AI 工作末日是谎言：Dan Shipper 对 2025 年工作的大胆预测

上次你上这个播客时，你有一个大胆的观点，说人们低估了 Claude Code。你简直对得离谱。这期节目的主题是，我们来聊聊你预测还会发生什么。

The last time you were on this podcast, you had this hot take that people were sleeping on Claude Code. You were so unbelievably right. The premise of this episode is we're going to go through what else you predict will happen.

AI 导致工作末日并不是真的。我非常非常看好产品经理和全栈设计师。

The AI job apocalypse is not really a thing. I am super super bullish on PMs and full-stack designers.

所以，你们在过去一年里人员翻倍了，这可不是人们预期中一家如此 AI 前沿的公司会做的事。

So, you guys are hiring doubled in people in the past year, which is not what people would have expected from a company that is so AI forward.

我同时极度拥抱 AI 又非常看好人类。自动化是个谎言。每个智能体都需要一个人类。我们有这么多自动化、这么多 AI，而我的工作量反而更大了。

I'm simultaneously extremely AI pilled and very bullish on humans. Automation is a lie. Every agent needs a human. We have so much automation, so much AI, and I also work way more.

创造力。感觉它会越来越有价值，让你从人们不断发布和推出的那些垃圾中脱颖而出。

Creativity. It just feels like it's going to be more and more valuable to stand out from all the slop that people are shipping and launching constantly.

模型通常做的是让昨天的人类能力变得廉价。于是它就被商品化了，不再有价值。人类做的事情是，我们走进去说，‘好，我们有昨天所有冻结的人类能力。我该怎么用它来做出新的、有趣的东西？’

What models do in general is they make yesterday's human competence cheap. And so, it becomes commoditized. It's not valuable anymore. What humans do is we go in there and we're like, 'Yeah, we have all this frozen human competence from yesterday. How do I use this like make something new and interesting?'

对于我们的工作方式将如何改变，你有什么预测？

What are some predictions for how the way we work is going to change?

它将以两种主要方式分化。一是每个人都会至少有一个可以对话、可以委派工作的智能体。二是你大部分工作实际上会在你的电脑上，在像 Codex 或 Claude Co-work 这样的环境中完成。

It's going to bifurcate in two main ways. One is everyone's going to have at least one agent that they talk to, that they can offload work to. Second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Claude Co-work.

你这里预测的是 SaaS 工具将在 Codex 或 Claude Code 内部运行。

What you're predicting here is the SaaS tools will run within Codex or Claude Code.

我认为 SaaS 末日论很蠢。我现在就会买 SaaS 股票。智能体做的是增加 SaaS 的用户数量，而不是消灭它。

I think the SaaS apocalypse is dumb. I would buy SaaS stocks right now. What agents do is increase the number of users of SaaS, not get rid of it.

很多人正在转向 CLI，试图在终端里工作。

A lot of people are moving to CLI and trying to work from the terminal.

我们快速经历了 CLI 时代。它持续的时候还不错，但我认为 CLI 已经结束了。

We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.

今天的嘉宾是 Dan Shipper，Every 的 CEO 兼创始人。Dan 和他的团队正在打造可能是最 AI 前沿的初创公司。因此，他们很大程度上生活在未来，那时 AI 在我们日常中扮演越来越大的角色。他们公司的每个人，包括所有非技术人员，都使用 Codex、Co-work 和 Claude Code 来完成大部分工作。这就是为什么，早在其他人之前，Dan 就看到了 Claude Code 和现在的 Cohere 的崛起，他在大约一年前上次上播客时就预测到了。所以我请 Dan 回来分享他目前对接下来一年工作方式将如何改变的最大预测。我们聊了到今年年底大多数公司的工作会是什么样子，我们工作的形态将如何变化，以及谁会在即将到来的未来中表现最好。提示一下，产品经理和设计师会做得很好。Dan 做了很多大胆的预测，其中许多是相当反共识的，我没想到他会这么说。我们将在整整一年后重新审视这次对话，看看他猜对了多少。在开始之前，别忘了访问 Lenny's Product Hunt dot com，为 Lenny 的新闻通讯订阅者独家提供一年免费的最热门、最精良的 AI 产品。话不多说，有请 Dan Shipper。

Today my guest is Dan Shipper, CEO and founder of Every. Dan and his team are building maybe the most AI forward startup out there. And as a result, are very much living in the future of how work is going to look as AI becomes a bigger and bigger part of our day-to-day. Everybody at their company, including every non-technical person, uses Codex and Co-work and Claude Code to get much of their work done. And this is why, way before anybody else, Dan saw the rise of Claude Code and what is now Cohere, which he predicted almost a year ago when he was on the podcast last time. So, I asked Dan to come back on the podcast to share his current biggest predictions for how work is going to change over the coming year for most people. We chatted about what work will look like at most companies at the end of this year, how the shape of the work we do will change, and who will do best in this coming future. Hint hint, product managers and designers are going to do very well. Dan makes a lot of bold predictions and many quite contrarian takes that I was not expecting him to say, and we are going to revisit this conversation exactly a year from today to see how much he got right. Before we get into it, do not forget to check out Lenny's Product Hunt dot com for a free year of the hottest and most well-crafted AI products in the world available exclusively to Lenny's newsletter subscribers. With that, I bring you Dan Shipper.

Dan，非常感谢你来到这里，欢迎回到播客。

Dan, thank you so much for being here and welcome back to the podcast.

谢谢邀请。和你在一起总是很愉快。

Thanks for having me. Always a pleasure to be with you.

上次你上这个播客时，你几乎是不经意地抛出了一个大胆观点，说人们低估了 Claude Code，尤其是 Claude Code 用于非工程工作，比如修复文件、整理硬盘，所有这些人们没想过的事情。没人谈论这个。那是一年前。你对此对得离谱。从那以后发生的事情简直不真实。他们构建了 Cohere，完全是基于这个非常具体的想法——用 Claude Code 做非技术工作。Codex 现在也开始涉足这个领域。我想你肯定注意到了。他们正在大力推动编码智能体的非技术用途。我觉得这也是 Anthropic 过去一年成功的重要原因，就是非技术人员怎么用这些东西？所以，你在这方面真是先知先觉。我甚至写了一篇新闻通讯文章，基于这个想法。我说，‘嘿，这很有趣。我应该深挖一下。’我问人们，‘你怎么用 Claude Code 做非工程工作？’然后我收到了好多例子，那成了我第二受欢迎的文章。所以，很明显，你对未来的走向有独特的洞察。所以，这期节目的主题是，我们来聊聊你预测未来还会发生什么，对于构建产品的人来说事情会如何变化。我认为，先让大家简要了解一下你和你的团队是如何运作的，从而让你拥有这种独特的视角，会很有帮助。所以，给我们讲讲你是怎么工作的吧。

The last time you were on this podcast, you had this kind of it was almost like an offhand hot take that people were sleeping on Claude Code and in particular Claude Code for non-engineering work, for just like fixing files, sorting your hard drive, just all these things that people hadn't thought about. Nobody was talking about this. This was a year ago. You were so unbelievably right about this. It's just like unreal what has happened since then. They built Cohere, which was this whole They built on this very specific idea using Claude Code for non-technical work. A Codex is getting into this now. I imagine you've been seeing this. They're like leaning into this non-technical use of basically coding agents. I feel like this has also been a big part of Anthropic's success over the past year, just like how do non-technical people use this stuff? Uh so, you were just so go on this stuff. I I I even wrote a newsletter post building on this idea. I'm like, 'Hey, this is you interesting. I should dig into this.' I asked people, 'How do you use Claude Code for non-engineering work?' And I just had like so many examples and it's like my second most popular post. So, uh clearly you uh you you have a unique glimpse into where things are heading. So, the premise of this episode is we're going to go through uh what else you predict will happen in the future, how things will change for people building products. And I think it'd be helpful to start with giving people a brief glimpse into just how you operate and how your team operates that gives you this unique lens into where things are going. So, just give us a sense of how you how you work.

谢谢。我很感谢你的介绍。嗯，是的，我认为预测未来的一件事，或者说我们在 Every 思考预测未来的方式，是你不想做的只是预言。你想做的是，嗯，就是一起生活在未来里。所以，Every 的每个人都是 AI 早期采用者。我们现在差不多有 30 人了。我记得我们上次访谈时是 15 人，所以过去一年规模翻了一番。我们都是早期采用者，我们有工程师、设计师、作者、编辑、销售、客服。每个人都有那么一点特质，就是‘哦，我喜欢探索。我喜欢实验。我很好奇，我完全投入 AI。’我认为这创造了一个小小的未来口袋，我们都一起生活在里面，我们就能稍微领先一点，因为在其他公司，人群是混合的：有早期采用者，有中间派，也有非常反对的人。另一件很酷的事是，由于我们的角色——嗯，你知道，评测模型，在 AI 领域有点品味引领者的意思——我们能在东西发布之前就接触到。所以我们可以做 alpha 和 beta 测试，某种程度上帮助引导方向，这非常非常酷。所以，当我思考预测未来时，实际上当你创造了这样一个环境，就只是注意正在发生的事情。

Thank you. Um I I really appreciate the introduction. Um and yeah, I think what one of the things about predicting the future or or the way that we think about predicting the future at Every is that you what you don't want to do is prognosticate. What do you What you want to do instead is um is just live in it together. So, everybody at Every is an AI early adopter. We're almost 30 people now. I think when when we did our interview we were 15, so we've doubled in size in the in the last year. We're all early adopters and we have engineers, we have designers, we have writers, we have editors, we have um sales people, we have customer service people. And everybody has a little bit of that um whatever that thing is where you're just like, 'Oh, I like to explore. I like to experiment. I'm very curious and I'm like super all in on AI.' And what I what that does, I think, is it creates this like little pocket of the future where we're all living in it together and we get to be a little bit further ahead cuz at any other company there's like a mix of people. There's early adopters, there's like there's sort of like the middle of the pack people and there's people who are that like very anti. And another thing that happens, which is really cool, is we get to because of our role um you know, reviewing models and and being a little bit of a tastemaker in AI, we get access to stuff before it comes out. So, we get to beta test and alpha test and kind of help help steer the direction of where things are going a little bit, which is very, very cool. And so, when when I think about predicting the future, um it's actually when you create an environment like that, it's actually just about um noticing what's going on.

我认为其中核心的一部分就是把它写下来。把你注意到的、对未来的构想表达出来，在某种程度上会让它变得真实——对你、对你的团队，以及互联网上任何读到它的人都是如此。所以 Claude Code 这件事，对我们来说是一个非常自然的过程。它刚出来的时候我们就试了，这算是我们的工作——我们会试用所有模型公司的新东西。当时它还有点早，但大概在 Sonnet 3.5 或 Sonnet 3.7 的时候，我们用它做了一次“氛围检查”，然后惊呼：“天哪，这太疯狂了，真的很好用。你可以……他们把代码编辑器去掉了。”从那时起，我们就开始转变。现在我们内部运行着六个软件产品，当时可能只有两三个。从那时起，我们开始转向一个世界：没人再看代码了，每个人都在用英语通过终端里的 Claude Code 和电脑对话。所以我看到了：“哦，这开始发生了。”然后因为我的工作有一部分就是去推动和玩这些东西，我就想：“不知道我能不能把它用在写作上？怎么用？”然后它就开始展开，你会觉得：“好吧，这还没准备好，但显然对我有用。”我们内部讨论的一件事，我称之为“伸手测试”——就是你早上醒来时，会不会自然而然地伸手去用它？

And I think a core part of it, too, is writing about it. I think articulating what you're noticing, articulating the future, kind of brings it about in a way that makes it real for you and your team and then anybody else on the internet who's reading it. So, the Claude Code thing, it was this very organic thing where for us, we tried Claude Code when it came out. That's sort of our job. We try all the new stuff from all the model companies. And at the time it was a little bit early. But right around, I think like Sonnet 3.5 or Sonnet 3.7, we were testing that to do our vibe check on it. And we were like, "Holy cow, this is crazy. This is really good. You can... They got rid of the code editor." So, from that point on, we just basically... At this point now we run six software products internally. At that time we ran maybe two or three. And from that point on, we just started shifting to a world where everybody was... No one was looking at the code. Everybody was talking to their computer in English using Claude Code in the terminal. And so, I was able to see, "Ooh, this is starting to happen." And then because my job is a little bit to just push and play with stuff, I was like, "I wonder if I could use this for my writing. Like, how could I do that?" And then it just starts to unfold and you're like, "Okay, this is not ready yet, but it's obviously useful for me." You know, one of the things that we talk about internally is what I call the reach test, which is like, do you just, when you wake up in the morning, do you reach for it organically?

我很喜欢你这种结合——你使用最新的东西，而且我认为，就像你说的，这也许是一种被低估的技能：你很善于自我觉察，能发现那些奇怪、新颖、不同、有趣的东西。所以这是一个很酷的组合，部分原因是你必须写下来，而且你真的写了。我认为这是一个人感知事物走向的完美配方。

I love this combination of you using the latest stuff, and I think this is, as you said, maybe an underrated skill: you're good at being self-aware of what's weird and new and different and interesting. So, that's a really cool combination, partly because you have to write about it, and you write about it. So, I think that's like the perfect recipe for someone having a sense of where things are going.

本期节目由本季的冠名赞助商 WorkOS 提供。OpenAI、Anthropic、Cursor、Vercel、Replit、Sierra、Clay 以及数百家其他成功公司有什么共同点？它们都由 WorkOS 提供支持。如果你正在为企业构建产品，你一定体会过集成单点登录、SCIM、RBAC、审计日志等大公司所需功能的痛苦。WorkOS 将这些交易障碍转化为即插即用的 API，并提供了一个专为 B2B SaaS 打造的现代开发者平台。我投资的每一家开始向上拓展市场的初创公司，最终都会与 WorkOS 合作。这是因为它们是最好的。无论你是试图拿下第一个企业客户的种子轮初创公司，还是正在全球扩张的独角兽，WorkOS 都是实现企业就绪和解除增长障碍的最快路径。它本质上就是企业功能的 Stripe。请访问 workos.com 开始使用，或者直接到他们的 Slack 频道，那里有真正的工程师在等待回答你的问题。WorkOS 让你能够通过令人愉悦的 API、全面的文档和流畅的开发者体验更快地构建。今天就前往 workos.com 让你的应用企业就绪。

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所以，我将这样组织这次对话，基本上有三个预测方向。第一，我们工作的方式在未来几年将如何改变。第二，我们即将从事的工作的形态会是什么样子，以及如何变化。第三，谁将在这种未来中最成功 / 你现在应该做什么、致力于什么才能在这种未来中成功？Lenny，我只有一个要求：一年后我们再来，然后你来打分。我想打分。好的，所以这是一年后的预测。那么，这是你对一年后情况的预测，还是说这是正在浮现的未来？

So, the way that I'm going to structure this conversation, there's going to be basically three buckets of predictions. One is how the way we work is going to change in the coming years. Two is how the shape of the work we're going to be doing is going to look like and change. And then three is who is going to be most successful in this future / what should you be doing and working on now to be successful in this future? Lenny, my only ask is we come on a year from now and then you score it. I want to score it. Okay, so this is a year from now. Okay. So, is this like your predictions for in a year this is what it's going to look like or this is like the emerging future?

我可能会说我没有确切的时间表。我认为我要谈的大部分东西在一年内会相当明显，但也可能需要更长时间。但我认为至少在一年的尺度上，它不会明显错误。也就是说，它应该看起来是在朝那个方向发展的，这样才算数。

I will probably say I don't have an exact timeline. I think most of the stuff that I'm going to talk about will be pretty apparent within a year, but it may take longer than that. But I think it should within at least a year be not obviously wrong. Like it should seem like it's moving in that direction to count.

好的。2027 年 5 月，我们会回顾你的预测。没错。太棒了。

Okay. May of 2027, we will review your predictions. Right. Amazing.

确认。

Confirmed.

好的，我喜欢这个。那么让我们深入吧。对于未来一年我们工作的方式将如何改变，你有什么预测？

Okay, I love this. Okay, so let's dive in. What are some predictions for how the way we work is going to change in the coming year?

这是我最喜欢的问题之一，因为如果你看那些基准测试，你会觉得：好吧，AI 基本上要抢走我们所有的工作了。I meter 有一个很酷的基准测试，它衡量最新模型能自主执行任务多长时间，然后发现……它叫什么来着？哦，像 Mythos 预览版，那个大家都很担心的 Anthropic 大模型，它能以 50% 的准确率执行 17 小时的任务。天哪，这太疯狂了。我认为这是真的，模型进步是指数级的。但我的经验和感觉是，一年后我们会回顾说，我们实际上有更多的工作要做。人类有更多的工作要做。即使模型在做事方面越来越强，这里有一个非常有趣的悖论。我对工作将如何改变，或者说一年后你将如何工作的预测是，它会在两个主要方向上分化，即你使用智能体的方式。第一，我认为你会做我们五年前设想的那种工作方式，即每个人——至少在公司里——都会有一个可以对话、可以委派工作的智能体。我们会讨论那是什么样子，但本质上就像 OpenClaw。第二，你大部分工作实际上会在你的电脑上，在一个像 Codex 或 Claude Cowork 这样的环境中完成，它成为你完成所有工作的操作系统，无论是你的邮件、你创建的文档，还是诸如此类的东西。它将在那种界面上进行。这正在成为清晰的竞争格局。所以我想按顺序讨论这两点。第一点是你将拥有可以委派任务的智能体，可能在 Slack 里，但也可以在别的地方。关于这一点，首先有趣的是，它的架构会是什么样还不清楚。

One of my favorite questions because I think if you look at the benchmarks, you're just looking at okay, like yeah, AI is going to just take all of our jobs basically, you know. I meter has this really cool benchmark where it measures how long the newest models can do tasks autonomously and it's like oh, it can... what's it called? Oh, like Mythos preview the big Anthropic model that everyone's so worried about, it can do tasks of 17 hours at 50% accuracy. It's like holy, that's crazy. And I think it is real. It's true and the progress like model progress is going up exponentially. And my experience and my feeling is that we will look back in a year and say we actually have a lot more work to do. Humans have a lot more work to do. Even as models get better at doing work, there's a really interesting paradox there. And my prediction for how work will change, or how you will be doing work in a year, is it's going to bifurcate in two main ways, how you use agents. One is you're going to be doing, I think, like what we figured you would be doing like 5 years ago when we thought about how work with AI works, which is everyone's going to have at least in their company at least one agent that they talk to that can do work, that they can offload work to. And we'll talk about what that looks like, but it's essentially like OpenClaw. The second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Claude Cowork that becomes the sort of operating system for how you do all of your work, whether that's your email, the documents you create, all that kind of stuff. It's going to be on that kind of a surface. That's becoming the clear competitive landscape. So there's... I want to go in order of those two. So the first one is you're going to have agents you delegate to, probably in Slack, but you know, anywhere. First thing that's interesting about that one is it's not clear what the architecture is going to be like for that.

每个人都会有一个智能体吗？每个团队都会有一个智能体吗？还是只有一个智能体？或者智能体是专门化的？就像有一个平行的影子组织架构。

Is everyone going to have an agent? Is every team going to have an agent? Is it going to be like just one agent? Is it like agent specializes? There's this parallel shadow org chart.

当 Open Claw 刚出来时，Every 公司内部每个人都用上了它，我当时非常确信这会是一个“每个人都有自己的智能体”的局面。那种平行组织架构的世界有一些非常有趣的地方。在那个世界里，智能体有点像你小小的镜像，这真的很酷、很有趣。就像你读过《黄金罗盘》吗？就像肩膀上有个小精灵，是你灵魂的一部分。我当时真的觉得那就是正在发生的事情。所以我非常热衷于个人智能体。但我已经完全转变了。我真的认为现在的模式会是一个超级智能体，也就是整个公司只有一个智能体。你开始在一些公司看到这种情况了。比如 Shopify 就有一个很有名的。Ramp 现在也有一个。我认为这背后有一些非常有趣的原因。我实际上仍然认为个人智能体时代会到来。但我们发现的是，Open Claw 被大肆炒作。每个人都觉得“我要设置它，太酷了”。然后每个人都意识到这工作量太大了。这东西老是出问题。我得摆弄它。我得能 SSH 到我的服务器，等等等等。而大多数至少要做工作的人就是不想花那个时间，或者做不到。驱动这一切的根本原因是，无论是 Open Claw 还是其他工具，为了让 AI 智能体现在有用，它真的需要一个关心它的人。它真的需要与某个人建立一种个人联系，这个人要看着它做什么，确保它做对了，并且对人们有用。一旦你切断这种联系，一旦有人说“啊，我不想维护这个愚蠢的 Open Claw”，智能体就不再那么有用了。这就是为什么我认为它已经开始转向每个公司一个智能体的模式。目前，理想情况是你基本上安排一个前部署工程师或类似角色的人，负责确保那个智能体为整个公司工作。然后你可能会有一些小团队智能体。我认为随着模型变得更独立，这种情况会下移，我们更有可能拥有更多个人智能体，因为我们不必摆弄所有内部细节。但我看到对我们和许多其他公司（包括模型公司本身）有效的模式是，对于那种异步智能体，你实际上有一个顶层的智能体，有时做所有事情，很多时候是做一种你决定公司每个人都需要智能体来做的特定工作，比如数据请求。然后我认为它会从顶层开始，然后逐渐向下渗透，你会有更专门的智能体和团队等等。其机制是智能体需要关心它们的人。

When Open Claw first came out, everyone internally at Every adopted it, and I was very convinced that it would be a everyone has their own agent. And there's some really interesting things about that world of a parallel org chart. Agents in that world sort of become little reflections of you, which is really cool and interesting. It's like if you ever read The Golden Compass, it's like having a little daemon on your shoulder, a little part of your soul. I really thought that's what was happening. And so I was very into personal agents. And I have completely flipped. I really think that the model for now is going to be a super agent, like one agent for the entire company. And you're starting to see this in some companies. So like Shopify very famously has one. Ramp has one now. And I think there are some really interesting reasons for that. I actually still think that the personal agent thing is coming. But what we found is there's all this hype with Open Claw. Everyone's like, 'I'm going to set it up. It's so cool.' And then everyone realizes it's way too much work. This thing breaks all the time. I got to fumble around with it. I got to be able to SSH into my server and blah blah blah. And most people to do work at least just don't want to spend that time or can't. The fundamental underlying thing that drives that is whether it's Open Claw or any other harness, in order for an AI agent to be useful right now, it really needs a human who cares about it. It really needs a human personal connection with someone who's watching what it does and making sure that it's doing the right thing and that it's useful for people. The minute you sever that connection, the minute someone's like, 'Ah, I don't want to maintain this dumb Open Claw,' is the minute the agent is not really that useful anymore. And that's why I think it has started to shift to a more one agent per company model. For now, the ideal is you basically set up a forward deployed engineer or someone with that sort of profile who's responsible for making sure that agent is working for the whole company. And then maybe you have some little team agents. I think as the models get better at being more independent, that will shift down and it'll be more likely that we'll have more personal agents because we don't have to fiddle with all the internals. But the model that I see working for us and for a lot of other companies, including the model companies themselves, is when it comes to the sort of async agents, it's really you have one agent at the top that's doing sometimes everything, a lot of times a particular kind of job that you've decided that everyone in the company needs an agent for, like data requests. And then I think it will start at the top and then sort of trickle down where you make it more specialized agents and teams and all that kind of stuff. The mechanism is agents need people who care about them.

这一点非常有趣，关于你需要“照料”你的智能体，因为你需要不断给它添加上下文。就像你说的，它会出问题，一旦工作量太大，你就会想，算了，忘掉这东西吧。我要回去用 Codex 或 Claude 之类的。确实如此。

That is so interesting, that point about you need to garden your agent because there's context you have to keep adding to it. It breaks as you said and it's just like once it's too much work, you're like, okay, forget this thing. I'm going to go back to Codex or Claude or something like that. Exactly.

好的，酷。所以这是一个很酷的机会。你在这里预测的是，公司会拥有这个超级智能体，每个人都可以和它对话。就像你说的，Shopify 有 River，我记得是叫这个名字。Ramp 的那个叫什么？我记不清了。好吧，可能有个有趣的名字。所以这就是预测。这是第一个预测。

Okay, cool. So this is a cool opportunity. So the idea that what you're predicting here is companies will have this super agent that everyone can talk to. As you said, Shopify's got River, I think it's called. What's the Ramp one called? I can't remember. Okay. It's probably got a fun name. So that's the prediction. That's the first prediction.

这是第一个预测。

That's the first prediction.

我们会从顶层的智能体开始，它们更通用，被公司里更多人使用，然后随着人们越来越习惯这些用例，它们会开始向下发展，变得更专门化，智能体也变得不那么难摆弄。就像它们工作得更好。

We will start with agents at the top that are more general and are used by more people in the company and then it will start to kind of grow down as people get more used to these use cases, they get more specialized and agents become less fiddly. Like they just work better.

你预测这主要会发生在 Slack 里吗？对于工作来说？是的，这似乎有道理。

And is this mostly going to be in Slack, do you predict? For work? Yeah, it seems to make sense.

我认为人们喜欢 Open Claw 上的绿色气泡。抱歉，是蓝色气泡。比如你可以用 iPhone 使用它，但我认为人们心里有点喜欢把个人和工作智能体分开。我们的 COO Brandon Gall 称之为“电脑跑腿”。有一整个领域是用个人智能体来做你的电脑跑腿，比如订我的杂货之类的。这方面有很多，我认为这会非常巨大，但我主要关注工作方面。我认为那主要会发生在 Slack 里。

I think people love having the green bubbles on Open Claw. I'm sorry, the blue bubbles on Open Claw. Like if you can use it with your iPhone, but I think there's this little thing in people's heads where they really like to keep their personal and work agents separate. Our COO Brandon Gall calls this computer errands. There's a whole territory of using personal agents for your computer errands. It's like order my groceries or whatever, and there's so much of that that I think this is going to be huge for, but I focus mostly on the work stuff. And I think that's going to happen mostly in Slack.

太好了。Slack 加油。我们要不要谈谈另一个工作界面？

Sweet. Go Slack. Should we talk about the other work surface?

当然。Codex 协同工作。好的，这是最后一个。我们开始吧。我对这个非常兴奋。我认为这是最酷的东西。基本上，Anthropic 在某个时候意识到，用 Claude Code，如果你把一个智能体放在你的电脑上，它在你的电脑上运行，它就能访问你能访问的一切。它使用终端，所以它基本上拥有超级权限。不仅如此，这些智能体真的知道如何使用终端，因为网上有大量相关内容。这创造了一个超级强大的编程范式，你知道，Anthropic 真的是第一个这样做的。OpenAI 有一段时间在我看来在这方面非常非常落后，然后最近在我看来已经超过了他们。这真的很有趣。但他们在这方面非常早。当人们还在把编码智能体或编码模型视为真正的结对程序员时，他们是第一批说“不”的人，并且成功地做到了。在他们之前有像 Devin 这样的人，我认为他有一个大的云环境，OpenAI 也尝试过这个，但真正的采用似乎发生在你把它放在你的电脑上时。所以，他们搞明白了这一点。

Absolutely. Codex co-work. Okay. This is the last. Let's do it. I'm so excited about this one. I think it's the coolest thing. So, basically what happened was Anthropic realized at some point that with Claude Code, if you put an agent on your computer and it runs on your computer, it has access to everything that you have access to. It uses the terminal, so it has basically superpowered access to it. And not only that, these agents really understand how to use the terminal because there's so much content online about that. And it created this superpowerful coding paradigm, which is you know, Anthropic was really doing it first. OpenAI for a while was in my opinion very, very behind on this, and then in my opinion has surpassed them recently. It's really interesting. But they were very early on this. When people were still thinking about coding agents or coding models as being really pair programmers, they were among the first to be like, 'No.' And do it successfully. Like there were people before them like Devin who I think had a big cloud environment and OpenAI tried this too, but the real adoption seems to have happened when you put it on your computer. So, they figured that out.

然后我认为他们和社区一起发现，一旦你的电脑上有一个能构建任何东西的编程智能体，它实际上对任何你想做的工作都非常有用。人们基本上开始破解 Claude Code 来做所有工作。所以，Anthropic 随后构建了 Co-Work，它是对 Claude Code 的一个更友好的封装，但本质上是同一回事。然后我认为 OpenAI 下了几个不同的赌注，但他们关于编程智能体的主要赌注是早期版本的 Codex，它非常技术性、超级聪明，但有点自闭。就像有点难——他们不太明白你的意思。他们完全按照你说的做。我认为大概三四个月前，在他们推出 5.3 的时候，他们开始转向这个方向：“哦，不，我们懂了。这个模型很快。它非常适合通用知识工作类型的任务。”然后他们推出了 Codex 桌面应用。我认为 Codex 桌面应用——如果你看看 Anthropic 学到的所有经验，他们从 Claude Code 走到了 Co-Work。你可以从 Anthropic 桌面应用 UI 的标签页中看到这一点。我认为 OpenAI 就像：“我们看到这趋势了。我们直接跳到那一步吧。”所以，我认为现在的 Codex——这是一场赛马。他们会有不同的定位。但我认为 Codex 现在是我的日常工具。我基本上所有时间都花在里面。我偶尔会切换一下，但我认为他们正在把范式做对，而且我很清楚，无论谁领先——因为我认为它会变化——无论谁领先，对我来说都非常明显，你做的所有工作都会在其中一个界面里。

And then I think they figured out, along with their community, that once you have a coding agent on your computer that can build anything, it's actually really good for any kind of work you want to do. And people started just hacking Claude Code essentially to do all of their work. So, Anthropic then built Co-Work, which is a little bit of a nicer wrapping around Claude Code, but it's fundamentally the same thing. And then I think OpenAI made a couple of different bets, but their main bet on a programming agent was the earlier version of Codex, which were very technical and super smart, but a little bit autistic. Like it was a little hard—they didn't quite get what you meant. They got exactly what you said. And I think maybe three or four months ago, around the time that they launched 5.3, they started to move in this direction of, "Oh, no, we get it. This model is fast. It's really good for general-purpose knowledge work type tasks." And then they launched the Codex desktop app. And I think the Codex desktop app takes—if you look at all the lessons that Anthropic learned, they went from Claude Code to Co-Work. And you can kind of see that in the tabs on the Anthropic desktop app UI. I think OpenAI was just like, "We see where this is going. Let's just skip to that." And so, I think Codex right now—this is a horse race. They're going to have different positions. But I think Codex right now is my daily driver. I spend all my time in it basically. I flip the card every once in a while, but I think they're getting the paradigm right and it's clear to me that whoever is in the lead—because I think it'll change—whoever is in the lead, it feels very obvious to me that all of the work that you do is going to be in one of those surfaces.

例如，当我在写文档时，Codex 在应用里有一个浏览器。它有一个应用内浏览器。当我在写文档时，我就进入我的一个 Codex 线程——每个项目我有一个线程——然后打开应用内浏览器。我去文档那里。我通常在 Proof 里做，这是我构建的一个在线 Markdown 编辑器。然后我就让 Codex 运行并在 Proof 里看着我。Codex 能看到我在做什么。我也能看到 Codex 在做什么。这一切都在一个地方，这是让 Claude Code 最初工作得很好的同一件事的延伸。我基本上感觉我有一个并行的工作伙伴，它不仅能在文档中回复和写作，还能去做研究。它可以用我的电脑做任何我能做的事情。这非常强大。我用它做所有事情。比如我已经连续 10 天收件箱清零了，如果你了解我，这很疯狂。我从来不是这样的。这是因为我真的让 Codex 用 Cora（我们的邮件智能体）收集我所有的邮件。然后它渲染一个小页面——我想我在 Anthropic 活动上给你看过——它渲染一个小页面，我就对着它独白，对每封邮件说话。我说：“好的，去研究这个。哦，这里有个来自律师的问题。你能收集过去四年的所有文档，整理成报告并发送吗？”它就照做了。所以所有我以前会拖延的事情，我现在都不怎么拖延了。

For example, when I'm writing a document, Codex has a browser in the app. It has an in-app browser. And when I'm writing a document, I just go into one of my Codex threads—I have one thread for every project—and I just open the in-app browser. I go to the document. I usually do it in Proof, which is this online markdown editor that I built. And then I just have Codex running and watching me in Proof. And Codex can see what I'm doing. I can see what Codex is doing. It's all kind of in one place, which is an extension of the same thing that made Claude Code work really well originally. And I basically feel like I have this parallel work buddy that not only can it respond and write in the document, but then it can go do research. It can go use my computer to basically do anything that I can do on my computer. And that's incredibly powerful. And I do this with everything. Like I've been in inbox zero for 10 days straight now, which if you know me is crazy. I'm never like this. And that's because I literally just have Codex gather all my emails with Cora, which is our email agent. And then it renders a little page—I think I showed you this at the Anthropic event—it renders a little page and I just like monologue into it and just talk at each email. I'm like, "Okay, go research this. Oh, here's a question from our lawyers. Can you go collect all the documents from the last four years and put them into a report and send them?" And it just does it. And so all the stuff that I would procrastinate on, I don't really procrastinate on anymore.

很长一段时间，我认为 AI 的最佳体验是把 AI 放在浏览器里。我认为相反的情况实际上正在发生，并且以一种我没想到的方式变得非常非常有价值，那就是把你一直在电脑上使用的 AI 智能体拿来，在里面放一个浏览器，这样它就能看到你做的所有事情。这只是一个神奇的组合，我认为现在非常罕见。你甚至在 Claude Code 里都做不到，因为他们不允许你在 Claude Code 内浏览外部网站。所以现在非常罕见，但我认为一年后会非常普遍。

For a long time, I thought the optimal experience of AI was going to be taking AI and putting it in a browser. And I think the reverse is actually starting to happen and be really, really valuable in a way that I did not expect, which is take the AI agent that you use all the time on your computer and put a browser in it so it can see everything you're doing. And that is just a magical combination that I think will be very uncommon now. You can't even do this in Claude Code because they don't let you browse external websites inside of Claude Code. So it's very uncommon now, but I think it will be super common in a year.

这比听起来可能更深刻。我听到的是，与其把 AI 嵌入 SaaS 工具，你预测的是 SaaS 工具将在 Codex 或 Claude Code 内运行。

This is more profound than it may even sound. What I'm hearing is instead of AI being baked into SaaS tools, what you're predicting here is the SaaS tools will run within Codex or Claude Code.

这是其中一个非常重要的二阶效应。所以，是的，就像我在用 Proof 或者任何网站，也许是 PostHog 之类的。我在我的智能体内部使用它。智能体可以访问网站，所以它可以访问我能访问的一切。它还能访问我的整个电脑。当我在那个网站上运行智能体时，我用的是我的 token，不是供应商的 token，也不是应用的 token。所以这把 SaaS 放回了它应有的位置：是的，你要让它对智能体友好。现在每个人都有 CLI。你要让 HTML 非常可用。你要确保 CLI 中发生的任何事情都能立即显示给用户。诸如此类。有很多问题要处理。但一旦你做到了，你实际上不需要再考虑有一个主要供用户使用的 AI 界面，也就是说你不需要在你的产品中自然地构建一个智能体。我认为你可以——而且还有一个非常有趣的分叉点我们得谈谈，那就是两个智能体比一个好。但就目前而言，有一个很酷的事情：以 Proof 为例，任何使用它的人，我都不用付 token 费用，因为他们把自己的 AI 带到 Proof 里。所以这改变了你作为 SaaS 公司构建的东西。你现在为人类和智能体同时使用而构建，它把你的利润率变回——嗯，我不再需要支付 token 费用了，因为用户会自带 AI。所以我认为这意义重大。

That is one really important second-order effect of this. So yeah, like I'm using Proof or really any website, maybe PostHog or whatever. And I'm doing it inside of my agent. And the agent has access to the website, so it has access to everything that I have access to. And it has access to my whole computer. When I run the agent on that website, I'm using my tokens. I'm not using the vendor's tokens. I'm not using the app's tokens. And so it puts SaaS back in its place where yeah, you want to make it friendly for an agent. And everyone's got a CLI now. You want to make the HTML really usable. You want to make sure that anything that happens in the CLI shows up for the user immediately. All that kind of stuff. There are a lot of issues to deal with. But once you do that, you actually don't really need to think about having an AI surface that's primarily going to be the thing that users use in the sense that you don't need to build an agent naturally into your product. I think you can—and there's another really interesting bifurcation of this that we should talk about, which is that having two agents is better than one. But for now, there's this really cool thing where with Proof for example, anyone who uses it, I don't pay for tokens because they just bring their AI to Proof. And so it changes what you build as a SaaS company. You build it now for both humans and agents to use at the same time, and it changes your margins back to well, I don't really have to pay for tokens anymore because the user is going to bring AI. So I think this is a huge deal.

所以你描述的是我们做的越来越多的工作，越来越多的专业工作，都将在 Codex 或 Claude Code 内进行。Cursor 怎么融入其中？有潜力吗？

So what you're describing here is more and more work that we do, more and more professional work, is it just going to happen within Codex or Claude Code. How where does Cursor fit into this? Is there potential there?

好问题。我认为 Cursor 也看到了很多相同的东西。在某些方面，他们有类似的东西，但更好。比如我认为 Cursor 的云实现比 OpenAI 或 Anthropic 的都好，也更先进。而且我认为 Cursor 至少到目前为止更明确地选择了一条路。他们更明确地选择为程序员服务。这可能会限制他们在这里能走多远。

That's a good question. I think that Cursor sees a lot of the same stuff. And in some ways they have some of the same stuff but it's better. Like I think that Cursor's cloud implementation is better than either OpenAI's or Anthropic's and is more advanced. And I think that Cursor has at least so far more distinctly chosen a lane. Like they're more distinctly choosing to be for programmers. And that may limit how far they get in here.

我觉得程序员的定义正在扩展，足以让他们拥有一个巨大的市场，但我不确定他们是否会直接跳进“用这个做幻灯片”之类的场景。不过很明显，每一家模型公司都开始意识到，拥有一个 harness（套件）来充分发挥模型能力是多么重要。所有平台都在朝着这样一个世界发展：当你调用 OpenAI 平台或 Anthropic 平台的模型时，不再只是简单的提示和响应，而是实际上在它们运行的云端计算机上运行模型，然后把结果返回给你。他们知道，为了获得模型的最佳效果，必须提供这种能力。所以你看，Anthropic 有云端管理的智能体。OpenAI 还没有回应，但我猜这也会发生。现在 Cursor 基本上被 SpaceX 收购了，虽然不是完全收购，但也差不多了。所以我认为人们开始意识到，不能只做模型部分，还必须有一个上层的 harness。我认为这种 harness 的终极形态是，我可以做任何类型的知识工作。Cursor 本身也面临一个艰难的决定：是只服务于程序员，还是扩展范围。对于那些不是 OpenAI 或 Anthropic 的产品构建者，如果这个预测成真，那么随着时间的推移，你的产品将被嵌入到这些智能体中使用。如果你是这样的公司，你会做些什么来为那个未来做准备？

Like I think the definition of programmer is expanding enough that they'll have a big market, but I don't know that they're going to jump into like okay, use this to make a slide deck or whatever. But it is really clear that every model company is starting to realize how important it is to have a harness to get the most out of the model. And so where all the platforms are moving is to a world where you're not just doing prompt and response when you call the model on the OpenAI platform, the Anthropic platform, you are literally running the model on a computer that is in the cloud that they run and then giving you the result out of it. And they know that in order to get the best results of the model they need to offer that. And so you see, Anthropic's got cloud managed agents. OpenAI does not have a response yet, but I assume that's going to happen. And now Cursor was just essentially acquired by SpaceX. It's not like a full acquisition, but it's close. So I think people are starting to realize I can't just do the model part of it. I have to have this harness above it. And I think the ultimate form of that harness is I can do any kind of knowledge work. Cursor itself feels like one of the things that it's going to be a hard decision for them whether to stay just for coders or not. So people building products that aren't OpenAI or Anthropic, if this proves to be true, the prediction here is they're going to be using your product over time inside of one of these agents. Is there something you would do if you were one of those companies to prepare for that future?

我会直接为此做准备。比如，每一款更经典的生产力软件，无论是 Slack、Word 文档、PowerPoint 还是其他，基本上都是为人类使用设计的。现在人们开始用 CLI，那是为智能体独立于人类使用而设计的。我认为我们正在进入一个新的范式：人类和智能体在同一项工作上协作，双方都在做事。你需要能看到智能体在做什么，智能体也需要能看到你在做什么。我们需要以一种无缝的方式来回交互。为此设计的软件将非常不同。例如，Proof 有很多东西都没有。我不需要 Word 文档那种格式、分页、制作表格之类的功能，因为智能体直接帮我做了。我不需要操心这些，它能把所有格式都处理好。所以，你可以把产品做得比传统产品简单得多，启动也更快。然后你还需要开始具备其他一些功能，因为智能体与软件交互的方式非常不同。比如，智能体可以同时做很多事情。它们可以对你的文档、幻灯片、代码库等同时做无数种操作。你如何向用户展示这些，将与你展示人类在文档中并发操作的方式截然不同。你需要审批功能。你需要一个收件箱，汇总所有将要发生或已经发生的事情。你需要日志和快速回滚的能力。所有这些考虑都会改变实际的产品。底层用户体验或底层基础设施也会不同，因为智能体可以在 3 秒内发出数十亿次请求。你该如何应对？这正是 GitHub 现在遇到问题的原因，因为使用 GitHub 的人数呈指数级增长，而实际上很多是人们的智能体在操作 GitHub。所以我认为这是一个全新的世界，你才刚刚开始窥见一斑。但其中有很多很酷的东西。例如，在 Proof 和我们的一些其他产品中，当有人遇到问题时，他们不会发邮件给支持团队，而是由他们的智能体发送 bug 报告。智能体的 bug 报告比人类的 bug 报告好得多。它会包含：我具体做了什么、精确的重现步骤、因为 Proof 是开源的，所以还有我认为代码库中哪里出了问题。然后我们收到报告，它变成一个 GitHub issue，然后我们可以直接派一个智能体去修复。虽然不能对所有事情都这样做，但这好太多了。你可以看到这种非常快速的闭环的雏形：我遇到一个问题，一个小麻烦，一个想要的小功能，一个小 bug，然后我的智能体直接去和公司的智能体沟通，公司的智能体就直接去修复了。我觉得这非常酷。

I would just prepare for that. So, for example, every more classic piece of productivity software, whether it's Slack or Word docs or PowerPoints or whatever, it's really mostly meant for a human to use. And now people are doing CLIs, so it's meant for an agent to use independently of a human. And we're moving into this new paradigm, I think, where the human and the agent are on the same piece of work together and they're both doing things. You need to have visibility into what the agent is doing. The agent has to have visibility into what I'm doing. We have to go back and forth in this sort of seamless way. And the kind of software that you make for that is going to be very different. So, for example, there's a lot of stuff that Proof doesn't have. I don't have to have a lot of the Word document kind of formatting or page breaks or making tables or whatever because the agent just does it. I don't need to worry about that. It can do all the formatting for me. So, you can make the products a lot simpler and faster to start than the legacy products are. And then there are all these other affordances that you need to start to have because the way agents interact with software is very different. So, for example, agents can do a lot at once. They can just do a billion different things to your document or your slide deck or your code base or whatever. And how you display that to the user is going to be very different than the way you might display a human being concurrent in your document and doing stuff. You need approval. You need a sort of inbox that summarizes, here's all the stuff that's going to happen or has happened. You need logs and the ability to roll it back real quick. So, there are all those kinds of considerations that change the actual product. And then the underlying UX of it or the underlying infrastructure you need is different, too, because agents can make a billion requests in like 3 seconds. So, how are you going to deal with that? This is exactly why GitHub is having problems right now because the number of people using GitHub has skyrocketed exponentially and it's really just people's agents in GitHub. So, I think it's a whole new world that is just starting, you're just starting to see like a little peek of it. But there are so many cool things about it. So, for example, in Proof and some of our other products, too, when someone has a problem, they don't email support. Their agent sends a bug report. And an agent bug report is way better than a human bug report. It has, here's exactly what I did, here's the exact repro steps, here's like Proof is open source, so here's what I think is going in the code base. And then we just get that, it becomes a GitHub issue, and then we can just send off an agent to fix it. And you can't do that with everything, but it's so much better. And you can see the glimmers of this very fast closed loop between I ran into something, a paper cut, a little feature I want, a little bug, and my agent just goes off and talks to the company agent, and then the company agent just goes and fixes it. That I think is incredibly cool.

那么，很多人正在转向 CLI 并试图在终端中工作，这是否是其中的一部分？你的预测是否包括人们会远离 CLI，转而与运行在身边的智能体进行交互？

So, is there a part of this that a lot of people are moving to a CLI and trying to work from the terminal? Is part of this prediction that people shift away from that and back to actually you interact with agents kind of running alongside them?

CLI 已经过时了。我们飞速经历了 CLI 时代。它曾经很好，但我觉得很明显了。并不是说 CLI 会完全消失。显然，它们已经存在了 30、40 或 50 年，还会继续存在。我认为在 Claude Code 非常流行的时候，或者说当 Claude Code 开始流行时，人们觉得成功的原因是它是 CLI，但我不这么认为。当你转向真正的 UI 时，你会意识到我们当初做 GUI 是有原因的。在 GUI 中工作更舒服。你可以在 GUI 中获得所有相同的好处，尤其是对于非程序员的工作。但我估计，每家公司里绝大多数技术人员已经不再把 CLI 作为主要工作界面了。我认为很多程序员偶尔还会切进去用一下，但他们基本上都在用 Codex、Claude Code、Cursor 这类工具。

CLIs are over. We speed ran the CLI era. It was nice while it lasted, but I think it's pretty clear. It's not that CLIs are going to completely go away. Obviously, they've been around for the last 30 or 40 or 50 years. They will continue to be around. And I think there was this moment when Claude Code was so popular, or when Claude Code was really starting to gain in popularity, that people were like the thing that's working is the fact that it's a CLI, and I don't think that's what it is. And when you move into an actual UI for this, you start to realize we made GUIs for a reason. And it's just nicer to be in a GUI. And you can get all the same benefits inside a GUI, especially for non-programmer work. But I would estimate that definitely the majority of the technical people inside every company are not using CLIs anymore as their main work surface. I think a lot of programmers are still flipping into it every once in a while, but it's more or less they're using Codex, Claude Code, Cursor, that kind of thing.

太好了。好的。我确实想把这一点说清楚。那么，回到预测的大局上，你预期有两种工作模式。一种是在公司内部的超级智能体，你很可能通过 Slack 与它聊天，它可以去执行任务和回答问题。另一种是在你的电脑上运行 Codex 或 Claude Code。

Awesome. Okay. I definitely wanted to make that part clear. So, coming back to the big picture of the prediction here, there are these two modes of work that you're anticipating. One is this kind of super agent within a company that you chat with through Slack most likely that can go off and do work and answer questions. And then there's on your computer running Codex or Claude Code.

而在此之中，你通常在电脑上做的所有工作，现在都将存在于 Codex 或 Claude Code 中，或者某个我们尚未意识到的第三方工具中。

And within that, all the work that you normally do on your computer is now going to be living within Codex or Claude Code, or maybe some third-party that emerges that we're not even aware of yet.

是的，而且你将在这些工具的内部浏览器中使用应用。

Yes, and you're going to use apps inside the internal browser of those tools.

哇，好吧。听你这么说，可能感觉没那么深刻，但这确实是我们工作方式的巨大改变。我们现在还没有一个在 Slack 里经常对话的 AI，而且目前也不是主要在 Codex 或 Claude Code 里工作。所以这实际上是一个相当大的转变。

Wow, okay. Listening to you talk about it, it may not feel as profound as it is, because this is a big change to how we work. We don't currently have an AI that we talk to regularly in Slack, and we also don't work currently mostly in Codex or Claude Code. So this is actually a pretty massive shift.

我也这么认为。在我们进入下一个预测之前，还有其他类似的观点吗？

I think so. Is there anything else along these lines before we get into our next prediction?

嗯，有几件事。我绝对不是智能体最大化主义者。我真的认为我们会使用很多不同的智能体。这对我来说似乎很清楚。而且我确实认为两个智能体比一个好。那么举个例子呢？当我让 Codex 与另一个智能体交互时，它可以提供比我打字多得多的关于我和我想要的上下文。它可以来回讨论那些我直接向智能体表达需要很长时间的事情。当你假设你的用户使用 Codex、Claude Code 或 Cowork 作为访问你应用的基本方式时，你会得到这种加速效果。

Well, a few things. I'm definitely not an agent maximalist. I really think we're going to have a lot of different agents that we use. Seems pretty clear to me. And I really do think that two agents are better than one. So what's a good example? When I have Codex interact with another agent, it can give so much more context about me and what I want than I would be able to type. And it can go back and forth talking about things that would take a long time for me to express directly to an agent. You get this speed-up effect when you assume that your users are using Codex or Claude Code or Cowork as their basic way to access your app.

一个非常简单的例子：我们有一个托管的 OpenClaw 产品，之前放在候补名单上。我们实际上不得不暂停，因为我们开始把人们从候补名单中移出来，而 OpenClaw 是一个非常难用的智能体框架。它发展得太快了。如果你是一个平台，当事情出问题时你无法修复。这非常困难。但在这个过程中我们学到的一件事是：如果你正在构建一个智能体产品或任何新的软件体验，你通常会假设要设置一个智能体，你需要构建一个小型网页界面或 Slack 工作流，询问人们：“好的，你是谁？你要用它做什么？你理想的梦想结果是什么？”或者任何你放在入职清单上的东西。相反，如果你只是划一条硬线：我们只服务使用 Codex 或 Cowork 的用户。那么会发生的是，你只需将提示粘贴到 Codex 或 Cowork 中。它会去与应用对话，应用可以是普通服务器或它自己的智能体。Codex 有大量关于你的信息，它可以提供：这是我和 Dan 一起做的所有事情。这是他可能想使用这个应用的所有方式，然后把它带回给我。这是一种非常定制化的体验。而且对于像智能体这样的技术产品，当出现问题时，我只需告诉 Codex，“去修复它。”Codex 就会去与应用对话，为我找出问题所在。所以我认为，当你假设每个人都有智能体，并且这些智能体以这种神奇而重要的方式相互对话时，整个范式开始改变。

And a really simple example: we have this hosted OpenClaw product, which we had on a waitlist. We actually had to deposit because we started taking people off the waitlist, and OpenClaw is just a very hard agent harness to make work. It's moving so incredibly fast. And if you're a platform for it, when things break you can't fix it. It's very hard. But one of the things we learned in that process is: if you're building an agent product or any new software experience, what you would assume to set up an agent is you need to build a little web interface or a Slack workflow that asks people, "Okay, who are you and what are you going to use this for? What's your ideal dream outcome?" Or whatever you'd put on an onboarding checklist. If instead you just make a hard line: we are only going to service users who use Codex or Cowork. What happens is you just paste a prompt into Codex or Cowork. It goes and talks to the app, and the app can be either a regular server or its own agent. And Codex has so much information about you that it can just give it: here's all the stuff I've been working on with Dan. Here's all the ways he might want to use this app, and then bring it back to me. It's this very custom experience. And also for a technical product like an agent, when something goes wrong, I can just tell Codex, "Go fix it." And Codex will go talk to the app and figure out what's going on for me. So I think the whole paradigm starts to change when you assume that everyone's got an agent and those agents are talking to other agents in this really magical and important way.

在我们开始之前，我还有几件事想提一下，因为要谈的太多了。你们中有人提到，SaaS 工具在使用时基本上不会消耗模型公司的 token。再多谈谈这个，因为这可能会改变未来 SaaS 公司的商业模式。这感觉像是一件大事。

There's a couple more things I want to touch on before we get started, because there's so much to talk about. One of you made this point about SaaS tools not using tokens from the model companies basically when using a SaaS tool. Talk a bit more about that, because that may change the business model for SaaS companies in the future. That feels like a big deal.

嗯，我认为这实际上可能会挽救他们的利润率。因为现在所有这些公司都急于在他们的产品中加入智能体，并认为“哦，智能体将是人们与我互动的主要方式。”这显然会消耗 token。而我实际上认为，一旦我把 Codex 或 Cowork 作为我的主要工作界面，我仍然想使用 SaaS。所以这是另一个好的预测。我现在会买入 SaaS 股票。我认为 SaaS 的末日已经结束，SaaS 股票在未来几年将大幅上涨。这不是投资建议，但你知道，我会买 SaaS 股票。所以我认为它节省了你的利润率，因为现在你的思考方式不是“我必须把 AI 构建到里面”，而是更像“我必须制作一个人类和 AI 都想要协作的软件”。这很难，但一旦你构建了它，它比假设每个人都花费 token 要便宜得多。而且我认为这是一个好生意。我如此看好 SaaS 的部分原因是：A，我们内部的每个人，就像我说的，我们都有智能体，我们都使用 Codex 之类的，但我们仍然支付大量的 SaaS 费用，而且我们的 SaaS 支出逐年增加。我们并不是像 vibe coding 那样每件小事都自己做，你知道的？而且我认为智能体所做的是增加 SaaS 的用户数量，而不是消除它。所以我认为 SaaS 公司将看到需求的疯狂激增，因为会有大量智能体以非常高的量使用这些产品。就像我说的，这是一个巨大的基础设施挑战。有很多有趣的定价挑战，但这让我非常看好 SaaS。

Well, I think it actually may save their margins. Because right now all these companies are rushing to add an agent to their offering and thinking, "Oh, the agent is going to be the main way that people interact with me." And that costs tokens, obviously. And I actually think once I have Codex or Cowork as my main work surface, I still want to use SaaS. So this is another good prediction. I would buy SaaS stocks right now. I think the SaaS apocalypse is done and SaaS stocks will be up majorly in the next couple years. Not investment advice, but you know, I would buy SaaS stocks. So I think it saves your margin because now the way you're thinking is not "I have to build AI into this." It's more like "I have to make a piece of software that humans and AI want to collaborate on together." And that's hard, but once you build it, it's a lot cheaper than assuming everyone's spending tokens. And I think it's a good business. And part of the reason I'm so bullish on SaaS is: A, everybody internally here, like I said, we've all got agents and we're all using Codex and whatever, and we still pay for a ton of SaaS, and our SaaS spend is up year over year. And we're not like vibe coding every single little thing, you know? And I think that what agents do is increase the number of users of SaaS, not get rid of it. So I think SaaS companies are going to see an insane spike in the amount of demand that they have, because there's going to be tons of agents using these products at a very high volume. And like I said, that's a huge infrastructure challenge. There are a lot of interesting pricing challenges, but it makes me very bullish on SaaS.

我喜欢这个。如果这次对话还有什么别的结论，Dan Shipper，SaaS 就是 AI 的未来。#发推文

I love that. If anything else comes out of this conversation, Dan Shipper, SaaS is the future of AI. #sendtweet

我只是喜欢……是的，这相当反主流。另一个有趣的点是你们在招聘，过去一年人数翻了一番，这对于一个如此 AI 前沿的公司来说，并不是人们所期望的。谈谈你的经历。

I love just... Yeah, this is quite contrarian. And the other interesting piece is that you guys are hiring, that you doubled in people in the past year, which is not what people would have expected from a company that is so AI-forward. Talk about what your experience there is.

好吧，我们实际上仍然需要人类。自动化是一个谎言，因为每当你自动化某件事时，为了确保自动化运行良好，你需要一个人类在上面确保它运行良好。所以我几年前写了一篇文章叫《分配》，关于分配经济，想法是人类与 AI 合作的方式将像成为管理者一样。关于管理者，你需要记住的是，管理者实际上花了很多时间工作。大多数管理者并不在海滩上；他们一直在与员工沟通，试图弄清楚：“好吧，我们如何让这个工作得好？我们如何让它更好？它做得怎么样？这个人做得怎么样？”诸如此类。而且我认为人类管理者和模型管理者之间有一些区别，但基本上它仍然需要大量的时间和注意力。

Okay, we still actually need humans. Automation is a lie, in the sense that every time you automate something, in order to make sure the automation is working well, you need a human on top of it making sure that it's working well. So I wrote this piece a couple years ago called "The Allocation" about the allocation economy, the idea that the way humans are going to work with AI is going to be like being a manager. And the thing you have to remember about managers is that managers actually spend a lot of time working. Most managers are not on the beach; they're checking in with their employees all the time, trying to figure out, "Okay, how do we make this work good? How do we make it better? How's it doing? How's this person doing?" All that kind of stuff. And I think there are some differences between being a human manager and being a model manager, but fundamentally it still requires a lot of time and attention.

我认为我们在模型讨论中忽略了这一点。原因之一是基准测试让 AI 看起来比实际更自主。我说的自主有特定含义，我会尽量解释清楚。这有点难以表达，但我自己体会到了，因为我一直感受到这个悖论：我们有了这么多自动化、这么多 AI，而我却工作得更辛苦了。

And I think that we kind of miss that in the model discourse. And one of the reasons is benchmarks make it look like AI is more autonomous than it is. And by autonomy, I mean something specific by autonomy, and I'm going to try to express this. It's like a little hard to express, but I learned this for myself because I've been feeling this paradox a little bit. I've been feeling the like we have so much automation, so much AI, and I also work way more.

我觉得这个悖论在我创建自己的基准测试时开始部分化解了。我做了个“高级工程师基准测试”，用来衡量 AI 与人类工程师的对比。我是怎么做的呢？我有个应用做证明，我一边用 vibe coding 方式开发它，一边处理其他事务。发布后，因为完全是 vibe coding 出来的，它开始频繁宕机，我修不好。非常尴尬，我丢尽了脸。产品本身是能用的，我们内部测试过，也有很多 beta 测试者，但发布后第二天，服务器每 10 分钟就宕一次。大家都看着我，我只能说“我不知道怎么回事”，然后对 Codex 说“修好它”。Codex 说“我不知道怎么回事”，或者说“我知道怎么回事，我修好了”，结果它又引发四个其他错误，就这样循环往复。我睡不着觉，vibe coding 太猛，手肘都得了滑囊炎。这算是个生活教训吧——vibe coder 肘。

And I think part of the paradox or part of the paradox started to like resolve for me a little bit when I made my own benchmark. So, I made this senior It's called a the senior engineer benchmark, and it's like, "How good is AI versus a human engineer?" And the way that I built it is again, have this app proof. I just vibe coded it on the side and I like while running the rest of every. And when we launched it, because it was completely vibe coded, it just started going down and I couldn't fix it. And it was very embarrassing. I had a lot of egg on my face. And like the product worked. We We tested it internally. We had a lot of beta testers, but like the day after launch, it was like just every like 10 minutes the servers would go down and people were looking at me and I'd be like, "I don't know what's going on." Like, "Codex, fix it." And Codex was like, "I don't know what's going on." Um or really Codex was like, uh "I do know what's what's going on. I fixed it." And then it it would cause four other errors, and then you're just going around in a circle and I wasn't sleeping, and I I I vibe coded so hard I got bursitis on my elbow. So, uh that's a There's a life lesson in there. Vibe coder elbow.

总之，我找了两位不同的高级工程师独立修复它，所以有了两套不同的代码重写方案，他们告诉我怎么做的。现在，当新模型发布时，我就给模型一个提示：“这是 vibe coding 出来的垃圾。如果你想从第一性原理重写，你会怎么写？去做吧。”所有模型直到 GPT 5.5 都只得了 30 分（满分 100），而人类高级工程师能得 80 多、90 多分。所以差距还很大。然后我试了 GPT 5.5，它得了 62 分。注意，这 62 分是 GPT 5.5 使用 Opus 4.7 计划的结果。Opus 4.7 计划非常好。GPT 5.5 是唯一一个有自主意识和信心直接删掉旧代码、从第一性原理重写的模型。其他编程模型总是试图修补边缘，说“哦，这活儿太大了，我打个补丁吧”，而你会说“不，我明确告诉过你别这样”。所以 GPT 5.5 的分数提升了 30 分，达到 60 分。很明显，一年之内它就能达到高级工程师水平，对吧？这让你脑海中浮现出某种画面，尤其是基于我给基准测试起的名字——我认为很多基准测试都是这样。

Um so, anyway, I got a I got actually two different senior engineers to fix it independently. So, I have two different rewrites of the code base that um tells me how they did it, right? And so, what I get to do is when we get new models I just give the new model a prompt. I say like, "This is vibe coded slop. If you wanted to rewrite it from first principles, how would you write it? Go do it. And all the models until GPT 5.5 got like a 30 out of 100. And senior like a human senior engineer gets like high 80s, low 90s out of 100. So, there's a lot to go. And then I tried GPT 5.5 and it got like a 62. And mind you, the 60 the 60 score was um GPT 5.5 using an Opus 4.7 plan. Opus 4.7 plans are very good. GPT 5.5 is the only model though that has the sense of agency and confidence to just like rip out old code and just like actually rewrite from first principles. Other coding models, they kind of like try they like end up papering over the edges around the edges and they're like, "Oh, this is a big job. Like I'll just do a little patch." And you're like, "No, I like specifically told you not to." So, GPT 5.5 there's like a 30-point bump in the score. 60 out of 100. It's like very it's very clear that in a year or less, it's going to be senior engineer level, right? And that gives you a certain picture in your mind, especially based on how I named the benchmark, which I think a lot of benchmarks do.

我可以告诉你，当达到那个点时，我很容易就能修改基准测试，让当前模型得零分。例如，看起来提示词不需要技巧或思考，就是“这是 vibe coding 垃圾，从第一性原理修复它”，但实际上我花了一段时间才找到一个既不泄露答案、又能让模型展现能力的提示词。我最初给的提示词，就是我在生产环境宕机时试图修复问题时用的：早上醒来，我说“好吧，昨天有四五起报告的问题。我希望你检查所有问题，制定一个解决所有问题的计划，然后去执行。”我敢预测，市场上所有编程模型一年后仍然会这样做——它们会认真对待这个指令。如果我说“这里有一堆问题，去修”，它们就会去修。但真正的人类高级工程师会查看代码库，然后说“这家伙根本不知道自己在干什么。”

And I can tell you that when we get to that point, I will be very it will be very easy for me to change the benchmark to zero out the current model. So, that gets a zero out of 100. And so, for example, uh it seems like there's no skill or no thought into the prompt, which is this is vibe code is slop like fix it from first principles, but actually it took me a a while to get to a prompt that didn't give away the answer, but uh uh but got the model to reveal what it's capable of. And the original prompt I gave it was the original prompt that I gave it when uh when I was trying to fix the issue in production was going down, which is like I'd woken up I'd I'd woken up in the morning and I was like, "Okay, we had four or five reported issues yesterday. I want you to go through all the issues and then come to like a make a plan for how to resolve all of them and go do it, right?" And every coding model on the market, and I I am I'm pretty sure this Here's a prediction. I'm pretty sure every coding model on the market will still do this in a year. Every coding model on the market will take that instruction seriously. And if I tell it, "Here's a bunch of issues, go fix it." They will just go try to fix the issues. What a actual human senior engineer does is they go look at the code base and they're like, "This is a piece of This guy doesn't know what he's doing."

然后他们会说：“我们实际上得重写很多代码，这很困难也有风险。我知道你不想听，但我们必须这么做。”如果你问模型“嘿，我们该这么做吗？”，它可能会得出同样的结论，但它不会主动去做。而且有很多激励因素阻止它这么做。即使它做了，也总有更高的框架等着我们。所以我认为，在思考基准测试进展时，从这个角度出发非常重要：基准测试是在我们能够清晰表述、能够评分的问题上提升的。还有很多人类工作，在你写下来之前是无法评分的，而思考如何提示或写下来的行为本身是无法衡量的，但这意味着即使基准测试饱和了，也不等于我们就能完全取代所有高级工程师。我认为这就是为什么尽管模型在自动化方面越来越好，我仍然在招聘工程师。

And then then they say, "We're going to have to like actually rewrite a lot of this and it's going to be hard and risky. I know you don't want to hear that, but like we're going to have to do that." And if you asked the model, "Hey, like should we do that?" It'll it'll probably it it'll probably get there, but it's not going to do it on its own. Um and it and there's a lot of incentives pushing against it doing that. And even if it does that, there's a there's always a higher frame for us to go. And so I think it's it's really important uh when when we think about benchmark progress to think about it from that perspective, which is benchmarks rise on problems that we've framed that we can articulate, that we can score. And there's a lot of work that's human work that uh it it can't be scored until you write it down, but the act of thinking to prompt it or write it down um is uh is something that you can't measure, but like kind of means that even if the benchmarks get saturated, it doesn't mean the same thing as we you totally replace all senior engineers. And it's I think it's why even though the models are getting better at automation, I still hire engineers.

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我最近在播客里提到一件事，听说关于你们拥有的那种人类编写的代码，数据标注公司正在购买 2021 年、2022 年之前、在 AI 成为主流之前编写的代码，这些数据非常有价值。

One thing I mentioned recently on the podcast, I heard that speaking of the code that you have of like humans writing code, data labeling companies are buying code that was written before 2021, 2022, before AI became a thing is like very valuable data.

原始人类代码。对，没错，完全正确。有趣的是，这正是用来构建这个模型的那种代码。嗯，有趣在哪里？我想澄清一下。我并没有让人完全手工编写代码。因为我觉得那样有点傻。比如，我不太在意，因为我知道如果一个工程师不用 AI，我不会和他合作。我不太在意。这就像，我会让人类和汽车赛跑吗？我可能不会那么做。但是，我会让一个开车的人类和另一个开车的人类比赛，看谁更好。在这种情况下，基准测试的结构是，这些人类工程师确实用了 AI，但他们用的方式是我做不到的，因为我不理解，也没有时间，说实话我也不想深入去理解代码库。我认为在考虑基准测试时，非常重要的一点是，AI 是一种广泛分布的技术，任何人都可以使用。当我们拿人类和 AI 做基准测试时，实际上我们总是在谈论一个使用 AI 的人类和另一个使用 AI 的人类，因为 AI 不会自己使用自己。它可能以某种递归的方式做到这一点，但在任何实际用例中，总有一个人类在旁边确保它正常工作。

Original human code. Yeah, exactly. That's exactly right. And it's so interesting that that's exactly the kind of code used to build this model. Well, what's interesting? So, I want to clarify there. So, I did not have a human write the code all by hand. Because I actually think that that's sort of it feels silly to me. Like, I don't really care because I know if an engineer is not using AI, like I'm not going to work with them. I don't really care. It's like it's sort of like am I going to race a human against a car? Like, I probably wouldn't do that. But, um I would race a human in a car versus another human in a car and say which one's better. And in this case, what the way the benchmark is structured is yeah, like these human engineers used AI, but they used it in a way that I could not cuz I didn't understand it and I didn't have time and I didn't really want to like go in and try to understand the code base, to be honest. And I think that's a really important thing when we think about benchmarks is AI is a broadly distributed technology that any human can use and when we are benchmarking against humans, AI against humans, we're actually really always talking about one human using AI versus another human using AI cuz AI doesn't use itself. It may be able to in this like slightly somewhat recursive way, but there's in any real use case, there's always a human like pretty close to it making sure that it's working.

好了，我试着总结一下第一个部分。有太多要聊的了。我列了一个小清单，基于你的预测，我认为人们应该做这些事情才能成功。我们最后也会讨论这个，但先提几点。第一，开始在工作中越来越多地使用 Codex 或 Claude Code，尤其是浏览器，使用其中的工具。第二，让你的智能体能够使用你的产品。如果你真的在做 SaaS 工具，本质上要让智能体容易成为用户。第三，开始考虑一些你可以协作的 Slack 机器人，比如尝试一些工具。我知道 Slack 有自己的 Slack 机器人，我觉得也很好，我还没用过，但人们真的很喜欢。所以，寻找一个可能成为你公司内部 AI 智能体的工具。尽快买入 SaaS 股票。

Okay, I'm going to try to wrap up our first bucket. There's so much to talk about. I've made a little list of things that I think people should do based on your predictions to be successful. We'll talk about this at the end, too, but just a few things. One is start using Codex or Claude Code more and more for the work you're doing and especially the browser, use tools inside of it. Two is allow your agents to use your products. If you're really going to SAS tool, make it easy for agents to be a user, essentially. Three is start thinking about some Slack bot that you can work with, like try out tools. Like I know Slack has their own Slack bot that I think is really good, too, and I haven't played with it, but people really like it. So, look for I guess a tool that could become the AI agent within your company. Buy SAS stock ASAP.

不是投资建议。我认为完全正确。我想稍微调整一下，当你考虑为智能体构建软件时，当前的模式是我构建一个智能体使用的 CLI，但它们的使用方式有点像任务被委托给智能体，智能体使用 CLI。而我认为未来的方向是，你和智能体一起使用应用。智能体可能用 CLI，但你用 Web 界面，两者需要同步。我认为这是一个非常有趣的新挑战。

Not investment advice. I think that's totally right. I will like my slight tweak is when you're thinking about building your software for agents, the current model is I'm building a CLI that an agent uses, but they're using it in a sort of like they're being delegated a task to the agent and the agent's using the CLI. And what we where I think it's going is you and the agent are using the app together. The agent's probably using the CLI, but you're using the web interface and they both need to be in sync. And that is I think a new challenge that's really interesting.

太棒了。在我们进入下一个类别之前，还有什么要说的吗？Bisas。这是标题。

Awesome. Anything else before we get to our next category? Bisas. That's the title.

哦，天哪。好的。第二个预测类别是关于我们工作形态的变化。你预测了什么？工作形态方面有很多有趣的事情。一旦你进入这个领域，你有异步的智能体可以委托任务，然后你有像 Codex Claude Code 这样的工作界面，事情就开始发生了。我们在内部经常看到的一件事，在大型模型公司也能看到，就是拉取请求的数量激增。你知道，我们有咨询或运营岗位的人，甚至是编辑，都在提交拉取请求。嘿，这真的很酷，这是一种非常不同的工作形态，你可以预期公司或用户中更高比例的人将做以前只有技术用户才能做的事情。这给另一端的人带来了压力，他们需要处理所有这些新代码。所以，我认为这会产生很多有趣的事情。例如，我之前提到的 Open Claw。Pete 每天在 Open Claw 上收到数千个拉取请求，然后他启动 5 万个 Codex 实例，筛选并合并其中大约一千个。这真的很疯狂。我认为这会越来越普遍。这引发了很多有趣的问题，比如应该合并哪个拉取请求？你知道，当你在流程的某一部分增加容量时，它会打破平衡。以前构建东西很难，现在很容易。所以，重点不是我们能不能构建，而是它是否与我们已构建的其他部分协调一致？我们如何保持整体的连贯性？还有，我们该删除什么？我认为 Entropic 在这方面做得很好。他们从 Claude Code 中删除了很多东西，以确保它不会臃肿。所以，我认为这类事情会大量发生。一方面，很多非技术人员可以做技术工作，而技术人员负责确保这些工作以连贯一致的方式融入产品或流程。而且，产品人员也会这样做。我认为这很酷。

Oh, man. Okay. So, the second category of predictions is around just the shape of the work that we're going to be doing is going to change. What do you predict? There's all this interesting stuff in terms of the shape of work. Like once you're in this land where you've got, you know, these async agents off that you delegate work to then you've got your like Codex Claude Code like work surface that that starts to happen. So, one thing that we see a lot internally and you also see this in the big model companies is the number of pull requests that you get is like skyrockets. You know, we have people, you know, in consulting or in ops roles or whatever who are or or editors just just making pull requests. And hey, that's really cool and it's a very different shape of work where you should you can expect that a higher percentage of your company or your users are going to be doing things that previously only technical users could do. And what that does is it creates all this pressure on the other end for the people who have to deal with all of the new code for how to deal with that. And so, I think there's a lot of interesting things that happen with that. Like so, for example, open claw, I mentioned that earlier. Pete gets like thousands of pull requests a day on open claw and then he has like and then he just spins up like 50,000 Codex instances and then sorts through them and then merges like a thousand of them. It's really crazy. I actually think that that's going to be more and more common. There's like it brings up a lot of really interesting questions around which pull request should you merge? And you know, when you whenever you add capacity in one part of your process, like it breaks things. It used to be really hard to build things, and now it's very easy. So, the point is not, can we build it? It's like, would it make sense with the rest of what we've built? And how do we keep a like sense of a coherent whole? And also, what do we delete? I think Entropic does this really well. Like they delete a lot of stuff from Claude Code to make sure that's not bloated. So, I think there's a lot of that going to happen. On one side, there's a lot of non-technical people can do technical work, and then technical people are in charge of making sure that that work gets into a product or into a process in a cohesive, coherent way. And also, their product people are going to be doing that, too. And I think that's quite cool.

我从人们那里听到的是，既然每个人都能做所有事情，比如工程师可以设计，产品经理可以编码，营销人员可以发布东西。这就产生了困惑，我的工作到底是什么？是啊。我到底负责什么？比如，我应该发布东西吗？我还是营销人员吗？这在世界上造成了大量的困惑和不确定性。

Something I'm hearing from people is that now that everyone can do everything, like engineers can design, PMs can code, marketing people can ship stuff. There's just this like confusion about what the hell is my job anymore. Yeah. What am I responsible for, exactly? Like, am I supposed to be shipping stuff? Am I still a marketing person? And it's just creating a lot of confusion and certainty in the world.

我认为这是真实的，我觉得每个人都很特别的一点是，每个人都是某种通才，喜欢涉足很多不同的事情，不管这个比喻是什么。我认为这最终会稳定下来，会感觉更正常。比如，营销人员仍然会做营销，即使他们接触网站。那只是现在营销的一部分。但我也认为，现在作为通才能走得更远，这真的很酷，尤其是对小公司来说。另一件有趣的事情是，肯定会出现一些新的工作角色。

I think that's for real, and one of the things that I think is special about every is everyone is sort of a generalist and really loves like having their fingers in a lot of different pots, or whatever the metaphor is. I think that'll probably settle down at some point, and it'll feel more normal. Like, marketing people are still going to do marketing, even if they're touching the website. Like, that's just part of marketing now. But I also think that you can get a lot further being a generalist now, and that's like really cool, especially for smaller companies. The other thing that I think is interesting is there are definitely some new job roles that are a thing.

而且越来越清楚的一点是，整个“前部署工程师”的概念我认为是真实的。它源于每个智能体都需要一个人类。即使你去那些大模型公司，他们内部也有这些智能体在运行。他们有团队在管理这些智能体，你知道吗？我不认为这些团队会消失。模型会变得更强大，智能体也会变得更强大，智能体的数量会增长，但人类仍然会管理它们。所以这看起来是一种非常特定的人。我们内部就有几个这样的人，他们负责确保你的智能体正常工作并做正确的事情。我们也做咨询，所以我们会把这些服务借给客户，我认为这是人们非常想要的东西，这也是另一个让你觉得“嗯，自动化本该取代工作，但它似乎只是创造了一个或多个工作”的地方。

And the thing that is becoming really clear is the whole forward deployed engineer concept I think is for real. And it comes out of every agent needs a human. Even like you go to the big model companies, they have these agents that run internally. They have teams of people that run these agents, you know? And I don't think those teams are going away. The models are going to get more powerful, the agents are going to get more powerful, and the number of agents is going to grow, but people are still going to manage them. And so that looks like a very specific kind of person. And you know, we have a couple of those people internally here, and it's like the people who are in charge of making sure your agents are working and doing the right thing. We also do consulting, so we lend that out to people, and I think that's a big thing that people want, and it's another one of those places where you're like, 'Hmm, automation was supposed to take away jobs, but it looks like it just created one or many.'

你知道，有一种特定类型的工程师非常喜欢，比如 Nitesh，他是我们团队的一员，符合这种前部署工程师的类别。他是一名 AI 工程师，大部分时间都在 Slack 上与我们的一个智能体对话。我们内部有一个叫 Claudie 的智能体，负责整个咨询业务。他花很多时间在 Slack 上。虽然也有代码，他会用 Claude Code 之类的工具，但很多工作就是和它对话，比如“你为什么做这种蠢事？我们来修一下，好吗？”所以我认为有些工程师喜欢这样，喜欢接触最新的事物，也喜欢在 workspace 里做这种工作，这看起来和传统的软件开发有点不同。你的感觉是，我们离智能体不需要人类还很远。你说了很多次，智能体需要人类，既有设置的部分，也有永远维护的部分。我觉得两者都很重要。我听到的是，这将在很长一段时间内都是一个工作。AI 不会聪明到能完全自动化，你无法完全自动化一段时间。

You know, and there's a specific type of engineer that really loves, you know, Nitesh, who's one of our uh who fits this. He's an AI engineer and he fits the sort of forward deployed category, and he's on our team. He spends most of his time actually talking to one of our agents in Slack. We have an agent internally called Claudie, which runs our whole consulting practice. And he spends a lot of time in Slack. Like there is code, and he is using Claude Code and other things like that, but a lot of it is just talking to it and being like, 'Why did you do this dumb thing? Let's fix that, you know?' And so there's certain kinds of engineers that I think love that and love having their hands on the latest thing, and also love making this like being that's like in the works in a workspace and it looks a bit different than more traditional building more traditional software. And your sense there is we're not going to we're not near a place where these agents don't need a human. You said that so many times now that agents need a human and there's kind of like the setup part and then there's the maintaining it forever part. It feels like both are important. Is what I'm hearing like this is going to be a job for a long time. AI is not going to get smart enough to just automate it you fully automate for a while.

是的，我同时极度充满 AI 信仰，也非常看好人类以及人类在确保 AI 正常工作中的作用。

Yes, I'm simultaneously extremely AI-filled and extremely bullish on humans and the role of humans in making sure that AI is working well.

有意思。好的，所以你提到的两个类别，一个是，我听到你之前描述的是，软件发布的速度和一切都在加快，这也意味着有更多的工作要审查这些粗糙的输出。我刚刚和一个数据科学的朋友聊天，他说他的团队，作为数据科学团队，以前的工作是做分析、回答问题、看实验是否有效。现在每个人都在做这些，分享结果，然后他们说“不，这不对”，他们大部分工作变成了审查糟糕的数据科学工作。这是一个问题，这意味着同样的事情也发生在工程师身上，意味着你需要更多，实际上你需要数据工程师和数据科学家，也意味着你还没有设置合适的系统或智能体来帮助你。所以在大公司内部，比如至少有一家公司有一个数据科学机器人，组织里的每个人都可以查询它，它连接到数据仓库，知道谁是谁，所以在仓库层面知道谁有权限访问什么。所有基本问题，因为有一个团队设置了这个机器人。所有人们可能想问的基本问题，它有时会出错，他们不断确保它正确。所以数据科学团队不必回答所有问题，因为有另一个团队在构建一个智能体来很好地处理这些。但如果那个团队不存在，数据科学家会讨厌他们的生活。是的。但这可能让工作没那么有趣，因为你只是坐在那里，整理别人粗糙的工作，而不是……

Interesting. Okay, so the two kind of buckets here that you're talking about one is like the way I think I hear what you described earlier is this the pace of shipping software and everything is just increasing which also means there's so much more work reviewing all this sloppy output. I was just talking to a data science friend and he was saying how his team is just as data science team is just their job used to be do analysis, answer questions, see if this experiment was a good was a was positive. Now it's just everyone's doing that and they're sharing the results and they're like no this is not correct and most of their job is now reviewing bad data science work. Which is a problem and it means that and the same thing is happening with engineers and it means that you need more like you actually need data engineers for this and you need data scientists and it means that you haven't set up the appropriate systems or agents to help you with this. So like the way that it works inside of the big mono companies for example at least one of them has literally a data science bot that every single person in the org can query that is hooked up to their data warehouse that knows who's who so that it knows at the warehouse level like who has permission to access what. And so all of the basic questions because they're there's a team that sets up this bot. All of the basic questions that people might want to ask that it sometimes gets that might get wrong that they're constantly making sure it's getting it right. And so the data science team doesn't have to answer all of the like questions because there's another team building an agent that that that is set up to do that really well. But if the team didn't exist the data scientist would hate their lives. Yeah. It does though make the job maybe less fun cuz you're just sitting there you know gardening people's sloppy work versus

我认为这实际上可以让工作变得更好，因为对于数据科学家来说，你现在不用处理所有那些愚蠢的请求。你处理的是更深层次的问题，这些对处理基本请求并构建智能体的团队来说更难。这就像过滤掉所有那些东西，这样你就可以集中精力。

What I think is like it can actually make the job better because for the data scientist you're now not dealing with all the silly requests. You're dealing with the deeper questions that are harder for the team who's dealing with all the basic requests and building an agent to do that. It's like filtering all that stuff out so you can focus.

我一直在思考一个问题。我本来没打算谈这个，但这是我一直在想的。问题是，哪个产品技术角色现在变化最小？比如工程师，现在 100% 的代码都是 AI 写的，这完全是一份不同的工作。产品管理，很多 PRD 你不需要写那么多，你可以直接发布代码，不需要等别人。设计，整个设计过程已经死了，根据最近的嘉宾所说，就像没有时间做整个设计过程，角色非常不同。数据科学现在的工作也非常不同。还有市场营销、销售。所以问题是，你认为到目前为止，哪个角色从根本上变化最小？

Here's a question I've been thinking about. I was not planning to talk about this but it's something that I've been thinking about. So the question is which product tech role is the least changed now. So like engineers 100% of code AI now. It's like a completely different job. Product management a lot of the you know PRDs are you don't have to write as much. You can ship code. You don't have to wait for people. Design the whole design process dead according to recent guests just like there's no time to do the whole design process very different role. Data science very different work now. There's marketing there's sales. So here's the question. What do you think is the least fundamentally changed role so far?

嗯，一个有趣的事情是，我不知道这算不算，但像 CEO 和投资者，他们是否使用这些东西似乎仍然非常可选。

Well one interesting thing is you know I don't know if this counts but like CEOs and investors it seems still very very optional whether or not they use this stuff.

嗯。看起来是这样。

Mhm. It seems that way.

我认为实际情况恰恰相反。根据我的经验，我们和很多高管和高级领导团队合作。我的经验是，你的公司能走多远取决于你的 CEO 在 AI 上走多远，这不是你能委托的事情。你必须亲自参与，否则你就没有直觉。但很长一段时间以来，看起来好像是，这是做工作的人必须做的，但我不需要做。我只需要告诉他们做什么。所以我认为，如果你是 CEO，你基本上可以让你的日常工作看起来非常相似。我认为在某个时候这会迅速改变，你会觉得“哦不，我落后太多了”，但就目前而言，或者甚至中层管理者，我认为这些人变化相当小。我觉得可能是销售，因为它非常非个人化。

I think the opposite is actually true. Like my experience we do a lot of this with senior executives and senior leadership teams. My experience is that your company's only going to go as far as your CEO goes in AI and it's not something you can delegate. You have to have your hands in it cuz you don't otherwise you don't have an intuition for it. But for a long time it has seemed like yeah, that's something that the people who are doing the work have to do but like I don't have to do that. Like I'll just tell them what to do. And so I think if you're a CEO, you kind of can get away with your day looking very similar. I think that will change rapidly at some point where it'll be like oh no, I'm like way behind but for now because or maybe even middle managers, like those kinds of people I think are it's fairly similar. I think like maybe sales because it's so so impersonal.

是的，这是我的选择。你知道，它正在悄悄进入 BDR 这类领域，我们可以处理很多 BDR 类型的查询。你只和真正想要的人交谈。而且你可以用它来做销售，做研究非常有用。

That's yeah, that's my vote. You know, it's sort of creeping up in the kind of BDR like we can deal with a lot of, you know, BDR type queries. You're only talking to like people who actually want it. And you can do it for sales it's like it's so useful to do research.

我最喜欢的 Codex 体验是我们在招聘一位学习与发展主管。我通常会发布职位信息，但我觉得纽约有一家叫 General Assembly 的公司，长期提供非常优质的技术教育。所以我想，曾在 General Assembly 工作过、现在又对 AI 感兴趣的人会很合适。我直接把要求输入 Codex，然后去忙别的事，回来时它已经找到了完美人选。他在 General Assembly 工作过，当过讲师，非常热衷 AI，还在 Twitter 上关注了我。于是我直接给他发了私信，并和他共进晚餐。这太疯狂了。以前这要花很长时间。这对销售、招聘等所有方面都非常有价值。

Like my favorite Codex experience is we're hiring a head of L&D. I always put out a job post, but I felt like there's this company called General Assembly in New York that's done really good technology education for a long time. So I thought someone who worked at General Assembly and is now into AI would be really good. I literally typed it into Codex, went off doing something else, came back, and it found the perfect guy. He worked at General Assembly, was an instructor, is super AI-pilled, and follows me on Twitter. So I just DM'd him and had dinner with him. That's crazy. That would have taken so long before. And it's super valuable for sales, recruiting, all that kind of stuff.

是的，我首先想到的是销售。漏斗顶端的 AI 在寻找和筛选线索等方面帮助很大。感觉销售人员的工作并没有根本性的不同。

Yeah, sales is where my mind went. The top of funnel AI is helping a lot with sourcing and qualifying things like that. It feels like the work of a salesperson is not fundamentally different.

而客户支持已经发生了根本性变化。这很有趣。销售方面，目前对那些人来说还不错。

And customer support has fundamentally changed. So that's interesting. Sales, so far so good for those folks.

好的。那么也许总结一下这个类别中关于工作形态如何变化的一些预测。我目前听到的是，工作中会有更多审查他人输出的部分。其次，会有很多几乎像照看 AI 智能体一样的工作，让它们做你想做的事，进行部署，然后一路维护，确保它们继续工作。在我们进入第三部分之前，还有其他补充吗？

Okay. So maybe summarizing some of the predictions in this bucket of just the shape of the work, how it's going to change. What I'm hearing so far is it's going to be a lot more reviewing of other people's output as a part of the work. And then two, there's going to be a lot of almost babysitting of AI agents to make them do the thing you want them to do for deploying and then just gardening them along the way, make sure they continue to do their work. Anything else before we get into our third bucket?

我更倾向于将其分为更少的照看智能体，更多的是你的前线部署团队在努力构建一个完整的系统，让知识较少的人也能使用该系统而不会做蠢事。这是一个非常有趣的工程挑战。我认为“照看”这个词让人觉得你只是在等它出错然后修复。有时确实如此，但我认为很大程度上这是一个极其有趣的工程挑战：构建一个系统，让组织中的其他人能够完成以前需要技术的工作。而如果你不是那些人，比如你是数据科学家之类的，你可以借助 AI 更深入地研究真正重要的问题，这些问题最终可能会渗透到前线部署工程团队的工作中，但更具生成性、更新颖，并且你在处理更难的问题。

I would sort of split it into less babysitting agents and more your forward deployed team is trying to build a whole system that makes it so that people who have less knowledge can use that system without doing something dumb. And that's a really interesting engineering challenge. I think babysitting kind of makes it feel like you're just waiting for it to mess up and then fixing it. That can be the case, but I think a lot of it is just an extremely interesting engineering challenge of building a system to enable everybody else in the organization to do what used to be a technical job. And then if you're not one of those people, like you're the data scientist or whatever, you can go a lot deeper with AI into really important questions that eventually probably filter into the work that the forward deployed engineering team is doing, but is more generative and more new and you're dealing with harder questions.

另一件我觉得非常有趣的事情是，我认为我们会阅读更多 AI 生成的文档和邮件，并且我们会喜欢它。我认为在编程领域我们已经这样做了，比如阅读计划文档。我不希望工程师手写计划文档，那会很傻。我认为同样的情况也适用于其他方面。你知道，当我们在 2025 年底做季度规划时，我们完全使用了 Notion 智能体。我们有一堆 Notion 智能体，或者实际上是一个 Notion 智能体，然后我们有一个公司层面的战略，公司里的每个人都与智能体对话。它会问他们去年发生了什么，进展如何，你的目标是什么，今年想做什么，你的指标是什么，它会提出质疑，然后它会问这与公司整体理念有何关联？诸如此类。然后我得到了这些非常棒的 AI 生成的战略报告或每个团队的季度计划。然后我可以查看，比如，谁需要互相沟通？哪些团队需要沟通但他们自己不知道？哪些计划质量低，哪些质量高？所有这些都让处理变得容易得多。我现在经常看到这种情况。我 consistently 收到 AI 生成的内容，AI 生成的文档有优劣之分。劣质文档是，他们制作它花费的时间比我阅读它还要少，而且他们不为每一行内容负责。所以我的期望是，如果你发给我一份 AI 生成的文档，我觉得很好。但如果我们在讨论时，你显然不知道里面写了什么，那绝对不行。不允许这样做。我认为这种对 AI 生成内容的反感会消失，因为当我的团队成员很好地指导 GPT-5.5 时，它能写出的战略文档比他们自己胡乱敲键盘要好得多。大多数人写战略文档真的很差。所以门槛很低。邮件也是如此。我现在大部分邮件都是由 GPT-5.5 和 Codex 写的。老实说，我宁愿它标明来自 GPT-5.5，我可能会改成那样。但前几天我有一次经历，我需要给一位投资者发邮件，我让 Codex 去做。Codex 通常会问我，但这次它没有，直接发送了邮件。我根本没看。然后我去已发送里查看，发现这正是我会发送的内容。所以很多时候它已经很接近了。它可能有点过于正式，还有一些小问题……但当你仔细想想，你的大部分邮件都是例行公事，平淡无奇。我当然希望由我来决定内容应该是什么，但具体的句子通常对我来说并不那么重要。有时它们非常重要。这是从一个作家口中说出的。我非常在意写作。我认为人类写作极其重要。而且我期望我们只发布人类写作的内容。实际上，我们发布的是人类和 AI 写作的混合，但我们总是标注出来。有时在某些事情上有一个 AI 合著者也不错。我绝对认为人类写作很重要，并且我认为对 AI 写作的反感或排斥是愚蠢的。

One other last thing that I think is really interesting is I think that we will be reading way more AI-generated writing in documents and emails and we will like it. And I think we are already doing this in coding where we read plan documents. Like I don't want an engineer to handwrite a plan document. That would be very silly. And I think the same is true. You know, when we did our quarterly planning at the end of 2025, we did it all with Notion agents. We had a bunch of Notion agents, or really one Notion agent, and then we had a top-level company strategy and then everybody in the company just talked to an agent. It asked them about what happened last year, how it went, what were your goals, what do you want to do this year, what are your metrics, it pushed back, and then it was like, how does this relate to the overall company idea? All that kind of stuff. And then I got these incredibly good AI-generated strategy reports or quarterly plans for each team. And then I could go in and be like, okay, who needs to talk to each other? Which teams need to talk to each other that don't know they need to talk to each other? And which one of these is actually low quality or high quality? All that kind of stuff makes it a lot easier to process. And I see that all the time now. I consistently get AI-generated stuff and there is a difference between an AI-generated document that's slop and not. And the slop one is it took them less time to make it than it takes me to read it. And they don't stand behind every line. So my expectation is, if you send me an AI-generated document, I think that's great. And if we talk about it and it's clear you have no idea what's in it, big no-no. Not allowed to do that. And I think this aversion to AI-generated stuff will go away because the kind of strategy document that GPT-5.5 can write when it's directed well by someone on my team is way better than them just dinking and dunking on the keyboard. Most people are really bad at writing strategy documents. So the bar is low. And same thing with email. Most of my email is written by GPT-5.5 and Codex right now. And I would honestly prefer it to say that it's coming from GPT-5.5 and I may change it to do that. But I had this experience the other day where I had to send an email to one of our investors and I asked Codex to do it. Codex knows to ask me and it usually does, but this time it didn't. And it just sent the email. And I didn't look at it at all. And I went to my sent and looked at it and I was like, oh, this is exactly what I would have sent. So it's pretty close to that a lot of the time. It can be a little over formal and there are a couple things that... but when you really think about it, most of your email is kind of rote. It's kind of prosaic. I definitely want to be the one to think about what it should say, but the actual sentences don't matter that much to me usually. Sometimes they do a lot. And this is coming from a writer. I care a ton about writing. I think that human writing is incredibly important. And I expect we only publish human writing. Well, actually, we publish a mix of human and AI writing, but we always label it. Sometimes it's nice to have an AI co-author on certain things. I absolutely think that human writing is important and I think that the reaction or the aversion to AI writing is silly.

你的意思是，在内部，如果你只是在处理规划、文档、邮件之类的事情，AI 写的东西就没那么可怕。而且就像你说的，人们已经在这么做了。很多时候你甚至更喜欢这样，因为人写得实在太差了。

And your point is internally, if you're just working on planning, documents, email, and things like that, it's much less scary that it's AI written. And to your point, people are already doing this. You almost prefer it a lot of times because people are really bad at it.

完全同意。我们对外部内容也是这样。比如我们发布的所有指南，通常都有智能体辅助，智能体是合著者，这些指南既供人类阅读，也供智能体阅读。这是因为，如果你在写一篇大型信息类文章——你经常这么做——要真正应用它，最好的方式就是让你的智能体把它吸收进去，记住：下次我做定价时，提醒我这份指南，我们一起过一遍。这样能更好地将想法落地，也能让你深入更多，因为智能体一秒钟就能读一万页。所以你可以跟人类讨论故事、重要的事情和核心想法，而智能体掌握所有细节，在你需要时为你应用。

Totally. We have this too for external stuff. Like we publish all these guides, and the guides are often agent-assisted. The agent is a co-author, and they're intended to be read both by humans and by agents. And that's because if you're writing a huge informational thing, I mean, you do this all the time. In order to really apply it, the best way to do that is just have your agent ingest it and remember: next time I'm doing pricing, remind me of this guide and we'll go through it together. It allows you to operationalize the ideas much better, and it allows you to go much deeper because agents can read like 10,000 pages in a second. So you can talk to the human about the story, the stuff that matters, and the core ideas, and the agent has all the details that it can then apply for you when you need it.

太棒了。在进入最后一个类别之前，这个类别还有什么要说的吗？

Awesome. Anything else in this category before we get into our final category?

没有了。

No.

好，那我们开始吧。最后一个类别是：在我们正在接近的 AI 未来中，谁会成功？未来一两年里，人们应该做什么才能成功？

Okay, let's do it. So, the final bucket is just: who will be successful in this AI future that we are approaching? What should people be working on to be successful in this next year or two?

我非常非常看好产品经理。我知道你的听众可能会喜欢这个。但让我确信这一点的轶事是，我们内部有个叫 Marcus 的家伙，他负责 Spiral，我们的写作应用。Marcus 是产品经理出身，之前负责 Axios 的写作产品，是产品经理，带一个大团队，做到了数千万美元的营收和 ARR。他休了一年假，然后彻底迷上了 AI，学会了很好地使用 Cursor。现在他可能用 Claude Code，但他之前很长一段时间都极度痴迷 Cursor。他算是轻度技术型：他知道什么是数据库迁移，如果需要看代码，我觉得他能看懂，但一年前我们绝不可能雇他来做这份工作。然而，编码模型已经足够好了，他能把自己已有的技术知识与非常敏锐的产品感、写作感和用户感结合起来。这太厉害了。他交付速度比团队里几乎任何人都快，而且他对每一个用户、每一次对话都有敏锐的洞察力——这意味着什么，我们如何把它整合成一个关于下一步方向的故事，我们需要修复哪些问题，等等。我觉得他感到解放了，因为他不需要组织整个团队来做这些事，他自己就能搞定。这非常令人印象深刻，也让我非常看好任何真正迷上 AI 的产品经理。

I am super, super bullish on PMs. And I know that your audience will probably love that. But my anecdotal case that has convinced me of this is we have this guy internally, his name is Marcus, and he runs Spiral, which is our writing app. Marcus is a PM by training. He previously ran Axios's writing product, was a PM, had a big team, and it got to tens of millions in revenue and ARR. And he took a year off that job and just got super AI-pilled. And just learned how to use Cursor really well. Now, I think he uses Claude Code, but he was extremely Cursor-pilled for a long time. And he's what I would call lightly technical. He knows what a database migration is. If he has to look at code, I think he can understand it, but we never could have hired him to do this job even a year ago. But the coding models have gotten good enough that he can pair the technical knowledge he does have with his really spiky product sense and sense for writing and sense for users. And it's so dangerous. He ships faster than almost anyone on the team, and he has such an eye for every single user, every single conversation, what does it mean, how do we collect it into a story about where we want to go next, what are the issues we need to fix, and all that kind of stuff. And I think that he feels liberated because he doesn't have to organize a whole team of people to do that. He can just do it. And it's super impressive, and it makes me very, very bullish on any PM who gets really AI-pilled.

这话我爱听，Dan。你让这里很多听众非常开心。我自己也一直这么说。你需要掌握的技能就是那些——构建的部分已经为你做好了。你需要擅长什么？弄清楚要构建什么，判断它好不好，找出要解决的问题。所以，我很高兴你亲眼看到这个变成了现实。我真的相信这一点。

Music to my ears, Dan. You're making a lot of very happy listeners here. I've been saying this for a long time, too. It's just like the skills you need to build are the things like the building out is done for you. What do you need to be good at? Figuring out what to build, figuring out if it's great, figuring out what problems to solve. So, I love that you're actually seeing this come to fruition. I really believe it.

这可能是我整个播客中评分最高的一期了。会有……

This could be the highest rated podcast episode of my whole podcast. There is going to be like...

没错。会没事的。统计回来了，产品经理回来了，你知道的。这是我做过的反主流观点最强的一期。

Hell yeah. It's going to be okay. Stats is back, PMs are back, you know. This is the most contrarian episode I've ever done.

天哪。好，那么我认为会成为超级强者的另一类人——同样，这是因为我们在内部看到了——是全栈设计师。如果你是一名设计师，并且一直使用这些工具，你太习惯了：我做了这个漂亮的交互，但工程师就是不想做，或者它没有按照我认为的方式实现，或者有各种问题。我看到我们内部和外部的很多设计师现在感到非常有能力去构建东西，因为他们觉得：我有这么多想法让东西看起来惊艳，有这些有趣的交互，而这正是用实时编码很难做到的，因为做出来都一个样，看起来都像垃圾。而他们能做出看起来截然不同的东西，现在他们真的能把它构建出来。当我们与他们在内部合作时，你会看到他们直接提交拉取请求。他们不再那么需要交接。有时需要，但很多时候他们直接提交拉取请求，东西就建好了，就这样。我认为这对公司运作方式来说是不可思议的，但这也是这些人变得更好、开始自己创业的巨大机会，因为他们现在能做出东西了。我认为设计师是非常有创造力的人，而 AI 对这样的人来说就像一个超级工具。

Oh my god. So, okay, so the other people that I think are going to be super power people, and I again this is because we see this internally, is full stack designers. If you're a designer and you're in these tools all the time, you're so used to: okay, I make this beautiful interaction and the engineer just doesn't want to do it, or it doesn't happen the way I think it should happen, or there's all this stuff. And I see so many designers for us internally or externally where they now feel so empowered to go build stuff, because they're like: I have all these ideas to make things look amazing and these interesting interactions, and that's the exact thing that's really hard to do with live coding because it just all looks the same, so it all looks like slop. And they can make stuff that looks so different, and now they can actually build it. And what you see when we work with them internally is now they're just making pull requests. They don't need to hand it off as much. Sometimes they do, but a lot of times they just make pull requests and the thing is built and that's it. And I think that's incredible for the way that companies work, but it's also a huge opportunity for those people to become much better and start their own thing, because they can make stuff now. And I think designers are such creative people, and I think AI is like a super tool for anyone like that.

我完全同意。即使有 Claude Design 和所有这些 AI 设计工具，一旦你看到它，你就会觉得那肯定是 Claude Design。而创造力，就像你说的，感觉会越来越有价值，以便从人们不断发布和推出的垃圾中脱颖而出。所以我完全同意。有趣的是，设计师岗位——我研究过就业市场——设计师岗位一段时间以来并没有增长。所以我等着看这是否会成为一个大趋势，就像我们需要更多设计师一样。

I so agree. Even though there is Claude Design, there's all these AI designing tools, once you see it, you're like that's definitely Claude Design. And they're like the creativity to your point is it just feels like it's going to be more and more valuable to stand out from all the slop that people are shipping and launching constantly. So, I completely agree. It's interesting that designer roles, I do research on the job market, and interestingly designer roles have not grown in a while. So, I'm waiting to see if that becomes a big trend, just like we need more designers.

嗯，这确实很有趣。我们拭目以待。

Hm, that is really interesting. We'll see.

是啊。我们拭目以待。这可能是预测的一个方法：人们是不是在招聘更多设计师？我不知道。这很有趣。好。所以产品经理、设计师在蓬勃发展。

Yeah. We'll see. We'll see. That might be a way to predict this: are people hiring more designers? I don't know. That is interesting. Yeah. All right. So that's PM, designer thriving.

产品经理、设计师蓬勃发展。

PM, designer thriving.

嗯，我也认为总体上 AI 工作末日并不是真的。当然，我们看到公司开始重组，我认为这很有道理。老实说，很多重组你可以说是 AI 导致的，但其实是之前过度招聘，公司业绩不佳，这些本来就要发生，AI 只是个很好的借口。但一些 AI 首席执行官谈论的大规模失业，我认为不会发生。我目前看到的模式——再说一次，我没有水晶球，但我确实觉得我们已经看到了足够多的新模型发布，可以大致了解趋势——是新模型发布，或者说模型总体上，会让昨天的人类能力变得廉价。

Um, I also just think generally the AI job apocalypse is not really a thing. Absolutely, we see companies starting to reorganize, and I think that makes a lot of sense. I think to be honest, a lot of the reorganization you can say it's AI, but it's like we over-hired and the company's not doing as well, and all that kind of stuff was coming, and this is a good excuse. But the mass unemployment thing that some AI CEOs are talking about, I think that's not going to happen. The pattern that I see so far, and again I don't have a total crystal ball, but I do feel like we've seen enough of the new model drops to have some sense of how this is going, is that what a new model drop does, or what models do in general, is they make yesterday's human competence cheap.

我的意思是，模型吸收了所有已经发生的数据，然后让这些数据能以极低的成本部署到你想要的任何场景中。这样一来，每个人都获得了一种新能力。它被迅速采用，突然之间这些东西就无处不在。就像突然之间谁都能做个落地页，于是到处都是新的落地页；突然之间谁都能写东西，于是到处都是垃圾推文。但有趣的是，因为这些内容都来自模型，而且大家用的基本上是同一个模型，如果你用最默认、最基本的方式去使用，它们看起来都一模一样。所以它就变得商品化了，不再有价值。而人类要做的是，我们走进去说，‘好吧，我们有昨天积累的所有冻结的人类能力。我该怎么用它来做出一些新的、有趣的东西？’我确实认为，从结构上看，由于模型的工作方式，以及模型公司为了使其合规和对齐而存在的财务激励，模型总是会落后于那些利用模型为自己非常特定的场景创造新专长或新事物的人。那些东西会被纳入模型，但同样，这又会为人们进一步向前推进创造空间。我认为这在几乎所有工作中都能看到一点影子，比如工程师。突然之间，人人都是工程师。但这并不意味着我们要解雇工程师。对工程师的需求反而更大了，因为你需要工程师来搞清楚，‘好吧，这些都是垃圾。这些东西到底该怎么放进我们的代码库？’我认为这是基准测试上升所无法真正捕捉的。而且感觉这需要很长时间才能改变。

So, what I mean by that is they ingest all this data of what has happened already and they make it really cheap to deploy that in whatever situation you want as your own. And what happens then is this is a new power that everyone has. So, it gets adopted super rapidly and suddenly that stuff is everywhere. It's like suddenly anyone can make a landing page, there are new landing pages everywhere. Suddenly everyone can write, there are slop tweets everywhere. But what's interesting is because it's all coming from these models and everyone's using basically the same models, it all looks the same if you use it in the most default basic way. And so, it becomes commoditized. It's not valuable anymore. And what humans do is we sort of go in there and we're like, 'Yeah, we have all this frozen human competence from yesterday. How do I use this to make something new and interesting?' And I really think that structurally, because of the way the models work, because of the financial incentives of model companies to make them compliant and aligned, structurally, there are always going to be trailing behind those people who are taking the models and using them to make new expertise or make new things that haven't been done that way before for their very particular situation. And that stuff is going to get incorporated into the models, but again, it will create room for people to push further ahead. And I think that you see this in a small way in pretty much all jobs, like engineers. Suddenly, everyone's an engineer. That doesn't mean we fire the engineers. There's way more demand for engineers because you need the engineers to figure out, 'Okay, this is all slop. How should this actually go in our code base?' And I think that's something that the benchmarks rising don't really capture. And it feels like a thing that will take a long time to change.

人们听到这个预测可能会觉得，‘好吧，工作末日不会来了。人们不会被全部解雇。人类的工作还会存在相当长一段时间。’这可能太让人安心了，因为你可能还是得改变自己的运作方式，才能在将来保住工作。你有没有一种感觉，就是‘这里有一些你需要做的事情，以免成为被裁掉的人之一？’

People may be hearing in this prediction here of just, 'Okay, the job apocalypse is not going to happen. People are not going to be all fired. There's going to be human jobs remaining for quite a while.' It may be almost too comforting because you may probably have to change the way you operate to still have a job in the future. Do you have any sense of just like, 'Here's what you need to do to not be one of these layoffs?'

是的。而且我认为这实际上非常重要。你唯一需要做的就是驾驭模型。这意味着无论你做什么，都要使用它们。你知道，我们讨论过 Codex 和 Copilot 如何成为工作的标准操作系统。如果你只是这样做，当新模型出现时，你尝试它们并弄清楚，‘好吧，现在它们有了新能力，我该怎么利用它们？’而不是‘我要试着忽略它，因为它让我害怕’，我认为这其实是理性的，是合理的反应。而且，如果你驾驭它们，它们会扩展你的能力，让你不会落后。你成为了未来的一部分，成为了工作方式的一部分，我认为我们会在很长很长一段时间内都需要这样做的人。

Yes. And I think that is actually super important. The only thing you need to do is ride the models. And that means use them for whatever it is that you do. You know, we've talked about how Codex and Copilot are becoming the standard operating system for work. If you're just doing that and when new models come out, you're trying them and figuring out, okay, how can I now, they have new powers, how can I use them instead of just being like, I'm going to try to ignore it because it makes me afraid, which I think is honestly rational, it's a reasonable response. And also, if you ride on top of them, they extend your powers in a way that doesn't leave you behind. You're part of the future and part of the way work happens and I think that we're going to need people doing that for a very, very long time.

我喜欢‘驾驭模型’这个说法。那么，比如说新模型出来了，你觉得在 Salesforce 工作的人，比如一个产品经理，他们应该怎么做才能驾驭模型？

I like this term 'ride the model'. So, what's like saying you all comes out, what do you think someone say working at I don't know, Salesforce. Say a PM at Salesforce, what should they do to ride the model?

嗯，有一件非常有趣的事情是，很多公司甚至限制了员工这样做，因为我不知道在 Salesforce 能不能用最新的模型，很多时候你得等，或者有各种限制。所以，你可能得在业余时间做。但我真的很喜欢对新模型做的事情就是玩。有些事情我知道它现在还做不到，但每当新模型出来，我总是会再翻一次石头，看看它现在能不能做到。比如上次它通不过高级工程师基准测试，我又翻了一次石头，现在它得了 60 分（满分 100），这已经很不错了。所以，驾驭模型的方法不是某一件具体的事情，因为模型总是在变，而是要充满好奇心和玩乐精神，把新模型应用到你关心的任何事情上，无论是你的工作还是工作之外的事情，并且不断翻石头，因为现在可能不行，但最终可能会行，很可能最终会行，而且你使用它的方式很重要。

Well, one of the things that's really interesting is a lot of companies handicap their employees from even doing this because like I don't know what model, I don't know if you can use the latest models at Salesforce, you know, like a lot of times you have to wait or it's, you know, whatever. So, maybe you have to do it on your own time. But, the thing that I really like to do with new models is play. And there are certain things where I know it can't quite do it yet, but when a new model comes out, I like always turn the rock over again to be like, can it do it now? You know, so, it could not do the senior engineer benchmark last time and I turned it over the rock over again and now it's 60 out of 100, which is really good. So, the way to ride the models is not one specific thing because they're always changing, but it is to be curious and playful, to apply the new model to whatever it is that you care about, whether that's your job or something outside of your job and to keep turning over rocks because it may not work now, but it may work eventually, it probably will work eventually and the way that you use it matters.

所以，很酷的一点是，我认为人们以为 AI 的前沿在旧金山。但实际上我并不这么认为。我认为 AI 的前沿在于 AI 与真实的人类在做某件事的交汇处。因为旧金山的人是在制造 AI，但他们实际上并不太了解如何使用它。他们不知道，或者至少他们不知道如何使用它的一切。他们需要看到别人怎么用。所以每当新模型出来，你就有机会成为世界上第一批发现它可能有什么用的人。这就像一次新的发现。我认为这就是为什么，比如我们在布鲁克林。但我真的认为我们比旧金山的人领先很多，因为我们只是把它们用于一切。如果人们持续这样做，我认为很难被淘汰。

So, what's really cool is that I think people think of the edge of AI as being in San Francisco. And I actually don't think that that's where it is. I think the edge of AI is wherever AI meets a real human doing something. Because the people in San Francisco, they're making it, but they don't actually know a lot about how to use it. They don't know, or at least they don't know everything about how to use it. They need to see how other people use it. And so whenever a new model comes out, you get to be one of the first people in the world to discover what it might be useful for. And that's like a new discovery. And I think that's why, for example, we're in Brooklyn. But I really think of us and I think we are quite far ahead of people in San Francisco because we just use them for everything. And if people do that consistently, I think it's going to be very hard to lose.

这是目前 AI 最令人惊叹的事情之一：无论你有多少钱或多少钱，你都能使用最先进的 AI 模型。它不是免费的，所以你需要一些钱。但你可以一发布就立刻获得它。也许唯一有优势的人是那些在 OpenAI 或 Anthropic 工作的人。但除此之外，它就这么可用。我知道我上周或几周前和你们一起参加了他们的代码活动，他们都在用 Mythos，我当时想，‘该死的。’真烦人。但我认为这完全正确。如果 IBM 发明了 AI，你可以打赌它不会是这样。它会贵得离谱，只有顶级公司才能用，而且他们会以最奇怪、最无趣的方式使用它。我认为 AI 是在美国、在硅谷文化中构建的，这一点非常重要，这种文化是‘我们要让智能便宜到无需计量’。这不是默认立场。这意味着每个人都拥有这个广泛可用的工具，我认为这太棒了。

That is one of the most amazing things about AI right now is no matter how much money you have or little money you have, you have access to the most advanced AI model. It's not free, so you need some money. But you can get it immediately when it comes out. Maybe the only people that have an advantage are the people working at OpenAI or Anthropic. But otherwise, it's just available. I know I was at their event with you, their code event with you last week or a couple weeks ago and they're all using Mythos and I'm like, 'God damn it.' So annoying. But I think that's totally true. Like that is if IBM had invented AI, you can bet it would not be like this. And it would be like a bajillion dollars and only the top companies could use it and they would be using it in the weirdest, most uninteresting ways. And I think it's really important that AI was built in America and in the Silicon Valley culture that's like we want to make intelligence too cheap to meter. That's not the default stance. And it means that everyone has this broadly accessible tool that they can use and I think that's amazing.

说得好。有趣的是，这也催生了历史上增长最快的公司、最大的公司。确实如此。不是那种方式——

It's such a good point and interestingly it's also created the most fastest growing companies in history, the biggest companies in history. That's true. Not the way to

那些硅谷的家伙很聪明。如果我把视角拉远来看，这真的很有意思。这有点像硬币的两面。一方面，其实很多东西没怎么变。SaaS 还在继续，工作没有消失。我们还在发邮件，还在用 Slack。很多工作都没变。另一方面，每个角色都变了。工程师不写代码了，产品经理不写 PRD 了，设计也变了。有趣的是，变化如此之大，又如此之小。我不知道。人们以为会是一个全新的世界，但很多方面其实还好。它会像现在这样继续，只是边缘多了很多东西。这就是我的感觉。我既兴奋，又觉得一切都变了。我非常看好它，看好我们将取得的进步等等。是的，我只是觉得有些事情会和现在很相似，这可能是好事。我认为，总的来说，我们对未来的直觉——我对此有一个模型——就像中世纪人们对地平线尽头的直觉一样：那里有龙吗？还是会掉进虚无？很多人都有一种强烈的直觉，觉得地平线那边会发生可怕的事情。也有人觉得会有不可思议的事情发生，会改变一切，我们会进入乌托邦，所有人都幸福。但当你真正到达那里时，你会发现有些东西很酷，有些东西不酷，那只是另一个地平线。我认为这就是思考未来的方式。在你开始看到它之前——我觉得我们能看到，因为我们在内部一直能看到——重要的是不要让你的思绪失控，不要想着“这会发生，那会发生”，因为你会讲一个在当时听起来很真实的故事，但后来你会发现，实际上它要复杂得多，某种程度上是“一切都变了，又什么都没变”。一旦你到了那里，你就会开始看到，哦，是的，这是真的。部分原因是 AI 公司很擅长用未来可能发生的事情吓唬我们。我认为这种情况正在改变。他们可能已经意识到，不应该让所有人都对危险感到恐慌。

Those Silicon Valley guys, they're smart. If I zoom out on the conversation, it's really interesting. There's a kind of these two sides to the coin. One is not a lot is actually like so much is not changing. SaaS continues, jobs not disappearing. We're still emailing each other. We're still working in Slack. Like a lot of the work not changed. On the other hand, every role transformed. Engineers don't write code. PMs don't write PRDs. Design and design, you know, it's like it's so interesting how much has changed, how much has not changed. I don't know. It's interesting that people think it's going to be this whole new world, but in many ways it's okay. It'll continue the way it is with a lot of stuff around the edges. That's how I feel. Like I'm simultaneously so excited and it feels like everything has changed. And I'm so bullish on it and the progress that we're going to make and all that kind of stuff. And yeah, I just I feel like there are these things where they're going to be pretty similar to how they are and that's probably good. And I think generally our intuitions about the future the model that I have of what our intuitions are about the future is the intuitions that people had in the Middle Ages about like what happened at the end of the horizon, you know, it's like are there dragons? Like does it drop off into nothingness or whatever? You know, like a lot of people have a lot of deep intuition that there's something terrible going to happen over the horizon. And also that some people are like there's something incredible. It's to change everything. We're going to all be happy as a utopia. And what happens is you get there and you're like, there's some really cool things, there's some not cool things, and it's just another horizon. And I think that's the way to think about the future. And until you get to that place where you're starting to see it, and I think we get to see it cuz we get to see it internally all the time, it's important not to let your mind get away from you and being like, this is going to happen and this is going to happen and whatever cuz you're going to tell a story that sounds so real in the moment, but later on you're like, actually it's much more complex than that and somewhere it's sort of a both everything has changed and nothing has. And once you get there, I think you're sort of starting to see like, oh yeah, this is a real thing. Part of it is that the AI companies are very good at scaring us about what might happen in the future. And I think that's actually shifting. I think that they've realized maybe we should not freak everybody out about the dangers.

我觉得那个公关策略完全说不通。我确实认为它是真诚的，但效果太差了，而且我觉得它也是错的。嗯。要不我们最后聊聊听众在未来一年应该做些什么来取得成功，随着世界的发展方向。写模型。我会尝试在 Codex 或 Co-work 中运行所有工作流，看看效果如何。如果你的公司不允许你在自己的时间里做，我会尝试一些智能体产品，比如 Open Claw 或 Hermes，或者对于不太懂技术的人，有 Victor，我们还有 1 + 1。我会熟悉这两种工作方式。然后试着找点乐子。我觉得现在太多人是因为害怕错过才做这些——比如我可能会失业，或者错过什么大事——而实际上找到有趣、有用的 AI 用途的最好方法就是做点开心的事。我们请过 Nikhil Singhal 上播客，他描述的方式是：你必须找到与 AI 相处的快乐时刻。一旦你发现“哇，我不敢相信 AI 为我做了这个。太棒了。我要继续构建更多东西。”是的，我同意。

I that PR strategy just does not make any sense to me. I do think that it's like genuine, but it's so ineffective and I think it's also wrong. Mhm. How about we end with maybe just like a few things listeners should do to be successful over the next year with the way the world is moving. Write the models. I would try all of your workflows in Codex or Co-work and see how that works. And if your company doesn't let you do it on your own time, I would try out some of these agent products like Open Claw or Hermes or for less technical people there's Victor, we have 1 + 1's. I would get comfortable with both of those ways of working. And try to like try to have fun. I think there's too much of I'm doing this because I have FOMO like it might I might lose my job or like I might miss out on this big thing or whatever and the best way to actually figure out interesting useful things to do with AI is to like do something enjoyable. We had a Nikhil Singhal was on the podcast and the way he described it is you got to find your moment of joy with AI. Once you find like, "Wow, I can't believe AI did this for me. This is awesome. I'm going to keep building stuff." Yeah, I agree.

如果你还没看到过，那就试着去找、去解决。我经常听到的说法是：在生活中或工作中找一个难题，看看 AI 能不能解决。去 loveabull、Claude Code、replit 看看。试着构建那个东西，通常你会觉得“天哪，这太酷了。”

If you haven't seen that yet, then it's just like try find try solving it. The thing I hear a lot is just find a problem in your life or work and see if AI can do it. Go to loveabull, go to Claude Code, go to replit. Try to build the thing and often it's like, "Holy this is so cool."

Dan，我们还有什么没聊到的吗？我们已经深入探讨了很多。你还有什么想分享的吗？在我们进入非常激动人心的快问快答环节之前，还有什么想预测或想说的吗？

Dan, is there anything else that we haven't covered? We've gone deep on so much. Is there anything else you want to share? Anything else you want to predict or just say before we get to our very exciting lightning round?

我觉得都聊到了。我们聊了很多。这太棒了，我很期待一年后看看我的表现如何，希望你能监督我。

I think we covered it. We did a lot. This is awesome and I'm very excited to see how well or poorly I do in a year and I hope that you hold me to it.

我们让 AI 给我们打分，怎么样？我们会像看舞蹈预测系列一样看待世界。好了，Dan Shipper，我们到了非常激动人心的快问快答环节。我有五个问题要问你。准备好了吗？

We're going to have AI score us. How about that? We'll look at the world like a dance prediction series. Well, with that Dan Shipper, we've reached our very exciting lightning round. I've got five questions for you. Are you ready?

准备好了。

I'm ready.

你经常向别人推荐哪两三本书？

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

显然是 Annie Dillard。Every 公司的每个人都要读《写作生涯》。入职时会拿到一本，必须读。不过只需要读最后一章。我觉得最后一章非常精彩，它处于写作、技术和未来的交汇点，探讨了与未来和时间的关系，我认为它把 Everything 的一切都浓缩在了一章里。写得非常好，而且我觉得 Annie Dillard 总体上就很棒。还有什么推荐呢？我就说说最近读过的几本我很喜欢的书吧。每当我喜欢什么，我就会告诉所有人。所以这些书我推荐过很多次了。我最近在读——我学到的一件事，我以前不知道——丘吉尔是个非常好的作家。他写了一整部二战史，是历史和回忆录的结合。我觉得这太酷了，因为他亲身经历了，他做到了。这和我们 Every 公司做的事情有某种联系。我们在构建东西，在写作，而能同时做这两件事的人很少见。所以丘吉尔的二战史非常棒。我刚读完第一卷，正在读第二卷。纳粹刚刚入侵法国。非常引人入胜。这是一本。另外，我最近对量子物理有点着迷。如果你深入研究，AI 其实对量子物理非常有用。

Obviously Annie Dillard. Everyone at Every has to read The Writing Life. Like when you join you get a copy and you have to read it. You only have to read the last chapter though. I think the last chapter is incredible and it is at the intersection of writing, technology, and the future and it's like its relationship to the future and to time and I think that's like it's everything about every like wrapped up into like a very tight chapter. It's so good and I think Annie Dillard just generally is fantastic. What else do I recommend? I'll just tell you a couple things that I've read that I like really liked recently and whenever I like something I always just like tell everyone about it. So, I have recommended these a lot. I've been reading one of the things I learned, which I didn't know, is Churchill's a really good writer. And he has a whole history of World War II that he wrote, and it's like a combination history and memoir. And I think that's so cool because he was there, you know, he did it. And there's something about what we do at everywhere. I feel some sort of kinship with that of like we're building stuff, we're writing stuff, and it's very rare to find people that also do that. And so, Churchill's history of World War II is fantastic. I just finished the first volume. I'm on the second volume. The Nazis just invaded France. Very captivating stuff. So, that's one. I also just I've been on a like a little bit of like a quantum physics like kick recently. AI is very actually very good for quantum physics if you get into it.

我刚刚读完一本叫《天使的严谨》的书。这是一部思想史，把海森堡（他提出了不确定性原理）、博尔赫斯（一位阿根廷小说家，写了很多精彩的短篇小说，现在因为跟 AI 关系密切而大受欢迎）和康德联系在了一起。非常酷，超级震撼，有很多跟 AI 有趣的重叠。强烈推荐。我觉得我们完全可以做一整期播客聊你的阅读和推荐的书，我知道这是你的爱好。我最近沉迷的是《权力掮客》。我想我去拜访你的时候聊过这本书。它永远看不完，但读纽约的历史出奇地引人入胜。

And there's this book called The Rigor of Angels that I just finished. It's a history of ideas that relates Heisenberg, who has his uncertainty principle, Borges, who's an Argentinian fiction writer who wrote a bunch of great short stories that are starting to get a lot of play now because they're very AI-related, and Kant. Very cool, super mind-blowing. Lots of interesting overlaps with AI stuff. Highly recommend. I feel like we could have a whole podcast episode about your reading and books you recommend. I know this is a passion of yours. My current obsession is The Power Broker. I think we talked about it when I was visiting you. It just never ends, but it's surprisingly compelling to read through the history of New York.

好，第二个问题。如果你有时间看电视的话，最近有什么你真的很喜欢的电影或剧集吗？

Okay, second question. What is a recent movie or show you really enjoyed if you have time for TV?

我最近看了很多篮球比赛，这是一方面。我今年成了尼克斯队的球迷，所以很有意思。不过我还看了一部迷你剧纪录片叫《黑暗巫师》，讲的是 Dean Potter。他就像是 Alex Honnold 之前的 Alex Honnold。他性格非常极端，什么都徒手攀岩，然后穿着翼装跳伞之类的。片子探索了他的心理和他的遭遇。我挺喜欢这类东西。还有一部叫《百尺巨浪》，讲的是那些尝试冲大浪的人。这些片子总让我想起创业者之类的。但《黑暗巫师》强烈推荐。

So, I've been watching a lot of basketball, so that's one. I became a Knicks fan this year, so that's really fun. But I recently watched a mini-series documentary called The Dark Wizard about this guy Dean Potter. He was like Alex Honnold before Alex Honnold was Alex Honnold. He has this very extreme personality where he's free soloing everything and then base jumping in a wingsuit and stuff like that. It's sort of exploring his psychology and what happened to him. I kind of like stuff like that. There's another one called 100 Foot Wave about people who are trying to big wave surf. There's something about that that reminds me of founders or whatever. But The Dark Wizard, highly recommend.

有没有你最近发现并且非常喜欢的产品？

Is there a product you recently discovered that you really love?

Codex。真的很好用。

Codex. It's really good.

你有没有一个经常在工作中或生活中回想的座右铭？

Do you have a favorite life motto that you often come back to in work or in life?

有，我有好几个。我在大学时给自己写的核心座右铭是：做值得写的事，写值得读的东西。还有一个人叫 Rob Brezsny，他在 AI 和冥想重叠的讨论中很受欢迎，这也是一个大话题。我真的很喜欢他。他已经去世了，但我认为他很了不起。我听过他很多演讲。有一次演讲只有一句话，但他说：当你面对困难的事情时，你要做的是能够从一种宽广和力量的角度去应对。这里面有一些非常有趣且重要的东西。很多关于冥想或者一般如何应对困难的讨论，更像是 David Goggins 那种方式——你只管冲。有时候那确实管用。但我也觉得，有时候当你面对事情时，比如我非常害怕 AI 会如何改变我的工作，对我非常有帮助的是问自己：我是在从宽广和力量的视角来看待这件事吗？如果不是，我能达到那种状态吗？因为从那个角度去处理会高效得多。这对我帮助非常大。

Yes, I have several. The core one that I wrote for myself in college was: do things worth writing about and write things worth reading. And then there's this guy Rob Brezsny, who's very popular in the AI meditation overlap discourse, which is also a big thing. I really like him. He's dead, but I think he's amazing. I listened to so many of his talks. There's this one talk where it's just one sentence, but he talks about when you're dealing with stuff that's hard, what you want to do is be able to relate to it from a position of spaciousness and strength. There is something really interesting and important in that. A lot of the meditation discourse or generally how do you deal with hard things is a little bit more like the David Goggins approach: you just got to go for it. Sometimes that can work. But also, sometimes when you're dealing with things, for example when I'm super afraid of how AI is going to change my job, it has been very helpful for me to be like: am I coming at this from a vantage point of spaciousness and strength? And if not, can I get there? Because it will be much more productive for me to deal with it from that place. That has been very, very helpful for me.

哇，我喜欢这个。好，我们最后一个问题，围绕这次对话的主题。我很好奇有没有一个 AI 工具你觉得仍然被低估了，最近人们还没注意到。

Wow. I love that. Well, our final question on the theme of this conversation. Curious if there's an AI tool that you think is still kind of underrated that you're just like recently people are sleeping on.

我不想这么说但我必须说：Codex。任何认识我的人都知道，最近我们在 Anthropic 的一个会议上，我告诉 Claude Code 的 Boris 和 Kat，你们一定要试试 Codex。它真的很好，你能用它做的事情非常不一样。尤其是如果你把它和 Anthropic 浏览器一起用，来处理邮件、查看分析数据之类的。它彻底改变了我的工作方式。如果我去找别的工具反而是对你不负责任，因为它就是那么好。

I hate to say this but I have to: Codex. Anyone who knows me, we were at this Anthropic conference recently and I'm telling Boris and Kat from Claude Code like you have to try Codex. It's just really good and the things you can do with it are so different. Especially if you're using it with the Anthropic browser to do things like your emails or check your analytics or anything like that. It has completely transformed the way I work. I would be doing you a disservice if I was searching for something else because it is that good.

靠，太厉害了。你觉得 Anthropic 能追上吗，还是说……？

Damn, that's wild. Do you feel like Anthropic can catch up or is this just like well?

不，是的，我觉得他们能。就像我说的，这会是一场赛马，不同的人会在不同时间领先。但我觉得现在 OpenAI 有点重新夺回了天命。之前几个月，大概六个月左右，比较艰难，但我认为他们回来了。

No, yes, I think they can. Like I said, I think it's going to be a horse race and different people will be ahead at different times. But I think right now OpenAI has gotten back the mandate of heaven a little bit. It was a rough couple months, like 6 months or so, but I think they're back.

有意思。那如果另一个变得更好你会换吗？

Interesting. And you'd switch if one became more?

我会的。人们会说，“哦，你是 OpenAI 赞助的吗？”我说，“不，我只是聊我喜欢的东西。”当 Claude Code 是我真正喜欢的东西时，我大力推荐过它。当它出现时，我就会说我喜欢什么。而且你说得对，在不同场景下同时使用两者很有价值。我会来回切换。我确实仍然经常用 Claude。

I would. People are like, "Oh, are you sponsored by OpenAI?" And I'm like, "No, I just talk about what I like." I was super loud about Claude Code when that was the thing I really liked. I'll just say what I like when it happens. And to your point, there's a lot of value in using both for different things. I switch back and forth. I truly do still use Claude a lot.

市场真大。好了，Dan，我们做到了。我们聊了这么多。我等不及一年后再回顾这个，或者说把这一期发出去，这样大家就可以开始为明年做规划了。最后两个问题。人们在哪里可以找到你，大家应该知道什么？然后听众怎么才能帮到你？

Such a big market. Well, Dan, we did it. We went through so much. I can't wait to revisit this in a year slash get this out so people can start planning for this next year. Two final questions. Where can folks find you and what should people know? And then how can listeners be useful to you?

你可以在 X 上找到我，账号是 Dan Shipper，s h i p p e r，还可以订阅 Every。请订阅 Every，every.to，every.to/subscribe。听众怎么帮到我？你知道，好好玩 AI。说真的，超级有趣。这不一定是直接对我有用，但当人们亲自上手，一起摸索而不是争论时，一切都会变得更好。你能做的最有用的事情就是找到在生活里用好它的方法，然后分享出来。

You can find me on X at Dan Shipper, s h i p p e r, and you can subscribe to Every. Please subscribe to Every, every.to. every.to/subscribe. How can listeners be useful? You know, have fun with AI. Seriously, it's super fun. It's not necessarily useful to me, but it makes everything better when people put their hands in it and just start figuring it out together rather than arguing about it. The most useful thing you can do is find ways to use it well in your life and share it.

Dan，非常感谢你来做客。

Dan, thank you so much for being here.

谢谢。

Thank you.

非常感谢你的收听。如果你觉得本期有价值，可以在 Apple Podcasts、Spotify 或你最喜欢的播客应用上订阅本节目。也请考虑给我们评分或写评论，这真的能帮助其他听众找到这个播客。你可以在 lennyspodcast.com 找到所有过往节目或了解更多关于本节目的信息。下期再见。

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