Adam Mosseri 谈 AI、品味与 Instagram 产品团队的未来

不，我认为品味至关重要。在一个更容易构建东西的世界里，更重要的是确保你的时间花在弄清楚一开始应该构建什么上。我认为那些能充分利用这一点的人，是那些对 AI 擅长什么、不擅长什么有清晰认识的人，并且对 AI 未来会擅长什么、不擅长什么有直觉或嗅觉。

No, I think taste matters a ton. In a world where it's easier to build things, it's more important to make sure that your time is spent figuring out what you should be building in the first place. The people who I think are going to make the most of it are the ones who are cleareyed about what AI is good at and what it's not good at and also have an instinct or a nose for what it will be good at and not good at.

Instagram 算法对人们行为的了解中，有什么是大家可能没意识到的？

What's something that the Instagram algorithm knows about human behavior that people may not realize?

我认为人们假设算法对每个人的兴趣和偏好有更详细的语义理解，但实际上并没有那么深入。

I think people assume that there's a much more detailed semantic understanding of everybody's interests and preferences in the algorithm than there is.

AI 内容的兴起对 Instagram 来说是逆风还是顺风，相比于其他平台？

Is the rise of AI content a headwind or a tailwind for Instagram versus other platforms?

我认为会是顺风，但在一个合成内容丰富的世界里，这也会是一个挑战。我实际上认为人们会去寻找创造力、真实性和人与人之间的联系。我不认为我们应该过滤掉 AI 内容。我认为我们应该让你知道内容是否是 AI 生成的。这很难，顺便说一句。

I think it's going to be a tailwind, but I think it's going to be a challenge in a world where there's an abundance of synthetic content. I actually think people are going to seek out creativity and authenticity and people. I don't think we should filter out AI content. I think we should let you know if content is AI content or not. That's hard. By the way.

随着 AI 不断蚕食产品开发生命周期的更多部分，你认为人脑在哪些方面仍然最有价值？

Where do you think human brains will continue to be most valuable as AI continues to eat more and more of that product development life cycle?

这是个好问题。

That's a great question.

所以今天我的嘉宾是 Adam Mosseri，Instagram 的负责人。每月有超过 30 亿人使用 Instagram，这相当于地球上每三个人中就有一个。这令人难以置信。在 Instagram 之前，Adam 设计并领导了早期的 Facebook 信息流。他还负责构建了 Facebook 排名算法的团队。8 年前，他从创始人 Kevin Systrom 和 Mike Krieger 手中接管了 Instagram。他是一名设计师转型为产品经理，再成为 Instagram 的领导者。Adam 也因作为 Instagram 产品演变过程中所有争议和变革的代言人而闻名，我们也会谈到这些。在开始之前，别忘了访问 lennisproduct.com，获取一年免费的最有趣、最精良的 AI 产品，仅限 Lenny 的新闻通讯订阅者。接下来，有请 Adam Mosseri。Adam，非常感谢你的到来。欢迎来到播客。

So today my guest is Adam Mosseri, head of Instagram. Over three billion people use Instagram monthly. That's one in every three people alive. It boggles the mind. Prior to Instagram, Adam designed and led the early Facebook news feed. He also ran the team that built the Facebook ranking algorithm. And 8 years ago, he took over Instagram from its founders, Kevin Systrom and Mike Krieger. He's a designer turned product manager turned leader of Instagram. Adam is also famous for being the face of all of the controversy and changes that come with evolving Instagram as a product, which we talk about. Before we get into it, don't forget to check out lennisproduct.com for a free year of the most interesting and well-crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you Adam Mosseri. Adam, thank you so much for being here. Welcome to the podcast.

谢谢邀请。很高兴来到这里。

Thank you for having me. Excited to be here.

你做产品已经很久了。你能看到 Meta 内部很多团队在 Instagram 的运作方式。2026 年的典型产品团队是什么样的？今天团队运作方式与几年前相比，最大的不同是什么？

You've been doing product for a long time. You get to see how a lot of teams operate across Meta within Instagram. What does just kind of like the canonical product team look like in 2026? What's kind of most different today in how teams operate slash should operate versus say a couple years ago?

今年变化很大。在像我们这样的大公司里，很长一段时间以来，典型团队是两三个 Android 工程师、两三个 iOS 工程师、两三个服务器工程师，可能还有一个全栈工程师、一个 PM、一个设计师、一个数据科学家，运气好的话还有一个研究员，大概就这些。所以差不多是 13 人左右，这是因为你需要为每个写代码的人配备一个能审查代码且熟悉代码库的人，并且这些职能更加专业化。我认为在初创公司情况非常不同，但今年正在改变。我们采用了所谓的 pods，也就是迷你团队，大约四到六名工程师，他们更偏向全栈。还有一个我们称之为 product staff 的角色，这是 PM 的演变。也就是说，一个 PM 可以利用我们提供的最新工具，做一些设计师、数据科学家和研究员的工作。然后根据需求配备他们需要的专家——如果做定价策略，就需要高级数据科学家；如果做体验上非常新颖的东西，就需要非常资深的产品设计师。所以我们尝试根据工作需求来组建团队，但最终核心团队要小得多，通常只有六到七人。这是我们今年刚刚发生的一个巨大转变。仅仅因为协调的人更少，他们通常能更快行动，做出更好的决策，减少委员会式的设计。我们经常谈论 AI 如何调整和提高生产力，这是其中一部分，但我认为另一部分就是小团队通常更有效。

It's changed a lot this year. So for the longest time at a big company like ours, the canonical team was something like two or three Android engineers, two or three iOS engineers, two or three server engineers, maybe a generalist, a PM, a designer, a data scientist, a researcher if you were lucky, and maybe that's about it. So you know on the order of a baker's dozen and that is a function of you know you want to have for anybody who's writing code someone who can review their code and that's who's familiar with that codebase and having these different functions that are more specialized you know I think it's very different at a startup but this year it's changing we've adopted what we call pods which are just mini teams where it's call it four to six engineers who are a bit more generalists. Uh one we call product staff which is sort of an evolution of the PM. So a PM who can do some of what a designer does and some of what a data scientist does and some of what a research does leveraging um the latest tools that we have for them. And then whatever specialist they need if they if they're doing something that requires a pricing strategy you need a senior data scientist. If you're doing something that is really novel from an experience standpoint, you need a very senior product designer. So we try to build the team based on the needs of the work a bit, but then end up with a much smaller core which is more on the order of six or seven usually. And that is a very big shift that's just happening to us this year. But they just by virtue of having less people to coordinate they can often move faster and make um better decisions a little bit less design by committee. So we talk a lot about you know AI adjusting and improving productivity and that's part of it but I think another part of it is just the small teams I think often are just more effective.

本期节目由本季的赞助商 WorkOS 提供。OpenAI、Anthropic、Cursor、Vercel、Replit、Sierra、Clay 以及数百家其他成功的公司有什么共同点？它们都由 WorkOS 提供支持。如果你正在为企业构建产品，你会感受到集成单点登录、SCIM、RBAC、审计日志等大公司所需功能的痛苦。WorkOS 将这些交易障碍转化为即插即用的 API，拥有专为 B2B SaaS 构建的现代开发者平台。实际上，我投资的每一家开始向上市场扩张的初创公司最终都与 WorkOS 合作。这是因为他们是最好的。无论你是种子期初创公司，试图获得第一个企业客户，还是正在全球扩张的独角兽，WorkOS 都是实现企业就绪和解除增长障碍的最快途径。它本质上是企业功能的 Stripe。访问 workos.com 开始使用，或者直接联系他们的 Slack，那里有真正的工程师等待回答你的问题。WorkOS 让你通过令人愉悦的 API、全面的文档和流畅的开发者体验更快地构建。今天就前往 workos.com 让你的应用企业就绪。

This episode is brought to you by our season's presenting sponsor, WorkOS. What do OpenAI, Anthropic, Cursor, Vercel, Replit, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by WorkOS. If you're building a product for the enterprise, you've felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand upmarket ends up working with WorkOS. And that's because they are the best. Whether you are 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. It's essentially Stripe for enterprise features. Visit workos.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to workos.com to make your app enterprise ready today.

我喜欢这个。那么在这个六到七人的团队中，组成是怎样的？你发现哪个角色变少了？如果规模缩小了 50%。

I love this. So on this team of six to seven, what's what's the makeup again? And which role are you finding you have less of? If you're going from the kind of the if you're going 50% size.

你只是少了专家，对吧？所以你可能根本没有专家。可能是四个工程师和一个 product staff，没有数据科学家、没有设计师、没有研究员、没有内容设计师。Product staff 是支持所有这些事情的通才。我的意思是，很明显，所有职能都开始相互融合，整个行业都在纠结这意味着什么。例如，在大公司，数据科学家做的很多事情相对机械。所以，他们做的一些事情更像是艺术和科学，而另一些则更像是拉取数据、数据管理。因此，我们内部正在构建一些工具来理解，比如一个传统的数据科学问题会是漏斗分析。如果你想看人们创建 Reels 的过程，你会查看所有步骤，以及人们在每个步骤的流失情况，然后找出可能改进的机会。

You just have less specialists, right? So you might not have any. You might be four engineers and a product staff and there's no data scientist, there's no designer, there's no researcher, there's no content designer. The product staff is the generalist that sort of supports all of those things. I mean, what's clearly happening is all the functions are starting to bleed into each other and the whole industry is wrestling with what that means. You know, a lot of what a data scientist does at a big company, for instance, is relatively mechanical. And so, you know, there's stuff that they do that is really more like, you know, art and science. And the stuff that's really more like just pulling data, data management, you know, so some of the tools that we're building internally to understand, for instance, a traditional data science question would be a waterfall. So if you wanted to look at people creating reels, you would look at all the steps and then how people fall off on each step and try to figure out where there might be opportunities to improve things.

那种基本的瀑布分析现在用我们的一些内部工具自动拉取要容易得多，而不必让数据科学家做一堆定制工作。所以产品人员现在可能就能做到，而一年前他们还做不到。结果你就有了这些更通才型的人。然后当你真正需要的时候，你有一个更资深、理想情况下或更有创意的专家。比如一个出色的产品设计师，或者一个天才的数据科学家或研究员。

That kind of basic waterfall analysis is much easier now to use some of our internal tools to just pull automatically, as opposed to having to have a data scientist do a bunch of bespoke work for that. So a product staff might be able to do that now, and they couldn't do that a year ago. So you end up with these generalists who have more generalist shapes. And then when you really need it, you have a more senior, ideally, or just more creative specialist. So a phenomenal product designer, or just a genius data scientist or researcher.

这太有意思了。这正是我刚听到的。我请了 Fiona Fong，Claude Code 和 Co-Work 的工程主管来播客。她是 Forced Attorney 的经理，她描述她现在雇佣的人是：一是有品位的构建者，能把一个想法从头到尾实现；二是在非常特定领域有深厚专业知识的人。

This is so interesting. It's exactly what I just heard. I had Fiona Fong, the head of engineering for Claude Code and Co-Work on the podcast. She's Forced Attorney's manager, and she described the people she hires now are: one, builders with great taste that can take an idea from end to end, and people with deep expertise in a very specific domain.

品味非常重要。我真的很同意。Boris 曾在 Instagram 工作。我喜欢看到他。他现在在 Threads 上到处都是。他有点像 Clark K 的代言人。他现在是个名人了。真的是。在现在这个如火如荼的世界里，我认为品味至关重要。所以在一个更容易构建东西的世界里，更重要的是确保你的时间花在弄清楚你应该首先构建什么上。实际上，现在很多设计师对自己的角色非常焦虑。你有其他通才在做设计，有工程师在做设计，产品人员也在做设计。但我其实很看好设计师，因为他们往往有品味，我认为这是更难被自动化取代的东西。所以设计有时还有其他挑战，但我现在很看好设计师。

Taste matters a lot. I really agree with that. Boris used to work at Instagram. I love seeing him. He's all over Threads now. He's sort of like the face of Clark K. He's a celebrity now. He really is. In a world that is on fire right now, I think taste matters a ton. So in a world where it's easier to build things, it's more important to make sure that your time is spent figuring out what you should be building in the first place. Actually, a lot of designers right now are very anxious about their roles. You've got these other generalists doing design, you've got engineers doing design, product staff doing design. But I'm actually pretty long on designers, because they tend to have taste, and I think that is something that is much more difficult to imagine being automated away. So there are other challenges with design sometimes, but I'm pretty long right now on designers.

我一直也这么觉得。因为构建如此容易，AI 产生的所有工作都那么……你可以看出这是 Claude 的设计。我知道你在这里做了什么。

I've always felt that too. As it is so easy to build, and all the work that AI produces is so like, you can tell this was Claude design. I know what you did here.

这是 Codex。

This is Codex.

嗯，它们都有自己的风格，对吧？就像你凭感觉编码你的应用。我们称之为“感觉编码”。你都会说，“哦，那是个 Codex 应用。”或者，“哦，那是个 Claude 应用。”

Well, they all have their vibe, right? Like you vibe code your apps. We call it vibe coding. You're all like, "Oh, that's a Codex app." Or, "Oh, that's a Claude app."

对。还有 Replit。还有 Lovable。你可以预测这些东西。

Right. And that's Replit. That's Lovable. You can predict these things.

是的。我一直也这么想，设计现在应该已经蓬勃发展才对。但出于某种原因，还没有。如果你看设计师的工作岗位，它们有点停滞不前。我觉得缺失的部分是产品经理那部分，即深刻理解业务、什么能推动增长、什么是成功，所有这些商业方面的东西，而不是品味方面的东西。

Yeah. I've always thought that too, that design should be thriving already now. For some reason, it hasn't yet. If you look at jobs for designers, they're kind of flatlining. I feel like the missing piece is the PM piece of deeply understanding the business and what will grow it and what success, all that stuff, the business side of it versus the taste side of it.

是的。我想你会看到，我们 Instagram 有个叫 Nate 的高级设计师，他刚转到了产品岗位。所以我认为你会看到的是，谈论设计角色和谁是优秀设计师会变得更难，因为他们不会只待在传统设计岗位上。如果你是一个出色的设计师，你可能在交互和视觉设计之外也有强烈的观点。你可能对产品策略、甚至业务和上市策略都有强烈的看法。所以我实际上认为我们一些最优秀的产品人员将来自设计和数据科学的转行者，他们只是想扩大自己的影响力。他们以前就能跨职能边界产生影响，但在这个职能边界被极度模糊的世界里，他们可以直接跳进来。所以当然，他们在纸面上技术上是个通才，但他们显然在某一类手艺上有独特而强大的能力。但他们有能力并有强烈观点，能跨其他部分或其他手艺做出明智决策。所以我不知道我所有最优秀的设计师是否都会留在设计领域。他们可能大多数会，但我可以想象很多非常优秀的人会转岗。不过我也应该检查一下我自己的偏见，因为我很久以前在 Facebook 就是从设计师起步的，后来转了岗位。

Yeah. I think you're going to see, we have a senior designer at Instagram called Nate who just transferred into product staff. So I think some of what you'll see is it will be harder to talk about design roles and who's a good designer, because they're not going to just stay in traditional design roles. If you're an amazing designer, you might have strong opinions outside of just the interaction and visual design. You probably have strong opinions on product strategy, even on the business, on the go-to-market. So I actually think some of our strongest product staff are going to be converts from design and from data science who are just looking to expand their reach. They were influential across functional boundaries before, but this world where those functional boundaries are just wildly blurred just allows them to jump in. So sure, they'll be technically a generalist on paper, but they clearly have a uniquely strong ability in one type of craft. But they've got the ability and strong opinions to make informed decisions across other parts or other crafts. So I don't know that all the strongest designers I have will all be in design. They probably will be the majority, but I can imagine a bunch of really strong ones moving roles. But I should also check my own bias here, though, because I started as a designer at Facebook way back when, and I switched roles.

不，设计师很棒。我是他们的忠实粉丝。所以这真的很有意思。一直有这种总经理模式，不同类型职能的人可以成为总经理。这个产品人员角色感觉也类似，不同职能的人可以成为产品人员。

No, designers are great. I'm a big fan. So this is really interesting. There's always been this GM model where different types of functions can become GMs. It's like this product staff role feels like a similar situation where different functions can become product staff.

是的。这在以前对产品经理来说也是真的，但现在更是如此。从某些方面来说，这可能是通才的时代，但我仍然认为这些真正出色的专家会有一个非常重要的角色，他们就是专注于深入。我希望我能那样。我一直很向往那些杰出的机器学习工程师或 AI 研究员或鞋匠。我觉得那是世界上最酷的事情，但那从来不是我的形状。我一直没有哪方面特别出色。我一直只是有广度。那一直是我的优势。

Yeah. And that was true of PM before, but it's just so much more true now. In some ways it's probably the age of the generalist, but I still think there's going to be a real important role for these really amazing specialists who are just all about going deep. I wish I was like that. I always romanticized the phenomenal machine learning engineer or AI researcher or shoemaker. I think that's the coolest thing in the world, but it's never been my shape. I've always never been great at anything. I've always just had range. That's always been my strength.

一样。好的。所以这个产品人员的想法，就是这些新的小组。这是你们在做的新事情。所以有这些小组团队：产品人员、工程师，可能还有一个专家，比如在定价算法之类的东西上深入。所以这告诉我的是，有一些相邻的角色可能在未来几年会更麻烦。比如数据科学，比如用户研究。你谈到了设计师的焦虑。有没有什么情况是，哦，也许这些群体里的人应该考虑转到其他角色？

Same. Okay. So this idea of product staff, so the idea is on these new pods. So this is like a new thing you guys are doing. So there's these pod teams: product staff, engineers, and maybe one specialist that's going deep on say pricing algorithm or something like that. So what this tells me is there are these adjacent roles that are maybe more in trouble over the years. Data science for example, user research for example. You talked about designers being anxious. Is there anything there of just like, oh, maybe folks in these groups should think about shifting to other roles?

我的意思是，到处都有焦虑。我和很多其他公司的很多人聊过，似乎现在有很多担忧，关于竞争、关于工作被取代、关于所有这些技术以及一切变化如此之快所带来的意外或不可预见的后果。所以这确实在发生。我认为你会看到职能界限继续模糊，但我仍然认为职能会有空间。它们只是会以不同的方式塑造。它们会更……它们不一定都是高级个人贡献者，但它们要么是高级的，要么正在走向高级。比如你不能只有一堆超级高级的数据科学家而没有新人，因为那样未来谁来做新的超级数据科学家。所以你需要招聘、指导和培养人才。也许团队整体更小，然后那些没有走向超级高级的人会转向更通才的角色。

I mean, there's anxiety everywhere. I've talked to a lot of people at a lot of other companies, and it just seems like there's a lot of concern right now about competition, about job displacement, about unintended or unforeseen consequences of all this technology and all this moving so quickly. So that's definitely happening. I think you will see the functional lines continue to blur, but I still think there will be room for functions. They'll just be shaped differently. They'll be more, they won't all be senior ICs necessarily, but they'll all be either senior or on their way to being senior. Like you can't just have a bunch of super senior data scientists and no new ones, because then who's going to be the new super data scientists in the future. So you need to basically hire and mentor and grow talent. Maybe the team is smaller overall, and then those who aren't on their way to being super senior move into more of a generalist role.

你刚才谈到了品味。这让我想到——你面试了很多人，也招了很多人。在这个世界里，你越来越看重哪些特质？又有哪些特质在下降，对你来说可能不那么重要了？

So you talked about taste. This makes me think about—so you're interviewing a lot of people, hiring a lot of people. What are some traits that are trending up in things that you look for more and more now in this world? And what are some traits that are trending down and maybe less important to you?

有些东西是不变的，对吧？很长一段时间以来，无论什么职能，我始终看重三件事：你有没有韧性？就是那种干劲，那种内心的火焰。你是不是学得快？还有，你是不是相当——最好是高度——有自我意识，这样你才能真正接受反馈，知道自己擅长什么。因为如果你具备这三样——有干劲、学得快、有自我意识——你最终几乎什么都能做好。但如果缺了任何一样，通常就会出问题。所以这算是现在招聘的基线。但对于那些在未来五到十年、随着变化如此剧烈而会更成功的人来说，我认为有两件事是我一直在鼓励自己去做的：保持好奇心，以及勇于尝试。我就是觉得你得去试，对吧？就像之前说的，没人真正知道会发生什么，你只需要愿意去尝试。这几乎——我不知道。你会说另一种语言吗？

I mean there are some things that are the same, right? So, for the longest time, almost no matter what the function, I always look for three things. Do you have grit? Like, you know, you've got some drive, some fire in your belly. Are you a quick learner? And are you reasonably, ideally, very self-aware so that you can actually take feedback and know what you're good at? Because if you're those three things—if you got fire in your belly, you learn quickly, and you're self-aware—you can kind of get good at anything eventually. But if any of those things are missing, there's usually an issue. So that's sort of the baseline right now for hiring. But for people who are going to be more successful over these next five or 10 years as things change so significantly, I think two things that I'm continuing to encourage myself to do are to stay curious and to put yourself out there. I just think you got to try things, right? To that point before where no one really knows what's going on, you just have to be willing to try things. It's almost—I don't know. Do you speak another language?

呃，俄语。是的。

Uh, Russian. Yeah.

是的。所以，当你学习另一种语言时，我认为最重要的事情之一，也是最好的预测指标之一——这是我的猜测，我没有这方面的研究——关于你是否能说好，就是：你愿意像个傻瓜一样吗？你愿意直接说出来，被纠正，不生气，然后越来越好？你只需要把自己推出去。对于所有这些新工具、模型和技术，我认为你只需要愿意去尝试。所以如果你保持好奇并去尝试，我认为你会学习、会适应。但如果你不好奇，或者不愿意犯错或尝试，我认为你会遇到大麻烦。或者至少我认为这会是一段非常艰难的时期。所以我认为这些是加分项，不仅对于在 Meta 这样的公司或 Instagram 这样的团队招聘，而且我认为在接下来的 10 到 20 年里，整个行业和多个行业都是如此。有没有一些特质我们可能在某些职能上不那么看重了？我认为有些角色仍然会有非常大的团队，所以你需要那些真正擅长管理大型组织的人。大型组织领导力本身就是一门手艺和技能。它实际上不同于管理。但我确实认为这类角色会减少。我认为我们会有更多的小团队，管理数千人的人会更少。所以不是说那份工作会消失，但我在招聘中会减少对这类人的需求，因为我的这类角色会变少。

Yeah. So, when you learn another language, I think one of the most important things, one of the best predictors—this is my guess, I don't have any research on this—about whether you're going to get good at speaking is: are you willing to sound like an idiot? Are you willing just to say it and be corrected and not be offended and then just get better and better? You just have to put yourself out there. And with all of these new tools and models and technologies, I think you just have to be willing to try stuff. So if you're curious and you try stuff, I think you'll learn, you'll adapt. But if you're not curious or you're not willing to make mistakes or try things, I think you're in a ton of trouble. Or at least I think it's going to be a really difficult time. So those I think are premiums, not just for hiring at a company like Meta or a team like Instagram, but I think across the industry and multiple industries over the next 10 or 20 years. Is there something that maybe we're looking for less of for some of these functions? I think that there are some that are still going to be very large teams and so you need people who are really good at managing large organizations. Large organizational leadership is its own craft and skill. It's actually different from management. But I do think there'll be less of those roles. I think we'll have more smaller teams and there'll be less people who manage thousands of people. So that's not that that job will go away, but that will be less of what I'm looking for in hires because I'm going to have less roles like that.

我从一些人那里听到，AI 几乎在重置人们的影响力和成功，比如一些在 AI 之前可能表现不佳的人，现在可以做他们以前不擅长的事情，或者 AI 现在让他们能够做到，然后他们蓬勃发展，构建各种东西，帮助其他人。你看到这种情况了吗？就像 AI 在提升一些人，也可能在降低一些人。

Something I'm hearing from a few folks is AI is almost kind of resetting people's impact and success in terms of some people that were maybe low performers pre-AI can now do things they were bad at, or AI now allows them to do, and now they're thriving, building all these things, helping other people. Do you see that at all? Just like AI is lifting other people up, maybe lowering some people down.

是的。我的意思是工作本身就不一样了。拿工程来说。工程曾经是——可能不是大多数，但很大比例，40%、50%、60%——写代码。现在不是了，尤其是如果你和这些实验室的任何人交谈，他们大部分时间都在规划和审查代码。这是一份非常不同的工作。你可能讨厌那样，而你可能只喜欢写代码，或者你可能喜欢那样，而你写代码并不那么快。所以谁成功取决于谁的优势与工具的需求和业务的需求相匹配。所以这确实在发生。另一件事是，有些人对于如何为其他职能做贡献有好主意，但缺乏机械或技术技能，而 AI 降低了这样做的门槛。然后突然他们就能做到了——对我来说，这有点好笑，因为当我被 Facebook 录用时，所有设计师都必须会编程。那是——我经历了一个技术面试环节。我们放弃了那个要求，因为太难招人了。但现在我得以在大概 10 年来第一次重新编程，而我并不是一个好工程师。状态好的时候我也只是个平庸的工程师，但现在我可以负责任地写代码，这真是太棒了。你在各个级别、资历和职能中都能看到这一点。设计师在编程，工程师在拉取数据并做强有力的分析，数据科学家在撰写设计提案。工具并不都是完美的。顺便说一句，我认为我们经常对 AI 的状态持有一种非常两极分化的二元看法，比如你是支持 AI 还是反对 AI？但人不是二元的。我昨天对团队说了这句话，工具的状态也不是二元的。它们在某些方面很出色，在其他方面却非常糟糕。而我认为最能充分利用它们的人，是那些对 AI 擅长什么和不擅长什么有清晰认识的人，同时也对下个月或几个月后它会擅长什么和不擅长什么有直觉或嗅觉。

Yeah. I mean the job is just different. Take engineering. Engineering used to be—maybe not majority but a large percentage, 40, 50, 60%—writing code. It's not now, especially if you talk to anybody at these labs, they're spending most of their time planning and reviewing code. That is a very different job. You might hate that and you might have loved just writing code, or you might love that and you might not have been that fast at writing code. So who succeeds is a function of whose strengths are aligned with the tools' needs and the business's needs. And so this is definitely happening. Another thing is you've had people who had good ideas about how to contribute to other functions but didn't have the mechanical or technical skills to do so, and AI reduces the barrier to do that. Then all of a sudden they can—like for me, it's kind of funny because when I got hired at Facebook, all the designers had to be able to program. That was like—I went through a technical loop. We gave up on that because it was too hard to hire people. But I now get to program again for the first time in maybe 10 years, and I am not a good engineer. I'm a mediocre engineer on a good day, but now I can write code responsibly, which is just an amazing thing. You're seeing this across all sorts of levels and seniority and functions. Designers who are programming, engineers who are pulling data and doing strong analyses, data scientists who are putting together proposals for designs. The tools aren't all great. By the way, I think too often we have this really polarized binary outlook on the state of AI, like are you pro-AI or anti-AI? It's like people aren't binary. I said that to the team yesterday, and the state of the tools isn't binary either. They're amazing at some things and remarkably bad at others. And the people who I think are going to make the most of it are the ones who are clear-eyed about what AI is good at and what it's not good at, and also have an instinct or a nose for what it will be good at and not good at next month or in a couple months from now.

你提到 AI 现在写了我们所有的代码。有人发了条推文，这个想法让我记了好几个月：还记得我们曾经可以免费写代码吗？

You mentioned that AI writes all our code now. Someone tweeted this idea that stuck with me for like months now: remember we used to be able to just write code for free.

我认为你仍然可以免费写代码。只是会用更小的模型。但没错。

I think you'll still be able to write code for free. It'll just be with a smaller model. But yes.

我想确实如此。

I guess that's true.

有些模型几乎免费，这太疯狂了。但想想成本。想想你现在为模型付多少钱，获得什么水平的智能，而一年前同样的价格你能得到什么？到了某个点，增量价值就不重要了。我们正在接近那个点。对于小项目和编程，我认为模型会变得更加重要。这周你有 Fable，显然还有 Anthropic 的 Mythos。我这周花了很多时间用它们。第一次我觉得：“哦，我是在和一个比我技术强得多、聪明得多的工程师对话。”下一个版本，一年后的模型——我需要为 Anthropic 6.0 的前沿 token 付费，还是 Fable 对我的所有副项目来说就足够了？很可能就够了。到那时可能也相当便宜了。

There are models that are close to free, which is crazy. But think about the cost. Think about what you pay for a model now and the level of intelligence you're getting, and at that same price point a year ago, what were you getting? At some point, the incremental value won't matter. We're getting there. With small projects and programming, I think the models will matter even beyond. This week you've got Fable and obviously Mythos from Anthropic. I spent a lot of time with that this week. For the first time, I'm like, "Oh, I'm just talking to a much more technical, much smarter engineer than I am." The next version, a year out from that model—do I need to pay for frontier tokens for Anthropic model 6.0, or is Fable just fine for all my side projects? Probably just fine. Probably pretty cheap by then, too.

是的。当 Kevin Whale 还在 OpenAI 担任 CPO 时，他在播客上说过一句名言：“这是模型有史以来最差的状态。”这仍然很难理解。哇。它只会变得更好。

Yeah. When Kevin Whale was on the podcast when he was CPO at OpenAI, he famously said, "This is the worst the model will ever be." Still hard to comprehend that. Wow. It's only going to get better.

那么，关于 token 花费、ROI 之类的事情，Meta 曾因 token 花费排行榜而闻名。这是个糟糕的主意。不应该有 token 花费排行榜。好吧，谈谈这个，以及你现在如何看待工程师和产品团队的预算？你是想花多少就花多少吗？有上限吗？你有没有找到什么行之有效的方法？

So, on this point of token spend, ROI, and things like that, Meta was famous for this leaderboard of token spend. It's a terrible idea. No leaderboards for token spend. Okay, talk about that and just how do you think about budgets for engineers and product teams at this point? Do you just spend as much as you want? Is there a cap? Have you figured out something that works well?

目前，我们通过关掉一些愚蠢的做法，设法稍微控制了成本。构建一个不创造太多价值的 token 焚烧炉并不难。一旦你审视投入的美元和产出的价值，你可能会说：“哦，那真是个坏主意。”所以现在我们没有对工程师设置 token 限制，实际上对任何人都没有。我认为最终会有必要设置限制，特别是如果成本在下降之前先上升的话。我认为成本最终会下降，原因我们刚刚讨论过。但我把它看作任何其他资源。我必须决定如何将算力分配给不同的团队，因为我的 GPU、CPU、存储、内存等数量有限。我必须决定如何将运营预算分配给各团队的标注预算。我必须决定如何将工资预算分配给各团队的人员编制。你可以想象，至少在一两年内，一名优秀工程师的消耗率可能等同于他们的工资或雇佣成本。在那个世界里，你可能需要设置一些上限。上限应该与公司对你以正 ROI 方式使用这些资源的信任程度成正比。但我认为上限是健康的。现在我们还没到那一步。我认为成本会上升，因为我们会使用更多 token，而不是因为价格必然上涨。但随后我认为价格会下降，因为所有这些前沿模型都会陷入一场价格战。所以走着瞧吧。我认为这会有点像过山车。

Right now we've managed to get the costs reined in a little bit by shutting down the silly things we were doing. It's not that hard to build a token incinerator that doesn't create a lot of value. As soon as you look at the dollars in and value out, you might just be like, "Oh, that's just a bad idea." So right now we don't have token limits for our engineers, actually for anybody I think. I think that'll eventually have to happen, particularly if costs go up before they go down. I think they'll eventually go down for the reasons we just talked about. But I think of it like any other resource. I have to decide how to deploy capacity to my different teams because I have a limited number of GPUs, CPUs, storage, RAM, etc. I have to decide how to deploy opex for labeling budgets across my teams. I have to decide how to deploy payroll for headcount across my teams. I think you can imagine, at least in a year or two, the burn rate of a strong engineer might be the same as their salary or cost of employment. In that world, you'll probably need to put in some caps. The cap should probably be proportional to the company's trust in your ability to use them in an ROI-positive way. But I can imagine caps being healthy. Right now we're not there. I think costs will go up because we'll just be using more tokens, not because prices will necessarily go up. But then I think prices will come down because all these frontier models will be in a bit of a pricing war. So we'll see. I think it'll be a bit of a roller coaster.

那么，回到你提到的这个想法：我们已经从自己编写所有代码发展到几乎所有代码都由 AI 编写。现在感觉转变不只是由 AI 编写，而是由 AI 一次性生成。现在的编程就是引导 AI，而你需要纠正它的频率就是编程本身。软件开发生命周期正在被 AI 慢慢吞噬。我想它会开始越来越多地帮助我们提出想法。我喜欢问人们的问题是：随着 AI 不断吞噬产品开发生命周期的更多部分，你认为人脑在哪些方面仍然最有价值？

So, coming back to this idea that as you said, we've evolved from writing all our code to now approaching all code being written by AI. And it feels like now the transition is not just written by AI, but one-shotted by AI. Coding now is steering AI, and how often you have to correct it is coding now. The software development life cycle is slowly being eaten by AI. It'll start helping us come up with ideas, I imagine, more and more. The question I like to ask people is: where do you think human brains will continue to be most valuable as AI continues to eat more and more of that product development life cycle?

品味，就像我们讨论过的，判断力，特别是关于战略的。你可能会从 AI 那里得到关于战略的反馈，但你不会很快让 AI 来制定战略。即使你这样做，也是在你自己设定的范围内：这是我的目标，这是我的愿景，这是我的约束，这是我的工作，这是我的预算。我认为这更像是管理。你在努力定义成功的样子，决定你对成功路径要给出多少具体指示，然后在这个过程中提供反馈。这本身就是一门手艺。看看一些相同的动态如何出现会很有趣。我相信，如果你作为领导者对团队过于具体指示，你会扼杀好主意。但如果你过于开放，有时团队会浪费时间走错方向。你给团队的自主权水平可能适用于未来的智能体，特别是当我们讨论的不仅仅是构建东西，而是首先决定构建什么时。我认为愿景是对你想达到的世界或产品状态的阐述。然后我认为战略是实现该愿景的一种有主见的路径。战略不能是“成为最好”或“变得出色”。它必须有争议性——一个理性的人应该能够不同意它，否则你可能只是在原始执行上竞争。我认为愿景和战略都将是我们大脑花费越来越多精力的地方，而执行则越来越少。

Taste, like we talked about, judgment, particularly around strategy. You might get feedback from an AI on a strategy, but you're not asking an AI to come up with a strategy anytime soon. Or if you are, it's within the context of bounds you set: here's my goal, here's my vision, here are my constraints, here's my job, here's my budget. I think it looks more like management. You are trying to define what success looks like, decide how prescriptive you want to be about the path to success, and then giving feedback along the way. That is its own craft. It'll be interesting to see how some of the same dynamics come up. I believe that if you are too prescriptive as a leader with a team, you end up stifling good ideas. But if you're too open-ended, sometimes teams just waste time going in the wrong direction. That level of autonomy you give a team might apply to agents in the future, particularly when we're talking not just about building something, but deciding what to build in the first place. I think of vision as an articulation of the world or the state of the product you want to get to. Then I think of strategy as an opinionated path to achieve that vision. A strategy can't be "be the best" or "be amazing." It has to be controversial—a reasonable person should be able to disagree with it, because otherwise you're probably just trying to compete on raw execution. I think both vision and strategy are where our brains will spend more and more of our cycles, and less on execution.

我一直认为 AI 应该非常擅长战略。因为你会想：这是市场，这是关于市场的所有信息，我们的竞争对手，我们的指标，我们的数字，我们的增长，所有这些。帮我找出如何获胜。你认为 AI 知道所有这些会非常擅长这个。我认为它可能可以。但我发现除非你非常积极地引导它，否则它并不擅长。我不是指引导它走向一个答案。我是指基于约束条件。事实证明，当你试图制定战略时，有很多事情需要考虑。你需要考虑技术状况、团队人员以及他们的动机和你能得到什么。有时想出一个处于风口浪尖的想法——它实际上会吸引一些最优秀的人才，而这种推动力会转向这个想法。

Something I have always thought is AI should be incredibly good at strategy. Because you would think: here's the market, here's all the information on the market, our competitors, our metrics, our numbers, our growth, all these things. Help me figure out how to win. You think AI knowing all that would be really good at this. I think it could be. I have found it's not unless you steer it pretty aggressively. And I don't mean towards an answer. I mean based on the constraints. It turns out when you're trying to come up with a strategy, there's a lot of things to consider. You need to consider the state of the technology, the personnel on the team and what's motivating them and what you can get. Sometimes coming up with an idea that is on the bubble—it's going to actually attract some of the best talent, and that kind of push then goes to the idea.

显然，竞争格局、像我们这样大公司的监管环境、合规环境，以及品牌的定位和存在理由，所有这些都必须考虑。我认为，如果你只是懒散地向 AI 要一个策略，你不会得到什么好东西。你会得到相当可预测的东西，可能竞争对手也会预料到你会这么做。我认为，如果你想要一个更有效的策略，你需要仔细思考所有需要考虑的不同输入。确保你引导 AI 也考虑这些因素。这需要对话和来回交流。但我认为，如果你愿意投入工作和时间，它绝对会有帮助，也绝对能让你更清晰。特别是如果你让它保持批判性。不过，不同模型的风格差异很大。

Obviously the competitive landscape, the regulatory landscape for companies as large as ours, and the compliance landscape, the identity and reason to exist for the brand. You have to consider all of these things. I think if you ask an AI just for a strategy lazily, you're not going to get something great. You're going to get something pretty predictable that probably the competition would expect you to do. I think if you want a really more effective one, you need to think long and hard about what are all the different inputs that need to be considered. Make sure you steer the AI in a way that it's considering those as well. And it needs to be a conversation and the back and forth. But I think if you're willing to put in the work and the time, it can definitely be helpful and definitely be clarifying. Particularly if you tell it to be critical. Different models have very different vibes though.

它们愿意接受反驳的程度。

How willing they are to be pushed back.

所以我建议选一个喜欢反驳的。

So I recommend picking one that likes pushing back.

是的。Mythos 已经变得很擅长说，“我做不到这个。我们继续吧。”

Yeah. Mythos has gotten really good at being like, 'I can't do this. Let's move on.'

Fun 一直有点混蛋，但我其实很欣赏这一点。我真的很欣赏。我真的不想遇到那种只会说“哦，你说得太对了。我很抱歉我那么说”的 AI。而是“不，等等。我想要真正的智慧。我不想要讨好者。”

Fun has always been a little bit of a jerk in a way that I actually appreciate. I truly appreciate it. I really do because I don't want one that's just like, 'Oh, you're so right. I'm so sorry I said that.' It's like, 'No, hold on. I want the real real sort of intelligence. I don't want the pleaser.'

你提到的关于人们对策略感到兴奋这一点非常有趣。我读过一篇文章，好像是科利·多克托罗写的。有一种概念叫半人马和反向半人马。半人马是人的身体马的下半身——这会有下文的，我保证。人的上半身，马的身体。是的。人类负责。我们更倾向于这样，我们想掌控。反向半人马，也就是我们要避免的 AI 情况，是 AI 控制我们，我们只是执行它的命令。那是马头人身。是的，没错。太可怕了。从某种意义上说，像优步司机和 DoorDash 外卖员，这就是他们的生活，这并不好。对很多人来说，危险在于，如果 AI 给我们制定策略，告诉我们该做什么，没人会愿意那样做。所以这是一个非常有趣的反驳点，我们几乎不希望 AI 告诉我们策略。

This point you made about people being excited about the strategy is such an interesting one. There's this idea that I read, I think Cory Doctorow wrote this. There's this kind of concept of a centaur and a reverse centaur. So centaur is a human body horse. This is going somewhere I promise. Human upper part, lower part horse body. Yeah. Where the human is in charge. And that's kind of we prefer that we want to be in charge. Reverse centaur, which is what we want to avoid with AI, is where the AI is controlling us and we're just doing its bidding. It's a horse head on a human body. Yeah, exactly. It's terrifying. Like in a sense like Uber drivers and DoorDash people, this is their life, which is not great. And this is the danger thing for a lot of people is like if it's giving us the strategy and telling us here's what we're built, like no one's gonna want to do that. So that's a really interesting counterpoint to we don't want AI to be telling us the strategy almost.

是的。不，我认为现在有很多事情需要小心。我当然不会仅仅因为你可以把某些工作流程外包给 AI 就认为你应该这么做。有些情况我认为是真正的双赢。有些情况我认为风险大于收益。

Yeah. No, I think there's a lot of things to be careful about right now. And I would certainly not just assume that because you might be able to outsource some workflow to AI that you should. There are certain ones where I think it's really just a win-win. There are certain ones where I think the risk outweighs the benefits.

本期节目由 Mercury 赞助。深受超过 30 万企业家喜爱的颠覆性银行，现在还有 Command。我是 Mercury 超过 6 年的客户，从未想过离开。Mercury 基本上就是由产品人而非银行家打造的银行。他们让发送发票、转账、为团队成员设置虚拟卡变得非常容易，我敢说甚至有趣。你的银行有 API、原生终端 CLI 或 AI 就绪的 MCP 服务器吗？我不这么认为。就在最近，他们推出了 Command，一个直接内置于 Mercury 的对话式界面，充当你的财务操作员。我一直在用 Command 转账、找出我在哪些类别上花钱最多、分析现金流，就在今天，我用它查了过去一年从某个特定赞助商那里赚了多少钱。我只是问：“过去一年我从 X 那里赚了多少钱？”10 秒后，我就得到了答案。这太酷了。访问 mercury.com 了解更多，几分钟内在线申请。Mercury 是一家金融科技公司，不是 FDIC 承保的银行。银行服务由 Choice Financial Group 和 Column NA（FDIC 成员）提供。

This episode is brought to you by Mercury. Radically different banking loved by over 300,000 entrepreneurs and now with Command. I've been a customer of Mercury's for over 6 years. I have never once thought about leaving. Mercury is basically what happens when banking is built by product people, not by bankers. They make it so easy, dare I say fun, to send invoices, move money around, set up virtual cards for folks on my team. Does your bank have an API, a terminal native CLI, or an AI ready MCP server? I don't think so. And just recently, they launched Command, a conversational interface built directly into Mercury, which acts as your financial operator. I've been using Command to transfer money around to figure out what categories I've been spending the most money in, analyze my cash flows, and just today, I used it to find out how much I've made from a specific sponsor over the past year. I just asked, 'How much have I made from X over the past year?' 10 seconds later, I have an answer. It is so freaking cool. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and Column NA, members FDIC.

好的，回到产品领导力，你一路走来学到的东西。我们之前聊过你学到的东西，你提到你合作过的一些最好的产品领导者，他们更像是策展人而非远见者。我想多听听这方面的内容。

Okay, going back to product leadership, things you've learned along your journey. We were chatting ahead of this about just things you've learned and one thing that you said about some of the best product leaders you've worked with is that they're less visionary and more curators. I'd love to hear more along these lines.

是的，我的意思是，你有时确实会遇到一些出色的产品领导者，他们就是想法机器，多产的想法机器。但我认为很多最优秀的人都有品味，有某种特质让真正优秀的人才愿意与他们合作，但最终他们更像是策展人。人员的策展人、想法的策展人、技术的策展人、策略的策展人，因为我不在乎我为某个领域招聘的负责人，策略是来自他们还是别人。我只在乎有一个出色的策略，每个人都认同，并且我们很好地执行了那个策略。所以我认为，一些最好的产品领导者，是的，他们有自己的想法。如果你自己没有一些想法，很难成为一个优秀的策展人，但他们接受现实，即他们自己无法想出所有东西。所以他们需要创造一个环境，让伟大的想法涌现出来，并被选择或决定。所以，你知道，我认为这不仅仅是策展想法，有时也是策展团队和人员。

Yeah, I mean you do sometimes find amazing product leaders who are just idea machines, just prolific idea machines. But I do think a lot of the best have taste, have something about them that really makes strong talent want to work with them, but end up sort of being curators. Curators of people, curators of ideas, curators of technologies, curators of strategies, because I don't really care if I'm hiring a strong lead for an area if the strategy comes from them or from somebody else. I just care that there is an amazing strategy and everyone has bought into it and that we are executing against that strategy well. And so I think that some of the best product leaders, yes, have ideas. It's hard to be a great curator if you don't have some of your own ideas, but they embrace the reality that they can't come up with everything themselves. So they need to create an environment in which great ideas bubble up and are chosen or decided upon. So, you know, I think it's not just about curating ideas, but it's sometimes about curating teams and people.

我喜欢这个观点。我非常同意。我觉得每个人加入团队时，都只想做愿景和策略，而不是实际动手的工作，而且没有 AI 介入。让我来做策略。我喜欢这个观点，其中蕴含着巨大的力量和价值，而人们低估了仅仅需要一个好的团队想法策展人的必要性。

I love that. I so agree. I feel like everyone's always joining a team and they just want to do vision strategy, just not actually hands-on work, and no AI is coming in here. Let me do the strategy. And I love this point that there's so much power and value and people underestimate just the need for just a really good curator of the team's ideas.

是的。有时这不仅仅是关于谁优秀或哪个想法好。也是关于在更广泛的背景下什么会奏效。例如，在团队建设方面，我一直在考虑的一个大问题不仅仅是“这个人是不是这个职位的强有力候选人？”而是这个人如何融入他们的领导团队。所以对于像信任与安全这样的领域，我有一个工程负责人、一个产品员工负责人、一个数据科学负责人、一个设计负责人、一个研究负责人。我需要确保这五个人互补。我需要确保他们，这更像是艺术而非科学，有良好的氛围，对吧？你知道，你需要信任和融洽的关系。

Yeah. Sometimes it's also not just who's good or what idea is good. It's also what is going to work given the broader context. So for instance on team building, a huge thing that I'm always considering is not just 'is this person a really strong candidate for this role?' It is how does this person fit into their leadership team. So if for some area like trust and safety, I have an engineering lead, I have a product staff lead, I have a data science lead, I have a design lead, I have a research lead. I need to make sure that those five complement each other. I need to make sure that they, this is more art than science, have a good vibe, right? You know, you need trust and rapport.

一个拥有高度信任和默契的领导团队几乎可以应对任何挑战。而缺乏信任或默契的领导团队，任何小事都可能变成大问题。所以这种化学反应，正如我所说，更多是艺术而非科学，但它同样重要。我认为一些最优秀的领导者，尤其是产品负责人，要么本能地、要么有意识地具备这种能力——他们有一种嗅觉，能组建出充满正能量和良好协作的团队。

A leadership team with strong trust and rapport can work through most anything. A leadership team without trust or rapport, like anything can become an issue. And so that chemistry bit is, like I said, much more art than science, but that also matters. And so I think some of the best leaders and product leaders specifically also either do that instinctively or consciously, but you know they have a nose for building teams that are going to have good energy and good collaboration.

温暖又模糊的东西。

Warm and fuzzy stuff.

对。但反过来也一样，对吧？在我的职业生涯中，有很多次，我觉得两个人非常出色，我甚至欣赏他们、喜欢他们，但他们就是合不来。你会意识到这不是能力问题，只是性格问题，有时候你不得不做出决断，把他们分开。

Yeah. Well, the flip is also true, right? Like I've had many times in my career I've had two people who I think are amazing and I even adore them and love them and they just can't get along. You're like this isn't a competency issue. This is just a personality issue and you just have to sometimes call it and split them.

我想转换话题，聊聊 Instagram 这个产品、这个平台，以及你们在那里学到的东西。我先问这个问题：Instagram 的算法对人类行为有哪些了解，是大家可能没有意识到的？

I want to transition to talk about Instagram the product, the platform, things you guys have learned there. Let me start with this question. What's something that the Instagram algorithm knows about human behavior that people may not realize?

最常见的误解其实恰恰相反。我认为人们以为算法对每个人的兴趣和偏好有非常详细的语义理解，但实际上并没有。过去 5 到 10 年，推荐系统领域的进步主要来自这些大型嵌入模型和其他技术，它们产生的产物是人类无法直接读取的。它们不可解读，就像巨大的向量。当然，我可以把向量展示给你，但那只是一堆数字，比如七维空间里的数字。所以当我们讨论算法是否“知道”某件事时，我们通常用语义化的方式思考：它知道我喜欢冲浪。但事实并非如此——它只有一个巨大的数字，恰好与冲浪相关。不过，我认为这种情况正在改变。LLM 带来的一个能力是，它们可以用文字——英语或你喜欢的任何语言——来描述那些之前不可解读的产物，至少是近似描述，即使不是直接含义。这就是我最近一直在做的事情——我本周发了一篇帖子，叫做“你的算法”。基本思路是，我们查看你互动过的所有内容，所有这些内容都在一个嵌入空间中。你可以把嵌入空间想象成一张地图，把大量视频映射到同一张地图上，相近或相似的视频会靠在一起。现在我们可以让 LLM 来描述地图的某个区域，它可能会说：“哦，那是深度手冲咖啡的装逼指南。”这真的很神奇。

One of the most common misconceptions is actually in the opposite direction. I think people assume that there's a much more detailed semantic understanding of everybody's interests and preferences in the algorithm than there is. Most of what's really driven the progress in the world of recommenders over the last 5 to 10 years have been, you know, these large embedding models and these other techniques that basically produce artifacts that cannot be read by people. They're not legible. They're like giant vectors. It's like, sure, I can show you the vector, but it's just going to be a bunch of numbers in like a seven-dimensional space. And so when we talk about does the algorithm know something, usually we think in these more semantic terms. It knows I like surfing and it's like it doesn't. It just has this big ass number that happens to correlate with surfing. That said, I think that is starting to change, right? I think that what one of the things that LLMs are enabling is they can describe in words, English or whatever language you prefer, what some of those previously illegible artifacts are, at least approximate to if not mean directly, right? So this is like the thing I've been really—I posted about this this week—this thing called your algorithm. Basically the idea is we take a look at all of the stuff that you've interacted with. And then all of that is in an embedding space. You can think of embedding space as a map. You can map a bunch of videos into the same map. And so videos that are close or similar. And now we can just have an LLM be like describe that part of the map. And it can be like oh that is like deep pourover coffee snobbery. And that's kind of amazing.

这太酷了，你可以让语言模型查看这些数字并推断出你感兴趣的主题。

That is so cool, that like you can ask the LM to look at these numbers and extrapolate, here's like the topic that you're interested in.

对。或者看视频本身。而且你也可以把概念嵌入到同一个空间里。我的意思是，嵌入技术实际上是 LLM 的底层技术，对吧？整个系统就是这样运作的。所以我们现在做的是让你“看到”你的算法。你可以看到我们认为你感兴趣的主题，并且可以调整它，添加或删除内容。但核心想法是，在这个社交媒体应用被推荐算法主导的世界里，给人们一些主动权。不过还有很多其他事情我们目前还做不到，但将来可以。比如，有些需求不是主题性的：我想要更多有趣的内容，我想多看到朋友，我不想看到我高中同学孩子的照片。或者，我不想连续看到七张照片，但六张没问题——只要你能想到的，我们都可以尝试。所以我们还有很多工作要做，我对此感到兴奋。但我认为，历史上一个误解是——直到最近，我们对你的了解并没有你以为的那么多。我们只是发现，哦，你点赞了这些照片，这些人也点赞了同样的照片，他们还点赞了其他照片，所以你可能也会喜欢那些照片。我简化了，但大致就是这样运作的。直到现在，我们才真正达到了人们多年来以为我们已经达到的复杂程度。

Yeah. Or look at the videos. And so the way you—both—you can also embed concepts into that same space. And so I mean embeddings are really the underlying technology underneath LLMs, right? That's how the whole thing works. And so what we do now is we let you, quote unquote, see your algorithm. You can see what topics we think you're interested in. And you can adjust it. You can add and remove things. But the idea here is giving people some agency back in a world where these social media apps are getting taken over by recommendations, but we can't do a lot of other things yet, which we will be able to do. You know, there are things that aren't topical that you might ask for. I want more fun content. I want to see my friends more. I don't want to see my high school kids' friends' kids photos. You know, I don't know. We can come up with—I don't want to see seven photos in a row, but I'm happy to see six photos in a—whatever your heart can, you know, whatever your mind can come up with. So we have a lot of work to do. And so I'm excited about that. But I think a misconception historically is until recently, we don't really know as much about you as you think. We were just like, oh, you liked these photos, these people also like those same photos and they like these other photos, so you might like those other photos. Like that's kind of how—I'm oversimplifying, but that's kind of how it worked. Now only now are we actually getting as sophisticated as I think people have assumed we've been for many years.

这真的很有趣。我想到的一个点是，每个人最终都会经历向算法驱动的广泛全球信息流的转变。人们总以为“我只想按时间顺序看到我认识的和关注的所有人，那将是我最喜欢的 feed”，但事实证明这总是错的。实际上，当 feed 变成算法推荐我们认为你会喜欢的内容时，你的参与度会高得多。

That is really interesting. One that comes to mind is kind of this transition everyone eventually goes through to this like algorithmic broad global feed. It always feels like people think I just want to see chronologically everyone I know and follow and that's going to be my favorite feed and it continues to be proven wrong. No, you actually engage a lot more when it's this algorithmic feed of things we think you will love.

是的，这很难，因为我理解——我本周发了一篇关于主动权的帖子，结果评论区被喷得很惨，这也是工作的一部分，我明白。但纯时间线有几个问题。一个是——这在一定程度上是个人兴趣与规模化效果之间的张力。如果你采用纯时间线，每个人的动机就是尽可能多地发帖，因为一旦发布，它就会出现在所有关注者的 feed 顶部。结果就是，feed 被专业内容淹没，通常是大型公司和出版商的内容——比如《纽约时报》一天能发 50 条。而你的好朋友可能一周才发一条，所以你的 feed 就被占领了。所以部分原因是设计系统时产生的激励——这就像设计一座城市。你需要思考：机制如何运作？会产生哪些激励？人们在这些激励下会如何行动？结果会怎样？另一个问题是，有时最有趣的内容并不是最新的。时效性是相关性的重要因素，但不是唯一因素。比如我姐姐昨晚订婚了，而她在德国。

Yeah, it's tough because I mean I get—I posted this week this thing about agency and I just got destroyed in the comments which is just part of the job. I get it, right. There are a couple issues with the chronological feed. So one is—and some of this is the tension between an individual's interests and what works when you scale it up, right? So if you do a pure chronological feed, the incentive for everybody is to just post as much as possible because it will always be at the top of everyone who follows you's feed as soon as you post. So what ends up happening is that the feed gets overwhelmed with professional content, with usually large company content and publishers because they get—you know, the New York Times can pump out 50 things a day. Your best friend won't—you might get one thing a week from them—and so your feed just gets taken over. So part of it is the incentives that emerge because when you design these systems it's almost like designing a city. You need to think about okay here's how the mechanics work. What are the incentives that arise? How are people going to act within those incentives? And then what happens? And the other thing is sometimes the most interesting thing was just not the most recent thing. Recency is an important input into relevance, but it's not the only one. My sister got engaged last night and you know she's in Germany.

不，她没有。如果她订婚了，那她已经结婚了。

No, she didn't. If she did, she's married.

她去年结婚了，所以我才想到这个例子。

She got married last year. That's why it was top of mind.

好吧。

All right.

但如果她真的订婚了，我可能会因为她在欧洲、有时差而错过那条消息。

But if she got engaged, you know, and I missed it because she lives in Europe and you know, we're different time differences.

我真的想看我兄弟的三明治照片，还是想先看我姐妹的东西？这很难。我很想找到一种方法来找到合适的平衡。我想给人们掌控体验的权利，但我认为这需要以一种创建合理系统的方式进行，不仅对我们作为企业很重要，对整体社区也很重要。我们尝试过默认按时间排序，你可以将其设为默认，但你不仅看到使用量下降，整体满意度也下降。做出那个选择的人当时可能很开心，但当你在几个月里被不太感兴趣的内容淹没时，我们进行大规模调查，发现人们对 Instagram 的满意度越来越低。

Do I really want to see a picture of my brother's po'boy sandwich, or do I want to see my sister's things first? It's tough. I'd love to figure out a way to find the right balance. I want to give people agency over the experience, but I think it needs to be in a way that creates a system that makes sense, not just for us as a business, which matters, but also for the overall community. We've tried chronological by default where you can make it default, and you see not only does usage go down, overall sentiment goes down. The individual who made that choice might be happy at the moment, but when you get pummeled with stuff you're less interested in over months, we run surveys at massive scales, and we see people start to become less and less satisfied with Instagram.

顺着这个思路，每个人都在问你关于 AI 和内容以及它如何影响一切的问题。我想问一个我没见别人问过的问题。AI 内容的兴起对 Instagram 来说是顺风还是逆风，相比于其他平台？这对你们有帮助还是有伤害？

Along these lines, everybody asks you about AI and content and how it impacts everything. I want to ask something I haven't seen someone ask you. Is the rise of AI content a headwind or a tailwind for Instagram versus other platforms? Does it help or hurt you guys?

我认为这会是顺风，但也会是一个挑战。不仅仅是因为内容更多了。显然，我们是一个注意力驱动的业务，一个广告业务。更多内容意味着可能更多注意力。但这并非免费。我觉得我们还不太擅长对 AI 内容进行排名。有好的 AI 内容，也有糟糕的 AI 内容。你应该看到你感兴趣的东西，而不是你不感兴趣的。但我确实认为，在一个权力从机构转移到个人的世界里——最简单的例子是体育，现在球员比球队更相关，我小时候不是这样——我们有必要投资于个人，特别是 Instagram 和创作者。我指的是广义的创作者：不仅仅是推广品牌内容的影响者，而是任何使用 Instagram 等平台来帮助他们做事的人——记者、艺术家、卖自己缝的围巾的人。他们以自己身份出现，创建和分享内容来实现他们想做的事。我们多年来一直朝这个方向倾斜。在一个合成内容丰富的世界里，我实际上认为人们会更寻求创造力、真实性和人，而不是更少。我认为这会帮助我们。这并不意味着我们的平台上不会有 AI 内容。会有坏的和好的 AI 内容，我们会像处理普通内容一样处理它：不安全的内容消失，有趣与否基于排名和个性化。但我认为人们会真正寻求其他观点，因为 Instagram 从来不只是关于内容；它总是关于内容背后的人、他们的观点、他们分享的原因、他们的视角。我认为这会变得更加重要，而不是更少。考虑到我们在很多方面不是最好的，但我们是最大的创作者平台——如果你看看我们如何定义创作者以及有多少人使用我们与其他平台相比——我认为这会是一个顺风，因为人们会寻求人。

I think it's going to be a tailwind, but it's going to be a challenge. Not just because it's more content. Obviously, we're an attention-driven business, an advertising business. More content means potentially more attention. But that's not for free. I don't think we're very good at ranking AI content yet. There's great AI content and crap AI content. You should see the stuff you're interested in and not the stuff you're not. But I do think that in a world where power is shifting from institutions to individuals across industries—the easiest example is sports, where players are more relevant than teams now, which wasn't the case when I was a kid—it behooves us to invest in individuals, specifically for Instagram and creators. I mean creators broadly: not just influencers promoting branded content, but anybody using platforms like Instagram to help do what they do—a journalist, an artist, someone selling scarves they sew. They're out there as themselves, creating and sharing content to achieve whatever they're trying to do. We've been leaning in that direction for many years. In a world with an abundance of synthetic content, I actually think people will seek out creativity, authenticity, and people more, not less. I think that will help us. That doesn't mean we won't have AI content on our platform. There will be bad and good AI content, and we'll handle it the way we normally handle content: unsafe goes away, interesting versus not interesting is based on ranking and personalization. But I think people will really seek out other points of view because Instagram was never just about the content; it was always about the person behind the content, their point of view, why they're sharing it, their perspective. I think that will become more important, not less. Given that we are not the best at many things but are the largest creator platform—if you look at how we define creators and how many use us versus other platforms—I think it'll be a tailwind because people will seek out people.

这与你之前的观点相关，像《纽约时报》这样的公司可以大量生产 AI 内容，而创作者则不然。所以你是说你也想防止这种情况，让个人尽管有这么多 AI 内容，仍能继续表现良好。

This connects to your earlier point that companies like the New York Times can pump out a bunch of AI content versus a creator. So you're saying you want to protect against that too, allow individuals to continue to perform well in spite of all this AI content.

如果你就是喜欢 AI 内容，那很好。你应该能有一个全是 AI 小镇的 feed。如果你不喜欢，那你的 feed 里就不应该有。我理解人们为什么担心；我并非对我们所处的整体范式转变和革命视而不见。但我认为我们不应该根据制作内容的工具来评判内容。我认为我们应该根据内容、观点、内容背后的人来评判。我不认为我们应该过滤掉 AI 内容。我认为我们应该让你知道内容是否是 AI 生成的。我认为我们应该让你更多地了解发布任何内容的人，这样你就可以根据了解他们是谁、在哪里、改了多少次个人资料、或者他们的个人资料是三天前还是三年前创建的，来做出是否相信或信任他们的明智决定。但我不认为我们应该根据你使用的工具来做价值判断。

If you just love AI content, great. You should be able to have a feed that's just AI town. And if you don't, then you shouldn't have it in your feed. I understand why people are concerned; I'm not oblivious to the overall paradigm shift and revolution we're sitting in. But I don't think we should judge content based on the tool that made it. I think we should judge it based on the content, the point of view, the person behind the content. I don't think we should filter out AI content. I think we should let you know if content is AI content or not. I think we should let you know more about the person who posted anything so you can make informed decisions about whether to believe or trust them based on knowing who they are, where they are, how many times they've changed their profile, or if their profile is three days old or three years old. But I don't think we should be making value judgments based on what tool you used.

有没有你喜欢的 AI 内容创作者，让你觉得‘这太好了。我喜欢看这些 AI 视频。’她叫什么？Plastic dream sequence？是这个吗？

Is there an AI content creator you love that you're just like, 'This is so good. I love watching these AI videos.' What is she called? Plastic dream sequence? Is that it?

我想我会查一下名字。Plastic dream sequence。我手机上有。就是那种玩偶、芭比娃娃，但它们以小剪影和片段唱歌。太棒了。有点奇怪但也很棒。很明显是 AI，没有假装不是，但它有非常清晰的创意和审美观点。每次我遇到一个，我都会想，‘是的，我们现在就这样做。我要看 30 秒。’

I think I'll check the name. Plastic dream sequence. I have it on my phone. It's these sort of dolls, Barbies, but they're singing songs in tiny little silhouettes and snippets. It's just amazing. It's a little weird but also kind of amazing. It's very clearly AI, not pretending not to be, but it has a very clear creative and aesthetic point of view. Every time I come by one, I'm like, 'Yep, we're doing this now. I'm going to watch this for 30 seconds.'

我把它调出来了。我想看，但我不打算看。

I have it pulled up here. I want to watch it, but I'm not going to.

太棒了。

That's awesome.

If Only AI，这是另一个。他在法国，我想是巴黎。他使用多种不同的工具和模型，但他试图创造这些梦境并让它们动起来。他用一个模型创建图像，另一个创建视频、音乐等。他显然有自己的审美。你可以把他看作画家，但这是他的工具。

If Only AI, that's another one. He's in France, I think Paris. He uses multiple different tools and models, but he tries to create these dreamscapes and animate them. He uses one model to create the image, another to create the video, music, etc. He's very clearly got his own aesthetic. You could think of him as a painter, but this is his tool.

在信息流中，是否有某种 AI 与人类对比的愿景？就像你说的，你可能想标记一下，你怎么看待用户？他们会是 AI 账户还是非 AI 账户？你怎么看，还是说这仍在进展中？

Is there kind of a vision of AI versus human in the feed? Do you think it'll like you said you maybe want to market like how do you think about people? Are they going to be like AI count, not AI count? How do you think about or is that still kind of a work in progress?

也许我们最终会达成一致，但标记内容和标记账户是有区别的，两者都既有用又有趣。所以，如果内容是由 AI 创建的，我认为你应该能够知道这一点。顺便说一句，这很难，因为我们现在可以检测到，但随着这些模型变得更好，我们可能会失去检测能力。因此，我们也应该非常小心地如实告诉你我们对自己的评估有多自信。嗯，但我认为你应该能够直接问：“嘿，这是 AI 吗？”我们应该能够告诉你。我们认为它可能是，或者我们不确定，或者绝对不是，或者绝对是。实际上，我认为从长远来看，标记相机拍摄的内容（即基本上是非 AI 内容）可能比标记 AI 内容更实用，原因有几个。

Maybe we'll end up in the same place, but there's a difference between marking content and marking accounts and they're both useful and interesting. So, if content was created with AI, I think you should be able to know that. That's hard by the way because we can detect that right now, but as these models get better, we might lose the ability to detect that. So, we should also be very careful to be honest with you about how confident we are in our own sort of assessment. Um, but I think you should be able to just ask be like, "Hey, is this AI?" And we should be able to tell you. We think it probably is or we're not sure or it's definitely not or it definitely is. I actually think we might be more practical to label camera captured content like basically non-AI content as opposed to labeling AI content long term for a couple of reasons.

但在账户层面，我认为这也很重要。确实有一种新的垃圾信息载体，就是这些虚假账户——顺便说一句，AI 创作者没问题，这本身不一定有错——但有一些垃圾信息载体试图滥用这一点，你知道，他们销售虚假的补品，就像一个 AI 僧侣，它没有明确表明自己是 AI，只是试图利用某种审美或某种刻板印象。我们需要想办法打击这种行为。所以，我确实认为我们应该确保你基本上只需要知道，然后你就可以做出自己的明智决定。这个账户是不是真人？这个内容是不是真实的内容？

But then at the account level, I think it also matters. There is definitely a new spam vector which is these fake accounts which by the way an AI creator that's fine there's nothing wrong with that necessarily but there is there are these spam vectors which are trying to abuse that and you know they're selling like you know bogus supplements and it's like an AI monk and it doesn't present it's not obvious that it's an AI and it's just trying to like take advantage of you know a certain aesthetic or a certain sort of stereotype like we need to figure out how to crack down on that. And so I do think we should be making sure that you know basically you just need to know and then you can make your own informed decision. Is the account a real person or not? Is the content a real piece of content or not?

当你想到社交领域的其他平台，你知道，内容平台，有没有什么功能或者处理事情的方式，他们做得很好，让你有点嫉妒或者印象深刻的？

When you think about other platforms in the space social, you know content platforms, are there any features or just like ways of approaching stuff that they do well that you're kind of jealous of or really impressed by?

是的，有很多。每个人。很多人做了很多事，因为对我来说，我们终于赶上的一件事，但我一直印象深刻的，是 TikTok 及其推荐系统发掘小众人才的能力。在排序和推荐的世界里，你可以谈论基于利用的排序。这听起来很糟糕，但它只是意味着使用你已有的数据。然后你可以谈论基于探索的排序，去尝试弄清楚某人可能感兴趣但他们自己甚至还没意识到的东西。通过向人们展示他们可能会喜欢的东西（因为很多人都喜欢）来提升互动要容易得多。而去弄清楚如何测试内容，比如“嘿，你肯定喜欢 Bieber，但你可能也喜欢 Afrobeats，所以我们给你展示一些 Afrobeats，看看会发生什么”，这要难得多。如果你做后者，这种基于探索的排序，我认为它对小众创作者和小型创作者非常有利，因为你给了他们一个机会去找到以前可能看不到他们、甚至不知道自己感兴趣的受众。所以，过去几年我们在排序上投入了大量资金，不仅是为了提高互动，还要提高原创性，增加爆款内容的数量，提高时效性以保持文化相关性。其中很多都受到了 TikTok 和字节跳动的启发。我认为我们正在赶上。实际上，有几个领域，据我们所知，我们已经领先于他们。有几个领域我们仍然落后，但我们已经看到了在任期内首次成为推荐领域最佳的前景。所以，我认为他们因启发了我们的很多工作而值得称赞。

Yeah, there's a bunch. Everybody. So many people do so much because I mean for me like one of the things that we are finally catching up with but I've been always very impressed with is TikTok and their recommender's ability to break small talent. In the world of ranking and recommenders, you can talk about exploitation-based ranking. That sounds terrible, but it just means like using the data you have. And then you can talk about exploration-based ranking, going and trying to figure out, you know, what someone might be interested in that they might even not know they're interested in yet. And it is much easier to move engagement by showing people stuff that, you know, they'll probably like because lots of people like it. It's much harder to go and figure out how to essentially test content so that we can see like hey maybe you sure you like Bieber but you might also like Afrobeats and so we're just going to like show you some Afrobeats and see what happens. If you do the latter, this exploration-based ranking, you can I think it's really good for niche creators and small creators because you give them a chance to find an audience that either wasn't going to see them before or didn't even know that they were interested before. So, we've invested a lot over the last couple years in ranking, not just increasing engagement, but increasing originality, increasing the number of pieces of content that break out, increasing recency to stay culturally relevant. And so a lot of that has been inspired by TikTok and ByteDance. I think we're catching up. There's actually a couple of those areas where I think by best we can tell we're ahead of them. There's a couple where we're still behind, but we have line of sight to I think being the best in class at recommendations for the first time during my tenure. So that's I think and they get a lot of credit for inspiring a lot of their work.

干得好。

Nice job.

嗯，我们拭目以待。我们还没到那一步。他们叫我“失望的爸爸”。我的团队总是说：“你能别那么像失望的爸爸吗？”所以，我正在努力更慷慨地给予人们应有的赞扬。

Well, we'll see. We're not there yet. They call me disappointed dad. My team is always like, "Can you ease up on the disappointed dad vibe?" So, I'm trying to be a little bit more generous about giving people their flowers.

就像你说的，但这是非常成功的领导者身上一个有趣的共同点，就是永不满足。

Like you say that, but that's an interesting common thread across really successful leaders is just never being satisfied.

是的。

Yeah.

总是感觉既是福也是祸。

Always feeling a blessing and a curse.

问题就在这里。确实如此。是的。关于创作者这部分。我认为这也是，你知道，人们抱怨这个全球算法没有向他们展示所有朋友，但我觉得这是这样做的好处，现在你可以让新创作者进入广泛受众，如果你有这种全球算法信息流，这对很多人来说真的很棒。

Here's all the problem. It is. Yeah. On this creator piece. I think that's also, you know, people complain about this global algorithm not showing them all their friends, but I feel like this is a benefit of what happens when you do this now that you can break new creators into wide audience if you have this kind of global algorithmic feed, which is really great for a lot of people.

我的意思是，我在外面谈论很多这些有争议的问题，在评论里经常被抨击，这没关系。我主要想传达的是，几乎总是有权衡的，对吧？那就是，你知道，不幸的是，你不能拥有一切。你知道，如果你想永远看不到不感兴趣的东西，那么你也只会一直看到最基础、最普遍、最低共同点的内容。你知道，你想发现新奇有趣的东西，那么你偶尔也会看到完全不对味的内容。但这不仅适用于排序。所有这些重大辩论都有权衡，对吧？你知道，隐私和安全，这两者是相互矛盾的。你知道，你是否希望公司扫描你的消息？这场辩论的双方都有一些非常重要的权衡。所以，一般来说，当我与那些对事情有强烈感受的人争论和辩论时，我通常不是试图说服他们。他们的想法通常已经定了。我只是试图为观看对话的其他人列举所有不同的利弊。

I mean, I'm out there talking about a lot of these contentious issues and I get beat up a lot in the comments, which is fine. My main thing here is just to try to communicate that there's almost always trade-offs, right? There's that, you know, you can't just have all of the things, unfortunately. You know, you want to never see something you're not interested in, then you're also just going to see like the most basic general lowest common denominator stuff all the time. You know, you want to discover new and interesting things. You're occasionally going to see stuff that was just a miss, you know. But this isn't just true about ranking. All these major debates have trade-offs, right? You know, privacy and safety, those two things are in tension. You know, do you want a company scanning your messages or not? There's some really significant trade-offs on both sides of that debate. And so generally speaking, when I argue and engage in debate with people who feel really strongly about things, I'm not usually trying to convince them. They usually their mind is usually made up. I'm just trying to enumerate all of the different puts and takes for the rest of the people watching the conversation.

说到在评论里被撕碎，你身处所有这些非常棘手的局面之中，改变信息流，你卷入了 Cambridge Analytica 诉讼，所有这些，你就像处在这么多争议的中心。这是你主动投入的吗？这是你需要做的事情。还是说扎克伯格说“亚当，你得成为所有这些事情的前台，冲进去”？这从何而来？

Speaking of getting torn apart in the comments, like you're so in the middle and thick of all of these really hairy situations, changing the feed, you're like in the Cambridge Analytica lawsuit, all this just like you're in the center of so much controversy and is that something you lean into? This is the thing I need to do. Or was it like Zuck being like Adam you got to be the front face of all the stuff and get in there? Like where does that come from?

这始于 News Feed。所以我以前在 Facebook 负责 News Feed，我的看法是，无论我们是否参与，辩论都会发生。

It started on newsfeed. So I used to run newsfeed at Facebook and I my take was that the debate was going to happen with or without us.

所以我们不妨参与进去。于是我开始在 Twitter 上非常活跃，特别是因为当时记者们都在那里。我觉得出现在他们的地盘上能显示一些谦逊。我的 Twitter 最终成了我生命中最黑暗的地方，因为我只关注了我们最大的批评者。这不是在贬低 Twitter，只是我自己的做法。事情就是这样开始的，然后慢慢发展起来。无论好坏，我们已经成为很多人日常生活中非常重要的一部分。我们影响着很多人，肩负着很多责任，也面临着很多变化，而变化会带来焦虑、压力和审视。我们做过很棒的决定，也犯过错误。我们被批评过，有些我觉得不公平，有些则很公平。所以我们只需要接受这场辩论会广泛发生。我认为我们最好还是谈论它，清楚地说明我们在做什么、为什么做、以及有哪些权衡。如果人们不同意，那也没关系。我们谈论我们所做的事情时，不一定能赢得朋友。但我认为从长远来看，人们从根本上更害怕他们不理解的事情，以及那些更隐秘、更难接近的事情。所以我一直努力以可接近和真实的方式出现。我犯过错误，有时享受其中，有时又讨厌它。但这就是开始的方式。很久以前，在马克的高级领导团队中也有过一场有趣的辩论，我们讨论的是，作为一家社交媒体公司，我们的沟通方式非常传统，比如新闻稿，为什么我们不直接使用自己的平台。我本来不在那场辩论中，但我把自己插了进去，试图调解。我认为这也是我最终被卷入的原因，因为马克说：“好吧，我们看看。你为什么不试试看效果如何？”

So we might as well participate. And so I started being really active on Twitter specifically because that's where journalists really lived at the time. And I thought it would show some humility to show up on their turf, so to speak. My Twitter ended up being the darkest place in my life because I just followed all of our biggest critics. That's not a dig on Twitter. That was just what I did. And that's where it started and it kind of slowly built from there. For better, for worse, we've become a really important part of daily life for a lot of people. We touch a lot of people. We have a lot of responsibility and there's a lot of change, and with change there's going to be anxiety and stress and scrutiny. We've made great decisions. We've made mistakes. We've been criticized for things that I think we've been criticized unfairly. We've been criticized fairly. And so we just need to accept that this debate is gonna happen broadly. So I just think it's better for us to talk about it and just be clear about what we're doing, why we're doing it, what the trade-offs are. If people disagree, that's okay. We're not necessarily winning over friends when we talk about what we do. But I think over the long run, people are fundamentally more afraid of things that they don't understand and about things where people are more secretive and less accessible. And so I've tried to show up in an accessible and authentic way. And I've made mistakes and I have enjoyed it at times and hated it at other times. But that's kind of how it started. There was also kind of a fun debate in Mark's senior leadership team a long time ago where we were just talking about how we're a social media company where we had a very conventional approach to communication, like press releases, and why don't we just use our platform. So I was not in that debate, but I stuck myself into that debate, trying to mediate it. And I think that was also a reason why I ended up getting sucked in, because Mark was like, "All right, well, let's see. Why don't you try and see how it goes?"

有什么东西能帮助你应对每次你说出人们不同意的话时涌来的仇恨？

What's something that helps you deal with the hate that flows at you every time you say something that people disagree with?

你试着把它放在视角中看待，对吧？你知道，这始于 2009 年我重新设计 Newsfeed 的时候。我们在 2009 年 3 月为 Facebook 推出了它。我当时是一名设计师，一名 IC 设计师，前端，入门级设计师。第一条评论非常贬低。我想是恐同和反犹太的，你知道，我们所有人都坐在那里。我们发布了这个东西，正在看评论流，第一条评论就是关于——他们不认识我，但说的是“哪个该死的、被屏蔽的、被屏蔽的设计了这个”，我心想，哦，是我。我崩溃了。我当时是个 25 岁的孩子，我不知道，我想了想，得出了一个想法：如果你每天在办公桌前花 30、40、50 分钟，在那里整理照片、给朋友写信、阅读，然后我过来重新整理了你的桌子，没有告诉你，没有警告你，甚至没有解释为什么。你会很生气，而且那是合理的。而当时发生的事情正是如此，只不过涉及数百万人。所以我试着把事情放在视角中看待。然后我试着抽身离开。花时间陪孩子，去户外。有些月份一点也不难熬，有些月份则真的让我很煎熬。

You try to put it in perspective, right? You know, so it started with I did the redesign of Newsfeed in 2009. We launched it, March of 2009 for Facebook. I was a designer. I was an IC designer, front, entry-level designer. And the first comment that came in was something pretty derogatory. I think it was homophobic and anti-semitic, you know, it was just like literally we're all sitting there. We launched this thing and we're just looking at the stream of comments and it's like the first one and it was specifically about they don't know me but it was like what expletive, censored, censored designed this and I was like oh it was me. And I was devastated. I was a 25-year-old kid and I don't know, I thought about it and I came to this idea that if you spent 30, 40, 50 minutes a day at your desk and you organized your photos there and you wrote letters to your friends there and you read there and then I just came and I rearranged your desk and I didn't tell you, I didn't warn you. I didn't even explain why. Like you would be pissed and that would be reasonable. And that was what was happening just, you know, with millions of people. So I try to put things in perspective. And then I try to step away from it. Get time with my kids, get time outside. There are months where it's really not hard at all and there are months where it's really, really grinds on me.

顺着这个思路，有一个著名的反转事件，当你把信息流重新设计成这种视频滚动体验时。全世界都在抗议。

Along those lines, there's a famous kind of reversal when you redesign the feed into this kind of video scrolly experience. There's this whole protest. The world protested.

是的，那相当艰难。

Yeah, that was pretty rough.

当时是什么感觉，比如，好吧，哇，我们其实不对，应该回头。是什么帮助你决定，好吧，我们改变方向。

What was kind of like, okay, wow, we're actually not right and we should go back. What was kind of what helped you decide, okay, let's change course.

实际上，那件事和三四个事情混在了一起。我们对信息流进行了重新设计，变成了视频查看器，那是对 iOS 上 4% 用户的一个测试。它不会全面推出，只是一个早期测试，用来了解这个想法的效果和反馈。我们也在大力投入 Reels，大力投入推荐，也就是来自你不关注账号的帖子。还有一些创作者因为他们的触达下降而不满，他们把这归咎于排名变化。这四件事全部混在了一起。我们有一些大牌创作者公开抨击我们。然后媒体报道了创作者的反弹，这又引发了更多创作者加入。所以我们最终在创作者社区和媒体之间形成了一种乘数效应或回声，但我们从未打算推出那个设计。那只是一个早期测试。我们知道它需要改进。实际上，我们后来继续发展视频，投资创作工具，投资排名和推荐，这推动了此后几年我们的大部分增长。我认为我们被推着走，好吧，我不确定，感觉不像，但我觉得……我真正的收获并不是我们不应该测试那个设计。我认为我们本可以做得更好来解释，也许快一点或慢一点。我认为我们只是推进得太快了。当你负责像 Instagram 这样的平台时，你需要合理且现实地考虑你能进化多少。现在，我宁愿每隔几年经历一次那样的反弹，继续进化并保持相关性，也不愿选择另一种情况，即我们没有视频、没有私信、没有故事、没有排名，我们现在也不会做这个播客。但积极投入的代价是，你偶尔会犯错，而且你肯定会为此付出代价。

So, actually that one got conflated with three or four things. We had a redesign of feed that went to the video viewer that was a test to 4% of users on iOS. It was not going to roll out. It was just an early test to get some sense and feedback on the idea. We were also leaning into Reels a lot. We were also leaning into recommendations, so posts from accounts you don't follow a lot. And there were also creators who were upset about the fact that their reach was going down and they were blaming ranking changes on that. Those four things got all conflated. We had some pretty big-name creators publicly slap us. Then the press covered that creator sort of backlash which then got more creators doing it. So we ended up with this little bit of like a multiplier effect or echo between the creator community and the press back and forth, but we were never going to launch that. That was an early test. We knew it needed work. We actually have continued to grow video and invest in creative tools and invest in ranking and invest in recommendations and that's driven most of our growth in the years since. I think we were pushed, well I don't know, doesn't feel like, but like it, I think we were... I think my real takeaway wasn't that we should have not tested that design necessarily. I think we could have done a bunch of things better to explain and maybe move a little fast, move a little slower. I think we were just pushing things a little bit too fast. And when you are responsible for a platform like Instagram, you need to be reasonable and realistic about how much you can evolve it. Now, I would much rather have backlashes like that every couple years or continue to evolve and continue to stay relevant than the alternative, which would have been like we didn't have video, we didn't have DMs, we didn't have Stories, we didn't have ranking, and we wouldn't be having this podcast right now. But the cost of leaning in is that you're going to occasionally make a mistake and you're going to definitely pay for it.

有趣的是，对于你们这种规模的公司来说，现在运行实验风险很大。一个人发现了，他们就会说“哦，糟了”。你需要有媒体策略。你不必主动沟通，但你需要有一个冷静的策略。比如，我们不能对任何可能有争议的设计变更或测试掉以轻心。

It's interesting how running experiments now is like very risky for companies at your scale. One person spots it and they're like, "Oh shit." You kind of need to have a press. You don't need to be proactive about communicating it, but you need to have a calm strategy. Like we can't for any design change or any test that could be controversial.

我们事先会讨论，比如‘好吧，不是会不会泄露，而是什么时候泄露，我们该怎么说？’我们是主动谈还是被动回应？不管怎样，信息是什么？因为你不能在没有测试的情况下向 30 亿人发布产品，但以我们的规模测试，又不可能指望没人报道。所以你必须在你甚至不确定要不要发布之前，就准备好怎么说。这让开发周期比以前更复杂了。

We talk about it beforehand and be like, 'Okay, not if it leaks, when it leaks, what are we saying?' Should we talk proactively or reactively? Either way, what's the message? Because you can't launch something to three billion people and not test it first, but you can't test something at our scale and not expect people to cover it. So you have to be ready to talk about it before you even know you want to launch it. It makes the development cycle more complicated than it used to be.

是啊。Anthropic 的增长负责人搞了个定价实验，结果在 Twitter 上炸了锅。他说，‘等等，1% 的人就在刷趋势了。’

Yeah. The head of growth at Anthropic launched an experiment with pricing and it just went crazy on Twitter. He's like, 'Hold that, 1% of people were just trending stuff.'

定价尤其要非常小心。我学到了，我们都学到了这些教训。我觉得我们应该多交流经验。

Pricing particularly is one you've got to be real careful with. I've learned, we've all learned these lessons. We should all share notes more. That's what I think.

这里有一条反面教材：如何避免让互联网恨你一整天。

Here's one not to: how to avoid the internet hating you for the day.

是啊。我很乐意多聊聊这个。

Yeah. I'm happy to talk to that more.

我觉得他还行。好了，我要带大家进入播客的两个固定环节：失败角落和热椅角落。失败角落：你做过什么巨大的失败，让你变得更好？

I think he's all right. Okay, I'm going to take us to two recurring corners on the podcast: Fail Corner and Hot Seat Corner. Fail Corner: what's something that you worked on that was a huge failure that helped you become better?

哦，有很多。我给你讲两个吧。在 Instagram 之前，我作为产品经理的第一个项目叫 Facebook Home，它有点像 Android 在操作系统层面的一个分支，还和 HTC 合作做了一款硬件。那是一次惨败。我在那一年半里学到的东西，可能比我职业生涯中任何一年都多。在那之前我只是个设计经理。我自封为产品经理，因为项目上的产品经理辞职了，我就一头扎进去，去理解运营商、OEM、认证，还有 Android 和操作系统。我学到了很多。我很高兴我结束了那个项目，因为它已经进行了很久。有时候，最好的做法就是去执行一个没有市场契合度的想法，只是为了判断这个想法本身到底好不好。我在 Instagram 期间犯的另一个大错误是，第一版 Reels 是建立在 Stories 之上的。Stories 当时势头很猛。那大概是 2019 年，我们想把 Reels 塞进 Stories，因为我们想把它建在增长最快的东西上。但那不是一个坚实的基础。Stories 的阅读完成率相对较低。Stories 的数量远远超过大多数人能看完的。所以大多数 Reels 根本没人看，然后就消失了。如果我们能在 2019 年夏天就推出 2020 年中那个版本的 Reels，我觉得 TikTok 不会像现在这么大。因为 TikTok 真正起飞是在疫情爆发的时候，很多人待在家里，想找点乐子，而且完全不介意手机开着声音。你看数据，2020 年他们爆发了，而我们没跟上。一方面，我是个设计师，我不想给产品加太多新东西，我想扩展已有的基础元素，这就是当时的想法。另一方面，我错了，从过去八年的整体业务来看，这算是一个很大的分岔路口。

Oh, a bunch. So I'll give you two maybe. Before Instagram, my first project as a PM was on a project called Facebook Home, which was a sort of fork of Android at the operating system level and a piece of hardware with HTC. It was a spectacular failure. I learned way more in that year and a half than in any year in my career, I think. I was just a design manager before that. I declared myself a PM because the PM on the project quit, and I just threw myself head first into understanding carriers, OEMs, certification, as well as Android and operating systems. I learned a ton. I'm happy I brought that project to an end because it had been going on for a long time. Sometimes the best thing you can do is execute an idea that doesn't have market fit well just to decide whether or not the idea was a good idea in the first place. Another big mistake I made during my Instagram tenure was the first version of Reels was built on top of Stories. Stories had a ton of momentum. This was 2019, I think, and we were trying to build Reels into Stories because we were trying to build into the thing that was growing the fastest. But it was not a strong foundation. The read-through rate on Stories is relatively low. There are way more Stories than most people have time to consume. So most of the Reels were never seen, and then they disappeared. If we had the version of Reels that we launched in mid-2020 in the summer of 2019, I don't think TikTok is as big as it is now. Because when they really took off was when the pandemic hit, and a bunch of people had a lot of time at home and were looking for a little bit of joy and were totally fine with their phone having sound on. If you look at the numbers, 2020 is when they exploded, and we were out of position. On one hand, I'm a designer. I'm trying to not add new things to the product. I'm trying to extend existing primitives, and that was the idea. On the other hand, I was wrong, and it's a pretty big fork in the road if you just look at the overall business over the last eight years.

我们给世界创造了大量经济机会，让 TikTok 得以成长。

We created a lot of economic opportunity in the world allowing TikTok to grow.

那很好。我很高兴他们存在。

That's good. I'm glad they exist.

好了，最后一个问题。我很好奇你对孩子的屏幕时间管理。我知道你有三个孩子。现在很多人担心 Instagram 对人们不好，对孩子也不好。很多科技高管在开发产品的同时，却不让孩子用设备。作为 Instagram 的负责人，你怎么看待孩子的屏幕时间？

Okay, final question. I'm curious about your screen time policy with your kids. I know you have three kids. There's a lot of concern these days about Instagram not being great for people, not for kids. A lot of tech executives don't let their kids use devices while they're building the product. As head of Instagram, how do you think about screen time with your kids?

对我来说，关键是设定界限。还有教育，以及和他们沟通。但我的孩子还太小，不能用社交媒体。他们分别是 10 岁、8 岁和 6 岁。不过他们每人都有一个 iPad。他们得自己挣屏幕时间。有不同的方式去挣。通常是坐下来做三次作业，每次半小时，这样周末就能得到总共 90 分钟。然后他们可以在周末用这些时间。你得设定界限，不能他们一要你就给。我觉得这很重要。然后我对他们在上面做什么很有主见。我批准他们安装哪些应用。我认为家长应该批准孩子下载到设备上的应用。我们在 Meta 从政策层面倡导这个已经很久了。我觉得这些很有帮助。也有一些例外。一个是飞机上。那纯粹是为了熬过去。我不知道你有没有——如果你是家长的话。就像，‘好吧，我们要飞 10 个小时或 8 个小时。嗯，熬过去就行。’另一个我开始在我 10 岁孩子身上尝试的是学校。我非常支持课堂上不用手机。这种情况越来越普遍。我认为这对教育可能有好处。我也知道在 AI 领域，有人担心孩子用 AI 会学不到批判性思维技能。这个担忧有道理。但我也担心孩子学不会利用 AI，然后处于劣势。所以这是个平衡。我觉得两者都需要。我和大儿子最近开始一起 vibe coding。他喜欢电子游戏。所以我说，‘好，我们做个游戏吧。’他做了一个 19 关的平台游戏，看起来像我小时候玩的 8 位版超级马里奥。但每一关都有自己的主题和怪物类型。还有一个商店，可以买不同的皮肤或武器。一个 10 岁、还用三个手指打字的孩子，只要一起坐几个小时就能做出这种东西，真是难以置信。但这更像是‘我想让你学会如何创造东西。我想让你思考，不只是玩游戏，我会和你一起坐在这里，我们一起做。’对我来说，这些才是重要的。

The key thing for me is boundaries. It's also about education and having conversations with them. But my kids are too young to use social media. They're 10, 8, and 6. But they each have an iPad. They have to earn their time. They have different ways to earn that time. It's usually about sitting down to do homework three times for half an hour each, which gets them a total of 90 minutes on the weekend. Then they can use that time on the weekend. You have to set that boundary where it's not just they ask for it and you give it to them. I think that matters a lot. Then I'm pretty opinionated about what they do on it. I approve what apps they have. I think parents should be approving what apps kids download onto their devices. We've been advocating for this at a policy level for a long time at Meta. I think those things help a lot. There are some exceptions. One is planes. It's just about surviving. I don't know if you've ever—for those of you who are parents. It's like, 'All right, you know, we're flying a 10-hour flight or eight-hour flight. Yeah, just get through it.' The other one I'm starting to experiment with my 10-year-old is schools. I'm pretty supportive of no phone in classrooms. That's happening more and more. I think that's probably good for education. I also know in the world of AI that there's concern about kids using AI and not learning critical thinking skills. That's a valid concern. But I also worry about kids not learning how to leverage AI and then being at a disadvantage. So that's a balance. I think you need both. With my eldest, we started vibe coding recently together. He loves video games. So I said, 'All right, let's make a video game.' He's made this 19-level platformer game that looks like an 8-bit version of Super Mario from when I was a kid. But each level has its own theme, its own types of monsters. There's a store where you can buy different skins or weapons. It's unbelievable what a 10-year-old who still types with three fingers can do with just a couple hours of sitting together. But that's more of a 'I want you to learn how to make things. I want you to be thinking, not just playing games, and I'm going to sit with you and we're going to do this together.' To me, these are the things that matter.

设定界限，把范围缩小到你认为对孩子健康的活动上。每个孩子都不一样。但我确实认为你希望你的孩子具备数字素养和 AI 素养，因为我觉得如果他们不具备，就会处于劣势。但你也不希望它变成毫无约束的混乱局面。

Boundaries, scoping it down to the activities you think are healthy for your kid. Every kid is different. But I do think you want your kids to be digitally literate, AI literate, because I think if they're not, they're going to be at a disadvantage. But you also don't want it to be a free-for-all.

这对我个人来说很有用，因为我有一个三岁的孩子，我正在努力搞清楚这些事情。所以，这很有帮助。

This is selfishly useful for me as a I have a three-year-old and I'm trying to figure all this stuff out. So, this is useful.

哦，是的。不，我们需要制定一个策略。

Oh, yeah. No, we need to figure out a strategy.

这太棒了。这确实是个事。而且你离得不远。你真的离得不远。再过几年就会实现。

It's amazing. It's a thing. And you're not that far off. You're really just not that far off. It's going to happen in a couple years.

明年五岁开始编程。就这么办。

Five coding next year. Let's do it.

我简直不敢相信，六个月前我试过，但完全不行。而现在有了更新的模型，效果惊人。

I couldn't believe I tried to do it six months ago and it just totally didn't work. And then now with newer models, it's been amazing.

他们选择的平台是什么？是用 Claude Code 的人吗？

What's their platform of choice? Are they a Claude Code person?

是的，我十岁的孩子现在正在用 Claude Code。但我们会看看。我们会看看情况如何。

Yeah, my 10-year-old is using Claude Code right now. But we will see. We'll see how that goes.

Adam，我要放你走了。非常感谢你来到这里。你真是个难得的好人。很明显，你多么清晰、多么真诚，而且你对每件事思考得多么深入。所以我真的很感激你能来。

Adam, I'm going to let you go. Thank you so much for being here. You're just such a gem of a person. It's just so obvious how clear, like how authentic you are and just like how deeply you think about everything. So I really appreciate you being here.

感谢你邀请我。我当你的粉丝很久了。终于能聊一聊，真好。

I appreciate you bringing me on. Been a fan for a long time. It's nice to finally get to have a conversation.

我很感激。真的很感激。让我问你最后一个问题，我问每个人的。听众们能怎么帮到你？

I appreciate that. I really appreciate that. Let me just ask you this final question to ask everyone. What's a way that listeners can be useful to you?

我只是觉得，你甚至不必把这个告诉别人，但请记住，这个世界和技术是复杂的，几乎总是存在权衡。你完全可以不同意我或我们做出的决定。但请记住，我们也是人，在这里努力做出这些决定，尽力做到最好。我实际上确实欢迎批评、指正和反馈，但要知道，这些有争议的辩论没有一个是像大多数人假装的那样简单。

I just think you don't even have to tell this to other people, but just remember that this world and technology is complicated and there are almost always tradeoffs. And you can totally disagree with the decisions I or we make. But just remember that we are people here trying to make these decisions, trying to do the best we can. And I actually do invite the criticism and the critique and the feedback, but know that none of these contentious debates are nearly as simple as most people pretend to make them out to be.

金玉良言，Adam，非常感谢你来到这里。

Wise words, Adam, thank you so much for being here.

荣幸之至。谢谢你，Lenny。

Pleasure. Thank you, Lenny.

再见，各位。非常感谢你们的收听。如果你觉得这期节目有价值，可以在 Apple Podcasts、Spotify 或你最喜欢的播客应用上订阅本节目。另外，请考虑给我们评分或写评论，这真的能帮助其他听众找到这个播客。你可以在 lennispodcast.com 找到所有往期节目或了解更多关于本节目的信息。下期节目再见。

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