OpenAI 内部：Tara Seshan 谈产品、雄心与 AI 工作的未来

今天的嘉宾是 Tara Seshan。Tara 在 OpenAI 负责 Codex 和 JBT work 的产品。我相信这是目前增长最快、对知识工作者来说最重要的 AI 产品。Tara 与 Andrew Amberino 共事，他最近也来过播客，是她的引擎经理。在加入 OpenAI 之前，Tara 在 Stripe 工作了六年，是最早的五位产品经理之一，多年被评为全公司前三的 Stripe 员工。她还在 Watershed 负责产品，做过创始人和 teal fellow，最重要的是，Tara 是我几年前创办的 Lenny 通讯社的三位研究员之一，这个项目旨在发掘最出色的新兴产品领导者。看到 Tara 在这个极其重要且有影响力的岗位上，我非常兴奋。在开始之前，别忘了访问 lenny's product.com，可以免费获得一年全球最热门、设计最精美的 AI 产品，仅限 Lenny 通讯订阅者。那么，欢迎 Tara Seshan。Tara，非常感谢你来做客，欢迎来到播客。

Today my guest is Tara Seshan. Tara leads product for both Codex and JBT work at OpenAI. I believe this is the fastest growing and arguably most important AI product for knowledge workers today. Tara works alongside Andrew Amberino who was a recent podcast guest. He's her engine manager. Prior to OpenAI, Tara spent six years at Stripe where she joined as one of the first five product managers and for many of those years she was named one of the top three Stripes across the entire organization of Stripe. She also led product at Watershed, was a founder and a teal fellow, and most importantly of all, Tara was one of the three Lenny's newsletter fellows, which is a program that I ran a few years ago to highlight some of the most amazing up-and-coming product leaders. I am so excited to see Tara in this new incredibly important and impactful role. Before we get into it, don't forget to check out lenny's product.com for a free year of the hottest and most beautifully crafted AI products in the world available exclusively to Lenny's newsletter subscribers. With that, I bring you Tara Seshan. Tara, thank you so much for being here and welcome to the podcast.

谢谢你，Lenny。我很高兴来到这里，见到你真好。

Thank you, Lenny. I'm so glad to be here. It's so nice to see you.

我更高兴。你在 OpenAI 已经快一年了，这在大多数地方是很短的时间，但在 AI 领域，这就像一辈子。

I'm even more glad. So you've been at OpenAI for just about a year now, which in most places would be a very short amount of time. In AI time, that's like a lifetime.

是的。

Yes.

我想你加入 OpenAI 时，应该对在前沿实验室工作是什么样子有所预期。我很好奇，真正在 OpenAI 工作最让你惊讶的是什么？最好好坏都说。

I imagine when you joined OpenAI, you had a sense of what it was going to be like to work at a frontier lab. I'm curious what's most surprised you about what it's actually like to work at OpenAI and ideally both good and bad stuff.

在 OpenAI 工作的很多事情对我来说都很熟悉，因为我之前在其他高增长、高人才密度、高强度、超速扩张的地方工作过。所以像“同事都很棒”或“紧迫感很强”这些感觉很熟悉。但最让我惊讶的是，我工作过的很多公司，实际上所有过去的公司都是创始人主导的，而 OpenAI 实际上是“无创始人”的，也就是说公司里的每个人，尤其是在自己领域里，本质上都有点像创始人。OpenAI 自上而下的指令程度，相比我之前工作过的地方极其有限。所以当我刚加入时，这既让人高兴，因为我之前也有过创业经历，我会想“太好了，我可以继续像创始人一样负责这个产品领域或这个团队”，而且我与市场的距离非常非常近。有时候在大公司，你会感觉与用户需求或市场要求隔绝，但在 OpenAI 完全不会。你会像创始人一样，尽一切努力为你的产品找到产品市场契合。但反过来说，也许更令人惊讶的是，我原本以为 OpenAI 会有一个秘密战略宝库，就像在过去的公司，你进来后会想“啊，这是支付圣经，这是我们如何看待支付和运营的”，但实际上 OpenAI 是开放的，所有关于世界应该是什么样子、产品应该如何构建、模型应该如何运作的想法，很快就会成为公开产品或公开信息的一部分。所以这对我来说既是非常积极的惊喜，也肯定改变了我做事的方式。

So many things about working at OpenAI felt familiar to me because I had worked at other places that were, you know, high growth, high talent, high intensity, hyperscaling mode places before. And so some of the things like, oh, my colleagues are so awesome or the urgency is really high felt very familiar. The part to me that actually felt the most surprising is that many companies I've worked for, in fact all the companies I've worked for in the past, have been founder-led and OpenAI is actually founderless, which is that everyone inside the company especially in their area is in essence kind of a founder to some extent. The level of top-down direction at OpenAI is extremely limited relative to places I've worked for prior. And so I think when I first got to the company that was both delightful and that I had come from like a founding journey before and I was like yes I can continue to feel like the founder of this product area or this team and you know the distance between me and the market is very very thin you know and sometimes at a larger company you feel insulated from what users want or feel insulated from like what the market demands but actually at OpenAI that you do not at all. You are doing everything it takes to get product market fit for your product akin to how a founder might. But the counter to this is that maybe the more surprising side of this is I came into the company expecting that there was a treasure trove of OpenAI secret strategy that I would be able to understand akin to how at past companies you come in and you're like ah yes this is like the payments bible and this is how we think about payments and operations and actually OpenAI is open like every sort of thought that exists in terms of this is how the world should look like or this is how product should be built or this is how the model should operate very very quickly becomes a part of the public product or a part of the public messaging. And so that to me was incredibly both positively surprising and just like a change in my operating mode for sure.

告诉我们，并没有一个秘密房间，里面运行着 AGI，还有掌握所有答案的总体规划。

Telling us there's not like the secret room with the AGI running there with the master plan that has all the answers.

或者至少我肯定不在那个房间里。但我觉得真正激励我的是，OpenAI 做的很多事情都会立刻变成用户能在产品中触摸和感受到的东西，这个循环比我见过的任何地方都快。

Or at least I'm not in that room for sure. But I think the piece that is really inspiring to me is that so much of what OpenAI does immediately becomes something that users can touch and feel in the product and that cycle is faster than anywhere else I have seen.

本期节目由本季赞助商 WorkOS 提供。OpenAI、Anthropic、Cursor、Replit、Sierra、Clay 以及数百家其他成功公司有什么共同点？它们都由 WorkOS 提供支持。如果你正在为企业构建产品，你一定体会过集成单点登录、SCIM、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，并提供一个专为 B2B SaaS 打造的现代开发者平台。说实话，我投资的每一家开始向高端市场扩张的初创公司，最终都会与 WorkOS 合作。那是因为他们是最好的。无论你是试图拿下第一个企业客户的种子轮初创公司，还是全球扩张的独角兽，WorkOS 都是实现企业就绪和释放增长的最快路径。它本质上就是企业功能的 Stripe。访问 workos.com 开始使用，或者直接去他们的 Slack，那里有真正的工程师等着回答你的问题。WorkOS 让你通过愉悦的 API、全面的文档和流畅的开发者体验更快地构建。今天就访问 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, 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, 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.

你在很多不同的地方做过产品经理，是一位资深的产品领导者。

You've been a PM at a lot of different places, a longtime PM leader.

在这个新世界里，你会失去什么？

What do you lose in this new world?

当市场更静态或更缓慢时，你有机会做一些类似大战略的工作，因为市场更可预测，或者你至少能理解所有部分。例如，支付在一定程度上是动态市场，但它也是成熟市场，你可以说：“如果我下这个赌注，我的竞争对手可能会下那个赌注”，或者从第一性原理非常严谨地推理所有可能的后续行动。事实上，那个市场的本质要求你这样做——赢家会比其他人思考得更严谨。如果你不严谨思考，就会表现为粗心，因为很多决策本可以预测。但在这个市场，很难理解未来会出现什么。它非常涌现、变化迅速、动态十足。最重要的是，必须紧密联系研究。所以，高产和实证远比学术或理论重要。我过去工作的许多公司都是非常学术和理论的地方。从写一份冗长的推理文档（几乎像博士论文）来规划未来，转变为“我如何尽快得到可以尝试并与用户测试的东西”，这确实是一个真正的转变。从理论到实证的转变起初让我感到不适应。我想：“我是不是没有尽职？我是不是不够深思熟虑？难道我不应该严谨地思考这一切吗？”但实际上，你必须尝试并尽可能多地学习。这意味着你需要做的思考是尽可能精准地确定你的核心假设。而假设的定义是最重要的。用 Shashir Rahul 的话说，什么是“iigen 问题”？什么是那个具体且最重要需要测试的东西？其他一切，你构思的任何其他大战略，都不相关。

When a market is more static or slow-moving, you have the chance to do some grand strategy-esque work because it's more predictable, or you can at least understand all the pieces. For example, payments is a dynamic market to some extent, but it's also established, and you can say, 'If I take this bet, my competitor might take that bet,' or reason from first principles very rigorously through what all the next actions might be. In fact, the nature of that market mandates that you do that—winners will think more rigorously than everyone else. And if you aren't thinking rigorously, it shows up as carelessness, because many of those decisions could have been predicted. But in this market, it's so hard to understand what is going to emerge in the future. It's very emergent, fast-changing, and dynamic. Most importantly, it's crucial to stay tied to the research. So being prolific and empirical is far more important than being academic or theoretical. Many past companies I've worked at were very academic and theoretical places. It was a real switch to go from writing out a long reasoning doc, almost like a PhD thesis, of what I think the plan should be for the next period, to instead asking, 'How do I get to something I can try out and test with users as fast as possible?' That switch from theoretical to empirical felt jarring at first. I thought, 'Am I not doing my due diligence? Am I not being thoughtful enough? Shouldn't I be thinking through all this with rigor?' But actually, you have to try stuff and learn as much as possible. That means the thinking you need to do is being as pointed as possible about your core hypothesis. And that hypothesis definition is the most important thing. What is the—to use the Shashir Rahul phrase—the 'iigen question'? What is that specific, most important thing to test? Everything else, any other grand strategy you concoct, is not relevant.

我很想多听听这个，因为那真的很有趣。这几乎是说，PM 角色中有些东西是不变的。太多东西在变，世界在变，但仍有这一部分甚至更重要。请多谈谈——你具体认为人们需要更关注什么？

I'd love to hear more about that because that's really interesting. It's almost like here's the thing of the PM role that is not changing. So much is changing, the world is changing, but there's still this piece that is even more important. Speak more to that—what specifically do you think people need to focus more on?

是的，PM 角色有很多外在的东西，比如按时执行、写各种具体的文档和演示文稿。但核心始终是：关于你的产品，你需要问的最本质的问题是什么？什么将决定你的产品是否成功？你如何测试？你如何查看结果？你如何将其反馈到循环中，完善假设并再次运行？这始终是 PM 的工作。这当然包括尝试理解用户、市场和你正在构建的实际技术，将这三者结合起来，形成最敏锐的假设，然后尽可能快速有效地进行测试。我认为这不仅没有改变，反而成为公司里最重要的事情。PM 这样思考，工程师这样思考，数据科学家这样思考，设计师也这样思考。每个人都专注于这个非常重要的问题定义和测试循环——我们到底在做什么，我们怎么知道它是否有效。从 PM 的角度看，这很棒，因为 PM 一直专注于把这件事做好。这始终是工作的核心，许多其他外在的东西都消失了，留下的就是每次都要做对的关键。

Yeah, there are so many trappings around the PM role, like running execution on time and writing all these specific docs and presentations. But the core has always been about what is the most essential question you need to ask about your product. What is the thing that will determine whether your product works or not? How do you test that? How do you look at the results? And how do you feed that back into a loop of refining your hypothesis and running it again? That has always been the PM job. That involves, of course, trying to understand users, the market, and the actual technology you're building, pulling those three together to make the sharpest hypothesis you can, and then making the test as fast and effective as possible. I think that has not only not changed, but it's become the most important thing at the company. PMs are thinking this way, engineers are thinking this way, data scientists are thinking this way, designers are thinking this way. Everyone has moved to focus on this really important problem definition and testing loop—what are we actually doing and how do we know if it's working. From a PM standpoint, it's great because PMs have always been focused on getting that right. That has always been the core of the job, and many of the other trappings have fallen away, leaving that as the key thing to get right every time.

你提到了循环这个概念，最近有很多讨论——几周前在 Twitter 上循环很火，感觉它仍然是知识工作广泛讨论的话题。我理解循环的方式基本上是：AI，这是成功的样子，去构建并弄清楚，直到你实现成功。你如何看待循环从软件工程扩展到产品管理和所有知识工作？你认为这会成为趋势吗？

You mentioned this idea of a loop, and there's a lot of talk these days—loops were so hot, I don't know, a few weeks ago on Twitter, and it feels like it continues to be a topic of discussion for knowledge work broadly. The way I understand a loop is essentially: AI, here's what success looks like, go off and build and figure it out until you achieve success. How do you think about this idea of loops expanding from just software engineering to product management and all knowledge work? Do you think that's going to be a thing?

我确实认为，未来的工作将越来越像掌舵而不是划桨。会有智能体与你合作，它们做很多划桨的工作，而你的角色越来越成为把船引向正确方向并指向正确方向。就此而言，我认为掌舵可能会变得越来越高层。掌舵曾经是“我写了这行代码，按 Tab”——哦等等，现在我指挥的东西更全面了，可能到目标层面，甚至更高。我认为掌舵将继续上升抽象层次，但最终仍由人来决定我们指向哪个方向，并根据反馈和额外数据，决定下一步要把这件事带向何方。掌舵的一部分是关于数据告诉你的，但很多是关于做出有主见的决定。我认为有时我们低估了直觉的力量，甚至低估了我们对未来期望的积极决定论。想象一下，“嘿，我希望产品看起来这样”，不是因为相反的策略同样可行，而是因为我希望世界朝着我推动的方向发展。我认为这始终是一种观点——至少目前——需要人来提供。所以我认为循环很棒。在越来越大的循环中运行智能体，让它们为你做更多划桨工作，这很好，但当前你仍然需要掌舵。我认为工作也将表现为与其他人在一组智能体上共同掌舵。将其他队友带入你和智能体之间的互动，其中智能体划桨而你掌舵，这感觉也非常有价值。

I do think that increasingly the future of work will look more like steering than rowing. There will be agents that you can work with that do a lot of the rowing, and your role increasingly becomes steering the ship in the right direction and pointing it in the right direction. To that point, I think that steering might grow higher and higher level. The steering used to be at the level of 'I wrote this line of code, press tab'—oh wait, now I'm directing something a bit more comprehensive, maybe to the goal level, maybe to an even higher level. I think the steering will continue to go up layers of abstraction, but ultimately it's still on a person to find which direction we're pointing this in and, given feedback and additional data, where I want to take this thing next. Some of the steering is about what the data tells you, but a lot of it is about making an opinionated call. I think sometimes we underrate the power of intuition or even positive determinism of what we want the future to be like. Picturing, 'Hey, I would like the product to look this way,' not because the converse is not an equally viable strategy, but because I would like the world to look the direction I'm pushing it in. That, I think, will always remain an opinion—at least right now—that is required from a person. So I think loops are awesome. Running agents in increasingly larger loops where they do more of the rowing for you is great, but right now you really still need to steer. I think work will also look like steering with other people over a group of agents that you work with together. Bringing in other teammates into that interaction between you and the agent, where it's rowing and you're steering, feels incredibly valuable.

这是一种非常有趣的描述方式。

That's such an interesting way of describing it.

这里也有两个想法浮现。一是如果每个人都能使用同样的工具，那么区分你的就是人本身，基本上就是这个人。否则，我们都只是在建造同样的东西。你可以用它。每个人都可以问：我们怎么赢？我们该做什么？然后那个几乎不公平的优势，几乎就是人类的大脑。

There's also like there's two thoughts here that come up. One is if everybody has access to the same tools, the thing that will separate you is the human, the person basically. Otherwise, we're all just going to be building the same thing. You could use it. Everyone could be asking how do we win? What do we do? And then the thing that almost unfair advantage almost is the human brain.

是的，我觉得这在某些方面很像时尚。当然有功能性的衣服，每个人都能穿，也能完成任务，但你穿什么，至少我怎么想我穿什么，很大程度上是关于我想表达什么样的个性，或者我想如何向世界展示自己。而让它引人注目的很大一部分原因是它与他人的表达形成对比。比如我做的衬衫之所以有声明性，只是因为它可能和其他人做的不同，或者和某些群体做的不同，或者表达了我属于某个群体的声明之类的。我认为我们构建的很多产品也有类似的主观性和艺术性。帕特里克·科里森（Patrick Collison）或者可能是约翰·科里森（John Collison）说过一句关于软件的好话：软件不像房地产，你投入钱就能获得价值。它更像电影制作，你可以投入很多钱，但这并不能保证电影成功或优秀。它伴随着某种艺术声明、主观性和艺术性。我认为这依赖于你或你的团队对产品有有趣的话要说。

Yeah, I think it reminds me a lot of fashion actually in some ways. Like there are certainly functional clothes that everybody can wear and gets the job done, but so much about what you wear at least or how I think about what I wear is about what statement I want to make about my individuality or how I want to reflect to the rest of the world. And a lot of what makes that compelling is how it contrasts with other people's expression. Like the shirt I make makes a statement only because it is maybe different than what everybody else is doing or different than some cohort of people are doing or makes a statement about my group membership or something of that kind. And I think a lot of the products that we build feel similarly opinionated and artistic. Like Patrick Collison has this really nice statement or maybe it's John Collison has this really nice statement about software which is that software is not like real estate. You don't put money in and get value out. It is a little bit more like film making where you can put a lot of money into a film but that doesn't guarantee that the film is successful or good. There is some artistic statement or is some opinionation and artistry that goes along with it. And I think that relies on you having something interesting to say or your team having something interesting to say about your product.

马蒂·卡根（Marty Cagan）很强调一个观点：当你对产品或功能有一个想法时，这个想法很少会成为最终的结果。你需要经历一个完整的过程来弄清楚它实际上应该是什么。感觉这就是你在这里说的：作为人类，你需要经历这个过程来理解它真正是什么，以及人们真正想要什么。它永远不会是“好的，明白了，去构建这个东西，我从一开始就懂了。”

There's something Marty Cagan is big on which is this idea that when you have an idea for a product or feature, rarely is that idea the thing that ends up being. There's this whole process you go through to kind of figure out what the hell actually it should be. And it feels like that's kind of what you're saying here is like you need to go through that process as a human to understand what it really is and what people actually want. It's never going to be like, "Okay, got it. Go build this thing. I got it from the beginning."

是的，当然，当然。而且这个循环，这些循环正变得越来越快。所以，你形成这些直觉的能力，获取形成这些直觉所需的信息，然后与人和智能体一起将其付诸行动，这才是关键。

Yeah. For sure. For sure. And that loop, those loops are moving faster and faster and faster. And so your ability to form those intuitions, get the information you need to form those intuitions, and then use that with people and agents to put that into action is the key.

我很好奇，你认为我们作为知识工作者，工作方式的下一个转变会是什么？感觉你不仅拥有一些其他人还没有的最先进的工具，而且你还和世界上最前沿的 AI 人士一起工作。你认为在接下来的三到六个月里，人们内部的工作方式中，哪些会成为我们使用这些 AI 工具的更普遍的方式？

I'm curious what you think the next shift will be in how we work just broadly as knowledge workers. It feels like not only do you have access to the most advanced tools that some people that other people don't yet also you work around the most AI forward people in the world. How are people working internally that you think will become kind of a more normal way we all work using these AI tools in the next I don't know 3 to six months?

是的，我认为这有两个方面。一是继续在越来越高的抽象层次上与智能体合作。也就是说，让智能体为你独立做越来越多的事情，你介入提供方向，然后让智能体继续“烹饪”，在更高的抽象层次上提供细节。这感觉像是人们越来越多地思考持久化智能体的方式，它们感觉像队友，像同事，你可以像和我团队中的某个人合作那样与它们合作：他们做一大堆工作，我提供输入，然后他们再做工作，我们以不同的节奏同步，查看彼此的进行中工作，并提供越来越多的反馈。感觉这种同事模式肯定是趋势。它感觉像是一种更自然的界面，让我们能够与智能体合作，我们在内部也已经看到了很多这样的情况。第二是，到目前为止，我与智能体的大部分工作都是一对一的，我和我的智能体一起工作。也许它会派生出一些子智能体来完成某些任务，但基本上是我和我的智能体在一起。这可能会与我同事和他们的智能体所做的事情脱节。所以有一段时间，内部每个人都在 Slack 上互相发送他们的 Codex 线程截图。我们就像，好吧，我想和你分享我是如何得到这个数字的。这是我如何得到这个数字的。这是我做法的截图。但这也不是最自然的协作方式。所以随着越来越多的工作由我们的智能体完成，我们难道不应该能够与我们的智能体一起完成工作吗？而实现这一点的最自然界面是什么？这些是我们正在思考的一些事情。

Yeah, I think there's two aspects to this. One is continuing to work with agents at higher and higher levels of abstraction. So letting the agent do more and more for you independently, coming in providing that steering, and then letting the agent continue to cook, like let the agent cook and provide details at higher orders of abstraction. Feels like the way people are increasingly thinking about agents that are persistent, that feel like teammates, that feel like co-workers, where you can work with them the way I might work with someone on my team, which is they do a whole bunch of work, I provide input, and then they do work again, and we sync up at different cadences, look at each other's in-progress work, and provide more and more feedback. It feels like that co-worker model is the way that things are certainly going. It feels like a much more natural interface for us to be able to work with agents, and we already see a lot of that internally as well. The second is that a lot of my work with agents thus far has been one-on-one, that I work with my agent. Maybe it's spawned some subagents to get some tasks done, but it's me and my agent together. And that is potentially divorced from what my colleagues are doing with their agents. And so there was a time where everyone internally was just like sending their Codex threads, screenshots of their Codex threads to each other on Slack. We're like, okay, well, I wanted to share with you how I got to this number. Here's how I got to this number. Here's a screenshot of what I did. But that's also not quite the most natural way for someone to collaborate together. And so as more and more work gets done with our agents, shouldn't we be able to get work done with our agents together? And what is the most natural interface to make that happen? And those are some of the things that we're thinking about.

聊天是我能想到的。这太有道理了。就像，好的，这是 Tara 的智能体，这是我的智能体。她做了一些分析工作。我会说，嘿，我的智能体，Lenny 的智能体，去检查一下，确保这是合法的，并且符合我对世界的看法。

Chat is what I'm picturing. That makes so much sense. It's like, okay, here's Tara's agent. Here's my agent. She did some work on some analysis. I'd be, hey, my agent, Lenny's agent, go check make sure this is legit and connects to the way I think about the world.

理想情况下，工作感觉像是一个多人游戏，我们所有人一起完成任务，引导我们的智能体，而我们的智能体继续处理越来越多像划船这样的战术性任务。

Ideally, work feels like a multiplayer game where all of us together are getting stuff done, steering our agents as our agents continue to take care of more and more of those like rowing tactical tasks.

有趣的是，这只是一个缓慢的信任进展，以及意识到这可以成为我们的工作方式。就像，好吧，去工作更长时间，你可以承担更多。就像一直有关于慢起飞、快起飞情景的讨论，每个人都害怕这种快速的 AI 起飞，就像它太聪明了，现在我们有大麻烦了。感觉我们非常像是在慢起飞的情景中，这很好，就像在缓慢迭代。感觉并不那么慢，但从某种意义上说，你知道，我们不像某个 300 智商的 AI，你知道的。

It's interesting how it's just been this like slow progression of trust and just like awareness that this can be how we work. Just this like, okay, go work for longer, you can take on more. It's just like there's been this talk of like the slow takeoff, the fast takeoff scenarios and everyone's afraid of this fast AI takeoff where it's like way too smart and now we're in big trouble. It feels very much like we're on the slow takeoff scenario which is good where it's just like slowly iterating. Doesn't feel that slow, but in a sense, you know, we're not like some 300 IQ AI like, you know.

我的意思是，模型非常聪明，但我认为很多让我们能够与智能体一起工作或让智能体处理越来越高层抽象的事情，当然与智能有关，它们执行长时间运行任务的能力以及它们能保持任务多长时间，但实际上也有一些非常务实、战术性的东西使这成为可能，比如在本地工作的智能体非常方便，因为它们可以访问你机器上的所有数据。要让智能体在云端成功，你必须构建大量的云基础设施才能使这成为可能。

I mean, the models are incredibly smart, but I think a lot of the things that have enabled us to then work with our agents together or have the agents take care of higher and higher order abstraction things certainly are about the intelligence, their ability to perform long-running tasks and how long they can stay on tasks, but also actually there are very meat and potatoes tactical things that make this possible like agents working locally are really convenient because they have access to all the data that's on your machine. To make an agent successful in the cloud, there is a ton of cloud infrastructure that you have to build to make that possible.

就像访问你的系统一样，智能体如何与那些存有你所有数据的第三方系统对话？就像一个你雇来的同事，你把他锁在房间里，从不给他访问 Google Docs、Slack 或公司数据库的权限，那他对你也没什么用。同样，一个被隔离的云智能体也不会那么有效。所以，让这些智能体变得有用并实现这些未来，很大程度上当然在智能层面，但很多也纯粹是战术性的，比如数据访问、云基础设施和可靠性这些方面，它们比一些更广泛的智能问题显得平淡得多，但在某些方面对最终效果同样重要。

And just like access to your systems, like how can agents talk to all these third-party systems that have all of your data? Just like a colleague who you hire, who you like lock into a room and never give them access to Google Docs, Slack, or the company database, would not be that useful to you. Similarly, a cloud agent that is similarly isolated will not be that effective. And so a huge part of making these agents useful and achieving some of these futures is on the intelligence side certainly, but a lot of it is also just really tactical, like data access, cloud infrastructure, and reliability pieces that feel much more prosaic than some of the broader intelligence questions, but matter in some ways just as much for end effectiveness.

这触及了本播客中反复出现的一个词：野心。我知道你也对此思考很多。感觉不仅是因为这些 AI 工具我们能更有野心，我们几乎需要更有野心，这对很多人来说并不自然，因为现在每个人都能轻松完成那些简单的事情。简单的事超级简单，难的事也容易。现在区分人和公司的，就是他们能有多大的野心。谈谈当我提到这种对野心的需求及其出现时，你会想到什么。

This touches on something else that has been coming up a bunch on this podcast: this word ambition. I know you think a lot about this too. It feels like not only are we able to be more ambitious because of these AI tools, we almost need to be more ambitious, which is not natural for a lot of people because everybody can now do all these easy things really easily. Like the easy stuff is super easy. The hard stuff is easy. And the thing that separates people now and companies now is just how ambitious they can be. Talk about what comes up when I talk about the need and the kind of the emergence of this need for ambition.

是的，我认为我们看到的最善于使用 AI 工具的人，不只是用它来自动化日常任务，而是用它来扩展自己能力范围。比如在过去，在所有这些 AI 出现之前，独角兽式的人才是一个有深刻产品感的人，恰好也是工程师，可能还是设计师。那个人总是独角兽式的招聘对象，因为他们能真正消除这些职能之间所需的翻译层级，能自己快速轻松地构建或构思一些东西，并让它运行起来，然后能与团队合作。我认为我发现的最引人注目的，也是我努力用这些工具做到的，以及我看到一些最成功的同事能做到的，就是真正扩展那些“在可能范围内”的事情，这样他们就能开始把头脑中的想法越来越多地实现到现实中，就像以前那种多面手能做到的那样。我们现在几乎都拥有那种超能力，我可以快速生成一套设计，去构建一个初始原型，找出正确的定价模型，并模拟所有场景。真的，可能性范围已经大大拓宽了。实际上，这在很多方面意味着，我有能力更像一个作者，就像之前关于电影的那个观点，更像一个作者，当我努力完成某件事并可能以更高的保真度实现我的愿景时。对我来说，这就是在追求新想法和新产品时能提升你野心的一部分。因为所有这些现在都触手可及，因为这套新能力现在在你的掌握之中，你可以尝试和获取。你不再真正受限。你的野心不再受限于你自己能执行什么、能沟通什么。它可以宽广得多。我认为做这件事最难的部分就是扩展你的思维。实际上，能力已经扩展得如此惊人。真正难的是扩展你对在极短时间内可能实现的事情的思考。对我来说，尝试做到这一点的最好方法是帕特里克·科里森在他的网站上，我想是 patrickcollison.com/fast，上面列出了所有那些在极短时间内完成、野心大得离谱的项目。现在对我来说，这份项目清单的显著之处在于，它们都存在于这些工具让你几乎瞬间学会如何构建某物或用一个问题提问之前，比如“嘿，你能立即为我总结这段非常复杂的文本或这本书吗？”或者我能尝试做所有这些以前对我来说不可能的事情，但现在我能做了吗？比如，我能快速生成，你能为我制作一个我可能有的想法的 CAD 模型吗？真正超出我能力范围的能力现在都在我的掌握之中。所以，如果那些快速项目在我们过去拥有的能力下是可能的，那么随着 AI 赋予我们的能力，我们难道不应该看到那些类型的不合理快速且有效执行的事情数量呈指数级增长吗？

Yeah, I think the people that we see who are most effective at using AI tools don't simply use it to automate routine tasks but use it to expand the set of things that they are capable of doing. Like back in the day, before all this AI stuff, the unicorn person was someone who was a really thoughtful product sense person who also happened to be an engineer who may also have been a designer. That person was always the unicorn hire because they were able to really flatten the layers of translation needed between all these functions and were able to build something or ideate something really quickly and easily themselves and get it up and running and then were able to work with a team and collaborate with the team on it. And I think the most compelling thing I found, that certainly I try to be able to do with these tools and I've seen some of my most successful colleagues be able to do with these tools, is really expand the set of things that are quote unquote within their range of possibilities so that they can start realizing more and more of what's in their head into reality, the way that someone who was previously a jack of all trades was able to do. We kind of all have that superpower now that I can spin up a set of designs on something and I can go build an initial prototype of it and I can figure out the right pricing model for it and model out all the scenarios. Really the set of possibilities have widened dramatically. And actually what that means in so many ways is that I have the ability to be more of an author, to that point earlier about film, like be more of an author as I try to get something done and realize my vision maybe to higher fidelity. And that to me is part of what can elevate your ambitions while pursuing new ideas and new products. Because all of these things are now within reach, because this new set of capabilities is now within your reach to be able to try and access. You're not really limited. Your ambitions are no longer limited by what you're capable of executing yourself, what you're capable of communicating. It can be so much wider. I think the hardest part about doing this is simply just expanding your thinking. Actually, the capabilities have expanded so dramatically. It is really expanding your thinking of what's possible in an unreasonably short time frame. And to me, the best way of trying to do that is Patrick Collison has on his website, patrickcollison.com/fast, I think, which is all of these projects that were unreasonably ambitious that were executed in a really really short time period. And what for me is now remarkable about that list of projects is that they all existed before these tools made it possible for you to learn how to build something almost instantly or ask it with one question, 'Hey, can you summarize this very complicated text or this very complicated book for me immediately?' Or can I try to do all of these things that were previously impossible to me, but now I'm able to do? Like, can I spin up, can you make for me like a CAD model of this idea that I might have? Really capabilities that were truly beyond my reach are now in my reach. And so if those fast projects were possible before with the capabilities we used to have, shouldn't we just see an exponential increase of the number of those types of unreasonably quickly and effectively executed things with what AI has given us?

你说得对，最难的部分就是记得去尝试，就是要想“哦，对，让我看看 Codex 能不能帮我做这个。”这就像一个新习惯，一个我们必须在脑子里建立的新东西。泰勒·考恩在他的网站上有这样一句话：大多数人低估了去对别人说“嘿，你能不能做你正在做的事情的更雄心勃勃的版本，或者你能不能更快地尝试这个，或者你能不能以 10 倍的规模尝试这个”的影响。在某些方面，当我想到现在产品经理做什么会非常有效，或者他们能做什么会非常有效时，我认为提升他人的野心或提醒他们这里可能实现什么，是产品管理角色中很大的一部分。比如当人们说，“嘿，我觉得我们可以用这种方式完成这个，或者我们可以在这个时间线内完成，或者这可能是它的第一个版本。”你现在的部分工作就是提升每个人的野心，说，“实际上，可能性上限是不是要高得多？我们难道不应该对我们在这里尝试的事情更有野心吗？或者，我们不能更快地尝试这个吗？”我认为那是一个很好的位置。就你能构建什么、什么是可能的，以及这份工作变得多么令人兴奋而言，这是一个很好的位置。

To your point, the hardest part is just remembering to even try, just to be like, 'Oh yeah, well let me see if Codex can do this for me.' It's just like a new habit, a new thing we have to build in our brain. Tyler Cowen has this statement on his site which is that most people underrate the impact of going to someone else and saying, 'Hey, couldn't you do what is like the more ambitious version of what you're doing, or couldn't you try this faster, or couldn't you try this at a 10x bigger scale?' And in some ways, again, when I think of what do PMs do that is incredibly effective now, or what can they do that is incredibly effective now, I think elevating others' ambitions or reminding them of what's possible here is a huge part of the product management role. Like when folks say, 'Hey, I think we can get this done in this way, or we can get this done by this timeline, or maybe this is the first version of it.' Part of your job now is to elevate everyone's ambitions and say, 'Actually, isn't the possibility ceiling meaningfully higher? Like, shouldn't we be more ambitious about what we're attempting here? Or like, couldn't we try this faster?' And I think that's a great place to be. It's a great place to be in terms of what you can build, what's possible, and in terms of how exciting the job becomes.

这太有趣了。我记得尼克·特利上过这个播客，他可能在你之前担任过这个职位。我想他现在在做企业相关的工作。他内部有个梗，这是最大加速吗？

That is so interesting. I remember Nick Turley was on the podcast who was maybe had the role before you. I think he's working on enterprise stuff now. He had this meme internally, is this maximally accelerated?

是的。

Yes.

Slack 里好像有个表情符号。这是最大加速吗？

There's like an emoji I think inside the Slack. Is this maximally accelerated?

这是最大加速吗？这完全是 OpenAI 的梗。

Is this maximally accelerated? It's totally an OpenAI meme.

另一个 OpenAI 梗，我和 Andrew Embraino 喜欢问团队的，就是“你已经在主用它了吗？”意思是，你是不是整天都在用这个产品来完成你的工作？我觉得，再加上“我们是不是尽可能有野心？”这关乎你尝试做的事情的范围和规模。我们是不是在最大程度地加速？我们是不是在尽可能快地推进？然后，“你已经在主用它了吗？”你在用它吗？你是不是把你所有的品味都用来判断这个东西是否有效、是否是人们真正想要的，并尽可能收紧那个反馈循环？对我来说，这就是产品开发的三个梗，我们现在必须尽可能广泛地传播它们。

The other OpenAI meme that Andrew Embraino and I love to ask the team is like, are you mainlining it yet? Which is like, are you using this product all day every day to get your thing done? And I think that in combination with, are we being as ambitious as possible? Which is about the scope and the scale of what you're trying to do. Are we maximally accelerated? Are we moving as fast as possible on it? And then, are you mainlining it yet? Are you using it? And are you bringing all your tastes to bear on whether this thing works and is something that people really want, and tightening that feedback loop as much as possible? Those, to me, are like the three memes of product development that we just have to spread as much as possible now.

我喜欢这个。这就像是新的“吃狗粮”。不是“吃狗粮”，你得“主用”它。

I love that. That's like the new dog fooding. Instead of dog fooding, you got to mainline it.

是的，完全正确。这在推文中体现得非常深刻。我主要通过这个看到你们团队的沟通方式，就是他们对产品有多痴迷，不断在问：我们能做得更好的是什么？现在什么让你烦恼？这是我们正在构建的东西。很明显，正如你之前所说，每个人都是自己产品的创始人，而且很明显他们如何作为外部观察者行事。内部还有其他梗吗？那些太有趣了。还有其他的吗，我不知道。

Yeah, exactly. And that shows so deeply in the tweets. This is mostly how I see your team communicate, of just like how obsessed they are with the product and are just constantly asking, what can we do better? What's bugging you now? Here's the thing we're building. It's very clear, to your point earlier, that everyone is just the founder of their product, and it's very clear how they act as an external observer. Are there any other memes internally? Those are so interesting. Any other, I don't know.

是的，我在想是否还有其他好的文化梗。当然，一个非常重要的就是“感受 AGI”，或者只是意识到 AGI 即将到来。对于它可能是什么样子或人们如何思考它，有很多结果。但让大多数人加入这家公司的一个巨大因素是相信 AGI 有益于人类的使命，并尽一切努力使之成为可能。既要实现 AGI，又要确保它对人类有益。在构建产品时，我必须在脑海中不断重复的另一句话是：我们是在为两到三个月后的模型构建吗？如果你为现在的模型构建，你会失败。如果你为你认为一年后模型会达到的状态构建，你也会失败。两种结果都同样错误。我相信很多人已经讨论过这个，但两种结果确实都同样错误。如果你太早，你就错了。如果你构建的东西过度关注过去模型的能力，你就完全错了。唯一的构建方式是瞄准两到三个月，并且要有这样的信念：模型会变得更好。我需要把模型能力视为这个产品的核心。我需要在我创建的产品结构中为模型让路。我如何确保这对两到三个月后的模型是正确的？

Yeah, I'm trying to think if there are other good cultural memes. Certainly a really important one is like feeling the AGI, or just being conscious of AGI coming. There are so many outcomes for what it could look like or how one thinks about it. But a huge part of what puts most people at this company is believing in that mission of AGI being beneficial and trying to do whatever it takes to make that possible. Both realization of AGI and ensuring that it is beneficial for humanity. And in building products, another constant refrain I have to keep in the back of my mind is: are we building for where the models are going to be in two to three months? You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Both outcomes are equally wrong. And I'm sure many people have talked about this, but both outcomes are really equally wrong. If you're too early, you're wrong. If you build something that was overly focused on a past model's capabilities, you're entirely wrong. The only way to build is two to three months, and having this beam of like, models are going to get way better. I need to think about the model capability as the center of this product. I need to get out of the way of the model in terms of the product constructs that I create. How do I ensure that this is right for the model in two to three months' time?

你怎么知道两到三个月是什么样的？这是一个具有挑战性的理解，尤其是在我们处于这种指数级增长的时候。这仅仅是直觉吗？研究人员有没有给你一些感觉？这是怎么运作的？

How do you know what two or three months is like? It's a challenging understanding, especially while we're on this exponential. Is it like just a gut feeling? Is there anything the researchers give you a sense? How does that work?

是的，当然，与研究团队就他们认为事情的发展方向进行非常紧密的沟通是极其重要的。这些事情并非完全是一个黑匣子，因为你大致知道，嘿，我们专注于这些特定的事情，比如我们希望模型在编码方面以这些特定方式变得更好，或者在写作方面以这些特定方式变得更好。所以我们当然有重点努力，让模型在特定能力上变得更好。因此，知道这一点，并确保产品开发尽可能与研究议程和路线图紧密相连，是非常重要的。

Yeah, certainly communicating really tightly with research on where they think things are going is incredibly important. These things aren't entirely a black box in that you kind of know, hey, we're focused on these particular things, like we would like models to be better at coding in these specific ways or better at writing in these specific ways. So we certainly have focused efforts on making the model better at specific capabilities. And so knowing where that is and ensuring that product development is as tied as possible to what research has as its agenda and its roadmap is really important.

我永远不会忘记的一句话是 Kevin Weil 上播客时说的。他当时是首席产品官。他说，这是模型有史以来最差的时候。这听起来很简单，但很难让人理解，这是有史以来最差的。现在说这句话几乎成了陈词滥调，但这是真的。这很荒谬。

A quote that I'll never forget is when Kevin Weil was on the podcast. He was chief product officer at that time. He said that this is the worst the models will ever be. And it sounds so simple, but it's just like it's hard to wrap your head around that this is the worst there will ever be. It's such a cliché almost now to say that, but it's true. It's absurd.

是的，这很荒谬。绝对荒谬。

Yeah, it's absurd. It's absolutely absurd.

天哪。好的。我想简单谈谈 ChatGPT 应用。好的，我现在正打开着它。

Oh man. Okay. I want to talk about the ChatGPT app briefly. Okay, so I have it open right now.

是的。

Yes.

好的，所以我在里面看到的是这样的。ChatGPT，然后有一个下拉菜单，有 ChatGPT 和 Codex，然后有这个切换，聊天和工作。Tara，这是怎么回事？这些都是什么？帮助我们理解每样东西是干什么用的，这会走向何方？你觉得它会保持这样吗？你是否已经想象了一个下一步？

Okay, so here's what I see in it. ChatGPT, and then there's a dropdown, and there's ChatGPT and Codex, and then there's this toggle, Chat and Work. Tara, what is going on? What are all these things? Help us understand what each of these things are for, and where does this go? Do you think it's going to stay like this? Is there a next step that you're imagining already?

我们的北极星是用户不需要在这些不同选项之间做决定。这里没有切换，你直接去对话框，输入你的任务，比如，我想构建一个非常棒的应用，帮助我的播客嘉宾在节目之前做研究之类的，它就会自动选择正确的工具。它会为你选择正确的模型来完成那件事。理想情况下，选择权不在用户身上，用户不必在这些不同概念之间做选择，也不必理解我们产品的局限性和能力。所以这当然是我们近期想要实现的目标。在 ChatGPT 和 Codex 之间选择，实际上是选择：你是一个开发者，想留在更面向开发的 UI 中，还是想在 ChatGPT 模式中获得同样的能力和功能？所以如果你是 Codex 用户，继续使用 Codex。你不会错过任何东西。尽可能继续使用它。但如果你是 ChatGPT 用户，想知道这些新的智能体能力是什么，你可能应该使用 ChatGPT 模式。然后当你在 ChatGPT 中时，如果你想进行对话，如果你想搜索，那就是聊天模式合适的地方。这是你熟悉和喜爱的聊天模式，每次都有更好的模型和更新的能力。但在工作模式中，底层是 Codex。我们移除了一些编码 UI，比如你不会在工作模式中突然看到一个工作树弹出，但它有同样的能力来完成任务，例如，生成一个非常复杂的财务模型。这在工作模式中都是可能的，我们看到人们，尤其是我提到的我们的企业财务团队，使用工作模式做令人难以置信的事情，以前这些事要么是手动的，要么需要团队中一个人的深厚专业知识，现在整个团队都能执行，或者只是提升了团队中每个人在时间线、能力或他们能完成的前沿方面的雄心。

Our north star here is that users do not need to make decisions between picking between all these different options. There is no toggle here that you go to the box, you type in your task, like, I would like to build a really awesome app that, I don't know, helps my podcast guests do research before episodes or something, and it will just pick the right harness. It'll pick the right model for you to be able to get that thing done. Ideally, the choice here is not on our users to have to pick between all these different concepts and understand not only what they are trying to do but understand the limitations and capabilities of our products. So that is certainly where we want to go in the near term. Picking between ChatGPT and Codex is really a choice for, are you a developer who wants to stay in a more development-oriented UI, or do you want to have the same power and capabilities in the ChatGPT mode? And so if you're a Codex user, keep using Codex. You're not missing out on anything. Continue using it as much as possible. But if you're a ChatGPT user who is like, what are these new agent capabilities, you should probably be in ChatGPT mode. And then when you're in ChatGPT, if you want to have conversations, if you want to search, that's where chat mode is the right thing. It's the same chat mode you know and love with better and better models and newer and newer capabilities every time. But in work mode, that's where under the covers this is Codex. We've removed some of the coding UI, like you're not going to see a work tree pop up all of a sudden in work mode, but it is the same power to get things done, to, for example, generate a really complex financial model. That's all possible in work mode, and we see people, especially I mentioned our corporate finance team, use work mode to do incredible things that were previously either manual or required deep expertise from one person on the team, become things that the whole team can execute, or just elevate the ambitions of everyone on the team in terms of timeline or capabilities or frontier of what they can get done.

好的，这真的很有帮助。

Okay, that's really helpful.

所以目前大概有三种模式：工程模式、聊天模式，还有做知识工作的模式。知识工作模式实际上就是 Codex 在做那些工作，但人们可能不知道 Codex 是什么，或者可能对它感到害怕。那个工作模式里有没有什么不只是 Codex 的东西？因为这真的很有意思。是有额外的 harness 调整让它感觉有点不同，还是只是同样的事情换了点不同的界面？

So there are kind of these three modes currently: the engineering mode, the chat mode, and then the do knowledge work mode. And the knowledge work mode is actually Codex doing all that work, but people may not know what Codex is, or may be afraid of it. Is there anything in that work mode that's not just Codex? Because that's actually really interesting. Is it like additional harness tweaks to make it feel a little different, or is it just the same thing with a little different UI?

这确实是在界面层面。所以工作模式和 Codex 模式——如果你去 Codex 让它生成一个惊人的财务模型来给你的产品定价，或者让它预测你未来六个月的收入，Codex 会做得和工作模式一样好。关键在于，当它做这些事的时候，你想在思维链中看到什么样的界面？你想看到什么样的技术细节？它同样强大，所以 Codex 用户不会因为不切换模式而错过任何东西。事实上，我们不希望他们切换——比如，留在 Codex 里做你想在 Codex 里做的所有事情，我们会根据你要求的事情展示合适的界面。确实，我们的北极星是把所有这些合并起来，这样用户就不必做这些决定。这种分离更多是关于我们如何尽可能地在用户所在的地方满足他们，无论是他们使用的产品，还是他们对概念的熟悉程度，并确保我们让每个人都能利用与智能体协作的优势，这已经完全改变了每个开发者的工作方式。我们应该对知识工作做同样的事情。

It's really at the UI level. So work mode and Codex mode—if you go to Codex and ask it to generate an amazing financial model to price your product, or tell me to predict my revenue for the next six months, Codex will do as good a job as work mode. It's really about while it's doing so, what kind of UI do you want to see in the chain of thought? What kind of technical detail do you want exposed to you? It's incredibly similarly powerful, so Codex users aren't missing out on anything by not switching modes. In fact, we do not want them to—like, stay in Codex and do all the stuff you want to do in Codex, and we will show you the appropriate UI based on the things you asked for. Truly, our north star is to merge all these things so that users don't have to make any of these decisions. The separation is really more about how can we meet people where they are as much as possible, in terms of the products that they use, in terms of their familiarity with concepts, and make sure that we are enabling everyone to take advantage of working with agents, which has transformed entirely the way every single developer works. We should do the same thing with knowledge work.

有道理。因为事情发展得太快，我想象有人会说，试试 Codex 吧，这肯定会很棒，然后它就火了，有了 1000 万月活用户，然后他们又说，等等，我们这是在干什么？我们有 ChatGPT，我们有 Codex，我们怎么……所以这就解释了为什么这些东西——在一段时间内不会感觉很明显和完美，因为你必须随着事情的成功和失败来调整，还有这些过渡期，比如，好的，现在让我们让人们朝着这个超级应用愿景前进。好的。我想象你工作中最难的部分之一，是如何平衡这个 1000 亿月活的产品 ChatGPT——可能是历史上最成功的消费产品——和 Codex 这个新东西，以及你们想尝试的其他新事物。你是怎么考虑的？就是如何平衡这些非常创新、快速发展的团队和产品，同时面对“有 10 亿人在用这个，我们可以大幅改变它”的情况。

It makes sense. Because things move so fast, I imagine somebody's like, let's try Codex, this is going to be awesome, and then it takes off and there's 10 million monthly active users, and then they're like, wait, what are we doing here? We got ChatGPT, we got Codex, how do we... So it makes sense why these things—it's not going to feel obvious and perfect for a while because you have to kind of adjust as things work and things don't work, and there are these transition periods of like, okay cool, now let's get people moving towards this vision of the super app. Okay. I imagine one of the hardest parts of your job is balancing this 100 billion MAU product, ChatGPT, maybe the most successful consumer product in history, with Codex, which is this new thing, and other new things that you guys want to try. How do you think about that? Just balancing these very innovative fast-moving teams and products with this like, okay, there's a billion people using this, we can change this dramatically.

是的，我认为这里最有趣的事情之一是，在 ChatGPT 网页版和桌面应用中推出工作模式并将这些整合在一起的目标之一，是着眼于那 10 亿使用 ChatGPT 的用户，并给他们带来越来越多的智能体能力。如果你把 AI 产品的第一个时代看作是聊天，那么这些产品的第二个时代显然是与智能体协作，而且主要是编码智能体。我们想把它带到更多领域，当然比如知识工作，这也是给所有这 10 亿 ChatGPT 用户带来工作能力的目标的一部分。当然，我们面临的产品挑战是，不仅要把这些带给他们，还要让它自然且易于采用。让它不是他们必须明确做出的决定。我们可以直接帮助他们做正确的事情。我们如何把它简化，让他们不需要考虑像 harness 这样的东西，这对 10 亿消费者来说感觉是疯狂的概念。所以这主要是挑战。然后当然，可能很快到来的第三个时代是如何与一个持久的同事协作，它能和你一起完成任务，也许还能和其他人协作。所以近期挑战的一部分是，我们正在向 10 亿可能还没有体验过智能体的人介绍智能体。我们如何以最简单、最自然、最易用的方式做到这一点？当然，我们还有很多工作要做才能实现这一点。但这也是我学到的一个教训，也许对比前 AI 时代或过去的产品经验，在之前的公司，打磨是王道。把每一个 UI 交互或每一个小细节都完全做对，比提前发布重要得多，因为时间对结果的影响没那么大。因此，如果每个角落都没有完美打磨，每件事都不完全正确，那还不如不发布。但我认为这个时代和这个产品体验真正引人注目和有趣的地方在于，当你对产品具有变革性有如此强烈的信念时，把产品交到用户手中比完美要好得多，而这种紧迫感和产品的推出非常重要。所以我们还有很多工作要做，让它对 ChatGPT 用户更易用、更容易上手，尤其是对那些可能甚至不是用它来提高生产力，而是用于消费类任务的用户，但完成比完美更好，我们还有更多事情要做。

Yeah, I think one of the most interesting things here is that one of the goals of launching work in ChatGPT web and launching it in the desktop app and bringing these things together was to look at those billion people who are using ChatGPT and bring them more and more of the agents' power. If you think about the first era of AI products as chat, the second era of these products is clearly working with agents, and primarily has been coding agents. We'd like to bring it to more domains, certainly like knowledge work, and that is part of the goal of giving all these billion ChatGPT users the power of work. Certainly the product challenge that's on us is how do we not only bring it to them but make it natural and easy to adopt. Make it not a decision they have to explicitly make. We can just help them do the right thing. How do we decomplexify it so they don't need to think about things like harnesses, which feel like crazy concepts for a billion consumers to understand. So that is primarily the challenge. And then of course, that third era that might come soon is how do you work with a persistent co-worker who is able to get things done with you, maybe collaboratively with other people. And so part of this challenge in the near term is we're introducing agents to a billion people who may not have experienced them yet. How do we do so in the easiest, most natural, and most usable way possible? Certainly, there's a lot more for us to do to make that happen. But part of this is also a lesson I've had, maybe contrasting pre-AI era or past product experience with this one, which is at previous companies, polish was king. Getting every UI interaction or getting every little thing completely right was way more important than shipping something early, because time didn't make as much of a difference in terms of the outcome. And so as such, if every corner wasn't perfectly polished and everything wasn't exactly correct, you might as well not ship it. But I think what's been really compelling and interesting about this era and this product experience has been getting the product in the hands of users when you have so much conviction that it's transformative is way better than perfect, and that urgency and that introduction of that product is so important. So we have a lot to do to make it more usable and easier for ChatGPT users, certainly especially for folks who are not maybe even using it for productivity but using it for consumer tasks, but done is better than perfect, and we have so much more to do.

是的，我记得这个应用刚推出时，有很多关于混乱的评论，而看到团队迭代和回应反馈的速度，正是我在这里听到的：把它推出去，弄清楚什么不行，人们怎么用它，快速迭代。感觉这就是现在的模式。

Yeah, I remember when this app first launched, there was a lot of comments about the confusion, and seeing how quickly the team iterated and responded to the feedback is exactly what I'm hearing here: get it out, figure out what's not working, how people are using it, iterate quickly. Feels like that's the model now.

当然，有些事情你可以在发布前继续迭代并获得反馈。我们总是有很多可以也应该做得更好的地方。但尽可能快地迭代并倾听正确的信号，无论这是发布前还是发布后，理想情况下是发布前，这才是关键。

And of course, there are things that you can continue to iterate and get that feedback prior to launching. And there's a lot that we can and should always do better. But iterating as quickly as possible and listening to the right signals is, regardless of whether that's pre-launch, post-launch, ideally pre-launch, the key thing.

本期节目由 Mercury 赞助播出。彻底不同的银行服务，现在还有 Spend 功能。我成为 Mercury 客户已经很多年了。我把所有企业银行业务都转到了 Mercury，说实话，我再满意不过了。这就是当在线银行由产品人而非银行家打造时的感觉。现在有了 Spend，你可以给团队发放个人卡，设置每人或每团队的花费限额，并让费用收据自动从 Gmail 或短信中提取。你甚至可以为你的 AI 智能体提供它们自己的卡，带有自己的限额和策略。大多数创始人的起步方式都一样：公司里每个人都用一张卡。它一直有效，直到失效。有人超支了，一张收据不见了。

This episode is brought to you by Mercury. Radically different banking, now with spend. I've been a Mercury customer for so many years now. I switched all my business banking to Mercury, and honestly, I could not be happier. It's what online banking feels like when it's built by product people, not by bankers. And now with Spend, you can give your team individual cards, set spending limits per person or per team, and have expense receipts automatically pulled in from Gmail or over text. You can even give your AI agents their own cards with their own limits and policies. Most founders start out the same way: one card used by everybody at the company. It works until it stops working. Someone goes over, a receipt disappears.

你花两天时间搞清楚谁花了什么、为什么花。Spend 是直接内置于 Mercury 的费用管理工具。你团队的所有卡片、预算和报销，都和你的业务思考放在同一个地方。不用追着问、不用人工审核、不用月底手忙脚乱。结果是团队能快速行动，创始人不再是瓶颈。了解更多并注册，请访问 mercury.com。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 提供，其成员为 FDIC 成员。IO 卡由 Patriot Bank NA 发行，其成员为 FDIC，依据 Mastercard International Incorporated 的许可。

You spend two days trying to figure out who spent what and why. Spend is expense management built directly into Mercury. All your team's cards, budgets, and reimbursements. All live in the same place as your business thinking. No chasing, no manual reviews, no end of month scramble. The result is a team that can move fast and a founder who is no longer the bottleneck. Learn more and get signed up at mercury.com. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided to Choice Financial Group in column NA members FDIC. The IO card is issued by Patriot Bank NA member FDIC pursuant to a license for Mastercard International Incorporated.

我在 Twitter 上注意到，过去几个月里，从 Claude Code 到 Codex 的氛围确实发生了转变。以前大家都在用 Claude。最近感觉人们开始倾向于 Codex，至少在 Twitter 上是这样，虽然那是个泡沫，但那里聚集了很多科技圈的人。我很好奇，在过去的三到六个月里，除了 Tara 加入并整顿局面之外，内部有什么变化？有没有什么你可以分享的，比如“我们搞定了这个、调整了那个、砍掉了这个”，是什么帮助扭转了氛围，让 Codex 变得如此成功？

Something I've noticed on Twitter is there's definitely been this vibe shift from Claude Code to Codex in the past few months. It used to be everyone was Claude. More recently, it just feels like people are leaning now towards Codex, at least on Twitter, which is a bubble, but it's where a lot of tech people are. I'm curious what's shifted internally in the past, I don't know, 3 to 6 months, other than Tara joining and shaping up the ship. Is there anything that you can share that's just like, okay, we figured this thing out, we shifted this, we cut this thing, what helped shift the vibes and help Codex become as successful as it is becoming?

你知道，我觉得有句话叫“开悟前，砍柴挑水；开悟后，砍柴挑水”。实际上，对于 Codex 应用，最初把它搭建起来并投入工作的团队，非常以用户为中心，迭代循环紧凑，真的在吃自己的狗粮，尽可能多地使用这个应用，把一切做好。人们开始意识到外部和 Twitter 上发生的变化，用户也开始真正注意到。但团队一直非常关注用户，非常关注迭代，变化只是市场赶上了而已。而且这个过程在内部没有改变。每个人仍然经常使用这个应用。每个构建它的人显然都是开发者，用它进行开发，不仅不断修复自己的问题，还努力倾听公司其他人的问题和用户的问题。

You know, I think there's like this phrase which is, before enlightenment, carry wood or carry water, chop wood, post enlightenment, carry water, chop wood, sort of thing. And actually with the Codex app, the team who initially got it up and running and were working on it were super user-focused, tight iteration loop, really dogfooded the thing, like mainlined the app as much as possible to get everything right. Folks started to realize that was happening externally and on Twitter and users started to really notice. But the team was always really focused on users, really focused on that iteration, and it was merely the market catching up that was the change. And that process has not changed internally. Everyone still constantly uses the app. Everyone who's building it obviously is a developer using it for development and is constantly fixing not only their own problems but trying to listen to other people in the company's problems and user problems.

这个回答真正有趣的地方在于它非常人性化。是你、是 Andrew、是 Tibo，是团队对客户和产品的痴迷。并不是 AI 才是答案，是人的差异造成了不同。

What's really interesting about this answer is it's the very human part of it. It's you, it's Andrew, it's Tibo, it's the team just being obsessed with the customer, the product. And it's not like AI was the answer. It's the humans that made the difference.

是的，我要把全部功劳归给团队。团队中的每个人都非常深思熟虑且独立。而且，OpenAI 里有很多创始人，几乎团队里的每个人，尤其是桌面团队，都像创始人一样关心每一个部分和每一个细节。当他们注意到某个领域应该改进时，他们会非常独立地去构建并让它运转起来。如果内部测试不顺利，比如人们不使用它，或者觉得它没用，他们就会迭代，最后再对外发布。但这个循环完全归功于团队中的个人，是他们让这一切发生。

Yeah, I'll give the team full credit here. Everyone on the team is incredibly thoughtful and independent. And to the point of, there are many founders at OpenAI, like almost everyone on that team, like the desktop team especially, like asks like founders and cares about every piece and every detail. And when they notice an area that should be better, they go build it very independently and get the thing up and running. And if it doesn't test well internally, like people aren't using it, if people don't find it useful, they'll iterate on it and then finally ship it externally. But that loop is full credit to the people on the team and individuals for making that happen.

你提到的角色重叠这个想法，工程师做 PM 的工作，你可能在发布原型、构建并可能推向生产。感觉这也带来了很多挑战。我听到很多人说，作为设计师，我现在的工作是什么？我负责什么？我不负责什么？作为营销人员，我在做什么？你注意到这些了吗？你在处理这些吗？对此有什么想法？

Something you touched on is this idea of roles overlapping, this idea of engineers doing PM work, you're doing probably shipping prototypes and building maybe shipping to production. It just feels like that also creates a lot of challenges. I hear from a lot of people like, what is my job now as a designer? What am I responsible for? What am I not responsible for? As a marketer, what am I doing? Is that something you notice? Is that something that you're dealing with? Just any thoughts along those lines.

我一直最喜欢在初创公司工作的一点，有时我在一家不小心成长为大型公司的初创公司开始，但主要还是主要在初创公司工作，就是你的角色几乎没有界限。一切和什么都不归你负责，最终你要为成功负责。实际上，Stripe 就是这样，工程师、产品经理、设计师之间没有界限。每个人都可以做任何事。所以实际上，我一直很喜欢这种心态，现在终于能力跟上了。但我真正关心的是，需要有人负责或承担核心责任，确保这个产品被用户使用？是人们想要的东西吗？质量高吗？有效吗？无论那个人是工程师、设计师、PM 还是其他人，总有一个 DRI。然后，为了实现这个目标需要做的任何工作，当然人们可以根据自己的亲和力和能力来承担。但我喜欢一个不在乎个人角色界限的团队，每个人都专注于让结果发生。

I think the thing I've always liked the most about working at startups, and sometimes I've started at a startup that accidentally grew into a large company, but largely primarily working at startups, is that there are very few boundaries around your role. That everything and nothing is your responsibility. Ultimately you're accountable for success. Actually, Stripe was very much this way, where there were no boundaries around what an engineer could do versus a product manager could do versus a designer could do. Everyone could do anything. So actually it kind of feels like I've always really loved that mentality, and now finally capability is catching up to that. But the thing I really care about is that someone needs to look after, or have core accountability for, is this product being used by users? Is it something that people want? Is it high quality? Is it effective? And whether that person is an engineer or a designer or a PM or whomever, someone is the DRI. And then whatever work needs to be done to make that possible, certainly people can pick it up based on their affinity, based on their capability. But I like a team that doesn't really mind what the boundaries are between individual roles, but everyone's just sort of focused on making the outcome happen.

与此相反的是，我也非常喜欢作为 PM 的手艺方面。PM 手艺有很多方面，我知道像 Shreyas、Marie Kagan 或 Shashir 这样的人，他们都推崇这些，我觉得很棒。有时候，也许这些问题来自，“等等，我非常热爱我领域的手艺。通过这种更灵活的团队合作方式来完成事情，我会不会失去提升和打磨手艺的机会？”我真的没有答案。我认为这是我们都在共同经历的，那就是我们手艺的某些部分实际上正在被模型抽象化，它们能非常有效地完成，也许比个人做得更好。你的手艺从能够做过去那个非常具体的任务，转变为现在应用到产品或学科的其他部分。但是的，这仍然是我在思考的问题，那就是如何平衡我作为团队一员并使用这些工具的渴望，以及被这些产品带来的高效所吸引的感觉，与我对手艺的热爱？是的，对工程师来说，一直手写代码真的很有趣，但现在人们不再那样做了。

The converse of this is I also really love the craft aspects of being a PM. Like there are so many aspects to PM craft that I know folks like Shreyas or maybe Marie Kagan or Shashir, like all these people have really espoused that I think are wonderful. And sometimes maybe some of these questions come from, wait, I so love the craft of my domain. By taking this more fluid approach to teamwork and collaboration to get something done, do I lose out on getting better and polishing my craft? And I truly don't have an answer for that question. I think it's something we're all experiencing together, which is some pieces of our craft are actually getting abstracted by models being able to do it really effectively, maybe better than individuals can. And your craft moves from being able to do that very specific task you did in the past to now applying it to some other part of the product or the discipline. But yeah, that is still a question I'm thinking about, which is how do I balance my desire to be part of a team and use these tools and feel so compelled by how effective one can be now with all these products, with my love of the craft? Yeah, it's really fun handwriting code for an engineer all the time, and one doesn't really do that anymore.

对，我正想说到这儿。现在工程角色的变化简直令人难以置信。

Yeah, that's where I was going to go. It's just like unbelievable how different the engineering role is now.

是啊。

Yeah.

就像你以前整天写代码，那是你的工作，而现在这不再是你的工作了。

It's like you used to write code all day. That was your job and that is no longer your job.

对。

Yeah.

而且这发生得如此之快，就像人们怀念亲手写代码时的那种心流状态，而现在做的事情完全不同了。

And that happens so quickly, like people mourn like the flow state of writing code manually yourself versus now what one does.

但我认为这是一个艰难的转变。

But I think it is a tough transition.

是啊。你知道有些人喜欢，有些人不喜欢。那是另一个话题了。顺着这个思路，我想问前沿 AI 领域的人一个问题：你认为人类大脑在未来哪里还会继续有价值？长期来看无法预测。我们还需要人类吗？希望如此，但我想说，在未来几年里，你认为人类大脑在哪里还会最有价值？

Yeah. And you know some people love it, some people don't. And that's a whole other topic. Kind of along those lines, something I'd like to ask people at the frontier of AI is where do you think human brains will continue to be valuable in the future? It's impossible to predict long term. Will we need humans? Hopefully, but I'd say in the next couple years just like where do you think human brains will continue to be most valuable?

我认为人类将继续作为责任主体而最有价值。所以，最终谁对结果负责？在某种程度上，你可以把与你合作的智能体看作你的下属。最终，谁拥有最终产品？它质量高吗？它是你想要它做和说的东西吗？这当然仍然是一个人，至少目前如此。尤其是在高度监管或需要直接人际接触的行业和地方，这对我来说非常合理。我认为人类大脑在表达方面也非常有价值。我之前提到过那个类比：软件不像房地产，它更像电影，你投入资金，但伟大的电影不会自动出现。最伟大的电影不是预算最大的那些。而且在构建软件时，有一种艺术性、观点性和表达性，让你觉得有一个人或一群人的作者身份。这部分对我来说仍然非常人性化，比如你选择构建什么以及它给人的感觉，这真是一个人类的问题。我还认为人类大脑在如何关心彼此、如何相互联系方面继续有价值。我工作中的这一部分一直非常人性化，而且实际上变得比以往任何时候都更重要。你和团队中的其他人交谈，共同想办法如何对一个领域充满热情，如何一起学习和工作，如何提升彼此的抱负。所有这些感觉起来仍然是人类的事情。是的，我认为人类大脑在这方面将继续非常有价值。话虽如此，我无法预测模型会怎样，但那些部分对我来说极其人性化。

I think humans will continue to be most valuable as an entity of accountability. So, who ultimately owns the outcome here? In some ways, you can think of your agent that you're working with as like your report. Ultimately, who owns what was the end product? Was it high quality? Was it the thing that you wanted it to do and say? That will certainly remain a person, at least for now. And especially in industries and places that are highly regulated or require a direct human interface, that makes a ton of sense to me. I think the human brain is also really valuable for expression. I'd mentioned earlier that analogy of software is not like real estate. It is more like a film where you could put money and a great film does not come out. Like the greatest films are not the ones with the biggest budgets. And given that there's a certain artistry and opinionation and expression in building software where you feel like there is some authorship by a person or a group of people. And that part remains to me so human, like what you choose to build and how it feels feels like such a human question. I also think the human brain continues to be valuable in like how we care for each other and relate to one another. That piece of my work has remained so human and actually has become more important than ever. The part where you talk to other people on your team and collectively figure out how you can be enthusiastic about an area, how you learn and work together, how you elevate each other's ambitions. All of that feels and remains such a human thing to do. Yeah, I think the human brain will continue to be so valuable in that regard. That said, I can't predict what'll happen with the models, but those pieces feel to me to be incredibly human.

我喜欢这个回答。你谈到了 AI 能力与实际应用之间的这种差距。人们总是说，感觉最大的差距之一是，好吧，我该拿它做什么？我很好奇，你在工作中使用 AI 的一些方式，可能会启发人们，比如“哇，我没想过可以这样用”。这里大概有两类。一类是，你的产品经理工作因为 AI 发生了哪些最大的变化，让你觉得“好吧，现在我用 AI 做这些”；另一类是，最近有没有什么超级有趣的创意用法，让你觉得“哦对，我应该试试这个”。

I love that answer. There's this idea that you talked about this idea of the overhang of what AI is capable of and what we're actually doing with it. People are always like, it feels like one of the biggest gaps is like, okay, what should I do with it? I'm curious, what are some ways that you use AI in your work that may inspire people like oh wow I didn't think about using it that way? There's kind of two buckets here. One is just like how your PM job has changed most thanks to AI that you're just like okay now I use AI for this stuff, and then what's like is there any super interesting creative uses of AI recently that you're like oh yeah I should try this.

我在工作中使用 AI 最令人兴奋的方式之一，是我现在经常构建站点。我不知道你有没有试过在……里构建站点。

One of the most exciting ways that I use AI in work is I actually build sites all the time now. I don't know if you've tried building sites in...

我没有。说说站点吧。

I haven't. Talk about sites.

站点是一个非常有趣、很棒的产品。你基本上可以在工作里构建一个站点作为展示性作品，但我实际上为任何东西都构建站点。我为团队建了一个站点，作为游戏，我们一起用站点玩游戏，因为站点有数据库。你可以构建站点，我实际上因为最近去背包旅行而建了一个站点。我建了一个路线站点，追踪我们要去的每个地方的海拔。我们旅行中的每个人都输入了他们的食物。这非常快速和高效。站点在某种程度上实现了艾伦·凯在 60 年代提出的可塑个人软件的梦想，即真正的个人电脑是拥有个人软件的。在某些方面，站点是让这成为可能的具体方式。我们都曾梦想制作个人软件，当然，使用 Notion 等工具的人尝试用所有这些块来配置可能的样子。但有了站点，它就是一个提示词。我实际上用提示词说，给我构建这个我需要的精确工具来完成这件事，它就做到了。它们可以共享，可以自动更新。你可以使用内部数据来构建仪表板，例如包含许多指标。而不是费力地制作某种幻灯片，站点是更动态的展示表面。

Sites is a really fun amazing product. You can basically build a site certainly in work as a presentational artifact, but I also build sites for literally anything. I built a site for the team as a game where we all played a game together using a site because sites have a database. You can build a site, I actually built a site because I went on a backpacking trip recently. I built a site of the route that tracked the elevation of everywhere we were going. Everyone on our trip inputted all their food. It was super fast and effective. Sites kind of realized the dream of malleable personal software that Alan Kay flagged in the 60s of like the true personal computer is one that has personal software. In some ways, sites are like the tangible way to make that possible. We had all once dreamed of making personal software, and certainly people with tools like Notion etc. try with all these blocks to configure what that could be. But with a site, it is literally a prompt. I literally with a prompt say like build me this exact tool that I need to get this thing done and it just does it. They're sharable. They can auto update. You can use internal data to build like a dashboard for example with lots of metrics. And rather than painstakingly laboring over some sort of slide deck, a site is just a way more dynamic surface for presentation.

你怎么使用站点？你需要做什么特别的事情，还是你告诉它创建站点？

How do you use a site? Do you have to do anything special or you tell it make create a site?

在 Codex 里，就说创建一个站点，比如，我不知道，为我的团队做一个黑手党游戏，它就会直接做。

In Codex, be like create a site that is, I don't know, a mafia game for my team and it will just do it.

而且我在想大写的 Site，但我想没关系。

And like I'm thinking capital S site but doesn't matter I imagine.

它就知道站点是什么。

It just knows what sites are.

所以是的，因为以前是，这里有一些源代码，去搞清楚部署到哪里。

So it's yeah because it used to be here's some source code go figure out where to deploy it.

而且你在这里说的是，它只是为你托管，你可以选择它是公开的，你可以选择它是否与你的团队共享，或者选择它是否对你私有。

And what you're saying here is it just host it for you and you can choose whether it's public, you can choose whether it's with your team or choose whether it's private to you.

嗯，它们很棒。随时轻松构建站点改变了我日常的工作方式，以前通常看起来像是创建许多文档、表格之类的工件。现在我只是经常制作站点。

Um, they're great. The easy reach of building a site all the time has changed what my day-to-day looks like, which often in previous worlds used to look like creating lots of artifacts like docs and sheets and whatever it might be. Now I just make sites all the time.

而且你可以通过，我想，Work 来做，还是你可以通过所有服务，Codex、Work？

And you could do that through I imagine work or can you do it through all the services Codex work?

你可以通过 Work 做。你可以通过 Codex 做。你可以在网页上做。你可以在手机上做。你可以在任何地方做。

You can do it through Work. You can do it through Codex. You can do it in the web. You can do it on mobile. You can do it anywhere.

好的。我刚刚开始创建一个关于 Tara Seshan 的站点。

Okay. I just kicked off a create a site about Tara Seshan.

太好了。

Great.

顺便问一下，你的姓是这么发音的吗？我还没问过。

Is that how you pronounce your last name by the way? I haven't asked.

呃，Tara Seshan，像 station 那个 s。

Uh, Tara Seshan like station.

好的，酷。好的，酷。嗯，站点。好的。既然我们在这个话题上，对产品经理还有其他快速提示吗？那是个很好的提示，因为我觉得很多人不知道站点。这非常有用。

Okay cool. Okay cool. Um, sites. Okay. Any other quick tips while we're on this topic for PM? Like that was a great tip because I don't think a lot of people know about sites. That's very useful.

是的，站点很棒。我真正喜欢的另一件事是在 Codex 中使用可视化。你用过斜杠可视化吗？

Yeah, sites are awesome. The other thing I really love is using visualize in Codex. Have you used slash visualize?

没有。

No.

哦，斜杠可视化（slash visualize）简直太令人兴奋了。你可以直接输入斜杠可视化，比如“可视化我至今的 ChatGPT 使用情况”之类的，它就会把你做过的所有事情拉进来，生成一个超棒的图表。我无数次在想，怎么才能不仅拉入一堆图表和数据，还能以一种易于理解、对我想讲的故事有用的方式呈现出来，而可视化让这一切变得极其简单。用起来真是出奇地令人愉悦。

Oh, slash visualize is incredibly exciting. You can just do slash visualize. Visualize my ChatGPT usage until now or something like that, and it will pull in all the things that you've done and create an amazing visualization for it. The number of times that I've been thinking about how do I not only pull in a bunch of charts and data but present them in a way that is understandable and useful for the story I'm trying to tell has been infinite, and visualize makes that incredibly simple. It is surprisingly delightful to use.

这就像，这些都是很好的例子，说明这里有很多我们甚至不知道或不理解的力量，而这就是你面临的挑战。一定要帮帮我们。

It's just like, these are such good examples of there's so much power here we don't even know about or understand, and that's the challenge you have here. Help us, for sure.

帮我们了解所有这些功能。这就是为什么像这样的播客也很有用。不可能把所有东西都塞进产品里。

Help us know all these things. That's why podcasts like this are also useful. Can't put it all in the product.

我要换个完全不同的方向。我想聊聊写作。我问了很了解你的布里·沃尔夫森，该问你什么。有趣的是，她之前也为我和谷歌的亚当·沃德做的播客对话提过问题。她说：“好，你应该问问她关于写作/思考的事。塔拉的简报是标志性的。帮我们理解是什么让你的写作、你的简报如此标志性，以及有没有什么技巧可能对想提高写作和文档撰写能力的人有帮助。”

I'm going to go in a totally different direction. I want to talk about writing. I asked Brie Wolfson, who knows you well, what to ask you. Funny enough, she suggested questions for the previous podcast conversation I did with Adam Ward from Google. So she said, "Okay, you should ask her about writing slash thinking. A Tara brief is iconic. Help us understand just what makes your writing, your briefs iconic, and any tips that might be helpful for people that are maybe trying to get better at writing and writing documents."

我非常坚信，我在工作中做两种类型的写作。一种是“作为思考的写作”，另一种是“作为报告的写作”。作为思考的写作，是我写一份简报，说明为什么我们应该构建某个产品，或者为什么我们应该采取某种策略，或者为什么，比如，一个大胆的观点。但作为报告的写作，是像“哦，我在总结我们团队这周做了什么，然后发一份报告”或者“这是我们这次发布或公告的计划”。作为报告的写作，我很乐意自动化，或者我一直用模型让它尽可能简单。但作为思考的写作，是我永远不会自动化的。我非常坚信，至少对我来说，经历一个过程，把某件事列成提纲，把它变成一定程度的散文，删减、编辑，不断迭代，是让我理清思路的最重要步骤之一。我认为大多数人实际上会一概而论，比如“我永远不会用模型来写作”或者“我总是用模型来写作”，而实际上对我来说，这两种一概而论都是错的。我认为你应该尽可能多地使用模型来做“作为报告的写作”，而当你像我一样（我认为很多人也这样）用写作来思考时，你不应该用它；你不应该用模型取代你的思考。但我过去的简报，因为我写很多作为思考的写作，我会钻进一个洞里，为一个新想法或产品写一份简报，花大量时间打磨那个特定的想法，把它拿给人们看，让他们尽可能攻击其中的想法，挑毛病，让它更强大，然后再拿给下一个人，做同样的事情。所以在 Stripe，这是我一次又一次做过很多很多次的事情，无论是启动一个新的产品领域，还是建议一个大的方向改变，或者分析一个问题并提出前进的道路。Stripe 是一个非常注重写作文化的公司。有很多人，比如杰夫·温斯坦，也非常热衷于在 Stripe 写作和分享简报。Stripe 是少数几个简报会在公司内部走红的地方之一。所以“作为思考的写作”在那里非常受重视，而我大部分这类写作工作就是在那里完成的。在 OpenAI，我认为我仍然一直在做“作为思考的写作”，但这里可分享的产物并不是真正意义上的长文档或那种工作证明。部分原因是时代变了，长文档不再是你想清楚某件事的标志，因为你很容易就能产出一份长文档，却表明你并没有想清楚。所以实际上，也许我在日常工作中经历的最大变化之一，一个可能令人震惊的变化是，我过去在文档中思考，然后把它转化为一种演示性的产物，那将是我表明自己想清楚了一个问题、这就是我们要做的、团队朝那个方向前进的标志。而现在我更倾向于“模型而非文档”，或者“原型而非文档”。如果我有一些人们可以尝试和互动的东西，或者更好的是，我有结果，比如我们试过这个，我们跑了 A/B 测试，这是结果，这就是为什么我认为我们应该朝这个方向走——那是一个比文档本身更好的沟通工具。所以我仍然一直在写数百份文档，但我是为自己写的。而且我不再真正为别人写了。那不再是交谈和沟通的最佳方式。这可能是我在这个时代与之前时代相比，个人经历的最大变化。

I really strongly believe that I do two types of writing at work. One is writing as thinking, and the other is writing as reporting. Writing as thinking is me writing a brief about why we should build a certain product or why we should take a certain strategy or why, like, maybe a spicy take. But writing as reporting is things like, "Oh, I'm summarizing the status of what our team has been up to this week and I'm sending over a report about it," or "This is our plan for this particular launch or announcement." Writing as reporting I happily automate, or I use the models all the time to make that as simple as it can be. But writing as thinking is something I never will automate. I really strongly believe that, at least for me, the act of going through and outlining something, turning it into some level of prose, cutting it and editing it, continuing to iterate on it, is one of the most important steps for me to get my ideas in line. I think most people actually will paint with a really broad brush, like "I will never use the models for writing" or "I always use the models for writing," and actually to me, both those broad brushes are wrong. I think you should use the models as much as possible for writing as reporting, and in as much as you think with writing as I really do—and I think a lot of people do—you should not use it; you shouldn't replace your thinking with it. But my briefs in the past, because I write so much as a way of thinking, is that I will go into a hole, write a brief for a new idea or a product, spend a ton of time refining that particular idea, shop it around with people, and have them attack the ideas in it as much as possible and poke holes, make it stronger, and then take it to the next person and do the same thing. And so at Stripe, this is something I did many, many, many, many times over, whether that was to kick off a new product area or to suggest a big change in direction or to analyze a problem and suggest a path forward. And Stripe is incredibly oriented as a writing culture. And there are many people like Jeff Weinstein who are also very into writing and sharing briefs at Stripe. Stripe is one of the few places where a brief will go viral inside the company. And so writing as thinking there is really prized, and that's where I did the majority of that writing work. At OpenAI, I think I still write as thinking all the time, but the shareable artifact here is not really a long doc or a sort of proof of work in that way. Partially because times have changed, and a long doc is not a signal that you thought through something because you can easily produce a long doc that indicates that you haven't. And so actually, maybe one of the biggest changes I've experienced personally in my day-to-day, which has been a big, maybe jarring change, is that I used to think in a document and then do some translation of that into a presentational artifact, and that would be my indication that I thought through a problem and this is what we're going to do and the team moves in that direction. And now I am way more on mocks not docs, or prototypes not docs. And if I have something that people can try and interact with, or even better, I have like results where we tried this, we ran an A/B test, here's the results, this is why I think we should go in this direction—that is a way better communication tool than the doc itself. And so I still write hundreds of docs all the time, but I do it for me. And I no longer do it for other people really. That no longer is the best way to talk and communicate. That is probably the biggest change I've experienced personally in this era versus the previous era.

这太有意思了。我真的很喜欢你关于在文档上获取大量反馈的建议。你知道，这听起来很明显，但你可以通过几乎作弊的方式，在迭代过程中获得大量反馈，从而得到一份标志性的文档或简报。

That is so interesting. I really liked your tip of getting tons of feedback on a doc. Like, you know, it sounds obvious, but you know, you can get to an iconic doc, brief by just cheating almost and getting lots of feedback on it as you're iterating.

是的，让它变得越来越强大，而不是像“酷，这是第一次”，而且这种情况很少见。

Yes, to make it stronger and stronger and stronger versus like, "Cool, here it is first time," and it's rarely.

我以前有一位经理告诉我，正确做法总是把文档写到 70% 完成，然后拿给你需要获得支持的人，把它从 70% 提升到 100%。这仍然是我一直在做的事情，因为很少有优秀的人愿意与一个完美打磨的成品想法互动。一个完美打磨的想法，他们的新想法只会被弹开，而一个有更多棱角和粗糙边缘的东西，他们也可以和你一起打磨。我认为那样把人带入过程，让文档成为底层产物，是我发现的最好的协作方式之一。

I previously had a manager who told me that the right thing to always do is write a doc to 70% completion and then take it to the people that you need buy-in from and get it from 70% to 100%. And that still is like a thing that I do all the time, because very few great people want to interact with a perfectly polished finished idea. Like a perfectly polished idea, their new ideas just bounce off of it, versus something that has more crags and more rough edges that they too can polish with you together. And I think that bringing people into the process that way, where a doc is like an underlying artifact for that, is one of the best ways to collaborate that I found.

你怎么看待 AI 脑腐病和开始过度依赖 AI？这是每个人都会面临的挑战。为什么不用这种魔力来帮助看待某件事，然后我们开始失去写作、阅读长文档的能力？你有没有做什么来避免这种情况？

How do you think about AI brain rot and starting to over-rely on AI? This is just a challenge everybody's gonna have. Why not use this magic to help look at something, and then we start to lose our ability to write, read long documents? Is there anything you do that you are trying to avoid that?

是的，我认为这种“作为思考的写作”的纪律是我日常采用的主要方法之一，以确保我的思考能力不会过度萎缩。

Yeah, I think this writing-as-thinking discipline is one of the main pieces that I employ in my day-to-day to make sure I'm not overly atrophying my thinking abilities.

我觉得我还是会尽可能把写作和报道外包给模型，但写作就是思考，这部分我必须自己做。我有一个个人信念：如果我要让别人读我的文档，我至少得先自己读那么多遍。我在开会时也这么想：如果我要召集一群人开会，我需要在开会前就准备好相当于所有人开会时间总和的工作量。所以为了保持思维敏锐，我会先自己写文档，确保我投入了至少相当于别人阅读时间的精力去写作和产出。我也不太依赖模型来润色我的文字，我觉得它在这方面做得并不好，尤其是在生成初稿时。但当然，在总结或把内容从一种格式转换成另一种格式时，我会经常让模型帮忙。

I think I will again outsource all writing and reporting as much as possible to the model, but writing is thinking, and I have to do that myself. I have this personal belief that if I'm going to make someone read my document, I have to at least read it first that number of times. I think about this in meetings too: if I'm going to call a meeting with a set of people, I need to have prepped the collective amount of time that people are going to spend in that meeting before the meeting. So when it comes to keeping my thinking sharp, I do that writing for the document myself first and make sure I've invested the collective amount of time I expect people to read it, at least in writing it and producing it. I don't really rely on the model for polishing my prose either, which I don't think it really does, especially not in generating the first version. But I do, of course, have the model help me a lot with summarization or translation of content from one format to another all the time.

所以你的意思是：想法、简报、计划，都由你自己作为人类来写。自己写，不要从 AI 开始，甚至不要用它来改进写作，保持这一切都是人类完成的。

So what I'm hearing is: write the idea, the brief, the plan yourself as a human. Write it yourself. Don't start with AI. And don't even use it to improve on the writing. Just keep that all human.

是的，至少对我来说，开头和结尾都是我自己。我可能会在中间用 AI 来研究特定元素、加入一些数据、去拉取一些数据，或者帮助反驳一些想法。

Yeah. At least for me, I start myself and I end myself. I might use AI in the middle to research specific elements, drop in some data, or go pull some data, or help push back on some ideas.

反驳一些想法。

Push back on some ideas.

是的，反驳一些想法，但开头和结尾都是你自己，这样不会削弱你的思考。

Yeah, push back on some ideas, but start yourself and end yourself with a piece of writing, and that doesn't deteriorate your thinking.

好的，关于 Sutter Hill 我想问最后一个问题。你的职业经历中有一个非常不寻常的步骤。你去找了你的 PMP 创始人，然后就成了 Sutter Hill Ventures 的入驻企业家（EIR），那是一家标志性的风投。人们可以查一下，Sutter Hill 出了很多了不起的公司。它有非常独特的创业方式，基本上是孵化公司，比如 Snowflake。那是怎么回事？你从那段经历中学到了什么？

Okay, one last question I want to ask about Sutter Hill. You had this very unusual career step. You went to your PMP founder person and then just like, okay, EIR at Sutter Hill Ventures, which is an iconic VC. People can look it up. A lot of amazing companies came out of Sutter Hill. It has a very unique way of approaching founding, where basically they incubate companies. Snowflake as an example. What was that about? What did you learn from that experience?

Sutter Hill 是一家标志性的公司，而且故意保持低调。如果你去 Sutter Hill 的网站，你会看到上面什么都没有。这家公司不张扬，尽量不引人注目，尽可能谦逊，但不知何故却造就了硅谷一些最具标志性的成功。他们有一种非常独特的孵化模式，由那里的杰出合伙人之一 Mike Spiser 开创，并接连取得成功。我觉得 Sutter Hill 对我来说最标志性的一点是，人们把找到产品市场契合点视为一种玄学，或者把建立一家数百亿美元的公司视为玄学——好像那是运气、是偶然、是所有因素凑在一起。但 Mike Spiser 已经做到了多次。所以显然有一种方法可以做到，显然有一条路线图可以实现，有一系列可以重复做的事情。这不只是运气，不只是玄学。有一本剧本，这本剧本就存在于 Sutter Hill 公司内部。他们知道如何在决策和下注时经常正确，也学会了如何在日常积累中做正确的事情来创建一家成功的公司——无论是如何组建企业销售团队、如何定位产品、如何建立初始创始团队。Sutter Hill 的招聘是无可比拟的卓越。他们有一个秘密工具叫 Reticle，里面有一张地图，记录了所有他们互动过的人，以及这些人互动过的 10 个最优秀的人。这让他们在这方面非常高效。所以我去 Sutter Hill 是因为在某种程度上，我的职业生涯一直围绕着如何尽可能多地找到产品市场契合点，无论是作为创始人、在 Stripe 开创新产品，还是加入像 Watershed 这样的初创公司。所以 Sutter Hill 是一个他们知道如何在 B2B 产品上找到产品市场契合点的地方，我想向他们学习。

Sutter Hill is an iconic firm and is intentionally a very illegible firm. If you go to the Sutter Hill website, you will see nothing on the website. It's a firm that doesn't operate loudly. It tries to operate as under the radar as possible, as modestly as possible, yet is somehow responsible for some of the most iconic successes that Silicon Valley has seen. And they have this very unusual incubation model, which Mike Spiser, one of the amazing partners there, started and has rolled out success after success. I think the thing that was most iconic to me about Sutter Hill is that people look at finding product-market fit as a dark art, or building a tens of billions of dollars company as a dark art—like, oh, it's luck, it's chance, it's all these things that must come together. Yet Mike Spiser has done it multiple times. So there is clearly a way to do it, there's clearly a roadmap for making that possible, there's a set of things one can do to get this repeatably. It's not just luck, it's not just a dark art. There is a playbook, as it were, and that playbook lives inside the firm Sutter Hill. They have figured out how to be right a lot in terms of calling shots and making bets, and they've learned how to be right a lot in terms of the daily compounding things that one does to create a successful company—whether that's how you set up your enterprise sales team, how you position your product, how you build the initial founding team. Recruiting at Sutter Hill is an unparalleled excellence. They have a secret tool called Reticle, where they have a map of everyone they've interacted with and the 10 best people that those people have interacted with. That helps them be so effective at this. So I went to Sutter Hill because in some way my career has been about how do I try to find product-market fit as many times as possible, whether that was as a founder, or in starting new products at Stripe, or in joining a startup like Watershed. So Sutter Hill is a place where they've figured out how to find product-market fit on B2B products, and I wanted to learn what I could from them.

你学到了什么？除了他们知道怎么做之外，你从那段经历中带走了什么？

What'd you learn? What's one thing you took away from that experience other than they know how to do it?

他们确实知道怎么做。我觉得在那里学到的一件非常让我惊讶的事情是，产品市场契合点固然重要，但实际上我大大低估了产品营销契合点。也就是说，你谈论产品的方式和营销产品的方式，甚至可以先于产品本身的构建。它可能应该来自对技术的深入理解与企业销售流程的理解的结合，而这种产品营销契合点、这种叙事、这种定位，实际上是在你构建产品体验之前就应该测试的正确东西。所以你应该去给 100 个人做推介，尽可能完善你的推介，把为什么这个东西具有变革性的营销叙事做对，然后才去承诺产品形态。而 Mike Spiser 在这门艺术上是无敌的。以前我总有点低估 PMM 的工作——我觉得它无所谓，它就是这些职能之间的粘合剂，还行。然后我意识到，做得好的 PMM 工作对公司的结果有多么大的变革性影响，实际上它可能是让公司成功的关键因素。

They definitely know how to do it. I think one thing that was very surprising to me that I learned there is that product-market fit is sure important, but actually I really underrated product-marketing fit. The idea that the way you talk about the product and the way you market it can precede actually even building the product. It should probably come from some sort of bringing together of understanding the technology deeply and then understanding the enterprise sales process, and then that product-marketing fit, that narrative, that positioning, is actually even before you build a product experience the right thing to test. So you should go pitch 100 people, figure out how to refine that pitch as much as possible, get the marketing narrative of why this thing is transformative right, and then only then go commit to the product shape. And Mike Spiser is unbeatable at this art. Previously I'd always kind of underrated PMM work—I was like, it's whatever, it's the glue between these functions, it's fine. And then I realized how transformative that work done excellently is to a company's outcome, and in fact can be the element that makes a company successful.

太棒了。我非常同意这个定位。我们在播客里经常讨论这个。

Amazing. I so agree with that positioning. We talk a lot about that on this podcast.

嗯。

Mhm.

好的，我快速给你看看生成的网站。我们聊天的时候它一直在运行。看看这个。

Okay, I'm going to show you what site got created real quick. It was running while we were talking. Check this out. Look at this.

哦，天哪。

Oh man.

比如，让它更棒，它就变得更棒了。

Like, make it more awesome, and it made it more awesome.

多产品。漂亮。看看这个，这是真正的设计。

Multiroduct. Beautiful. Look at this. This is like legit design.

看看你还有引言。大信念。小团队。从买家开始。

Look at you got quotes. Big conviction. Small teams. Start with the buyer.

你觉得这个作为你的网站怎么样？你的新网站。

How do you feel about this being your website? Your new website.

我确实觉得顶部那张我大概 19 岁的照片很搞笑。但除此之外，我很喜欢这个网站。

I do think that the picture of me at maybe 19 years old at the top is really funny. But yeah, otherwise I love the site.

好的，干得好。

Okay, good job.

看起来不错。

Looking good.

我觉得那是我在 Stripe 的工牌照片。

I think that was my badge photo from Stripe.

哦，哇。太棒了。我喜欢它做的——我已经取消分享了，但我喜欢它在你的头周围做了个小东西。太可爱了。

Oh wow. Amazing. I love that it built—I already unshared it, but I love that it built a whole little thing around your head. So cute.

呃，Tara，你还有什么想分享的吗？在我们进入非常精彩的快问快答环节之前，你还想谈谈别的吗？

Uh Tara, is there anything else that you wanted to share? Anything else you want to uh touch on before we get to our very exciting lightning round?

是的，我们在产品构建中，尤其是在这个新时代的 ChatGPT 工作中，一直在思考的一件事是，知识工作和编码实际上有着根本的不同。我们学到的令人惊讶的一点是，编码是如此以输出为导向，当你让它执行编码任务时，你可以通过测试来验证它是否正确或良好地完成了任务。你可以试一试，看看它是否有效。就像有一种基于输出进行验证的方法。但知识工作不同，我不能简单地看最后的演示文稿，看到数字，比如“哦，成功率 90%”之类的，就真的相信它。我真的需要思考过程、输入、推理以及它是如何一步步进行的。

Yeah, one thing that we've been thinking about a lot in product building, especially with ChatGPT work in this new era, is how knowledge work and coding are actually fundamentally different. And one of the surprising things we learned as a part of that is that coding is so output oriented that when you ask it to do a coding task, you can verify whether it did the task correctly or well via tests. You can try it out and see if it works. Like there is a way to validate it based on the output. But knowledge work is different in that I can't simply look at the deck in the end and see the numbers. Oh, it's like 90% success or whatever in the deck and actually believe that. I really need to think about the process and the inputs and the reasoning and how it went along the way.

所以在产品中体现出来的是，我们已经做的和必须继续做的很多工作，就是继续让产品适应知识工作，这意味着更多地专注于让 ChatGPT 成为你的协作者，让你看到所有进行中的工作，看到它的引用和输入，帮助你和模型一起走完这段旅程，最终得到那个输出，这样你最终会知道：“哦，等等，这个东西是对的，这个东西是好的，这个东西是有用的。”

And so in terms of how that looks in the product, like a lot of work that we have done and have to continue to do is continue to adapt the product to knowledge work, which means way more focus on making ChatGPT your collaborator, allowing you to see all the in-progress work, see its citations and inputs, help you go on the journey with the model to get to that end output such that you know in the end that oh wait, this thing is right, this thing is good, this thing is useful.

这当然在产品体验中体现得很多，但也应该体现在推理和思维链等方面。比如，你是否应该在这个过程中看到更多的引用，以了解它是如何得出那个最终状态和数据的？线程的界面非常适合编码，但它是否也是你查看知识工作所有这些内容的正确场所？有太多重大而重要的产品问题。因此，当我们考虑将人类协作者引入你的工作时，我们也需要考虑如何让模型更多地成为你的协作者，与你一起完成任务。

And that shows up certainly in the UX of the product quite a bit, but also should show up in things like the reasoning and the chain of thought. Like should you see more citations along the way, for example, of how it got to that end state in that data? Is the surface of a thread, which is so suited to coding, the right place for you to see all of that for knowledge work as well? There's so many big important product questions. And so as we think of maybe bringing in human collaborators into your work, we also need to think about how we can make the model more of a collaborator with you as you get things done together.

这真是个好观点。我在想象一个高管会议，你试图向高管推销你的计划，告诉他们你认为我们应该做什么。其中很大一部分是帮助他们看到你为此所做的工作，所有的步骤。所以，你需要 AI 向你展示它所做的同样的工作，本质上就是工作证明，而工程领域则不同，我不需要知道你做的所有小的架构决策，只需要知道它看起来怎么样，是否通过了我们所有的测试。所以，这两种模式有多么不同，这真是一个很好的观点。

That is such a good point. I'm imagining an exec meeting where you're trying to pitch the exec on here's what the plan is. Here's what I think we should be doing. So much of that is helping them see here's the work I did to get there. Here's all the steps. And so it makes sense that you need the AI to show you that same sort of work that it did, the proof of work essentially, versus engineering where like okay I don't need to know all of the little architectural decisions you made, just what does it look like, is it passing all the tests that we have. So that is a really good point just how different those two models are.

还有上下文的问题。它是否拥有做你想让它做的事情所需的上下文？它是否知道，能否看到你的电子邮件，能否看到你所有的 Ocean 文档？

And also there's like the context. Does it have the context it needs to do the thing that you want it to do? Does it know, can it see your email, can it see all your and Ocean Docs?

嗯。

Mhm.

呃，真是个好观点。所以，我看到了你工作中的挑战。我认为所有这些工作都是一个产品。棘手，棘手。呃，太棒了。在我们进入非常精彩的快问快答环节之前，还有什么要说的吗？

Uh, such a good point. So, I see the challenge in your job. I think all this work is one product. Tricky, tricky. Uh, amazing. Anything else before we get to our very exciting lightning round?

是的。我们开始吧。

Yeah. Let's jump into it.

那么，我们进入了非常精彩的快问快答环节。我有五个问题要问你。准备好了吗？

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

是的。

Yes.

你发现自己最常向别人推荐的两三本书是什么？

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

我真正推荐给人们的一本书是威廉·芬尼根的《野蛮的日子》。我不知道你读过没有。它讲述了一个《纽约客》记者的生活，以及他如何爱上冲浪并以此为激情。我从这本书中学到的是，一个人可以深深地热爱、投入，并让某件事成为你的人生目标，即使你并不擅长它。这本书讲述了他爱上冲浪的艺术，以及他在知道自己永远无法达到完美的情况下，仍然追求卓越和完美。对于我认为人们应该如何继续生活，这是一个非常引人入胜且具有变革性的故事。我真的很喜欢那本书。

One book I really recommend to people is Barbarian Days by William Finnegan. I don't know if you've read it. It's about a life of a man who is a New Yorker reporter, but how he fell in love with surfing as his passion. The thing I took away from the book is that one can be deeply passionate and dedicated and have something be your life purpose without you being good at it. And it is about the art of falling in love with surfing and his striving for excellence and perfection whilst knowing that he will like never reach it. It is such a compelling and transformative story for how I think one should continue to live our lives. I really really love that book.

另一本我可能会推荐的书是《安娜·卡列尼娜》。我最近在重读经典，我喜欢《安娜·卡列尼娜》，因为它像一本有层次的书。我认为在这个新时代，我们将要做的很大一部分事情是转变自己，或者让自己踏上旅程，去做不同于我们习惯的事情。当我想到那本书时，我想起我 13 岁读它时，基本上理解了情节。当我 17 岁读它时，我理解了欧洲历史的动态和阶级斗争。然后当我 30 岁读它时，我想：“哦，这是一个关于女人和人类的故事。”它只是让我想起成长，以及随着你的成长，用多种不同的视角看待同一件事是可能的。我认为这也是我们在考虑职业生涯时面临的挑战。

Another book that I might recommend as a book that people should read. I really love Anna Karenina. I've been rereading the classics lately and I love Anna Karenina because it's like a book of layers. And I think that a huge part of what we're going to have to do in this new era is transform ourselves or take ourselves on a journey to do different things than what we were used to. And when I think about that book, I think about when I was 13 and I read it, I understood basically the plot. When I read it at like 17, I understood the European history dynamics and like the class warfare. And then when I read it at 30, I was like, "Oh, this is like a story about a woman and humans." And it just reminds me of growth and that it is possible to look at the same thing through multiple different lenses as you continue to grow. Which I think is kind of the challenge that's ahead of all of us as we consider our careers as well.

这两本书都很有趣，我觉得它们也能和我们现在所处的时代以及 AI 联系起来。呃，我最近也读了一本

It's interesting on both these like I could connect to AI in the time we're living in now too. Uh I also read recently read an

你觉得怎么样？

What did you think?

今年早些时候。太棒了。我以前从未读过。我在一个书单上看到它，上面写着“世界上最聪明的人读过什么”，那是一整份书单，而这是我唯一没读过的。所以我想我得读读它。是的。呃，它太棒了。呃，有人剧透了结局，这让它不那么令人惊讶。我不想剧透任何东西。没有剧透。呃，我还觉得它很长，但现在我在读《权力掮客》，它就像为新的总统任期设定了标准，到现在我已经读了大半辈子了。

Earlier this year. Amazing. I've never read it before. I saw it on a book list of like here's what the smartest people in the world have read and it's like a whole list of books and that was one that I hadn't read. So I'm like I got to read that. Yeah. Uh it was amazing. Uh someone gave away the ending which kind of made it less like surprising. I don't want to give anything away. No spoilers. Uh and I also felt like it was very long and but now I'm reading the Power Broker which is like set the new president for a long been reading it for half my life at this point.

我喜欢《权力掮客》。我强烈推荐给人们的另一件事是，如果有人关注 Substack，比如西蒙·黑兹尔的 Substack，他会对重要的书进行慢读。他做过《战争与和平》，我想他正在做《狼厅》，或者他已经做过了，那是希拉里·曼特尔的书，就像一章一章地读。这似乎是读《权力掮客》或《战争与和平》甚至《安娜·卡列尼娜》的唯一方式，就像一章一章地读。

I love the Power Broker. Another thing I highly recommend to people is if anyone follows the Substack, like Simon Hazel's Substack where he does a slow read of important books. So he did one of War and Peace and he's doing one of Wolf Hall I think or he did one of Wolf Hall which is a Hillary Mantel book, like take it chapter by chapter. That's like the only way to read something like the Power Broker or War and Peace or even Anna Karenina, like chapter by chapter.

说到这个，有个人告诉我，我忘了是谁，有一个《99% 隐形》播客对《权力掮客》的读书俱乐部解读，共 13 集，每集一两个小时，他们一次读几章，然后讨论，还有像彼得·蒂尔和 AOC 这样的特别嘉宾，以及住在那个地区的人，他们谈论每一个故事，读起来和听他们的分析都很有趣，然后他们还邀请罗伯特·卡罗来过几次，太棒了。

Speaking of that, there's a someone I forget who told me this, there's a 99% Invisible book club breakdown of the Power Broker where it's 13 episodes, an hour or two each, and they go through a couple chapters of the book one at a time and talk about it and they have special guests like Petage and AOC and folks that lived in that area and they talk about every, you know, the story and it was so fun to read and listen to their analysis of it and then they have Robert Caro come on a couple times on the That's amazing.

是的。好建议。好的，我们继续快问快答。呃，如果你有时间看的话，你最近最喜欢的电影或电视剧是什么？

Yeah. Hot tip. Okay, we'll keep going with our very lightning round. Uh, favorite recent movie or TV show you've really enjoyed if you've had time to watch anything.

当然，我看了《奥德赛》。我觉得这是一部了不起的电影。它是关于 AI 的，或者我的个人看法是，它是关于 AI 的，或者说克里斯托弗·诺兰对 AI 如何改变社会的看法，我很喜欢。我强烈推荐看《奥德赛》。他是一位了不起的导演，把艺术性和商业成功结合起来，我认为没有其他现代导演能做到。我最近还看了电影《罗生门》，黑泽明的那部，它开创了通过多视角讲述故事的手法，到最后你永远不知道真相是什么。那种电影手法是黑泽明开创的。它让我想到在限制条件下能做出什么。那部电影是 50 年代拍的，是黑白的，但它是如此完美，如此有品味、创新、令人惊叹的创意典范。看那部电影让我想到，我的 iPhone 里有他拍那部电影时一百倍的威力和工具。那我还有什么借口不提升我的野心，做出更好的东西呢？

Of course, I watched The Odyssey. I found it to be an incredible film. It's about AI, or my hot take is that it's about AI, or Christopher Nolan's view on how AI transforms society, which I loved. I highly recommend watching The Odyssey. He's an incredible director and has bridged artistry and commercial success in a way that I think no other modern director has done. I also recently watched the film Rashomon, the Akira Kurosawa film that pioneered the technique of telling a story through multiple perspectives, where you never know what was true in the end. That technique in film was pioneered by Kurosawa. It reminds me what one can do under constraints. That film was made in the 50s, it was black and white, and yet it is so perfect and such a tasteful, innovative, amazing example of creativity. Watching that film reminds me that I have a hundred times the power and tools that he had making that film, in my iPhone. So what's my excuse for not elevating my ambitions and making better stuff?

一切都归结于野心。关于《奥德赛》，我还在努力搞票。太难了。我一开始没上心，现在一个月内都看不到了。哪儿都没座位。

It all comes back to ambition. On The Odyssey, I'm still trying to get tickets. It's so hard. I slept on it and now it's impossible for like a month. There are no seats anywhere.

凯文·克拉克这周早些时候帮我们在 Metreon 弄到了晚上 10 点的票。太棒了。

Kevin Clark got us tickets at 10 PM at the Metreon earlier this week. It was so good.

下次叫我。我加入。

Next time, call me. I'm in.

天哪，我有机器人在刷票。我还有人专门在弄。我有个朋友。我们都在试着找……

Oh man, I have bots running on it. I have a person working on it. I have a friend. We're all trying to find a...

太棒了。你会喜欢的，我等不及听你看完后的想法。如果你同意我的看法，它是关于 AI 和道德崩塌的。

It's amazing. You're going to love it, and I can't wait to hear what you think after you see it. If you agree with me that it is about AI and the collapse of morality.

好，别剧透。希望这期节目播出时我已经看过了。但如果还没有，谁有门路请告诉我。我想去看 IMAX 全效果的 Metreon 那种。是的。

Okay, no spoilers. Hopefully by the time this comes out, I have seen it. But if not, if anyone has hookups, please tell me. I'm trying to do the IMAX full power Metreon sort of thing. Yeah.

好。下一个问题。你最近发现的最喜欢或有趣的 AI 产品，最好不是 OpenAI 的产品，但如果你想也可以说。

Okay. Next question. Favorite or interesting AI product that you've recently discovered, ideally not an OpenAI product, but you can also go there if you want.

哦。我是说，当然，我最喜欢的 AI 产品是 ChatGPT，还有用酷站和编解码器可视化东西，这很神奇。但在 OpenAI 产品之外，我最喜欢的 AI 产品是我朋友为我做的产品，因为现在人们真的可以做到。我觉得这太酷了。我非常喜欢“舒适软件”运动，就是为五个朋友做软件工具，然后你们一起用。所以我有个朋友叫塞巴斯蒂安，他做了一个很酷的 AI 应用，能把任何东西变成播客，放进你的小播客应用里。他还为我们的朋友做了一个很棒的私密社交网络，叫 GATS。这正是我认为未来应该有的样子，人们应该做完全满足自己和朋友需求的软件。

Ooh. I mean, of course, my favorite AI product is ChatGPT and using cool sites and visualizing stuff in codecs, which is amazing. But outside of OpenAI products, my favorite AI products are products that my friends make for me, because now actually people can do that. I think that's so cool. I'm such a huge fan of the cozy software movement, where you make software tools for like five of your friends and you guys use it together. So I have a friend named Sebastian who made a really cool AI app that turns anything into a podcast and puts it in a little podcast app for you. He also made a really great private social network for our friends, and it's called GATS. It is exactly what I think the future should be, which is people should make software that exactly meets their and their friends' needs.

GATS 代表什么？是某种内部笑话吗？

What does GATS stand for? Is that some inside joke?

不是。或者至少如果是个内部笑话，我也不知道。但它是那个地方……就像私密 Twitter，也许适合一小群朋友。我在那个产品上了解到最有趣的东西。

It is not. Or at least if it is an inside joke, I don't know it. But it is the place that I... It's like private Twitter, maybe for a small group of friends. And I learn the most interesting things on that product.

就像 WhatsApp，但不是……

It's like a WhatsApp, but not...

是的。完全正确。完全正确。

Yes. Exactly. Exactly.

播客应用很有趣，但我觉得我喜欢的版本是，在你的播客订阅里真的有播客，然后新集数会添加你想读的东西之类的。

The podcast app is interesting, but I feel like the version that I would love is actual podcasts in your feed of podcasts, and then new episodes just get added of things you want to read or whatever.

是的，它就是这样的。它会把它放进你的 Apple 播客订阅里。你看着。

Yeah, that's what it does. It drops it in your Apple Podcasts feed. You watch.

太棒了。我想要这个。太好了。我怎么帮我？帮我订阅这个。

Amazing. I want this. It's great. How do I help me? Help me subscribe to this.

当然。

For sure.

好。太棒了。好。还有两个问题。你有没有一个最喜欢的人生格言，在工作或生活中经常用到？

Okay. Amazing. Okay. Two more questions. Do you have a favorite life motto that you find yourself coming back to often in work or in life?

我在工作中经常用到的人生格言，其实是托妮·莫里森关于工作的三条见解。让我很快调出来。

My life motto that I come back to all the time in work is actually Toni Morrison's three takes on work. Let me pull it up really quickly.

太棒了。

Amazing.

好，是四件事。来自她的文章《你做的事，你成为的人》。第一：无论做什么工作，都要做好，不是为了老板，而是为了自己。第二：你成就工作，而不是工作成就你。第三：你真正的生活是和家人在一起。第四：你不是你所做的工作，你是你这个人。

Okay, it's four things. It's from her essay, "The Work You Do, the Person You Are." The first one is: whatever the work is, do it well, not for the boss but for yourself. The second is: you make the job, it doesn't make you. The third is: your real life is with your family. And the fourth is: you are not the work you do, you are the person that you are.

我起鸡皮疙瘩了。哇。太好了。我想这就是你置顶在 Twitter 个人资料上的内容。是的，因为我记得看到过。

I got tingles. Wow. So good. And I think that's what you have pinned to your Twitter profile. Yes, because I remember seeing that.

太酷了。

So cool.

好。也许我们可以在你谈论的时候在屏幕上展示一下。我喜欢这个。我喜欢这是记住事情的好方法。就把它贴在 Twitter 顶部，因为每次我去 Twitter，哦，又看到了。

Okay. Maybe we'll show that on the screen as you're talking about that. I love that. I love that's a great way to remember something. Just stick it to the top of your Twitter, because every time I go to Twitter, oh, there it is again.

好。最后一个问题。你当年是泰尔奖学金得主。泰尔奖学金。泰尔还是泰尔？泰尔。泰尔。是的。多么棒的校友群体。天哪。这真是一个伟大的想法和项目。那个时代有没有什么有趣的故事可以分享，比如“哇，那太疯狂了”之类的？

Okay. Final question. You were a Thiel Fellow back in the day. Thiel Fellow. Thiel or Thiel? Thiel. Thiel. Yeah. What an alumni group. Holy moly. It's such a great idea and program. Any story from that time that might be fun to share, something that's like, oh wow, that was crazy?

我不知道你引以为傲的其他泰尔奖学金得主。其他……面试是什么样的？我不知道那些。

I don't know any other Thiel Fellow that you're proud of. Any other... What was the interview like? I don't know anything along those lines.

是的，我……

Yeah, I...

泰尔奖学金是我人生的转折点。没有它，我不会是现在的我。也许到了这样的程度：有些关键时刻，你告诉人们提升他们的野心，他们做到了，这改变了他们。那是一个有人来找我，提升我的野心，说“不，你能做到。你不必走你原来的路”的时刻。真的，我永远感激他们能那样做。我现在经常一起工作的一个泰尔奖学金得主是阿里·温斯坦，他创立了一家公司叫 Sky，被 OpenAI 收购了。在此之前，他创立了公司并在 Apple 工作过一段时间，因为他们收购了他之前的公司。阿里是我见过的最有创造力的思想家之一，真正是 Mac 上所有酷事的专家。阿里领导我们 OpenAI 很多计算机使用方面的工作，他发布了很多很棒的东西。他的创造力、他对工作的热情和他对技艺的热爱真的激励了我。阿里是个酷家伙。但我正在想那个时代有没有什么好故事，感觉……

The Thiel Fellowship was an inflection point in my life. I wouldn't be where I am without it. Maybe to the point that there are key moments where you can tell people to elevate their ambitions and they do, and that changes them. That was a moment where someone came to me and elevated my ambitions and said, "No, you can do this. You don't have to take the path that you were on." And truly, I'm eternally grateful for them being able to do that. One of the Thiel Fellows that I get to work with all the time now is Ari Weinstein, who founded a company called Sky that was acquired by OpenAI. Prior to this, he founded and worked at Apple for a while because they acquired his previous company. Ari is just one of the most creative thinkers I've ever seen and is truly the expert on all the cool things you can do on a Mac. Ari leads a lot of our computer use stuff at OpenAI, and he's shipped a whole bunch of great things for computer use. His creativity and his joy in what he does and his love of his craft really inspire me. Ari's a cool guy. But I'm trying to think of a good story from that time that feels...

在你思考的时候，我来给不知道的人解释一下泰尔奖学金，如果我错了请纠正。基本上，彼得·泰尔说：“嘿，人们不应该上大学。”

As you think about it, I'll explain the Thiel Fellowship for people that don't know this, and correct me if I'm wrong. Basically, Peter Thiel is like, "Hey, people shouldn't go to college."

相反，他们应该尝试去构建自己真正想要的东西，给你 10 万美元，让你不用上大学，而是去追随自己的抱负。大致是这样吗？

Instead, they should just try building something that they want and you get $100,000 to not do college and instead just go follow your ambition. Is that roughly correct?

是的，完全正确。当时你和另外 19 个人在一起。每年大约 20 人，因为这是“20 岁以下 20 人”项目。

Yeah, that is exactly right. And you're with 19 other people at the time. It was like 20 people every year because it's 20 under 20.

这个项目持续了多少年？现在还在进行吗？

And how many years did it go on for? Is it still going?

我想它还在继续，但我觉得在最初的四五年里，人数一直限制在 20 人左右。

I think it's still going, but I think it was constrained at the 20 number for like the first four or five years or something like that.

我觉得我那年发生的一件很疯狂的事是，我是该奖学金项目的第二届学员。他们决定把整个过程拍成纪录片，在 CNBC 播出。所以我们的整个——我申请奖学金的演讲，上台展示我要做的项目，所有这些都不幸地留在了 YouTube 上。所以如果你真想看我 19 岁时做些尴尬的事，那里就有。

I think a really crazy thing that happened my year is that I was the second every year of the fellowship. They decided to make it all a documentary on CNBC. And so our whole, my pitch for the fellowship, getting up on stage and presenting the idea I was going to do, all of that is unfortunately live on YouTube. So if you really want to see me as a 19-year-old doing something embarrassing, it's there.

当然，那一届奖学金学员中最杰出、最成功的人之一是 Dylan Field，他不仅才华横溢，而且非常善良。能和他们一起工作，我感到非常幸运。

Of course, one of the most amazing and successful people who came out of that batch of the fellowship is Dylan Field, who is not only an incredible talent, but also like a very kind person. And yeah, feel very lucky to be able to work with those folks.

太棒了。有意思的是，我觉得大家一提到泰尔奖学金就会想到 Dylan。

Amazing. Yeah, it's interesting that Dylan's like the guy I think everyone thinks of when they think of Thiel Fellows.

是的，是的。

Yeah. Yeah.

多么响亮的品牌。好了，Tara，这期节目太棒了。你有什么想宣传的吗？想让大家关注什么？听众怎样才能帮到你？

What a brand. Okay, Tara, this was incredible. Is there anything you want to plug? Anything you want to point people to? And how can listeners be useful to you?

我想宣传或推荐什么？也许他们应该使用 ChatGPT 桌面应用。他们应该在网页上使用 ChatGPT，试试 Work 功能。不幸的是，它只是一个小开关。他们可以切换过去试试 Work。让它做点酷的事情。让它为你建一个网站。也许从这开始，或者让它做一个关于你 ChatGPT 使用情况的小可视化块。这是开始亲身体验这东西力量的非常酷的方式。而且他们能做的用例列表是无限的。我很乐意。

Anything I want to plug and point people to? Maybe they should use the ChatGPT desktop app. They should use ChatGPT on the web and try Work. It's like unfortunately a little toggle. They can toggle over to it and try out Work. Ask it to do some cool thing. Ask it to build a site about you. Maybe to start or ask it to make a little visualization block of your ChatGPT usage. It's a really cool way to start experiencing the power of this stuff very intimately. And the list of use cases they can do from that, you know, are infinite. And I'm happy.

我有个更好的主意。我有个更好的主意。让它建一个网站，告诉你可以用 Work 做什么。

Here's a better idea. Here's a better idea. Ask it to build a site to tell you what you could do with Work.

太好了。

Great.

那肯定行。

That will work.

解决所有问题。好的。我打断你了。抱歉。你还想补充或说什么？

Solve all the problems. Okay. I interrupted you. I apologize. What else were you gonna add or say?

是的，我主要想说的是，去下载 ChatGPT 应用吧。在网页上使用它。更具变革性的是，去移动端试试。然后坐一次长途地铁或类似的长途车程。当你没信号出来后，事情已经为你完成了。这部分感觉超级神奇。你不再需要一直开着笔记本电脑到处走。你终于让这些东西在云端运行，做真正的工作。

Yeah, my main plug is yeah, go download the ChatGPT app. Go use it on web. Even more transformatively, go try it on mobile. Then take like a long subway ride or something like that or a MUN ride. And when you pop out after having no service, the thing is done for you. That's the part that feels super duper magical. You're not like wandering around with your laptop open the entire time. You've finally got these things running in the cloud doing real work.

是的，最后一点我本来想提的，但我觉得这是当今移动端产品中被严重低估的一个元素，而且这主要是移动端独有的功能。就是云端部分。

Yeah, that last piece I was going to bring up, but that's I think a really underappreciated element of the product today on mobile and it's most that's just a mobile only feature. The cloud piece.

不，它无处不在。

No, it's everywhere.

无处不在。好的。太棒了。所以在你的移动应用上，你可以进入 ChatGPT，切换 Work，让它做一些工作，你不需要真的——它不是本地运行的。它在云端运行。它会继续工作直到完成，然后你可以和它聊天。这感觉真的很简单，但这是一个非常强大的功能。好的。Tara，在我们放你走之前，还有什么要说的吗？

It's everywhere. Okay. So amazing. So on your mobile app, you can go to ChatGPT, toggle Work, ask it to do some work, and you don't need to actually have the it's not running locally. It's running in the cloud. It'll go keep doing work until it's done, and then you could chat to it. So like that feels like really simple, but that's a massively powerful thing. Okay. Anything else, Tara, before we let you go?

没有了，就这些。

No, that's it.

好的。谢谢，这期节目太棒了。非常感谢你参加。

Okay. Thanks, this was awesome. Thank you so much for doing this.

非常荣幸。

Such a pleasure.

从当年的奖学金项目到现在，真是一段旅程。我会在开场白里多聊聊这个。

What a journey since the fellowship back in the day. I'll talk about that more in the intro.

是的。

Yeah.

好了。感谢你的到来。

All right. Well, thanks for being here.

谢谢。

Thank you.

大家再见。非常感谢收听。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留下评论，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.