# Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI · 中英对照逐字稿

- 原节目：Latent Space: The AI Engineer Podcast
- 英文原始来源：https://www.latent.space/p/chatgpt-work
- 中文译制版入口：https://www.xiaoyuzhoufm.com/episode/6a69df93b581962ce2bd6935
- 时长：01:09:28
- 方法与限制：英文来自已验证的原始节目 transcript/caption；中文由 Codex 逐段翻译，未做逐字人工校对，公开引用前请回到英文原文与音频复核。

## 中英对照逐字稿

### [00:00:00–00:00:07] Swyx

**EN**  We’re here in the studio with Akshay from OpenAI. Welcome.

**中文**  我们现在正在演播室里，和来自 OpenAI 的 Akshay 一起。欢迎。

### [00:00:07–00:00:08] Akshay Nathan

**EN**  Thank you.

**中文**  谢谢。

### [00:00:08–00:00:32] Swyx

**EN**  And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It’s been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.

**中文**  还有我们可靠的联合主持人 Vibhu。你们最近发布了 ChatGPT Work，你负责 Core Product Engineering。你一路走来经历了很长的历程。我觉得很有意思的是，你最开始做的是 no-code 或 low-code，包括 Walrus 和 Airtable。从某种程度上说，ChatGPT Work 就像“超级应用中的超级应用”：它是终极的 no-code，你只需要写一段 prompt。

### [00:00:32–00:01:33] Akshay Nathan

**EN**  Yeah. It’s funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there’s this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It’s funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that’d be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what’s going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we’ve been up to is, like, the manifestation of that.

**中文**  是啊。事情兜兜转转又回到了原点，很有意思。我职业生涯最初做的是消费金融科技。之后我一直有一个假设：工程师通过代码能做到的事情，如果能以更容易使用的方式带给更多人，那会非常神奇。我们当时做过一家创业公司——那还是 LLM 和视觉 LLM 出现之前——想用 AI 做自动化测试。当时的东西有点粗糙，但我们已经尽力而为。后来我在 Airtable 工作了一段时间，依然围绕同一个命题：如果能把数据库，或者数据库背后的基础构件带给普通人，会非常有用。但当 LLM 登场后，很明显，缺失的那一块终于出现了：它正是让所有人在不必理解底层原理的情况下获得代码魔力所需的技术。所以我认为，这次发布以及我们一直在推进的很多事情，都是这个想法的具体体现。

### [00:01:33–00:01:44] Vibhu

**EN**  How was stuff when you joined? So you joined OpenAI 2023. Now we’ve got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?

**中文**  你刚加入时是什么样？你是在 2023 年加入 OpenAI 的。现在我们的产品多了很多，有 ChatGPT、Codex app、ChatGPT Work。情况变了吗？

### [00:01:44–00:02:40] Akshay Nathan

**EN**  I think the more interesting thing is how things haven’t changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn’t changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we’re probably gonna, like, try different products and have different things that succeed and don’t. But the vision has stayed the same, and the mission has stayed the same, and we’re starting to see the pieces, fall together, and that’s really cool.

**中文**  我觉得更有意思的是，哪些东西没有变。我记得加入时公司大约有五百人。我当时担心的一点是，我想找一个更早期的项目，这里还会不会足够像创业公司？加入以后我发现：“这里比我能想象的任何地方都更像创业公司。”直到今天这一点也没有真正改变。那种自下而上的抱负，以及任何人都能做任何事、提出想法并把它发布出去的能力，非常酷。使命层面真正吸引我的是，把前沿智能带给每个人：构建 AGI，再把它带给所有人。我们当时也承认，这个愿景不会沿着一条直线实现；我们大概会尝试不同产品，有些成功，有些失败。但愿景和使命始终不变。现在我们开始看到各个部分逐渐拼到一起，这真的很酷。

### [00:02:40–00:02:52] Swyx

**EN**  You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you’re bringing into your work now?

**中文**  你之前做过 Enterprise。很多人从未接触过 ChatGPT Enterprise。你在那里学到的、现在带进这份工作的一件事是什么？

### [00:02:52–00:03:56] Akshay Nathan

**EN**  I think how there’s no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you’d get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it’s interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it’on the flip side, it also means that, like, you don’t know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.

**中文**  我学到的是，Enterprise 里没有一种方案能适用于所有人。我记得 ChatGPT Enterprise 早期，我们和客户及各种人交流。那大概是 ChatGPT 发布一年后，所有人都非常兴奋，想把 AI 引入企业。各种团队纷纷成立，比如拿着巨额预算的“AI 部署团队”。如果你问大家为什么兴奋、想解决什么，起初得到的往往是一些基础答案，比如“我们有这些上下文、数据和各种资料”。但如果继续问：“你们希望 AI 在工作场所支持哪个具体用例？”答案就会呈现出巨大的差异，出现各式各样的需求。这很有意思：使用这些模型和产品时，你面前有一个框，什么都可以对它说，这是它的魔力；但反过来，这也意味着你不知道该拿它做什么。在 Enterprise 里，很重要的一部分就是在用户所在的位置与他们会合：理解他们试图解决的用例，然后教他们怎样使用 AI 在那里获得杠杆。

### [00:03:56–00:04:01] Swyx

**EN**  Do you meaningfully differentiate that from forward-deployed engineering?

**中文**  你认为这和 forward-deployed engineering 有实质区别吗？

### [00:04:01–00:04:24] Akshay Nathan

**EN**  I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who’s, like, looking at their computer or looking at their phone, like, it’s our job in the product to, like, be enabling them and showing them where to go. So we’re really excited about that.

**中文**  这里既有 go-to-market 的一面，也有产品的一面。我认为产品侧必须有人负责。无论我们的 FDE 模式做得多好，归根结底，如果用户正看着电脑或手机，那么通过产品赋能他们、告诉他们该往哪里走，就是我们的职责。所以我们对此非常兴奋。

### [00:04:24–00:04:39] Vibhu

**EN**  Do you think there’s been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don’t know what to do with it, or have things changed?

**中文**  过去三年的采用情况发生变化了吗？期间出现过一些阶跃式变化，比如 reasoning models 等。Enterprise 仍然面临同样的问题吗——它像个黑箱，人们不知道用它做什么——还是情况已经变了？

### [00:04:39–00:05:27] Akshay Nathan

**EN**  We’re seeing now that, like, there’s this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we’re seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it’s connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there’s, like, this, like, 10x or 100x bigger market where, like, they don’t yet get that, or they don’t yet see that. And so I think that’s the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That’s where we wanna play, especially with ChatGPT Work.

**中文**  我们现在看到采用量大幅上升。所有人都非常兴奋。感觉很多人——数百万乃至数亿人——都在使用 ChatGPT，也大体理解了怎样与 AI 协作。但每解锁一种新能力，比如现在的 agents，可能仍然只有一批 early adopters 真正理解它。他们知道：“我们什么都能做；只要确保有正确的上下文、连接到正确的工具，并由你监督，任何事情都有可能。”但还有一个大十倍甚至一百倍的市场，他们还不理解或还没看到这一点。所以我认为，这是下一个阶段。回答你的问题：采用已经发生，而且增长很快，但机会远比这大。这正是我们想要参与的空间，尤其是通过 ChatGPT Work。

### [00:05:27–00:05:50] Swyx

**EN**  Yeah. well, let’s, let’s skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we’re officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.

**中文**  好，那我们直接说 ChatGPT Work。它大概一个月前才发布。促成它的决策过程是什么？这里涉及“超级应用”的整体合并——官方是这么叫的吗？你们也停用了 browser。请概括一下你过去几个月做这个产品的经历。

### [00:05:50–00:06:34] Akshay Nathan

**EN**  Yeah. It feels like forever now, but it’s only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they’re all using Codex for, their use cases. That part’s cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, like

**中文**  是啊，感觉已经过了很久，其实只有几个月。我想最突出的一个推动因素是：当我们发布 Codex，甚至只是内部开始使用 Codex 时，令我们非常惊讶的是，OpenAI 内部非开发者的采用出现了真正的拐点。我想我们最近公布过一些相关数据。在产品开发过程中，我会参加 UXR session，和内部人员交谈。最令我印象深刻的是：你去找战略财务、市场营销之类的团队，他们都在用 Codex 处理自己的用例。这本身很酷，但真正令我印象深刻的是，人们对自己在用 Codex 感到非常自豪。就像……

### [00:06:34–00:06:36] Swyx

**EN**  It’s like, “I’m not supposed to be using it, but I am.”

**中文**  就像：“按理说我不该用它，但我正在用。”

### [00:06:36–00:07:17] Akshay Nathan

**EN**  It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it’s, like, a tricky thing, right? There’s many ways you can go about it. And so that’s what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.

**中文**  确实有这种感觉。他们觉得自己很早就接触到了这个新事物，同时也觉得自己拥有了超能力。我们当时意识到，Codex 的力量、agents 的力量，不只属于开发者，而且这种情况比我们原先想象的来得更早。我们已经拥有一个庞大的分发基础，很多人了解并喜爱 ChatGPT；问题是，怎样把这种能力展示给他们、带给他们？这是一个困难而棘手的产品问题，可以有许多做法。我们把长期推进的方向称为 Merge 和 Super App，最后以 ChatGPT Work 发布：核心问题就是怎样做到这一点。

### [00:07:17–00:07:36] Swyx

**EN**  How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there’s a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?

**中文**  你如何区分这些产品？也就是它们分别面向谁？Codex 最初甚至只是 CLI，后来有了 app；现在 ChatGPT、Codex 和 ChatGPT Work 又合并了。它是向普通用户、Enterprise 还是工作场景开放的入口？你怎样定位它？

### [00:07:36–00:07:42] Akshay Nathan

**EN**  I think we want to get it to position it for if you’re doing work-related things, for lack of a better word, right?

**中文**  我想我们希望把它定位为：如果你正在做与工作相关的事情——姑且用这个说法——就可以使用它。

### [00:07:42–00:09:26] Akshay Nathan

**EN**  I think productivity is what, like, the pillar that I support. Like, that’s the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there’s also personal productivity, right? And, like, I think ChatGPT Work is I’ve seen people do things in their personal lives that you wouldn’t classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn’t receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there’s all these things that, like, you, work-related or productivity-related things, I think that’s what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don’t want a user to get stuck in a tab or an experience where they don’t get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you’re in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there’s, like, some opinions that go behind that, but we do want We don’t want the user to need to choose which experience they’re in.

**中文**  我负责的支柱叫 productivity，这也是团队的名字。之所以叫 productivity，而不是 enterprise 或 work，是因为还存在个人生产力。ChatGPT Work 能处理一些个人生活中的事情，严格说它们不算工作，但这些 agents 很擅长。最近 Slack 上有人分享：他的包裹没收到，却从 Amazon 或快递公司那里拿到了投递照片。他让 ChatGPT Work 查清包裹在哪里。这个 agent 非常执着，它拿着照片查看了附近大量房源信息，准确找出了包裹所在的公寓楼，并给了他一些信息。所以我认为，无论叫工作相关还是生产力相关的事情，这就是我们希望产品覆盖的范围。你还问到 Codex。我们认为 Codex 是一个会长期存在的品牌，但我们有一条原则：不希望用户被困在某个 tab 或 experience 里，因而无法获得产品的全部能力。因此，你在桌面端产品 Codex 部分能做的一切，在 ChatGPT Work 里也能做，反之亦然。不过我们做了一些有明确倾向的产品决策：例如身处 Git repo 时，要向最终用户暴露多少 Git 状态；要多突出 agents 的思考过程和 diff，是否默认就展示 diff；安全方面，则要如何做 sandboxing，并在不同状态下设置合适的默认值。这些背后都有具体取舍，但我们不希望用户必须选择进入哪一种 experience。

### [00:09:26–00:09:49] Swyx

**EN**  That is a good goal for AGI, right? Like, people don’t want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It’s not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?

**中文**  这对 AGI 来说是个好目标，对吧？人们不想费心选择自己要哪个版本的 AGI，只想让 AGI 替自己决定。我能得到一个明确答案吗？我不是很清楚：Codex harness 和 ChatGPT Work harness 是同一个吗？差别只是 UI affordances，还是 prompt 层甚至更深处也有区别？

### [00:09:49–00:10:16] Akshay Nathan

**EN**  So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you’re in. On the UX side, there’s opinionated takes that we have when you’re in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.

**中文**  harness 是同一个，是共享的。我们在两个产品中都改进了 harness，让它更适合知识工作，尤其是 plug-ins、computer use 和 artifacts 相关场景。无论进入哪个 experience，你都能获得这些能力。UX 层面则有我们明确的设计取向：处于 Codex mode 时 UX 应该是什么样、怎样运作；此外还有我提到的一些 sandbox 差异。但底层 harness 和 capabilities 应当相同。

### [00:10:16–00:10:23] Swyx

**EN**  I’m just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?

**中文**  我只是有点好奇。也许我们可以……有没有一个 query，分别在两种 mode 中运行时会明显不同？

### [00:10:23–00:10:42] Akshay Nathan

**EN**  Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you’ll see, like, the diffs of, like, the sheet that it’s creating and stuff like that, and the file edits. But in Work you won’t be able to see that.

**中文**  有。我试过让它在两种 mode 中创建一个退休计算器 spreadsheet 之类的东西。在 Codex mode 里——可能需要处于 repo 中——你会看到它创建 sheet、编辑文件等产生的 diff；但在 Work 里看不到这些。

### [00:10:42–00:10:55] Swyx

**EN**  I think that’s, that’s super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn’t productivity everything?

**中文**  这就很清楚了。我还想深入了解你的 productivity team。除此之外还有哪些团队？首先，顶层团队除了 productivity 还有什么？难道所有东西不都算 productivity 吗？

### [00:10:55–00:10:55] Akshay Nathan

**EN**  So

**中文**  那么……

### [00:10:55–00:10:55] Swyx

**EN**  Science?

**中文**  Science？

### [00:10:55–00:11:33] Akshay Nathan

**EN**  We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there’People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there’s so much more inside to create images. And there’s so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there’s teams focused on enterprise and infrastructure and API and stuff like that, so.

**中文**  我们有专注于 ChatGPT 的团队，也就是面向消费者的核心 chat experience。我不认为那全都属于 productivity。人们每天用 ChatGPT 搜索、琢磨怎样给亲友写消息、学习一个新主题，等等；还会用它创建图像。chat 内部有太多事情，数亿用户在使用，值得投入一个非常专门的团队。另外还有专注于 enterprise、infrastructure、API 等方向的团队。

### [00:11:33–00:11:34] Swyx

**EN**  I will bring it up.

**中文**  我把它调出来。

### [00:11:34–00:11:43] Swyx

**EN**  Yeah. So I have them both running. This is ChatGPT Work. There’s a Codex version here. I picked “Five Little Ducks” song, so this will take a while.

**中文**  好了，我让两边都运行起来了。这边是 ChatGPT Work，另一边是 Codex 版本。我选了《Five Little Ducks》这首歌，所以要跑一会儿。

### [00:11:43–00:11:43] Akshay Nathan

**EN**  Huh.

**中文**  哦。

### [00:11:43–00:11:48] Swyx

**EN**  I think we’ll just keep it in the background and, as they finish, we’ll look into some of the differences.

**中文**  我们就让它们在后台运行，等完成后再看看有什么差异。

### [00:11:48–00:11:53] Akshay Nathan

**EN**  Yeah. But immediately, I think if you flip back to the Codex version you’ll see that,

**中文**  可以。不过你马上就会发现，如果切回 Codex 版本……

### [00:11:53–00:11:54] Swyx

**EN**  That it assumes

**中文**  它会默认假设……

### [00:11:54–00:11:54] Akshay Nathan

**EN**  Like the

**中文**  就像那个……

### [00:11:54–00:11:56] Swyx

**EN**  It assumes Git. Yeah. Yeah.

**中文**  它默认假设你在用 Git。对，对。

### [00:11:56–00:12:07] Akshay Nathan

**EN**  The, like, dynamic island assumes that you’re in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.

**中文**  那个 dynamic island 默认假设你处在 Git repo 里。你可能会错过一些内容，因为有一部分位于实际的 chain of thought 中，涉及这些变更以及我们如何展示它们。不过，是的。

### [00:12:07–00:12:14] Swyx

**EN**  Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let’s not do it?” Like, what’s the thinking behind that?

**中文**  有没有什么违反直觉的例子？比如你原本想发布某项功能，收到反馈后却说：“不，还是别做了。”背后的思考是什么？

### [00:12:14–00:12:17] Akshay Nathan

**EN**  In, ChatGPT Work?

**中文**  在 ChatGPT Work 里吗？

### [00:12:17–00:12:22] Akshay Nathan

**EN**  I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it’s like, why

**中文**  我们原本可以选择的一个方向，是让不同 experience 完全分开。也就是说，为什么要……

### [00:12:22–00:12:23] Swyx

**EN**  Different apps.

**中文**  做成不同的 app。

### [00:12:23–00:13:45] Akshay Nathan

**EN**  Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I’m, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we’re, we’re building, this technology is giving people leverage. Like, the things, maybe it’s the more mundane parts of your job or parts that, like, if you were able to automate, you’d be able to share more ideas faster or whatever, like, you’re able to do now. And because of that, like, that might blur the lines between someone who’s, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn’t box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It’s this thesis that, like, eventually things are gonna come together and we don’t wanna be Like, we wanna be prescriptive about when to be in either experience, but we don’t want to box anyone in.

**中文**  没错，做成不同 app，或者即使在同一个 app 中，也做成完全不同的 experience。为什么要把它们全部合并？Codex 已经深受喜爱，为什么还要把这些产品放在一起？这里的直觉是，AI 正在剧烈改变我们所有人的工作。几乎每隔几个月，我一觉醒来就觉得自己做的事情与几个月前完全不同。我的假设——更准确地说，是我们的假设——是，我们构建的技术正在给人们增加杠杆。你现在可以自动化工作中较为单调的部分，或者那些一旦自动化就能让你更快分享更多想法的部分。结果是，只写代码、写战略文档、策划活动、做市场、做播客等角色之间的界线可能会模糊，而且会随时间进一步模糊。根据“你是谁”划出硬边界会很困难。我们应让用户可以选择，但不应把他们限制在盒子里。为此我们投入了大量工作来保持 primitives 一致；比如 plugins 在这个产品、ChatGPT 和 cloud 之间是统一的。背后的命题是，最终这些东西会汇合。我们希望对何时使用哪种 experience 给出明确引导，但不想限制任何人。

### [00:13:45–00:14:02] Swyx

**EN**  I wonder if there’s users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can’t imagine what that was, but maybe they’re more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you’ve seen it.

**中文**  我在想，会不会有些用户非常习惯旧的 ChatGPT harness，而它现在实际上被 Codex harness 取代了。我想象不出旧 harness 具体是什么，也许它更偏对话。你能比较一下这两种 harness 吗？毕竟只有你见过。

### [00:14:02–00:14:08] Akshay Nathan

**EN**  Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,

**中文**  可以。我认为 ChatGPT 现有的 harness 今天依然存在，就在这个 app 里。

### [00:14:08–00:14:09] Swyx

**EN**  The classic, right?

**中文**  就是 classic，对吧？

### [00:14:09–00:14:09] Akshay Nathan

**EN**  The

**中文**  那个……

### [00:14:09–00:14:13] Vibhu

**EN**  You just start a new chat, and you don’t go under Work, right?

**中文**  你只要新建一个 chat，不进入 Work，对吧？

### [00:14:13–00:14:13] Akshay Nathan

**EN**  Yeah. If you start

**中文**  对。如果你新建……

### [00:14:13–00:14:14] Vibhu

**EN**  So

**中文**  所以……

### [00:14:14–00:14:16] Akshay Nathan

**EN**  A new chat and go to chat, then you’re, you’re talking to ChatGPT with the instant model.

**中文**  一个新 chat 并进入 chat，你对话的就是使用 instant model 的 ChatGPT。

### [00:14:16–00:14:21] Vibhu

**EN**  Oh, we can technically do another. But on instant.

**中文**  哦，从技术上说我们还可以再开一个，不过要用 instant。

### [00:14:21–00:14:26] Swyx

**EN**  Yeah. So this one’s not gonna code or it’s gonna be in line. It’s on a in line in a sandbox.

**中文**  对。所以这个不会去写代码，或者说它会以内联方式运行，在 sandbox 里以内联方式处理。

### [00:14:26–00:14:27] Akshay Nathan

**EN**  It’ll

**中文**  它会……

### [00:14:27–00:14:27] Vibhu

**EN**  Oh, that’s cool

**中文**  哦，这很酷。

### [00:14:27–00:14:30] Akshay Nathan

**EN**  We try to push you to go to Work if you’re creating a spreadsheet. Yeah, but this is

**中文**  如果你在创建 spreadsheet，我们会尽量引导你进入 Work。对，不过这个是……

### [00:14:30–00:14:34] Swyx

**EN**  And this is a router decision? Sorry. Is it a router decision?

**中文**  这是 router 做出的决定吗？抱歉，是 router decision 吗？

### [00:14:34–00:14:48] Akshay Nathan

**EN**  This is the decision that, the model is making, and then, like it sees that you’re able to. or you’re trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?

**中文**  这是 model 做出的决定。它看到你有能力——或者说你正试图做一件在 Work mode 中更适合完成的事情。不过我想你的问题是，chat，也就是 ChatGPT chat harness，有哪些优势？

### [00:14:48–00:15:10] Swyx

**EN**  It’s more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let’s call it the ‘01 era, until now, and now it’s being replaced by the Codex harness effectively. And they’re, they’re overlapping somewhat, but I’m curious what changed if there is.

**中文**  我的问题更宽泛：我想做一部 harness engineering 的口述史。ChatGPT harness 从我们姑且称为“01 时代”的时期一直支撑到现在，现在实际上正被 Codex harness 取代。两者有一部分重叠，但如果它们确实发生了变化，我很好奇具体变了什么。

### [00:15:10–00:16:24] Akshay Nathan

**EN**  My perspective on this is, like, there’s, there’s, there’s there’s like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we’re, we’re really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we’ve been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I’m sure there’ll be work down the road in order to get things to be, equivalently capable in all scenarios. But it’s just a question of, like, what we’ve been focusing on the product on historically and what we’re focusing on now.

**中文**  我的看法是，这里一直在经历分化、汇合、再分化、再汇合的循环。在 chat 中，对于我前面提到的许多用例，比如搜索或学习，我们非常重视 latency、personality 等不同方面。人们之所以喜爱 ChatGPT，是因为我们长期以来一直针对这些东西优化和投入。我们从 Codex 学到的是，如果让 agent 访问一台计算机这样近乎无限灵活的环境，它就能做非常强大的事情。所以，当我们思考知识工作该选哪种 mode 时，把它带入这种 computer environment 对我们来说更自然。我们也许会对不熟悉计算机细节的用户隐藏一部分底层内容，但仍然让他们获得同样的力量。不过最终，我们希望所有地方都有这种能力，也希望在用户所在的位置与他们会合。我相信未来还会继续工作，让各种场景都具备同等能力。现在的区别，只是我们过去对产品关注什么、以及目前关注什么。

### [00:16:24–00:16:44] Vibhu

**EN**  I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there’s also the new models you’ve released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all these

**中文**  除了 harness 以及何时使用 Codex、ChatGPT 或 Work，你们还发布了新模型，对吧？这方面有什么建议？人们喜欢精打细算地选择，比如只在 high reasoning 下用 Terra，某类任务要在这里用 Sol，忽略其他这些……

### [00:16:44–00:16:46] Akshay Nathan

**EN**  There’s 32 options.

**中文**  一共有 32 种选项。

### [00:16:46–00:16:59] Vibhu

**EN**  But, that being said, for people that are expanding, so, productivity trying stuff for work that don’t have the breakdown of what all this is what’s, what’s the advice, right?

**中文**  话虽如此，那些正在扩展用法、尝试将它用于 productivity 和工作的用户，并不了解所有这些选项的区别。给他们的建议是什么？

### [00:16:59–00:18:09] Akshay Nathan

**EN**  Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That’s happening again. I think it’s like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we’ve we’ve chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we’re, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you’re not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.

**中文**  在给建议之前，首先必须说明：没有这些模型，这一切都不可能实现。你前面问过 Work 的灵感来自哪里。我提到早期我们观察到 Codex 的使用情况，但那也是因为模型在变得强大得多。现在这种变化又发生了一次，我认为又迎来了一次阶跃式跃升。至于使用建议，我们希望默认配置就是尽可能好的选择。我们希望对默认值做出明确判断，因此选了一套我们认为最适合所有人的默认配置。同时，我们也为 power users 提供了底层选项。可以说，现在选项或许太多了，我们正在简化。你可以提高 reasoning level，也可以在需要时切换不同 model class，但默认值应当适合大多数 use case。因此我对多数人的建议是坚持使用默认值。等你遇到某种情况，觉得可以尝试另一套配置，比如成本侧的效率或智能侧的质量没有达到预期，再修改默认值，看看能否得到更好结果。但我们认为默认配置应该已经足够好。

### [00:18:09–00:18:24] Swyx

**EN**  I have, I’m just gonna run something by you since you have way more experience than me. I’ve recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.

**中文**  我想说个自己的用法，请你判断一下，毕竟你的经验比我多得多。我最近一直用 Sol Lite 搭配 goal；我的想法是，用 goal 增强 reasoning effort，但接受更多次终止和 turn。

### [00:18:24–00:18:29] Swyx

**EN**  Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?

**中文**  和 Sol Ultra 或 Sol Extra High 相比，这样理解对吗？

### [00:18:29–00:18:31] Akshay Nathan

**EN**  Yeah. It’s hard to say because

**中文**  很难一概而论，因为……

### [00:18:31–00:18:33] Swyx

**EN**  Yeah. It’s like an interaction effect.

**中文**  对，这里存在 interaction effect。

### [00:18:33–00:18:46] Akshay Nathan

**EN**  exactly. It’s like there’s a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it’s doing the right thing?

**中文**  没错。这取决于个人偏好，也就是你自己喜欢怎样与模型协作。你希望出现多少次你所说的 termination，好让你能够介入引导，或确认它正在做正确的事情？

### [00:18:46–00:19:29] Akshay Nathan

**EN**  I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you’ll be able to make consistent progress in a way that’s verifiable over time. But I think for most tasks, they don’t fall into either of those buckets. And so like at least when they’re starting, and so that’s why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.

**中文**  总的来说，每个人都应该尝试适合自己的方式。我认为 Ultra 或 multi-agent setup 最适合两类任务：极其复杂、开放式探索的任务，或高度可并行化的任务。即使用 goal，我认为它也最适合那种能够持续推进、且随时间推移可以验证进展的任务。但多数任务至少在刚开始时并不属于这两类。所以最好的第一步还是先试默认配置，再看接下来需要往哪个方向调整。

### [00:19:29–00:19:36] Swyx

**EN**  Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.

**中文**  对。你们做了一个 slider，这对减少选择时的焦虑非常有帮助。

### [00:19:36–00:19:38] Vibhu

**EN**  It’s nice on mobile at least. There’s a nice slider there.

**中文**  至少在手机上很好用，那里有个不错的 slider。

### [00:19:38–00:19:39] Swyx

**EN**  It’s nicer.

**中文**  确实更好看。

### [00:19:39–00:19:40] Vibhu

**EN**  I haven’t tried it.

**中文**  我还没试过。

### [00:19:40–00:19:44] Swyx

**EN**  So you have the advanced view there, but if you click advanced view. Yeah.

**中文**  你这里看到的是 advanced view，不过如果点 advanced view……对。

### [00:19:44–00:19:46] Vibhu

**EN**  Ooh, it’s just a nice slider. Yeah.

**中文**  哦，就只是一个很舒服的 slider。对。

### [00:19:46–00:19:48] Swyx

**EN**  Very pretty, very colorful.

**中文**  非常漂亮，颜色也很丰富。

### [00:19:48–00:20:04] Akshay Nathan

**EN**  Yeah. The idea was here was like reduce it to like one dimension even though there’s multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.

**中文**  对。这里的思路是，即便实际存在多个维度，也把它压缩成一个维度，为用户投影到单一轴线上：一端代表速度和效率，另一端代表质量和详尽程度。

### [00:20:04–00:20:07] Swyx

**EN**  I am just puzzled that it uses Sol so much, like the lower

**中文**  我只是很困惑，为什么它这么多地方都用 Sol，比如较低的……

### [00:20:07–00:20:07] Vibhu

**EN**  No

**中文**  不。

### [00:20:07–00:20:08] Swyx

**EN**  Grounds I would’ve used

**中文**  档位我原本会用……

### [00:20:08–00:20:09] Vibhu

**EN**  I think the slider, if I’m not mistaken, is

**中文**  如果我没记错，这个 slider 用的是……

### [00:20:09–00:20:10] Swyx

**EN**  Terra.

**中文**  Terra。

### [00:20:10–00:20:11] Vibhu

**EN**  Oh, it is.

**中文**  哦，真的是。

### [00:20:11–00:20:22] Swyx

**EN**  Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.

**中文**  对，看到了吧？所以他们把 Terra 预设为只有轻量这一档。不过我认为应该有更多人使用 Terra。一方面，Sol 总是用尽 capacity。

### [00:20:22–00:20:24] Vibhu

**EN**  I’m the reason. Here’s ten minutes of our

**中文**  这都是我造成的。这是我们花了十分钟做出来的……

### [00:20:24–00:20:25] Swyx

**EN**  There you go

**中文**  出来了。

### [00:20:25–00:20:26] Vibhu

**EN**  Retirement calculator.

**中文**  退休计算器。

### [00:20:26–00:20:28] Swyx

**EN**  Oh, that’s the Excel thing working for you.

**中文**  哦，这是那个 Excel 功能在替你工作。

### [00:20:28–00:20:28] Vibhu

**EN**  This is,

**中文**  这是……

### [00:20:28–00:20:28] Swyx

**EN**  Oh my God. Look at that

**中文**  天啊，看看这个。

### [00:20:28–00:20:41] Vibhu

**EN**  This is work, and then Codex is still cooking, so we’ll get back into it. I think it’ll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.

**中文**  这是 Work 的结果，Codex 还在处理中，我们稍后再回来。我觉得看看它的 thought process 和 reasoning 会很有意思。而且 Work 这边已经跑了八分钟，Codex 仍然没完成。

### [00:20:41–00:21:01] Swyx

**EN**  Yeah. And by the way, so I’ve, do Gabriel Chua? He’s part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it’s agentic Excel.

**中文**  对。顺便说一下，你认识 Gabriel Chua 吗？他在 OpenAI Singapore 团队。他给我展示这个时，我相当震惊，因为它看起来就像 Excel，也确实能编辑 Excel 文件。你们并没有付 Excel license 的费用，对吧？但它不知怎么就能正常使用，而且是 agentic Excel。

### [00:21:01–00:21:05] Akshay Nathan

**EN**  Yeah. one of the big like pushes that we made for this launch was like artifacts, right?

**中文**  对。这次发布我们重点推动的一件大事就是 artifacts。

### [00:21:05–00:21:16] Akshay Nathan

**EN**  Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you’ll see that there’s been pretty dramatic improvements in the quality of these artifacts and then also on the product side.

**中文**  model 侧也是如此。如果把它与之前的 GPT-5.5、GPT-5.4 比较，你会看到这些 artifacts 的质量有了非常显著的提升；product 侧也一样。

### [00:21:16–00:21:23] Vibhu

**EN**  The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like it

**中文**  UX 侧也非常夸张，比如 hosted sites 等。你不再需要自己托管一个小网页，就像……

### [00:21:23–00:21:46] Swyx

**EN**  Oh, I have a story about that. I can do, a separate thing. I’ll need to take the visuals here, but we-we’ll, we’ll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?

**中文**  哦，我有个这方面的故事，可以单独讲。我需要把这里的画面录下来，我们之后会切回去。你们有没有做 co-training？因为你们正推进这次重大变更，而且 GPT-5.6 和 ChatGPT Work 在同一天发布。model training team 与 harness team 之间相互影响了吗，还是发布日期只是碰巧落在同一天？

### [00:21:46–00:22:24] Akshay Nathan

**EN**  I think the we collaborate heavily with the research teams, and I think that’s like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you’re seeing, like underneath the hood, there’s a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it’s not necessarily that you wouldn’t need an Excel license. This is stage one, right? Like, this is probably not what you meant when you’re like making a retirement calculator.

**中文**  我们与 research team 的合作非常紧密，我认为这是这份工作最神奇、也最有趣的部分之一。就拿 artifacts 来说，你现在看到的东西背后有大量工作：我们需要确保拥有合适的 infrastructure，才能训练模型并让它在这方面变得更好；product 侧则需要提供恰当的 experience，让用户能与模型围绕这样的 artifact 协作。事实上，这整个 viewer 背后的直觉是：重点未必是你不再需要 Excel license。这只是第一阶段。你说“做一个退休计算器”时，大概并不只是想得到现在这个结果。

### [00:22:24–00:22:24] Vibhu

**EN**  Yeah, you can iterate very easily. Yeah.

**中文**  对，你可以非常方便地迭代。

### [00:22:24–00:22:39] Akshay Nathan

**EN**  You wanna iterate and like when you’re seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.

**中文**  你会希望继续迭代。如果你看到的内容与最终在 Excel 中看到的、或者你把文件发给 Sean 后同事会看到的内容高度一致，我认为迭代会容易得多，也会增强你对产品的信任。

### [00:22:39–00:22:46] Vibhu

**EN**  When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?

**中文**  你说“同事会看到”，那你们是否设想过围绕 artifacts 进行多人、多团队协作？你们考虑过哪些事情？

### [00:22:46–00:22:48] Swyx

**EN**  You can already share it, right?

**中文**  现在已经可以分享了，对吧？

### [00:22:48–00:23:04] Akshay Nathan

**EN**  Yeah. It’s inter It’s something that, we’re actively thinking about. one thing that, we’ve noticed internally without talking too much about the roadmap is that like there’s many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I’ll ping them back the answer.

**中文**  对，这件事很有意思，也是我们正在积极思考的。在不透露太多 roadmap 的前提下，我们注意到内部常发生一种情况：有人发消息问我一件事，我把问题交给 ChatGPT Work，再把答案发回给对方。

### [00:23:04–00:23:04] Akshay Nathan

**EN**  And then I’ll be thinking like

**中文**  然后我就会想……

### [00:23:04–00:23:07] Vibhu

**EN**  Like the simplest would be, the three of us are just all on one hosted.

**中文**  最简单的方式应该是，我们三个人都直接待在同一个 hosted 环境里。

### [00:23:07–00:23:28] Akshay Nathan

**EN**  Exactly. And I’ll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there’s so much context like in the rollout and stuff that could be interesting.

**中文**  没错。我会想：这个流程里真的需要我吗？也许我确实重新表述了对方的问题，或者从某些特定上下文中提取了信息。但我把答案交还给对方的过程本身也是有损的：我给出的只是自己对 ChatGPT Work 产出内容的理解。可是在底层，rollout 等过程里还有大量上下文，可能都很有价值。

### [00:23:28–00:23:28] Vibhu

**EN**  Yeah, it’s

**中文**  对，这……

### [00:23:28–00:23:33] Swyx

**EN**  So like the answer was preemptively respond to every inbound request?

**中文**  所以答案是：提前响应每一条传入请求？

### [00:23:33–00:23:36] Akshay Nathan

**EN**  No, it was just like literally like this is what I do sometimes as my job.

**中文**  不是，我说的只是我工作中有时确实会这样做。

### [00:23:36–00:23:39] Swyx

**EN**  I know you copy-paste and then you’re just a message forwarding service

**中文**  我知道，你复制粘贴一下，自己就只是个消息转发服务。

### [00:23:39–00:23:39] Akshay Nathan

**EN**  Yeah. Yeah, exactly

**中文**  对，对，正是这样。

### [00:23:39–00:23:40] Swyx

**EN**  From AI to AI.

**中文**  从一个 AI 转给另一个 AI。

### [00:23:40–00:23:52] Vibhu

**EN**  But I think it’s interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don’t realize until they try or someone shows you, and then you’re like, “Oh, okay. Okay, I see.”

**中文**  不过我觉得这很有意思。它能帮助人们理解自己能提出什么要求、能委派哪些事情。很多时候，人们在亲自尝试或有人演示之前并不知道这种能力；一旦看到，就会说：“哦，明白了，原来如此。”

### [00:23:52–00:24:07] Swyx

**EN**  I think it’s als there’s also like a, light security issue, where like you’re the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I’m not supposed to see it. And that there’s no way I would know because I’m not supposed to know what I don’t know.

**中文**  这里似乎还有一个轻微的安全问题：你本人充当了 permissions layer。按道理我也可以查询你能查询的一切并获得自动回复，但也许其中有些内容本来不该让我看到。而且我根本无从知道，因为我本就不应该知道自己不知道什么。

### [00:24:07–00:24:22] Akshay Nathan

**EN**  Especially as like, with ChatGPT Work, we’re, we’re asking you to connect your plug-ins and, it’s pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that’s something I think we need to preserve, so that’ll be definitely a challenge.

**中文**  尤其是 ChatGPT Work 会要求你连接自己的 plug-ins，还会从本地文件等地方提取内容。agent 能访问的上下文极其私人，我认为我们必须保护这一点，所以这肯定会成为一项挑战。

### [00:24:22–00:24:41] Swyx

**EN**  There’s Excel, there’s PowerPoint, there’s Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there’s like OpenAI Airtable? Like what does that look like if you ever ended up doing it?

**中文**  现在有 Excel、PowerPoint、Docs——工作的三大件。你们还会考虑哪些工作格式？比如你曾在 Airtable 工作。未来会出现类似 OpenAI Airtable 的东西吗？如果真的做，会是什么样？

### [00:24:41–00:24:43] Akshay Nathan

**EN**  It’s a really good question. I think,

**中文**  这是个非常好的问题。我想……

### [00:24:43–00:24:46] Akshay Nathan

**EN**  one that you didn’t bring up was Sites, and I think that was

**中文**  有一个你没提到的就是 Sites，我认为它是……

### [00:24:46–00:24:46] Swyx

**EN**  Sites

**中文**  Sites。

### [00:24:46–00:26:02] Akshay Nathan

**EN**  A core part of this launch. There’s one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who’s on like our corporate finance team, and like we were mentioning how like now when they have these reports that they’re, they’re working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they’re just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it’s like, it’s like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn’t support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it’s been really valuable.

**中文**  这次发布的核心部分。大家经常谈论 Sites 的一面，尤其在 Twitter 或 X 上，把它当成 prototyping tool。实际上，这次发布中也发生了类似情况。你们前面提到的 model slider，几乎完全是在一个 Site 里开发出来的；设计、工程和产品团队就在 Site 中一起尝试 affordance，感受它的效果。但我认为另一个较少被讨论的方面，是把 Sites 当作知识工作的 artifact。前几天我和 corporate finance team 的一位同事交流。他们每个月以团队形式制作报告，过去这些内容放在 slide deck 和 spreadsheet 里，现在全部放到 Sites 中；Sites 成了团队协作的载体。原因是它在某种程度上拥有更高 bandwidth。PowerPoint 和 Excel 这类工具非常灵活，但最终总会遇到边界：要么作为人类，你不知道某项功能怎么用；要么产品本身就不支持。但 site 可以做任何事，你提出任何要求，都可以获得相应结果。一旦人们看到这种魔力，我认为它就会非常有价值。

### [00:26:02–00:26:17] Swyx

**EN**  Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I’m a fan of this game called Strata. It’s, it’s like a little board game that you

**中文**  好，我展示一下自己的 case study。这里包含所有热门话题，包括 ChatGPT Work、GPT-5.6、token billionaire、token maxing、Sites 和 auto research。我喜欢一款叫 Strata 的游戏，是一种小型 board game，你可以……

### [00:26:17–00:26:32] Swyx

**EN**  That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thing

**中文**  用实体积木来玩，积木像这样叠在上面。周末我拍了大约三十张照片，直接全部扔进 ChatGPT。消耗了十七亿 token 之后，出来了这个 site，里面有一个完全可以玩的版本。

### [00:26:32–00:26:32] Akshay Nathan

**EN**  Wow

**中文**  哇。

### [00:26:32–00:26:45] Swyx

**EN**  With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that’s, that

**中文**  它包含 3D、积木放置等全部功能。因为原版需要实体积木，我需要让朋友练习并提高水平，这样我才能和他们对战。此外，我还可以在上面训练 AI，而这就……

### [00:26:45–00:26:46] Akshay Nathan

**EN**  That’s your auto research

**中文**  这就是你的 auto research。

### [00:26:46–00:27:28] Swyx

**EN**  That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they’re, they’re gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn’t gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it’s created this lab, panel. Where is there a, is there a shortcut for a site that is created?

**中文**  这就进入 auto research 了。你想训练自己的 AI，再让它们彼此 self-play。我需要同时设置两个 AI。现在是 AI 对 AI，它们会自己对弈。AI 起初表现很差，然后你需要定义 loss function，让它们逐渐变好。我不打算全程监督，因为当时人在 San Mateo 参加会议。我最后让系统对此进行 auto research 并创建 benchmarks，但参数实在太多，我不可能全部读完。于是我开始让它做一个 site，它就创建了这个 lab panel。已经创建的 site 有没有快捷入口？

### [00:27:28–00:27:33] Akshay Nathan

**EN**  You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.

**中文**  你应该能在 sidebar 顶部进入 Sites。就是左侧 sidebar。

### [00:27:33–00:27:35] Swyx

**EN**  This one? Oh, left?

**中文**  这个吗？哦，左边？

### [00:27:35–00:27:36] Akshay Nathan

**EN**  Yeah. Just scroll all the way to the top.

**中文**  对，一直滚到最上面。

### [00:27:36–00:27:39] Swyx

**EN**  Oh. Oh, it says Sites. Oh, there you go. Yeah.

**中文**  哦，写着 Sites。找到了，对。

### [00:27:39–00:27:40] Akshay Nathan

**EN**  Ooh.

**中文**  哦。

### [00:27:40–00:28:41] Swyx

**EN**  So it create, it creates the sites. I don’t, I don’t think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there’s also a huge sprawl. Like look at how long this thing is. There’s so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it’s an interesting transition from Markdown effectively that you’re putting out to, you’re putting out a whole functional site.

**中文**  它会创建这些 sites。我觉得这还不完全是我想要的，但给你看一下它产出的东西。作为 research artifact，准确传达正在做什么非常重要。它输出了这个东西，后来我开始把它公开发布。我把它移出了 Sites，因为我需要的 database 和 infrastructure 超出了 Sites 提供的范围。但你可以从这种研究输出开始摆弄，思考训练 AI 时究竟在调哪些 hyperparameter。我当时还试着总结 scaling laws，做各种 game optimization。你能直接把这些内容以 research artifact 的形式展示出来：我不再需要阅读 ChatGPT 的输出，而是阅读 Site 的输出。但它也会严重膨胀。看看这个东西有多长、数字有多少，确实非常让人不知所措，所以之后我还得开始删减。不过，从实际输出 Markdown 转向输出一个完整、可运行的 site，这种过渡很有意思。

### [00:28:41–00:28:57] Akshay Nathan

**EN**  I think Markdown just isn’t that optimal for people to read, right? Might as well just write HTML website and I don’t know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they’re quite verbose. I don’t need a lot of this information.

**中文**  我认为 Markdown 本来就不太适合人阅读，对吧？还不如直接写成 HTML website。我不知道，我觉得这里可以做很多定制。你可以用 skills 来说明自己的需求。我注意到它们相当冗长，而我并不需要这么多信息。

### [00:28:57–00:28:58] Swyx

**EN**  It’s very verbose.

**中文**  确实非常冗长。

### [00:28:58–00:29:05] Akshay Nathan

**EN**  So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don’t, right?

**中文**  所以，把 site 并排放在旁边的好处是，你可以直接迭代，明确哪些要、哪些不要，对吧？

### [00:29:05–00:29:11] Swyx

**EN**  Yeah. I don’t know if, any that triggers any stories for you of how it’s run internally. Am I doing this right?

**中文**  对。不知道这会不会让你想起它在内部怎样运行的故事。我这样做对吗？

### [00:29:11–00:29:53] Akshay Nathan

**EN**  Yeah. I think that this is like a workflow that we’re seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it’s just HTML, you can like. It’s infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There’s more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they’re very, they’re long and verbose, could be broken up. I’m sure that there’s still something to do there.

**中文**  对。我认为各种不同团队都开始使用这类 workflow：过去以 deck 等形式存在的 canonical artifact，现在正在变成 site。因为 site 就是 HTML，所以几乎无限灵活。比如 slide deck 中某项内容会显得埋得很深，而你希望突出它，就可以让它成为 hero image。我认为人们正开始意识到这一点。当然，要让这些内容更容易协作，还有很多工作要做。你提到它们很长、很冗余，应该拆分；我相信这里仍有改进空间。

### [00:29:53–00:29:54] Swyx

**EN**  They’re super long. Yeah.

**中文**  它们真的特别长。对。

### [00:29:54–00:30:07] Akshay Nathan

**EN**  Yeah. But I think we’re starting to see that like there is this aspect of this is a really interesting, format, for people to use, that’s like much more flexible than what they ever had before.

**中文**  对。不过我们开始看到，这是一种非常有意思、可供人们使用的格式，灵活程度远超他们过去拥有的任何东西。

### [00:30:07–00:30:18] Swyx

**EN**  I think your job also comes becomes meta. You’re not designing the products. You’re designing a product to make products, and I’m curious how you manage that.

**中文**  我觉得你的工作也变得更 meta 了。你不是在设计产品，而是在设计一个用来制造产品的产品。我很好奇你如何管理这件事。

### [00:30:18–00:30:41] Akshay Nathan

**EN**  I think one thing that we’ve been Like when we look at the UX, like that we’ve been thinking a lot about is how can we balance like simplicity with capability? Like if we’re designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can’t put that all in front of you because you’ll get overwhelmed.

**中文**  我们在观察 UX 时一直反复思考：怎样平衡 simplicity 与 capability？正如你所说，如果我们设计的是一种用来构建其他东西的产品，那么它能构建的东西非常多。但我们不能把一切都直接摆在你面前，否则你会不知所措。

### [00:30:41–00:30:41] Vibhu

**EN**  Yes.

**中文**  是的。

### [00:30:41–00:31:19] Akshay Nathan

**EN**  And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there’s so much that can be done, I think the balance that we’re constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it’s using the right tools, it’s pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.

**中文**  过去 ChatGPT 也遇到过类似问题或挑战，但现在能做的事情更多，这个问题尤其突出。我们一直试图达成的平衡是：给用户足够的 UI surface，让他们可以充分表达，告诉 agent 自己需要什么，并验证它使用了正确的工具、从正确的来源提取信息等；随后 UI 就应该让开。与此同时，我们怎样建立合适的 system，通过直接展示而不是口头说明，让用户理解它能做什么？因为如果用户真想通过 AI 获得超能力，很大一部分就在于他们如何发现下一个 use case，以及再下一个 use case。

### [00:31:19–00:31:39] Vibhu

**EN**  Yeah. It’s interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn’t take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I’ve got a similar version. not with all the auto research and whatnot, but

**中文**  对，很有意思。我觉得每个人的做法也都不同。我做了同一款游戏的类似版本，既没拍棋盘照片，也没拍规则。只把任务放进 goal，十八分五十三秒、消耗大量 token 后，就得到了一个类似版本。虽然没有那些 auto research 等功能，不过……

### [00:31:39–00:31:40] Akshay Nathan

**EN**  You gotta do all the latest trends.

**中文**  最新潮流都得加上。

### [00:31:40–00:31:45] Vibhu

**EN**  And yeah, I did it with, did it with Codex, not Work, but it’s interesting, right?

**中文**  对。我是用 Codex 而不是 Work 做的，但很有意思，对吧？

### [00:31:45–00:31:51] Akshay Nathan

**EN**  Yeah. And this is GPT Image generating the pro avatars. Very good for game design. Like

**中文**  对。这些专业感很强的头像是 GPT Image 生成的，它很适合游戏设计。

### [00:31:51–00:31:52] Vibhu

**EN**  And

**中文**  而且……

### [00:31:52–00:31:54] Akshay Nathan

**EN**  A lot of game designers were like really into GPT Image for assets.

**中文**  很多游戏设计师都非常喜欢用 GPT Image 制作素材。

### [00:31:54–00:32:15] Vibhu

**EN**  I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It’s, it’s a pretty niche game. It couldn’t find how to do this on its own.

**中文**  我想说，更广泛的结论可能是，我们这样做主要是为了测试工具。这也是对刚发布的 GPT-5.6 的测试。我之前曾用 GPT-5.5 做过这个游戏。那时我必须把规则提供给它，因为这是个很小众的游戏，它无法自己找到该怎么做。现在我已经不再需要……

### [00:32:15–00:32:15] Akshay Nathan

**EN**  Oh, yeah.

**中文**  哦，对。

### [00:32:15–00:32:16] Vibhu

**EN**  GPT-5.6

**中文**  GPT-5.6……

### [00:32:16–00:32:21] Akshay Nathan

**EN**  It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.

**中文**  它属于 out-of-distribution 情况，这也是我特别想测试 GPT-5.6 能力的原因。

### [00:32:21–00:32:27] Vibhu

**EN**  But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?

**中文**  不过，随着 Work 和各种新东西发布，这些都只是我们从侧面测试产品的方法，对吧？

### [00:32:27–00:32:31] Akshay Nathan

**EN**  Yeah. It’s some private eval. That is not this private.

**中文**  对，算是一项 private eval，只不过现在已经没那么 private 了。

### [00:32:31–00:32:36] Vibhu

**EN**  But also valuable because now you can send this to your friends and I learned about this game through seeing this.

**中文**  但它也很有价值，因为你现在可以把这个发给朋友，而我就是看到它以后才知道这款游戏的。

### [00:32:36–00:32:39] Akshay Nathan

**EN**  It’s a hard game. He’s very good.

**中文**  这游戏很难，他玩得非常好。

### [00:32:39–00:33:02] Vibhu

**EN**  It’s good to when no one is competing with you. But yes, it’s a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.

**中文**  没有人和你对战时，游戏会比较容易。不过，是的，这就是典型的 RL 问题：通过 self-play 逐步训练出你的游戏 AI。你可以看到，工作多么容易变成个人事务，个人事务又多么容易变成工作。我为个人兴趣做的东西，直接影响到了同事，因为我把它给他们看了。他们会说：“原来 GPT 能做到这个？”我想这大概就是你们的增长策略。

### [00:33:02–00:33:18] Akshay Nathan

**EN**  Yeah. The show not tell is a big piece that, I think we’ve we’re not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.

**中文**  对。“展示而不是讲述”是非常关键的一部分，我觉得我们还没有完全解决：怎样直接向人们展示产品能做的一切，而不是试图通过文章、onboarding 等方式教给他们。

### [00:33:18–00:33:19] Akshay Nathan

**EN**  So meeting them in the moment.

**中文**  也就是在当下这一刻与他们会合。

### [00:33:19–00:33:37] Vibhu

**EN**  It’s a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you’re like, “What do you mean? You don’t, you don’t need.” your job is to tell. And then. But the product people are like, “Well, we don’t need you if our product is intuitive enough.” So

**中文**  这对我的职业来说有风险，因为我以前做 developer relations。你的工作是展示，结果现在产品在说：“什么意思？已经不需要你了。”你的工作其实也是讲述。但产品团队会说：“如果我们的产品足够直观，就不需要你了。”所以……

### [00:33:37–00:33:50] Akshay Nathan

**EN**  Yeah. that’s the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they’ve done in the past, exactly where they are on the adoption journey. So I think that’s like gonna be a super big opportunity.

**中文**  对，这就是模型的魔力。你可以针对用户的具体需要，定制怎样讲述或展示：他们关心什么、过去做过什么、目前处于 adoption journey 的哪个位置。我认为这里会有非常大的机会。

### [00:33:50–00:34:12] Vibhu

**EN**  Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don’t wanna segment different people into different buckets, right? It’s also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?

**中文**  现在定制化展示似乎越来越容易了，对吧？人们有不同的 use case。虽然你说不希望把不同人划入不同类别，但为不同类别的人提供差异化内容也没那么难。不过问题是，你说你的团队负责一个更宽泛的方向，当时用的词是什么？Productivity？

### [00:34:12–00:34:12] Akshay Nathan

**EN**  Productivity.

**中文**  Productivity。

### [00:34:12–00:34:12] Vibhu

**EN**  Productivity. So how

**中文**  Productivity。那么怎样……

### [00:34:12–00:34:14] Akshay Nathan

**EN**  Which is now work.

**中文**  也就是现在的 Work。

### [00:34:14–00:34:28] Vibhu

**EN**  Is it work? Is there another distribution that we’re not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn’t targeting?

**中文**  它就是 Work 吗？还有什么尚未覆盖的分发渠道吗？是否有某一群人需要 ChatGPT、Codex 或 Work 之外的其他东西？还有哪些大众产品目前没有瞄准的领域？

### [00:34:28–00:35:51] Akshay Nathan

**EN**  I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that’s where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there’s inherent challenges with like, this show not tell thing that we’re talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they’re doing in their lives. And we’re already seeing that a little bit. Like this game example that you have is, something that’s like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I’m doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.

**中文**  我把它看作一个按顺序推进的过程。愿景是把有用的 agents 带给每个人。我们从 developers 开始，因为 developers 历来属于 early adopters，愿意忍受更多摩擦、自己完成设置等，Codex 就从这里起步。下一个机会是我们所谓的 general knowledge work，也就是围绕 developers 的其他所有职能。从 developers 扩展到这个群体时，会自然遇到一些挑战：比如我们前面说的“展示而非讲述”，需要让产品更容易理解，也要引入对这个群体比对 developers 更重要的新能力，包括 artifacts、computer use 等。下一阶段则是把从 general knowledge work 中学到的东西带给所有人，无论他们生活中在做什么，就像我们把从 developers 那里学到的经验带进 general knowledge work 一样。我们已经看到了一点迹象。你这个游戏案例正处在娱乐、个人生活与职业生活的边界。我在家也会全职使用 ChatGPT Work，什么事情都拿它做。前几天我用它制定 meal plan，并保存到它拥有的 computer environment 中，以后可以随时回来继续使用。现在每个人都这样做了吗？大概没有，因为产品名称上写着 Work。但最终，我们希望大家都能走到那里。

### [00:35:51–00:35:52] Vibhu

**EN**  ChatGPT life.

**中文**  ChatGPT Life。

### [00:35:52–00:36:07] Akshay Nathan

**EN**  Yeah, exactly. ChatGPT cooking. But I think there’s a lot of, there’s a lot of opportunity there, but I see it as like, we’re, we’re built we built a foundation in software engineering, and we’re gonna take the same learnings that we take from software engineering to knowledge work to everyone.

**中文**  对，没错。ChatGPT Cooking。这里有很多机会。我的看法是，我们先在 software engineering 中打下基础，再把从 software engineering 中学到的经验带到 knowledge work，最后带给所有人。

### [00:36:07–00:36:30] Vibhu

**EN**  Do you have any power user advice? I feel like, there’s a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there’s a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you’ve found that help bridge that gap?

**中文**  你对 power user 有什么建议吗？我感觉有一群人会完全沉浸其中，用它做所有事，全天二十四小时都在线；而这群人与另一类人之间存在差距，后者会说：“好，我工作时会用，偶尔使用，有时输入几个问题。”你有没有什么建议、经验或结论，能帮助弥合这道差距？

### [00:36:30–00:36:52] Akshay Nathan

**EN**  I think a couple things that I’ve seen is like, one, that it really helps to broaden your imagination of what’s possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it’s like, wow, it’s like it can. Like,

**中文**  我观察到几件事。第一，扩大你对“什么是可能的”的想象非常有帮助，即使对我来说这也是一种学习。技术进步得太快了；某件事即使三个月前还会让人觉得“模型绝对做不到”，现在却会令人惊讶地发现，它真的可以。

### [00:36:52–00:36:52] Swyx

**EN**  Give an example

**中文**  举个例子。

### [00:36:52–00:37:09] Akshay Nathan

**EN**  We’re going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there’s a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousness

**中文**  我们内部现在正处于 review cycle。大家一直说模型擅长这类事情，还有一种老生常谈的说法：“好吧，没有人想写 review，我们直接用 AI 写。”但认真说来……

### [00:37:09–00:37:10] Swyx

**EN**  And it can evaluate it as well.

**中文**  而且它也能做评估。

### [00:37:10–00:38:27] Akshay Nathan

**EN**  Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I’ve found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they’ve like the things that they’ve done to make a difference, highlighting like, wins that they’ve had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they’ve caught, reviews, Slack, everything. And so it’s, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn’t even I tried using it, but it was not at all helpful. And this time it’s been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what’s possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you’re in, whether it’s your life or your work, the more valuable it becomes, and it’ll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.

**中文**  对，没错。认真说，以前产出的只是一些粗制滥造的内容；虽有一点帮助，但谈不上真正提高生产力。现在我发现，model 在很多方面能做得比我更好，尤其是在这个 environment 中，它可以提取人们正在做什么、他们做过哪些产生影响的事情等上下文，还能突出他们取得而我可能根本没有看到的成果。它能访问一切：code、他们发现的问题、reviews、Slack 等。因此它在这个领域极其强大。仅仅六个月前、也就是上一个 cycle，我也试过用它，但完全没有帮助；这一次却非常有用。所以我最大的建议也许是，不断拓宽对可能性的想象，即使某件事过去已经试过。另一点是，你投入的信息越多，回报越大。尤其在 model 能访问电脑上所有内容的 environment，或者在 ChatGPT Work 中，你可以不断创建 artifacts、保存到 library，模型以后仍然可以访问。无论是生活还是工作，你给它的领域信息越多，它就越有价值，而且会以意想不到的方式体现价值。比如它可能主动从某段上下文中提取信息，这是你自己都没想到的。不过前提是，它首先得能访问那些工具或上下文。

### [00:38:27–00:38:46] Swyx

**EN**  One thing I just wanna talk about the review stuff because I’m still that’s a very sensitive thing and you’re, you’re a founder, you’ve managed people, you’ve hired people. As manager myself, I’m very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don’t care.

**中文**  我想再谈谈 review，因为这仍然非常敏感。你做过 founder，也管理和招聘过员工。我自己作为 manager，对于发布任何 LLM 生成的内容都很谨慎，尤其当它关系到具体的人，因为那会让人觉得你根本不在乎。

### [00:38:46–00:38:57] Swyx

**EN**  Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what’s the etiquette?

**中文**  按理说，OpenAI 的人会更愿意接受由 GPT 做 eval 和评级。但这方面有没有非正式规则？相关 etiquette 是什么？

### [00:38:57–00:39:08] Akshay Nathan

**EN**  Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That’s the place where it’s incredibly helpful.

**中文**  我认为 etiquette 是：我绝不会仅仅通过 AI 写一份东西，再把它当成对某人的 review。刚才说的更多是收集上下文，这才是它极其有帮助的地方。

### [00:39:08–00:39:09] Swyx

**EN**  So it’s just search.

**中文**  所以本质上只是搜索。

### [00:39:09–00:39:09] Akshay Nathan

**EN**  Yeah, exactly.

**中文**  对，没错。

### [00:39:09–00:39:10] Swyx

**EN**  It’s agentic search. Yeah.

**中文**  是 agentic search。

### [00:39:10–00:39:39] Akshay Nathan

**EN**  It’s like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it’s all there’s a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you’re able to do so much more, it’s easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.

**中文**  它就像 agentic search，但比过去更容易定制和引导。这里正在形成一个 flywheel：有了 Codex 和 ChatGPT，人们现在能做的事情比过去任何时候都多；而能做的事情越多，也越容易漏掉一些内容。因此我们需要使用这些相同的工具，跟上每个人产生的全部影响，并理解自己能在哪里提供帮助。

### [00:39:39–00:39:50] Swyx

**EN**  I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?

**中文**  我经营的是一家小公司，所以搜索起来容易。但以 OpenAI 的规模，加上你们在 Slack 里产生那么多消息，你认为它会漏掉一些东西吗？

### [00:39:50–00:39:52] Akshay Nathan

**EN**  Probably, but I think that I also miss things.

**中文**  大概会，但我自己也会漏掉。

### [00:39:52–00:39:53] Swyx

**EN**  Like, it doesn’t matter, right?

**中文**  所以没关系，对吧？

### [00:39:53–00:39:53] Vibhu

**EN**  I think sometimes it’s

**中文**  我觉得有时它……

### [00:39:53–00:39:54] Swyx

**EN**  Like it’s, as it needs to be human-level

**中文**  也就是说，它只需要达到 human-level。

### [00:39:54–00:39:56] Akshay Nathan

**EN**  It’s all relative, right? Yeah.

**中文**  一切都是相对的，对。

### [00:39:56–00:40:20] Vibhu

**EN**  Sometimes it’s nice when it finds things you wouldn’t, right? Like right now, my Codex system prompts, they’re set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it’ll be like: Oh, there’s this project you did like four months ago. Here’s a note that we had, and it randomly pulls it back into context that I would never do, I haven’t thought about.

**中文**  有时候，它找到你自己找不到的东西会很惊喜。比如我现在的 Codex system prompts 设置为：每个 project 都有独立的 notes.md，把学到的内容写进去；global 层的文件则可以从所有 project 中提取信息。所以它偶尔会说：“哦，你四个月前做过这个项目，这是当时记下的一条 note。”然后随机把它重新拉回上下文，而我绝不会自己想起这件事。

### [00:40:20–00:40:41] Vibhu

**EN**  And I’m like, okay, this is quite superhuman, right? Like, stuff that would. And, it’ll save like hours on chunking of stuff or find something that’s already been done. I’m like, as much as it might miss stuff, I would too, but it’s very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It’s just marked down files that get pulled whenever they want.

**中文**  这会让我觉得它相当超人类。它能在信息分块上省下几小时，或者找到别人已经做过的东西。即便它会漏东西，我自己也一样会漏；但当它找到东西时确实非常有用。而且我的方案并没有经过精心设计，只是一些 Markdown 文件，系统需要时随时提取。

### [00:40:41–00:41:13] Akshay Nathan

**EN**  Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there’s so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that’s going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.

**中文**  对。我有个有趣的故事。最近为了准备这次发布，团队连续几个月都在全力推进。期间 Slack、Docs 和其他地方出现了大量讨论与闲聊。团队里有个人设置了 scheduled task，也就是一种 automation，查看正在发生的一切，挑出最好的 meme，再发布到我们的共享 channel。这里有两件很酷的事。第一，我认为模型正在逐渐开始变得有幽默感。

### [00:41:13–00:41:13] Swyx

**EN**  Funny. Nice.

**中文**  有趣，很好。

### [00:41:13–00:41:43] Akshay Nathan

**EN**  Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that’s like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that’s emerging, which is just like to find information that you otherwise would not know of.

**中文**  一年前完全不是这样。第二点正如你所说：它们能以意想不到的方式找到你没想到的内容，并建立你没想到的联系。这对生成 meme 很有帮助，因为它会带来真正令人惊讶、也因此显得好笑的东西。虽然这不是该技术最具生产力的用途，却确实揭示了一种正在形成的能力：发现那些你原本根本不会知道的信息。

### [00:41:43–00:41:58] Swyx

**EN**  Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they’re announcing ten million users. Does it feel different? You’ve been through a lot of launches.

**中文**  说到这次发布，我基本上已经把它称为很长时间以来最成功的一次发布。就我个人而言，我觉得它甚至比 5.0 更成功，而且他们宣布用户已达一千万。感受不同吗？你经历过很多次发布。

### [00:41:58–00:42:46] Akshay Nathan

**EN**  I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we’ve been on for a long time. Like I said, we saw the magic of Codex internally, and then we’re like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that’s like huge and super exciting. The flip side of that is like, there’s so much more to do too. Like, that’s also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that’s the next step from here. But yeah, I think extremely pumped about how it’s going so far and the opportunities.

**中文**  我觉得它像一次阶段性集大成。更准确地说有两点。第一，它确实像长期愿景和使命的一次汇聚。正如前面所说，我们在内部看到了 Codex 的魔力，非常兴奋地想把它带给更多人。现在看到它真正发挥作用，看到我们达成你提到的分发目标和用户数字，我认为意义非常大，也极其令人兴奋。另一方面，要做的事情仍然太多，而这同样让人兴奋。ChatGPT 这个几乎被所有人等同于 AI、也受到大家喜爱的产品拥有数亿用户。一千万当然很棒，但我们需要把它带给每个人，让每个人都感受到这种魔力。这就是接下来的阶段。总之，我对目前的进展和机会都非常振奋。

### [00:42:46–00:43:11] Swyx

**EN**  Awesome. I did want to also Because I’ve, I’ve, I’ve been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they’re same harness. The whole point is that you don’t, you can’t, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn’t that the default on ChatGPT or no?

**中文**  太好了。我还想问一点，因为我一直密切关注这个数字。它在某个时间点从只统计 Codex users，变成统计 Codex 加 ChatGPT Work，因为两者用的是同一个 harness，重点就在于无法分别统计。ChatGPT users 大概有十亿吧？那为什么这个数字没有直接跳到十亿？它不是 ChatGPT 的默认选项吗？

### [00:43:11–00:43:14] Akshay Nathan

**EN**  We don’t default you into ChatGPT Work if you’re on ChatGPT

**中文**  如果你正在使用 ChatGPT，我们不会默认把你带进 ChatGPT Work。

### [00:43:14–00:43:15] Swyx

**EN**  If you’re free. Yeah

**中文**  尤其是免费用户。对。

### [00:43:15–00:43:36] Akshay Nathan

**EN**  It’s also only available to paid users right now. And I think there’s like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it’ll be a journey.

**中文**  目前它也只对 paid users 开放。我认为还需要一个过程：让用户理解这个产品的价值、让他们尝试、从反馈中学习，再不断改进。目标是让尽可能多目前喜爱 ChatGPT 的人感受到 ChatGPT Work 的力量，但我认为这会是一段旅程。

### [00:43:36–00:43:44] Swyx

**EN**  Yeah. And Codex will still be alive as a brand for the foreseeable future. And we’ll just toggle between them as needed for UI stuff.

**中文**  好。Codex 作为品牌在可预见的未来仍会存在，我们只会根据 UI 需要在它们之间切换。

### [00:43:44–00:44:11] Akshay Nathan

**EN**  Yeah, I think it’s even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there’s, there’s so much more that we can do to make Codex great specifically for, software development, and we’ll continue to do that. This doesn’t take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.

**中文**  我认为情况甚至比这更明确。我们完全打算继续认真对待 developers。developers 长期以来一直是我们的核心市场，而且为了让 Codex 更适合 software development，我们还有太多可以做的事，也会继续做下去。这次合并完全不会削弱它。反而，它应当提升 Codex 这类产品的效用，因为现在你可以在编写 diff、创建 artifact，或围绕某个要素进行搜索之间无缝切换。

### [00:44:11–00:44:20] Swyx

**EN**  I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.

**中文**  我确实好奇，这些术语会在多大程度上渗透给非技术用户。比如，如果我想要一个 artifact，他们是不是必须学会说 artifact？还是……

### [00:44:20–00:44:23] Akshay Nathan

**EN**  It’s funny, like we call it artifacts internally ‘cause that’s what the teams call it.

**中文**  很有意思，我们内部确实叫它 artifacts，因为各团队就是这么称呼的。

### [00:44:23–00:44:23] Swyx

**EN**  It’s nice. Yeah.

**中文**  这个词不错。对。

### [00:44:23–00:44:38] Akshay Nathan

**EN**  But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they’re used to, right? So if, ChatGPT Work is good at creating slides, they’ll say ChatGPT Work is good at creating slides, and that’s what we want.

**中文**  但在外部没有人这么说，也没有人把它叫 artifact。人们通常会用自己习惯的方式描述事物。如果 ChatGPT Work 擅长制作 slides，他们就会说 ChatGPT Work 擅长做 slides，而这正是我们想要的。

### [00:44:38–00:45:06] Swyx

**EN**  One big Another, it’s July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that’s I think a lot of people’s first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.

**中文**  还有另一件大事。现在是 2026 年 7 月，OpenAI 相关领域发生的一件大事是 OpenClaw。我认为很多人第一次真正把 agent 的能力用于大量个人事务，也以类似方式跨入工作场景。据我了解 OpenClaw 仍然独立，但你自己经历过 OpenClaw 时刻吗？有没有从 OpenClaw 带到 Codex、或反向带回去的经验？任何方向都可以。

### [00:45:06–00:45:10] Akshay Nathan

**EN**  I think there’s a lot of inspiration. I did go through my own OpenClaw moment. I,

**中文**  我认为其中有很多启发。我自己确实经历过 OpenClaw 时刻。我……

### [00:45:10–00:45:10] Swyx

**EN**  Yeah, tell the story

**中文**  好，讲讲这个故事。

### [00:45:10–00:46:14] Akshay Nathan

**EN**  Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there’s like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it’s like a work-related thing, right? It’s like not work necessarily, but it’s like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.

**中文**  我和妻子设置过一个 OpenClaw，尝试管理家里的所有事情。其实事情没有特别多，但它相当有用。我们给了它一个 calendar，它开始替我们创建 events 等。后来运行它的 laptop 坏了，我们一直没机会重新启用。不过这里确实有很多启发。在 ChatGPT Work 的 web 和 mobile 端，你可以访问一个 persistent computer environment，能在里面存文件，而且这些文件会跨 session 保留，目的就是支持这类 use case。我们团队中有一位成员，现在用 ChatGPT Work 做他以前在 OpenClaw 上做的事情，而且感觉已经完全迁移过来了：包括 workout planning 和 meal tracking。它同样属于“与工作相关”的事情——未必真是工作，但处于 personal productivity 范畴。它具备相同的 primitives：scheduled tasks、在 file system 中存储文件、长期引用这些内容的能力。因此，我们开始看到同类 use case 涌现，这很酷。

### [00:46:14–00:46:20] Swyx

**EN**  Is there a point that ChatGPT Work completely replaces OpenClaw? they’re independent, so.

**中文**  ChatGPT Work 将来会不会彻底取代 OpenClaw？不过它们相互独立，所以……

### [00:46:20–00:47:45] Akshay Nathan

**EN**  Yeah, I’m, I’m not close to it, so I can’t speak to the OpenClaw roadmap, but I don’t think so. I think that there’s gonna be, there’s always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that’ll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don’t have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that’s the goal. It’s like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you’re doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There’ll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.

**中文**  我不太接近这个项目，所以无法评论 OpenClaw 的 roadmap，但我认为不会。这个团队构建了非常出色的 open source technology，对它的需求会一直存在。我们可以从产品中汲取启发。听说和使用过 ChatGPT 的人，远多于用过 OpenClaw 的人。如果我们能把 OpenClaw 的魔力带给这些人，就算成功。ChatGPT Work 侧有一点我们非常坚持：core experience 是用户来到产品中，与这个 agent 开始一段 conversation 或 session——叫什么都可以。产品的魔力在于，你当下什么都能做。我们希望做成这样一个产品：你不需要点击按钮或去另一个地方，财务 app 或其他任何产品中的功能，都可以在同一处获得。所以目标是打造一个通过 plugins 扩展的 system，让你连接完成任务所需的工具：既可以处理 financial task；如果做 science work，也能扩展 system，让你编写相关技术内容并获得良好表现。我们支持的领域总会存在各自 best-in-class 的产品，但希望 core experience 能承载尽可能多的魔力。

### [00:47:45–00:47:50] Swyx

**EN**  Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?

**中文**  对。你认为自己以前在 Wealthfront 能做的所有事情，现在都可以在 ChatGPT Finance 里完成吗？

### [00:47:50–00:48:17] Akshay Nathan

**EN**  I tried it. like ChatGPT doesn’t yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that’s all possible with ChatGPT today. So, I feel like at least that component’s replaced for me.

**中文**  我试过。ChatGPT 还不能替我托管现金和资产，所以这部分暂时不行。但我在那里时，我们研究过 retirement planning、financial planning、budgeting 等完整的一块内容。现在有了 finances plugin，这些在 ChatGPT 里都可以做到。所以至少对我来说，这一部分已经被替代了。

### [00:48:17–00:48:27] Swyx

**EN**  I haven’t really plugged it in yet. I’m somewhat scared to look at the answer. Like that’s honestly like the same reason for health and finances. Like I’m like, no.

**中文**  我还没有真正把它接进去，多少有点害怕看到答案。说实话，健康和财务我都是同样的心态：“还是别了。”

### [00:48:27–00:48:48] Akshay Nathan

**EN**  It’s really good. It’s really cool how we were talking about like the agentic search aspect a little bit earlier, but like, it’s really cool how like, in conventional UX, like if the more power you wanna give to a user, the more like knobs and bells and whistles you need to add. Like, for like these finance and budgeting apps, like there’s always like a bunch of the different filters and like search bars and stuff like that. But like now, like with the right

**中文**  它真的很好用。我们前面谈过 agentic search，而这里很酷的一点是：在传统 UX 中，你想给用户的能力越强，就需要添加越多 knobs、bells and whistles。比如 finance 和 budgeting app 总有大量不同 filter、search bar 等。但现在，只要具备正确的……

### [00:48:48–00:48:57] Vibhu

**EN**  Connect-connectivity to the right data, you can have whatever you want. You can ask any question you want and into that box and get the answer, and I think that’s super powerful.

**中文**  数据连接，你想要什么都可以。你可以在那个框里问任何问题并得到答案，我认为这非常强大。

### [00:48:57–00:49:08] Akshay Nathan

**EN**  I think it’s also nice to just have it centralized in one space, right? You have different health apps. I have one for a smart scale, a watch, all these different things. It’s just nice to centrally co-locate it.

**中文**  我觉得把它们集中到一个地方也很好。你可能有不同的 health app，我有一个连接 smart scale，还有 watch 等各种设备；把数据集中放在一起就是很方便。

### [00:49:08–00:49:40] Vibhu

**EN**  Which is, part of the whole thing of OpenClaw, right? Like that you would have, personal OS, which presumably ChatGPT wants to become. I do think that just relying on, like, just-in-time pulling of data for, let’s say, through via MCP, CLI, API, whatever you do, still not enough. Like I come from a bit of a data engineering background, like you still want like a data warehouse or some caching or semantic layer. do you feel that or do you already have that?

**中文**  这也是 OpenClaw 整个理念的一部分，对吧？你会拥有一个 personal OS，而 ChatGPT 大概也想成为它。但我确实认为，仅靠 just-in-time 拉取数据——无论通过 MCP、CLI、API 还是其他方式——仍然不够。我有一些 data engineering 背景，觉得依然需要 data warehouse、cache 或 semantic layer。你也有这种感觉吗，还是你们已经具备了这些？

### [00:49:40–00:50:23] Akshay Nathan

**EN**  I can’t speak to like all the details on how everything works, but I think it depends on the access pattern, right? Like if you want an answer immediately, then yes, it’s very difficult to do that if you need to pull from all of these sources. But a lot of the like use cases that we wanna enable in ChatGPT Work aren’t necessarily something that you need immediately. It’s more like a task that you want the agent to go and do, and that’s gonna take a certain amount of time. And, with things like programmatic tool calling and stuff now, like some of that time and sub-agents and stuff, like some of that is also parallelizable. And so it’s possible I think it’s very possible that there’s a, the ceiling on what can be done, with MCPs and like calling out to these third-party services has been raised substantially. So we’re really excited about that.

**中文**  我不能透露一切如何运作的所有细节，但我认为这取决于 access pattern。如果你需要立刻得到答案，而系统必须从所有来源拉取信息，确实非常困难。但 ChatGPT Work 想支持的许多 use case 未必需要立即完成；更多时候，你希望 agent 去执行一个需要一定时间的任务。现在有 programmatic tool calling 等能力，其中一些时间可以通过 sub-agents 等方式并行。因此我认为，通过 MCP 和调用这些 third-party services 能完成的事情，其上限已经显著提高。我们对此非常兴奋。

### [00:50:23–00:50:45] Vibhu

**EN**  You mentioned sub-agents. I gotta double-click on that. Ultra is a new mode. You have special affordances in ChatGPT itself to show off the agents. Can’t really do much with them, to be honest. Like just watch. what have been, what have been your experiences, any design issues that you would call out to other builders building with sub-agents?

**中文**  你提到了 sub-agents，我得深入问一下。Ultra 是一种新 mode，ChatGPT 本身也有专门的 UI 来展示这些 agents。说实话，你不能对它们做太多，只能看。你有过哪些体验？对于其他正在用 sub-agents 构建产品的人，你会特别指出哪些设计问题？

### [00:50:45–00:51:33] Akshay Nathan

**EN**  I think it’s goes back to the balance that I was raising earlier about like, showing builders the power of the tool, but also creating enough of an abstraction to not overwhelm them. I think with sub-agents, the thing that we wanted to show is that you can take a task that, has many parallel tracks or, is complicated in a way that, sub-agents can handle, and this product is for you. Like, the model can accomplish those goals or try to accomplish those goals. And so like that’s the point of like showing them in the product and that’s where we-we’ve gone with the design. There’s another, iteration of this where like you can see exactly what they’re doing and things like that, which I think is like, could converge on like overwhelming, with information. And so this is like the deliberate trade-off that we made for now.

**中文**  这又回到我前面说的平衡：既要向 builders 展示工具的力量，也要创建足够的抽象层，避免让他们被信息淹没。对于 sub-agents，我们想展示的是：你可以交给系统一个有许多并行分支、或复杂到适合 sub-agents 处理的任务，而这个产品能为你完成它；model 能够实现或至少尝试实现这些 goals。这就是在产品中展示它们的目的，也是目前的设计方向。另一种迭代方式是让你看到它们具体在做什么，但那可能逐渐演变成信息过载。因此，目前的方案是我们有意做出的 trade-off。

### [00:51:33–00:51:35] Vibhu

**EN**  You do display quite a lot of transcripts.

**中文**  你们确实展示了相当多的 transcript。

### [00:51:35–00:51:36] Akshay Nathan

**EN**  Right. Right.

**中文**  对，对。

### [00:51:36–00:51:36] Vibhu

**EN**  Or do you

**中文**  还是说你们……

### [00:51:36–00:51:37] Akshay Nathan

**EN**  I think it’s hidden by default though, right?

**中文**  不过默认情况下它是隐藏的，对吧？

### [00:51:37–00:51:38] Vibhu

**EN**  Do you want to display more than that?

**中文**  你想展示得更多吗？

### [00:51:38–00:51:39] Akshay Nathan

**EN**  No, it’s hidden by default. Yeah.

**中文**  不，默认是隐藏的。对。

### [00:51:39–00:52:15] Vibhu

**EN**  Some people could want more. So I’m one of those people that will throw a lot of stuff at goal, and pretty much every goal I’ll tell it to use sub-agents. Seems redundant, right? But every time I’m like, “Okay, use sub-agents where possible.” And I have a lot of people, a lot of friends that recommend and do the same. Whereas I’ll sometimes talk to people that are like, “Okay, this is where I want you to use sub-agents for this sub-task,” and I’m sure they would appreciate seeing into how they’re being used. For me, it’s primarily like two things, right? One is net time efficiency, so span out across sub-agents. Two is probably cost, right?

**中文**  有些人会希望看得更多。我就是会把大量东西交给 goal 的那种人，几乎每个 goal 都会要求它使用 sub-agents。听起来有点多余，对吧？但我每次都会说：“可以时就使用 sub-agents。”我也有许多朋友推荐并采用同样做法。另一些人则会非常具体地说：“这个 sub-task 要在这里使用 sub-agents。”我相信他们会希望看到 sub-agents 如何被使用。对我来说主要有两点：第一是总体时间效率，让任务分散到多个 sub-agents；第二大概是成本。

### [00:52:15–00:52:41] Vibhu

**EN**  Don’t use big, expensive model. Offload to a lot of smaller, cheaper models. And some people want that level of control. So if you have repetition in what you’re doing, right? Say I want something built where I want it to consistently do this every day, I might wanna go in and fine-tune sub-agents here, sub-agents there. So you can see both, but I think if I’m not mistaken, it’s hidden by default. There’s a dropdown that goes a lot where I’m like, okay I’m just gonna keep, using.

**中文**  不要使用昂贵的大模型，而是分派给许多更小、更便宜的模型。有些人需要这种控制级别。如果你做的事情具有重复性，比如希望系统每天稳定执行某项任务，就可能想进去微调这里和那里的 sub-agents。所以两种需求都存在。不过如果我没记错，它们默认隐藏在一个 dropdown 里。我通常会说，好吧，我就继续这样用。

### [00:52:41–00:52:42] Akshay Nathan

**EN**  Oh, you can change the model that they use.

**中文**  哦，你可以更改它们使用的 model。

### [00:52:42–00:53:02] Vibhu

**EN**  I know I tell them to be steered. I’ll say my I know Anthropic offers this in Cloud Code. You can tell Fable to use Sonnet or Opus to use Sonnet as sub-agent, so pretty trivial thing. You tell it to span out sub-agents with Sonnet, it’s cheaper, faster. I would assume if it’s not there, it could be built there. But I think there’s a side of

**中文**  我知道我可以通过指令引导它们。比如 Anthropic 在 Cloud Code 里提供类似能力，你可以让 Fable 使用 Sonnet，也可以让 Opus 把 Sonnet 当作 sub-agent，这其实非常简单。你让它用 Sonnet 扩展出 sub-agents，就会更便宜、更快。如果当前没有，我想也可以构建出来。但我认为还有一面是……

### [00:53:02–00:53:04] Akshay Nathan

**EN**  It’s too many toggles.

**中文**  toggle 太多了。

### [00:53:04–00:53:07] Vibhu

**EN**  It’s not a toggle. It’s just, you tell it in chat.

**中文**  它不是 toggle，你只需要在 chat 中告诉它。

### [00:53:07–00:53:07] Akshay Nathan

**EN**  You’re prompting it. Yeah.

**中文**  也就是通过 prompt。对。

### [00:53:07–00:53:34] Vibhu

**EN**  The way I do it is prompt it, right? And I think this is something that gets abstracted unless it’s something you built for repetition, right? So if I’m building something, say that’s, podcast prep, right? Research into people, do a very deep extensive research, that I might wanna configure to cheaper, faster model just for web search, right? I can see a world in which you want both. I think the default is pretty good right now, where it’s hidden, but you can drop down and get some more info into what’s done.

**中文**  我的方式就是通过 prompt。我认为，除非是为了重复执行而构建的东西，否则这些细节可以被抽象掉。比如我在构建 podcast prep，需要对受访者进行非常深入、广泛的 research；那么仅仅负责 web search 的部分，我可能会配置为更便宜、更快的模型。我能想象两种模式都有人需要。当前默认值相当不错：信息默认隐藏，但你可以打开 dropdown，查看更多已完成工作的细节。

### [00:53:34–00:53:47] Vibhu

**EN**  I know people talked a lot about it on GPT-5.6’s launch. this thing loves to use a lot of sub-agents and causes the ChatGPT app to just crash because it’s so processor-heavy. But,

**中文**  GPT-5.6 发布时很多人都讨论过：它特别爱使用大量 sub-agents，导致 ChatGPT app 因为处理器负载太高而直接崩溃。不过……

### [00:53:47–00:53:52] Akshay Nathan

**EN**  For what it’s worth, that’s not my experience. Yeah, I haven’t had a crash from sub-agents.

**中文**  仅就我的体验而言并非如此。我还没遇到过由 sub-agents 导致的崩溃。

### [00:53:52–00:54:12] Vibhu

**EN**  I haven’t either. I have We both have big laptops. But I know people brought it up. There was a topic of discussion that we didn’t see the same, but it is another vibe eval, right? People are like, “Okay, the amount of sub-agents Sol is wanting is crazy.” And I’m like, “I think this is okay. I think it’s good.” But just stuff people bring up.

**中文**  我也没有。我们都有性能很强的 laptop。不过我知道有人提过，这是个讨论话题，只是我们没遇到同样情况。这也算另一种 vibe eval：有人说“Sol 想用的 sub-agents 数量太夸张了”，而我会说“我觉得可以，这很好。”总之是用户提到的一些现象。

### [00:54:12–00:54:33] Akshay Nathan

**EN**  I think when we launched the product too, we weren’t as opinion about like who is Ultra for and like when should they be using it. And since then we’ve made some changes to like, require you to turn it on and find it in the advanced setting ‘cause that’s who it is for. It’s for like power users who understand what’s gonna happen because it also, depending on your use case, can use more of your limits as well.

**中文**  我觉得产品刚发布时，我们没有足够明确地说明 Ultra 适合谁、应该何时使用。此后我们做了一些调整，要求用户主动开启，并到 advanced settings 中找到它，因为它就是给这类人准备的：理解即将发生什么的 power users。根据你的 use case，它也可能消耗更多使用额度。

### [00:54:33–00:54:33] Vibhu

**EN**  Yes.

**中文**  是的。

### [00:54:33–00:54:36] Akshay Nathan

**EN**  So that’s where I think a lot of the feedback was coming from.

**中文**  所以我认为很多反馈都来自这里。

### [00:54:36–00:54:39] Vibhu

**EN**  It’s okay. Reset the limits. Always reset the limits.

**中文**  没关系，重置额度。永远重置额度。

### [00:54:39–00:54:55] Akshay Nathan

**EN**  Well, it’s, today we’re resetting because of this. I wanna change topics to one last piece of the harness, memory. A lot of people are commenting on memory recently. ChatGPT’s new memory system used to suck, it’s not very good. And then this guy also the same thing, and Samir, who you presumably work with

**中文**  好吧，今天就是因为这个才在重置。我想换到 harness 的最后一部分：memory。最近很多人评论 memory。ChatGPT 的新 memory system 以前很糟糕，现在变得很好。这个人也表达了同样看法，还有你大概共事过的 Samir……

### [00:54:55–00:55:27] Akshay Nathan

**EN**  Talking about memory. What can you say there? I think that, Samir and the team have made a ton of and then the research teams have made a ton of, updates and improvements over time. I think when I talk to friends, family members about what they love about ChatGPT, like the fact that it knows them, that they feel like their ChatGPT is their ChatGPT, I think comes up probably number one. In ChatGPT Work, in the Cloud, like by default, all conversations like inherit from your ChatGPT memory, so you’ll know they’ll know context about you, and they’ll also be able to write back to this memory.

**中文**  都在谈 memory。你能透露什么？Samir 及其团队、还有 research teams 随时间做了大量更新与改进。我和朋友、家人聊他们喜欢 ChatGPT 的什么时，“它了解我”“我的 ChatGPT 就是属于我的 ChatGPT”，大概总会排在第一位。在 cloud 的 ChatGPT Work 中，默认情况下所有 conversation 都会继承你的 ChatGPT memory，因此它们会知道与你有关的上下文，也能把新信息写回 memory。

### [00:55:27–00:55:31] Vibhu

**EN**  With it, like a small text write. Like you tell me when you’re writing, right? Is it

**中文**  它写入的是一小段文本吗？比如写入时会告诉我，对吧？还是……

### [00:55:31–00:55:36] Akshay Nathan

**EN**  No, it’s part of the same like memory V3 system that we launched.

**中文**  不是，它属于我们发布的同一套 Memory V3 system。

### [00:55:36–00:55:37] Vibhu

**EN**  Yeah, Memory V3, yeah.

**中文**  对，Memory V3。

### [00:55:37–00:55:51] Akshay Nathan

**EN**  So I think that’s been really powerful because, going from ChatGPT to ChatGPT Work feels like an extension of what I’ve already been doing with the product for sometimes many years. So that’s been awesome, and it’s awesome to see that like people are recognizing the improvements here.

**中文**  我认为这非常强大，因为从 ChatGPT 转到 ChatGPT Work，会感觉像是延续了自己此前有时长达多年的使用经历。这很棒，看到人们注意到这里的改进也令人高兴。

### [00:55:51–00:56:05] Vibhu

**EN**  Is there So it’s a retrieval problem, right? Like, are you retrieving the right things? Are you over-focusing on the wrong things? Is there like a more false positive or false negative, if that makes sense? Like, what’s the bigger problem?

**中文**  这本质上是 retrieval problem，对吧？你有没有检索到正确内容？会不会过度关注错误的内容？如果这样说合理的话，false positive 与 false negative 哪一种是更大的问题？

### [00:56:05–00:56:21] Akshay Nathan

**EN**  So I don’t work on memory directly so it’s hard to say what the bigger problem is with like certainty. But I think you’re right. I think that like, the there’s two sides of it. It’s like, making sure it knows things about you, but then also having the EQ to like bring those things up at the right moments proactively or surprising you in ways that are positive, not negative.

**中文**  我没有直接负责 memory，所以很难确定地说哪一个问题更大。但你说得对，这里有两面：一方面要确保它了解与你有关的事情，另一方面还要具备 EQ，在合适时机主动提起这些内容，或以正面的、而非负面的方式带给你惊喜。

### [00:56:21–00:56:29] Akshay Nathan

**EN**  So I think it’s a very challenging problem, but something that I think we feel very there’s a huge opportunity to get right, which is like why we’ve made like big investments in it.

**中文**  所以这是个非常具有挑战性的问题，但我们认为一旦做好会带来巨大的机会，这也是我们大力投入的原因。

### [00:56:29–00:56:58] Vibhu

**EN**  How do you see the side of, okay, when you’re building ChatGPT for work different than the regular chat app, different than Codex, managing memory across different projects, collaboration and whatnot, how do you see the side of what’s separate from the harness, right? So if I have four threads on one project any learnings on how to build memory systems there? For background as well, to steer it a bit, is when you do chat style applications, I’d say you have a lot of one-offs, right?

**中文**  当你构建面向工作的 ChatGPT——它不同于普通 chat app，也不同于 Codex——还要管理不同 project 之间的 memory、协作等，你如何看待那些独立于 harness 的部分？比如我在同一个 project 里有四条 thread，关于怎样构建这里的 memory system，有什么经验？再补充一点背景以便限定问题：制作 chat style application 时，会有很多一次性对话，对吧？

### [00:56:58–00:57:08] Vibhu

**EN**  When you switch to work it might be something you’re doing for a month, something you do a lot, right? Now, as I add more sessions, there’s a lot more than just single-threaded, right?

**中文**  切换到 Work 后，可能是在做一个持续一个月的项目，或者经常反复做的事。随着我增加更多 session，情况就不再只是 single-threaded，而会复杂得多。

### [00:57:08–00:57:10] Vibhu

**EN**  And there might be memory there.

**中文**  而且那里可能存在 memory。

### [00:57:10–00:57:55] Akshay Nathan

**EN**  I think first I challenge that like the depth of the memory or the like value of it is like fundamentally different across chat and work. Like it is true that like, there are a lot of like shorter sessions on chat, but I think, the ChatGPT, the product has had like a ton of longevity, in, as long as this technology has been around and people use it for work-related, like productivity-related things already today. And so I think we found that there’s a lot of value. I found this my personal usage, like all these one-offs add up over time into something like quite durable and like quite a good representation of who I am. I know like from time to time, something will go viral on X about like, ChatGPT telling you everything it knows about you, and people are always surprised like how deep that is.

**中文**  首先，我不太认同 chat 与 work 之间 memory 的深度或价值存在根本差异。确实，chat 中有很多较短的 session，但在这项技术存在的时期里，ChatGPT 这个产品本身已经拥有很长的使用历史，而且今天人们早就在用它做与工作和 productivity 有关的事情。我们发现其中价值很大。我自己的使用体验也是如此：所有这些 one-off 随时间累积，会形成非常持久、也相当准确的个人画像。我知道 X 上时不时会流行让 ChatGPT 告诉你它所知道的关于你的一切，而大家总会惊讶它了解得如此深入。

### [00:57:55–00:57:57] Vibhu

**EN**  The fun roast me?

**中文**  那个好玩的“roast me”？

### [00:57:57–00:58:20] Akshay Nathan

**EN**  Exactly. So like, I think like the That’s all to say that like I think there’s a lot of depth there in the existing, ChatGPT product, and so that’s why I think we think it’s valuable to bring into the work product. But the other reason I brought that up is because I think like hopefully we can use some of the same fundamental primitives and systems to extend memory here as well, and I know this is something that the team that focuses on this is like working through right now.

**中文**  没错。总而言之，我认为现有 ChatGPT 产品里的 memory 已经非常深入，所以我们觉得把它带进 Work 产品很有价值。我提出这一点的另一个原因是，希望我们可以使用一些相同的 fundamental primitives 和 systems 来扩展这里的 memory。我知道专注这个方向的团队目前正在研究。

### [00:58:20–00:58:33] Vibhu

**EN**  I wanted to bring up one element of memory, which I honestly don’t really use much, and I’m curious if you do: Chronicle, which was, is up on screen right now. It’s a super memory or like what is it?

**中文**  我想提 memory 的一个组成部分。说实话我不怎么用，很好奇你会不会用：就是现在屏幕上的 Chronicle。它是一种超级 memory，还是别的什么？

### [00:58:33–00:59:24] Akshay Nathan

**EN**  I think the idea is that like it can learn from, how you’re using your computer and like it’s another input source, into memory. And, I think it’s, experimental right now and something that like isn’t default off. But I’d recommend that you try it. I think that it’s like quite interesting how It goes back to a conversation we were having earlier on like, you were asking like, “Does it Can ChatGPT miss things?” Like does it, on Slack, when it’s searching, does it miss things? ‘Cause there’s such a volume of stuff, right? And like it’I, you can ask the same question about like everything that you’re doing on your computer. Like, is it gonna know everything that you’re doing? Is it gonna capture the intent and stuff like that? Probably not, but like it probably will find things that you might not know about. And then if it can surface those to you in relevant times, in proactive ways, like when you’re doing tasks, and I found at least that it can be quite helpful. So it’s worth trying.

**中文**  它的思路是从你使用电脑的方式中学习，作为 memory 的另一个输入来源。我认为它目前仍是 experimental，而且并非默认关闭的功能。不过我建议你试一下。这很有意思，因为它又回到我们前面的对话：你问“ChatGPT 会漏东西吗？”它在搜索 Slack 时会不会漏掉内容，毕竟信息量太大？同样的问题也可以套到你在电脑上做的一切：它会知道你做的所有事情吗？会捕捉到 intent 等信息吗？大概不会，但它很可能发现一些你自己不知道的东西。如果它能在相关时刻、你执行任务时主动把这些内容呈现出来，我至少发现它可能非常有帮助。所以值得一试。

### [00:59:24–00:59:27] Vibhu

**EN**  So mostly for insights and longer term.

**中文**  所以它主要用于 insights 和长期信息。

### [00:59:27–00:59:37] Akshay Nathan

**EN**  Yeah, exactly. Like insights and it builds context that makes, that can make you more productive on certain tasks. But it’s, it’s hard to describe without feeling it.

**中文**  对，正是如此。它提供 insights，并建立能让你在特定任务中更高效的 context。但如果没有亲身体验，很难把它描述清楚。

### [00:59:37–00:59:48] Vibhu

**EN**  I will say you can feel it pretty well. Like the idea of what they’re saying here, right? Just check through my memories or check through my logs and add skills. Pretty underrated, right?

**中文**  我得说，这种感觉其实很明显。就像他们这里说的：检查我的 memories 或 logs，然后添加 skills。这种用法相当被低估，对吧？

### [00:59:48–01:00:01] Akshay Nathan

**EN**  But that’s automations. You can repeat that using a cron job. Checking through your memories and creating skills. But I think the creation of the memories from Chronicle itself is like what’s different. It’s like you have much deeper memories because you have Chronicle on.

**中文**  不过那属于 automations，可以用 cron job 重复执行，也就是检查 memories 并创建 skills。但我认为，由 Chronicle 本身创建 memories 才是不同之处。开启 Chronicle 后，你会拥有深得多的 memories。

### [01:00:01–01:00:10] Vibhu

**EN**  It’s there. I don’t use it much, but maybe I just, I need more examples. I imagine you guys use a lot of it internally, so I’m always fishing for use cases.

**中文**  它确实在那里。我用得不多，也许只是需要更多示例。我想你们内部会大量使用，所以我一直在寻找 use case。

### [01:00:10–01:00:13] Akshay Nathan

**EN**  I would just try turning it on and then like

**中文**  我建议直接把它打开，然后……

### [01:00:13–01:00:14] Vibhu

**EN**  It just auto works? Like it

**中文**  它会自动工作吗？也就是说……

### [01:00:14–01:00:18] Akshay Nathan

**EN**  Yeah, and seeing like where it might start helping you. I think you’d be surprised.

**中文**  对，然后看看它从哪些地方开始帮到你。我觉得结果会让你惊讶。

### [01:00:18–01:00:44] Vibhu

**EN**  Yeah. Amazing. I think that was, about it in terms of like the overall, coverage of ChatGPT Work. I think there’s been a lot of like good progress and discussion on building and all these things. There’s a lot of like ex-founders in the community, in OpenAI as well. Do you think that things have changed a lot? like your overall reflection of building, pre-AI and post-AI.

**中文**  好，太棒了。我想 ChatGPT Work 的整体内容差不多都覆盖到了。在构建产品及相关方面，已经有很多很好的进展与讨论。无论社区还是 OpenAI 内部，都有许多 former founders。你认为情况发生了很大变化吗？回顾 AI 前与 AI 后，构建产品的总体感受是什么？

### [01:00:44–01:01:32] Akshay Nathan

**EN**  I think things have changed a ton. I think it’s like super exciting to see how quickly you can go to, from idea to something real today. whereas like even before, like I think, five, 10 years ago, like it’s fast if you were scrappy and, like, willing to build the minimal viable thing. But, like, now the extent of what you can build is, like, much broader. And I think that also, like, what we’ve seen internally building is, like, that gives you an opportunity to validate much more quickly, to talk to users, to talk to internal doctors, et cetera, and, like, make sure you’re on the right track. And, like, that loop I think has been has become more closed than ever before, and that’s, like, a win for product development. I think it’s a win for consumers and users too because ideally that means they’re getting much more better much better products out the gate.

**中文**  我认为变化非常大。今天从想法走到真实产品的速度之快，令人非常兴奋。即使在五到十年前，如果你很灵活、也愿意只构建 minimum viable thing，速度同样可以很快；但现在可以构建的东西范围宽广得多。从内部开发中我们也看到，这让你能更快验证、与用户和 internal dogfooders 等交流，并确认方向是否正确。我认为这个 loop 变得前所未有地紧密，这是 product development 的胜利；对消费者和用户也是好事，因为理想情况下，这意味着他们一开始就能拿到好得多的产品。

### [01:01:32–01:01:33] Vibhu

**EN**  Does it mean your teams are smaller?

**中文**  这是否意味着你们的团队变小了？

### [01:01:33–01:01:50] Akshay Nathan

**EN**  I think there’s much more to do now. So I think people can accomplish more individually or in a small team than they were that would require more people than before. But there’s, at the same time, there’s also more to do, so I think the teams are much more ambitious.

**中文**  我认为现在要做的事情多得多。个人或小团队确实能完成过去需要更多人才能做的工作，但与此同时，值得做的事情也更多，所以团队的雄心变得更大。

### [01:01:50–01:01:58] Vibhu

**EN**  Have you seen any changes in scopes of roles and building teams and how we used to have teams, say, a few years ago versus what ideal teams look like now?

**中文**  与几年前组建团队的方式相比，你是否看到角色范围和团队构成发生变化？如今理想团队是什么样？

### [01:01:58–01:02:08] Akshay Nathan

**EN**  I think we’ve seen a blurring in the lines between, like, the typical product development functions, like between, like, EM/PM, engineer, designer, et cetera. Like

**中文**  我们看到典型 product development 职能之间的界线变得模糊，比如 EM、PM、engineer、designer 等角色之间。

### [01:02:08–01:02:27] Vibhu

**EN**  Yeah, I wanna bring up this quote. There will be, only four jobs left in tech. There’s AI slop cannon, the people who just, like, they’ll burn a bunch of tokens. And then there is SRE, the people who. people who are more responsible. There’s grown-ups who sell things, and then there’s hot people.

**中文**  对，我想引用这句话：科技行业最终只会剩下四种工作。第一种是 AI slop cannon，也就是只管消耗大量 token 的人；第二种是 SRE，也就是那些更负责任的人；还有负责销售的大人，以及长得好看的人。

### [01:02:27–01:03:07] Akshay Nathan

**EN**  This is an interesting take. I think my suspicion is that there’s everything everyone will be, like, shaped in a way, in that, like, AI will enable everyone to become a generalist. Like, things that, like, I never would be able to, like, come up with a design before and, like, even now, like, I don’t have maybe, like, the visual taste required, but I can iterate on something with the help of AI. But then people will have a specialty, and that’s, like, the straight line in the T or the upward line in the T. And so, like, you can have a specialty that you’re interested in. With the help of AI, you can go deeper and become better at over time, but then you’ll also be a generalist. And so with that foundation, the way you can accomplish is, like, almost limitless.

**中文**  这是个有意思的观点。我的猜测是，每个人都会以某种方式被重塑，因为 AI 会让所有人都成为 generalist。比如以前我完全无法做出 design；即使现在也许仍缺乏所需的 visual taste，但借助 AI，我可以不断迭代。与此同时，人们仍会拥有一项 specialty，也就是 T 型能力中的竖线。你可以选择自己感兴趣的 specialty，在 AI 帮助下不断深入、逐渐变得更强，同时又具备 generalist 能力。建立在这个基础上，一个人能完成的事情几乎没有上限。

### [01:03:07–01:03:15] Vibhu

**EN**  What are you bottlenecked by in terms of specialties? Like, do you need more designers? Do you need more slop cannons? Do you need more hot people?

**中文**  从 specialty 角度看，你们的瓶颈是什么？需要更多 designer、更多 slop cannon，还是更多长得好看的人？

### [01:03:15–01:03:37] Akshay Nathan

**EN**  I think the bottleneck some becomes, like, ideas and taste. I think because anyone can build now, I think, it really is the era of, like, bottoms-up ambition. And because there’s so much to be built, like, you’re always gonna be bottlenecked by, the amount of ideas and amount of things that you’re doing at any given time.

**中文**  瓶颈会转变为 ideas 和 taste。因为现在任何人都可以构建，真正进入了 bottoms-up ambition 的时代。又因为值得构建的东西太多，无论什么时候，ideas 的数量和你同时推进的事情数量都会成为瓶颈。

### [01:03:37–01:03:39] Vibhu

**EN**  Do you think models help solve that?

**中文**  你认为 models 能帮助解决这个问题吗？

### [01:03:39–01:03:40] Akshay Nathan

**EN**  Models?

**中文**  Models？

### [01:03:40–01:04:01] Vibhu

**EN**  Yeah. I have the example of, like, I have a front-end design skill that’s like, they give me four drastically different examples of what this looks like. Sure, it burns a lot of tokens, but. And then I’ll mostly just condense down, “Okay, I like this part. I like this part. Let’s draw these together.” And it’s like, yeah, I had a vision, but, like, I don’t know.

**中文**  对。比如我有一个 front-end design skill，它会给我四个差异极大的方案来展示成品可能是什么样。确实会消耗很多 token，但接下来我通常会收敛范围：“好，我喜欢这里，也喜欢那里，把这些部分合起来。”也就是说，我心里有个 vision，但自己又不完全清楚。

### [01:04:01–01:04:41] Akshay Nathan

**EN**  I would say that the one automation that I would love to work and it doesn’t work is bring me new ideas, right? somehow LLMs are just not it. One interesting part about ideas is, like, they’re not, like, in a vacuum. It’s, like, not. They usually come from somewhere and, like, in product development, like, they’re coming from talking to users or reacting to, friction that you’re seeing or feedback, building on some foundation that you already had planned out before, whatever. And so I think that’s where, like, I think there will always be value in these, like, generalists that we talked about, like, closing that loop and then having coming up with those ideas that are grounded in that feedback or talking to users, whatever it is.

**中文**  我会说，有一种我很想让它实现却始终不奏效的 automation，就是“给我带来新想法”。不知道为什么，LLM 就是不太擅长。ideas 有一个有趣之处：它们不是凭空产生的，通常都有来源。在 product development 中，想法可能来自与用户交谈、对观察到的 friction 或 feedback 作出反应，或建立在此前已规划的某个基础上。因此我认为，我们前面谈到的 generalists 始终有价值：他们能闭合这个 loop，并提出扎根于反馈、用户交流或其他真实来源的 ideas。

### [01:04:41–01:04:46] Vibhu

**EN**  Cool. You were gonna. You lead the productivity team. How do you define productivity?

**中文**  很好。你刚才准备说……你负责 productivity team。你怎样定义 productivity？

### [01:04:46–01:05:25] Akshay Nathan

**EN**  I think our mission is to make it possible for people to do things that they weren’t able to do before. And right now we’re thinking about it from the perspective of knowledge work. And so when I look at knowledge work, I think about people are no longer siloed by their roles. They’re no longer siloed by maybe the, background or training that they have. Like, no matter what function you’re in, you can suddenly build things. You can suddenly get access to data that you otherwise might not be able to interpret, et cetera. And then I think that extends to your personal life, where we want to give you leverage at the end of the day. Like, we want the models and the product to be able to give you leverage so that you can, create time for yourself to do the things that you love.

**中文**  我认为我们的使命是，让人们能够做到以前做不到的事情。眼下我们从 knowledge work 的角度考虑它。审视 knowledge work 时，我认为人们不再被自己的角色隔离，也不再受背景或训练限制。无论处于哪种职能，你突然都能构建东西，也能访问原本可能无法解读的数据等。接着它会延伸到个人生活。归根结底，我们希望给你杠杆：希望 models 和 product 能带来杠杆，让你为自己创造时间，去做真正热爱的事情。

### [01:05:25–01:05:29] Vibhu

**EN**  Does that also translate to a way to measure productivity? Like, what is new?

**中文**  这是否也能转化为衡量 productivity 的方式？新的指标是什么？

### [01:05:29–01:05:30] Akshay Nathan

**EN**  The end is

**中文**  最终是……

### [01:05:30–01:05:31] Vibhu

**EN**  How do you measure leverage?

**中文**  你们怎样衡量 leverage？

### [01:05:31–01:05:44] Akshay Nathan

**EN**  I think we haven’t figured this out yet. Part of the reason is it’s so diverse. Everyone has different goals, and really the true measurement is, like, their ability to achieve that goal. Did we help you or did we not?

**中文**  我认为我们还没有解决这个问题，部分原因是它太多样了。每个人的 goal 都不同，真正的衡量标准其实是他实现 goal 的能力：我们究竟帮到你了吗？

### [01:05:44–01:05:48] Akshay Nathan

**EN**  And it’s very difficult without knowing what that goal is up front and also tailoring it for every individual.

**中文**  如果事先不知道 goal 是什么，而且还要针对每个人定制，衡量起来非常困难。

### [01:05:48–01:05:52] Vibhu

**EN**  And the thumbs up and thumbs down from ChatGPT doesn’t give you anything, right?

**中文**  而 ChatGPT 的 thumbs up 和 thumbs down 也提供不了什么信息，对吧？

### [01:05:52–01:05:56] Akshay Nathan

**EN**  You don’t know if they’re thumbs downing the content of the answer, the vibe of it

**中文**  你不知道他们点 thumbs down 是因为答案内容，还是因为它给人的感觉……

### [01:05:56–01:05:56] Vibhu

**EN**  Oh, yeah

**中文**  哦，对。

### [01:05:56–01:06:06] Akshay Nathan

**EN**  Whether or not it helped them with their goal. I think that’s difficult. But it’s something that I think we will need to figure out and the industry at large will need to figure out because, that’s how we measure success, if this is what we’re, we’re

**中文**  也不知道答案是否帮助他们实现 goal。这很难。但我认为我们以及整个行业都必须解决它，因为如果这正是我们要做的事情，它就是衡量成功的方式。

### [01:06:06–01:06:15] Vibhu

**EN**  Do you think it’s changed, productivity and how you measure it? you said there’s a lot more work that can be done, a lot more scope. has it changed?

**中文**  你认为 productivity 及其衡量方式已经改变了吗？你说现在可以完成的工作多得多、scope 也大得多。这带来变化了吗？

### [01:06:15–01:06:31] Akshay Nathan

**EN**  I think it was always true that what you really wanted to measure is, like, was your team, was the individual, were you personally able to hit the goal, or are you closer to hitting that, whatever your goal is, right? But I think previously we used proxies for this. So, like, code commits or

**中文**  我认为真正应当衡量的东西始终没变：你的团队、个人、或者你自己，是否实现了 goal，或者是否更接近实现那个 goal。但过去我们会使用 proxies，比如 code commit 或……

### [01:06:31–01:06:31] Vibhu

**EN**  Lines of code

**中文**  代码行数。

### [01:06:31–01:06:33] Akshay Nathan

**EN**  Lines of code or whatever.

**中文**  代码行数之类的。

### [01:06:33–01:06:34] Vibhu

**EN**  Story points.

**中文**  Story points。

### [01:06:34–01:06:36] Akshay Nathan

**EN**  Yeah, exactly. Story points. And, like

**中文**  对，没错，story points。还有……

### [01:06:36–01:06:38] Vibhu

**EN**  They’re coming back, by the way.

**中文**  顺便说一句，它们又要回来了。

### [01:06:38–01:07:02] Akshay Nathan

**EN**  maybe. But that is for a part of the change. And, like, I think with AI now, those proxies starting to fall apart. Like, you, the number of tokens you use or the number of pull requests you make are, like, no longer, like, maybe as hypercorrelated with that, is your team able to hit the goal or are they on track to hit their goals? So I think we’ll need to come up with new, measurements.

**中文**  也许吧。不过这正是变化的一部分。有了 AI，这些 proxy 开始失效。你使用的 token 数或提交的 pull request 数，可能已经不像过去那样与团队是否实现 goal、是否正按计划实现 goal 高度相关。所以我们需要提出新的 measurement。

### [01:07:02–01:07:06] Vibhu

**EN**  For the managers listening, give them one thing to try.

**中文**  给正在收听的 managers 一件可以尝试的事情。

### [01:07:06–01:07:54] Akshay Nathan

**EN**  I think for me, what’s important is like at-bats. Are we as a team building the muscle to have not just quantity of at-bats, but quality? Like, are we able to go all the way from, like, generating an idea, building it out, getting the feedback, reacting to that feedback, validating or invalidating the hypothesis, going on to the next idea? Are we able to do that really efficiently? And like, that goes to like, the actual like code that’s being written or the designs that are being made or the specs that are being written, whatever, but also the culture of the team. Like, do we have the humility and, are able to like go through that process many times and stay motivated and excited throughout that? so that’s the thing that like I think is important now, especially when we’re on the frontier of this technology and like there’s so much to build, there’s so much to do. That’s probably the most important thing that we look at.

**中文**  对我来说，重要的是 at-bats，也就是一次次真正上场实践的机会。作为团队，我们是否在锻炼这种能力，不只追求 at-bats 的数量，也追求质量？我们能否完整走过从产生 idea、构建出来、获取 feedback、回应 feedback、验证或推翻 hypothesis，再进入下一个 idea 的全过程？能否非常高效地做到？这既涉及实际写出的 code、做出的 design、写下的 spec 等，也涉及团队文化。我们是否足够谦逊，能多次经历这个过程，并在整个过程中始终保持动力和兴奋？我认为现在这点尤其重要，因为我们正处在技术前沿，还有太多东西可以构建、太多事情可以做。这大概是我们最重视的事情。

### [01:07:54–01:08:06] Vibhu

**EN**  Any traps people fall into around measuring productivity with your teamwork on. I feel like there’s a lot of, okay, we added a lot of LMs. We have dashboards for this and that, but not much has changed, right?

**中文**  围绕团队工作衡量 productivity 时，人们会掉进哪些陷阱？我觉得常见情况是：“好，我们加了大量 LM，也为这个和那个做了 dashboard”，但实际并没有发生太大变化，对吧？

### [01:08:06–01:08:09] Akshay Nathan

**EN**  That is the trap, yes.

**中文**  对，这就是陷阱。

### [01:08:09–01:08:18] Vibhu

**EN**  And the broader source of the question is for the managers and teams building, how should they approach this?

**中文**  更广泛的问题是，对于正在构建产品的 managers 和 teams，他们应该怎样处理这件事？

### [01:08:18–01:08:57] Akshay Nathan

**EN**  I think maybe the trap is like conflating motion and progress. I think motion is much easier now than ever before because of the tooling that we have. But progress requires you to be like very prescriptive and deliberate about like what you’re trying to achieve, and it goes back to our question of measurement, right? Like you wrote we were talking about like, can we, OpenAI, like figure out how to measure productivity for our users? That’s, that’s a very hard problem because of the diversity. But like as a team, like you should have a really prescriptive and deliberate view on like what progress looks like for you and for your team. And if you don’t have that, then it’s very easy to conflate these two things.

**中文**  陷阱或许是把 motion 与 progress 混为一谈。借助现有 tooling，制造 motion 比以往任何时候都容易；但要取得 progress，你必须非常明确、有意识地界定自己想实现什么。这又回到 measurement 的问题。我们前面讨论 OpenAI 能否找出衡量用户 productivity 的方法。由于用户目标极其多样，这是个非常困难的问题。但作为团队，你应当对自己和团队所说的 progress 究竟是什么，形成非常明确而审慎的判断。缺少这个定义，就很容易混淆 motion 和 progress。

### [01:08:57–01:09:08] Vibhu

**EN**  I think at-bats is a really great thing. I’m, I’m really glad. I like the discussion between motion and progress. I think that’s a quote that we’re gonna feature on the write-up. You’ve been very generous with your time. Thank you so much and congrats on ten million.

**中文**  我认为 at-bats 是个很棒的概念，我很高兴听到它。我也喜欢对 motion 与 progress 的讨论，这句话大概会出现在我们的文章里。你非常慷慨地给了我们这么多时间，非常感谢，也祝贺你们达到一千万用户。

### [01:09:08–01:09:09] Akshay Nathan

**EN**  Yeah, thank you for having me.

**中文**  谢谢邀请我。

### [01:09:09–01:09:28] Vibhu

**EN**  The next one at a hundred in two months. Two weeks. Thank you.

**中文**  下一次就达到一亿——两个月？两周吧。谢谢。