别为 2027 做规划：OpenAI 如何以 90 天为周期打造产品

好了，我们开始了。我在后台就警告过他们我们要从什么话题开始，因为我知道有很多关于 OpenAI 的问题。有很多关于产品开发未来的问题。但我想谈的是——

All right, we're here. And I warned them backstage what we were going to start this talk about because I know there's a lot of questions about OpenAI. There's a lot of questions about the future of product development. But what I want to talk about is—

你想谈什么，Claire？

What do you want to talk about, Claire?

那个开关。我想谈谈每个 AI 产品都有两张脸。它有左开关和右开关。今天有很多关于迭代产品开发、发布不完美的东西、把东西推向市场的讨论。所以 Tara，我想请你跟我聊聊发布不完美的东西而不是一个开关，以及你如何做出这些决定，把你知道现在可能不是完美的东西、但最终是正确的东西推向市场，以构建正确的产品。

The toggle. I want to talk about the fact that every AI product has two faces. It has left toggle and right toggle. And there's been a lot of conversation today about iterative product development and shipping things that are imperfect and getting things into the wild. And so Tara, I just want you to talk to me about shipping imperfect things instead of a toggle and how do you make these decisions about putting things in the wild that you know may not be the perfect thing right now but are the right thing ultimately to build the right product.

当然。我先说这一点：我是亚洲父母的孩子。我不习惯做不完美的事情。你每次都必须输出完美的东西。我也在 Stripe 工作了大约七年，Stripe 的一切都经过深度打磨和深思熟虑——你有没有想过 100 遍，你有没有把这个枚举命名得完全正确？我认为在这个时代发布东西的一个重大变化是，紧迫性真的很重要，要把功能推向市场。而且，在发布之前你可以做很多理论化的工作，但没有什么能比看到用户尝试、使用，然后从中迭代的实际经验证据更重要。所以我真的不得不转变心态，从如何先验地做到完美，转变为如何发布东西并尽可能快速有效地迭代。所以关于那个开关，它是完美的解决方案吗？不，它是不完美的解决方案。它当然不是理想的发布产品。但真正重要的是把这个智能体框架——或者说使用智能体完成任务的能力——交到超过十亿使用 ChatGPT 的用户手中。所以我们需要发布一些东西，让我们能够做到这一点，同时不破坏开发者的工作流程。

For sure. I will say this to start: I'm the daughter of Asian parents. I'm not used to doing imperfect things. You have to output something perfect every time. I also worked at Stripe for like seven some odd years and everything at Stripe is deeply polished and deeply considered—did you think through it 100 times and did you name this enum the exact correct thing? And I think what has been a big change about shipping things in this era is actually urgency really matters in terms of getting the functionality out there. And also there's a lot of theorizing that one can do before you ship something, but nothing compares to the actual empirical evidence of seeing users try it, use it, and then iterating from there. So I've really had to shift my mindset from how do you get it perfect a priori versus how do you ship something and iterate on it as quickly and effectively as possible. So with the toggle, is it the perfect solution? No, it is an imperfect solution. It is certainly not the ideal thing to ship. But what was really important was getting this agentic harness—or the ability to use an agent to get things done—in the hands of the over a billion users that use ChatGPT. And so we needed to ship something that allowed us to do that without disrupting developers' workflows.

作为如此不完美的解决方案。你知道吗，我确实相信一年后我们可能不会再进行同样的关于挣扎的对话。我们之前也聊过，现在你必须构建并丢弃很多东西，无论是代码还是产品。我很好奇你如何决定废弃什么、保留什么并保持活力。

As such an imperfect solution. And you know what, I do believe that in a year maybe we won't be having the same conversation about the struggle. And we've had this chat before where we've talked about right now you have to build and throw away a lot of stuff, whether it's code or product. And I'm curious how you think about deciding what to deprecate and what to keep around and keep alive.

你知道，那个开关就是一个很好的例子，我认为有一个明显的下一步，就是没有开关，对吧？就像有一个从步骤 A 到步骤 B 的故事。如果你能带着用户一起踏上旅程，那么他们就会更容易接受变化、转折和产品演进。特别是如果他们欣赏他们能够理解并有一定透明度地了解幕后实际发生的事情。所以我认为，只要你有一个连贯的故事，用户能够跟随，并且考虑到他们投入的注意力，你实际上可以做很多事情，而不会真正烧毁任何桥梁。

You know, the toggle is a good example of this where I think there's an obvious sort of next step which is like no toggle, right? Like there's a story that evolves from step A to step B. And if you can take users on the journey with you, then they're a lot more accepting of the change and the turn and the product evolution. Especially if they appreciate that they can have an understanding and some transparency over what's actually going on behind the scenes. So I think as long as you have a coherent story that users can follow along with and, with the amount of attention that they're paying, there's actually a lot that you can do and not really burn any kind of bridges with them.

是的。我很好奇，你知道，那个开关可能不完美，我们可能构建很多必须丢弃的东西，但你在你曾经或即将从事的产品中确实运用了良好的判断力，也确实运用了谨慎和理性。在如此快速发展的产品构建中，你坚持哪些约束？是什么让你保持高质量标准？

Yeah. And I'm curious, you know, that the toggle is maybe imperfect and we may build a lot of stuff that we have to throw away, but you do exercise good judgment and you do exercise caution and reason inside the products that you've worked on or are about to work on. What constraints do you hold really strongly when building product that's moving so fast? What keeps that quality bar high for you?

我认为有几个不同的要素。一个当然是：我们是否在为使用者做加法？我们是否在释放额外价值？这个产品里是否有真正有用的东西？第二个是我们有一个内部标准：拿一个产品，内部发布，试用，让所有人使用，如果内部采用率不够——这不是一个具体的数字或确切的标准——但有一个标准：留存好吗？人们是否发现它令人愉悦或出乎意料地棒？它是否解锁了模型的新用例？它是否在做一些新颖的事情？我认为那种感觉是：这是在增加他们的体验。这是在解锁一组新功能。然后在这个时代可能最重要的是：这是否瞄准了模型 2 到 3 个月后的状态，而不是过度锚定于现在或过度未来主义而无法使用？这是否处于那个完美的区域——我不知道——足够 AGI 化，我认为这就是这里的标准。所以：它有价值吗？用户关心吗？对用户来说是否出乎意料地棒？它是否与模型能力一致？它是否让模型不受阻碍地发挥出色？

I think there's a few different elements of it. One is certainly: are we doing something additive for users? Are we unlocking additional value? Is there something genuinely useful in this product? I think the second is that we have this internal bar of taking a product, shipping it internally, trying it out, having everyone use it, and if there isn't enough uptake internally—and that's not a specific number or exact bar—but there is: is it retaining well? Do people find something delightful about it or something surprisingly great about it? Is it unlocking a new use case of the models? Is it doing something novel? I think that sense for this is adding to their experience. This is something that is unlocking a new set of functionality. And then probably most importantly in this era is: is this aiming for 2 to 3 months of where the models will be, rather than being overly anchored in the present or being overly futuristic and unusable? Is this at that perfect zone of being—I don't know—sufficiently AGI-pilled, that I think is the bar here. So: is it valuable? Do users care about it? Is it surprisingly great for users? And is it in line with model capabilities? And is it getting out of the way of the model doing a great job?

Nan，你呢？你认为哪些约束有助于你构建出色的产品？

Nan, what about you? What constraints do you think help you build great product?

最大的约束老实说是人们的理解力和吸收你所提供内容的能力，对吧？我们经常谈论能力过剩，但模型能做这么多，人们不一定在利用所有这些能力。我认为这就是差距所在，对吧？所以无论你投入什么，如果人们吸收这些变化和吸收那种能力的能力不存在，那么这就是你面临的真正约束过程。

The biggest constraint is honestly people's understanding and ability to absorb what you're giving them, right? We talk a lot about capability overhang, but models can do so much and people aren't necessarily taking advantage of all those abilities. I think that's where the gap is, right? So it doesn't matter what you put into it, if the ability for people to absorb those changes and absorb that capability isn't there, then that's the real bounding process that you're dealing with.

你知道，我们现在在聊，你们两位正在做 Codecs 和相关工作，有很多商业用户包括我自己都围绕依赖这个工具建立了业务，我想你知道我在企业领域待了一段时间，很多人说 B2B 客户无法吸收变化。我的意思是，客户成功人员告诉我他们无法吸收变化。销售人员告诉我销售人员无法吸收变化。就像我们无法吸收变化的速度。你觉得这在 AI 时代仍然成立，还是你觉得这个说法现在已经被打破了？

You know, and we're talking right now, you two are working on codecs and work and there's a lot of business users including myself that have built businesses around relying on this tool and I think you know I spent a minute and a half in enterprise and a lot of people say that B2B customers cannot absorb change. I mean like customer success people tell me they can't absorb change. Sales people tell me sales people can't absorb change. Like we cannot absorb the rate of change. Do you feel like that still stands true in the AI era or do you feel like that statement is broken right now?

其中一些确实是真的，我认为当前的速度对大多数企业来说可能感觉快得离谱——就像快得离谱，功能和进步的盛宴。对某些人来说确实感觉可能令人不堪重负。但与此同时，如果你不向这些企业发布最新和前沿的东西，你最终会被超越。一个很好的例子是，我们大多数企业都在使用聊天。

Some of it is certainly true in that I think the current pace probably to most enterprises feels beyond breakneck—like something beyond breakneck, a la palooza of features and advancements. It certainly feels maybe to some overwhelming. But at the same time, if you're not shipping the latest and the frontier to these enterprises, you'll end up getting leapfrogged. So a great example of this is that most of our enterprises were using chat.

所以他们只是在提问、得到回答，而与此同时，智能体革命正在发生。我们需要尽快把智能体交付给这些企业，否则他们可能会考虑其他产品。他们有自己的节奏去消化变化，而我们需要交付这个巨大的革命性变化——它打破了他们所有的流程，也打破了他们对企业应如何接收新更新的所有认知，这样他们才能真正释放更多价值。这就是做产品的一条老生常谈：做用户真正需要的，而不是用户嘴上说需要的。我觉得这一点在这里比以往任何时候都更适用。

So they were merely asking questions and getting answers, and whilst that was happening, the agent revolution was going on. We needed to get agents to these enterprises as fast as possible, otherwise they might consider alternative products. While they have their own pace of absorbing changes day to day, we needed to ship this giant revolutionary change that broke all of their processes and broke all of their conceptions about how enterprises should receive new updates, so that they could actually unlock more value. It's one of those things — it's a truism of building product: do what your users need, not what your users say they need. And I think that applies here almost more than it ever has before.

既然你提到了智能体，我想聊聊这个话题，因为我确实觉得我们正处在智能体时代，我自己也已经在智能体时代里待了一阵子了。我想跟你辩论一下——

I want to talk about agents since you brought them up, because I do think we're in the age of agents, and I have certainly been in the age of agents for a little bit. And I want to have a debate with you —

哦天哪。

Oh gosh.

——或者我想听听你的看法。也许我们不用辩论，也许我们会达成一致。单一身份智能体 vs. 多身份智能体：一边是我电脑上那个我整天跟它说话的、我心爱的 Codex，另一边是我那 40 个 Grok 机器人，它们在我业务的不同环节里承担着关键作用。我确实觉得，作为产品构建者、作为做智能体的人，我们正看到这种分化：是把一切集中到一个能做任何事的通用智能体身份上、让模型自己去发挥，还是手工打磨 soul MD、为具体任务做小小的微智能体？但我很好奇，带着新鲜视角来看，从设计原则的角度，你在这个光谱上落在哪里——如果有的话？

— or I want your opinion. Maybe we won't have a debate, maybe we'll agree. Single-identity agents versus multi-identity agents: my beloved Codex docked on my computer that I talk to all day versus my 40 Grok bots that are load-bearing across different parts of my business. I do think as product builders, people who are building agents, we're seeing this divergence: do we centralize on a central, generic agent identity that can do anything and let the models get out of the way of the models, or do we artisanal-craft soul MDs and have little micro agents for the job? But I'm just curious, coming in with fresh eyes, where do you land, if anywhere, on that spectrum from a design-principles perspective?

我觉得在设计这个的时候，最成功的尝试其实都试图贴合人性，对吧？我们有数百万年的进化，帮助我们理解如何在脑子里同时装下不同的东西。而 40 个智能体是相当多的，对吧？你问任何人，你愿意管理 40 个直接下属吗——有些人，比如黄仁勋也许可以——但很多人看到这个都会说，我不确定我能不能真的跟上这么多条并行的线。所以我觉得这里面有个东西：我们需要把这些东西归组成一束束的活动。如果你问人们，嘿，你是怎么管理你的智能体的？通常你会听到的是，嗯，首先我有一个幕僚长智能体，它替我管理其他所有智能体。好吧，你这就是作弊了，对吧？你不是在管理七个，你是在管理一个在替你带团队的东西。所以我觉得，一旦你让人们这么干，这种动态立刻就会浮现出来。

I think when designing this, the most successful efforts have really tried to map onto human nature, right? We have millions of years of evolution that help us understand how to hold different things in our heads. And 40 agents is quite a lot, right? If you asked anybody, would you manage 40 direct employees — some, you know, Jensen Huang might do it — but a lot of people would kind of look at that and be like, I don't know if I could actually keep up with that many threads ongoing. So I think there's something in there where we need to group these things into bundles of activity. If you ask people, hey, how do you manage your agents? Usually what you'll hear is, well, first I have this chief-of-staff agent that manages all the other ones for me. It's like, okay, you've just cheated, right? You're not managing seven, you're managing one that's kind of running your team. So I think that dynamic immediately comes to the front as soon as you let people do this kind of stuff.

那你呢？你怎么看？

What about you? What do you think?

我的想法是——这里面当然有哲学层面的东西：你是要一个统一的实体，还是要这些更小的实体？但实际上，这里面有很多非常具体的问题，比如你怎么考虑数据访问和权限？如果你有一个实体，它是在服务账号和你自己的账号之间切换吗？你怎么把它以用户容易理解的方式呈现出来？如果你有一个单一智能体，它有分段记忆吗——比如每次它加入一个私密 Slack 频道，它是不是几乎变成了另一个智能体？它在 Slack 频道里是被“分离”了吗，就像《人生切割术》那样？所以对我来说，回答这个问题其实要回到非常具体的用例，并且大概要把所有这些边界情况都想清楚：记忆应该如何表现，它应该如何与每个人对话，它应该用他们的凭证，还是用一套通用凭证？这个话题上当然可以有哲学层面的观点，但对我来说，这里有太多实际的产品考量——用例在决定这件事上太重要了。

My mind with this — certainly there's a philosophical element to it: do you have this one unified entity, do you have these smaller entities? But actually there are a lot of really tactical questions that go into it, like how do you think about data access and permissions? If you have one entity, is it switching between using a service account or your account? How do you manifest that to the user in an easy way to understand? If you have a single agent, does it have segmented memory — like, every time it joins a private Slack channel, is it almost a different agent? Is it severed in the Slack channel, in the Severance sense? So actually, to me, answering this question goes back to really specifically the use case, and probably thinking through all of those edge cases: how should the memory behave, how should it talk to each person, should it be using their credentials or should it be using a universal set of credentials? There are philosophical things one could opine on in this topic, but to me there are just so many practical product considerations — the use case matters so much in deciding this.

我喜欢这两个回答的地方在于，我觉得对产品人——在座所有人，加上设计师——我们这门手艺有这两面。一面是非常人性的一面，你要试着——这里有《盗梦空间》，还有《人生切割术》——有《盗梦空间》那一面：我怎么进入用户的头脑，他们的心智模型是什么，他们如何与世界交互，我们的产品用什么最自然的方式触及并解决他们的问题。然后还有系统与架构思维，也就是：为了把这个东西以现实的方式呈现到世界上，我需要把所有这些系统、所有平台组件、所有边界情况都想清楚，确保我们能以合理的方式把它完整地交付给用户。你觉得这就是当下设计和构建优秀智能体所需要的两种技能吗？还是说，在你设计或思考智能体式体验时，你觉得还有其他硬技能是你现在比以往更依赖的？

What I love about these two answers is, I think for product folks — everybody in the audience, plus designers — there are these two sides of the craft that we do. There's this very human side, where you're trying to — there's Inception and Severance, I guess — there's the Inception of how do I get in the mind of a user, what are their mental models, how do they interface with the world, what is the most natural way for our products to touch their problems and solve them. And then there is systems and architect thinking, which is: in order to manifest this into the world in a realistic way, I need to think through all these systems, all these platform components, all these edge cases, to make sure that comprehensively we can even deliver this to the user in a sensible way. Do you think those are the two skill sets you need to design and build great agents in this moment? Are there other hard skills you feel like you're relying on now more than ever as you design or think about agentic experiences?

你描述的是系统思维加上用户共情。这些是非常经典的产品管理理念，对吧，是我们在“从前”应用到另一个技术宇宙里的东西。但现在我们必须从头开始，从这些原则出发重新思考这些产品应该被塑造成什么样。所以我觉得这些非常重要，但归根结底，它们只是旧原则换了层皮。我可能还会加一点——我之前提到过，我以前在 Stripe 工作，那是一家非常学术的公司，我父母也非常学术，我喜欢抽象地思考这些东西——但我想说，除了用户共情和系统思维，你真正需要的第三样东西是一种不折不挠的劲头：去试，然后不断迭代，而且你在过程中会承受很多痛苦。就是有一种不停地试、试、试、试，看什么行得通的东西。

You're describing systems thinking plus user empathy. These are very classic product-management types of ideas, right, that we applied to a different universe of technology in the before times. But now we have to start from scratch and from those principles rethink how these products want to be shaped. So I think those are very important, but ultimately they're just the old principles in disguise. I would maybe add a thing to that, which is — again, I mentioned I used to work at Stripe, which is a very academic company, and my parents are very academic, and I love thinking about those things in the abstract — but I'd say the third thing you really need, in addition to user empathy and systems thinking, is a relentlessness to try it out and keep iterating, and you're going to take a lot of pain while you do that. There's just an element of relentlessly trying and trying and trying and trying to see what works.

还有从那些反馈回路中学习的能力——这当然一直都是必需的，只是时钟速度大大加快了。

That, and the ability to learn maybe from those feedback loops — that certainly has always been necessary, but just the clock speed has gone way up.

是啊，你一直在往很多墙上扔很多意大利面。其实，是你的幕僚长智能体在让其他所有智能体往墙上扔意大利面。

Yeah, you're throwing a lot of spaghetti against a lot of walls all the time. Actually, your chief-of-staff agent is telling all the other agents to fling spaghetti at the walls.

说到系统思维和平台，我真的很好奇，因为我们所有人都在寻找工具——当然包括你构建的那些——来提升生产力和效率。

You know, talking about systems thinking and platform, I am really curious, because all of us are looking to tools — certainly ones that you build — for productivity and efficiency.

我在后台跟人说过，我现在太喜欢 computer use 了。甚至不用给我 MCP。我就想把 computer use 指向你的 UI。然后，你知道，我不在乎 token。恭喜你卖给我 token。嗯，但你知道，我在想我们过去有的工具和现在的工具之间的交集是什么，你构建什么，你为什么搭建平台。我很好奇，当你在努力提升人们的工作效率时，你如何看待更广泛的生态系统。

And I was telling somebody backstage, I just love computer use now. Like don't even give me an MCP. I want to point computer use at your UI. And then, you know, I don't care the token. Congratulations on me selling me tokens. Um, but you know, I think about what is the intersection between the tools we had and the tools we have now, what you build, what you platform for. I'm curious how you just think about the broader ecosystem as you're trying to enable productivity for people at work.

所以我认为我们经常思考平台和 ChatGPT 作为平台，哪些功能是原生进入平台的。哪些功能是我们可能允许人们构建的，无论是第一方还是第三方作为插件或附加接口，这就是它与生态系统的关系。

So I think we think a lot about platforms and ChatGPT as a platform and what is the functionality that natively goes into the platform. What is functionality that we might allow folks to build in, whether that's first party or third party as plugins or additional interfaces on top of it, and that's kind of how that relates to the ecosystem.

那么在那个平台中，我们需要向第三方会议应用暴露哪些钩子，以便它最有效地让你在 codecs 中使用 codecs、使用 computer use 等所有这些工具。嗯，但我不认为这就像什么应该在我们的平台或我们的生产力系统中， versus 在其中一个应用中那样二元。

So in that platform what are the hooks that we need to expose to a third-party like meetings app so that it can be most effective for you use in codecs using codecs using computer use like using all the these tools um but I don't think it's so binary as like what should be in our platform or in our productivity system versus in one of these apps.

我认为我们应该提供一系列不同的工具，比如也许像插件一样能够获取你所有的会议数据并理解你接下来需要做什么。但如果那不起作用，那些系统接口不好，那么总有 computer use 作为下一层或作为后备，就像如果用户想要完成这个任务，而你知道预构建的工具或生态系统工具不可用，那么总有其他东西可以依靠。所以我认为更多的是在平台中按层次构建这些能力，将它们作为一组钩子暴露出来，第三方开发者和第一方开发者可以钩入并构建连贯的体验，拥有正确的系统使这些体验彼此可组合。然后最终所有这些都只是服务于用户的目标，当他们试图完成任务时，如果第一层不起作用，就有第二层让他们能够完成任务，当你遍历所有这些能力时。

I think we should offer an array of different tools like for perhaps like a plugin to be able to like get all your meetings data and understand you know what you need to do next. But if that doesn't work and those systems don't uh interface well then there's always computer use as like a next layer or as like a fallback to be like well if the user wants to get this task done and you know the pre-built tools or like ecosystem tools aren't available for it then there's like always something else that they can fall back on. And so I think it's more about structuring these capabilities in the platform in terms of layers, exposing them as like a set of hooks that third-party developers and first-party developers can like hook into and build cohesive experiences, having the right system for those experiences to be composable with one another. And then ultimately all of this is just serving the user's goals of as they try to get their task done, if like the first layer doesn't work, there's like a second layer for them to be able to accomplish their task as you like traverse all those capabilities.

你如何看待集成体验，比如在一个平台中完成所有事情，让我们住在我们的房子里， versus 扩展到这种生态系统，你如何构建——我的意思是，当你说话时我在想的是，你有一个非确定性模型，在自然语言界面之上，功能上有一台计算机可以点击并用你不控制的第三方工具和集成做任何事情，而你仍然试图构建一个全面的、高质量的、品牌良好的、受人喜爱的体验，作为产品领导者，你如何攀登那座山？你有什么天才想法要带进来帮助我们吗？

What do you think about kind of like integrated experiences like do it all in one platform, let's live in our house versus kind of expanding into this ecosystem and how do you build I mean the thing that I was thinking as you were talking is like you have a non-deterministic model on top of a like natural language interface functionally with a computer that can click and do anything with third party tools and integrations you don't control and you're still trying to build like a comprehensive of highquality good branded beloved experience like how do you even climb that mountain as a product leader? Do you have any what's what genius are you going to bring in to help us here?

呃，你看，我不知道这是不是一个新想法或独特想法，但完成所有工作与完成除了最后一英里之外的所有事情之间有很大区别。而最后一英里有时感觉比一开始就不行还要糟糕。如果一开始就不行，你可以说，哦，我就自己做吧，或者我试试另一条路之类的。但如果它完成了 99%，然后突然崩溃，你就会觉得，这实际上是一种更糟糕的未兑现承诺。我认为 computer use 很多时候感觉很好的地方在于它总是能工作，对吧？它可能有点慢，或者你知道，它可能花费很多 token，就像你说的，但它会一直把工作完成。而且你知道，如果生态系统，你知道，我们等它赶上，提供正确的钩子和正确的 MCP 等等，它会 100% 为你完成工作。我认为最终这就是让它感觉超级棒的原因。我认为在所有这些复杂性和变化中，正确的体验——生态系统、界面、模型本身、像 computer use 这样的工具、所有这一切的不可预测性——是用户几乎应该感觉像，你知道，像 Charles Emmes 的名言，你的用户几乎应该感觉像你家里的客人，你已经预料到他们想做的所有事情，并且温柔地为他们提供。所以你知道，我们应用中的用户应该感觉我们已经预料到他们的需求。在某种程度上，模型越来越多地做到这一点。

Uh look I I I don't I don't know if this is a the a new or unique idea but there is a a huge difference between getting um like all of the job done versus getting everything except for the last mile. And the the last mile honestly feels sometimes worse than just like it's a non-starter. If it's a non-starter, you can just like look, I'm just going to do it myself or I'll try another route or whatever it is. But if it gets like 99% there and then it just like barfs, then you you're like, well, this this is this is actually a worse sort of like unfulfilled promise. And I think what feels great about computer use a lot of times is that it will always work, right? It might be a little bit like slow or like, you know, it might cost a lot of tokens like you were talking about, but like it will going to get the job done all the way. And you know, if as the ecosystem kind of, you know, we wait for it to catch up, right, and to provide the right hooks and the right MCPs and all that kind of stuff, it will 100% do the job for you. And I I think that ultimately that's that's what makes it feel super great. I think the right experience amidst all those complexities and changing things, the ecosystem, the interface, the model itself, the like tools like computer use, the unpredictability of all of it is that like the user should almost feel like uh you know like the Charles Emmes quote that your that your user should almost feel like a guest in your home like you've anticipated all the things that they want to do and you've like provided for them gently. Like so should you know our user in our app feel like we've anticipated their needs. And in some ways it's like the model increasingly doing that.

嗯，我得告诉你，每次我走进你的家，它都告诉我该更新了。

Well, I have to tell you, every time I go into your home, it tells me it's time to update.

是的。是的。是的。是的。获取最新获取最新版本。这就是我得到的欢迎，Matt。就像我走进去，就像，你想要雷诺吗？我给你雷诺。

Yeah. Yeah. Yeah. Yeah. Get get the latest get the latest build. That is the welcome, Matt, that I get. It's like I walk in, it's like, you want a Renault? I got a Renault for you.

嗯，我很好奇。你知道，你在设计这个产品的同时它正在热销。你知道，很多事情，Nan，正如你所说，是旧的东西。用户同理心、系统思维，但也有一些新东西，对吧？有一些新技能，我很好奇与研究合作，因为我认为观众中很多人职业生涯中没有做过这件事，因为我认为这对产品人员或产品领导者来说非常新。你从进入这个新角色中学到了什么，你真的必须与研究合作？

Well, I I'm curious. You know, you're you're designing this product while it's flying off the shelf. You know, a lot of the things, Nan, as you said, are are old things. user empathy, systems thinking, but there are some new things, right? There are some new skills and t I'm curious about working with research because this is something I don't think a lot of folks in the audience have done in their careers because I think it's very new for product people or product leaders. What have you learned from going into this new role where you really have to collaborate with research?

是的，我要说对我来说，来到 OpenAI 后与研究合作完全是新的。所以我当然不是这个话题的专家。我会给你像新手的观点，像《康涅狄格 Yankee 在亚瑟王朝廷》那种观点。嗯，我进来与研究合作的观点是，哦，与研究合作不同于与工程合作。这可能对这里每个人都很明显，但我想，哦，这你知道，这相当不同。嗯，就我能为与研究合作添加什么而言，对我来说是带来非常非常具体的用例，非常清楚地了解用户想做什么以及他们的目标，有样本记录。所以理解字面上会话是什么，它看起来像什么，以及为什么模型没有做它需要做的事情。

Yeah, I will say for me working with research was entirely new coming to OpenAI. So I'm certainly not the like expert on this topic. I will give you like the you know novices's view like Connecticut Yankee and King Arthur's Court sort of view of this. Um my take coming in working with research was oh working with research is different than working with engineering. Like that might be obvious to everyone here but I was like oh this you know it is quite different. Um, in as much as like I can add add something to uh working with research, it is for me to come with very very specific use cases, a really clear idea of what like users want to do and their goals to have done like sample readings. So understand like literally what was the like session and what did it look like and why did the model not do what it needed to do.

如果我能写评估，那就像我在那里一样。学习如何写好评估并尽可能多地写评估，是最重要的事情。而且，只要我能说，用这个提示和这些技能，我就能用模型达到这个结果，那这就是我能带到后训练的理想情况，然后说，看，现在我们如何从一开始就把这种能力后训练到模型中？但我认为，这首先需要理解这是一种完全不同的工作方式，我以前真的不知道怎么做，然后达到足够擅长带来具体用户数据的程度，然后如果他们足够信任你，你就可以写评估来启动那个迭代循环。

If I can write evals, that is like I'm there. Learning how to write good evals and writing evals as much as possible is like the most important thing. And in as much as I can say with this prompt and these skills I was able to achieve the outcome with the model, that is like the ideal thing I can take to post-training and be like, look, now how can we post-train this capability into the model from the beginning? But I think it starts with first understanding that this is like a wholly different way of working that I really didn't know how to do before, and then getting to the point where you're good enough at bringing the specific user data, and then if they trust you sufficiently, you can then write the eval to get that iteration loop going.

作为产品领导者，你觉得自己生活在多远的未来？就像，你知道吗，因为我确实认为存在这样的时间视野：我可以解决眼前的问题，有东西要修。我知道接下来会发生什么，我可以预测产品的走向，然后还有明年的模型会在这里。作为建立在这个不断变化的基础之上的领导者，你觉得你必须生活在哪个时间点？

How far in the future do you feel like you live as a product leader? Like are you, you know, because I do think there's this time horizon of like I could fix what's right in front of my face and there's stuff to fix. There's I know what's coming next and I can anticipate where the product's going, and then there's like the models next year are going to be out here. Where do you feel like you have to live as a leader building on top of that foundation that's moving?

你知道吗，你一说“你生活在未来吗？”我就想，我生活在当下。我不生活在未来。我生活在当下。嗯，不，我确实（生活在未来一点）。我认为我必须生活在未来一点。对我来说，理想是两到三个月。不能太自以为是，说这是未来几年的样子，因为我认为我们对未来的预测几乎总是错的。就像我有一个个人文档，记录了我认为会发生但没发生的错误预测。只是为了提醒自己，我无法预测未来几年。同时，如果你只为今天而建，你就会被落下。所以我经常重复这一点，但大约是两到三个月后。

You know, as soon as you said that, like do you live in the future? I was like I live in the now. Like I don't live in the future. I live in the now. Um no, I do. I think I have to live a little bit in the future. To me it's like the ideal is two to three months. Can't be too big-brained and be like this is the future in years because I think that inevitably our predictions of that are almost always wrong. Like I have a personal doc of incorrectly predicted things that I thought were going to happen and didn't happen. Um just to remind myself of like my inability to predict the future years out. And then at the same time if you build exactly for today you're going to get left behind. So it's like I constantly repeat this but it's like two to three months out.

我想再次拉上观众。今天是结束的时候了。让我们互动起来。现在谁在做 2027 年度规划？谁刚刚听到她说你不能超过 60 或 90 天？我认为这是根本的紧张关系。我们都——这太难猜了。但并不是每个企业都一样。

I do want to just let's pull the audience again. It's the end of the day. Let's get interactive. Who right now is doing 2027 annual planning? And who just heard her say you can't go out more than 60 or 90 days? I think this is the fundamental tension. We're all—it's like so hard to even guess. But it's not the same for every business.

是的，就像再次用一个我熟悉的例子，Stripe 的业务与 OpenAI 的业务非常不同。支付市场的动态看起来与之前大致相似，当然在其他方面加速了，但你知道那些力量，你可以建模，比如，这是牛市情况、熊市情况、基准情况，而对于 OpenAI 的业务，你就是做不到。好吧，也许 Sarah Frier 可以，她是专家，我肯定不如她。但对我来说，我很难像我能感觉到“哦，我理解 Stripe 的杠杆”那样预测 OpenAI 的业务。所以也许规划很多个月是合适的，但我认为这涉及理解你所处市场的节奏，并理解给定那个市场的节奏和动态，我应该以什么节奏思考和规划未来。

Yeah, like again just to use an example I'm familiar with, like Stripe's business is very different than OpenAI's business. The dynamics of the payments market look largely similar to how they looked before, certainly accelerated in other ways, but you like know the forces and you can kind of model like, oh, here's the bull case, bear case, base case in a way that for OpenAI's business, you just like cannot do. Well, maybe Sarah Frier can and she's the expert and I'm not as good as her for sure. But like I—it's hard for me to predict OpenAI's business the way I can feel like oh I understand the levers in Stripe's. And so maybe it is appropriate to plan many many months out, but I think it involves understanding the pace of the market you're in and understanding given the pace and the dynamics of that market at what cadence should I be thinking and planning in the future.

所以你听到的是 Stripe 的产品经理仍然必须做年度规划。

So what you're hearing is Stripe PMs still have to do their annual planning.

哦，当然。祝你们好运。就像，是的，当然。

Oh, for sure. Good luck to you all. Like, yeah, for sure.

好的，让我们稍微谈谈。让我们以“过去与现在”来结束。嗯，我想，你知道，我们作为 26 岁的新人，在产品领域已经有一分半钟了。经典产品经理技艺中哪些仍然重要？你认为哪些是新近的必备条件？

All right, let's talk a little bit. Let's close out with then and now. Um, so I want, you know, we've all been in product for a minute and a half as fresh 26-year-olds. What still matters from a classic PM craft? And what do you think is like newly table stakes?

你知道，我认为重点会变化，对吧？就像有很多原因，但你知道，我们谈到了吸收率和能力过剩。所以我认为现在可能比以前更重要的事情是引导流程。我想我们谈过，你知道，人们谈过引导流程是重要的事情，但它也有点具体，很多工程师不喜欢做这个之类的，但我认为要真正获得很多成功，思考初始体验是什么？你如何让人们真正理解你的产品是什么，这可能是现在比以前重要得多的事情。

You know, I think the emphasis changes, right? Like for a lot of reasons, but you know, we talked about just like absorption rate and like capability overhang. So I think something that probably matters way more now than it's done before is onboarding. I think we've talked about, you know, people have talked about onboarding is an important thing, but it's also something where it's a little bit specific and like a lot of engineers don't love working on it and things like that, but I think really to get a lot of success, like thinking about what is the initial experience? How do you get people to really understand what your offering is like constantly is probably something that matters way more now than it does before.

Tara，你怎么看？

Tara, what do you think?

是的，我认为用户理解隐私、他们的数据以及如何使用，比以往任何时候都重要得多。就像用户理解你产品的规范。以前任何不可预测的行为你都可以蒙混过关，因为有一些设置菜单或弹出窗口可以出现。但现在如果智能体半自主地做事，你真的需要确保在任何给定时刻，用户都能预测你引入的功能或原语可能会发生什么。就产品思维或作为产品经理起步而言，在某些方面听起来会是一个非常无聊的答案，但所有通常的东西仍然适用，除了经验性测试和更快行动的重要性只增不减。所以就像是的，做你以前做的所有其他事情，比如深入细节，同时拥有高层和低层视角，真正理解设计，真正理解产品，但也要更快地做所有事情，与用户测试已经变得重要得多。

Yeah, I think users understanding privacy, their data, how that's used is way more important than it always has been. Like users understanding norms of your product. Before any sort of unpredictable behavior you could get away with because there was some settings menu or some popup that could appear. But now if the agent is doing things semi-autonomously, like you actually really need to make sure that at any given point a user can predict what is probably going to happen with a feature or a primitive that you're introducing. And in terms of just product thinking or getting up and running as a PM, in some ways it's going to sound like a really boring answer, but all the usual stuff still applies except the importance of testing things empirically and moving faster has only increased. So it's like yeah do all the other stuff you used to do which is like get in the details, have the high level and the low level at the same time, really understand the design, really understand the product, but also do it all faster and test with users has just become way way more important.

我还有一点，Naan 会成为受害者，当你在开放的 Slack 中时，OpenAI 的产品和工程团队非常可私信，非常容易接触，非常显眼。我的意思是，他们经常收到我的反馈 UYU ID。就像我不喜欢 Astra 说的。我有态度。所以我很好奇，你知道，当然我在自己的生活中尝试过这样做，但你觉得现在直接的用户关系正在成为产品领导者的必备条件吗？就像可私信的产品经理这个想法，我认为我在 OpenAI 看到很多，我很好奇，保持贴近地面，你是否觉得这变得越来越重要。

I have one more that Naan is going to become a victim of when you are in the open Slack, which is the OpenAI product and engineering team is highly DMable, highly accessible, highly visible. I mean, they're getting like feedback UYU IDs from me on the regular. Like I didn't like what Astra said. I had an attitude. And so I'm curious, you know, certainly I have tried to do this in my own life, but do you feel like direct user relationships right now are becoming table stakes for product leaders? Like this idea of the DMable PM I think is one that I see a lot out of OpenAI and I'm curious, staying close to the ground, if you feel like that's becoming more and more important.

我的背景是开发者和企业。所以我一直是一个可私信的产品经理，人们总是可以给我发消息并要求功能。嗯，但我觉得有点新的是，对于一个大型消费产品或者每个人都使用的产品，做到同样水平的可接触性。嗯，所以在某些方面，这对我来说并不正常，就像你当然应该与你的用户直接接触。正如我之前提到的，你对研究的价值在于具体了解用户想要什么以及他们的用例是什么。

My background is in developers and enterprise. So I've always been a DMable PM in the sense that people could always message me and ask for features. Um, but I think what kind of feels new is that for a large consumer product or a product that everybody uses to do the same level accessibility. Um, so in some ways it doesn't feel normal to me like of course you should be in direct contact with your users. And as I mentioned earlier, like your value to research is knowing specifically what the users want and what their use cases are.

所以，你越能成为一个可以私信的 PM 或工程师就越好。比如，如果你不喜欢 Astra 的输出，我其实在想，嗯，Claire 应该把那些输出发给我。知道那里发生了什么会很有趣。那种东西才是重要的，部分上这就是你增加价值的方式。但我很好奇 Rene 的看法。

And so the more you can be a DMable PM or engineer the better. Like if you didn't like Astra's output, I was actually thinking, like, hmm, Claire should send me those outputs. Like it would be really interesting to know what's happening there. Like that stuff is like the important thing, in part that's how you add value. But I'm curious Rene's take.

是的。我觉得你曾在开发者工具领域工作过，对吧？工程师极其挑剔，极其注重细节。通常你要问到第二或第三个追问，才能得到好的反馈。我觉得很多 PM 正感受到这种变化，对吧？因为如果你在设计一个具有自然语言界面的智能体式体验，人们遇到的问题，比如 Astra 游戏的态度，它们相当微妙，对吧？这需要一些解释，比如它到底说了什么？上下文是什么？你惹它生气了吗？你需要各种追问。所以我认为拥有更直接、更深层的关系在那里会结出很多果实，对吧？也许你以前无法利用这一点。

Yeah. I think you know you worked in dev tools, right? And engineers are extremely picky. They're extremely detail oriented. Often you're not going to get good feedback until the second or third follow-up question. And I think that's what a lot of PMs are feeling like a change about, right? Because if you're designing an agentic experience with natural language interfaces, the problems that people encounter like Astro Game is attitude, like they're pretty subtle, right? That bears some explaining like what exactly did it say? What was the context? Did you make it mad? Like all sorts of follow-up questions that you need to have. So I think having a more direct deeper relationship bears a lot of fruit there, right? That maybe you wouldn't have been able to take advantage of before.

你们俩都没收到我的私信。所以，我非常期待未来 90 天你们的表现。我们会建个群聊，会很有趣。好，最后一个问题。当 Dan 和 Anthropic 团队在这里时，他问，一年后产品工艺、角色会有什么不同。我想谈谈这些东西。那么，你觉得产品会有什么不同，比如我们接触的东西？网站上的按钮、体验，你觉得这一切会走向何方？因为我觉得每周、每个月，我们都会想出一个新的形态，一个新的重要东西。作为产品思考者，很好奇。你认为 2027 年什么会大放异彩？我有一个答案，就是语音。

You neither of you have gotten my DMs. So, I'm very excited for the next 90 days for you all. We'll put us in a group chat. It'll be really fun. Okay, last question. So, when Dan was up here with the Anthropic team, he asked, you know, what in a year about the product craft, about the role will be different. I want to talk about the stuff. So, like what do you feel is going to be different about products like what we touch? What the buttons on the site, the experiences, like where do you feel like this is all going? Because I feel like every week, every month, we come up with a new form factor, a new thing that matters. It's curious as product thinkers. What do you think is going to be big in 2027? I have an answer for you and it's voice.

语音。

Voice.

我喜欢语音。你试试 GPT Live。我喜欢语音。就像我们是话痨。

I love voice. You try out GPT live. I love voice. Like we're yappers.

话痨。Hillary Fidley。话痨 API 天才。语音改变了我的工作方式。我就想和电脑说话。我不喜欢……这好多了。在某些方面感觉自然多了。我相信你们都给父母做过技术支持。语音的存在把我为家人做的技术支持减少到了不可思议的程度。比如我在 OpenAI 给咱们的人力团队做新产品的入职培训，语音的存在改变了游戏规则。对很多人来说，这是一个非常直观的界面。这是他们互动的自然方式。就像 Nan 之前说的，你必须考虑人性和我们习惯的方式，我们几千年来进化出的与他人互动的方式，说话感觉如此自然，语音——模型终于擅长这个了。它越来越快。语音，我非常看好语音。

Yappers. Hillary Fidley. The yappers API genius. Like voice has changed the way I work. Like I just want to talk to computer. Like I do not like it's so much better. It feels so much more natural in some ways. Like I'm sure all of you have been tech support for your parents. Like the existence of voice has reduced the amount of tech support I've had to do for my family to an incredible degree. Like I was onboarding our own people team at OpenAI onto a new product and the existence of voice changed the game. Like it is such an intuitive interface for so many people. It's like the natural way they interact. To the thing Nan said earlier about like you kind of have to think about human nature and the way we are used to like the way we've evolved over millennia to interact with others like speaking feels so natural like voice is the models are finally getting good at it. It is getting faster. Voice I'm like so bullish on voice.

语音。你呢？

Voice. What about you?

嗯，我觉得可能像自动驾驶这样的东西，可能会在明年变得更加重要和突出。你知道，我认为人们的一个大问题是空输入框问题，对吧？无论你使用的是像 ChatGPT 这样的通用工具，还是更行业特定的工具，你都有同样的问题，就像，好吧，我有产品了。然后呢？嗯，现在你得学会如何使用它，对吧？然后，这些产品现在很智能。它们真的智能。所以让它们使用自己，帮助你，让这成为一个非常非常温和的入门引导，我认为这可能会是一件非常正常的事情，我们会回顾并说，为什么以前不是这样？

Um I think I think probably like something that looks a lot like self-driving is probably going to be the thing that really you know becomes much more important and prominent in the next year. You know, I think a big problem people have is like, you know, empty input box problems, right? And it doesn't really matter if you're using a general tool like ChatGPT or something more industry specific like you have the same it's like, okay, I have the product. Now what? Well, now you got to learn how to use it, right? And then so having these products are smart now. They're like literally intelligent. So having them kind of use themselves and sort of to help you out and make that a really really gentle on-ramp I think is probably going to be a very normal thing that we're going to look back on be like why why didn't it always work that way?

好，我们需要记录员做我们的问责伙伴。所以我们有语音，有自动驾驶。我要说硬件，取代我们随身携带打开的笔记本电脑。嗯，所以是硬件。是的。我想要一个小宝宝机器人在这里。一个小盒子里的 Codex，这样我就不必做——她在点头——我就不必做这个。这是我的预测。所以不管谁记了笔记，我们看看谁对 2027 年年度热门产品的预测是对的。大家感谢这些来自 OpenAI 的可爱领导者们。Claire，谢谢。

Okay we need the notetakers to be our accountability buddy. So we got voice, we got self-driving. I'm going to say hardware that replaces carrying around our laptop open. Um so hardware. Yeah. Like I want like a little baby baby bot over here. A little Codex in a box where I don't have to do She's nodding where I don't have to do this. That's my prediction. So whoever took notes, we're going to see which of us were right on what 2027 hot product of the year. Everybody thank these lovely leaders from OpenAI for coming. Claire, thank you.