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现在每个人都能做任何事。
Everyone can be everything now.
产品经理也能写代码。
PMs can ship code.
设计师也能写产品需求文档。
Designers can write PRDs.
工程师也能做产品。
Engineers can product.
大家现在都很困惑，不知道自己的工作到底该做什么。
And there's this confusion and frustration of what is my job anymore.
每当新技术出现，都会先经历一阵混乱，然后才慢慢找到方向。
Anytime a new technology comes along, you go through a storming phase before you go through the forming phase of things.
我们现在就正处于这个阶段。
We are in the middle of that right now.
我不觉得这意味着我们应该把AI关回笼子里，干脆不用了。
I don't think that means we should put AI back into the box and say, let's not use it.
如果我们每个人都能成为创造者，那还需要分不同的职能吗？
If we all become builders, will we still need separate functions?
我依然认为，精湛的技艺非常重要，而且短期内不会消失。
I still see a craft excellence that's really important that I don't think is going away anytime soon.
优秀工程师依然稀缺，出色的数据科学家稀缺，真正的创造力也稀缺。
I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce.
如果你看看奈飞早期的文化手册，强调高自主性、高度自由、市场顶尖薪酬——现在我听到的顶级AI实验室，不正是这样运作的吗？
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.
奈飞的文化一直把卓越当作一种运营系统。
Netflix's culture has always been excellence as an operating system.
它意味着拒绝去做那些大公司常做的事，而且能坦然接受这种不安定感。
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.
要实现这种状态，关键要素是什么？
What are the ingredients to make this happen?
人才密度是不可妥协的，还要能坦然面对风险。
Talent density is the non negotiable, being very comfortable with risk taking.
如果事情进展不顺，别以为流程就能解决一切。
In cases where things are not going well, not assume that process is gonna fix it.
在这个AI领域里，你在职业发展路径上增加了哪些新内容？
What have you added to the career ladders within this AI world?
我们需要更多系统思维型人才，能横跨各个业务领域，提炼出我们真正需要的基础模块。
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 gonna need.
那人们该怎么学会这种能力？
How do people learn this?
有个小技巧。
Small trick.
每当你面对一个问题时，往后退一步，想想你对整个大环境有哪些默认的假设。
Each problem you're trying to solve, step out one click to the what am I assuming is true about the broader space.
今天我们的嘉宾是Netflix的产品与技术负责人伊丽莎白·斯通。
Today, my guest is Elizabeth Stone, product and technology officer at Netflix.
这是伊丽莎白第二次做客这个播客。
This is Elizabeth's second visit to the podcast.
她第一次来做客时，还是首席技术官，那期节目至今仍是这个播客最受欢迎的之一。
Her first visit when she was just the CTO was, for the longest time, one of the most popular episodes of this podcast.
你很快就会明白为什么。
You'll soon see why.
这次对话太精彩了，因为两年前半前我们聊的时候，AI才刚起步。
This is such a killer conversation because when we chatted two and a half years ago, AI was only starting to emerge.
作为长期负责工程、产品和数据科学的负责人，伊丽莎白对行业走向和值得关注的方向有着独特的见解。
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.
在加入Netflix之前，伊丽莎白曾担任Lyft的科学副总裁、Nuna的首席运营官、The Analysis Group的经济学家，以及美林证券的交易员。
Prior to Netflix, Elizabeth was VP of science at Lyft, chief operating officer at Nuna, an economist at The Analysis Group, and a trader Merrill Lynch.
在开始之前，别忘了去Lenny's productcast.com注册，免费领取一整年独家提供给Lenny通讯订阅者的全球最热门、最精良的AI产品。
Before we get into it, don't forget to check out Lenny's productcast.com for an entire year free of the hottest and best crafted AI products in the world available exclusively to Lenny's newsletter subscribers.
接下来，有请伊丽莎白·斯通。
With that, I bring you Elizabeth Stone.
伊丽莎白，非常感谢你今天来到这里。
Elizabeth, thank you so much for being here.
欢迎回到我们的播客。
Welcome back to the podcast.
谢谢。
Thank you.
能再次受邀来做客，我感到非常荣幸。
I'm honored to be here once and now twice.
没错。
That's right.
这对我来说真是难得的殊荣。
That's a rare, a rare treat for me.
你可能不知道，你第一次来的时候，那一期成了我播放量第二高的节目。
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.
很长一段时间里，你仅次于布莱恩·切斯基。
You're right behind Brian Chesky for the longest time.
天哪。
Wow.
我既惊喜又有点不服气，想知道怎么才能冲到第一。
I I'm pleasantly surprised and also mildly competitive of how do I get to the first spot,
不过这个我们先放一放。
but I'll set that
好的。
aside for now.
是的。
Yeah.
这正是我们的机会。
That's this is our this is our shot.
布莱恩确实了不起，我就不再争这个了。
Brian's amazing, so I'll let that one go.
对。
Yeah.
他确实厉害，现在还有一大堆搞AI的新人正势头正猛呢。
He is and then there's just like all these fancy AI people that are just coming, you know, coming in hot.
到现在已经两年半了，变化真的很大。
So it's been two and a half years at this point, a lot's changed.
很明显，AI 让每个人都能做很多事情了。
Obviously AI, something AI is allowing people to do is everyone can kind of be everything now.
产品经理能写代码，设计师能写产品文档，工程师也能做产品，人人都能跨领域做事。
This idea of PMs can ship code, designers can write PRDs, and engineers can product, and everyone's everything.
这场讨论涉及好几个方面。
There's a bunch of elements of this conversation.
我听不少人说过，他们现在很困惑，也很沮丧，不知道自己的工作到底该做什么。
One is that I've heard from people that there's also this kind of confusion and frustration of, like, what is my job anymore?
作为产品经理，或者设计师，我到底该负责哪些事？
Like, what am I responsible for as a PM, as a designer?
你有这种感受吗？
Is that something you've experienced?
在奈飞，我确实经常听到这种说法。
I hear it within Netflix for sure.
我觉得，每当一种新技术出现，尤其是像生成式AI这样具有颠覆性的技术，总会经历一段混乱期，然后才慢慢进入有序阶段。
I think any time a new technology comes along, especially one that's as transformative as Gen AI, you go through a storming phase before you go through the forming phase of things.
而我们现在正处在这一阶段的中间。
And I think we are in the middle of that right now.
但这并不意味着我们应该把AI重新关回笼子里，说‘算了，别用了’，就因为它打乱了我们对各自角色的原有认知。
I don't think that means we should put AI back into the box and say, Let's not use it, because this complicating all of our preconceived notions about our roles.
但我确实认为，我们必须更谨慎地思考：如何在获得好处的同时，把代价降到最低。
But I do think it means we have to be much more thoughtful about how do we get the benefits while reducing the costs.
我觉得，大家去尝试如何更快地构思点子、快速原型、写出初步代码来验证想法，这是一件好事。
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?
但我真的认为，每个人都应该直接把代码部署到生产环境吗？
Do I believe that means anyone should be shipping code to production?
每个人都应该去干所有事情吗？
That everyone should actually be doing everything?
大概不用。
Probably not.
但我觉得让人们去探索可能性是好事。
But I think that it's good for people to be exploring what's possible.
就像我之前说的，产品和技术团队在一起的好处是，如果业务问题很明确，那么角色之间保持一定的灵活性是合理且健康的。
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.
因为这样就不必等工程团队准备好才能做原型，产品和设计可以更快推进，但还是要和工程伙伴一起思考：怎么把这个东西产品化，怎么扩展，该设哪些边界。
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.
所以我不认为专业能力会过时。
So I don't think it makes the functional expertise obsolete.
我觉得这意味着团队得更适应这种模式——它可能帮我们在某个方向上跑得更快。
I think it means that teams have to be more comfortable with maybe this helps us move faster in a certain direction.
从组织角度看，为了让这件事更清晰、减少挫败感，我们需要一些关键前提，才能获得收益而不是代价。
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.
比如：明确数据的权威来源，设定上线代码或重大改动前的测试规范，区分哪些场景可以信任AI的输出，哪些需要人工复核以确保质量，还要不断强调：最终责任仍在人类身上。
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.
就算代码是AI写的，或者我帮着做了个不熟悉的分析，也不代表就可以推卸责任——你创造的东西，你依然要负责。
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.
所以我觉得，投资于这些核心基础设施和实践，不断强调对结果的责任感，有助于平衡‘可能做到什么’和‘实际应该做什么’之间的关系。
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, like, what's possible with what we should actually be doing.
本集节目由本季的冠名赞助商WorkOS倾情呈现。
This episode is brought to you by our season's presenting sponsor, WorkOS.
OpenAI、Anthropic、Cursor、Vercel、Replit、Sierra、Clay，还有数百家成功公司，它们有什么共同点？
What do OpenAI, Anthropic, Cursor, Vercel, Replit, Sierra, Clay, and hundreds of other winning companies all have in common?
它们全都依赖WorkOS提供支持。
They are all powered by WorkOS.
如果你在开发面向企业的产品，一定经历过集成单点登录、SCIM、RBAC、审计日志等大型企业所需功能的痛苦。
If you're building a product for the enterprise, you've felt the pain of integrating single sign on, SCIM, RBAC, audit logs, and other features required by large companies.
WorkOS把这些阻碍交易的难题，变成了开箱即用的API，专为B2B SaaS打造的现代化开发者平台。
WorkOS turns those deal blockers into drop in APIs with a modern developer platform built specifically for b to b SaaS.
我投资的每一个初创公司，只要开始往高端市场拓展，最终都会选择WorkOS，因为它们确实是最棒的。
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.
无论你是刚起步、想拿下第一个企业客户的种子期初创公司，还是正在全球扩张的独角兽企业，WorkOS都是让你快速达到企业级标准、突破增长瓶颈的最快路径。
Whether you are a seed stage startup trying to land your first enterprise customer or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise ready and unblocking growth.
这本质上就是为企业级功能打造的 Stripe。
It's essentially Stripe for enterprise features.
访问 workos.com 开始使用吧，或者直接去他们的 Slack，那里有真正的工程师等着回答你的问题。
Visit workos.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions.
WorkOS 让你通过优雅的 API、详尽的文档和流畅的开发体验，更快地构建产品。
WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience.
现在就去 workos.com，让你的应用快速具备企业级能力。
Go to workos.com to make your app enterprise ready today.
你再次做客播客真的很棒，因为咱们上次聊的时候，AI 还没像现在这样彻底改变世界。
What's really awesome about having you back on the podcast is we chatted, like, before AI was a massive transformation in the world.
所以今天我们能聊聊这个有趣的转变，看看我们所有人经历了怎样的变化。
So it's a really cool arc that we can explore here, shift that we've all gone through.
回到产品和边缘团队的角色，我想知道过去两年半里，这些角色变化有多大。
Coming back to the roles of the product and edge teams, I'm curious how much these roles have changed in the last two and a half years.
如果从产品工程、设计、数据科学、用户研究这些角色来看，哪些变化最明显？
If you think about product engineering, design, data science, user research, which roles have changed most?
哪些角色变化最小？
Which roles have changed least?
过去两年半里，最不一样的地方是什么？
Like what's most different in the last two since two and a half years ago?
你已经提到一些了。
So you've mentioned some of the things.
我再复述一遍，然后稍微展开一下。
So I'll I'll reiterate them and then maybe build.
我发现，产品经理、设计师和数据科学家现在能在没有工程师深度参与的情况下，把产品开发推进得更远了，这跟几年前不一样。
So I have found that PMs, designers, data scientists are able to get farther in the product development life cycle before engineering really needs to be front of the line in unlocking things than was true a couple years ago.
我说这话时也留了点余地，因为就像我们之前说的，如果成千上万的原型都不是围绕真正重要的业务问题，那也没意义。
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.
工程团队也清楚，我们是在解决一个关键问题，设计师和产品经理会率先推动想法的成型。
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.
但这不是闭门造车，也不是盲目试错，看哪个能碰巧成功。
But it's not working in a vacuum, and it's not throwing a bunch of spaghetti at the wall to see what sticks.
但当问题选对了，就要认真对待，确保大家方向一致。
But when it's the right problem, approach in a thoughtful way with some alignment on that.
我看到产品、设计和数据科学团队现在更倾向于快速做出可验证的原型来测试假设。
I've seen product design data science move faster in the direction of let's get to something that's testable on this hypothesis.
这其实就是做原型、写代码的过程。
So that's prototyping, that's writing code.
另一件我觉得特别有价值的事是，Netflix内部积累了大量信息。
The other thing I've seen as being very valuable is we have a lot of information running around in the virtual walls of Netflix.
我们几十年来做过无数实验，收集了大量用户洞察，也从各业务部门获得了反馈，过去最大的挑战是如何充分利用这些长期积累的知识，来判断当前的问题哪些路径有潜力，哪些经验可以直接借鉴。
We have experiments we've run over decades, we have insights from consumers, we have input from stakeholders across the business, and that was a problem that really presented a challenge of like, how do we get the most out of that long history of knowledge and learnings to say, let's apply that to the problem we've got now to move faster in this is a promising path, or this is something that we've learned something about and we could leverage here.
AI 在提炼信息、分析海量数据、提炼核心洞察方面能力非常强。
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.
我不建议完全依赖它，但确实能帮我们快速起步。
I would hesitate to rely on that exclusively, but I think it's a head start.
就连我每天的工作中，也不用再发邮件打扰别人问：‘我们哪年做过什么研究？当时想解决什么问题？做了什么测试？’——现在几乎能瞬间找到答案。
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.
然后我就能自己整理出想法，看看这里面有什么值得关注的，直接跳过几步，去判断有没有什么可以行动的地方。
Then I can form my own, here's what I find interesting about this, and I have now skipped a couple steps towards, is there something actionable here?
这其实就是数据分析、建模和信息提炼，我注意到越来越多的人开始这么做，回应你最初的问题。
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.
现在不再只是那些待了二十年、见过所有实验或知道去哪里找资料的专家才能做这些事了，产品和技术团队的各个职能都能更快地做到。
So instead of that needing to be only the experts who were here for twenty 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.
对我们来说，更大的突破是，财务、内容和广告等业务方也能自己做这些。
And a big unlock for us is our business stakeholders sitting in finance and content and advertising can do that as well.
然后他们带着初步的假设回来，再和数据科学家、工程师们深入合作。
And then bring back an initial hypothesis where they want to work more deeply with the data scientists and engineers and so on.
关于提出假设、快速原型、深入思考问题这些环节，感觉正在加速，而且各个部门都能更灵活地参与进来。
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.
但我还是能看到各自的优势。
But I still see comparative strengths.
数据科学家依然最擅长判断：这个数据靠不靠谱？
So data scientists are still going to be experts at can we trust this data?
我们这样理解对吗？
Are we interpreting it the right way?
这里该用数据，还是该靠判断？
What's the data versus judgment that we should be applying here?
产品经理依然擅长问：我们有没有真正把要解决的问题定义清楚？
A product manager is still going to be exceptional at saying, have we really framed the what of this, like the problem we're solving in the right way?
工程师对‘怎么做’依然有他们的专长。
An engineer still has a craft around the how.
这个方案能扩展到多大规模？
How does this scale?
什么叫高质量？
What does high quality look like?
按照我们现在的设计和部署方式，会带来哪些新问题？
What problems is this gonna create for us based on how we build and deploy something?
所以，我还是能看到这些各自独特的优势。
So I still see the nuggets of that comparative advantage.
只是我们现在能更顺畅地推进很多以往总会遇到阻碍的环节。
It's just that we're able to move more fluidly in a lot of steps that normally we would have blockers on.
这里头有太多有意思的东西了。
There's so much interesting stuff here.
你刚才提到的最后一点，其实我也一直在想。
One is this last point you made, something I've been thinking about.
如果我们每个人都变成建设者，那还需要专门的职能分工吗？
If we all become builders, will we still need separate functions?
在AI部门，现在流行一种技术专家的趋势——没有固定头衔，你可以做任何事，不用非得被归到某个类别里。
There's this like member of technical staff trend that is happening for us at AI where it's like, okay, we don't have a title, you could be anything, you don't have to be in a bucket.
你刚才的意思是，即使大家会更多地跨职能协作，产品、工程、数据科学、设计这些专业角色依然有不可替代的价值，我理解得对吗？
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, tell me if I'm hearing you correct, in having this specific discipline and skill and background.
我依然认为，各个专业领域里的技艺精进非常重要，这种价值短期内不会消失，哪怕工作边界变得模糊了。
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.
这又回到我之前说的：终究还是得有人来确保我们做的事是合理的。
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.
我们正在以最符合Netflix用户和业务利益相关者需求的方式解决正确的问题。
We're solving the right problems in a way that is best for Netflix members or business stakeholders.
如果我跟工程师、数据科学家或设计师聊，他们会发现，现在他们掌握的语言比以前多得多，这得益于AI工具的帮助。
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.
但当我想到这种专业技艺，以及他们对‘什么是优秀’的理解时，依然有些东西是无法被取代的。
But there's still something that is not replaceable when I think about the craft and how they think about what good looks like.
这种感觉在各个层级上都是一样的。
And that feels true across all levels.
我依然觉得顶尖的工程能力很稀缺，出色的数据科学能力很稀缺，真正的创造力也很稀缺。
And, you know, I still find great engineering to be scarce, great data science to be scarce, great creativity to be scarce.
所以，是的，有些事情确实变简单了，但在我的认知里，这些核心价值并没有消失。
So, yes, some things are easier, but that hasn't dissolved in my mind.
随着AI工具和大语言模型的兴起，你们有没有发现某些岗位的招聘需求在增加，比如工程、产品管理或设计，而另一些岗位的需求在减少？
Are there functions that you are finding you are hiring more of, like the pie chart pie expanding, say for engineering or PM or design or something, and then functions you need less of with AI tooling and LLMs rising?
我不确定这能完全对应到具体职能上，但我可以告诉你，我们现在更需要、也看到更多的是哪些方面。
I'm not sure that it matches exactly to functions, but I can tell you what we're having, we're seeing more of, we need more of.
在人工智能时代，我们需要更多具备系统思维的人。
We need more systems thinkers in a world with AI.
不同部门的表现形式可能不一样，但我可以举几个例子。
That looks a little bit different across functions, but I could play out a couple examples.
在Netflix的核心基础设施团队和中央工程部门，过去让我们成功的关键是，各个业务团队能快速响应具体问题，自行推进解决方案。
So in our core infrastructure team at Netflix and central engineering, a lot of what made Netflix successful over time was that local teams with specific business problems could move fast to deliver.
他们通常并不觉得必须走中央统一规划的路子。
They very often were not feeling like they needed to be on a central paved path.
他们自己搭建所需的系统，直接解决问题、产生影响。
They built the stack that they needed to solve the problem and have the impact.
但在人工智能时代，智能代理跨多个系统运作，需要统一的数据源，这时候，建立一套被广泛采纳的标准化路径就变得尤为重要——这样既能最大化效益，又能设置必要的边界，确保我们做的工作是可靠的。通用的基础设施、统一的路径，把核心能力一次性做好、复用起来，变得比以往更关键。
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.
所以我们正在招聘更多能横跨各个业务领域、从中提炼出人工智能时代所需基础模块的人才。这是其中一个视角，同时我们也意识到，过去让Netflix成功的模式，未必能带我们走向未来。
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.
为了在未来的环境中保持敏捷，我们必须打造更强大的基础设施。
And we're going to have to have a stronger set of infrastructure to move quickly in this future.
所以，工程师的定位更偏向分布式系统、基础设施，以及系统思维，而不是单纯的业务领域专长。
So that means that engineering profiles are more distributed systems, more infrastructure, more of that system thinking mindset than a local business expertise.
当然，我们依然保留着在个性化、广告和内容分发方面非常深入的专家。
Though, of course, we still have people who are deep in personalization and advertising and content delivery.
对我们来说，核心基础设施和系统思维更像是在原有基础上的补充。
So it's more something additive for us to have that core infrastructure and systems thinking.
再拿设计来说，我们的体验设计团队必须打造模板和系统化的思维，明确Netflix应有的优秀用户设计标准，这样才能让大量非专业设计师也能开发出风格统一、融入整体用户体验的产品。
If I take another example, like design, it's extremely important that our experience design team is developing templates and again, systems thinking for what does great user design look like at Netflix so that they can enable lots of people, including those who are not designers by training, to develop products that are coherent, that fit into the end to end member experience.
如果任由设计语言和用户交互方式各自为政，最后搞出一堆拼凑起来的怪东西，我真会坐立不安。
I get really nervous about having different design languages or different types of user interactions and shipping Frankensteins, basically.
所以，我们在招聘设计师时，同样看重系统思维能力。
So designers need to then be the people we're hiring, Again, for design, systems thinking.
我们该怎么思考模板、品牌表达，以及什么是好的用户体验？
How do we think about templates and expression of the brand and what a good user experience looks like?
Netflix到底应该是什么样子？
And what is Netflix?
这就是Netflix独有的独特优势。
And like the Netflix differentiated special sauce.
现在我们设计团队里，需要具备这种思维方式的人比以前更多了，不再只是专注于为某个具体产品设计某个功能。
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?
每个部门现在都在往宏观视角转变，这要求我们重新调整现有团队的技能，同时也要招聘具备这种能力的人才。
So there's this stepping back to look at the big picture that I think is happening in every single function, and that requires some, yeah, reorientation of skills among the existing team and also hiring people who've got that type of expertise.
归根结底，这是一种思维模式的转变。
And across all of it, it's a mindset shift.
我们不会招那些不乐于探索、不愿尝试新事物、无法接受快速变化和模糊性的人，也不能接受对工作方式和协作边界日益模糊感到不适的人。
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.
无论是对已经在Netflix的员工，还是新加入的成员，这种好奇与创新的思维比以往任何时候都更加重要——至少在我从事这个领域的这段时间里是如此。
That's true for people who are already at Netflix and people who we are adding to the team, that that curiosity innovation mindset has not it's not been more important, at least in the time that I've been working in this field.
说到系统思维，它现在变得越来越重要，是因为节奏太快了，我们必须投入平台、框架和设计语言，教会大家举一反三，避免卡壳？还是有别的原因？
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 phish so they can not be blocked, or are there other reasons?
我觉得主要是速度的问题。
I think it's probably velocity.
平台确实能带来杠杆效应。
So platforms do have a benefit of leverage.
总的来说，无论有没有AI，平台都能让大多数团队先完成80%的工作，省得他们重新造轮子。
So in general, that that's an opportunity with or without AI for a platform to get most teams 80% of the way there, and then they don't have to reinvent those building blocks.
我们公司在各个业务板块上的尝试越来越多，要开发的东西也更丰富，所以平台思维特别重要。
We have more bets that we're making across the business, more things we're trying to build, so platform mindsets are good.
对于Netflix来说，把平台当作关键支撑，这种想法其实还比较新。
And it's something that is relatively more recent for Netflix to think about that being a real critical enabler.
在AI时代，还需要一种脚手架式的支撑结构。
There is also the sense of a scaffolding in a world of AI.
不只是速度变快了，还有更多人从事着各种新类型、不同以往的工作，就像我们之前说的。
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.
在这种情况下，怎么考虑权限和身份管理，是个值得思考的问题。
And there's risk that comes with how do you think about access and identity in that situation?
那安全性又该怎么保障呢？
How do you think about security in that situation?
你怎么看待交付高质量的代码、设计和用户体验？
How do you think about shipping high quality code and design and user experiences?
我不觉得让每个开发者都得自己去琢磨‘什么是好的标准’、‘该注意哪些边界和限制’能走得长远。
And so I I don't think it scales well to have each person who's building something have to go figure out, could you remind me what good looks like here and what are the bumpers or guardrails I should keep in mind?
我们得把这些规范融入到我们的标准流程和工作方式里。
I think we need to in encode that in our paved paths and our ways of working.
比如在数据科学和分析领域，要明确告诉团队：数据源在哪里、该怎么解读、如何访问、该用还是不该用，以及哪些类型的数据要特别小心。
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.
一个拥有数千人的组织，不能再靠‘大家都知道’或者‘去找那个懂的人’来解决问题。
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.
这个问题在AI出现之前就已经存在了。
So this was a challenge that was there before AI.
但有了AI之后，这个问题变得更紧迫了。
It's probably a more urgent challenge with AI.
我喜欢用AI或者其他新技术来推动改变——我们其实早就知道该做这些事了，现在正是加倍投入的好时机。
And I like the idea of using AI or any new tech to motivate, like we knew this is work we needed to do, no time like the present to invest in that more heavily across the team.
我觉得另一个原因是，现在智能代理承担了大量工作，给它们提供上下文、框架和设计规范，能大大提升效率。
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.
确实，奈飞的一个愿景是，未来会有大量智能代理参与工作，因此你必须能够对整个流程进行推理和判断。
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.
人类负责引导方向，明确我们到底要解决什么问题。
You know, the humans are the ones guiding, what's the problem we need to solve?
我们产出的东西是否真正有影响力、质量够高？
Do I feel like what we're producing is impactful and high quality output?
但具体的工作会由人类和智能代理共同完成。
But the work will be done by both humans and agents.
这既带来了效率和优势，也带来了风险。
And that creates velocity and benefits and it creates risks.
我觉得这一点特别重要，尤其是从工程角度来说，我们必须找到一种方式，既能让人快速推进，又不会给公司带来不必要的风险。
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.
这和珍妮·文之前在播客里说的完全契合。
This connects so directly with Jenny Wen was on the podcast.
她曾负责Claude Code和Cowork的设计，提出过一个‘设计流程已死’的观点。
She was head of design for Claude Code and Cowork, and had this whole design process is dead kind of thesis.
她的意思是，现在没时间搞传统设计流程了，设计师更多是引导团队、指明方向、随时调整，有空的时候再想大局。
The pitch there is just there's no time for design, the design process, and instead as a designer, you're just kind of steering people and pointing them in the direction and adjusting, and also thinking big picture when you have the time.
这跟你说的很像——先搭建好平台让人快速推进，就没时间为每个新功能走一遍完整的设计流程了。
And it feels like that's kind of what you're describing here is like create the platform for people to move fast, then there's no time for like design process of a specific new feature.
我对这个说法有点矛盾，因为我们还是希望借助基础设施和系统思维，让更多人做出有优秀设计的作品。
I have mixed feelings about that because I we do wanna enable with infrastructure and systems thinking more people to do great work with strong design as part of it.
既然新技术带来了这个机会，为什么不充分利用呢？
Why not take that opportunity that the new tech provides?
但对于我们最重要的任务，设计依然是解决问题的关键。
But for our most important priorities, design is critical to solve things in the right way.
所以，我们依然会为重要的设计工作留出时间。
So we do still make time for important design work.
只是速度可以更快而已。
It can move faster.
设计师手里的工具更多了，能以更快的速度做出出色的作品，提供更多方案，更快地学习、迭代和测试。
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.
但我觉得，如果因为写代码和做数据分析变快了，就认为设计和深度设计思维被边缘化了，那可就错了。至少对于像Netflix这样的大型消费产品来说，我们一旦失去设计，就会丢掉让Netflix出众的关键之一——产品、技术和设计共同把复杂性藏起来，打造出无缝的用户体验。
But I think it would be a mistake to say design and deep design expertise and thinking gets squeezed out just because we can write code faster, we can do data analysis faster, that feels like, at least for a large scale consumer product like Netflix, I feel like we would lose one of the things that makes Netflix great, which is the product, technology, and design makes a lot of complexity invisible and makes for a seamless customer experience.
这种设计思维必须是核心的一部分。
That's a design mindset that has to be core to it.
工作方式可能会变，但我认为这种思维不会消失。
So the work itself might look different, but I don't think we lose the mindset.
这个反驳太精彩了。
That's an awesome counterpoint.
所以我听到的是，我们越来越看重那些技能和特质：系统思维，以及对变化和未来保持开放、兴奋的态度，而不是固守旧方法。
So what I'm hearing is kind of trending up skills, attributes you look for, systems thinking, and this kind of mindset of being comfortable and excited about change and what's coming and not being stuck in your own ways.
那你觉得哪些东西在走下坡路呢？
What are you finding is trending down?
以前你很看重，但现在不太在意了？
What are you less looking for that you used to value more highly?
我觉得那种非常狭窄、深度专精的时代已经越来越受限了。
The days of very narrow, deep specialization feel more limited to me.
我还能举出一些例子，说明我们依然需要这种专精，比如某些行业或技术领域，全世界真正懂的人寥寥无几。
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.
我们团队里就有这样的例子，比如编码或者播放系统的工作原理，这些都曾为奈飞带来过非常创新和独特的突破。
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.
我依然相信，这些领域需要专业的技术人员。
I still believe we need specialized practitioners in those spaces.
但总的来说，跟五年前或十年前相比，我觉得我们现在的专才少了，更多人是通才，能灵活应对多种方向。
But as a general rule, compared to five or ten years ago, I would believe we have fewer specialists and more people who are generalist or adaptable in multiple directions.
这种灵活性可以体现在不同职能领域之间。
And that could be adaptable across functional expertise.
也可以体现在不同类型的工程工作之间。
It could be adaptable across flavors of engineering.
比如，我能不能同时驾驭后端和前端系统？
So can I navigate both back end and front end systems?
我能不能借助那些拥有深厚专业知识的基础设施？
Can I hook into infrastructure with a lot of expertise?
我觉得现在的思维方式应该是：我能快速学会这些，这又回到了系统思维的问题上。
I think the mindset now needs to be, I can learn that quickly, and that goes back to the systems thinking.
所以我认为，比起过去，专家们更容易掌握更广泛的技术工具。
So I think specialists can learn to have a broader array of tools more easily than was true in the past.
因此，我们可能需要更少的专家，因为人才现在能朝这个方向成长。
So we need fewer of them perhaps because talent's able to grow in that direction.
但一味坚持狭窄的专业领域，让我隐隐担心：这会不会限制了人们探索不同方向、持续成长的思维？
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?
我也不想把自己的看法限定得太死，但重要的是，即使是专家，也该保持一种心态：我想尝试新的解决问题的方式，而不是一味沿用过去的老办法。
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.
当你提到专家时，是指前端这样的角色吗？
And when you say specialist, are you thinking like front end?
比如前端工程师和后端工程师的区别，还是说还有其他类型？
I'm a front end engineer versus a back end, or are there other
或者可能是某个领域内的专业知识，比如我是支付领域的专家。
Or versions it could be a domain set of knowledge of, yeah, Like I'm a deep payments expert.
我是支付领域的专家。
I'm a payments expert.
我是广告交易平台设计方面的专家。
I'm an ads marketplace design expert.
我精通工作室制作所使用的这套特定工具。
I am an expert in this very specific tooling that studio productions use.
他们的专业技能和领域专长是有优势的，前提是这个人愿意成长，去思考：这真的还是解决这个问题的最佳工具或方式吗？
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?
从工程角度看，我觉得技术栈的层级越来越不需要那么专精，而那些不太可能保持不变、或缺乏变革动力的工具也是如此。
So I think it's the layers of the stack from an engineering perspective that there's less specialty, and then tools that are unlikely to be static or like to have a lot of inertia around them.
我希望看到的是能够创新、想象未来版本的人，所以我们更需要这类人才。
I would think like we would want people who are able to innovate and imagine like what's the future version of this, and so we want more talent like that.
太好了。
Awesome.
所以回到系统思维这个话题，听到这些的人可能会想：好吧，我得提升自己的系统思维能力。
So coming back to the systems thinking piece, people hearing this are like, okay, I gotta work on my systems thinking skill set.
那人们该怎么培养这种能力呢？
How do people develop the skill?
还是说，就是得长时间做下去，参与很多复杂的项目？
Other, is it just do it for a long time, work at a lot of complex projects?
我想起一本大家都常提到的书，封面上是个弹簧玩具，叫《系统思考》。
Like I think of this book that everyone always references with the slinky on the front, Thinking and Systems.
对，那人们到底该怎么学这个呢？
Yeah, how do people learn this?
有个小技巧。
Small trick.
每当你面对一个问题时，试着往后退一步，想想：在解决这个问题时，我对整个大环境有哪些默认的假设？
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?
我曾经接到一个任务，要为奈飞的会员体验开发一个新功能。
So I was given a task to build some new feature for the Netflix member experience.
让我停一下，想想我们真正要解决的用户核心问题是什么？
Let me take one beat and think about what is the bigger consumer problem we're trying to solve here?
这个功能打算支持什么样的内容类型？
What's the type of content that this feature is going to be able to support?
我原本计划的实现方式，能不能适应多种内容类型的扩展需求？
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?
或者它可能是一种能力，能被多个团队整合进平台的整体服务中。
Or it could be something that's a capability that then is contributed to a platform set of offerings from multiple areas.
我这个功能解决的用户问题，是不是Netflix在未来娱乐生态扩张、追求个性化和沉浸式体验时最需要解决的核心问题之一？
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?
这些问题其实不用把整个海洋都煮开。
Those are all questions that like, you don't have to boil the whole ocean.
你不需要去解决Netflix的整体战略，或者我们跟竞争对手相比定位如何。
You don't have to solve for Netflix's Netflix's overall strategy and who are we relative to competition.
你只需要聚焦自己负责的部分，把眼前的问题往上提一层，重新问一问自己。
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.
别在质疑阶段耗太久，不然就卡住了，没法往前推进。
I wouldn't spend too long in the questioning state because then you're stuck, then you're not making forward progress.
但我认为这能帮大家从系统角度思考，去问自己：我们是不是在用正确的方式解决真正对用户重要的问题。
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.
换种说法，就像你刚才说的，我想到的是，如果你是自己的经理，他们会怎么看？他们的视野不止于你这一团队、这个问题和KPI，而是更宏观的全局。
Another way, as you describe it, another way I'm thinking about it is like, if you were your manager, how would they, what's their broader perspective across not just your one team and problem and KPI, but the larger picture?
多年来我得到的建议和这个很像：我能不能用一种方式做好自己的工作，来帮经理更好地完成他们的工作？
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?
如果我把自己负责的所有事情，从经理的角度重新想一遍——不只是产品和技术，还包括财务、内容和其他业务板块——我自然就会跳出局部，思考这些部分怎么才能协同起来，让整体大于部分之和。
And so if I thought about all the things I'm directly responsible for, And but I thought about it from the perspective of my manager, so not just product and tech, but finance and content and other parts of the business, I would naturally zoom out and think about how all these component pieces need to come together and how the whole could be greater than the sum of the parts.
我觉得这种思维方式很有价值。
I think that's useful thinking.
对工程师来说，思考如何留下更完善的系统、如何打造高质量且能扩展的方案，这不仅关乎帮经理，也关乎帮同事——这正是我们工程原则的核心：做对整个组织有利的事，而不是只顾自己的一亩三分地，这同样是系统思维。
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 colleague, 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.
所以这不光是资历问题，更是你解决问题和构建方案的广度：你的方案对同事有没有用？你留下的东西，能不能为未来的新创新打下更好的基础？
So it's not just seniority, but it's breadth of the way I solve this problem and I build this, is it going to be useful to my colleagues, and am I going to leave a stronger version of things for the future set of innovations that we want to make?
这真是非常实用的建议。
That is awesome tactical advice.
让上司过得轻松点，从职业发展角度看总是没错的。
Making your manager's life easier is always a good tactic, career wise.
有几个原因，是的。
Several reasons, yeah.
顺着这个话题说一下，我知道你们最近刚推出了职业发展通道和级别，以前是没有这些的。
Following the thread a little bit, I know you all added career ladders and levels recently, was like a new thing, you used to not have these things.
所以围绕这个方向，你们在AI领域给职业通道增加了哪些新内容？是希望员工更关注哪些方面，还是说没怎么改？
So kind of all on that thread, what have you added to the career ladders within this AI world, if anything, that you find you want people to lean into more, you're looking to more, or or not?
你们没调整职业通道和绩效评估的标准吗？
Like, did you not change your career ladders and performance, you know, criteria?
到目前为止，我们的做法不是在每个级别上具体说明AI如何改变要求，而是为Netflix所有员工和招聘团队设置了一个统一的期望——AI素养。
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.
但不同岗位对它的具体表现形式会不一样。
And what that looks like is going to vary by function.
这会根据你职业生涯所处的阶段有所不同。
It's going to vary based on where you are in your career.
可能取决于你的职级、岗位类型，或者你负责的工作内容。
That that could be what level you're in or what type of role or persona work you're doing.
但我们都希望达到一种对AI的熟练运用，这一点其实很难明确定义。
But the aspiration for AI fluency, which is a tough thing to define.
那是不是意味着你要有探索和实验的心态？
So does it mean that I have an experimentation mindset?
还是说你要清楚AI在哪些地方有用、哪些地方没用？
Does it mean that I know where AI is useful and not useful?
又或者意味着你真的用AI亲手做过一些东西？
Does it mean that I've actually built things using AI?
我觉得，关于AI熟练度在职业发展体系中的体现，以及我们如何谈论它，几乎每个季度、甚至每个月、每天都在变化，因为技术本身进步得太快了。
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.
所以最有用的做法，不是把它细化到每个职级或岗位，而是鼓励所有人朝着‘AI熟练度’这个方向努力——这不意味着为了用技术而用技术，而是要在真正有用的地方合理使用，并具备判断力，同时保持开放心态，愿意尝试新事物。
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.
这是所有岗位都必须具备的基本要求。
That's the non negotiable for all roles.
就连奈飞最高层的职位也是如此，即使我们日常不写代码，也必须对AI有深入的理解。
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.
这一点已经发生了变化。
So that's changed.
这种变化也体现在我们的招聘流程中——面试时，我们会关注候选人如何思考AI和技术，他们平时用什么工具，对变化和探索的适应能力如何。
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.
甚至在编程面试中，我们也允许候选人使用AI工具，因为这已经是工作中不可或缺的一部分。
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.
这些确实是我们做出的调整，但我认为这远未结束，我们正处在变革的中心。
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.
我打算继续沿着这个思路聊下去。
I'm just going to keep following this thread.
显然，AI对编程带来了颠覆性的影响。
Obviously AI is transformative for coding.
这对快速原型开发来说是个巨大的突破。
It's a big unlock for prototyping.
Netflix 还有哪些 AI 应用场景是大家可能没注意到或者没想到的？
Are there other use cases of AI at Netflix that have been really impactful that people may not think about or not realize?
我想到两个例子。
So there's two that come to mind.
第一个是数据分析和信息提炼，也就是用这些工具来梳理我们手头的所有洞察，跟之前说的差不多。
So the first is data analysis, distillation of information modeling, which is, you know, using the tools to get our arms around all the insights we have, similar to what I mentioned before.
我们做过哪些实验？
What experiments have we run?
针对某个问题，我该关注哪些关键指标？
What are the metrics that I should be looking at for a certain problem?
我们做过哪些用户研究？
What's the consumer research that we've done?
这些工作速度更快、质量更高，但前提是你要确认结果可靠，跟身边的数据科学家确认：我用的是不是权威数据源？
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?
但这一点真的特别有用。
But that's been a great one.
就我个人而言，我用这些工具最多的就是这一块。
And that's one personally that I would say I most use some of these tools for.
这已经超出了原型设计和编程，延伸到了通用的分析思维，以及把数据转化为行动和洞察。
So that goes beyond prototyping and coding to general analytical thinking and translating data to action and insight.
另一个方面是在内容制作环节，应用场景非常多。
The other one is on the content production creation part of the business, which has lots of applications.
在生成式AI出现之前就已经是这样了。
This was true before Gen AI.
当时机器学习和人工智能就已经深度应用于很多制作工具了。
So ML and AI were deeply used in a lot of the production tools.
我们用它们来大规模制作宣传素材，还有字幕和配音的本地化工作。
We've used them to think about how to create promotional assets at scale, how to localize in subtitles and dubs.
生成式AI让创意构思方面的影响力实现了质的飞跃。
So Gen AI is a big step function in where the impact can be in creative ideation.
我们把这些叫做前期预演，简单说就是在正式开拍、召集团队之前，先把创作者的构想真实地呈现出来。
We call those things like pre visualization or basically bringing a creator's vision to life before you even get into the you bring people to a set and start to actually go through the production itself.
后期制作中也有很多应用场景。
There's lots of use cases in post production.
我们最近收购了一家公司叫InterPositive，由本·阿弗莱克创办，他们开发了一套模型和功能，让你在拍摄完成后还能重新打光、调整构图、重拍镜头，甚至修改对白，这些改动能显著提升内容质量，但始终由导演或创作者主导——他们会说：‘我想试试别的办法，让这个构想更完整。’
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 or creator saying, You know what, I would like to try something else to bring this vision to life.
这种影响非常有前景，我们看到很多制作项目都在使用各种工具，有些是内部开发的，有些则是通过其他供应商提供的，专门用于内容创作场景。
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.
再说到内容如何对接产品，我之前提到过本地化、字幕和配音，还有如何大规模生成高质量的预告片、图片和视觉素材，这些都能帮助作品在全球范围内精准触达观众，这些都是AI带来的重要杠杆。
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.
所以，这远远超出了原型设计或编程的范畴，真正进入了创意应用的领域。
So that, again, goes well beyond prototyping or coding to some of the creative use cases.
你可以想象，这些技术不仅适用于电影和电视剧的制作，也适用于广告、营销和品牌推广活动，这些正是我们正在探索的方向。
And you can imagine that just like they work for studio productions for film and TV, they work for advertising, they work for marketing, off service campaigns, and so those are all areas that we're exploring.
本集由Mercury赞助播出，这是一家深受三十多万创业者喜爱的全新银行服务，现在更推出了Command功能。
This episode is brought to you by Mercury, radically different banking loved by over 300,000 entrepreneurs, and now with Command.
我已经是Mercury的客户六年多了。
I've been a customer of Mercury's for over six years.
我从来没想过要换银行。
I have never once thought about leaving.
Mercury就是当银行由产品经理而不是银行家来打造时的样子。
Mercury is basically what happens when banking is built by product people, not by bankers.
他们让开票、转账、给团队成员设置虚拟卡片变得特别简单，甚至可以说很有趣。
They make it so easy, dare I say fun, to send invoices, move money around, set up virtual cards for folks on my team.
你的银行有API、原生命令行工具，或者支持AI的MCP服务器吗？
Does your bank have an API, a terminal native CLI, or an AI ready MCP server?
我觉得没有。
I don't think so.
最近，他们推出了Command，一个直接内置于Mercury中的对话式界面，相当于你的财务助手。
And just recently, they launched Command, a conversational interface built directly into Mercury, which acts as your financial operator.
我一直在用Command来转账、分析我花得最多的类别、查看现金流，就在今天，我还用它查了过去一年从某个赞助商那里赚了多少钱。
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.
我只要问一句：过去一年我从X那里赚了多少钱？
I just ask, how much have I made from X over the past year?
十秒钟后，答案就出来了。
Ten seconds later I have an answer.
这简直太酷了。
It is so freaking cool.
访问 mercury.com 了解更多，几分钟内即可在线申请。
Visit mercury.com to learn more and apply online in minutes.
Mercury 是一家金融科技公司，并非 FDIC 保险银行，银行业务由 Choice Financial Group 和 column NA 提供，两家均为 FDIC 成员。
Mercury is a fintech company, not an FDIC insured bank, banking services provided through Choice Financial Group and column NA, Members FDIC.
你提到 Netflix 很早就开始投入人工智能和机器学习了。
You mentioned how Netflix has been very early to AI and ML for a long time.
年轻人可能不记得了，但你们当年搞过一个叫‘Netflix 奖项’的竞赛。
Younger people may not remember this, but y'all had this contest to optimize The Netflix Prize.
对，就是那个 Netflix 奖项，这正好说明你们在人工智能和机器学习方面有多早入局。
Yeah, the Netflix Prize, like, like just show an example of how early you were to AI and ML.
当时有个百万美元的奖金，奖励能稍微优化一下Netflix推荐算法的人，谁优化得最多就拿走。
People, there was, I think it was a million dollar prize to optimize the Netflix ranking algorithm a little bit, like whoever could optimize it the most.
我记得冠军只把算法提升了几个百分点，大概就是这样。
And I think the winner optimized it by a few percentage points, something like that.
这在当时可是件大事。
It was like a huge deal.
全球一大批顶尖人才都参与进来了。
All these super smart people got around the world.
这种事还发生过好几次，对吧？
And it happened a few times, right?
你刚才说的，简直就像替我说了一样。
I mean, you said it on my behalf.
每当有人问Netflix是怎么看待AI的，特别有必要提一下这一点：这对我们来说从来不是新鲜事，尤其是在个性化推荐方面，它一直是我们为会员提供卓越体验的核心。
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.
我们平台上的内容量太大了，而且还在不断增长，这是我们的一个挑战，而让用户更容易发现内容，正是Netflix要解决的关键难题。
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.
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使用人工智能和机器学习，正是实现这一目标的方式。
And using AI and ML has been a way to do that.
你要把合适的内容，在合适的时机，推给合适的人。
You want to personalize right title for the right person at the right moment.
这个问题变得越来越难了。
That problem gets harder.
随着我们的内容库越来越丰富——不只是影视，还包括游戏、直播和播客——个性化变得愈发重要，它直接决定了用户体验。
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.
我们可以借助大量历史数据，来思考：现在该怎么解决这个问题？
So we can take a lot of that history and say, Okay, well now how do we solve this problem?
因为技术变得更强大了，这让我们在明确问题本身上有了先发优势，也更清楚这个问题对Netflix和会员有多关键。
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.
就像我之前提到的，在内容创作方面，人工智能和机器学习早就应用在视觉特效和语言本地化等领域了。
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.
现在我们想的是，当技术更强大了，下一个阶段会是什么样子？
Now we say, what's the next era of that when the tech is more powerful?
在这两种情况下，最终都用到了奈飞的核心优势——把娱乐和科技结合起来，确保我们始终领先一步，提供更好的体验。
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.
我很喜欢这一点，它早已融入我们的历史，至今仍是我们的强项，而且未来也必须继续是强项，因为我们要应对的挑战规模太大了——既要覆盖如此广泛的娱乐内容，又要保持出色的用户体验。
So I love that it's part of our history, it still continues to be a strength, and it's gonna have to be a strength given the size of the challenges we're facing around the breadth of entertainment while keeping a great experience.
是的。
Yeah.
我记得那时候大家还叫它机器学习，AI？根本没人提，觉得那太遥远了。
And I love that back then it was called machine learning, and AI was like, no, no.
这根本不是AI。
This it's not AI.
AI？绝对不可能，永远都不会有那一天。
AI is never never never that's never gonna happen.
那只是机器学习而已。
It's just machine learning.
可现在呢，什么都被称作AI，其实里面很多还是机器学习。
Well, then all of a sudden, you call everything AI, and some of it's machine learning.
没错。
That's
对。
right.
所以我通常会说，这得看情况，得看眼下最贴切的描述方式。
So I I try to you know, it depends, like, the thing that is of the moment to describe.
所以现在我们都把它们统称为人工智能了。
So I think we bucket all of it as AI now.
是的。
Yeah.
而且很多人工智能的应用并不是生成式的，要是细究起来，能聊的东西太多了。
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.
但总的来说，大家应该都不会惊讶，Netflix 正在广泛运用各种人工智能技术，而且对未来的可能性充满热情。
But in general, I don't think it would surprise anyone that Netflix is using a broad array, and with so much excitement about what's possible.
对在这里工作的人来说，有趣的是，只要你对技术如何赋能创意、用户产品或基础设施特别感兴趣，我们这儿全都有这类问题，而人工智能正是这一切的核心。
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.
而且别忘了，即使Netflix不把自己标榜为一家AI公司，这一点依然成立。
And it's good not to forget that that's true even if Netflix isn't branded as an AI company.
AI是我们用来实现优质娱乐和科技成果的得心应手的工具。
AI is a tool that we're very comfortable using to get these great entertainment and technology outcomes.
另一件特别有意思的事，继续夸一下Netflix——如果你看看Netflix早期的文化手册，还有我们上次聊的内容，就能发现一些关键点，比如高度自主性。
The other really interesting thing, just to kind of keep complimenting Netflix here, If you look at the early culture deck of Netflix, and also our conversation last time, things that emerge from that are things like high agency.
这从一开始就是Netflix的核心：高度自主、高度人才密度、自下而上的思维、快速试错和上线，这些现在正是顶尖AI实验室天天在谈的东西。
This was like something core to Netflix in the beginning, high agency, autonomy, high talent density, very bottom buzz up thinking, super quick experiments and launching, paying This top of is all stuff that every AI, like this is what I hear constantly now from how the top AI labs operate.
所以我们现在走到这儿了，而Netflix早就一直在那儿了。
So we're all ending here, and this is where Netflix has been forever.
是啊，他们很早就看懂了什么能让人才真正出色。
Yeah, it's a little prescient in understanding what makes talent incredible.
我一直在想Netflix的这些文化特质，听起来可能有点技术宅，但我觉得它们就像一套‘卓越操作系统’。
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.
所以这些文化要素的终极目的，并不是为了它们本身。
So the goal of all those cultural elements wasn't the end bone themselves.
并不是说我们要尽量让人承担更多责任。
It wasn't, let's just make sure people have as much responsibility as possible.
或者我们不喜欢繁琐的流程，所以干脆就别搞那些东西。
Or let's, you know, we don't like process, so let's make sure that we don't have any of that.
真正核心的理念是，只有给予人们充分的自主权和责任感，把决策权下放到组织最底层，招聘值得信赖的优秀人才，让他们凭判断做出正确决定，才能达到卓越。
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.
这样做的结果不仅是产出惊人，还能激发更强的动力和责任感。
And that ends out driving incredible outcomes, plus a lot more motivation and sense of responsibility.
这意味着团队里的每个人都能感受到：我被赋予了很大的权限，也承担着相应的责任，这里发生的一切都与我息息相关。
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.
我自己觉得，当你知道自己被赋予了这样的信任和责任，自然就会想把事情做到最好。
And I myself feel like when you know you're carrying that level of trust and accountability, you want to do your best work.
所以，Netflix的文化一直以追求卓越为目标，这一点其实非常合乎直觉。
And so there's something that feels very intuitive about Netflix's culture has always been aiming at excellence.
当你拥有顶尖人才，又不加干涉、不被流程拖累，让他们自由发挥，反而能取得更好的成果。
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.
所以我觉得，新兴公司正在捕捉到一些让我们感到特别熟悉的东西。
And so I do think that the newer era companies are picking up on something that is feeling very familiar to us.
但这并不是一件容易做到的事。
And it's not something that comes easily.
文化不是一成不变的。
So having culture is not a static thing.
随着公司规模扩大，你要解决的问题发生变化，文化也必须随之成长和演变。
Culture needs to grow and evolve as the company gets bigger, the types of problems you're solving change.
但追求卓越、相信顶尖人才需要自由发挥最大潜力这一理念始终没变，这依然是我们的独特优势。
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.
我特别喜欢这个理念。
I love this concept.
把卓越当作一种操作系统。
Excellence as an operating system.
这其实是一种很系统的思维方式，用来构建一家公司。
It's very systems thinking, you might say, for how to set up a company.
没错，伦尼。
Exactly, Lenny.
所以
So
对于所有听这个节目的人来说，大家都希望把卓越当成一种运营体系——谁会不想要呢？大家可能想知道，具体要怎么做才能实现这一点。
for people that, like everyone listening to this will want excellence as an operating system, like who would not want this, it'd be helpful for people to hear what are kind of the ingredients to make this happen.
首先，当然是高人才密度，只招最顶尖的人。
One is obviously high talent density, just hiring only the best.
第二，是责任感。
Two is accountability.
其实就是输入和输出的关系。
There's like the input and the output essentially.
输入顶尖人才，让他们负起责任，同时给予充分的自主权。
Input amazing people, the top people, make them accountable, give them autonomy.
如果创始人在听，你觉得打造这种以卓越为核心的运营体系，最关键的支柱是什么？
What would you say kind of like the pillars of creating this excellence as an operating system if people, if founders are listening to this, like I want them to do that.
人才密度是绝对不能妥协的。
Well, talent density is the non negotiable.
必须从这一点开始。
You have to start with that.
如果没有人才密度，你就不可能在组织的各个层级建立起对决策的信心，也无法让员工敢于冒险、快速创新——这正是奈飞文化中卓越的重要组成部分，也就是对承担风险感到坦然。
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.
我们不是试图避免失败，而是在失败发生后迅速恢复。
We don't try to avoid failures, we try to recover quickly when we have them.
我觉得已经有不少很好的例子了。
Think I there's been great examples of that.
我们进军直播领域就是一个绝佳的例子，当时我们清楚知道这会不完美，但也知道能快速学习，最终变得更强。
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.
看到团队如何应对这段经历，我从未如此为他们感到骄傲。
I've never been prouder of the team seeing how we worked through that.
所以你必须拥有高人才密度，相信员工能理解你提供的背景信息，具备出色的判断力和冒险精神，并为公司争取最佳的结果。
So you have to be talent density, comfortable that people are gonna 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.
你必须清楚，你所做的一切是为了给用户和奈飞创造价值。
You have to be very clear that what you're doing is driving outcomes for consumers and Netflix.
奈飞很重要，奈飞的会员也很重要。
So it's Netflix matters, Netflix members matter.
这跟个人成就或个人偏好没关系。
It's not about my own personal success or what I prefer.
这种卓越的运营体系里，还包含一种无私的精神。
So there's a selflessness that is part of this excellence operating system.
另外我想说，有些事情对人来说其实很反直觉。
And then the other thing I would say is some of the things that are, they're really unnatural for humans to do.
我可以举几个例子，比如有时候我看到别人做决定，心里会想：要是我来选，可能会不一样。
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.
这真的是最好的选择吗？
Like, is that really gonna be the best thing?
但在奈飞的文化里，我的职责不是每件事都要插手、否决或质疑，尤其是那些不关键、不会出大问题的决定，就让他们自己做，从中学习。
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.
事后可以问问他们，觉得这次怎么样？
And ask for those reflections afterwards of like, how did it go?
也许我当初想错了。
Maybe I was wrong.
也许那个决定其实特别好。
Maybe the decision was a great one.
这跟鼓励冒险有关，帮助大家学会坦然做决定，毕竟不可能每一步都对，但每次都能学到东西。
But that it's related to the risk taking and the like help people learn how to feel comfortable making their own decisions, especially when they're not all going to be the right decisions and they're going to learn something tough from it.
我当初也从我的上司和同事那里感受到过，他们说：这是你的决定。
I felt that myself from my boss and my peers saying, This is your decision.
我可以提供建议。
You know, I can provide input.
我可以帮你一起头脑风暴。
I can help you brainstorm.
但最终，这决定属于你。
It's yours in the end.
这真的不是天生就会的。
And it just doesn't come naturally.
当责任重大，又觉得自己要为公司的一切负责时，让别人去承担风险，确实让人很不安。
When the stakes are high, when I feel responsible for what the org's doing to let people lean into risk can be uncomfortable.
而且我觉得，当事情不顺的时候，也不能想当然地觉得流程能解决一切。
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.
过去几年我学到的一点是，每当计划遇到困难，我从来没听过谁说：‘我们终于找到了完美的计划方式。’
So if, oh, 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.
也没人说过：‘我们找到了完美的反馈、评级和薪酬流程。’
Or the perfect way to go through feedback and leveling and compensation.
但每次我们一看情况不对，就增加流程，结果花的时间更多，效果却没见好。
But every time we saw that and we added more process, we spent more time without getting better outcomes.
所以这又是件很不自然的事——大家一遇到复杂困难，第一反应就是想用一堆规则把问题简化，可实际上，我们更该想想：有没有更灵活、更有创意的方式来规划、做决策、定优先级，真正带来更好的结果。
And so it's another unnatural thing that I think everyone's inclination when things are hard and complicated is you think you're simplifying the problem by putting a lot of constraints around it, but it actually goes against the like, is there a more creative way to plan or to make people decisions or to make prioritization decisions that actually get us to better outcomes.
因此，我们要抵抗那种大公司常做的做法，经常得学会在不安中保持从容。
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.
这在我这个角色里是实实在在的感受。
So that's something I feel in my role.
我相信Netflix的很多人也有同感，因为你得刻意不去做那些常规的事。
And I would believe a lot of people at Netflix feel it because you try not to do the thing that is standard.
这话听着简单，但我太懂你的意思了——出问题的时候，你第一反应就是：‘到底哪里出了岔子？’
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?
赶紧定个流程，别再让这事重演。
Let's put a process in place to avoid this from happening.
但你其实是在说，得忍住这种冲动，因为流程一多，效率就慢了，真正优秀的人也不愿意待在一个到处是检查清单、审批环节和关卡的地方。
And what you're saying is like, 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 processes and gates and things
不，我觉得优秀的人更希望知道，出了问题会有无责复盘，他们会发自内心地承担责任，想着：‘我怎么才能确保这事不再发生？’
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 gonna say, How do I make sure this doesn't happen again?
不是靠流程，而是想：‘我该怎么把这些经验分享出去？’
Not with process, but with like, How could I share these learnings?
我该怎么调整自己的工作方式，下次才能做得更好？
How could I do work differently to make sure that I get to a better outcome next time?
当你信任人们去反思、学习和成长时，长远来看，结果会好很多。
When you are trusting people to take those reflections and learn and grow, I think you get much better outcomes over time.
团队也会变得更强大，我觉得这正是我们作为领导者的责任——打造一支坚韧、持久且懂得如何产生重大影响的团队。
And you get a much stronger team, which I think is part of our role as leaders of like, trying to grow a team that is resilient and durable and knows how to have great impact.
你不是要掌控一切。
You're not trying to control everything.
这正是打造高人才密度团队的关键。
Which is a key to building a team with high talent density.
这方面其实有两个角度，我想简单聊聊。
There's kind of, there's two sides of this that I want to chat about briefly.
一个是招聘，另一个是留住人才。
One is the hiring and the other is, keeping the people.
你以‘留任测试’闻名，上次我们提到过，这对很多人来说挺反直觉的。
So, you're famous for the Keepers Test, we talked about this last time, another unnatural thing for people.
想了解具体内容的人可以去听上一次的对话，但这几年这个测试有什么变化吗？
People that wanna understand what this is, they can listen to the first conversation, but has that, how has that evolved over the last couple of years?
‘留任测试’这个理念，现在还是文化的核心部分吗？
That's still a core part of the culture, this idea of the keeper's test?
很多人提到‘留任测试’，总觉得那是决定某人不适合岗位、要让他离开的那一刻，以及相关的谈话。
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.
但其实更多时候，它是用来讨论某人表现多么出色、在岗位上多么出色。
But it's equally commonly used to have a conversation about how extraordinary someone is, how well they're doing in a role.
因为通常的起点，是我对直接下属说，或者他们问我：‘我在你的留任测试里过得去吗？’
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?
而绝大多数情况下，我的回答都是：‘我一定会拼尽全力留下你。’
And the lion's share of the time, my response is, I would fight so hard to keep you.
我会跟你聊聊你做得特别棒的几件事，你的优势在哪，你带来了多大的影响。
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.
然后再说说，你还能怎么做得更好。
Here's how you could be even better.
所以，这其实是一个开启积极正面对话的契机。
So it's an entry into a conversation that is very positive and uplifting for people.
但核心问题是：我通过keeper测试了吗？
But the framing is, do I pass the Keeper test?
当然，也有更难的情况，就是我在评估某人是否通过keeper测试，或者他们主动来问你。
And then of course, there's the harder situations where I'm evaluating does someone pass the Keeper test or they're asking me.
这可能是最难开口的一句话了：说实话，你目前还没通过。
And this is the toughest thing to say, to be honest, you're not passing that right now.
在某些情况下，我觉得你还有希望，但这需要具体的反馈和明确的里程碑。
I think you could get there in some cases, and that comes with feedback and what are those milestones.
而在另一些情况下，我们会说：我们已经尽力了，但看不到成功的路径。
Or in some cases, you're saying, We've really tried and I don't see the path to success.
所以，它是一个能引导出各种对话的起点和锚点。
So it's it's an anchor and an entry point for a conversation that can go lots of different directions.
我喜欢它的原因在于，它让反馈变得有规律，能定期检查进展，有时逼着你直面棘手的问题，而不是躲开。
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.
要留住优秀人才，你也必须明确告诉他们：你做得非常好。
Or to keep great talent, you do need to say you're doing great.
这能让人们感受到被认可、被重视，这一点很重要。
Like that's an important part of making people feel recognized and valued.
所以我并不希望让人觉得我们对这个做法只有负面的看法。
So I don't want it to come across that we just have this very negative view of it.
其实它也有积极的一面。
I think there's this positive side of the coin as well.
太好了。
Awesome.
为了让大家明白这是怎么回事，不用再去听另一期播客，我简单解释一下。
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.
Netflix文化里有一条：当你有下属时，要一直问自己，以我现在的了解，今天我会雇用这个人吗？如果不会，那最好还是让他离开。
The idea here, a part of the Netflix culture, is that, when you, have people reporting to you, you should always be thinking, if I were to, would I hire this person today, knowing what I know about them, and if not, then I should probably let them go.
这样做的目的是保持高标准，绝不只是因为人已经在了，就随便留着。
And the idea there is to keep the high bar, to not ever just like settle, Okay, this person, they're here, I guess we'll keep them around.
这样理解对吗？
Is that roughly the way to understand it?
是的，这其实也是前面观点的延伸——如果今天这个人来告诉我他要走，我会努力挽留他吗？
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?
还是说，我内心其实松了一口气，觉得换个人来做这个职位可能更好？如果是这样，那我早就该采取行动，早点和他谈了。
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 have taken action and having that conversation sooner.
我特别认同你说的，要维持这样的标准，真的得面对很多让人不舒服的事。
I love, as you said, it's such so many uncomfortable things you have to do to maintain.
对。
Yeah.
‘留人测试’是其中之一，核心是留住人才，保持团队的活力和上下文的清晰，而不是对领导者搞控制。
Keeper test is one, maintaining talent, entity, context, not control among leaders.
我们常说要高度协同但保持松散耦合，也就是说，流程越轻越好，只要能确保大家清楚优先级并顺利执行就够了。
We talk about being highly aligned but loosely coupled, which is where light process, you know, the minimum to make sure we're clear on the priorities and we can execute them is what we're solving for.
这些做法，其实都不是人类或大规模组织天然会去做的。
All of these things are not things that human beings or organizations at scale tend to do.
所以，要持续用心，才能守住让Netflix变得特别的那些东西。
So it's constant diligence to try to maintain the thing that's made Netflix a special place.
归根结底，真正吸引并留住人才的是工作本身和企业文化，而这对我们实现商业成功至关重要。
Because in the end, it's the work and the culture that attracts people and retains people, and we need that to be a successful business.
这正是我正想说的。
So that's exactly where I was going to go.
要让这一切运转起来，你得吸引最优秀的人才。
So to make this work, you need to attract the best people.
一直以来，吸引顶尖人才都特别难。
It's always been very hard to attract the best people.
现在这事儿简直难到离谱，那么多钱在乱飞，那些炫酷的AI实验室，竞争太激烈了。
Feels insanely hard these days, with the amount of dollars flying around, the fancy AI labs, so much competition.
简直人人都在抢人，情况太疯狂了。
There's like everyone's just you know, it's it's crazy.
你发现什么办法能真正说服顶尖人才，让他们选择来Netflix，而不是去那些更光鲜的公司？
What have you found to be, effective in convincing the top people to still come to Netflix and and join versus all the other fancy places they can go?
是的。
Yeah.
我们一直以来都在和很多人抢人才。
We've always had a lot of competition for talent.
现在可能感觉更激烈了，但我们的团队确实有顶尖人才。
It might feel more pronounced right now, but we we have great talent on the team.
这可能不用多说，但我还是想明说出来，因为我真心这么认为。
Maybe that goes without saying, but I feel like I should say it out loud because I believe it.
Netflix里既有新加入的精英，也有长期留任的骨干，人才实力非常强。
We have incredible talent in Netflix, recent hires, long tenured people.
我总是对团队所做的工作感到由衷钦佩。
I'm always impressed by the work that the team is doing.
所以我并不觉得我们吃亏了，或者好人都被别的公司挖走了——事实上，很多顶尖人才都留在了Netflix。
So I don't feel like we've suffered or like other companies are vacuuming up all the good people because so many of them I do think sit at Netflix.
我们确实需要更清楚地说明，什么样的人和什么样的人才最适合Netflix，而不是像那些前沿实验室那样的公司。
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.
在Netflix工作的人，必须对技术的实际应用、对打造产品解决特定问题充满热情。
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.
你得真心喜欢娱乐产业。
You have to love entertainment.
你得热爱大规模的消费类产品。
You have to love consumer products at scale.
你得喜欢这种全球化的特性。
You have to love the global nature of that.
有很多非常优秀的人，恰恰就爱这个结合点。
There are a lot of incredibly talented people who love that sweet spot.
我就是其中之一，既懂科技、产品，又热爱娱乐，怎么把这些东西巧妙地融合在一起，做出令人惊叹的东西？
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?
你可以用AI，用其他技术和产品来实现，但你得真的被这个方向点燃，才会对Netflix的很多职位充满热情。
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.
如果你更被像前沿模型公司那种基础性工作所吸引——那当然也很棒，但那是另一种类型的人。
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.
那是另一种不同的问题领域，是你想投身的方向。
It's a different like, here's the problem space that I want to work in.
但我认为，对技术应用充满热情、并能将其与日常生活中热爱的东西——比如Netflix——联系起来的人，根本一点都不缺。
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.
这正是让我每天早上充满干劲的原因，我相信也激励着团队里的很多人。
And so that gets me up in the morning, and I think it gets a lot of the team members up.
我们经常聊到，这种独特的机会，恐怕只有Netflix的人才能把握，或者换到其他深度应用技术的行业，也是一样。
And we have this conversation about, like, that's something special that only talent at Netflix can do, or fill in the blank for another industry that's deep in the application of it.
我觉得这真的很鼓舞人心。
I think that's inspiring.
我想聊聊我在AI这个领域里最近一直在思考的几个问题。
I want to kind of touch on a couple of things that I've been thinking about in this world of AI that we're approaching.
一个是新人的问题。
One is junior people.
现在的感觉是，大家都忙着招那些已经证明自己很厉害的资深人才，人才密度高，标准也高。
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 you know, high talent density, high bars.
但AI让很多事情变得太容易了，新人可能反而学不到真本事——比如初级工程师、初级产品经理、初级设计师，他们该怎么一步步成长为出色的资深人才呢？
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, like there's just, like how do new people become these awesome senior people?
你有没有想过什么特别的问题？
Is there anything you've- you think about?
你们在招初级员工吗？
Are you hiring junior people?
如果初级员工没机会学习、没路径成长为资深人员，你觉得会怎么样？
How do you think about this- if this hap- what happens with junior people not necessarily learning or having a path to learn to become the senior person?
我们仍在招聘初级员工，他们对我们的人才战略非常重要。
We are still hiring junior people, and they're really important to our talent strategy.
我们依然有实习生项目，也有应届生项目，这在几年前对我们来说还是新的。
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.
几年前之前，我们只招聘各个岗位上有丰富经验的人才。
So prior to a few years ago, we were only hiring more experienced talent across all the functions.
现在我们会直接从本科和研究生项目中招聘新人，并且会继续这么做。
Now we do hire people straight from undergrad and graduate programs, and we'll continue to do that.
即使在AI让很多事情变简单的今天，我们之前也谈过心态和对AI的熟悉程度。
So even in a world of AI where some things are easier, we were talking earlier about mindset, AI fluency.
根据我的经验，年轻人通常更开放包容。
From my experience, younger folks are more open minded.
他们对这些新的工作方式往往更得心应手。
They tend to be more native in some of these new ways of working.
像奈飞这样的公司，年轻人也特别了解娱乐行业怎么变、消费者行为怎么变，以及产品和技术如何影响他们使用的应用。
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.
这种能力对我们团队来说特别重要。
That's really important to have on our team.
新毕业生作为工程师，他们的身份是一方面；但更重要的是，作为一个二十出头的年轻人，他们对世界有自己的见解，而且对变化中的世界适应得非常好。
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 20s and has a perspective on the world that is highly valuable and a comfort with the way the world is changing.
所以我说，这是人才战略中至关重要的一环。
So that's why I say it's a critical part of our talent strategy.
就算你入职后面对的是五年前、十年前根本不存在的AI工具，我依然认为，对专业技能的掌握至关重要。
To the, okay, so you step into the role and you have AI tools that didn't exist five or ten years ago, I would say mastery of the craft is still very important.
作为团队成员，我对自己提交的代码质量负责，对自己打造的产品质量、设计和用户体验负责——这些责任永远不会消失。
So going back to as the team member, I am 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.
所以，当我想到团队里那些经验较浅、职业生涯初期的成员时，我们必须同样投入精力，让他们明白什么是优秀的工作标准。
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.
这是如何使用这些工具，但你依然要对结果和产出的质量负责。
This is how you use these tools, but you still take accountability for what the outcomes are, what the quality of the output is.
我之前也提到过，我觉得真正的精通和技艺上的卓越依然非常稀缺。
And I think I mentioned this earlier, I find that mastery and that craft excellence scarce still.
所以我们一定要把这一点教清楚。
So we want to make sure we're teaching that.
我理解大家的顾虑：如果我不再像以前那样亲力亲为，那我该怎么获得这些能力呢？
I think it's a valid concern of like, how do I get that if I'm not as hands on as I would have had to be?
但你依然要负责审查代码、测试代码、诊断问题，清楚什么才是好的产品。
But you still carry responsibility for reviewing code, testing code, being able to diagnose problems, knowing what a good product looks like.
我觉得，真正稀缺的是那种能解决重要问题、设计精良的卓越产品能力。
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.
所以我认为，技艺上的精通，它的核心重要性并不会消失。
So I don't think that craft mastery, the importance of it is going away.
我们培养和提升人才的方式可能得变了，因为他们会用不一样的工具。
Probably the way we train and grow talent has to change because they're going to use different tools.
我敢说，经验少的新人也会教我们这些老一辈很多新东西。
And I can guarantee you that earlier career talent is going to be teaching older folks like me many new things too.
所以我觉得这是双向的。
So I think it goes in both directions.
你觉得工程领域在未来五到十年会变成什么样？
Where do you think engineering goes in the, I don't know, five, ten years?
你认为人们还得懂代码吗？
Do you think people need to still understand code?
还是说将来会有一层抽象，根本不用学 C++、Java、Python 这些东西？
Or do you think there's this abstraction layer that sits on top where you don't even have to learn C plus plus Java, Python, whatever?
我觉得能用 Python 或 C++ 写代码，和理解代码、计算机系统、产品是怎么运作的，这两者是不一样的。
I think there's a difference between being able to write lines of code in a particular language like Python or C plus plus and understanding how code, computer systems, products work.
而我认为后者是不会消失的。
And I don't think the latter is going away.
如果我们完全依赖智能代理来掌握所有语言、编写所有代码，那我们就无从判断一个产品到底好不好了。
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?
它到底是个糟糕的产品吗？
Is it a bad product?
当它没按预期运行时，我们怎么知道问题出在哪？
Is it working as we expected when it doesn't?
就像我之前说的，我们承担了很大的风险。
Like I mentioned earlier, we take a lot of risk.
我们快速试错，也快速修复。
We fail fast, we recover fast.
这就要求我们真正理解这些系统是如何运作的。
That requires an understanding of how are these systems working.
我可能会用智能代理帮我理解这些内容，更快地发现异常或故障，然后快速排查。
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.
但我依然得对我们要构建的东西有清晰的理解，知道它怎么工作，这样才能判断它好不好，也知道该怎么修复。
But I still need to have a fluency of like what is this thing that we're building and how does it work so I know if it's good and I know how to fix it.
我不确定，但我希望这种意识别消失，毕竟我们做的这些东西，要想让世界变得更好，就得真正理解自己造出来的是什么。
I don't know, I hope that doesn't go away because, you know, that's like a how do we make the world a better place through the stuff that we're building, I think requires some understanding of what we've built.
我听到的，觉得挺有道理的是，你可能不用亲自写代码，但你得懂它在干什么。
What I'm hearing, which makes sense, is you may not have to write the code, but you have to understand it and what's happening.
但作为一个不写代码的人，要真正内化这种理解，难道不是难得多吗？
But it's so much harder to just, as a person not writing it, to actually, you know, have that instilled in you?
我觉得这正是现在学习曲线特别陡峭的地方。
I think that's one of the things that the learning curve is very steep on right now.
看看这些模型或代理生成的一些代码，真的很难看懂。
So looking at some of the code that some of these models or agents are writing, they're very hard to follow.
我知道性能提升了，可完全说不清为什么。
It's like, I know I'm getting better performance from this, but I have no idea why.
万一出问题了，我根本不知道该怎么修。
And if this thing breaks, I'm going to have no idea how to fix it.
这让我很不安。
That makes me uncomfortable.
可能是因为我还在适应这个新世界的路上，还不知道该怎么操作。
You know, maybe that's because I'm still on that learning curve of like how do we operate in that world?
我们需要什么样的测试方法、推理方式和理解框架，才能真正安心地使用它呢？
Like what's the set of tests or rationalization and understanding that we need to have to get comfortable with it?
但乍一看，这种东西显得特别陌生，也让人很不安。
But at first glance, it looks very unfamiliar and very unsettling.
我相信，随着时间推移，工程实践会慢慢适应这种变化，大家会变得熟练，知道怎么引导新技术、新代理和新功能，确保对输出结果充满信心。
So I think engineering over time will evolve to be comfortable with that and have fluency in it and know how to guide new tech and agents and new capabilities to make sure that we feel really good about what the output is.
我想知道，该怎么形容这种变化呢？我始终惊讶于短短两年内，工程工作方式竟然发生了这么大的转变。
I wonder what the metaphor is for this, where this like, it's I continue to be astounded by how much engineering has transformed in like two years.
简直像是换了一个完全不同的工具箱。
It's like a completely different drop down.
以前你得坐在IDE前一行行写代码，现在呢，你跟代理聊天、审代码，一天就能提交上百个合并请求。
You're just you used to sit there in an ID and write code, and now you're just talking to agents and reviewing code and shipping 100 PRs a day.
这感觉就像是工程变革的速度被大大加快了。
It feels like it's an acceleration of how much engineering has changed.
但如果你回看过去十年、二十年，也会有同样的感受。
But if you looked over the last ten years or twenty years, you would say the same thing.
只是现在变化的速度更快了，我们很难理解过去几年里变化竟如此迅猛，但工程、数据科学或产品领域出现这种重大转变并不陌生，就像电影制作一样。
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.
如果看过去一百年，变化简直天翻地覆，全靠技术进步和新工具的引入。
If you look over the last one hundred years, it's unbelievably different because of technology and new tools that we've brought to it.
感觉整个周期正在不断加速。
Just feels like the cycle is speeding up.
明白了。
Okay.
我想稍微聊一下娱乐行业。
I wanna talk about entertainment for for a brief moment.
我很好奇，未来五到十年，娱乐方式会怎么变化。
Just I'm curious just like how entertainment will change over time in the next five, I don't know, five, ten years.
现在呢，我们打开Netflix，刷刷剧，看看视频。
Just you know, today, we open up Netflix, check out some shows, watch some videos.
这事儿好长时间都没啥变化，就是那种‘酷’的感觉。
It hasn't changed in a while, just that idea of, like, cool.
我就想把《地平线》一集不落全看完。
I'm gonna watch the pit and just watch it all.
我想看部电影。
I'm gonna watch a movie.
我有TikTok，有Instagram，一堆刷不完的内容。
I got TikTok, I got Instagram, feeds of stuff.
你觉得五年后，我们娱乐的方式会有多大不同？
Like, how much different do you think this will be in, I don't know, five years, the way we entertain
我觉得Netflix已经在变了，因为未来的娱乐不会只有一种形式，现在就已经不是了。
I think it's already changing at Netflix because entertainment is not gonna be one thing in the future, and it's already not one thing now.
我们之所以拓展到电影电视之外，是因为用户期待更多元的形式、设备和一天中不同场景的体验，Netflix必须把这些都做好，才能满足甚至超越他们的期待。
So part of the reason that we are going beyond film and TV in our offering is because there's an expectation that consumers have of much greater variety across formats, devices, moments of the day that Netflix needs to be able to serve well in order to meet consumer expectations and hopefully exceed them over time.
所以我们考虑加入移动端和电视上的云游戏、直播内容、播客，还有和更多现在入驻Netflix的创作者合作。
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.
所有这些让娱乐的内涵变得更丰富了，而Netflix有能力去定义并拓展这种边界。
All of those things create a greater breadth of what entertainment is, and Netflix is able to define and expand that.
这也对Netflix会员体验提出了更高的要求：我们该怎么把这些内容组织得让人一目了然？
And it puts a higher bar expectation on how do we make sense of that for a Netflix member?
我们想让你能无缝地从听比尔·西蒙斯的播客，直接跳到看美式橄榄球比赛——因为这是Netflix提供的内容之一，再过渡到玩最新款的FIFA云游戏。
So how do we show you this very seamless journey from, I listen to the Bill Simmons podcast to I watch quarterback because I love that as one of the Netflix offerings in the more, you could say, traditional film or TV space to I play the most recent FIFA cloud game.
我想在电视和手机上都能做到这一点，因为我经常在外移动，希望能在一天中的不同时间点发现并参与这些内容。
And I want to be able to do that on 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.
这正是Netflix正在构建的体验，我相信它会随着时间推移变得越来越自然流畅。
That's already a journey that we're building into Netflix, which I think will become stronger and stronger over time.
未来的娱乐不会只有一种形式，它必须更个性化、更沉浸、更互动，让你感觉像是进入了一个世界，可以根据你当下的兴趣，从各个方向自由探索。
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.
Netflix面临的挑战是，必须让内容的发现和参与比现在简单得多。
And the challenge Netflix has is we've got to make discovery and engagement much easier than it feels today.
我们有海量内容，但用户可能会觉得它们支离破碎，尤其是当你把市面上所有其他平台都考虑进去的时候。
We have tons of content, it can feel very fragmented, especially when you consider all the services or offerings out there.
我认为Netflix在理解如何跨娱乐、产品和技术解决这个问题方面有着得天独厚的优势。
And I think Netflix is very well positioned to understand how to solve that problem across entertainment, product, and tech.
另一个关键因素显然是人工智能。
The other element of this is AI, obviously.
作为一个局外人，看到科技圈对AI这么狂热，真的挺有意思的。
As an outside observer, it's like so interesting to see how in tech it's like AI, I love it.
这代表未来。
It's the future.
这是最好的方向。
It's the best.
但在好莱坞，人们却说：别搞了，赶紧停吧。
In Hollywood, it's like, no, shut it down.
这两种态度并存。
There's a
混合的。
mix.
范围非常广。
A very wide array.
Netflix 的角色就是为创作者提供他们想用的任何工具，帮他们实现自己的创意。
So Netflix's role in this is to enable creators with whatever tools they want to use to bring their vision to life.
总会有一些创作者或导演站在完全反对的一端，说：绝对不行。
There are going to be some creators or filmmakers who are on the end of the spectrum that says, Absolutely not.
不要用人工智能。
No AI.
这不是我做制作的方式。
That is not how I do production.
这和我的理念不符。
It's not consistent with my vision.
这没问题。
That's fine.
我们照样和这些创作者合作。
We work with those creators.
还有另一批创作者，我觉得人数正在不断增加，他们对探索一个问题非常感兴趣：这些生成式AI工具能不能实现以前根本做不到的事？
There's other creators, a growing number of them I would say, who are very interested in exploring, Wait, can these Gen AI tools make something possible that wasn't possible before?
我能不能用一种全新的方式来讲故事？
Can I tell a story in a new way?
我能不能让这个故事质量更高，更能打动观众？
Can I make that story higher quality and more resonant for audiences?
我能不能在如何呈现故事时，做出更富有创意的尝试？
Can I do things that are extra creative in how I think about bringing a story to life?
我们同样支持他们，也支持那些处于中间地带的创作者们。
And we support them as well, and we support all the folks who are in the in between.
说实话，我们能站在这个位置上非常重要，因为娱乐内容不可能只有一种形态。
I mean, that's a really important position for us to be in, again, because entertainment is not going to be one thing.
不可能只有一种形式。
There's not going to be one format.
我觉得未来会有一些电影和剧集依然保持传统风格，同时也会出现一些全新的形式，由那些极具想象力的创作者们推动诞生。
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.
Netflix 希望参与到这个过程中，这就意味着我们提供的工具和合作方式必须足够灵活，要以赋能创作者为核心，而不是死板地只走一条路。
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.
我觉得人们会对 AI 内容的质量感到惊讶。
I think people are going to be surprised by just how good AI content is.
比如斯宾塞·普拉特的视频，大家都说：这真有意思。
Like, Spencer Pratt's videos are just like, everyone's like, this is entertaining.
明显是 AI 做的，但就是让人看得很着迷。
Obviously AI, but it's so interesting.
你觉得有一天，整部剧都是 AI 做的，大家还会特别喜欢吗？
Do you think do you think we'll get to a place where it's just like, the whole TV shows are AI and people love it?
我很难想象娱乐内容会脱离人类的核心作用。
I have a hard time picturing entertainment that doesn't have humans at the heart of it.
人类在故事创作中的参与，我认为是一种稀缺而珍贵的能力。
So that's humans in the creation of the storytelling, which I think is a scarce and valuable skill.
讲故事和人性是分不开的，关键在于理解什么能打动人心。
Storytelling is one and the same with humanity and knowing what connects with people.
所以我认为，人类始终会是故事的核心部分。
So I think humans will be part of the, will always be a core part or a critical part of the story.
至于屏幕上那些缺乏人性的角色，我觉得看下去就没什么吸引力了。
And I think watching characters on screen who don't have that humanity feels less compelling to me.
真正打动人的，是通过故事看到另一个人，看他如何演绎角色、如何传递情感。
And what the power of storytelling really is to like see another human and to watch how they perform a role or like bring an emotion to life.
这完全是人类独有的东西。
That's such a human element.
AI能帮我们把这些东西呈现出来吗？
Will AI help to bring that to life?
它会不会在制作过程中发挥重要作用，影响作品的质感和氛围？
Will it play a material part in some of those productions or how we get them to look and feel a certain way?
是的，肯定能。
Yeah, definitely.
但我想象不出，没有人类作为根基的版本会是什么样子。
But I don't see the version of it that doesn't have the human as the backbone.
有一句话常被误认为是萨尔曼·鲁西迪说的，说的是：孩子一出生，先要吃喝，接着就要听故事。
There's a quote that I think is misattributed to Salman Rushdie, which is, When a child is born, they first ask for food and water and projection, and then they ask for, Tell me a story.
从古至今，讲故事一直是社群、人际联结和情感交流的核心部分。
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.
我很喜欢这个想法——科技能放大这种力量，以全新、独特又激动人心的方式让故事活起来。
So I love the idea that technology can amplify that and can bring that to life in very new, novel, exciting ways.
但如果有人说，故事可以没有人性作为核心，那总觉得少了点什么。
But if to say storytelling wouldn't have that humanity at the center feels like something would be missing.
未来几年，我们会看到很多令人震惊的东西。
We're gonna see some wild shit over the years coming
当然，这一点毫无疑问。
on Oh, I'm over sure.
这一点是肯定的。
There's no question about that.
而且其中很多都会非常有趣。
And a lot of it could be very entertaining.
说实话，我也不否认这一点，但我认为未来会涌现出各种各样的内容，而网飞必须站在核心位置，去塑造并实现这些可能，这正是我们的目标。
You know, I don't debate that either, but I think there's going to be a broad range, and I think Netflix needs to be at the center of shaping that and bringing that to life, which is our plan.
太棒了。
Amazing.
我们今天聊得挺全面的，伊丽莎白。
Well, we covered a lot of ground, Elizabeth.
在进入我们激动人心的快问快答环节之前，你还有什么想补充的吗？有没有哪一点特别想再强调一下，让听众们记住？
Before we get to our very exciting lightning round, is there anything else that you wanted to share, leave listeners with, maybe double down on, from things we've talked about?
可能我前面已经提到了，但我还想再强调一下：现在正是在娱乐行业做产品最令人兴奋的时刻。
It probably came across throughout, but I would underscore that this is a really exciting time to be building products in entertainment.
我们刚才聊到的那些变化——技术的演进、用户行为的转变，还有‘娱乐’本身的定义。
Everything we talked about of like, what's changing in the tech and consumers, and like, what is entertainment?
我们正处在一个创新速度空前的阶段。
We're at this unbelievable high velocity innovation period.
这正是我选择留在网飞的原因。
So it's what keeps me at Netflix.
我觉得在这里工作特别有意思。
I think it's a fun place to be.
如果我不再强调一下，我觉得这确实没错，那我就欠妥了。
I would be missing something if I didn't reinforce that I think that's true.
作为整个行业，我们有时候太专注于技术本身或功能，反而忽略了大局——我们真正要做的，是打造让用户真心喜爱的产品。
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.
我们要做的，是创造让人喜爱的优质娱乐内容，这才是他们最珍视的东西，我绝不希望这个核心被埋没。
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.
当然，背后有很棒的技术和产品支撑，但最终最让人振奋的，是我们能为全球用户带来什么。
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?
说到这一点，
And along
最近恰恰相反，市场上新消费产品和用户体验少得可怜，几乎没什么成功的消费类初创公司，而AI恰恰提供了一个新的机会，我觉得Netflix就是少数几家持续推出出色消费产品和商业模式的公司之一。
those lines, there's been such the opposite of glut, a drought of a consumer, new consumer products, consumer experiences, like there's very few success, like 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.
这种公司真的没几个。
There's just not that many of them.
我们接下来要
We're gonna
继续保持优秀的表现。
keep good job.
那个
That
好了，我们现在进入激动人心的闪电问答环节，有五个问题要问你。
Well, that, we've reached our very exciting lightning round, we've got five questions for you.
你准备好了吗？
Are you ready?
好的，我准备好了。
Okay, I'm ready.
那你能说说，你最常推荐给别人的两三本书是哪些？
All right, what are two or three books that you find yourself recommending most to other people?
我得挑些和上次不一样的书。
I have to come up with different books than I said last time.
记不起那本书的名字了，但听起来很不错。
Don't remember the word, but that sounds great.
还是挺有怀旧感的。
Still like a good throwback.
我想到两本，一本是《进入空气稀薄地带》，作者是约翰·克拉库尔；另一本是《说谎者的扑克牌》，作者是迈克尔·刘易斯。
So two that are coming to my mind, Into Thin Air, John Krakauer and Liars Poker, Michael Lewis.
我以前在华尔街工作，喜欢跟人聊聊过去那些年的情形。
So I I worked on Wall Street, and I like reminding people what it was like in the way back time.
最近看过什么特别喜欢的电影或剧集吗？虽然你人在Netflix，这个问题可能有点难，但我很好奇你会想到什么。
Favorite recent movie or TV show you really enjoyed, which is maybe too hard for someone working at Netflix, but I'm curious what comes up.
能列出来的太多了。
The list is very long.
最近看的是《非凡的生灵》，是我妈推荐的。
The most recent I watched, Remarkably Bright Creatures, after a recommendation from my mom.
特别催泪。
It's a tearjerker.
聊聊讲故事中人性的那一部分。
Talk about the human part of storytelling.
最近你发现的最让你喜欢的产品是什么？
Favorite product you recently discovered that you really love?
对我的健康和福祉至关重要的东西？
Critical for my health and well-being?
Eight Sleep 这个产品。
Eight Sleep.
你有没有一句经常在工作或生活中回味的人生格言？
Do you have a favorite life motto that you often come back to in work or in life?
我常常回想父母在我很小的时候就教给我的那些道理。
I often go back to the things that my parents instilled in me in very early times.
可能有点老生常谈，但每天总会发生点好事。
So the risk of repeating maybe, first, something good happens every day.
留意这些事，哪怕在最紧张的时候也别错过。
Watch for it, even in the most stressful times.
第二点，最后那百分之五的努力，往往才是关键所在。
And second, that the last 5% of effort usually makes all the difference.
这些话太棒了。
These are awesome.
真的说到我心坎里了。
They hit me.
最后一个问题是。
Final question.
我对这个一无所知，但你提到你正在参加某种骑行活动。
I don't know what anything about this, but you mentioned you're doing some kind of cycling event.
是啊。
Oh, yeah.
跟我们说说具体情况吧。
Tell us what's going on.
你这是在搞什么？
What are you doing here?
所以我丈夫和我计划去一趟，在环法自行车赛最后一周沿着赛道骑行。
So my husband and I are doing a trip where we ride alongside the Tour de France for the last week of the race.
环法赛总共三周，最后一周有很多山地赛段，所以我们每天早上可以骑一段路线，下午再去看比赛。
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.
这可不是胆小的人能承受的。
Not for the faint of heart.
所以我正在加紧训练，好让自己能充分享受这些骑行。
So I'm trying to train up so I can enjoy those rides.
本来应该是
It's supposed
度假嘛，毕竟。
to be a vacation after all.
天哪。
My god.
我太喜欢这次度假了。
I love this vacation.
我们就只是去骑一骑。
We're just gonna race.
所以这算是一场比赛吗？
So is it like a race?
我特别喜欢职业体育。
I love professional sports.
能亲身参与其中真的很有趣。
It's fun to be able to participate in it.
那这是真的比赛呢，还是你就随便骑，轻松过完路线？
So is this like racing, or you just kind of try to go as nonchalantly through the course?
你就轻松骑就行。
You go nonchalantly.
那可太惊险了。
That's the very hairy.
我想我之前提过。
Think I mentioned.
山上的山坡。
Hill on the hills.
这既考验体力，也考验意志，虽然不是比赛，但我也不想落在最后。
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.
所以我得保持足够的状态，跟上队伍。
So I got to be comfortable enough to hold my own.
真厉害。
Wow.
我很喜欢，这跟你平时的工作完全不一样。
I love how different this is from your job.
这样挺平衡的，能让我多接触大自然，也能换个角度看问题，我很期待。
It's a good balance, and it gets me outdoors and gives me some nice perspective, so I'm looking forward to it.
伊丽莎白，你太棒了。
Elizabeth, you are awesome.
最后两个问题。
Two final questions.
如果大家想关注你或者联系你，可以在哪里找到你呢？
Where can folks find you online if they want to follow you, reach out for maybe anything that came up?
听众们又能怎样帮到你呢？
And how can listeners be useful to you?
想找我或者了解我们正在做的工作，最好的地方其实是Netflix技术博客，我把很多聊过的内容都发在那里了。
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.
我们正努力更主动地分享那些有趣的工作进展。
We're trying to do a better job communicating about the fun stuff we're working on.
所以那里通常是第一个该去的地方。
So that's a good first stop, usually.
至于听众怎么帮忙，就是多试试我们推出的新东西。
And then how listeners can be useful, try all the new stuff that we're putting out there.
关注我们的直播活动，玩玩新游戏，试试我们移动端新推出的短视频功能Clips，然后给我们反馈。
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.
我们就是想把这些做得更好。
So we wanna make it better.
这些项目很多都是我们从零开始的全新尝试。
And a lot of these things are new zero to one efforts for us.
所以我们正努力尽快把它们做到出色。
So we're trying to get to great and excellent as quickly as possible.
我很喜欢这种‘作业’——去刷Netflix就行。
I love that the homework is go watch Netflix.
你当然也可以看别的内容，告诉我们怎么改进，但我更想知道Netflix怎么才能做得更好。
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.
太棒了。
I love it.
我这就去刷剧。
I'm gonna go I'm gonna go do that.
伊丽莎白，非常感谢你今天再次来到这里。
Elizabeth, thank you so much for being here and being here again.
谢谢。
Thank you
谢谢邀请我。
for having me.
每次都特别开心。
Always fun.
大家好。
Hi, everyone.
非常感谢你们收听。
Thank you so much for listening.
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Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast.
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You can find all past episodes or learn more about the show at lenny'spodcast.com.
下一期再见。
See you in the next episode.
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