这期《Lenny’s Podcast》不是在介绍一个新按钮，而是在追问：当 AI 不再只是回答问题，也不再只是一次性替你完成任务，知识工作会变成什么样？主持人 Lenny Rachitsky 采访了 OpenAI 的 Tara Seshan，围绕 Codex、ChatGPT Work、产品经理的工作方式，以及人在 AI 时代还要负责什么展开讨论。Tara 是 OpenAI 的 Codex 与 ChatGPT Work 产品负责人；Lenny 在节目中介绍，她此前在 Stripe 工作六年，是最早加入的五位产品经理之一，之后负责过 Watershed 产品，也做过创业者和 Teal Fellow。最值得留意的，是一条从产品形态延伸到职业判断的线索：人可能越来越少亲自“划船”（row），越来越多负责“掌舵”（steer）；但方向、品味、雄心、验证和责任，并没有因此自动交给模型。

Tara 在节目开场把 AI 产品分成三个时代：第一个时代是聊天（chat），第二个时代是与代理（agents）协作，第三个时代可能是与一个能够持续记住上下文、反复工作、和你共同完成事情的“持久型同事”（persistent coworker）协作。这里的“第三个时代”是她对近未来的判断，不是节目宣布已经完成的产品形态。下面先拆开这套判断，再看它在现有产品和日常工作中长什么样。

Tara 对三个时代的划分，重点不在模型名称，而在人与 AI 的工作关系发生了什么变化。

Tara 用另一个比喻说明这项变化：未来的工作更像 steering than rowing——“掌舵多于划船”。划船是把每一个具体动作亲手做完；掌舵则是决定目的地、设定方向、观察航线、在关键节点修正。代理承担的“划船”越多，人的工作就越接近提出目标、判断是否走对、以及决定下一步往哪里走。

她认为这种掌舵会不断上移抽象层级。过去，程序员可能亲自写一行代码，再接受自动补全；后来，用户可以让代理完成一个更完整的功能；再往后，人可能直接描述目标，让代理持续推进。这个过程并不意味着人只说一句话就可以离场。Tara 特别强调：现在仍然需要人掌舵。代理做了更多执行，人的方向判断反而更重要。

更进一步，协作对象也可能不再是“我和我的代理”这一对一关系。Tara 描述了一种还在思考中的界面：不同的人带着各自的代理一起工作，代理之间也可以互相检查和协作。她把理想状态形容成多人游戏：人们共同推进目标，同时掌舵一组代理完成更多战术工作。这是节目中的产品设想，不等于对现有功能的完整描述。

节目没有把“代理变聪明”简单归结为模型参数更大。Tara 说，代理要做长时间、较高抽象层级的工作，当然需要更强的推理和执行能力，但还需要一批不那么显眼、却同样决定结果的基础设施：

她用了一个直观的类比：如果你雇了一个同事，却把对方锁在房间里，既不给 Google Docs 权限，也不给 Slack 和公司数据库权限，这个同事自然不会很有用。云端代理也是一样。让代理“会做事”不仅是让模型更聪明，还要让它能够在合适的权限和上下文里做事。

这也解释了为什么 Tara 不把现在的变化描述成单纯的“快起飞”或“慢起飞”。从用户体验看，代理可以逐步承担更长、更复杂的任务；从产品工程看，数据权限、系统接入和可靠性等“土木工程”一样的工作，决定了模型能力能不能真正转化为结果。

Tara 认为，AI 让产品经理失去的，是在相对稳定的市场里做大段预测的空间。

她以支付行业作对比。支付虽然也会变化，但它是一个相对成熟、结构更容易理解的市场。产品经理可以推演竞争对手的反应，围绕一项下注思考接下来可能发生的几步。可是 AI 市场非常快，也非常“涌现”（emergent）：未来会出现什么、模型会在哪个能力上突然变好，很难仅靠一份长战略文档预测出来。

所以她把重心从“理论上想完整”移到了“尽快形成假设并测试”。产品经理真正要问的，是产品最重要、最具体的那个问题——Tara 借用了 eigen question 这个说法，可以理解为“当前最值得先验证的核心问题”。找到这个问题之后，再把测试做得尽可能快、尽可能有效。

她并不是说思考不重要，恰恰相反，她认为问题定义变得更重要了。变化在于：

Tara 给出的产品时间窗口很具体：既不能只为模型现在的能力设计，也不能直接为一年后的能力设计；更合理的窗口是两到三个月。 如果产品只适配今天，模型能力一提升，产品结构可能很快过时；如果产品直接押注一年后的能力，今天又可能根本无法使用。两边都可能错。两三个月意味着产品要紧贴研究进展，同时保留足够近的可验证性。

这不是一个可以机械套用的预测公式。Tara 也承认，判断“两三个月后的模型能力”本身很难。她给出的工作方法是：和研究人员保持高频沟通，理解研究正在集中解决哪些具体能力，再让产品构造尽量不要挡住模型未来的能力。

Lenny 提出一个判断：当所有人都能轻松完成简单工作时，人与人、公司与公司之间的差异，可能越来越取决于能把目标想多大。

Tara 认为，最有效使用 AI 的人，不只是把重复劳动自动化，而是把 AI 用来扩大自己能做的事情集合。过去，既懂产品、又能写代码、还懂设计的人是稀缺的“全能型”人才，因为这个人能减少不同职能之间的翻译成本，快速把想法变成原型。Lenny 在节目中把 Tara 形容为 Stripe 最早的五位产品经理之一；这段背景来自主持人的节目介绍，不是本文对她履历的独立核验。现在，AI 让更多人可以生成设计、搭建原型、分析定价方案、模拟不同情景，于是过去超出个人执行范围的事情，开始进入“可以试一试”的范围。

执行能力的边界被推远之后，最难的部分不一定是“做不做得到”，而是“有没有想到要试”。Tara 说，新的习惯是：面对一个以前觉得太难的想法，先问一句，Codex 能不能帮我做一个版本？她引用 Tyler Cowen 网站上的一个建议：人们低估了这样一句话的力量——去问别人，能不能做得更有雄心、更快，或者放大十倍规模。

她把产品团队里反复出现的三个说法放在一起：

最后一句是对传统 dogfooding 的强化。Dogfooding 指团队自己使用正在做的产品；Tara 所说的 “mainlining” 更强调持续、深入地使用，让团队不断被自己的真实问题撞到。她认为，团队只有把产品用进自己的日常工作，才会更快知道什么烦人、什么缺失、什么值得修。

这套逻辑也解释了 Codex 近期“风向变化”的节目内解释。Tara 不把变化归因于某个神秘的内部转折，而是说团队一直在紧密使用产品、快速迭代，外部用户和社交媒体只是后来注意到了这种工作方式。这里的“市场追上来了”是她对现象的描述，不是独立统计结论。

节目中，Lenny 打开 ChatGPT 应用，看到 ChatGPT 与 Codex 的选择，以及 chat 和 work 的切换，于是请 Tara 解释这些选项。

Tara 给出的 North Star（北极星目标）是：用户不应该被迫在一堆模式、模型和产品概念之间作选择。理想情况是，用户只需要描述任务，例如“帮我做一个为播客嘉宾准备研究的应用”，系统就能自动选择合适的 harness 和模型。

这里的 harness 可以先理解为“包住模型、为模型提供工具、权限、工作环境和执行流程的一整套产品外壳”。Tara 在节目中没有把它定义成一个面向消费者的独立产品，而是用它解释为什么同一个模型在不同工作任务里需要不同的支持方式。她的核心观点是：这些内部概念不应成为普通用户的学习负担。

在节目录音所处的产品状态下，Tara 用以下方式区分几个入口：

她特别说，Work mode 与 Codex mode 的能力本身可以相近，差异主要在用户看到什么界面、是否暴露工作树和技术细节，以及产品如何适配用户已经熟悉的概念。她举的例子是：在 Codex 里要求它做一个财务模型、给产品定价或预测未来六个月收入，Work mode 也可以做同类工作；用户不必为了使用同样的能力而理解底层产品的全部结构。

因此，OpenAI 面临的产品问题不是把更多按钮塞进 ChatGPT，而是把代理能力带给大量原本只把 ChatGPT 当作聊天工具的人，同时让他们不必先学习“代理”“harness”等概念。Tara 承认还有很多地方需要改进，但她更看重先把有变革潜力的能力交到用户手里，再根据使用反馈迅速修正。

Tara 没有只用抽象愿景谈 AI。她在节目中列举了自己使用 Codex / Work 的几个具体场景。这些例子更适合被理解为产品负责人的个人使用案例，而不是对所有用户的效果保证。

她经常让 AI 直接生成 site。Site 在这里不是“给你一段源代码，然后让你自己找地方部署”，而是一个可以生成、托管并立即使用的交互式页面或小应用。她举了几类例子：

她把这件事和 Alan Kay 在 20 世纪 60 年代提出的“个人电脑应当拥有个人软件”的理想联系起来。过去，只有工程团队能把一个小工具做出来；现在，个人可以快速生成更贴合自己和小团队需求的软件。Site 的意义不在于网页本身，而在于“做一个只为这个场景服务的小工具”变得容易。

节目中还谈到发布范围：Tara 表示，用户可以选择把 Site 设为公开、只与团队共享，或保持私有。她说自己可以在 Codex、网页和移动端发起这类任务。具体可用范围和界面会随产品变化，本文不把这一段个人演示扩写成长期功能承诺。

另一个用法是 Codex 里的 /visualize。Tara 举例说，可以让系统“把我到目前为止的 ChatGPT 使用情况可视化”，由它调取已有使用信息并生成图表或视觉呈现。这个案例说明，AI 不只是生成文字或代码，也可以把一堆数据重新组织成更容易理解的界面。

节目的结尾，Tara 建议听众在手机上开启 Work，给它一个任务，然后坐一段较长的地铁或其他公共交通。等人重新联网时，任务可能已经在云端继续运行并完成，用户再回来查看结果或继续对话。

这个例子最能体现她说的“持久型同事”体验：人不需要一直守着屏幕，代理可以在后台继续工作。但它仍然是节目中的体验描述，实际效果取决于任务、产品版本、权限、网络和运行状态，不能据此保证任何任务都会在离线期间完成。

Lenny 问，若所有人都能使用同样的工具，什么会成为新的不公平优势？Tara 没有把答案归结为更熟练地写提示词，而是谈到了人的品味、表达、选择和作者性。

她用时装作比：功能性的衣服人人都能穿，但人穿什么也在表达“我想让世界看到怎样的自己”。软件也有类似的一面。她引用节目中提到的一个比喻：软件不像房地产，不是投入资金就必然得到相应价值；它更像电影，投入更多钱并不保证电影更好。好的产品需要某种作者性的判断，来自个人或团队“想对世界说什么”。

她认为未来仍然特别需要人的地方包括：

但她没有回避损失。她承认，工程师过去可以整天手写代码并进入心流，而现在更多时候是在让代理写代码、检查结果、继续指导。某些具体工艺会被技术抽象掉，人的专业能力可能转移到更高层级，也可能让一部分人感到失去熟悉的工作方式。Tara 对如何平衡效率与对工艺的热爱，明确说自己“还没有答案”。这个未决问题本身，也是这期访谈的重要信息。

节目后半段最可操作的部分，是 Tara 对写作的区分。她把工作中的写作分成两类：

这类内容包括总结本周进展、整理发布计划、把一种格式的信息转换成另一种格式。Tara 认为，这部分可以尽可能使用模型自动完成，因为它主要是在传递已经形成的内容。

这类内容包括论证为什么要做一个产品、为什么要采用某种策略、对一个方向提出有争议的判断。对 Tara 来说，写作过程本身是把想法排出来、写成句子、删改、继续迭代的过程。这个过程不能被“生成一份看起来完整的文档”替代。

因此她的工作流是：自己开始，自己结束。她可以在中间用 AI 查资料、补数据、把内容转换成另一种格式，或者让 AI 反驳自己的想法；但她不让模型替自己生成第一版思考，也不把“把文字润色得更漂亮”当成思考完成的证明。

这也改变了文档在团队里的角色。过去，一份写得很长、很完整的文档本身就能证明作者认真想过；现在，长文档很容易被自动生成，长度不再是思考的证据。Tara 更看重：

她并没有放弃写文档。她仍然会为自己写大量文档，但这些文档不一定是给别人阅读的最终沟通载体。一个成熟的协作方式，是把文档写到大约 70% 完成，再带给需要参与和支持的人。一个完全抛光的方案会让其他人的新想法弹开；带着缺口和棱角的方案，反而让别人可以一起打磨。

Tara 还指出，编码和知识工作在验证方式上不一样。代码通常可以通过测试检验：功能是否运行、测试是否通过、输出是否符合要求。知识工作则不能只看最后一页演示文稿，就断定里面的数字和结论是可靠的。

知识工作者需要知道：

因此，Tara 认为 ChatGPT Work 这类产品需要让用户看到更多引用、输入和过程线索，让人能和模型一起走到最后的结果，而不是只接收一个看似合理的答案。她把这种“共同检查过程”视为知识工作代理和编码代理之间的重要差异。

Lenny 还问到 Tara 曾经在 Sutter Hill Ventures 担任 EIR（Entrepreneur in Residence，驻留创业者）的经历。节目中提到，她此前在 Stripe 工作六年，是 Stripe 最早的几位产品经理之一；之后负责过 Watershed 产品，也做过创始人。Lenny 还介绍她曾是 Teal Fellowship 的参与者，以及 Lenny’s Newsletter Fellows 之一。这些经历解释了她为什么反复关注“如何找到产品市场匹配”。

Tara 说，Sutter Hill 给她最重要的启发之一，是她过去低估了 product-marketing fit（产品—营销匹配）。大家常谈 product-market fit（产品—市场匹配），也就是产品是否真正解决了市场中的问题；但在她看来，产品如何被描述、销售和定位，同样可能决定产品能否成功。

她从 Sutter Hill 学到的顺序是：

她把这看成对传统顺序的修正：不是先把产品全部做出来，再想怎么解释；而是先验证“人们是否理解并愿意接受这个价值叙事”，然后再投入资源确定产品形状。这个结论来自 Tara 对自身经历的总结，不是对所有 B2B 创业项目成功率的统计判断。

第一，AI 产品的下一步不是单纯把聊天答案做得更长，而是让 AI 以代理和同事的方式持续工作。持久型同事的关键是持续上下文、长时间执行、系统访问和反复同步，而不是多一个模式开关。

第二，执行成本下降之后，人的稀缺性会集中到目标、品味、雄心、验证和责任。人可以让代理划船，但仍要决定去哪儿、为什么去、什么算到达，以及结果是否值得相信。

第三，产品与个人工作都要缩短从假设到反馈的距离。Tara 的“两三个月”不是一个精确预测模型，而是一种工作纪律：不要被今天的能力锁住，也不要把产品押在遥远未来；和研究、用户及真实使用保持紧密循环。

如果你想判断这期是否值得完整收听，建议优先听 00:11 左右关于 steering / rowing 和持续代理的部分，00:20 左右关于基础设施与上下文的部分，00:35 左右关于 ChatGPT Work 与 Codex 的产品取舍，00:49 左右关于 Sites 和 Visualize 的使用案例，以及 00:53 之后关于“写作作为思考”的讨论。这几段共同回答了一个问题：当 AI 可以替你做越来越多事情时，人到底要把时间放回哪里。

以下按讲话人分段。每段先列英文自动转写，再列中文翻译。

If you think about the first era of AI products as chat, the second era of these products working with agents, that third era that might come soon is how do you work with a persistent coworker who is able to get things done with you?

如果你把人工智能产品的第一个时代看作是聊天，把这些产品的第二个时代看作是与代理合作，那么可能很快到来的第三个时代是，你如何与一个能够与你一起完成事情的持久同事合作？

There's this idea of the overhang of what AI is capable of and what we're actually doing with it.

有一种关于人工智能能力的悬而未决的问题以及我们实际上在用它做什么的想法。

It's so hard to understand what is going to emerge in the future. 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. Only way to build is two to three months.

很难理解未来会出现什么。如果你按照模型现在的状态去构建，你会失败。如果你按照你认为模型一年后的状态去构建，你也会失败。这两种结果同样是错误的。唯一的构建方式是两到三个月。

What if you had to most adapt to and adjust in how you operate as a PM in this world?

如果你必须在这个世界中最适应并调整你作为产品经理的运作方式，会怎样呢？

Being prolific and empirical is way more important than being academic or theoretical. Rather than writing out some long reasoning doc, instead it's like, how do I get to something I can try out and test with users as fast as possible?

多产且实证比学术或理论更重要。与其写一份冗长的推理文档，不如思考，我如何才能尽快做出可以让用户尝试和测试的东西？

It feels like not only are we able to be more ambitious, we almost need to be more ambitious, which is not natural for a lot of people.

感觉上，我们不仅能够更加有野心，几乎还需要更加有野心，而这对很多人来说是不自然的。

Elevating others' ambitions or reminding them of what's possible here is a huge part of the product management role.

提升他人的抱负或提醒他们这里的可能性是产品管理角色的重要部分。

I'm curious what's most surprised you about what it's actually like to work at OpenAI.

我很好奇，真正去 OpenAI 工作时，最让你感到惊讶的是什么。

I came into the company expecting that there was a treasure trove of OpenAI secret strategy. And actually, OpenAI is open.

我加入这家公司时，以为这里有一个 OpenAI 秘密策略的宝库。实际上，OpenAI 是开放的。

Today, my guest is Tara Seshan. Tara leads product for both Codex and ChatGPT Work at OpenAI. I believe this is the fastest growing and arguably most important AI product for knowledge workers today. Tara works alongside Andrew Ambrosino, who was a recent podcast guest. He's her eng 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'sProductPass.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.

今天，我的嘉宾是塔拉·塞尚。塔拉负责 OpenAI 的 Codex 和 ChatGPT 产品。我认为这是目前知识工作者中增长最快、可以说最重要的 AI 产品。塔拉与 Andrew Ambrosino 一起工作，Andrew 最近也是播客的嘉宾。他是她的工程经理。在加入 OpenAI 之前，塔拉在 Stripe 工作了六年，最初是作为前五名产品经理之一加入的。 在很多年里，她被评为整个 Stripe 组织中排名前三的 Stripe 员工之一。她还曾在 Watershed 领导产品工作，是创始人之一并且是 Teal Fellow，更重要的是，Tara 是三位 Lenny's Newsletter Fellow 之一，这是我几年前主办的一个项目，旨在突出一些最出色的新兴产品领导者。我非常高兴看到 Tara 担任这个全新、极其重要且具有影响力的角色。 在我们开始之前，不要忘记访问 Lenny'sProductPass.com，获得一年的全球最热门、最精美的 AI 产品，这些产品仅向 Lenny 的新闻通讯订阅者提供。接下来，我为大家带来 Tara Seshan。Tara，非常感谢你来到这里，并欢迎你参加播客。

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

谢谢你，莱尼。我很高兴能来到这里。见到你真好。

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.

我更加高兴了。所以你在 OpenAI 工作差不多一年了，在大多数地方，这算是非常短的时间。在人工智能的时间里，这就像一辈子一样。

Yes.

是的。

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.

我想象当你加入 OpenAI 时，你大概对在前沿实验室工作是什么样的有一些概念。我很好奇，实际上在 OpenAI 工作的经历中，什么让你最感到惊讶，希望能包括好的一面和不那么好的一面。

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 companies. And OpenAI is actually founders-led, 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... Tara Seshan, OpenAI product lead for Codex and ChatGPT Work. They discuss persistent AI coworkers, steering versus rowing, product strategy, model capabilities, ambition, and AI-era work. The more surprising side of this is I came into the company expecting that there was a treasure trove of like OpenAI secret strategy that I would be able to understand akin to how, you know, at past companies you come in and you're like, oh, 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 products 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 like the public messaging. And so that to me was incredibly both positively surprising and just like a change in my operating mode for sure.

在 OpenAI 工作的许多方面让我感到非常熟悉，因为我之前曾在其他一些地方工作过，你知道，那些地方也是高速增长、高人才、高强度、超速扩张的模式。所以有些事情，比如同事非常棒或者事务紧迫感很强，让我感觉非常熟悉。对我来说，真正让我感到最惊讶的是，我之前工作的许多公司，实际上我过去工作的所有公司，都是由创始人领导的公司。 实际上，OpenAI 是由创始人领导的，这意味着公司内部的每个人，特别是在他们的领域，某种程度上在本质上都有点像创始人。与我之前工作过的地方相比，OpenAI 的自上而下的指导程度极为有限。所以我认为，当我刚加入公司时，这既是…… Tara Seshan，OpenAI Codex 和 ChatGPT 产品负责人。 他们讨论了持久的 AI 同事、引导与划船、产品策略、模型能力、雄心以及 AI 时代的工作。更令人惊讶的是，我进入公司时本以为会有一大堆类似 OpenAI 秘密策略的宝藏，我可以理解，就像以往在其他公司时，你会进来想着，哦，是的，这就是支付圣经，这就是我们对支付和运营的思考方式。实际上，OpenAI 是开放的。 像所有存在的关于‘这个世界应该是什么样子’、‘产品应该如何构建’或‘模型应该如何运作’的各种想法，很快就会成为公共产品的一部分，或者成为公共信息的一部分。对我来说，这既令人非常惊喜，同时也确实改变了我的运作方式。

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

告诉我们那里没有像秘密房间里运行着通用人工智能并拥有包含所有答案的主计划这样的东西。

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've seen.

或者至少我可以肯定我不在那个房间里。但我认为真正激励我的部分是，OpenAI 所做的很多事情都能立即成为用户在产品中可以触摸和感受到的内容。而且这种循环比我见过的任何地方都快。

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, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand upmarket ends up working with WorkOS. And that's because they are the best. Whether you are a seed stage startup trying to land your first enterprise customer or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise-ready and unblocking growth. It's essentially Stripe for enterprise features. Visit WorkOS.com to get started or just hit up their Slack where they have actual engineers waiting to answer your questions. WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to WorkOS.com to make your app enterprise-ready today. You've been a PM at a lot of different places, a long-time PM leader. What do you lose in this new world?

本集节目由我们本季的主要赞助商 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。如果你正在为企业打造产品，你一定体验过整合单点登录、SCIM、RBAC、审计日志以及大公司所需的其他功能的痛苦。 WorkOS 将那些交易障碍转化为即插即用的 API，其现代开发者平台是专为 B2B SaaS 构建的。我投资的每一家开始向高端市场扩展的初创公司最终都会选择与 WorkOS 合作。这是因为他们是最棒的。无论你是一家正在努力赢得首个企业客户的种子阶段初创公司，还是一家正在全球扩展的独角兽公司，WorkOS 都是实现企业级准备并解除增长障碍的最快途径。 它本质上是面向企业功能的 Stripe。访问 WorkOS.com 开始，或者直接加入他们的 Slack，他们有真正的工程师等待回答你的问题。WorkOS 让你通过令人愉快的 API、全面的文档以及顺畅的开发者体验更快地构建应用程序。前往 WorkOS.com，让你的应用今天就做好企业准备。你曾在很多不同的地方担任产品经理，是一位长期的产品管理领导者。在这个新世界里，你会失去什么？

When a market is more static or a market is more slow-moving, you have the chance to actually do some grand strategy-esque work because it's more predictable, or you can at least understand all the pieces. As an example, payments is certainly a dynamic market to some extent. It's also an established market, and you're able to say, ah, yes, if I take this bet, my competitor might take this other bet, or reason from first principles very rigorously through what all the next actions might be. And in fact... The nature of that market mandates that you do that. Like, winners will think more rigorously than everybody else. And if you aren't thinking rigorously, it shows up as sort of like carelessness because a lot of those decisions that you made could have been predicted. But in this market, it's so hard to understand, like, what is going to emerge in the future. It's very emergent. It's very fast changing. It's really dynamic. And most importantly, it's like, Very, very important to stay tied to the research. And so actually being prolific and being more like empirical is way more important than being like maybe more academic or theoretical. And I think lots of the past companies I've worked at have been very academic and theoretical places. And it was a real switch to go from rather than writing out some like Thank you for joining us. And what that means is the thinking you need to do is being as pointed as possible about what your core hypothesis is. And that hypothesis definition is the most important thing. Like what is actually, to use the Shashira Rahul Tro phrase, like the eigen question, what is like that specific most important thing to test? And everything else, like any other grand strategy you concoct, is not relevant.

当一个市场更静态或市场发展较慢时，你实际上有机会进行一些类似大战略的工作，因为它更可预测，或者至少你可以理解所有的部分。例如，支付市场在某种程度上确实是一个动态市场。它也是一个成熟的市场，你能够说，啊，是的，如果我下注这个，我的竞争对手可能会下注另一个，或者非常严格地从第一性原理推理出所有可能的下一步行动。 事实上……那个市场的性质要求你必须那样做。比如，赢家会比别人更严谨地思考。如果你不严谨思考，就会显得有些粗心，因为你做出的很多决定本来是可以预测的。但在这个市场中，要理解未来会出现什么非常困难。它非常新兴，非常快速变化，非常动态。 最重要的是，保持与研究的紧密联系是非常非常重要的。因此，实际上多产和更倾向于经验主义比可能更学术或理论更为重要。我认为我过去工作的很多公司都是非常学术和理论化的地方。从写一些类似“感谢您加入我们”的东西转变过来，真的是一个很大的变化。 这意味着你需要做的思考是尽可能明确你的核心假设。这个假设的定义是最重要的。就像用 Shashira Rahul Tro 的话来说，就是本征问题，具体来说，就是那个最关键的需要验证的东西是什么？而其他任何你构思的宏大战略都不相关。

I'd love to hear more about that because that's really interesting as almost like here's the thing of the PM role that is not changing. So much is changing. The world is changing. But like there's still this piece that is even more important.

我很想听更多关于那方面的内容，因为那真的很有趣，就像是关于产品经理角色的某些本质是不变的。虽然很多事情都在变化，世界也在变化，但仍然有这一部分更为重要。

Speak more to that of what specifically that you think people need to focus more on. Yeah, there were so many trappings around the PM role of like, you know, running like execution on time and writing all these specific docs and presentations, etc. But the core of it has always been about what is like the most essential question you need to ask about your product? Like, what is the key? They discuss persistent AI coworkers, steering versus rowing, product strategy, model capabilities, ambition, and AI-era work. Pulling those three things together to make the most sharp hypothesis you can, and then making the test as fast and effective as possible. And I think that is not only not changed, but it's become the most important thing at the company to be able to do. EMs are thinking this way. Engineers are thinking this way. Data scientists are thinking this way. Designers are thinking this way. Everyone has sort of moved to focus their efforts on this really, really important problem definition and testing loop. What are we actually doing and how do we know if it's working?

更具体地谈谈你认为人们需要更多关注的内容。是的，围绕产品经理角色有很多琐事，比如按时执行、撰写各种具体的文档和演示等。但是它的核心一直是关于你需要问自己产品的最本质的问题是什么？关键是什么？ 他们讨论了持久的 AI 同事、引导与划船、产品策略、模型能力、雄心以及 AI 时代的工作。把这三件事结合起来，提出你能做出的最尖锐的假设，然后尽可能快速有效地进行测试。我认为，这不仅没有改变，而且已经成为公司最重要的能力。工程经理是这样思考的。工程师是这样思考的。数据科学家也是这样思考的。 设计师们就是这样思考的。每个人都在某种程度上转向专注于这个非常非常重要的问题定义和测试循环。我们实际上在做什么，我们怎么知道它是否有效？

And from a PM standpoint, like, it's great because PMs have always been really focused on trying to get that stuff right. That has always been the core of the job. And actually, many of the other just trappings of the job have, like, fallen away. And that remains, like, the key thing to get right every time. You mentioned this idea of a loop. And there's a lot of talk these days, like 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. And the way I understand a loop, 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 just this idea of loops expanding from just software engineering to product management to all knowledge work? Do you think that's going to be a thing?

从产品经理的角度来看，这很棒，因为产品经理一直非常专注于努力把事情做对。这一直是工作的核心。实际上，工作的许多其他附带部分已经消失了，而这仍然是每次都必须正确完成的关键。你提到了循环的概念。最近关于循环的话题很多，不知道几周前在推特上，循环非常火。 而且感觉它似乎仍然是知识工作中广泛讨论的话题。就我理解的循环而言，本质上，人工智能，这就是成功的样子。去构建和探索，直到取得成功。你如何看待将循环的概念从仅软件工程扩展到产品管理再到所有知识工作的这个想法？你认为这会成为一种趋势吗？

I do think that increasingly the future of work will look more like steering than rowing in the sense that there will be agents that you'll be able to 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. And to that point, I think that that steering might grow higher and higher and higher level. The steering used to be at the level of, I wrote this line of code, press tab, to, oh, wait, now I'm directing something a little bit more comprehensive to maybe the goal level, maybe to an even higher level. I think the steering will continue to... Thank you for joining us. Even sort of like positive determinism of what we want the future to be like, 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 like the direction that I'm pushing it in. And that, I think, will always remain a priority. And so I think that loops are awesome. Running agents in increasingly, increasingly larger loops where they're doing more and more of that rowing for you is great. But right now, you really still need to steer. Yeah. And I think work will also look like steering with other people over a group of agents that you guys work with together. Bringing in other teammates into that interaction between you and the agent where it's rowing in your steering feels also incredibly valuable.

我确实认为，未来的工作会越来越像掌舵而非划桨，因为将会有一些可以协作的代理来完成大量的划桨工作。而你的角色则会越来越倾向于掌舵，把船引向正确的方向。就这一点而言，我认为这种掌舵可能会变得越来越高层次。 过去的控制只是处于这样的层次：我写了这一行代码，按下 Tab，然后，哦，等等，现在我正在指导一些更全面的事情，可能是目标层面，甚至可能是更高的层面。我认为控制将会继续……感谢你加入我们。 甚至有点像对我们想要的未来的积极决定论，比如想象，嘿，我希望产品看起来是这样的，不是因为相反的做法同样不可行，而是因为我希望世界看起来像我推动它的方向。我认为这将永远是一个优先事项。所以我认为循环是很棒的。 在越来越大的循环中运行代理，让它们为你做越来越多的‘划桨’是很棒的。但现在，你仍然真的需要掌舵。是的。我认为工作也将像是和其他人一起管理一组你们共同工作的代理一样。将其他队友引入你和代理之间的互动中，让代理在你掌舵时划桨，这同样非常有价值。

That's such an interesting way of describing it. 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 this, is the human, the person, basically. Otherwise, we're all just going to be building the same thing. You could use it. You could, everyone could be asking, how do we win? What do we do? And then the thing that almost the unfair advantage almost is the human brain.

这是一种非常有趣的描述方式。这里也有两个想法浮现出来。一个是，如果每个人都能使用相同的工具，那么能让你与众不同的就是——就是人，也就是个人。否则，我们都会在做同样的事情。你可以使用它，大家都可能在问，我们如何获胜？我们该做什么？然后，几乎可以说，几乎是那种不公平的优势，就是人的大脑。

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... And so I think that's a really good way to start. Opinionated and artistic. Like Patrick Halzen has this really nice statement, or maybe it was John Halzen, has this really nice statement about software, which is that software is not like real estate. You don't like put money in and get value out. It is a little bit more like filmmaking, 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 like auteur statement or is some opinionation and artistry that goes along with it. And I think that is... relies on you having something interesting to say or your team having something interesting to say about your product.

是的，我觉得它在某种程度上实际上让我想起了时尚。比如，确实有一些功能性的衣服，每个人都可以穿，而且可以完成工作。但关于你穿什么，至少对我来说，更多的是关于我想通过穿着表达出我个人特色的声明……所以我认为这是一个非常好的起点——有观点且富有艺术性。 就像 Patrick Halzen 提出了一个非常好的观点，或者也许是 John Halzen，他对软件有一个非常好的表述，那就是软件不像房地产。你不会只是投入资金然后获得价值。它有点像电影制作，你可以在一部电影上投入大量资金，但这并不能保证电影会成功或好看。它涉及到某种作者理念，或者说某种观点性和艺术性。我的意思是，我认为这是... 依赖于你有一些有趣的话要说，或者你的团队对你的产品有一些有趣的话要说。

There's something Marty Kagan is big on, which is this idea that when you have an idea for a product or a 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.

Marty Kagan 特别强调的一点是，当你有一个产品或功能的想法时，很少有想法最终会成为现实。你需要经历整个过程来弄清楚它到底应该是什么。感觉这就像你在这里所说的——你需要作为一个人经历这个过程，以了解它真正是什么以及人们实际上想要的是什么。它永远不会只是‘好吧，明白了’那么简单。 去建造这个东西。我从一开始就明白了。

Yeah, for sure. For sure. And 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. Yeah.

是的，当然。绝对是的。而且那些循环运行得越来越快。因此，你形成直觉、获得形成这些直觉所需的信息，然后将其与人和智能体一起运用并付诸行动的能力是关键。是的。

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 other people don't yet, also you work around the most AI-filled, 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, three to six months?

我很好奇你认为作为知识工作者，我们工作的下一个大趋势会是什么。感觉不仅你可以使用其他人还没有的最先进工具，而且你周围还充满了最具人工智能意识、以人工智能为主导的人。你认为内部人是如何工作的，这种方式会在未来三到六个月内，成为我们所有人使用这些人工智能工具的更普遍的工作方式吗？

Yeah. 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 coworkers, 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. We sync up at different cadences, look at each other's in-progress work, and provide more and more feedback. It feels like that coworker model works. The second is that A lot of my work with agents thus far has been one-on-one. I work with my agent. Maybe it spawns some sub-agents 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.

是的。是的，我认为这有两个方面。一是继续与抽象层次越来越高的智能体合作。所以让智能体越来越多地为你独立完成工作，自己介入提供指导，然后让智能体继续处理。就像，让智能体处理并在更高层次的抽象中提供细节，这感觉是正确的方式。 人们越来越多地在考虑那些具有持续性、感觉像队友、像同事的代理，你可以像与团队中的某人合作一样与他们合作，即他们完成大量工作，我提供输入，然后他们再次工作。我们在不同的节奏上同步，查看彼此的进行中的工作，并提供越来越多的反馈。感觉这种同事模式是可行的。 第二点是，我迄今为止与代理的许多工作都是一对一的。我与我的代理合作。也许它会产生一些子代理来完成一些任务，但主要是我和我的代理一起工作。而这可能与我的同事们与他们的代理所做的事情脱节。因此，有一段时间，大家在内部只是通过 Slack 相互发送他们的 Codex 线程，以及 Codex 线程的截图。 我们就是说，好吧，我想和你分享我是如何得到这个数字的。就是这样得到这个数字的。这是我操作的截图。但这也不是人们协作时最自然的方式。因此，随着我们与代理完成的工作越来越多，我们是不是应该能够和代理一起完成工作呢？而实现这一点最自然的界面是什么？这些就是我们正在思考的一些问题。

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.

聊天就是我想象的那样。这太有道理了。就像，好的，这是塔拉的经纪人，这是我的经纪人。她做了一些分析工作。我会说，嘿，我的经纪人，莱尼的经纪人，去检查一下，确保这是真的，并且符合我看待世界的方式。

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.

理想情况下，工作感觉就像一个多人游戏，我们一起完成任务，指导我们的代理，而我们的代理则继续处理越来越多的类似划桨的战术任务。

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 in the slow takeoff scenario, which is good, where it's just like slowly iterating.

有趣的是，这只是一个缓慢的信任进展和对这种工作方式的意识的过程。就是那种，好的，可以工作更久，你可以承担更多。一直以来都有关于慢速起飞和快速起飞的讨论，每个人都害怕这种快速的人工智能起飞，就像它变得非常聪明，然后我们就陷入大麻烦。 感觉就像我们处于缓慢起飞的情景，这很好，就像在慢慢迭代一样。

It doesn't feel that slow, but in a sense, you know, we're not like some 300 IQ AI, like, you know. 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. 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 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, never give them access to like Google Docs and Slack and the company database would not be that useful to you. Similarly, like 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 are on the intelligence side, certainly. But a lot of it is also just really tactical, like data access, like cloud infrastructure and reliability pieces that feel... Yeah, they feel much more prosaic than some of the broader intelligence questions, but matter in some ways just as much for that end-effectiveness.

感觉上并不那么慢，但从某种意义上来说，你知道，我们不像某些 300 智商的人工智能。我的意思是，这些模型确实非常智能，但我认为让我们能够与我们的代理一起工作，或者让代理处理越来越高阶的抽象事务的许多因素，肯定与它们的智能、执行长期任务的能力以及它们能在任务上持续多久有关。 但是实际上，也有一些非常基础的战术性方法使这一点成为可能。局部工作的代理非常方便，因为它们可以访问你机器上的所有数据。要让代理在云端成功运行，你必须构建大量的云基础设施来实现这一点，就像访问你的系统一样。比如，代理如何与拥有你所有数据的这些第三方系统进行通信？ 就像你雇佣的同事一样，如果你把他们锁在一个房间里，从不让他们访问 Google 文档、Slack 和公司数据库，对你来说并不会太有用。类似地，同样被隔离的云代理也不会那么有效。因此，使这些代理有用并实现某些未来目标的一个重要部分，当然是在智能方面。 但很多也确实只是非常战术性的，比如数据访问，像云基础设施和可靠性这些部分，感觉……是的，它们感觉比一些更广泛的情报问题更平凡，但在某些方面对于最终效果同样重要。

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. Yeah. 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.

这触及了在这个播客中经常出现的另一个话题，这个词——野心。我知道你也经常思考这个问题。是的。感觉不仅因为这些人工智能工具我们能够更有野心，我们几乎需要更有野心，但这对很多人来说并不自然，因为现在每个人都可以轻松完成所有这些容易的事情。简单的事情非常容易，困难的事情也变得容易了。 现在把人和公司区分开来的东西就是他们能有多雄心勃勃。谈谈当我谈到这种雄心需求的出现时会提出什么内容。

Yeah, I think the people that we see who are most effective at using AI tools don't simply use it to automate rote tasks, but use it to expand the set of things that they are capable of doing. Like back in the day, you know, before all this AI stuff, the unicorn person was someone who was a really thoughtful product sense person. 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 a team on it. And I think that... And so, I think that's a really good way to start to think about what's in your head. into the reality, the way that someone who was previously like jack of all trades was able to do. We kind of all have that superpower now that I can like spin up a set of designs on something and I can like go build an initial prototype of it. And I can, you know, 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, to that point earlier about film, be more of an auteur 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 success. New ideas and new products that 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 like what you're capable of executing yourself, what you're capable of communicating. It can be so much, 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 timeframe. And to me, the best way of trying to do that is Patrick Halzin has like on his website, PatrickHalzin.com slash 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 a… Can you make for me like a CAD model of this idea that I might have? Like 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?

是的，我认为我们看到的那些在使用 AI 工具方面最有效的人，并不是简单地用它来自动化重复性任务，而是用它来扩展他们能够做的事情的范围。就像过去，你知道的，在所有这些 AI 东西出现之前，独角兽型的人是一个具有非常敏锐产品感觉的人，同时也是一个工程师，可能还是一个设计师。 那个人一直是独角兽式的招聘对象，因为他们能够真正简化各个职能之间所需的翻译层次，并且能够自己非常快速轻松地构建或构思某些东西，并使其运行起来。然后还能与团队合作，在团队中协作。我认为……所以，我认为这是一个非常好的方式来开始思考你脑子里有什么。 进入现实，就像一个以前是样样通的人能够做到的那样。我们现在都有那种超能力，我可以快速制作一套设计，然后去构建它的初步原型。我还可以，知道吗，弄清楚它的正确定价模型，并模拟出所有的情景。实际上，可能性集合已经大大扩大了。 实际上，这在很多方面意味着，我有能力——回到之前关于电影的那个观点——在我尝试完成某件事情并实现我的愿景时，更像一个作者，也许能够以更高的忠实度实现我的愿景。对我而言，这正是追求成功的过程中，能够提升你抱负的一部分。新的想法和新产品，因为所有这些事情现在都触手可及，因为这套新的能力现在你也能够尝试并使用，你实际上并不受限制。 你的抱负不再仅受限于你自己能够执行的能力，或者你能够沟通的能力。它可以如此之大，如此广泛。我认为做这件事最难的部分仅仅是扩展你的思维。实际上，能力已经显著扩展。真正是在以一个不合理短的时间内扩展你对可能性的思考。 对我来说，尝试做到这一点的最佳方式是，Patrick Halzin 在他的网站 PatrickHalzin.com/fast 上展示了所有这些项目，我想，这些项目都非常雄心勃勃，但却在非常非常短的时间内完成。对我来说，现在令人震惊的是，这些项目都存在于这些工具出现之前，而这些工具现在可以让你几乎立即学会如何构建某样东西，或者只需一个问题就能得到答案。 嘿，你能立即为我总结这篇非常复杂的文章或这本非常复杂的书吗？或者我可以尝试做以前对我来说不可能做到的所有事情，而现在我能够做到吗？比如，我能启动一个…你能为我做一个我可能有的想法的 CAD 模型吗？就像以前完全超出我能力范围的真正能力，现在我都可以实现了。 所以，如果那些快速项目以前在我们拥有的能力下是可能的，那么考虑到人工智能给我们的能力，我们难道不应该看到这些类型的不合理地快速且高效执行的事情数量呈指数增长吗？

To your point, the hardest part is just remembering to even to 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 and new like thing we have to build in our brain.

说到你的观点，最难的部分就是记得去尝试去做，就像，哦，是的，让我看看 Codex 能否为我做这个。这就像是我们必须在大脑中建立的一种新习惯和新事物。

Tyler Cowen has this statement on his site, which is that you most people underrate the impact of going to someone else and saying. Hey, couldn't you, 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 like, 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. Yeah. 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, yeah, it'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, yeah, how exciting the job becomes.

Tyler Cowen 在他的网站上有这样一句话，他说大多数人低估了去找别人说话的影响。嘿，你能不能……，你现在做的事情有没有更雄心勃勃的版本？或者你能不能更快地尝试这个？或者你能不能以十倍的规模来尝试这个？从某种意义上来说，当我想 PM（产品经理）们现在到底做些什么非常有效？或者他们现在能做些什么非常有效时，我会想到这个。 我认为提升他人的抱负或提醒他们这里可能实现的事情，是产品管理角色的重要组成部分。就像当有人说，嘿，我认为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之前完成，或者也许这是它的第一个版本。是的，你现在的部分工作就是提升每个人的抱负，并说，实际上，可能性的上限是不是明显更高？比如，我们在尝试的事情上是不是应该更有野心？ 或者，比如说，我们不能尝试更快一点吗？我觉得那是一个，嗯，是一个很棒的地方。就你可以构建的东西、可能性而言，这是一个很棒的地方，就职务变得多么令人兴奋而言，也是一个很棒的地方。

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 at Enterprise stuff now. He had this meme internally, is this maximally accelerated?

那真是太有趣了。我记得 Nick Turley 曾经在播客上，他可能在你之前担任这个角色。我想他现在在做 Enterprise 方面的工作。他内部有个梗，是“这是最大限度加速吗？”

Yes.

是的。

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

我觉得在 Slack 里有一个表情符号。这是最大限度加速了吗？是的。

Is this maximally accelerated is totally a OpenAI meme. The other OpenAI meme that Andrew Ambrosino 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, like, the scope and the scale of what you're trying to do, Are, is this 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, we just have to spread as much as possible now. I love that. That's like the new dogfooding. Instead of dogfooding, you got to mainline it. Yeah, exactly.

‘Is this maximally accelerated’ 完全是一个 OpenAI 的梗。另一个 Andrew Ambrosino 和我喜欢问团队的 OpenAI 梗是，你们现在已经在主用它了吗？意思是，你们是否每天整天都在使用这个产品来完成自己的事情？我认为，这句话与‘我们是否尽可能地有野心’结合起来，后者是关于你尝试做的事情的范围和规模。‘Is this maximally accelerated？’ 我们是否在尽可能快地推进它？ 然后，你现在是在直接使用它吗？你在用它吗？你是否在尽可能运用你的各种感受来判断这个东西是否有效，是否真的是人们想要的，并尽可能地缩短反馈循环？对我来说，这就像是产品开发的三个核心理念，我们现在必须尽可能广泛地传播。我喜欢这个。这就像是新的内部测试（dogfooding）。不是内部测试，而是你必须直接上手体验。对，完全正确。

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 like, it's very clear how, 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 others? I don't know.

这在推文中表现得非常明显。这基本上就是我看到你们团队沟通的方式——他们对产品有多么痴迷，以及他们不断地在问：我们能做得更好吗？现在有什么让你烦恼的？这是我们正在构建的东西。就像，你早先说的那样，非常清楚地表明，每个人都是自己产品的创始人。而且也很清楚他们是如何……像外部观察者一样行动的。内部还有其他梗吗？那些真的很有趣。还有其他的吗？ 我不知道。

Yeah, I'm trying to think if there's 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 AGI. 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 just 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. Like, 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? How do you know what two or three months is like? It's like a challenging understanding, especially while we're on this exponential. Is it like it's just a gut feeling? Is there anything the researchers give you a sense? How does that work? Yeah, certainly communicating really tightly with research on where they think things are going is incredibly important. Like these things aren't entirely like 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. Um, so we, 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, is really important.

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 的到来。AGI 有很多可能的结果。它可能的样子或者人们如何看待它，但让这家公司大多数人投入的一个重要原因，是相信 AGI 使命的价值，相信它能够带来益处，并且尽一切努力让这一目标成为可能，包括实现 AGI，同时确保它对人类有益。 在构建产品时，我必须时刻记在心里的另一个不断重复的提醒是，我们是在为模型两到三个月后的发展而构建吗？如果你为模型现在所处的状态而构建，你就失败了。如果你为你认为模型一年的状态而构建，你也会失败。两种结果都是同样错误的。我相信很多人都谈过这个问题，但两种结果确实同样错误。如果你太早，你就错了。 如果你构建的东西过于关注过去模型的能力，那你完全错了。构建的唯一方法是……两到三个月。而且这些类似模型的水平会变得更好。我需要把模型能力作为这个产品的核心。我需要在我创建的产品结构上让位于模型。我如何确保两到三个月后这对于模型仍然是正确的？ 你怎么知道两三个月是什么样子？这就像是一种具有挑战性的理解，尤其是当我们处在这个指数增长阶段时。是不是只是凭直觉的感觉？研究人员有没有给你一些感觉？这是怎么运作的？是的，当然了，与研究团队紧密沟通他们认为事情将会发展的方向是非常重要的。像这些事情并不是完全的黑箱，你基本上知道，嘿，我们专注于这些特定的事情。 就像我们希望模型在这些特定方式的编码或写作上表现得更好一样。嗯，所以我们确实专注于让模型在特定能力上表现得更好。因此，了解这一点并确保产品开发尽可能与研究的日程和路线图相结合是非常重要的。

A quote that I'll never forget is when Kevin Wheel was on the podcast, he was chief product officer at that time. Uh, 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 just like wrap your head around that, that this is the worst that will ever be. Like, it's such a cliche almost now to say that, but it's, it's true. It's absurd. This is like.

我永远不会忘记的一句名言是，当时凯文·惠尔在播客上，他那时是首席产品官。他说，这是模型将会达到的最糟糕状态。听起来很简单，但真的很难去理解，就是这是模型最糟糕的状态。现在说这句话几乎成了一种陈词滥调，但它是真的。这简直是荒谬。就像是这样。

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

是的，这太荒谬了。真的太荒谬了。

Oh, man. Okay. I want to talk about ChatGPT, the app, briefly. Okay, so I have it open right now. Yes. 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 do you think this goes? Is it going to stay like this? Is there like a next step that you're imagining already?

哦，天哪。好的。我想简要谈谈 ChatGPT 这个应用程序。好的，我现在已经打开它了。是的。好的，这是我看到的：ChatGPT，然后有一个下拉菜单，里面有 ChatGPT 和 Codex。然后有一个切换按钮，聊天和工作。Tara，这到底是怎么回事？这些东西都是干什么的？帮我们理解每一项的用途。你觉得它会保持现在这样吗？你已经在想接下来的步骤了吗？

Our North Star here is that users do not need to make decisions between picking between all these different options. Ideally, 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, like, do research before episodes or something like that. Yes, do that. 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 are they 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, do you want to stay in sort of like 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. Like, continue using it as much as possible. But if you're a ChatGPT user who is like, what are these new agentic 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. You're not going to see a work tree pop up all of a sudden in work mode, but it is the same power. They discuss persistent AI coworkers, steering versus rowing, product strategy, model capabilities, ambition, and AI-era work.

我们的北极星目标是用户不需要在所有这些不同选项之间做出选择。理想情况下，这里不应该有切换。你只需进入框中，输入你的任务。比如，我想要开发一个非常棒的应用程序，我不知道，比如帮助我的播客嘉宾在节目之前做研究之类的。是的，就这么做。它会为你选择合适的工具。它会为你选择正确的模型，让你能够完成这件事。 理想情况下，这里的选择不应由我们的用户来做，让他们必须在所有这些不同的概念之间进行选择，并不仅要理解他们想要做什么，还要理解我们产品的局限性和功能。这当然是我们希望达到的目标。在短期内，在 ChatGPT 和 Codex 之间进行选择实际上是一个问题……你是希望保持更多面向开发的用户界面，还是希望在 ChatGPT 模式中拥有相同的能力和功能？ 所以，如果你是 Codex 用户，就继续使用 Codex。你没有错过任何东西。尽可能多地继续使用它。但如果你是一个 ChatGPT 用户，想知道这些新的智能代理功能是什么？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，那么聊天模式就是正确的选择。 这是你熟悉并喜爱的聊天模式，每次都有越来越强大的模型和越来越新的功能。但在工作模式下，在幕后，这就是 Codex。我们移除了一些编码用户界面。在工作模式下，你不会突然看到工作树弹出，但它拥有同样的强大功能。他们讨论了持久的 AI 同事、引导与划桨、产品策略、模型能力、雄心以及 AI 时代的工作。

Work mode to do incredible, incredible things that were previously either manual or required deep expertise from one person on the team become things that the whole team can be able to 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. Okay, that's really helpful. So there's kind of like these three modes currently. There's like the engineering mode, the chat mode, and then the… Do knowledge work mode. And the knowledge work mode, it's actually Codex doing all that work, but people may not know what Codex is, may be afraid of it. Is there anything in that work mode that's not just Codex? Because that's actually really interesting. Is there additional harness tweaks to make it feel a little different, or is it just the same thing with a little different UI?

工作模式可以做以前需要人工操作或团队中某个人具备深厚专业知识才能完成的令人难以置信的事情，现在变成整个团队都能够执行的事情，或者只是提升团队中每个人在时间表、能力或他们能完成的前沿工作的抱负。好的，这非常有帮助。所以目前大概有三种模式，一种是工程模式，一种是聊天模式，然后是……做知识工作模式。 而知识工作模式，实际上是 Codex 在做所有的工作，但人们可能不知道 Codex 是什么，可能会害怕它。那种工作模式中有什么不仅仅是 Codex 的部分吗？因为那实际上非常有趣。有额外的控制调整使它感觉有些不同，还是只是同样的东西，加了一个稍微不同的界面？

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 something like that, or like tell me, predict my revenue for the next six months or something like that, Codex will do as good a job as work mode. It's like really about whilst it's doing so, what kind of UI do you wanna see in the chain of thought? What kind of technical detail do you want exposed to you? It's like incredible, it's similarly powerful. And so Codex users aren't missing out on anything. Thank you for joining us. 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.

这真的是在用户界面层面。所以工作模式和 Codex 模式，如果你切换到 Codex 并让它生成一个惊人的财务模型来为你的产品定价，或者类似的，让我预测未来六个月的收入之类的东西，Codex 会像工作模式一样做好。关键是在执行过程中，你希望在思路链中看到什么样的用户界面？你希望呈现给你的是什么样的技术细节？ 这简直令人难以置信，力量同样强大。因此，Codex 用户不会错过任何东西。感谢你加入我们。在他们使用的产品方面，在他们对概念的熟悉程度方面，尽可能多地做到，并确保我们让每个人都能够利用代理工作，这已经完全改变了每一个开发者的工作方式。我们应该在知识工作方面做同样的事情。

It makes sense. There's this, like, because things move so fast, like, 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. So it makes sense why these things, you know, like, it's not going to feel... OpenAI is a production of Codex. This hundred 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, I don't know, just balancing these very innovative, fast-moving teams and products with this, like, okay, there's a billion people using this. We can't change this dramatically.

这很有道理。就是，因为事情发展的太快了，我想象有人会想，好吧，我们试试 Codex。这会很棒的。然后它爆火了，有了一千万月活跃用户。然后他们就会想，等等，我们在做什么？我们有 ChatGPT，我们有 Codex。所以很容易理解为什么这些东西会这样，你知道，这不会让人觉得……OpenAI 是 Codex 的产物。 这个拥有一千亿月活用户的产品 ChatGPT，也许是历史上最成功的消费产品之一，与 Codex 一起，这个新东西以及你们想尝试的其他新东西。你怎么想这个问题？只是，我不知道，只是在平衡这些非常创新、快速发展的团队和产品与这个问题，比如，好吧，有十亿人在使用这个产品。我们不能对它做出剧烈的改变。

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 agent's power. Like, if you think about the first era of... AI products as chat. The second era of these products is clearly working with agents and, you know, 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 chat 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 take, 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, like that third era that might come soon is how do you work with a persistent coworker 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, like, if every corner wasn't, like, 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, hey, it's transformative, like, is way better than perfect. And that urgency and that...

是的，我认为这里最有趣的事情之一是，启动 ChatGPT Web 和桌面应用的一个目标，以及将这些功能整合在一起，是为了关注那些使用 ChatGPT 的十亿用户，并为他们带来越来越多的代理功能。你想想，第一时代的 AI 产品是以聊天为主的。而第二时代的这些产品明显是与代理协作，主要是以编码代理为主的。 我们当然希望将其带到更多领域，比如知识工作。这也是赋予所有这些数十亿聊天用户工作能力目标的一部分。当然，我们面临的产品挑战是，如何不仅将其带给他们，还要让其自然且易于采用，使其不成为他们必须明确做出的决定。我们可以帮助他们做正确的事情。我们如何简化它，使他们不需要去思考…… 像安全带这样的东西，对于十亿消费者来说就像是疯狂的概念，难以理解。所以这主要是挑战。当然，可能很快到来的第三个时代是，如何与一个能够和你一起完成事情、可能还会与其他人协作的持续性同事一起工作？因此，近期的部分挑战是……我们正在向十亿可能还没有使用过它们的人介绍代理。 我们如何以最简单、最自然、最易用的方式做到这一点呢？当然，要实现这一点，我们还有很多工作要做。但其中一部分也是我在对比 AI 之前的时代或过去的产品经验时学到的一个教训，那就是在以前的公司中，打磨是王道。 把每一个用户界面交互或者每一个小细节都做到完全正确，比提前发布某个东西重要得多，因为时间对于最终结果的影响没有那么大。因此，如果每一个角落都没有被完美打磨，一切都不完全正确，那你不如干脆不发布它。但我认为的是... 关于这个时代和这个产品体验的内容真的很引人入胜且有趣，当你非常确信，‘嘿，它是变革性的’，远胜于完美，把产品交到用户手中是非常重要的。而这种紧迫感以及那种...

The introduction of that product is so important. So we have a lot to do to make it more usable and easier for chat users, certainly, especially for folks who are not maybe even using it for productivity, but using it for like consumer tasks. But yeah, done is better than perfect. And we have so much more to do. 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 respond to the feedback is exactly what I'm hearing here is get it out, figure out what the hell's, what's not working, how people are using it, iterate quickly. It feels like that's the model now.

那个产品的推出非常重要。因此，我们有很多工作要做，以使它对聊天用户更易用、更简单，尤其是对于那些可能甚至不是为了生产力而使用它的人，而是用于消费任务的人。不过，是完成总比完美好。我们还有很多事情要做。 是的，我记得当这个应用刚推出时，有很多关于困惑的评论，而看到团队是如何快速迭代并回应反馈的，正是我在这里听到的——推出它，弄清楚到底哪些不起作用，人们是怎么使用它的，然后快速迭代。感觉现在这就是模型。

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 is the key thing.

当然，你可以继续进行迭代并在发布前获取反馈。我们可以做的以及应该做得更好的事情还有很多。但尽快迭代并倾听正确的信号是很重要的，无论这是发布前还是发布后，理想情况下，发布前是关键。

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, 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 and column NA members FDIC. The IO card is issued by Patriot Bank, NA member FDIC, pursuant to a license from MasterCard International Incorporated. Something I've noticed on Twitter is there's definitely been this vibe shift from Cloud Code to Codex in the past few months. Yeah. It used to be everyone was cloud code this, cloud code that. 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, three to six 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?

本集由 Mercury 赞助，彻底不同的银行体验，现在加入消费功能。我已经做了多年的 Mercury 客户。我把所有的商业银行业务都转到了 Mercury，说实话，我非常满意。这就是产品人员而非银行家打造的在线银行的感觉。现在通过 Spend，你可以为你的团队成员发放个人卡，设置每个人或每个团队的消费限额，并且可以自动从 Gmail 或短信中获取费用收据。 你甚至可以为你的 AI 代理分配它们自己的卡片，设定各自的限额和政策。大多数创始人一开始都是这样做的。公司每个人都使用同一张卡。这样可以使用，直到出现问题为止。有人超支了，有收据丢失了，你花两天时间去弄清楚是谁花的、为什么花的。Spend 是直接嵌入 Mercury 的费用管理工具。你团队的所有卡片、预算和报销都与公司的业务思考汇集在同一个地方。 无需追逐，无需人工审核，无需月底手忙脚乱。结果是一个能够快速行动的团队，以及一个不再成为瓶颈的创始人。了解更多信息并注册，请访问 Mercury.com。Mercury 是一家金融科技公司，而非受 FDIC 保险的银行。银行服务由 Choice Financial Group 提供，并且 column NA 是 FDIC 成员。IO 卡由 Patriot Bank 发行，Patriot Bank 是 FDIC 成员，根据万事达国际公司许可发行。 我在推特上注意到的一件事是，过去几个月 Cloud Code 到 Codex 的氛围确实发生了变化。是的，以前大家都是在说 Cloud Code 的这个那个。最近的情况是，至少在推特上，人们似乎更倾向于 Codex，虽然那只是一个信息泡沫，但很多科技人士都在那里。我很好奇过去三到六个月内部发生了什么变化，除了 Tara 加入并改造团队之外。 有没有什么你可以分享的，就像，好的，我们搞清楚了这个问题，我们调整了这个，我们删掉了这个，是什么帮助改变了氛围，并帮助 Codex 变得如此成功？

You know, I think there's like this phrase, which is before enlightenment, 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, again, user-focused, tight iteration loop, really dog-fooded 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, to some extent, like 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. Actually, what's sort of remarkable is that the mode of operating hasn't changed. It's always been... The same thing I had mentioned earlier, like, are we elevating our ambition sufficiently? Are we maximally accelerating progress? And are we mainlining it as much as possible? And I think it's great that users and folks on Twitter have noticed, but that operation, like, the full credit to the team, like, that hasn't changed.

你知道，我觉得有一句话，大概是这样的：开悟之前，挑水、劈柴；开悟之后，挑水、劈柴。实际上在使用 Codex 应用时，最初让它运行起来并工作的团队非常注重用户体验，迭代循环紧密，真正地自用这个应用，尽可能频繁地使用这个应用来确保一切都正确。 人们开始意识到这种情况在外部发生，并在推特上，用户开始真正注意到。但团队始终非常关注用户，非常关注那种迭代。而这仅仅是，在某种程度上，就像市场赶上了一样。那就是变化。而这个过程在内部并没有改变。每个人仍然不断使用这个应用。 显然，每个在构建它的人都是开发者，用它来进行开发，并且不断地不仅修复自己的问题，还尝试倾听公司其他人的问题以及用户的问题。实际上，有点值得注意的是，操作模式没有改变。它一直都是……就像我之前提到的那样，比如，我们是否足够提高了我们的雄心？我们是否最大限度地加速了进展？我们是否尽可能地维持这种状态？ 我认为用户和推特上的人注意到这一点很棒，但那个操作，全都要归功于团队，这一点没有变。

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

关于这个答案真正有趣的地方在于它非常有人性。这就是你，这是 Andrew，这是 Thibaut，这是团队，就像对客户或产品的痴迷一样。这并不是说 AI 是答案，而是人类才创造了不同。

Yeah, I'll give the team deserves like full credit here. Everyone on the team is incredibly thoughtful and independent and to the point of like there are, you know, many founders at OpenAI, like almost everyone on that team, like the desktop team especially, like acts 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 like ship it externally. But that loop is full credit to like people on the team and individuals for making that happen.

是的，我会给团队应该得到的全部荣誉。团队里的每个人都非常有想法、独立，而且直截了当。就像你知道的，OpenAI 有很多创始人，几乎团队里的每个人，尤其是桌面团队的成员，都像创始人一样行事，关心每一个部分和每一个细节。当他们注意到某个地方可以改进时，他们会非常独立地去构建，并让事情顺利运行起来。 如果它在内部测试表现不好，比如人们不使用它，如果人们觉得它没用，他们会对它进行迭代，然后最终发布给外部。但这个循环完全归功于团队和个人的努力，使其成为可能。

Something you touched on is this idea of roles overlapping, this idea of like, you know, engineers are doing APME work, you're doing probably shipping prototypes and building, maybe shipping to production. I don't know. Just, it 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?

你提到的一个问题是角色重叠的这个想法，比如，你知道的，工程师在做 APME 的工作，而你可能在做原型的运输和构建，也许还要运送到生产。我不知道。只是，这似乎也带来了很多挑战。我从很多人那里听到，作为一个设计师，我现在的工作是什么？我负责什么？作为一个市场营销人员，我不负责什么？我在做什么？这是你注意到的现象吗？ 那是你正在处理的事情吗？就是类似这样的想法吗？

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, like, everything and nothing is your responsibility. Ultimately, you're accountable for success. Actually, like, Stripe was very, very much this way, where there are no... Boundaries around what a engineer could do versus a product manager could do versus a designer could do. Everyone could do anything. And 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 the or have core accountability for is this product being used by users? Is it something that people want? Is it And so, I think that's a really good question. They discuss persistent AI coworkers, steering versus rowing, product strategy, model capabilities, and AI-era work. 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 technology. 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 interesting. 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 like the, yeah, it's really fun handwriting code for an engineer all the time. And one doesn't really do that anymore.

我认为我一直最喜欢在创业公司工作的原因，有时我加入的创业公司偶然成长为大公司，但大部分情况下主要是在创业公司工作，是你的角色几乎没有界限。也就是说，一切都是你的责任，也可能什么都不是你的责任。最终，你要对成功负责。实际上，Stripe 就非常非常像这样，这里没有... 工程师可以做什么、产品经理可以做什么、设计师可以做什么的界限。每个人都可以做任何事情。所以实际上，我一直很喜欢这种心态，现在能力终于赶上了这种心态。但我真正关心的是，有人需要负责或承担核心责任，这个产品是否被用户使用？是否是人们想要的东西？所以，我认为这是一个非常好的问题。 他们讨论了持续存在的 AI 同事、掌舵与划桨、产品策略、模型能力以及 AI 时代的工作。也许这些问题中的一些来自于，等等，我真的很热爱我领域的技艺。通过采取这种更灵活的团队合作方式来完成某件事，我是否会失去提升和打磨自己技艺的机会？而对于这个问题，我真的没有答案。 我认为这是我们大家都在一起经历的事情，那就是我们工艺的一些部分实际上正被技术抽象化。模型能够非常有效地完成这些任务，甚至可能比个人做得更好。而你的工艺也从过去能够完成那个非常具体的任务，转变为现在将其应用到产品或学科的其他部分。不过，是的，这仍然是我在思考的问题，这很有趣。 我如何平衡自己想成为团队一员并使用这些工具的愿望，以及对现在使用这些产品可以达到的高效感到非常吸引的感觉，与我对像那样、是的，对工程师来说一直手写代码的喜爱呢。而且现在已经不再真正那样做了。

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

是的，那就是我本来打算去的地方。只是……工程师的角色现在变化得太不可思议了。

Yeah.

是的。

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

就像你以前整天写代码一样。那是你的工作。而现在那不再是你的工作了。

Yeah.

是的。

And that happened so quickly.

而且那发生得如此之快。

Like you did not write code. I see people mourn like the flow state of writing code manually yourself versus now what, what one does. But I think it is a, um, yeah, it's a, it's a, it's a tough transition.

就像你没有写代码一样。我看到人们哀叹自己手动写代码的心流状态与现在所做的事情之间的差别。但我认为这是一个，嗯，是的，这是一个，这是一个艰难的转变。

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 what we need humans, hopefully. But I'd say in the next, I don't know, next couple of years, just like, where do you think human brains will continue to be most valuable?

是的，你知道，有些人喜欢它，有些人不喜欢。这又是一个完全不同的话题。沿着这个思路，我想问一下处于人工智能前沿的人们，你认为未来人类的大脑在哪些方面仍然会有价值？从长远来看，我们希望能预测人类的需求是不可能的。但我想说，在接下来的，我不知道，几年里，你认为人类大脑在哪些方面仍然会最有价值？

I think humans will continue to be the most valuable as a, certainly as a, um, They discuss persistent AI coworkers, steering versus rowing, product strategy, model capabilities, and AI-era work. And especially in industries and places that are highly regulated or require like a direct human interface like that, 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... 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 has actually 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 will happen with the models, but those pieces feel to me to be incredibly, incredibly human.

我认为人类将继续是最有价值的，当然作为一个，嗯，他们讨论持续存在的 AI 同事、指挥与划桨、产品战略、模型能力以及 AI 时代的工作。尤其是在高度监管的行业和需要直接人类接口的地方，这对我来说非常有意义。我认为人类的大脑在表达方面也非常有价值。我之前提到过那个软件类比，不像房地产。 这更像是一部电影，你可能投入了资金，但好电影却未必出现。就像最伟大的电影并不是那些预算最大的电影。而且在构建软件时，有某种……有某种艺术性、观点性和表达性，你会感觉到是某个人或一群人的创作。对我来说，这部分仍然是如此人性化。你选择构建什么以及它的感觉，感觉像是一个非常人性化的问题。 我也认为人类的大脑在我们如何关心彼此和相互关系方面仍然非常有价值。我的工作中这部分一直保持着很人性化的特质，而且实际上变得比以往任何时候都更重要。那部分你与团队的其他人交流，共同探讨如何对某个领域保持热情，如何一起学习和工作，以及如何提升彼此的抱负。所有这些都感觉并且仍然是非常人性化的事情。 是的，我认为在人类大脑方面，它将继续如此有价值。话虽如此，我无法预测这些模型会发生什么，但那些部分对我来说感觉是非常非常人类的。

I love that answer. There's this idea that you talked about, this idea of this 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, what's the most... 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 like super interesting creative uses of AI recently that you're like, oh yeah, I should try this?

我喜欢这个答案。你提到的有一个想法，就是关于人工智能的能力与我们实际使用它的方式之间的差距。人们总是感觉，其中最大的差距之一就是，好吧，我应该如何使用它？我很好奇，你在工作中使用人工智能的一些方式是什么，这些方式可能会启发别人，让他们想到，哦，原来可以这样使用它。这里大致有两个方向。一是关于什么是最…… 由于人工智能，你的产品经理工作最大的变化是什么？就是你会想，好吧，现在我用人工智能做这些事情。那么最近有没有什么特别有趣的创造性人工智能用途，让你觉得，哦，是的，我应该试试这个？

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 Codex. I haven't.

我在工作中使用人工智能的最令人兴奋的方式之一是……我现在实际上一直在建网站。我不知道你是否尝试过在 Codex 中建网站。我没有。

Talk about sites.

谈论网站。

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 I built a site of the route that, like, tracked the elevation of everywhere we were going. Everyone on our trip, like, inputted all their food. It was, like, super fast and effective. Sites kind of realized the dream of, like, malleable personal software that Alan Kay, you know, flagged in the 60s of, like, the true personal computer is one that has personal software. In some ways, sites... They discuss persistent AI coworkers, steering versus rowing, product strategy, model capabilities, and AI-era work. They can use like an auto update. You can use like internal data to build like a dashboard, for example, with lots of metrics. And rather than like painstakingly laboring over some sort of like slide deck, a site is just a way more dynamic surface for presentation.

Sites 是一个非常有趣、了不起的产品。你基本上可以建立一个网站，当然在工作中，可以作为一个展示的产物。但我也为任何事情建立网站。我为团队建立了一个作为游戏的网站，我们都用这个网站一起玩游戏，因为网站有数据库。你可以建立一个网站。实际上我建立了一个网站，因为我去了一次徒步旅行，我建立了一个记录路线的网站，跟踪我们去的每个地方的海拔。 我们旅行中的每个人，都像，把他们所有的食物都输入了。它，好像，非常快而且有效。Sites 有点实现了像 Alan Kay 在 60 年代指出的可变个人软件的梦想，你知道的，真正的个人电脑是拥有个人软件的。从某种程度上说，Sites……他们讨论持久的 AI 同事、引导与划桨、产品策略、模型能力以及 AI 时代的工作。他们可以使用类似自动更新的功能。 你可以使用类似内部数据来构建像仪表板这样的东西，例如，包含大量指标。而且，与其像费力地制作某种幻灯片一样，一个网站只是一个更动态的展示表面。

How do you use a site? Do you have to do anything special? Or you tell it, let me create a site?

你怎么使用一个网站？你需要做什么特别的事情吗？还是你只告诉它，让我创建一个网站？

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

在 Codex 中，就像是，创建一个网站，一个，我不知道，是为我的团队准备的黑手党游戏。然后我就会去做它。

And like, I'm thinking capital S site, but it doesn't matter, I imagine. It just knows what sites are.

而且，我在想大写的 S 站点，但我想这并不重要。它只是知道什么是站点。

Yeah.

是的。

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

所以是的，因为以前是这样的，这里有一些源代码，你去找出该部署在哪里。

Yeah.

是的。

And what you're saying here is it just hosts it for you and immediately you can use it.

你在这里说的是，它只是为你托管，然后你就可以立即使用它。

You can choose whether it's public, you can choose whether it's with your team, or choose whether it's private to you. 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.

你可以选择它是否公开，可以选择是否与团队共享，或者选择仅对你自己私密。它们非常棒。随时轻松建立网站的能力改变了我日常的工作方式，以前通常需要创建大量文档、表格或其他各种东西。现在我只是一直在创建网站。

And you can do that through, I imagine, work or can you do it through all the surfaces? Codex, Work, ChatGPT, Chat?

我想你可以通过工作做到这一点，或者你可以通过所有界面做到吗？Codex、工作、ChatGPT、聊天？

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

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

Okay. I just kicked off a creative site about Tara Seshan. Great. Is that how you pronounce her last name, by the way? I haven't asked you.

好的。我刚刚启动了一个关于塔拉·塞山的创意网站。太好了。顺便问一下，她的姓氏是这样读的吗？我还没问过你。

Tara Seshan, like station.

塔拉·塞尚，像车站一样。

Seshan, okay, cool. Sites, okay, any other quick tips while we're on this topic for Pete? Because that was a great tip because I don't think a lot of people know about sites.

Seshan，好，酷。网站，好，在我们讨论这个话题时，还有其他快速给 Pete 的建议吗？因为这是一个很棒的建议，我觉得很多人都不知道关于网站的事情。

That's very useful. Yeah, sites are awesome. The other thing I really love is using Visualize in Codex. Have you used slash visualize? Yes. 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...

那非常有用。是的，网站很棒。我真的很喜欢的另一件事是在 Codex 中使用 Visualize。你用过 slash visualize 吗？是的。哦，slash visualize 非常令人兴奋。你只需输入 slash visualize，比如说 visualize my ChatGPT usage until now 之类的，它就会把你做过的所有事情都提取出来，并为它创建一个惊人的可视化效果。 我思考过很多次，不仅要收集一堆图表和数据，还要以一种……的方式展示它们。

Thank you for joining us. I asked Bree 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 Cursor. So she said, OK, 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.

感谢你加入我们。我问了很了解你的 Bree Wolfson 该问你什么。有趣的是，她给出了我之前与 Cursor 的 Adam Ward 对话播客的提问建议。于是她说，好吧，你应该问她关于写作/思考的问题。Tara 的 brief 非常具有标志性。帮我们理解一下是什么让你的 brief 如此标志性，以及对那些可能想在写作和撰写文件方面提高的人有哪些有用的建议。

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 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 or something like that. Writing is reporting. I happily automate, or I use the models all the time to make that as simple as it can be. But writing is thinking is something I never will automate. I really strongly believe that the, 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, uh... We'll 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, like, 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 write a... 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 like to kick off a new product area or to suggest a big change in direction. And so writing is thinking there is really prized. And that's where I did like 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 automatically. That indicates that you haven't. And so actually, the point, maybe one of the biggest changes I've experienced personally in my day-to-day, which has been a big, maybe jarring change, is 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 try this, we ran an A-B, here's like the results. This is why I think we should go in this direction. That is a way better communication tool than like 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. Like, 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.

我真的非常坚信，我在工作中进行两种类型的写作。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篇简报，说明为什么我们应该开发某个产品，或者为什么我们应该采取某种策略，或者为什么可能有一个辣味的观点。 但是作为报告的写作是这样的，比如，哦，我在总结……我们团队本周的工作情况，我会发送一份报告，或者这是我们针对特定发布或公告的计划之类的事情。写作就是报告。我很乐意自动化，或者我一直使用模型来让这尽可能简单。但写作就是思考，这是我永远不会自动化的。 我真的非常坚信，至少对我来说，经历并列出大纲、把它变成某种程度的散文、剪辑和编辑、不断迭代，是我整理思路的最重要步骤之一。我认为大多数人实际上会用非常笼统的方式来看待。比如，有人会说‘我从不使用模型来写作’，或者‘我总是使用模型来写作’。而实际上，对我来说，这两种笼统的看法都是错误的。 我认为你应该尽可能多地使用这些模型来进行写作和报告。而且在你认为写作的过程中，如同我确实认为的那样，我想很多人也是这样认为的，你不应该使用它。你不应该用它来替代你的思考。但我过去的简报，因为我写作如此频繁作为思考的方式，就是我会写一个... 我会进入一个闭门状态，为一个新想法或产品写一份简报，花大量时间完善那个特定的想法，把它拿给别人看看，让他们尽可能地挑剔其中的想法、找漏洞，使它更强大，然后再拿给下一个人，重复同样的过程。在 Stripe，这就是我做了无数次的事情，无论是为了启动一个新产品领域，还是建议一个大的方向性改变。 因此，写作就是思考，这一点确实非常珍贵。这也是我在 OpenAI 完成大部分写作工作的地方。我想我现在仍然一直把写作当作思考，但这里可以共享的成果并不是真正的长文档或那种形式的工作证明，部分原因是时代已经变了，长文档不再意味着你认真思考过某件事，因为你很容易自动生成一篇长文档。那反而显示出你没有认真思考。 所以实际上，重点是，也许是我在日常生活中亲身经历的最大变化之一，这种变化可能很大甚至有些令人震惊，以前我习惯先在文档中思考，然后再把这些思考转化为展示用的成果。这是我认为自己已经理清问题并决定接下来该做什么的标志，然后团队就沿着这个方向前进。而现在，我更多地依赖于模型，而不是文档，或者说原型，而不是文档。 如果我有一些东西，人们可以尝试和互动，或者更好的是，我有实验的结果，我们做了 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。这比单纯的文档本身是更好的沟通工具。所以我仍然一直写成百上千的文档，但我是为自己写的，而不再是为别人写。因为那已经不再是最好的沟通方式了。 那可能是我在这个时代相比于上一个时代亲身经历到的最大的变化。

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 slash brief by just cheating almost and getting lots of feedback on it as you're iterating to make it stronger and stronger and stronger versus like, cool, here it is, first time, and it's really going to be amazing.

那真是太有趣了。我真的很喜欢你关于在文档上获取大量反馈的建议。就是说，你知道，这听起来很显而易见，但是……你知道，你几乎可以通过不断获取大量反馈来迭代文档，让它越来越强，最终得到一个标志性的文档/简报，而不是像‘好吧，这是第一次，它会非常棒’那样直接呈现。

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 like a perfectly polished, finished idea, like a perfectly polished idea. Their new ideas just like 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 like bringing people into the process that way where like a doc is like an underlying artifact for that is one of the best ways to collaborate that I found.

我以前有一个经理告诉我，正确的做法总是先把文档写到 70%的完成度，然后带给你需要获得支持的人，从 70%完成度推进到 100%。我现在仍然经常这样做，因为很少有优秀的人愿意与一个完美打磨好的、完成的想法互动，就像一个完美打磨的想法一样。 他们的新想法就像是在它上面反弹，而不是在一些有更多槽沟和更粗糙边缘的东西上，你们可以一起打磨。我认为以这种方式让人们参与到过程中，例如一个文档作为潜在的载体，是我发现的最佳协作方式之一。

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

你如何看待人工智能脑萎缩和开始过度依赖人工智能？那只是每个人都将面临的挑战。为什么不利用这种魔法来帮助查看某些东西呢？然后我们开始失去阅读长篇文档的能力。有什么事情是你正在尝试避免做的事情吗？

Yeah, I think this writing is 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. I think I will, again, outsource all writing is reporting as much as possible to the model, but writing is thinking I have to do myself. And I have this like 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. Or I think about this in meetings, too, that 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. And so when it comes to keeping my thinking sharp, I do that writing for the document myself first and make sure I've invested time. Like the collective amount of time I expect people to read it, at least in writing it and producing it. And I don't really rely on the model either for polishing my prose, which I don't think it really does, or especially not in like generating the first version. But I do, of course, have the model help me a lot when it's like summarization or like translation of content from one format to the other all the time.

是的，我认为写作是一种思维训练，这是我日常生活中使用的主要方法之一，以确保我不会过度削弱我的思维能力。我想，我会尽量把所有写作的报告部分外包给模型，但写作作为思维，我必须自己完成。我还有一个个人信念，如果我要让别人阅读我的文档，我至少必须自己先读几遍。 或者我在会议中也会考虑这一点，如果我要召集一组人开会，我需要在会议之前准备好这些人将在会议中花费的总时间。因此，当涉及到保持思维敏锐时，我会先自己为文件写作，确保我已经投入了时间。比如我期望别人阅读的总时间，至少在写作和制作它的时候，我自己已经投入了这个时间。 而且我也不太依赖模型来润色我的文章，我觉得它实际上并不擅长这个，尤其是生成初稿时更不用说了。但当然，当涉及到总结或者将内容从一种格式翻译成另一种格式时，我确实经常让模型帮我很多忙。

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 even don't use it to improve on the writing. Just keep that all human.

所以我听到的意思是，作为一个人自己写下想法、简要说明和计划。自己写。不要从人工智能开始。甚至不要用它来改进写作。就保持完全人工完成。

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

是的，至少对我来说，我自己开始，也自己结束。中间我可能会用人工智能来研究具体元素，或插入一些数据，或者去获取一些数据，或者帮我一把。

Or push back on some ideas.

或者对一些想法提出反对。

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

是的，对一些想法提出反对意见。但从自己开始，也以一篇文字作品结束，而这不会削弱你的思考。

Okay, one last question. I want to ask you about Sutter Hill. You had this very unusual career step. You went to your PMPM 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 is an example. What was that about? What did you learn from that experience?

好吧，最后一个问题。我想问你关于 Sutter Hill 的事情。你有一个非常不寻常的职业步骤。你去了你的 PMPM 创始人那里，然后就像是，好的，在 Sutter Hill Ventures 做 EIR（企业家研究员），这是一家标志性的创投公司。人们可以去查一下。很多了不起的公司都是从 Sutter Hill 出来的。它有一种非常独特的创业方式，基本上是培养公司。Snowflake 就是一个例子。这是怎么回事？你从那段经历中学到了什么？

Sutter Hill is an iconic firm and is intentionally a very illegible firm. Like, if you go to the Sutter Hill website, you will see nothing on the website. It is 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 Spicer, who is 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 like a tens of billion dollar company as a dark art. Like, oh, it's luck. Oh, it's chance. Oh, it's all these like things that must come together. Yet Mike Spicer has done it multiple times. And so there is like clearly a way to do it. There's clearly a roadmap for making that possible. There is like 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 of the firm Sutter Hill. And 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… They discuss persistent AI coworkers, steering versus rowing, product strategy, model capabilities, ambition, and AI-era work. Where they have a map of everyone that they've interacted with and the 10 best people that those people have interacted with that helps them be so, 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. And 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.

萨特希尔是一家标志性的公司，而且故意是非常难以理解的公司。比如，如果你访问萨特希尔的网站，你几乎看不到网站上的任何内容。它是一家不高调运营的公司。它尽量低调地运作，尽可能谦逊，但却以某种方式对硅谷一些最具标志性的成功负有责任。 他们有一个非常不同寻常的孵化模型，这个模型是 Mike Spicer——那里一位非常出色的合伙人——创建的，并且已经成功地多次推广。我认为对我来说 Sutter Hill 最具标志性的事情是，人们认为寻找产品市场契合度是一门神秘的艺术，或者将公司打造成为几十亿美元的企业也是一门神秘的艺术。大家会觉得，哦，这是运气，哦，这是机缘，哦，这些事情必须全部凑到一起。然而，Mike Spicer 已经多次成功做到这一点。 所以显然有一种方法可以做到这一点。显然有一个实现这一目标的路线图。有一系列可以做的事情来重复实现这一点。这不仅仅是运气，也不仅仅是神秘的技巧。可以说，有一份操作手册，而这份手册存在于 Sutter Hill 公司内部。他们已经找到了在做决策和下注方面经常正确的方法。 他们学会了在很多方面正确地处理问题……他们讨论持久性的人工智能同事、引导与划桨、产品策略、模型能力、抱负以及人工智能时代的工作。他们有一个图谱，显示他们与每个人的互动情况，以及这些人互动过的 10 个最佳人选，这帮助他们在这方面非常非常有效。所以我去了萨特希尔，因为在某种程度上，我的职业生涯一直在思考如何尽可能多次地寻找产品与市场的契合点。 无论是作为创始人，还是在 Stripe 推出新产品，或者加入像 Watershed 这样的初创公司。所以 Sutter Hill 是一个他们已经掌握了如何在 B2B 产品上找到产品市场契合度的地方。我想从他们那里学习我能学到的东西。

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

你学到了什么？除了他们知道如何做之外，你从那次经历中学到的一个东西是什么？

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 proceed actually even building the product It should probably come from some sort of bringing together of understanding the technology deeply and then understanding like 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 like pitch a hundred 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 and only then go commit the, okay, this is exactly the product shape. And Mike Spicer is like unbeatable at this art. Previously, I'd always kind of underrated PMM work. I was like, oh, it's whatever. Like 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.

他们绝对知道如何去做。我认为让我非常惊讶的一件事是，我意识到产品与市场的匹配固然重要，但实际上，我真的低估了产品营销匹配。这个理念是，你谈论产品的方式和营销产品的方式实际上可以先于产品的开发。它可能应该来自某种结合深刻理解技术和理解企业销售流程的方式。 然后那个产品营销契合度、叙事和定位实际上甚至在你构建产品体验之前，就是正确的测试对象。所以你应该去向一百个人推介，尽可能地完善那个推介，弄清楚为什么这个东西具有变革性的营销叙事，然后只有在那之后才去确定，好的，这就是确切的产品形态。而迈克·斯派塞在这门艺术上是无与伦比的。 以前，我总是有点低估 PMM 的工作。我曾经想，哦，没什么大不了的。就像是这些职能之间的粘合剂，没关系。然后我意识到，那些出色完成的工作对公司的成果有多么具有变革性，实际上可以成为让公司成功的关键因素。

Amazing. I so agree with that. Positioning, we talk a lot about that on this podcast. Okay, I'm going to show you what sites got created real quick. It was running while we were talking. Check this out. Look at this.

太棒了。我完全同意。定位，我们在这个播客里经常谈到这个。好的，我要快速给你展示一下创建了哪些网站。它在我们说话的时候一直在运行。看这个。看看这个。

Oh, man.

哦，天啊。

As like, make it more awesome. And it made it more awesome. Multiproduct. Beautiful. Look at this. This is like a legit design. Look at it. You got quotes. Big conviction, small teams. It's true. Start with the buyer. How do you feel about this being your website, your new website?

就像，把它做得更棒。而它确实变得更棒。多产品。漂亮。看看这个。这就像是一个真正的设计。看看它。你有引用。大信念，小团队。这是真的。从买家开始。对于这是你的网站，你的新网站，你感觉如何？

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. Okay. Good job. It's looking good. Good job, Sykes.

我确实觉得顶部那张我大约 19 岁时的照片真的很有趣。不过，除此之外，我很喜欢这个网站。好吧。干得好。看起来不错。干得好，赛克斯。

That was my badge photo from Stripe. Oh, wow. Amazing. I love that it built. I already unshared it, but I love that it built the whole little thing around your head. So cute. Tara, is there anything else that you wanted to share, anything else you want to touch on before we get to a very exciting lightning round?

那是我在 Stripe 的胸牌照片。哦，哇。太棒了。我喜欢它的构图。我已经取消分享了，但我喜欢它围绕你头部构建的小东西。太可爱了。Tara，在我们进入非常激动人心的闪电问答环节之前，你还有什么想分享的，或者还有什么想谈的吗？

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. 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. 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, And they also discuss the potential of the product to be able to be used in the future. 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, oh, wait, this thing is right. This thing is good. This thing is useful. 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? 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. Yeah.

是的，我们在产品开发中，尤其是在这个新阶段使用 ChatGPT 工作时，思考的一件事就是知识工作和编程实际上根本不同。作为其中的一部分，我们学到的一个令人惊讶的事实是，编程非常以输出为导向，当你让它完成一个编程任务时，你可以通过测试来验证它是否正确或执行得好。你可以尝试一下，看看是否有效。有一种方法可以基于输出来验证它。 但是知识工作有所不同，因为我不能仅仅在最后看一下演示文稿就看到数字。哦，好像演示文稿里显示成功率是 90%之类的，然后真的相信它。我真的需要思考过程、输入、推理以及整个过程是如何进行的。因此，就产品的表现而言，我们已经做了很多工作，并且必须继续做下去。此外，他们还讨论了产品未来可能被使用的潜力。 查看其引用和输入，帮助你与模型一起进行探索，以达到最终的输出，这样你最终就会知道，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。这当然会在产品的用户体验中有所体现，但也应该在推理和思路链条中有所体现。例如，你是否应该在过程中看到更多的引用，展示它是如何在那些数据中达到最终状态的？ 所以当我们考虑可能将人类合作者引入你的工作时，我们也需要思考如何让模型在你们共同完成任务时，更像是你的合作者。是的。

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, 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. 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 Notion docs? Such a good point. So I see the challenge in your job. You make all this work as one product. Tricky, tricky. Amazing. Anything else before we get to our very exciting lightning round?

这是一个很好的观点。我在想象一个高管会议，你正在尝试向高管推销，告诉他们计划是什么，我认为我们应该做什么。很多时候，这都是在帮助他们看到，我为达到这个目标所做的工作。这是所有的步骤。 所以，很有道理，你需要人工智能向你展示同样类型的工作，本质上就是工作证明，而不是工程方面的东西，比如，好吧，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？它是否通过了我们所有的测试？所以这是一个非常好的观点，这两种模型有多不同。另外还有上下文的问题。它是否具备完成你想让它完成的任务所需的上下文？它知道吗？ 它能看到你的电子邮件吗？它能看到你所有的 Notion 文档吗？这是一个非常好的观点。我明白你工作的挑战了。你把所有这些工作做成一个产品。棘手，棘手。令人惊叹。在我们进入非常令人兴奋的闪电问答之前，还有其他事情吗？

Yeah, let's jump into it.

是的，我们开始吧。

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

到此，我们来到了非常刺激的闪电问答环节。我有五个问题要问你。你准备好了吗？

Yes. Yes.

是的。是的。

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

你发现自己最常向别人推荐的两三本书是什么？

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 like 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. Another book that I might recommend as like a book that people should read is 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 like transform ourselves or like 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 that. 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 like a woman and humans. And it just reminds me of like 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 all of us, ahead for all of us as we consider our careers as well.

我真正推荐给大家的一本书是威廉·芬尼根的《野蛮日子》。我不知道你是否读过。这本书讲述了一位纽约记者的生活，但他如何爱上冲浪成为他的激情。我从书中得到的启示是，人可以对某件事充满热情和奉献，并且让它成为你的生活目标，即使你并不擅长它。 它讲的是像爱上冲浪的艺术，以及他在追求卓越和完美的过程中，明知道自己可能永远也达不到的努力。这是一个如此引人入胜和具有变革性的故事，让我觉得这是我们应该继续生活下去的方式。我真的，非常喜欢那本书。我可能会推荐的另一本文本是，我真的很喜欢《安娜·卡列尼娜》。我最近一直在重读经典作品。 我喜欢《安娜·卡列尼娜》，因为它就像一本有层次的书。我认为，在这个新时代，我们将不得不做的一件重要事情就是变革自己，或者带着自己去经历一些我们以前不习惯的事情。当我想到那本书时，我会想到我 13 岁读它的时候，我明白了基本的情节；当我 17 岁读它的时候，我理解了欧洲历史的动态，以及阶级斗争。 然后当我三十岁读它时，我觉得，哦，这就像是一个关于女人和人类的故事。它让我想起了成长，也让我意识到，随着不断成长，是有可能用多种不同的视角来看同样的事物的。我认为，这就是我们所有人在考虑职业发展时面临的一种挑战。

It's interesting how both these, like, I could connect to AI in the time we're living in now, too. I also recently read Anna Karenina. What did you think? 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, it was amazing. Someone gave away the ending, which kind of made it less surprising. I don't want to give anything away. No spoilers. And I also felt like it was very long, but now I'm reading The Power Broker, which has set the new precedent for a long time. I've been reading it for half my life at this point.

有趣的是，这两个作品，我也能在我们现在所处的时代与人工智能建立联系。我最近还读了《安娜·卡列尼娜》。你觉得怎么样？今年年初。太棒了。我以前从没读过。我在一个书单上看到它，比如，世界上最聪明的人读过的书，这上面有一整列书，而这是我没读过的一本，所以我就想，我得读这本书。是的，真是太棒了。有人透露了结局，这让它的意外性大打折扣。 我不想剧透。没有剧透。而且我也觉得它非常长，但现在我正在读《权力掮客》，它为长篇著作设定了新的标准。我到现在为止已经读了它半辈子了。

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 the Hilary 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. It's like chapter by chapter.

我喜欢《权力经纪人》。我还强烈推荐的一件事是，如果有人关注 Substack，比如 Simon Hazel 的 Substack，他会慢速阅读重要的书籍。他做过《战争与和平》的慢读，我觉得他也做过或者正在做《狼厅》的慢读，《狼厅》是希拉里·曼特尔的书。就是一章一章地读。读《权力经纪人》或者《战争与和平》，甚至《安娜·卡列尼娜》唯一的方法就是一章一章地读。

Speaking of that, there's a, someone, I forget who, told me this. There's a 99% visible 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 Pete Buttigieg and AOC and... Folks that lived in that area and they talk about every, you know, the story. And it was so, it's so fun to read and listen to their analysis of it. And then they have Robert Caro come on a couple of times. Whoa, that's amazing. Yeah. Hot tip. Okay, we'll keep going with our very lightning round. Favorite recent movie or TV show you've really enjoyed, if you've had time to watch anything. Okay.

说到这个，有个人，我忘了是谁，告诉我这个。有一个几乎百分之九十九可见的读书俱乐部分解《权力掮客》，分为 13 集，每集一到两个小时。他们每次讲解书中的几章，并进行讨论。他们还有特别嘉宾，比如皮特·布蒂吉格和 AOC，还有……住在那个地区的人，他们讲述书中的每一个故事。阅读和听他们的分析真是太有趣了。 然后他们请来了罗伯特·卡罗，来了几次。哇，太棒了。是的。小提示。好吧，我们将继续我们的快速问答环节。最近你最喜欢的电影或电视剧是什么，如果你有时间看过什么的话。好的。

Of course, I watched The Odyssey. I found it to be an incredible, incredible film. It is 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 and I highly recommend watching The Odyssey. He is just 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, which is the Akira Kurosawa film that for the first time did that technique of telling a story through multiple people's perspectives where you never know what was true in the end. That technique and film was pioneered by Kurosawa. And it reminds me what one can do under constraints. That film was made in like the 50s. It was black and white. They're like, you know there's a guy holding the camera, and yet it is so perfect. And it is such a tasteful, innovative, amazing example of creativity. And what I'm reminded of watching that film is like, I have 100 times the power and tools that he had making that film in my iPhone. And like, what's my excuse for not elevating my ambitions and making better stuff?

当然，我看过《奥德赛》。我觉得这是一部令人难以置信的电影。它是关于人工智能的，或者我的个人看法是它是关于人工智能的，或者是克里斯托弗·诺兰对人工智能如何改变社会的看法，我非常喜欢，我强烈推荐观看《奥德赛》。他真是一位了不起的导演，并且以一种我认为没有其他现代导演做到的方式，桥接了艺术性和商业成功。 我最近也看了电影《罗生门》，这是黑泽明的作品，它首次使用了通过多个人的视角讲故事的技巧，你永远不知道最终什么是真实的。这个技巧和电影都是由黑泽明开创的。这让我想起人在有限条件下可以做到的事情。这部电影大概是在五十年代拍的，是黑白片。你能看到有个人在拿着摄影机，但是它却非常完美。 它是如此有品味、创新、惊人的创造力的例子。看那部电影时让我想起的是，我在 iPhone 里拥有的力量和工具是他拍那部电影时的 100 倍。那么，我不提升自己的抱负、制作更好的作品的借口是什么呢？

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

拉里·赫里布曼斯基 一切都回来了，野心。在《奥德赛》上，我仍然在尝试买票。太难了。我考虑了一晚上，现在好像一个月内根本不可能买到票。哪儿都没有座位。

Kevin Kwok got us tickets at 10 p.m. at the Metreon earlier this week. It was so good.

Kevin Kwok 本周早些时候在 Metreon 为我们拿到了晚上 10 点的票。那真的太棒了。

Next time, call me. I mean, let me see you. Oh man, I have like bots running on it. I have a person working on it. I have a friend.

下次，给我打电话。我是说，让我见见你。哦，天哪，我在上面好像有机器人在运行。我有人在做这件事。我有一个朋友。

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.

这太棒了。你会喜欢的。我迫不及待想听你看完后的想法。如果你同意我的观点，它是关于人工智能和道德崩溃的。

Okay, no spoilers. Hopefully by the time this comes out, I have seen it, but if not, if anyone has hookups, please tell me. And I'm trying to do like the IMAX full power Matron sort of thing. Yeah. Okay, next question. Favorite or interesting AI product that you've recently discovered? If ideally not OpenAI product, but you know, you can also go there if you want.

好的，不剧透。希望到这件事出来的时候，我已经看过了，但如果没有，如果有人有渠道，请告诉我。我也想尝试那种 IMAX 全力模式的体验。嗯，好，下一个问题。你最近发现的最喜欢或最有趣的人工智能产品是什么？理想情况下不是 OpenAI 的产品，但你如果愿意也可以提到它。

Ooh, I mean, of course, my favorite AI product is ChatGPT and using cool sites and visualize stuff in Codex, 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, like, the cozy software movement where you, like, make software tools for, like, five of your friends and you guys use it together. And so I have a friend named Sebastian who… Made a really cool AI app that turns anything into a podcast and puts it in your, like, a little podcast app for you. And 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.

哦，我的意思是，当然，我最喜欢的人工智能产品是 ChatGPT，以及使用很酷的网站和在 Codex 中进行可视化，这太棒了。但在 OpenAI 产品之外，我最喜欢的人工智能产品是我朋友为我做的产品，因为现在实际上人们可以做到这一点。我觉得那太酷了。我是一个非常大的粉丝，比如说 cozy 软件运动，你会为你的五个朋友制作软件工具，然后你们一起使用它。 所以我有一个朋友叫塞巴斯蒂安，他……制作了一个非常酷的人工智能应用程序，可以将任何内容转换成播客，并放到你的一个小播客应用里。他还为我们的朋友们制作了一个非常棒的私人社交网络。它叫 GATS。这正是我认为的未来应该是的样子——人们应该开发完全满足他们和朋友需求的软件。

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

GATS 代表什么？那是某种内部笑话吗？

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 like a group of, a small group of friends. And I learned the most interesting things on that product.

不是的，或者至少如果它是一个内部笑话，我也不知道。但它就是我使用的那个地方，有点像私人 Twitter，或许是给一小群朋友的。我在那个产品上学到了最有趣的东西。

It's like a WhatsApp, but not. Yes, exactly. The podcast app is interesting, but I feel like the version that I would love is it's actually like podcasts in your feed of podcasts. And then just new episodes get added of things you want to read or whatever. Yeah, that's what it does.

这就像是 WhatsApp，但又不是。是的，完全正确。这个播客应用很有趣，但我觉得我真正想要的版本实际上就像是在你的播客频道里看到播客一样。然后新的剧集会被加入你想要收听的内容，不管是什么。是的，这就是它的功能。

It drops it in your Apple podcast feed. Oh, amazing.

它会把它放到你的苹果播客订阅中。哦，太棒了。

I want this. It's great. Help me subscribe to this.

我想要这个。这很棒。帮我订阅这个。

For sure.

当然。

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?

好的，太棒了。好的，再问两个问题。你有没有一个最喜欢的人生格言，经常在工作或生活中回想起它？

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

哦，我在工作中经常回想的生活格言实际上是托尼·莫里森对工作的三种看法。让我，嗯，快速把它找出来。

Amazing.

惊人。

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. Okay. 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.

好的，有四件事情。这些来自她的文章《你所做的工作，你是怎样的人》。第一，无论工作是什么，都要做好，不是为老板，而是为你自己。第二，你塑造工作，而工作不会塑造你。第三，你真正的生活是在家人身边。第四，你不是你所做的工作，你是你自己的人。

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. So cool. Okay. Maybe we'll show that on screen as you're talking about that. I love that. I love that 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. Mm-hmm. 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. Just like, so it's such a great idea and program. Any story from that time that might be fun to share? Something that's like, oh, that was crazy. 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? Yes.

我起了鸡皮疙瘩。哇。太棒了。我觉得那正是你固定在你的推特主页上的东西。是的，因为我记得看到过。太酷了。好吧，也许我们可以在你谈论它的时候在屏幕上展示一下。我喜欢这个。我喜欢这种记忆方式，把它固定在推特顶部。因为每次我上推特，哦，它又在那里了。嗯。好的，最后一个问题。你当年是 Thiel 学者。Thiel 学者？Thiel 还是 Thiel？Thiel。Thiel，对。 多么棒的校友团体啊。天哪。就像，所以这是一个非常棒的想法和项目。那个时候有没有什么有趣的故事可以分享？一些会让人觉得‘哇，那真疯狂’的事情。我不知道。还有其他你为之自豪的 Thiel 奖学金获奖者吗？还有其他人吗？采访是怎样的？我不知道。类似这样的事情吗？是的。

Yeah, the two fellowship was an inflection point in my life. I wouldn't be where I am without it. Maybe to the point of like there are key moments where you can tell people to elevate their ambitions and they do and that changes them. Like 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, you know, take the path that you were on. And truly, I'm eternally grateful for them being able to do that. One of the Teal 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. And 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 brilliant. The expert on what are all the cool things you can do on a Mac. And so Ari leads a lot of our computer use stuff at OpenAI, and he's shipped a whole bunch of great things for computer use. But yeah, his creativity and his joy in what he does and his love of his craft really inspires me. And Ari is a cool guy. But I'm trying to think what is a good story from that time that feels...

是的，那两个奖学金是我人生的一个转折点。没有它们，我不会有今天的成就。也许可以说在某些关键时刻，你可以告诉别人提升他们的抱负，他们真的做了，这改变了他们。那正是有一个人走到我面前，提升了我的抱负，并对我说，不，你可以做到，你不必按照你原来的路径走。真心地，我永远感激他们能够做到这一点。 我现在经常一起工作的 Teal 伙伴之一是 Ari Weinstein，他创立了一家公司叫 Sky，后来被 OpenAI 收购。在此之前，他创立过公司，并在苹果工作了一段时间，因为苹果收购了他之前的公司。Ari 是我见过的最有创意的思想家之一，非常聪明。他是研究在 Mac 上可以做的所有酷炫事情的专家。 所以阿里在 OpenAI 负责我们很多电脑使用相关的工作，而且他已经完成了很多很棒的电脑使用相关的项目。不过，他的创造力、对工作的热情以及对自己技艺的热爱真的让我很受启发。阿里是个很酷的人。但我在努力想，那段时间有什么好的故事，感觉...

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. 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?

当你考虑这件事时，我来给那些不知道的人解释一下 Thiel 奖学金，如果我说错了请纠正我。基本上，Peter Thiel 的意思是，嘿，人们不应该上大学。相反，他们应该尝试去做一些他们想做的事情，而且你可以获得 10 万美元，不上大学，而是去追随你的梦想。大致上是这样吗？

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

是的，确实如此。完全正确。很酷。而且你每次都和另外 19 个人在一起。每年大约就是 20 个人，因为这是 20 岁以下的 20 人计划。

How many years did it go on for? Is it still going?

它持续了多少年？它还在进行吗？

I think it's still going, but I think it was like constrained at the 20 number for like the first four or five years or something like that. Yeah, 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. 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, I feel very lucky to be able to work with those folks.

我觉得它还是在进行，但我觉得前四五年它好像被限制在 20 这个数字左右。是的，我觉得我那年发生的一件非常疯狂的事情是，我是那个奖学金的第二个受奖人。他们决定把整个过程拍成一个在 CNBC 播的纪录片。所以我整个——我为奖学金的提案，上台展示我打算做的想法，所有这些不幸地都在 YouTube 上有现场视频。 所以如果你真的想看到我作为一个 19 岁的人做一些尴尬的事情，它就在那。当然，从那批奖学金中走出来的最了不起、最成功的人之一是迪兰·菲尔德，他不仅才华出众，而且是一个非常善良的人。是的，我觉得能够和那些人一起工作非常幸运。

Amazing. Yeah. It's interesting that Dylan's like the guy I think everyone thinks of when they think of Teal Phillips.

太棒了。是啊。有趣的是，Dylan 就像是我认为每个人在想到 Teal Phillips 时都会想到的人。

Yeah. Yeah.

嗯。嗯。

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?

真是一个品牌。好吧，Tara，这太棒了。你有什么想推荐的东西吗，想指引大家去关注的东西吗，听众们怎么能帮到你？

Anything I want to plug and point people to, maybe they should use the ChatGPT desktop app. They should use ChatGPT in 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 visualize block of your ChatGPT usage. It's a really cool way to start, like, experiencing the power of this stuff. Yeah.

我想推荐给大家的任何东西，也许他们应该使用 ChatGPT 桌面应用。他们应该在网页上使用 ChatGPT 并尝试工作。遗憾的是，这有点像一个切换开关。他们可以切换过去试着工作。让它做一些很酷的事情。也许让它帮你建立一个关于你自己的网站作为开始。或者让它制作一个小的可视化块来展示你的 ChatGPT 使用情况。这真的是一个很酷的方式来开始体验这些东西的强大功能。是的。

Thank you for joining us.

感谢您加入我们。

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 muni 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，我的主要推荐是，嗯，去下载 ChatGPT 应用。去网页上使用它。更有变革性的是，去手机上试试。然后像坐一次长地铁或者类似的公交车之类的。当你在没有信号的情况下出来时，事情已经为你完成了。那部分感觉特别神奇。你不用整天开着笔记本到处走。你终于让这些东西在云端运行，做真正的工作。

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.

是的，我本来想提到最后这一点，但我认为这是目前移动端产品中一个非常被低估的元素。而且这主要是移动端独有的功能，云端部分。

No, it's everywhere.

不，它到处都是。

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, it feels like really simple, but that's a massively powerful thing. Okay. Anything else, Tara, before we let you go?

它无处不在。好吧，真是太棒了。所以在你的移动应用上，你可以进入 ChatGPT，切换到工作模式，让它去做一些工作。而且你实际上不需要在本地运行，它是在云端运行的。它会一直做工作直到完成，然后你可以和它聊天。所以，这感觉很简单，但这是一个非常强大的功能。好了，Tara，在我们让你走之前，还有什么要说的吗？

No, that's it.

不，就这些。

Okay.

好的。

Thanks, Len.

谢谢你，Len。

Tara, this was awesome. Thank you so much for doing this.

塔拉，这太棒了。非常感谢你做这件事。

Such a pleasure.

非常高兴。

What a journey since the fellowship back in the day. I'll talk about that more in the intro. Yeah. All right. Well, thanks for being here.

自从当年的奖学金以来，真是一段旅程。我会在介绍中更多地谈到那件事。是的。好吧。谢谢你们的到来。

Thank you.

谢谢你。

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