何思考 AI 或技术，在日常或当前工作中用什么，对变化和探索的适应度如何。甚至在 coding interview 中，我们当然允许候选人使用 AI 工具，因为这会成为现在工作的一部分。
30:00-40:00
30:59 | Lenny Rachitsky
英文原文：
I'm just 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?
中文翻译：
我继续沿着这条线问。显然，AI 对编码的改变非常大，也是原型制作的一大解锁。在 Netflix，还有哪些真正有影响力、但人们可能没想到或没意识到的 AI 用例？
31:16 | Elizabeth Stone
英文原文：
So, there's two that come to mind. So, the first is data analysis, distillation of information, modeling, which is 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 on, 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 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, InterPositive, that was started by Ben Affleck, that built a set of models and capabilities. That allow you after you've shot something to relight, reframe, reshoot, change dialogue in ways that are very impactful to get higher quality content. Are still led by the filmmaker, creator saying, "You know what? I would like to try something else to bring this vision to life." But that impact is extremely promising and we're seeing lots of productions leverage different tools, some of them built in-house, some of them that we enable through other vendors for those content creation use cases. And then as we think about how content comes to the product, I mentioned localization, subtitles and dubs, but also how we create high quality trailers, images, artwork at scale that then we can use to help make sure that titles find their audiences around the world. Those all are huge levers when we think about the AI impact. So, that again, goes well beyond prototyping or coding to some of the creative use cases. And you can imagine that, just like they work for studio productions for film and TV, they work for advertising, they work for marketing, off-service campaigns. And so, those are all areas that we're exploring.
中文翻译：
我想到两个。第一个是数据分析、信息蒸馏和建模：用这些工具梳理我们拥有的所有洞察。比如，我们做过哪些实验？某个问题应该看哪些指标？我们做过哪些消费者研究？只要确认结果有效，并和本地数据科学家一起确认「我用的是事实基准数据吗」，速度和质量都会高很多。这是一个很好的用例，也是我个人最常使用这些工具的地方之一。
它已经超越原型和编码，进入一般性的分析思考，以及把数据转化为行动和洞察。
第二个是内容生产和创作。这个领域在生成式 AI 之前就有很多应用：ML 和 AI 长期被深度用于生产工具；我们用它们大规模制作宣传资产，也用来做字幕和配音本地化。生成式 AI 让潜在影响出现了一个很大的跃升。
在创意构思阶段，我们把一些工作叫作 pre-visualization，基本上是在真正进入制作、把人带到片场之前，把创作者的愿景先变成可见的东西。后期制作也有很多用例。我们最近收购了一家由 Ben Affleck 创办的公司 InterPositive，它构建了一套模型和能力，让你在拍摄之后重新打光、重新构图、重新拍摄，甚至改变对白，从而以非常有影响力的方式获得更高质量的内容。
当然，方向仍然由电影人或创作者掌握，他们会说「我想尝试另一种方式来实现这个愿景」。但这种影响非常有前景，我们看到很多制作使用不同工具：有些是 Netflix 自建的，有些是通过其他供应商提供的。
再看内容如何进入产品：除了字幕和配音本地化，还包括大规模制作高质量预告片、图片和 artwork，帮助作品在世界各地找到观众。这些都是 AI 影响内容业务的重要杠杆，而且远远不只是原型或编码。你可以想象，它们不仅适用于影视制作，也适用于广告、营销和站外活动。这些都是我们正在探索的领域。
34:01 | Lenny Rachitsky
英文原文：
This episode is brought to you by Mercury, radically different banking loved by over 300,000 entrepreneurs and now with Command. I've been a customer of Mercury's for over six years. I have never once thought about leaving. Mercury is basically what happens when banking is built by product people, not by bankers. They make it so easy, dare I say fun to send invoices, move money around, set up virtual cards for folks on my team. Does your bank have an API, a terminal native CLI or an AI-ready MCP server? I don't think so. And just recently, they launched Command, a conversational interface built directly into Mercury, which acts as your financial operator. I've been using Command to transfer money around, to figure out what categories I've been spending the most money in, analyze my cash flows. And just today, I used it to find out how much I've made from a specific sponsor over the past year. I just ask, how much have I made from X over the past year? 10 seconds later I have an answer. It is so freaking cool. Visit mercury.com to learn more and apply online in minutes. Mercury is a FinTech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column NA Members FDIC. You mentioned how Netflix has been very early to AI and ML for a long time. Younger people may not remember this, but y'all had this contest to optimize the-
中文翻译：
本集由 Mercury 赞助。Mercury 是一家截然不同的银行，已经受到超过 30 万名创业者的喜爱，现在推出了 Command。我使用 Mercury 已经六年多，从来没有想过要离开。Mercury 就像「由产品人而不是银行家打造的银行」：发送发票、转账、为团队成员设置虚拟卡，都很简单，甚至可以说很有趣。你的银行有 API、终端原生 CLI 或支持 AI 的 MCP server 吗？我想没有。
最近他们推出了 Command，这是直接嵌入 Mercury 的对话式界面，可以充当你的财务运营员。我一直用 Command 转移资金、查看自己花费最多的类别、分析现金流。就在今天，我还用它查某个赞助商过去一年给我的收入。我只要问「过去一年我从 X 赚了多少钱？」，十秒后就能得到答案，实在太酷了。访问 mercury.com 了解更多并在线申请，几分钟即可完成。Mercury 是金融科技公司，不是 FDIC 保险银行。银行服务由 Choice Financial Group 和 Column NA 提供，成员均为 FDIC 保险机构。
你提到 Netflix 很早就开始使用 AI 和 ML。年轻一点的人可能不记得了：你们当年举办过一个优化——
35:25 | Elizabeth Stone
英文原文：
The Netflix prize.
中文翻译：
Netflix Prize。
35:26 | Lenny Rachitsky
英文原文：
Yeah, the Netflix prize. Just show an example of how early you were to AI and ML. I think it was a million dollar prize to optimize the Netflix ranking algorithm a little bit, whoever could optimize it the most. And I think the winner optimized it by a few percentage points, something like that. And it was a huge deal. All these super smart people got around the world and it happened a few times, right?
中文翻译：
对，Netflix Prize。你可以举这个例子说明 Netflix 多早就开始做 AI 和 ML。那好像是 100 万美元奖金，用来优化 Netflix 的排名算法；谁优化得最多就能获奖。我记得获胜者把它优化了几个百分点左右，应该还不止一次？
35:49 | Elizabeth Stone
英文原文：
I mean, you said it on my behalf. Often, when there's questions about how is Netflix thinking about AI, it's great to remind people of exactly that point, that this is not new to us. 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 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 and 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 both cases, it ends up taking a strength that Netflix has, which is marrying entertainment, and technology, and making sure we stay ahead of the game to deliver things that are even better. So, I love that it's part of our history. It still continues to be a strength and it's going to have to be a strength, given the size of the challenges we're facing around the breadth of entertainment, while keeping a great experience.
中文翻译：
你已经替我说了。每当有人问 Netflix 如何看待 AI，提醒大家这一点都很有用：AI 对我们并不新鲜。尤其在个性化方面，它一直是给会员提供出色体验的核心。
我们拥有的内容广度不可能靠人直接处理；内容越来越多，而让发现越来越容易，正是 Netflix 面临的挑战之一。AI 和 ML 一直是解决办法。你希望在正确的时刻，为正确的人个性化推荐正确的作品。目录越精彩、内容越广，这个问题就越难；而且目录不再只有影视，还有游戏、直播和播客。个性化以及它所带来的体验会变得更重要。
我们可以利用过去的积累来问：现在技术更强了，我们该怎样解决这个问题？这段历史让我们在确定要解决什么问题、以及为什么 Netflix 必须为会员解决它时，拥有领先起点。
创意业务也是一样。我刚才提到，AI 和 ML 长期用于视觉特效和语言本地化。现在要问的是：技术更强之后，下一阶段会是什么？两边最终都在发挥 Netflix 的优势：把娱乐和技术结合起来，并保持领先，交付更好的体验。
我很喜欢它属于 Netflix 的历史，而且仍然是我们的优势。面对内容广度带来的挑战，同时保持优秀体验，它未来也必须继续成为优势。
37:33 | Lenny Rachitsky
英文原文：
Yeah. And I love that back then it was called machine learning and AI was like, no, no, it's not AI. AI is never going to happen. It's just machine learning.
中文翻译：
对，我喜欢那时它叫 machine learning，而 AI 好像会说：「不，不，这不是 AI，AI 永远不会发生。」这只是 machine learning。
37:41 | Elizabeth Stone
英文原文：
Well, then all of a sudden, we call everything AI and some of it's machine learning.
中文翻译：
后来突然之间，我们把所有东西都叫作 AI，而其中一些其实是 machine learning。
37:45 | Lenny Rachitsky
英文原文：
That's right. That's right.
中文翻译：
没错，没错。
37:48 | Elizabeth Stone
英文原文：
It depends, the thing that is of the moment to describe. So, I think we bucket all of it as AI now.
中文翻译：
这取决于当下要用什么概念来描述它。我觉得现在我们把所有这些东西都归进 AI 这个桶里了。
37:54 | Lenny Rachitsky
英文原文：
Yeah. And that's cool.
中文翻译：
是的，这很有意思。
37:56 | Elizabeth Stone
英文原文：
And there's a lot 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, I don't think it would surprise anyone that Netflix is using a broad array 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'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.
中文翻译：
而且有很多 AI 用例并不是生成式用例。我们可以深入讨论各种具体事情，但总体来说，这应该不会让人惊讶：Netflix 正在使用非常广泛的一组能力，因为大家对可能性都很兴奋。
对在 Netflix 工作的人来说，有趣之处在于：如果你真正热爱技术在创意出口、消费产品或基础设施上的应用，我们这里有所有这些问题，而 AI 正位于它们的中心。即使 Netflix 没有被贴上 AI 公司的标签，也不要忘记，AI 是我们非常熟悉的一种工具，帮助我们获得更好的娱乐和技术结果。
38:37 | Lenny Rachitsky
英文原文：
The other really interesting thing, just to 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 was something core to Netflix in the beginning. High agency, autonomy, high talent density, very bottoms-up thinking, super quick experiments and launching, paying top of market. 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.
中文翻译：
另一个很有意思的地方是——我继续夸 Netflix——如果你看 Netflix 早期的文化手册，也回顾我们上次的谈话，会看到一些反复出现的特征：高度主动性，这是 Netflix 从一开始就重视的东西；自主权、高人才密度、自下而上的思考、非常快速地实验和发布、按市场顶端付薪。这正是我现在不断听到的顶尖 AI 实验室的运作方式。所以我们最后都回到了这里，而 Netflix 其实一直如此。
39:13 | Elizabeth Stone
英文原文：
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 bone themselves. It wasn't, let's just make sure people have as much responsibility as possible. We don't like process, so let's make sure that we don't have any of that. It was instead a very strongly held opinion that you get to excellence by giving people a lot of agency and accountability, by pushing decisions as deep in the organization as possible, hiring great people who can be trusted to have good judgment and make good decisions. And that ends up driving incredible outcomes, plus a lot more motivation and sense of responsibility. It means every person on the team can feel like I'm being given a lot of keys and a lot of accountability for what happens here. And I myself feel like when you know you're carrying that level of trust and accountability, you want to do your best work. And so, there's something that feels very intuitive about Netflix's culture has always been aiming at excellence. And when you have great talent and you give them the ability to do their best work without micromanaging it or drowning it in process, you actually get much better outcomes. And so, I do think that the newer era companies are picking up on something that is feeling very familiar to us. And it's not something that comes easily. So, culture's not a static thing. Culture needs to grow and evolve as the company gets bigger, the types of problems you're solving, change. But the notion that 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.
中文翻译：
这有点像 Netflix 很早就看懂了什么能让人才变得出色。我把 Netflix 文化的这些方面想成「把卓越当作操作系统」，听起来可能有点 nerdy。
所有这些文化元素的目标，并不是它们本身。不是说「让每个人承担尽可能多的责任」，也不是说「我们不喜欢流程，所以完全不要流程」。真正的观点是：要获得卓越，就要给人很多主动权和责任，把决策尽可能下沉到组织深处，招聘值得信任、能够做出良好判断和决定的优秀人才。
这样会带来惊人的结果，也会带来更多动力和责任感。团队里的每个人都会觉得，自己被交付了很多钥匙，也被交付了很多对结果的责任。当你知道自己承载了这样的信任和问责，就会想把工作做到最好。
所以，Netflix 的文化一直在追求卓越，这件事很有直觉性。当你拥有优秀人才，允许他们做好工作，而不是微观管理或让流程把他们淹没，结果就会好得多。新一代公司正在发现一些对我们很熟悉的东西，但这绝非轻易可以做到。
文化不是静态的。公司变大、要解决的问题改变，文化也必须成长和演化。但「我们追求卓越，并相信优秀人才需要空间做出最好的工作」这个观念没有改变，我认为它仍是我们的 special sauce。
40:00-50:00
41:11 | Lenny Rachitsky
英文原文：
I love this concept. Excellence as an operating system. It's very systems thinking you might say for how to set up a company.
中文翻译：
我喜欢这个概念。「把卓越当作操作系统」——你可以说，这是一种设置公司的系统思维。
41:18 | Elizabeth Stone
英文原文：
Exactly, Lenny.
中文翻译：
完全正确，Lenny。
41:21 | Lenny Rachitsky
英文原文：
So, everyone listening to this will want excellence as an operating system. Who would not want this? It'd be helpful for people to hear what are the ingredients to make this happen. One is obviously high talent density, just hiring only the best. Two is accountability. There's the input and the output, essentially. Input, amazing people, the top people, make them accountable, give them autonomy. What would you say are the pillars of creating this excellence as an operating system, if founders are listening to this and like, I want them to do that?
中文翻译：
所有听众都会想要一套「把卓越当作操作系统」的做法，谁不想要？如果创业者听到这里说「我也想这么做」，你能否讲讲它的组成要素？第一显然是高人才密度，只招聘最好的人。第二是问责：本质上有输入和输出，输入是优秀人才，输出是让他们承担责任、拥有自主权。你认为打造这种卓越操作系统的支柱是什么？
41:51 | Elizabeth Stone
英文原文：
Well, the talent density is the non-negotiable. 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, 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 I would make a different decision. 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 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 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 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. I can provide input. I can help you brainstorm. It's yours in the end." And 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.
中文翻译：
人才密度是不可谈判的。你必须从这里开始。没有它，就不可能对组织各层的决策有信心，也不可能允许人们承担风险、快速创新。
在 Netflix 文化里，卓越的重要部分是对承担风险感到自在。我们不试图避免失败，而是试图在失败发生时快速恢复。我们进入直播业务就是一个很好的例子：我们愿意承担大量风险，知道不可能一开始就完美，也知道可以快速学习，并因此变得更好。看到团队怎样处理这件事，我从未如此为他们骄傲。
因此，你需要人才密度，需要相信人们会利用你提供的上下文，拥有强判断力、愿意冒险，并为对业务最好的结果而努力。你还必须非常清楚，自己的工作是在为消费者和 Netflix 带来结果。重要的是 Netflix，是 Netflix 会员，而不是我的个人成功或我的偏好。卓越操作系统里有一种无私。
还有一点是，有些事情对人类来说非常不自然。我举几个需要学着适应的例子。有时我看到某个决定，会想「我会做出不同决定，这真的是最好的选择吗？」但在 Netflix 文化中，尤其对我来说，职责不是每次都介入、推翻或否决。只要事情不重大、不至于烧毁整个公司，就让那个人做决定、从中学习，然后事后请他们复盘。也许我才是错的，也许那个决定其实很好。
这和承担风险有关，也是在帮助人们习惯自己做决定，尤其是知道并非所有决定都会正确、并且会从困难结果中学到东西。我自己也经历过老板和同事对我说：「这是你的决定。我可以提供意见，可以和你一起头脑风暴，但最终它是你的。」这不是人类自然会做的事。风险很高、我觉得自己要为组织负责时，让别人承担风险会令人不安。
在事情进展不顺时，也不要假设流程会解决问题，这同样是例子。
44:36 | Elizabeth Stone
英文原文：
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, 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 would believe a lot of people at Netflix feel it, because you try not to do the thing that is standard.
中文翻译：
过去几年我学到的一点是：当规划很困难时，我从没听人说过「我们找到了完美的规划方法，或者完美的反馈、定级和薪酬方法」。但每次遇到问题、增加更多流程，我们只是花了更多时间，并没有得到更好的结果。
还有一件对人类来说不自然的事：当事情困难又复杂时，大家往往觉得给问题加很多约束是在简化它。其实，这违背了另一个问题：有没有更有创意的方式来规划、处理人员决策或优先级决策，让结果真正变好？
因此，我们要抵抗大公司通常会做的事情，经常让自己自在地处在不适之中。这是我在岗位上感受到的，也是我相信很多 Netflix 员工都会感受到的，因为你要努力不去做那个最标准的动作。
45:39 | Lenny Rachitsky
英文原文：
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 you need to resist that, because that slows things down. And the best people don't want to be working in a place with all these checklists and processing gates and things like that.
中文翻译：
听起来很容易，但我完全明白你的意思：有人搞砸了，你会想「好，到底哪里出了问题？我们制定一个流程，避免这件事再次发生」。而你说的是必须抵抗这种冲动，因为它会拖慢速度，优秀人才也不想在充满清单、流程和关卡的地方工作。
45:58 | Elizabeth Stone
英文原文：
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 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. And you get a much stronger team, which I think is part of our role as leaders, of 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.
中文翻译：
不。优秀人才希望知道，会有一次不追责的复盘，而且他们会感到对自己的行为负有很强的责任，于是会说：「我怎样确保这件事不再发生？」答案不是增加流程，而是思考怎样分享这些教训、怎样改变工作方式，让下一次得到更好的结果。
当你信任人们去反思、学习和成长，长期结果会好得多，团队也会更强。这是我们作为领导者的一部分职责：你要培养一支有韧性、能持续产生很大影响的团队，而不是试图控制一切。
46:41 | Lenny Rachitsky
英文原文：
Which is a key to building a team with high talent density. There's two sides of 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 keeper's 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 how has that evolved over the last couple of years? That still a core part of the culture, this idea of the keeper's test?
中文翻译：
这是打造高人才密度团队的关键。这里有两面，我想简单聊聊：一面是招聘，另一面是留住人才。你因为 keeper test 出名，我们上次谈过。它对人们来说又是一件不自然的事。想了解它的人可以听第一期，但过去几年它怎样演变？keeper test 仍然是文化核心吗？
47:05 | Elizabeth Stone
英文原文：
It's often cited in a way where you think of keeper's 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 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 this is the toughest thing to say, "To be honest, you're not passing that right now. I think you could get there in some cases." And that comes with feedback and what are those milestones. Or in some cases you're saying, "We've really tried and I don't see the path to success." So, it's an anchor and an entry point for a conversation that can go lots of different directions. And the thing I like about it is it's good hygiene on feedback and checking in on how things are going and forcing a tough conversation sometimes, instead of shying away from it. Or to keep great talent, you do need to say you're doing great. That's an important part of making people feel recognized and valued. So, I don't want it to come across that we just have this very negative view of it. I think there's this positive side of the coin as well.
中文翻译：
人们经常这样引用它，好像 keeper test 只是决定让某个人离开、判断其是否适合岗位的那一刻，以及围绕这件事展开的谈话。但它同样经常用于谈论某个人有多么出色、在岗位上做得多好。
因为它的入口可以是我对直属下属说，或者对方问我：「我在你的 keeper test 上表现如何？」大多数时候我的回答是：「我会非常努力地留住你。让我讲讲你做得特别好的地方、你的优势、你产生了哪些影响，以及你还可以怎样更好。」它可以成为非常积极、让人受到鼓舞的谈话入口，只是框架是「我通过 keeper test 了吗？」
当然，也会有更困难的情况：我要评估某人是否通过，或者对方问我时，我必须说：「坦白讲，你现在还没有通过。我认为在某些情况下你可以做到。」这会伴随反馈和里程碑；或者有时你会说：「我们已经努力过了，我看不到成功的路径。」
所以，它是一个锚点和入口，可以把谈话带向很多方向。我喜欢它，因为它是反馈和检查工作状态的良好习惯，有时会迫使人们面对困难谈话，而不是逃避。要留住优秀人才，你也必须告诉他们「你做得很好」，这是让人感到被认可和重视的重要部分。我不希望大家以为我们对它只有非常负面的看法，它也有积极的一面。
48:53 | Lenny Rachitsky
英文原文：
Awesome. I guess just to explain to people what this is so they don't have to go listen to a whole other podcast, 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, 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 settle. Okay, this person, they're here, I guess we'll keep them around. Is that roughly the way to understand it?
中文翻译：
我先简单解释一下，这样大家不必再去听另一期完整播客。Netflix 文化的一部分是：当有人向你汇报时，你应该一直思考——如果知道现在所知道的一切，我今天还会招这个人吗？如果不会，可能就应该让他离开。核心是保持高标准，不要满足于「这个人已经在这里，那就先留着吧」。这样理解大致对吗？
49:21 | Elizabeth Stone
英文原文：
Yeah. And the way it can... it's sort of a corollary to that. If that person came to me today to say they were leaving, would I fight to keep them or not? Or would I say, if my sense is relief of, oh, yeah, it probably would be better to have someone else in this role, I should've taken action and having that conversation sooner.
中文翻译：
对。它还有一个相应的反向问法：如果那个人今天来告诉我他要离开，我会不会努力挽留？如果我的第一反应是松一口气，觉得「是的，也许让别人来做这个岗位会更好」，那就说明我本应更早采取行动、开始那场谈话。
49:41 | Lenny Rachitsky
英文原文：
As you said, it's so many uncomfortable things you have to do to maintain this culture.
中文翻译：
正如你说的，要维持这种文化，就必须做很多令人不舒服的事情。
49:47 | Elizabeth Stone
英文原文：
Yeah. Well, the keeper test is one, maintaining talent density, context, not control among leaders. We talk about being highly aligned, but loosely coupled, which is where light process, the minimum to make sure we're clear on the priorities and we can execute them is what we're solving for. All of these things are not things that human beings or organizations at scale tend to do. So, it's constant diligence to try to maintain the thing that's made Netflix a special place. Because in the end, it's the work and the culture that attracts people and retains people. And we need that to be a successful business.
中文翻译：
是的。keeper test 是其中一件；此外还有保持人才密度，以及领导者之间的「提供上下文，而不是控制」。我们说要高度一致、松散耦合，也就是只保留让优先级清晰、能够执行所必需的最少流程。
这些事情都不是人在组织规模扩大后通常会做的，所以要一直保持警惕，努力维持让 Netflix 成为特殊工作场所的东西。归根结底，吸引并留住人才的是工作和文化，而我们需要它们来成为一家成功的企业。
50:00-60:00
50:22 | Lenny Rachitsky
英文原文：
That's exactly where I was going to go. So, to make this work, you need to attract the best people. It's always been very hard to attract the best people. It feels insanely hard these days with the amount of dollars flying around, the fancy AI labs, so much competition. It's crazy. What have you found to be effective in convincing the top people to still come to Netflix and join versus all the other fancy places they can go?
中文翻译：
这正是我想问的。要让它运转，你得吸引最好的人。吸引最好的人一直很难，而现在似乎格外难：大量资金涌向顶尖 AI 实验室，竞争太激烈了。你发现哪些做法能让顶尖人才仍然选择 Netflix，而不是他们可以去的其他热门地方？
50:49 | Elizabeth Stone
英文原文：
Yeah. We've always had a lot of competition for talent. It might feel more pronounced right now, but we have great talent on the team. Maybe that goes without saying, but I feel like I should say it out loud because I believe it. We have incredible talent at Netflix, recent hires, long tenured people. I'm always impressed by the work that the team is doing. So, I don't feel like we've suffered or other companies are vacuuming up all the good people, because so many of them I do think sit at Netflix. It does feel like we have to be more explicit about the types of people and talent that tend to thrive at Netflix versus other companies, like some of the Frontier Labs. So, people at Netflix have to be passionate about the application of technology and the application or building products to solve a certain set of problems. You have to love entertainment. You have to love consumer products at scale. You have to love the global nature of that. There are a lot of incredibly talented people who love that sweet spot, I am one of them, between tech and product and entertainment. And how do you make those things come together in a way that's remarkable? And you use AI to do it. You use other technologies and products to do it, but that has to be something that drives you to be really excited about a lot of the roles at Netflix. If instead you're inspired 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... 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 gets me up in the morning and I think it gets a lot of the team members up. And we have this conversation about 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.
中文翻译：
我们一直面临人才竞争，现在可能感觉更明显，但团队里确实有很多优秀人才。也许这是不言自明的，但我还是想明确说出来，因为我相信这一点：Netflix 有出色的人才，有新加入的，也有服务很久的人。我总是对团队正在做的工作印象深刻。
所以我不觉得我们受到了伤害，也不觉得其他公司把所有好人才都吸走了，因为很多优秀人才仍然在 Netflix。我们现在更需要明确说明：什么类型的人更容易在 Netflix 蓬勃发展，而不是在某些 Frontier Labs。
Netflix 的人必须热爱技术的应用，也必须热爱用产品解决某一组问题；必须喜欢娱乐，喜欢大规模消费产品，也要喜欢它的全球性。有很多极其优秀的人，热爱 tech、product 和 entertainment 的交叉地带——我就是其中一个——热爱思考如何把它们结合起来，产生令人惊叹的结果。你可以用 AI，也可以用其他技术和产品，但这必须是让你对 Netflix 许多岗位感到兴奋的东西。
如果你更受 frontier model 公司正在做的基础工作启发，那同样令人兴奋，但属于不同的 persona、不同的问题空间。我们并不缺少那些对技术应用极其兴奋、并能把技术和自己每天喜爱、使用的东西联系起来的人，比如 Netflix。这让我每天早上起床，也让很多团队成员起床。
我们会说，这是只有 Netflix 人才做得特别好的事情；其他深度应用技术的行业也可以把空白处填上。我认为这很有激励性。
52:54 | Lenny Rachitsky
英文原文：
I want to touch on a couple things that I've been thinking about in this world of AI that we're approaching. One is junior people. It feels like everyone's just like... This is a good example. You're hiring a lot of awesome senior people that have proven they're awesome and high talent, density, high bars. Also, just AI makes it so easy to do stuff that people may not be learning how to do anything. Like junior engineers, I'm thinking, or junior PMs, junior designers. How do new people become these awesome senior people? Is there anything you think about? Are you hiring junior people? How do you think about this? What happens with junior people not necessarily learning or having a path to learn to become the senior person?
中文翻译：
我想谈谈 AI 世界里我一直在思考的几件事。第一是初级人才。现在大家似乎都在说：你们招聘了很多已经证明自己很优秀、人才密度和标准都很高的资深人士；但 AI 让很多事情变得太容易，人们可能根本没学会做事。比如初级工程师、初级产品经理、初级设计师。新的人才怎样成长为优秀的资深人才？你们还招初级人员吗？如果初级人才没有学习机会或成长路径，会发生什么？
53:39 | Elizabeth Stone
英文原文：
We are still hiring junior people and they're really important to our talent strategy. So, we still have an intern program, we still have a new grad program, which was new for us as of a few years ago. So, prior to a few years ago, we were only hiring more experienced talent across all the functions. Now we do hire people straight from undergrad and graduate programs and we'll continue to do that. So, even in a world of AI where some things are easier, we were talking earlier about mindset, AI fluency. From my experience, younger folks are more open-minded. They tend to be more native in some of these new ways of working. For a company like Netflix, they're also very fluent in how entertainment is changing, how consumer behaviors are changing, how product and tech is influencing that in the products that they're using. That's really important to have on our team. So, there's the part of the persona, which is who are you as a new grad who's an engineer? But there's also, who are you as someone who's in their early twenties 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 five 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 is. And I think I mentioned this earlier, I find that mastery and that craft excellence scarce still. So, we want to make sure we're teaching that. I think it's a valid concern of, how do I get that if I'm not as hands-on as I would've 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. I think that's a very scarce skill to say, this is excellence in a product that solves a problem that matters and in how it's designed. So, I don't think that craft mastery, the importance of it is going away. Probably the way we train and grow talent has to change, because they're going to use different tools. And I can guarantee you that earlier career talent is going to be teaching older folks like me many new things, too. So, I think it goes in both directions.
中文翻译：
我们仍然招聘初级人才，他们对人才战略非常重要。我们仍有实习项目，也有几年前才建立的新毕业生项目。几年前以前，我们在所有职能上只招更有经验的人；现在我们会直接从本科和研究生项目招聘，并且会继续这样做。
即使在 AI 让一些事情变容易的世界里，心态和 AI fluency 仍然重要。以我的经验，年轻人更开放，也往往更自然地适应一些新的工作方式。对 Netflix 来说，他们还很了解娱乐如何变化、消费者行为如何变化，以及产品和技术如何影响他们正在使用的产品。这些对团队都很重要。
所以这里有两个层面：作为一名刚毕业的工程师，你是谁；以及作为一个二十岁出头的人，你对世界有什么看法、是否适应世界的变化。后者本身就很有价值。这就是为什么我说初级人才是人才战略的关键。
至于进入岗位后面对过去五到十年不存在的 AI 工具，我会说，对专业能力的掌握仍然非常重要。作为团队成员，我要为自己提交到生产环境的代码质量负责；要为自己构建的产品质量、设计方式和用户体验负责。这些都不会消失。
如果看团队里更初级、职业生涯更早期的人才，我们需要投入同样多的辅导，告诉他们什么是好的工作、怎样使用这些工具，同时仍然对结果和输出质量承担责任。我前面说过，我仍然认为这种掌握和 craft excellence 很稀缺，所以必须教会他们。
如果不再像过去那样亲手做那么多，怎样获得这些能力，确实是合理的担忧。但你仍要负责审查代码、测试代码、诊断问题，知道好的产品是什么样。能够判断「这是一款解决重要问题、设计出色的产品」，我认为是非常稀缺的能力。
所以 craft mastery 的重要性不会消失。人才培养方式可能必须改变，因为他们会使用不同的工具。我可以保证，职业早期的人才也会教会像我这样的年长者很多新东西。我认为这是双向的。
56:26 | Lenny Rachitsky
英文原文：
Where do you think engineering goes in, I don't know, five, 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?
中文翻译：
你觉得工程在五到十年后会走向哪里？人们还需要理解代码吗？还是说会出现一个抽象层，人们甚至不用学 C++、Java、Python 之类的语言？
56:42 | Elizabeth Stone
英文原文：
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 what is this thing that we're building and how does it work, so I know if it's good and I know how to fix it. I don't know. I hope that doesn't go away, because 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.
中文翻译：
我认为，能够用 Python 或 C++ 这样的特定语言写出代码行，和理解代码、计算机系统以及产品如何运作，是两件不同的事。后者不会消失。
如果我们把所有语言、所有代码都交给 agent，而人不知道为什么某个东西是好产品还是坏产品、不知道它在出问题时是否按预期运行，就会有问题。正如我前面说的，我们承担很多风险，快速失败，快速恢复；这要求我们理解这些系统如何工作。
我可以使用 agent 帮我理解这些事情，帮助我更快发现异常、识别故障和分流处理，但我仍需对正在构建的东西及其工作方式保持流畅理解，才能知道它是否好，也知道怎样修复。我希望这种能力不会消失，因为我们要通过构建的东西让世界变得更好，而这需要对所构建之物有一定理解。
57:46 | Lenny Rachitsky
英文原文：
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 have that instilled in you.
中文翻译：
我听到的意思是：你可能不必写代码，但必须理解代码和正在发生的事情。但一个人不亲自写代码，要让这种理解真正沉淀在自己身上，会难很多。
57:59 | Elizabeth Stone
英文原文：
I think that's one of the things that the learning curve is very steep on right now. So, looking at some of the code that some of these models or agents are writing, they're very hard to follow. It's like, I know I'm getting better performance from this, but I have no idea why. And if this thing breaks, I'm going to have no idea how to fix it. That makes me uncomfortable. Maybe that's because I'm still on that learning curve of, how do we operate in that world? 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.
中文翻译：
我认为这正是目前学习曲线非常陡峭的地方。看看某些模型或 agent 写出的代码，它们很难读懂。你可能知道它带来了更好的性能，却完全不知道为什么；如果它坏了，也完全不知道怎么修。这让我不舒服。
也许这是因为我自己还处在学习曲线上：我们如何在那个世界里运作？需要什么测试、推理和理解，才能真正适应它？但第一眼看上去，这些代码很陌生，也很令人不安。
我认为，随着时间推移，工程会演化出对这种方式的适应能力，会拥有相应的流畅度，知道怎样引导新技术、agent 和新能力，确保我们对输出结果真正有信心。
58:48 | Lenny Rachitsky
英文原文：
I wonder what the metaphor is for this where this... I continue to be astounded by how much engineering has transformed in two years. It's like a completely different dropdown. We used to sit there in NID and write code, and now you're just talking to agents and reviewing code and shipping 100 PRs a day.
中文翻译：
我在想，这件事的比喻是什么？过去两年工程变化之大，仍然让我惊叹。以前我们坐在 IDE 里写代码，现在只是和 agent 对话、审核代码，一天发布 100 个 PR。
59:05 | Elizabeth Stone
英文原文：
It feels like it's an acceleration of how much engineering has changed. But if you looked over the last 10 years or 20 years, you would say the same thing. So, 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 totally unfamiliar that engineering or data science or product would have these big shifts, just like how filmmaking works. If you look over the last 100 years, it's unbelievably different because of technology and new tools that we've brought to it. Just feels like the cycle is speeding up.
中文翻译：
这感觉像是工程变化速度加快了。但如果回看过去十年或二十年，你也会说同样的话。只是现在有什么东西移动得更快，我们很难理解过去几年变化得如此迅速。
不过，工程、数据科学或产品发生巨大转变，并不是完全陌生的事，电影制作也是如此。回看过去 100 年，由于技术和新工具，电影制作已经完全不同。只是现在这个周期正在加速。
59:46 | Lenny Rachitsky
英文原文：
Okay. I want to talk about entertainment for a brief moment. I'm curious just how entertainment will change over time in the next, I don't know, five, 10 years. Just today, we open up Netflix, check out some shows, watch some videos, it hasn't changed in a while. Just that idea of like, oh, I'm going to watch The Pitt and just watch it all. I'm going to watch a movie. I got TikTok, I got Instagram, feeds of stuff. How much different do you think this will be in, I don't know, five years, the way we entertain ourselves?
中文翻译：
好，我们短暂谈谈娱乐。我想知道未来五到十年，娱乐会如何变化。现在我们打开 Netflix，看几个节目、看几段视频；这种方式已经有一阵子没变了。比如「我要看《The Pitt》，一口气看完」「我要看一部电影」。同时我还有 TikTok、Instagram 的内容流。五年后，我们娱乐自己的方式会有多大不同？
60:00-70:00
60:15 | Elizabeth Stone
英文原文：
I think it's already changing at Netflix, because entertainment is not going to be one thing in the future and it's already not one thing now. So, part of the reason that we are going beyond film and TV in our offering is because there's an expectation that consumers have of much greater variety across formats, devices, moments of the day, that Netflix needs to be able to serve well in order to meet consumer expectations and hopefully exceed them over time. 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 or 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 listened 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 the challenge Netflix has is we've got to make discovery and engagement much easier than it feels today. We have tons of content, it can feel very fragmented, especially when you consider all the services or offerings out there. And I think Netflix is very well-positioned to understand how to solve that problem across entertainment, product and tech.
中文翻译：
Netflix 已经在改变了，因为未来的娱乐不会只有一种，现在也已经不止一种。我们扩大产品范围、超越影视，部分原因是消费者期待在不同格式、设备和一天中的不同时间里获得更多样化的体验；Netflix 必须很好地服务这些期待，并且希望不断超越它们。
所以，当我们想到手机和电视上的游戏、云游戏、直播内容、播客，以及如今加入 Netflix 服务的更广泛创作者，这些都会扩展「娱乐」的边界。Netflix 能够定义并拓展它，同时也提出更高要求：如何让 Netflix 会员理解这一切？
比如，你可以从收听 Bill Simmons 播客，无缝走到观看《Quarterback》——因为你喜欢这部更传统意义上的 Netflix 影视内容——再走到玩最新的 FIFA 云游戏。你可以在电视和手机上玩，因为此刻你在移动中；你可以在一天的不同时间发现、参与内容。
这已经是我们正在构建进 Netflix 的旅程，未来会越来越强。因此，娱乐的未来不会只有一个东西，而会更个性化、更沉浸、更互动。它会像一个可以从很多方向探索的世界，取决于你当下想要什么。
Netflix 的挑战是，让发现和参与比今天更容易。我们有大量内容，它可能让人感到碎片化，尤其当你把所有服务和产品都算进来时。我认为 Netflix 很适合解决横跨娱乐、产品和技术的这个问题。
62:19 | Lenny Rachitsky
英文原文：
The other element of this is AI, obviously. As an outside observer, it's 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.
中文翻译：
其中另一个显然是 AI。作为外部观察者，我觉得很有意思：科技圈的反应是「AI，我爱它，它是未来，是最好的东西」；好莱坞则是「不，关掉它」。
62:33 | Elizabeth Stone
英文原文：
There's a mix. There's a very wide array. So, Netflix's role in this is to enable creators with whatever tools they want to use to bring their vision to life. There are going to be some creators or filmmakers who are on the end of the spectrum that says, "Absolutely not. No AI. That is not how I do production. It's not consistent with my vision." That's fine. We work with those creators. There's other creators, a growing number of them I would say, who are very interested in exploring, wait, can these GenAI tools make something possible that wasn't possible before? Can I tell a story in a new way? Can I make that story higher quality and more resonant for audiences? Can I do things that are extra creative in how I think about bringing a story to life? And we support them as well and we support all the folks who are in the in between. I think 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, we only do this one way.
中文翻译：
实际情况是各种声音都有，范围非常广。Netflix 在这里的角色，是让创作者使用他们想要的工具，把愿景变成现实。
有些创作者或电影人会站在光谱的一端，说：「绝对不要。不要 AI。这不是我的制作方式，也不符合我的愿景。」没问题，我们会和这些创作者合作。
还有其他创作者，而且我认为人数正在增加，他们非常愿意探索：「等等，这些生成式 AI 工具能不能让以前做不到的事情变得可能？我能不能用新方式讲故事？能不能让故事质量更高、更能引起观众共鸣？能不能以更有创意的方式把故事带到现实？」我们也会支持他们，还会支持处在中间状态的所有人。
对我们来说，这是一个很重要的位置，因为娱乐不会只有一种形式。会有感觉传统的影视，也会有优秀创作者带来全新格式。Netflix 想参与其中，因此需要在工具、合作方式和创作者支持上保持灵活，而不是规定「我们只用一种方式」。
63:54 | Lenny Rachitsky
英文原文：
I think people are going to be surprised by just how good AI content is. Spencer Pratt's videos are just like... everyone's like, wow, this is entertaining. Obviously, AI, but it's so interesting. Do you think we'll get to a place where just whole TV shows are AI and people love it?
中文翻译：
我觉得人们会对 AI 内容最终能有多好感到惊讶。Spencer Pratt 的视频就很典型：大家会说「哇，这很有娱乐性」，明明知道是 AI，却还是很有意思。你认为我们会走到这样的阶段：整部电视剧都是 AI 做的，但人们仍然喜欢？
64:09 | Elizabeth Stone
英文原文：
I have a hard time picturing entertainment that doesn't have humans at the heart of it. So, that's humans in the creation of the storytelling, which I think is a scarce and valuable skill. Storytelling is one and the same with humanity and knowing what connects with people. So, I think humans will always be a core part or a critical part of the story. And I think watching characters on screen who don't have that humanity feels less compelling to me. And what the power of storytelling really is, to see another human and to watch how they perform a role or bring an emotion to life, that's such a human element. Will AI help to bring that to life? Will it 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.
中文翻译：
我很难想象娱乐可以没有人处在核心位置。这里的人包括故事创作中的人；我认为这是一种稀缺而有价值的能力。讲故事和人性、以及知道什么能与人连接，本来就是一回事。
所以我认为，人永远会是故事的核心或关键部分。看着屏幕上的角色，却感受不到那种人性，对我来说会缺少吸引力。故事真正强大的地方，是看见另一个人，看着他们扮演角色，或者把情感带到现实中，那是非常人性的元素。
AI 会帮助把这些东西变成现实吗？它会在某些制作中发挥重要作用，或者帮助作品获得某种外观和感觉吗？当然会。但我看不到一种让人类不再作为骨架的版本。
65:13 | Lenny Rachitsky
英文原文：
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 protection and then they ask for, tell me a story."
中文翻译：
有一句我觉得被错误地归给 Salman Rushdie 的话，大意是：「孩子出生后，最先要的是食物、水和保护；然后他会说，给我讲个故事。」
65:27 | Elizabeth Stone
英文原文：
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 to say storytelling wouldn't have that humanity at the center feels like something would be missing.
中文翻译：
从时间的起点开始，讲故事就是社区、社交网络、人类情感和连接的重要部分。所以我喜欢技术能够放大它，用非常新颖、令人兴奋的方式把它带到现实中。
但如果说讲故事不再以人性为中心，我会觉得少了某种东西。
65:57 | Lenny Rachitsky
英文原文：
We're going to see some wild shit over the years coming out of all this.
中文翻译：
这些东西未来几年会带来一些非常疯狂的结果。
66:00 | Elizabeth Stone
英文原文：
Oh, there's no question about that. And a lot of it could be very entertaining. 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.
中文翻译：
毫无疑问，其中很多会很有娱乐性，这一点我也不争论。但我认为它会有很广的范围，而 Netflix 需要站在塑造并实现这些可能性的中心，这就是我们的计划。
66:15 | Lenny Rachitsky
英文原文：
Amazing. Well, we covered a lot of ground, Elizabeth. Before we get to our very exciting lightning round, is there anything else that you wanted to share, leave listeners with, maybe double down on some things we've talked about?
中文翻译：
我们已经聊了很多，Elizabeth。在进入很令人期待的闪电问答前，还有什么想分享、留给听众，或者想再次强调的吗？
66:27 | Elizabeth Stone
英文原文：
It probably came across throughout, but I would underscore that this is a really exciting time to be building products in entertainment. Everything we talked about of what's changing in the tech and consumers and what is entertainment. We're at this unbelievable high velocity innovation period, so it's what keeps me at Netflix. I think it's a fun place to be. I would be missing something if I didn't reinforce that I think that's true. I also think that as an industry, we spend a lot of time sometimes talking about the pure tech or the capability and we sort of lose the forest for the trees. We're trying to build great consumer products that people love. We're trying to make great entertainment that people love and it's their favorite thing, that I don't want that to be lost 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.
中文翻译：
节目中应该已经反复说到了，但我想强调：现在是在娱乐领域构建产品的极好时机。技术、消费者和娱乐都在变化，我们正处于一个不可思议的高速创新阶段，这也是我继续留在 Netflix 的原因。我觉得这里很有趣，如果不强调这一点，反而会遗漏什么。
我还认为，作为一个行业，我们有时花很多时间讨论纯粹的技术或能力，结果只见树木、不见森林。我们要构建的是人们喜爱的消费产品，要做的是人们喜爱的、甚至最喜欢的娱乐。我不希望这些被丢在一边。
当然，底层有惊人的技术和产品工作，但最终最有激励性的事情是：我们为全世界的人带来了什么？
67:23 | Lenny Rachitsky
英文原文：
And along those lines, there's been such a... the opposite of glut and drought of a consumer, new consumer products, consumer experiences. There's very few success. Almost no consumer startup works 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. So, good job.
中文翻译：
沿着这个方向，市场上好像出现了消费产品和消费体验的反向「旱涝」——很少有新的消费产品真正成功，几乎没有消费创业公司能做成，而 AI 似乎给了其他东西成功的机会。我觉得 Netflix 是少数仍然持续交付出色消费产品和业务的公司与品牌之一，这样的公司真的不多。做得好。
67:48 | Elizabeth Stone
英文原文：
We're going to keep that up.
中文翻译：
我们会继续保持。
67:49 | Lenny Rachitsky
英文原文：
Well, with that, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?
中文翻译：
那么，我们进入非常激动人心的闪电问答。我有五个问题，你准备好了吗？
67:54 | Elizabeth Stone
英文原文：
Okay, I'm ready.
中文翻译：
好了，我准备好了。
67:56 | Lenny Rachitsky
英文原文：
All right. What are two or three books that you find yourself recommending most to other people?
中文翻译：
好。你最常向别人推荐的两三本书是什么？
68:02 | Elizabeth Stone
英文原文：
I have to come up with different books than I said last time.
中文翻译：
我得推荐一些上次没说过的书。
68:04 | Lenny Rachitsky
英文原文：
I don't remember what they were, but that sounds great.
中文翻译：
我不记得上次是什么，但这样很好。
68:06 | Elizabeth Stone
英文原文：
I still like a good throwback. So, two that are coming to my mind, Into Thin Air, Jon Krakauer and Liar's Poker, Michael Lewis. So, I worked on Wall Street and I like reminding people what it was like in the way back time.
中文翻译：
我仍然喜欢好的老书。现在想到两本：《Into Thin Air》，作者 Jon Krakauer；以及 Michael Lewis 的《Liar's Poker》。我在华尔街工作过，喜欢让人们回想很久以前那段时间是什么样。
68:23 | Lenny Rachitsky
英文原文：
Favorite recent movie or TV show you really enjoyed? Which is maybe too hard for someone working at Netflix, but just what comes up.
中文翻译：
最近特别喜欢的电影或电视剧是什么？对 Netflix 的人来说也许太难了，但你此刻想到什么就说什么。
68:29 | Elizabeth Stone
英文原文：
The list is very long. The most recent I watched, Remarkably Bright Creatures, after a recommendation from my mom. It's a tear-jerker. Talk about the human part of storytelling.
中文翻译：
片单太长了。最近看的是《Remarkably Bright Creatures》，是我妈妈推荐的。它让人流泪。说到讲故事中的人性，这就是一个例子。
68:41 | Lenny Rachitsky
英文原文：
Favorite product you've recently discovered that you really love?
中文翻译：
最近发现、而且非常喜欢的产品是什么？
68:44 | Elizabeth Stone
英文原文：
Critical for my health and wellbeing, Eight Sleep.
中文翻译：
对我的健康和身心状态至关重要：Eight Sleep。
68:48 | Lenny Rachitsky
英文原文：
Do you have a favorite life motto that you often come back to in work or in life?
中文翻译：
工作或生活中，你经常会回想的一句人生格言是什么？
68:53 | Elizabeth Stone
英文原文：
I often go back to the things that my parents instilled in me in very early times. So, 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.
中文翻译：
我常常回到父母很早就灌输给我的一些东西。可能说出来有重复的风险，但第一句是：「每天都会发生一些好事。留意它，即使在压力最大的时刻也一样。」第二句是：「最后 5% 的努力通常会带来全部的不同。」
69:17 | Lenny Rachitsky
英文原文：
These are awesome. They hit me. Final question. I don't know anything about this, but you mentioned you're doing some kind of cycling event.
中文翻译：
这两句很棒，也打动了我。最后一个问题。我对此一无所知，但你提到自己要参加某种自行车活动。
69:26 | Elizabeth Stone
英文原文：
Oh, yeah.
中文翻译：
对。
69:28 | Lenny Rachitsky
英文原文：
Tell us what's going on. What are you doing here?
中文翻译：
告诉我们具体是什么？你要做什么？
69:30 | Elizabeth Stone
英文原文：
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 three 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 a vacation after all.
中文翻译：
我和丈夫要去旅行，沿着环法自行车赛最后一周的路线骑行。环法持续三周，最后一周有很多山地赛段。我们每天早上骑一部分赛道，下午观看比赛。
这不是一件适合胆小者的事，所以我在努力训练，好让自己享受骑行。毕竟这应该是度假。
69:57 | Lenny Rachitsky
英文原文：
My God, I love this vacation. We're just going to race. So, is it like a race?
中文翻译：
天啊，我喜欢这种度假。我们就是去比赛。所以，这算比赛吗？
70:00-72:00
70:02 | Elizabeth Stone
英文原文：
I love cycling. I love professional sports. It's fun to be able to participate in it.
中文翻译：
我喜欢骑自行车，也喜欢职业体育。能够参与其中很有意思。
70:06 | Lenny Rachitsky
英文原文：
So, is this like racing or you just kind of try to go as nonchalantly through the course?
中文翻译：
所以这是在比赛，还是你们只是尽量不慌不忙地骑完整条路线？
70:11 | Elizabeth Stone
英文原文：
You go nonchalantly.
中文翻译：
你会不慌不忙地骑。
70:12 | Lenny Rachitsky
英文原文：
But still there [inaudible 01:10:13]?
中文翻译：
但还是会有 [听不清 01:10:13]？
70:12 | Elizabeth Stone
英文原文：
I think I mentioned... Yeah.
中文翻译：
我想我提到过……对。
70:12 | Lenny Rachitsky
英文原文：
Other hills.
中文翻译：
还有其他山坡。
70:15 | Elizabeth Stone
英文原文：
It's physically and mentally challenging. And 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.
中文翻译：
这在身体和精神上都很有挑战。它不是比赛，但我也不想落在队伍最后，所以必须达到足够舒服的状态，能够跟上大部队。
70:25 | Lenny Rachitsky
英文原文：
Wow. I love how different this is from your job. And it feels like someone [inaudible 01:10:29].
中文翻译：
哇，我喜欢这和你的工作多么不同。感觉就像有人 [听不清 01:10:29]。
70:30 | Elizabeth Stone
英文原文：
It's a good balance and it gets me outdoors and gives me some nice perspective. So, I'm looking forward to it.
中文翻译：
这是很好的平衡，也能让我到户外去，获得一些新的视角。所以我很期待。
70:36 | Lenny Rachitsky
英文原文：
Elizabeth, you're 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?
中文翻译：
Elizabeth，你太棒了。最后两个问题。如果听众想关注你、联系你，或者继续了解刚才提到的内容，可以在哪里找到你？他们怎样才能对你有帮助？
70:45 | Elizabeth Stone
英文原文：
The best place to find me and some of the work we're doing or reach out is the Netflix tech blog, actually, where we're putting a lot of things that I've been talking about up there. We're trying to do a better job communicating about the fun stuff we're working on. So, that's a good first stop, usually. And then how listeners can be useful? Try all the new stuff that we're putting out there. Watch the live events, play the games, have fun with the new vertical video feed that we have on mobile called Clips. Send us feedback. So, we want to make it better. And a lot of these things are new zero to one efforts for us, so we're trying to get to great and excellent as quickly as possible.
中文翻译：
最好的地方其实是 Netflix Tech Blog。我们会在那里发布很多刚才谈到的工作，也在努力更好地沟通那些有趣的事情。所以通常那里是很好的第一站。
听众可以怎样帮忙？试试我们推出的所有新东西。观看直播活动，玩游戏，体验手机上新的竖屏视频流 Clips，给我们反馈。我们想把它做得更好，而其中很多是 Netflix 从零到一的尝试，所以我们正在努力尽快达到优秀。
71:26 | Lenny Rachitsky
英文原文：
I love that the homework is go watch Netflix.
中文翻译：
我喜欢这份作业：去看 Netflix。
71:29 | Elizabeth Stone
英文原文：
You can also watch other things. Tell us how we can be better, but I'm definitely interested in how can we be better at Netflix.
中文翻译：
你也可以看其他东西。告诉我们怎样变得更好，但我确实很想知道 Netflix 怎样才能变得更好。
71:36 | Lenny Rachitsky
英文原文：
I love it. I'm going to go do that. Elizabeth, thank you so much for being here and being here again.
中文翻译：
我喜欢。我要去做。Elizabeth，非常感谢你来到节目，也感谢你再次来节目。
71:41 | Elizabeth Stone
英文原文：
Thank you for having me. Always fun.
中文翻译：
谢谢你邀请我。每次都很愉快。
71:43 | Lenny Rachitsky
英文原文：
Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.
中文翻译：
再见各位，感谢大家收听。如果你觉得这期节目有价值，欢迎在 Apple Podcasts、Spotify 或你常用的播客应用上订阅。也请考虑给节目评分或留下评论，这会帮助其他听众找到它。所有往期节目和更多信息都可以在 lennyspodcast.com 找到。我们下期再见。
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