The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell

... our goal with Cursor is to invent a new type of programming, a very different way to build software. So a world kind of after code, I think that more and more being an engineer will start to feel like being a logic designer, and really, it will be about specifying your intent for how exactly you want everything to work.

我们开发 Cursor 的目标，是创造一种全新的编程方式，一种截然不同的软件构建方式。因此，我认为，在一个从某种意义上说代码已成为过去的世界里，工程师会越来越像逻辑设计师。真正重要的是清晰地表达意图，精确说明你希望一切如何运作。

What is the most counter-intuitive thing you've learned so far about building Cursor?

到目前为止，在开发 Cursor 的过程中，你了解到的最反直觉的事情是什么？

We definitely didn't expect to be doing any of our own model development. And at this point, every magic moment in Cursor involves a custom model in some way.

我们最初完全没有想到会自行开发模型。如今，Cursor 中的每一个惊艳时刻，都以某种方式运用了定制模型。

What's something that you wish you knew before you got into this role?

在担任这个角色之前，有什么事情是你希望自己能更早知道的？

Many people you hear hire too fast, I think we actually hired too slow to begin with.

人们经常谈论招聘过快的问题，但我认为，我们一开始招聘得太慢了。

You guys went from $0 to 100 million ARR in a year and a half, which is historic. Was there an inflection point where things just started to really take off?

你们仅用一年半时间，就将年度经常性收入从 $0 提升至 100 million，创造了历史。在此期间，是否出现过某个拐点，让一切突然开始迅猛增长？

The growth has been fairly just consistent on an exponential. And exponential to begin with feels fairly slow when the numbers are really low, and it didn't really show off to the races to begin with.

我们的增长始终相当稳定，基本保持着指数级增长。指数级增长在初期基数较小时，其实会让人觉得相当缓慢，并不会从一开始就呈现出一骑绝尘的态势。

What do you think is the secret to your success?

你认为你们取得成功的秘诀是什么？

I think it's been...

我认为是……

Today, my guest is Michael Truell. Michael is co-founder and CEO of Anysphere, the company behind Cursor. If you've been living under a rock and haven't heard of Cursor, it is the leading AI code editor, and is at the very forefront of changing how engineers and product teams build software. It's also one of the fastest growing products of all time, hitting 100 million ARR just 20 months after launching, and then 300 million ARR just two years since launch.

今天的嘉宾是迈克尔·特鲁尔。Michael 是 Anysphere 的联合创始人兼首席执行官，该公司开发了 Cursor。如果你一直与世隔绝，尚未听说过 Cursor，那么它是一款领先的人工智能代码编辑器，正处于改变工程师和产品团队构建软件方式的最前沿。它也是史上增长最快的产品之一：发布仅 20 个月，年度经常性收入便达到 100 million；发布仅两年，便达到 300 million。

Michael's been working on AI for 10 years. He studied computer science and math at MIT, did AI research at MIT and Google, and is a student of tech and business history. As you'll soon see, Michael thinks deeply about where things are heading, and what the future of building software looks like. We chat about the origin story of Cursor, his prediction of what happens after code, his biggest counter-intuitive lessons from building Cursor, where he sees things

Michael 已在人工智能领域深耕 10 年。他曾在麻省理工学院学习计算机科学和数学，也曾在麻省理工学院和 Google 从事人工智能研究，同时深入钻研科技史与商业史。你很快就会看到，Michael 一直在深入思考未来的发展方向，以及未来的软件构建方式。我们探讨了 Cursor 的诞生故事、他对代码之后的世界的预测、他在打造 Cursor 的过程中总结出的最重要且反直觉的经验，以及他认为软件工程师未来

going for software engineers, and so much more. Michael does not do many podcasts. The only other podcast he's ever done is Lex Fridman, so it was a true honor to have Michael on. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of Perplexity, Linear, Superhuman, Notion, and Granola. Check it out at lennysnewsletter.com, and click bundle.

将走向何方等诸多话题。Michael 很少参加播客，他此前唯一参加过的另一档播客是 Lex Fridman，因此，我对能够邀请到 Michael 深感荣幸。如果你喜欢这档播客，请在常用的播客应用或 YouTube 上订阅并关注。此外，如果你成为我的新闻通讯年度订阅用户，就能免费使用一年 Perplexity、Linear、Superhuman、Notion 和 Granola。请前往 lennysnewsletter.com，点击 bundle 了解详情。

With that, I bring you Michael Truell. This episode is brought to you by Eppo.

接下来，有请迈克尔·特鲁尔。本期节目由 Eppo 赞助播出。

Eppo is a next-generation A/B testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams. Companies like Twitch, Miro, ClickUp, and DraftKings rely on Eppo to power their experiments. Experimentation is increasingly essential for driving growth, and for understanding the performance of new features, and Eppo helps you increase experimentation velocity, while unlocking rigorous deep analysis in a way that no other commercial tool does.

Eppo 是由 Airbnb 和 Snowflake 的前员工为现代增长团队打造的新一代 A/B 测试和功能管理平台。Twitch、Miro、ClickUp 和 DraftKings 等公司都依靠 Eppo 支持实验。实验对于推动增长和了解新功能的表现正变得越来越重要。Eppo 可以帮助你加快实验速度，同时提供其他商业工具无法实现的严谨深度分析。

When I was at Airbnb, one of the things that I loved most was our experimentation platform, where I could set up experiments easily, troubleshoot issues, and analyze performance all on my own. Eppo does all that and more, with advanced statistical methods that can help you shave weeks off experiment time, an accessible UI for diving deeper into performance, and out-of-the-box reporting that helps you avoid annoying, prolonged analytics cycles.

我在 Airbnb 工作时，最喜欢的工具之一就是我们的实验平台。我可以在平台上轻松设置实验、排查问题，并自行完成表现分析。Eppo 不仅能够实现这一切，还运用了先进的统计方法，可以帮助你将实验周期缩短数周；同时，它还提供易于使用的 UI，便于你深入分析表现，并通过开箱即用的报告功能，避免烦琐而漫长的分析周期。

Eppo also makes it easy for you to share experiment insights with your team, sparking new ideas for the A/B testing flywheel. Eppo powers experimentation across every use case, including product, growth, machine learning, monetization, and email marketing. Check out Eppo at geteppo.com/lenny, and 10X your experiment velocity. That's getE-P-P-O.com/lenny.

Eppo 还能让你轻松地与团队分享实验洞察，激发新想法，推动 A/B 测试飞轮持续运转。Eppo 支持多种使用场景下的实验，包括产品、增长、机器学习、商业变现和电子邮件营销。欢迎访问 geteppo.com/lenny 了解 Eppo，将实验速度提升 10 倍。网址是 getE-P-P-O.com/lenny。

This episode is brought to you by Vanta. When it comes to ensuring your company has top-notch security practices, things get complicated fast. Now, you can assess risk, secure the trust of your customers, and automate compliance for SOC 2, ISO 27001, HIPAA, and more with a single platform, Vanta. Vanta's market-leading trust management platform helps you continuously monitor compliance, alongside reporting and tracking risk. Plus, you can save

本期节目由 Vanta 赞助。要确保公司拥有一流的安全实践，相关工作很快就会变得复杂。现在，你可以通过一个平台评估风险、赢得客户信任，并自动完成 SOC 2、ISO 27001、HIPAA 等合规工作，这个平台就是 Vanta。Vanta 领先市场的信任管理平台可以帮助你持续监控合规情况，同时报告和跟踪风险。此外，你还可以

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使用 Vanta AI 填写安全调查问卷，节省数小时。加入全球数千家公司的行列，利用 Vanta 自动收集证据、统一管理风险并简化安全审查。访问 vanta.com/lenny，即可享受 Vanta 提供的 $1,000 优惠。网址是 V-A-N-T-A.com/lenny。

Michael, thank you so much for being here. Welcome to the podcast.

迈克尔，非常感谢你来到这里。欢迎做客本播客。

Thank you. It's great to be here. Thank you for having me.

谢谢。很高兴来到这里，也感谢你的邀请。

When we were chatting earlier, you had this really interesting phrase, this idea of what comes after code. Talk about that, just the vision you have of where you think things are going in terms of moving from code to maybe something else.

我们之前交谈时，你提到了一个非常有意思的说法，也就是“代码之后的世界”这一概念。能否谈谈这个概念以及你对未来的设想？你认为我们会如何从代码走向其他形式？

Our goal with Cursor is to invent sort of a new type of programming, a very different way to build software, that's kind of just distilled down into you describing the intent to the computer for what you want in the most concise way possible, and really distilled down to just defining how you think the software should work, and how you think it should look. With the technology that we have today, and as it matures, we think you can get to a place where you can invent a new method

我们打造 Cursor 的目标，是发明一种全新的编程方式，一种截然不同的软件构建方式。归根结底，就是让你以尽可能简洁的方式向计算机描述自己想要实现的目标，并将其真正提炼为：你认为软件应该如何运行，以及它应该呈现何种形态。借助现有技术，并随着这些技术逐渐成熟，我们认为，最终可以发明出一种新的软件构建方法，其抽象层级[听不清 00:05:16]更高，效率也更高，

of building software that's [inaudible 00:05:16] higher level, and more productive, in some cases, more accessible too. And that process will be a gradual moving away from what building software looks like today. I want to contrast it with maybe the vision of what software looks like in the future that I think... A couple of visions that are in a popular consciousness that we at least have some disagreement with. One is,

在某些情况下，也更容易上手。这个过程将使我们逐步摆脱当今的软件构建方式。我想将其与人们对未来软件形态的一些设想进行对比。至少对于大众普遍认知的几种设想，我们并不完全认同。其中一种观点认为，未来构建软件的方式

there's a group of people who think that software building in the future is going to look very much like it does today, which mostly means text editing, formal programming languages, like TypeScript, and Go, and C, and Rust. And then there's another group that kind of thinks you're just going to type into a bot, and you're going to ask it to build you something, and then you're going to ask it to change something about what you're building, and it's

仍会与今天非常相似，主要还是编辑文本并使用形式化编程语言，例如 TypeScript、Go、C 和 Rust。另一种观点认为，你只需向机器人输入文字，让它帮助你构建某个东西，再要求它修改你正在构建的内容。整个过程就像使用 Slackbot 式的聊天机器人，仿佛是在与自己的工程部门对话。

kind of like this chatbot, Slackbot style where you're talking to your engineering department.

我们认为，这两种设想都存在问题。对于聊天机器人

And we think that there are problems with both of those visions. I think that on the chatbot style end of things... And we think it's going to look weirder than both. The problem with the chatbot style end of things is that lacks a lot of precision. If you want humans to have complete control over what the software looks like, and how it works, you need to let them gesture at what they want to be changed in a form factor that's more precise than just,

我们认为，未来的形态会比这两种设想都更加奇特。聊天机器人模式的问题在于，它非常不精确。如果想让人类完全掌控软件的形态和运行方式，就需要让他们以比简单说出“把我的应用改成这样”更精确的形式，明确指出自己想要修改的内容，而不是脱离整个应用，

"Change this about my app." In a text box, removed from the whole thing. And then the version of the world where nothing changes we think is wrong, because we think the technology is going to get much, much, much better. And so a world after code, I think that it looks like a world where you have a representation of the logic of your software that does look more like English, you have written down... You can imagine in [inaudible 00:07:08] form,

只在文本框中输入这句话。我们也认为，世界不会发生任何变化的设想是错误的，因为我们相信这项技术会取得极大的进步。因此，代码之后的世界可能会呈现出这样的形态：人们能够以一种更接近英语的方式呈现并写下软件逻辑。可以想象，它会采用[听不清 00:07:08]的形式，

you can imagine in kind of an evolution of programming language towards pseudocode. You have written down the logic of the software, and you can edit that at a high level, and you can point at that. And it won't be the impenetrable millions of lines of code, it'll instead be something that's much Terser, and easier to understand, easier to navigate. But that world where the kind of crazy, hard to understand symbols start to evolve towards something

也可以想象，编程语言会逐渐向伪代码演进。人们可以写下软件逻辑，从更高层面进行编辑，也可以直接指向需要修改的部分。它不再是令人难以理解的数百万行代码，而是更加精简、易于理解和浏览的内容。在那个世界里，那些复杂、晦涩的符号会逐渐演变成

that's a little bit more human-readable, and human-editable, is one that we're working towards.

更便于人类阅读和编辑的形式，而这正是我们努力迈进的方向。

This is a profound point. I want to make sure people don't miss what you're saying here, which is that what you're envisioning in the next year essentially is kind of when things start to shift, is, people move away from even seeing code, having to think in code in JavaScript and Python, and there's this abstraction that will appear, essentially pseudocode, describing what the code should be doing more in English sentences.

这是一个非常深刻的观点。为了确保大家准确理解你的意思：你设想在未来一年左右，事情将开始发生根本性转变。人们甚至不再需要看到代码，也不必再使用 JavaScript 和 Python 代码进行思考，而是会出现一个本质上属于伪代码的抽象层，以更接近英语句子的方式描述代码应该完成什么。

Yep. We think it ends up looking like that, and we're very opinionated that that path goes through existing professional engineers, and it looks like this evolution away from code. And it definitely looks like the human still being in the driver's seat, and the human having both a ton of control over all aspects of the software and not giving that up. And then also the human having the ability to make changes very quickly, having a fast duration loop and not just

我们认为，最终将会形成这种形态。我们坚信，这条路径要以现有的专业工程师为起点，并呈现为一个逐渐摆脱代码的演进过程。人类显然仍会占据主导地位，全面掌控软件的各个方面，而不会放弃这种控制权。同时，人类也能够迅速做出更改，形成快速的迭代循环，而不是仅仅

having something in the background that's super slow and takes weeks, go do all your work for you.

让某个运行缓慢、需要数周才能完成任务的后台程序代替自己完成所有工作。

This begs the question for people that are currently engineers, or thinking about becoming engineers, or designers, or product manager, what skills do you think will be more and more valuable in this world of what comes after code?

这就引出了一个问题：对于目前已经是工程师的人，或者正在考虑成为工程师、设计师或产品经理的人，你认为在代码之后的世界里，哪些技能会变得越来越有价值？

I think taste will be increasingly more valuable. And I think often when people think about tastes in the realm of software, they think about visuals, or taste over smooth animations, and coloring things, UI, UX, et cetera on the visual design of things. And the visual side of things is an important part of defining a piece of software, but then, as mentioned before, I think that the other half of defining a piece of software is the logic of it, and how the thing works.

我认为，品味会变得越来越有价值。人们谈到软件领域的品味时，往往想到视觉效果、流畅的动画、配色、UI、UX 等视觉设计。视觉层面是定义一款软件的重要组成部分，但正如前面提到的，定义软件的另一半在于其逻辑，即软件如何运行。

And we have amazing tools for specing out the visuals of things, and then when you get into the logic of how a piece of software works, really, the best representation we have of that is code right now. You can kind of gesture at it with Figma, and you can gesture at it with writing down notes, but it's when you have an actual working prototype. And so I think that more and more, being an engineer will start to feel like being a logic designer, and really, it will be about

我们已经拥有非常出色的工具，可以详细定义各种视觉效果；但在呈现软件运行逻辑方面，目前最有效的形式仍然是代码。人们可以使用 Figma 粗略表达，也可以通过书面笔记加以描述，但真正关键的是制作一个实际可用的原型。因此，我认为未来的工程师会越来越像逻辑设计师，其真正的重点将是

specifying your intent for how exactly you want everything to work. It'd be more about the whats, and a little bit less about how exactly you're going to do things under the hood.

准确说明自己希望一切如何运行。重点会更多地放在“做什么”上，而不再过度关注底层具体如何实现。

I think taste will be increasingly important. I think one aspect of software engineering, and we're very far from this right now, and there are lots of funny memes going around the internet about some of the trials and tribulations people can run into if they trust AI for too many things that comes to engineering, around building apps that have glaring deficiencies, and problems, and functionality issues. But I think we will get to a place where you'll be able to

我认为，品味会变得越来越重要。目前，我们距离这种状态还很遥远。网上也流传着许多有趣的梗，描述人们在工程领域过度信任人工智能后可能经历的种种曲折，例如构建出的应用存在明显缺陷、各类问题和功能故障。但我认为，我们最终会达到这样一种状态：人们可以

be less careful as a software engineer, which, right now, is an incredibly, incredibly important skill. We'll move a little bit from carefulness, and a little bit more towards taste.

不再像现在的软件工程师一样谨小慎微，尽管这种谨慎目前仍是一项极其重要的能力。届时，我们需要的谨慎会减少，而对品味的要求会增加。

This makes me think of vibe coding, is that kind of what you're describing when you talk about not having to think about the details as much, and just kind of going with the flow?

这让我想到了氛围编程。你所说的是不必过多考虑细节，只需要跟随感觉进行创作吗？

I think it's related. I think that vibe coding right now describes exactly this state of creation that is pretty controversial, where you're generating a lot of coding, you aren't really understanding the details. That is a state of creation that then has lots of problems, you don't really... By not understanding the details under the hood right now, you then very quickly get to a place where you're kind of limited at a certain point,

我认为两者存在关联。现在所说的氛围编程，准确描述了一种颇具争议的创作状态：人们生成了大量代码，却并不真正理解其中的细节。这种创作状态随后会引发许多问题。由于不了解底层细节，人们很快就会遇到瓶颈，

where you create something that's big enough that you can't change. And so I think some of the ideas that we're interested around, how do you give people continued control over all the details when they don't really understand the code? I think that solutions there are very relevant to the people who are vibe coding right now. I think that right now, we lack the ability to let the tastemakers actually have complete control over the software. One of the issues also with

因为他们构建出的内容规模过大，最终无法再进行修改。因此，我们关注的一个问题是：当人们并不真正理解代码时，怎样才能让他们始终掌控所有细节？我认为，这方面的解决方案与目前进行氛围编程的人密切相关。现在还没有一种方法，能够让真正有品味的人完全掌控软件。氛围编程还存在另一个问题，

vibe coding, and letting taste really shine through from people is, you can create stuff, but a lot of it the AI making decisions that are unwieldy and you don't have to control over.

这个问题也会妨碍人们真正展现品味：人们虽然可以创造出产品，但其中许多决定是由人工智能做出的，而这些决定既难以驾驭，也无法由人掌控。

One more question along these lines. You threw out this word taste. When you say taste, what are you thinking?

沿着这个话题，我想再问一个问题。你刚才提到了“品味”这个词。你所说的品味，具体指什么？

I'm thinking having the right idea for what should be built. It will become more and more about effortless translation of, here's exactly what you want built, here's how you want everything to work, here's how you want it to look. And then you'll be able to make that on a computer, and it will less be about this kind of translation layer of, you and your team have a picture of what you'd want to build, and then you have to really painstakingly, labor-intensive,

我所说的品味，是指对应该构建什么形成正确的想法。未来的重点将越来越偏向于轻松地把这些想法转化为现实：明确自己想构建什么、希望一切如何运行，以及希望它呈现出怎样的形态。随后，人们就能在电脑上将其实现，而不再高度依赖复杂的转化过程。以往，你和团队需要先在脑海中形成想要构建的产品图景，再煞费苦心地投入大量人力，将其转化为电脑能够执行和解读的格式。我认为，

lay out that into a format that a computer can then execute and interpret. I think less than the UI side of things, maybe taste is a little bit of a misnomer, but just about having the right idea for what should be built.

如果不局限于 UI 层面，“品味”这个词或许并不完全准确，但归根结底，它指的就是对应该构建什么形成正确的想法。这个观点很精彩。稍后我还会回到这些话题，但现在想先把视角

Awesome. Okay. I'm going to come back to these topics, but I want to actually zoom us back out to the beginnings of Cursor. I have never heard the origin story, I don't think many people know how this whole thing started. Basically you guys are building one of the fastest growing products in the history of the world, it's changing the way people build products, it's changing careers, professions, it's changing

拉远，回顾 Cursor 创立之初的经历。我从未听过它的起源故事，很多人可能也不知道这一切是如何开始的。可以说，你们正在打造世界历史上增长最快的产品之一。它正在改变人们打造产品的方式，改变人们的职业和专业领域，也在改变许多其他事物。这一切是如何开始的？早期创业历程中，有哪些时刻令你印象深刻？

so much. How did it all begin? Any memorable moments along the journey of the early days?

Cursor 最初可以说是在为一个问题寻找解决方案，而它的诞生在很大程度上

Cursor kind of started as a solution search of a problem, and a little bit where it very much came from reflecting on how AI was going to get better over the course of the next 10 years. There were kind of two defining moments, one was being really excited by using the first beta version of Code Pilot, actually. This was the first time we had used an AI product that was really, really, really useful, and was actually just useful at all, and wasn't just a vaporware kind of demo thing.

源于我们对人工智能在未来 10 年中将如何进步的思考。其中有两个决定性的时刻。第一个时刻出现在我们使用 Code Pilot 的首个测试版时，当时我们感到非常兴奋。这是我们第一次使用真正实用的人工智能产品。它确实具有实际用途，而不只是一个徒有其表、虚无缥缈的演示。它不仅是我们使用过的第一款实用人工智能产品，

And in addition to being the first AI product that we'd use that was useful, Code Pilot was also one of the most useful, if not the most useful dev tool we'd ever adopted, and that got us really excited. Another moment that got us really excited was the series of scaling on papers coming out of OpenAI and other places that showed that even if we had no new ideas, AI was going to get better and better just by pulling on simple levers,

Code Pilot 还是我们采用过的最有用的开发工具之一，甚至可能是最有用的一款，这让我们非常兴奋。另一个令我们兴奋的时刻，是 OpenAI 等机构发表的一系列有关规模扩展的研究论文表明，即使没有任何新想法，只要调整一些简单的因素，人工智能也会变得越来越强，

like scaling up the models, and also scaling up the data that was going into the models.

例如扩大模型规模，以及增加输入模型的数据量。

And so at the end of 2021, beginning of 2022, this got us excited about how AI products were now possible, this technology was going to mature into the future. And it felt like when we looked around, there were lots of people talking about making models, and it felt like people weren't really picking an area of knowledge work and thinking about what it was going to look like as AI got better and better. And that set us on the path to an idea generation exercise,

2021 年底至 2022 年初，我们开始对如今人工智能产品所展现的可能性感到兴奋，并相信这项技术会逐渐成熟。环顾四周，我们发现很多人都在讨论构建模型，却似乎没有人真正选定一个知识工作领域，思考随着人工智能能力不断增强，这个领域将发生怎样的变化。这促使我们开始探索各种构想。

it was like, "How are these areas of knowledge work going to change in the future as this tech gets more mature? What is the end state of the work going to look like? How are the tools that we use to do that work going to change? How are the models going to need to get better to support changes in the work? And once scaling and pre-training ran out, how are you going to keep pushing for technological capabilities?" And the misstep at the beginning of Cursor is

我们思考的问题包括：“随着这项技术日益成熟，这些知识工作领域未来将如何变化？工作的最终形态会是什么样？我们用来完成这些工作的工具将如何改变？为了支持工作方式的变化，模型需要如何提升？一旦规模扩展和预训练的潜力耗尽，又该如何继续推动技术能力进步？”Cursor 创立之初曾走过一段弯路。

we actually worked on... We sort of did this whole grand exercise, and we decided to work on an area of knowledge work that we thought would be relatively uncompetitive, and sleepy, and boring, and no one would be looking at it, because we thought, "Oh, coding's great, coding's totally going to change with this AI, but people are already doing that." So there was a period of four months to begin with, where we were actually working on a very different idea,

我们进行了一整套宏大的推演，最终决定进入一个我们认为竞争相对较小、沉寂、无趣且无人关注的知识工作领域。当时我们的想法是：“编程当然很好，也必然会因这种人工智能而发生改变，但已经有人在做了。”因此，最初四个月里，我们实际上在开发一个完全不同的项目。

which was helping to automate and augment mechanical engineering, and building tools for mechanical engineers. There were problems from the get-go in that.

这个项目旨在帮助实现机械工程的自动化并增强相关能力，为机械工程师开发工具。但它从一开始就存在问题。

Me and my co-founders, we weren't mechanical engineers. We had friends who were mechanical engineers, but we were very much unfamiliar with the field. So there was a little bit of a blind man and the elephant problem from the get-go. There were problems around, how would you actually take the models that exist to today and make them useful for mechanical engineering? The way we netted out is, you need to actually develop your own models from the get-go.

我和几位联合创始人都不是机械工程师。虽然我们有一些从事机械工程的朋友，但对这个领域确实非常陌生，因此从一开始就如同盲人摸象。此外还有一个问题：究竟该如何利用现有模型，让它们真正服务于机械工程？我们最终得出的结论是，必须从一开始就自行开发模型。

And the way we did that was tricky, and there's not a lot of data on the internet of 3D models of different tools and parts, and the steps that I expect to build up to those 3D models, and then getting them from the sources that have them is also a tricky process too.

但具体实施起来十分棘手。互联网上没有太多关于各种工具和零件的 3D 模型数据，也缺少按步骤构建这些 3D 模型的过程数据。从掌握这些数据的来源处获取数据，同样十分困难。

But eventually what happened was, we came to our senses, we realized we're not super excited about mechanical engineering, it's not the thing we want to dedicate our lives to. And we looked around, and in the area of programming, it felt like despite a decent amount of time ensuing, not much has changed, and it felt like the people that were working on the space maybe had a disconnect with us, and it felt like they weren't being sufficiently ambitious about

最终，我们清醒过来，意识到自己对机械工程并没有特别强烈的热情，也不想把一生都投入其中。随后我们环顾四周，发现尽管已经过去了相当长的时间，编程领域却没有发生多少变化。我们认为，这个领域的从业者或许与我们的看法存在分歧。

where everything was going to go in the future, and how all of software creation was going to blow through these models. And that's what set us off on the path to building Cursor.

在我们看来，他们对于未来的发展方向，以及整个软件创造过程应如何充分利用这些模型，缺乏足够的雄心。正因如此，我们才踏上了打造 Cursor 的道路。

Okay. So interesting. Okay, so first of all, I love that... This is advice that you often hear of go after a boring industry because no one's going to be there, and there's opportunity. And sometimes it works, but I love that in this journey, it's like, "No, actually, go after the hottest, most popular space, AI coding, app building." And it worked out. And the way you phrased it just now is, you didn't see enough ambition potentially, that you thought there

这段经历很有意思。我尤其认同其中的一点。我们经常听到这样的建议：应该进入一个无趣的行业，因为那里无人涉足，所以存在机会。有时这种策略确实行得通，但你们的经历传达了另一层意思：“其实应该进入最热门、最受关注的领域，也就是人工智能编程和应用开发。”而且你们成功了。你刚才提到，其他参与者的雄心可能还不够。

was more to be done. So it feels like that's an interesting lesson. Even if something looks like, "Okay, it's too late, there's GitHub, Code Pilot's out there." Some other products. If you notice that they're just not as ambitious as they could be, or as you are, or you see almost a flaw in their approach, that there's still a big opportunity. Does that resonate?

这意味着该领域仍有更多事情可做，我认为这是一个很有意思的启示。即使某件事看起来已经为时过晚，例如 GitHub 已经存在，Code Pilot 也已经推出，市场上还有其他产品，但如果你发现它们没有达到本可实现的目标，雄心不及你，或者其方法存在某种根本缺陷，就说明这里仍然蕴藏着巨大机会。你是否也有同感？

That totally resonates. A part of it is, you need there to be leapfrogs that can happen, you need there to be things that you can do. And I think the exciting thing about AI is, in a bunch of places, and I think this is very much still true of our space, and can talk about how we think about that and how we deal with that, but I think that just the ceiling is really high. And yes, if you look around,

我完全有同感。部分原因在于，你需要找到实现跨越式突破的机会，找到一些能够真正做成的事情。我认为，人工智能令人兴奋的一点是，很多领域都存在这样的机会。在我们所处的领域，如今尤其如此。我们可以进一步讨论自己如何看待和应对这件事，但我认为它的上限确实非常高。

probably even if you take the best tool, any of these fields, there should be a lot more that needs to be done over the next few years. Having that space, having that high ceiling, I think is unique amongst areas of software, at least the degree to which it is high with AI.

即使考察这些领域中最好的工具，未来几年应该仍有大量工作可做。如此广阔的空间和如此高的上限，在各个软件领域中都很独特；至少，人工智能所带来的上限之高是独一无二的。

Let's come back to the IDE questions. So there's a few routes you could have taken, and other companies are doing different routes. So there's building an IDE for engineers to work within and adding AI magic to it, there's another route of just a full AI agentic dev product, and then there's just a model that is very good at coding, and focusing on building the best possible coding model. What made you decide and see that the IDE path was the best route?

我们回到集成开发环境这个问题。当时你们其实有几条路线可供选择，其他公司采取的路线也各不相同。例如，可以打造一个供工程师使用的集成开发环境，再为其加入人工智能能力；也可以直接打造一个完全由人工智能代理驱动的开发产品；还可以只开发一个非常擅长编程的模型，专注于打造尽可能出色的编程模型。是什么让你们认定集成开发环境是最佳路线？

The folks who were from the get-go working on just a model were working on end-to-end automation programming. I think they were trying to build something very different from us, which is, we care about giving humans control over all of the decisions in the end tool that they're building. And I think those folks were very much thinking of a future where end-to-end, the whole thing is done by AI, and maybe the AI is making all the decisions too. And so,

那些从一开始就只专注于模型的人，目标是实现端到端的自动化编程。我认为，他们想打造的产品与我们的设想截然不同。我们关心的是，在人们最终构建的工具中，让人类掌控所有决策。而他们更多是在设想这样一种未来：一切都以端到端的方式全部由人工智能完成，甚至所有决策也都由人工智能作出。

one, there was a personal interest component. Two, I think that always, we've tried to be intense realists about where the technology is today, very, very, very excited about how AI is going to mature over the course of many decades. But I think that sometimes people... There's an instinct to see AI do magical things in one area, and then kind of anthropomorphize these models, and think it's better than a smart person here, and

首先，这与个人兴趣有关。其次，我们始终努力以极其务实的态度看待当下的技术，同时又对人工智能在未来几十年的逐步成熟感到极为兴奋。但人们有时存在一种本能：看到人工智能在某个领域展现出惊人的能力，便会将这些模型拟人化。

so it must be better than a smart person there. But these things have massive issues, and we... From the very start, our product development process was really about dogfooding, and using the tool intensely every day. And we never wanted to ship anything that wasn't useful to us, and we had the benefit of doing that because we were the end users part of our product. And I think that that instills a realism in you around where the tech is right now, and so

人们会认为，既然人工智能在这里比聪明人做得更好，那么它在那里也一定能胜过聪明人。但这些模型存在很严重的问题。从一开始，我们的产品开发流程就非常强调内部试用，每天都会高强度地使用这个工具。任何对我们自己没有用处的功能，我们都绝不会发布。我们恰好具备这样做的条件，因为自己就是产品的最终用户。这使我们能够对当前技术所处的阶段形成务实的认识。

that definitely made us think that we need the humans to be in the driver's seat, the AI cannot do everything. We're also interested in giving humans that control too for personal reasons, and so that gets you away from just your model company that also gets you away from just this end-end stuff without the human having control. And then the way you get to an IDE versus maybe a plug-in to an existing coding environment is the belief that programming is going to flow through

由此我们意识到，必须由人类掌舵，人工智能不可能包办一切。出于个人层面的原因，我们也希望将这种控制权交给人类。因此，我们不会只做一家模型公司，也不会只开发没有人类掌控的端到端产品。至于选择集成开发环境，而非为现有编程环境开发插件，则源于我们对编程未来的判断。

these models, and the active programming is going to change a lot over the course of the next few years. And that the extensibility that existing coding environments have is so, so, so limited, so if you think that the UIs may change a lot, if you think that the form factor programming is going to change a lot, necessarily need to have control over the entire application.

我们相信，未来的编程将借助这些模型进行，而且未来几年内，编程活动也会发生巨大变化。现有编程环境的可扩展性十分有限。因此，如果你认为用户界面可能发生很大变化，也认为编程的形态必然会发生很大变化，就未必需要掌控整个应用程序。

I know that you guys today have an IDE, and that's probably the bias you have of this is maybe where the future is heading, but I'm just curious, do you think a big part of the future is also going to be AI engineers that are just sitting in Slack and just doing things for you? Is that something that fits into Cursor one day?

我知道你们目前拥有一个集成开发环境，这可能使你们更倾向于认为未来会朝这个方向发展。不过我很好奇，你是否认为未来的大量工作也会由人工智能工程师完成，它们会留在 Slack 里替你处理事务？Cursor 将来会涵盖这种场景吗？

I think you'll want the ability to move between all of these things fairly effortlessly, and sometimes I think you will want to have the thing kind of go spin off on its own for a while, and then I think you'll want the ability to pull in the AI's work, and then work with it very, very, very quickly, and then maybe have it go spin off again. And so these kind of background versus foreground form factors, I think you want that all to work well in one place. And

我认为，用户应该能够相当轻松地在所有这些方式之间切换。有时，你会希望让人工智能独立运行一段时间；随后，你会希望把它完成的工作调回前台，再迅速与它协作处理；之后，可能又会让它继续独立运行。这种后台与前台相结合的交互形态，应该能够在同一个地方顺畅运作。

I think the background stuff, there's a segment of programming that it's especially useful for, which is type of programming tasks where it's very easy to specify exactly what you want without much description, and exactly what correctness looks like without much description.

我认为，后台运行尤其适合一类编程工作：无需太多描述，就能准确说明自己的需求；同时也无需太多描述，就能准确界定怎样才算正确。

Bug fixes are a great example of that, but it's definitely not all of programming. So I think that what the IDE is will totally change over time, and our approach to having our own editor was premised on, it's going to have to evolve over time. And I think that that will both include, you can spin off things from different surface areas like Slack, or your issue tracker, or whatever it is, and I think that will also include the pane of glass that you're staring at is going

修复 bug 就是一个很好的例子，但编程显然不全是这类工作。因此，我认为集成开发环境的形态会随着时间推移而彻底改变。我们选择开发自己的编辑器，前提就是它必须不断演变。这既包括用户可以从 Slack、问题跟踪器或其他任何界面发起任务，也包括用户持续注视的屏幕界面。

to change a lot. We just mostly think of an IDE as the place where you are building software.

这一界面也将发生巨大变化。我们主要把集成开发环境视为构建软件的地方。

I think something people don't talk enough about with talking about agents and all these AI engineers that are going to be doing all this stuff for you, is basically we're all becoming engineering managers, with a lot of reports that are just not that smart, and you have to do a lot of reviewing, and approving, and specifying. I guess thoughts on that, and is there anything you could do to make that easier? Because that sounds really hard. Anyone

在谈论智能体，以及所有这些将替人类完成各种工作的人工智能工程师时，有一件事尚未得到充分讨论：我们基本上都会变成工程经理，管理一大批能力有限的下属，并且不得不进行大量审查、审批和任务说明。你如何看待这一点？有没有办法让这个过程变得更轻松？因为这听起来确实很困难。

that has had a large team, being like, "Oh my god, all these junior people just checking in with me doing not high quality work over and over." It's just like, "What a life. It's going to suck."

任何带过大型团队的人都会想到：“这么多新人反复找我确认，交付的工作质量又不高。”他们难免会担忧：“这样的日子该如何继续？体验一定非常糟糕。”

Yeah. Maybe you [inaudible 00:23:12] one-on-ones with [inaudible 00:23:15].

也许你会与[听不清 00:23:12]召开一对一会议，讨论[听不清 00:23:15]。

So many one-on-ones.

一对一会议太多了。

Yeah. So the customers we've seen have most success with AI I think are still fairly conservative about some of the ways in which they use this stuff. And so I do think today, the most successful customers really lean on things like our next edit prediction, where your coding is normal, and making the next into actions you're going to do. And then they also really lean on scoping down the stuff that you're going to hand off to the bot, and for a fixed percent of

根据我们的观察，那些最成功地运用人工智能的客户，在某些使用方式上仍然相当谨慎。就目前而言，这些客户非常依赖我们的“下一步编辑预测”等功能。这类功能让用户能够像平常一样编程，同时预测接下来要执行的操作。此外，他们也很重视缩小交给机器人的任务范围。假设你用于审查智能体或人工智能所生成代码的

your time spent reviewing code, from an agent, or from an AI overall, you could... There's two patterns. One is, you could spend a bunch of time specifying things up front, the AI goes and works, and then you then go and review the AI's work, and then you're done. That's the whole task.

时间占比是固定的，那么可以采用两种模式。第一种是先花大量时间把需求说明清楚，再让人工智能工作，随后审查人工智能完成的工作，至此结束整个任务流程。

Or you can really chop things up. So you can specify a little bit, AI writes something, review, specify a little bit, AI writes something, review. Autocompletes all in the way of that spectrum. And still we see often the most successful people using these tools are chopping things up right now, and keeping things fairly [inaudible 00:24:28].

第二种是真正拆分任务。你可以先说明一部分需求，让人工智能编写一些内容，然后进行审查；再说明一部分需求，让人工智能继续编写，之后再次审查。自动补全位于这条连续谱的一端。目前我们仍然经常看到，最成功地使用这些工具的人会拆分任务，并让每个任务都保持相当[听不清 00:24:28]。

That sounds less terrible. I'm glad there's a solution here. I want to go back to you guys building Cursor for the first time. What was the point where you realized this is ready? What was a moment of, "Okay, I think this is time to put it out there, and see what happens"?

这样听起来就没那么糟了。很高兴这里有解决方案。我想回到你们最初开发 Cursor 的时候。你们何时意识到它已经准备好了？在哪一刻，你们认为：“是时候发布它，看看会发生什么了”？

So when we started building Cursor, we were fairly paranoid about spinning for a while, without releasing to the world. And so to begin with too, we actually... The first version of Cursor was hand-rolled. Now we use VS Code as a base, like many browsers use Chromium as a base, and hit foot off of that. To begin with, we didn't, and built the prototype of Cursor from scratch, and that involved a lot of work. We had to build our own... There were a

刚开始开发 Cursor 时，我们很担心闭门造车太久，迟迟不向外界发布。Cursor 的第一个版本完全是从零手工打造的。现在，我们以 VS Code 为基础进行开发，就像许多浏览器以 Chromium 为基础一样。最初我们并未采用这种方式，而是从零开始构建 Cursor 的原型，这需要完成大量工作。我们必须自行开发现代

lot of things that go into a modern code editor, including support for many different languages, and navigation support for moving amongst the language, error tracking support for things.

代码编辑器所涉及的许多功能，包括支持多种不同的语言，以及提供代码跳转、错误跟踪等功能。

There's things like an integrated command line, the ability to use remote servers, the ability to connect to remote servers to view and run code. And so we kind of just went on this blitz of building things incredibly quickly, building our own editor from scratch, and then also the AI components. It was after maybe five weeks that we were living on the editor full-time, and had thrown away our previous editor,

此外，编辑器还需要具备集成命令行、使用远程服务器，以及连接远程服务器查看和运行代码的能力。于是，我们开始了一轮极速开发，从零构建自己的编辑器，同时开发人工智能组件。大约五周后，我们开始全职使用这款编辑器，放弃了此前使用的编辑器，

and we're using a new one. And then once it got to a point where we found it a bit useful, then we put it in other people's hands, and had this very short beta period. And then we launched it out to the world within a couple of months from the first line of code, I think it was probably three months. And it was definitely a, "Let's just get this out to people and build in public quickly." The thing that took us by surprise is we thought we would be building for a couple

转而使用这款新编辑器。当它发展到具备一定实用性时，我们便将其交给其他人使用，并进行了一段非常短的 Beta 测试。从写下第一行代码算起，仅仅几个月后，我们就向全世界正式发布了它，大约用了三个月。我们的想法非常明确：“直接把它交到用户手中，快速进行公开开发。”令我们意外的是，我们原以为会在很长一段时间内只为几百

hundred people for a long time. And from the get-go, there was an immediate rush of interest, and a lot of feedback too. That was super helpful, we learned from that. That's actually why we switched to being based off of VS Code instead of just this hand-rolled thing. A lot of that was motivated by the initial user feedback, and then had been iterating in public from there.

名用户开发。但从一开始，人们就表现出了强烈的兴趣，也提供了大量反馈。这些反馈非常有帮助，我们从中学到了很多。我们之所以转向以 VS Code 为基础、放弃完全手工打造，很大程度上正是受早期用户反馈的推动。从那以后，我们一直在公开迭代。

I like how you understated the traction that you got. I think you guys went from $0 to 100 million ARR in a year, year and a half or something like that, which is historic. What do you think was the key to success of something like this? You just talked about dogfooding being a big part of it. You built it in three months, that's insane. What do you think is the secret to your success?

我很喜欢你如此轻描淡写地描述你们取得的成绩。我记得你们用了一年或一年半左右，就从 $0 做到了 100 million 年度经常性收入，创造了历史。你认为这类产品取得成功的关键是什么？你刚才提到，内部试用是其中非常重要的一部分。你们只用了三个月就把产品做了出来，令人难以置信。你认为你们成功的秘诀是什么？

The three-month version wasn't very good, and so I think it's been a sustained paranoia about, there are all of these ways in which this thing could get better. The end goal is really to invent a very new form of programming that involves automating a lot of coding, as we know today.

三个月时的版本并不好。关键在于，我们始终怀有一种不安，认为这款产品的方方面面都有改进空间。最终目标是创造一种全新的编程形式，将我们今天所熟悉的大量编码工作自动化。

And no matter where we are with Cursor, it feels like we're very, very far away from that end goal, there's always a lot to do. A lot of it hasn't been over rotated on that initial push, but instead is the continued evolution of the tool, and just making the tool consistently better.

无论 Cursor 发展到什么阶段，它似乎都距离最终目标非常遥远，始终还有许多工作要做。成功并非源于我们一直依赖最初的冲劲，而是因为工具在持续演进，我们也一直在改进它。

Was there an inflection point after those three months where things just started to really take off?

在那三个月之后，是否出现过某个拐点，让业务真正开始腾飞？

To be honest, it felt fairly slow to begin with, and maybe it comes from some impatience on our part. I think there's the overall speed of the growth which continues to take us by surprise.

坦率地说，最初的发展速度似乎相当缓慢，这也可能是因为我们有些心急。不过，整体增长速度至今仍不断令我们感到意外。

I think one of the things that has been most surprising too is that the growth has been fairly just consistent on an exponential, of just consistent month-over-month growth, accelerated at times by launches on our part and other things. But an exponential to begin with feels fairly slow and the numbers are really low, and so it didn't really feel off to the races to begin with.

另一件最令人惊讶的事是，增长始终相当稳定，呈指数级上升，每个月都保持稳定增长，并且仍在不断加速。有时，我们发布新产品或发生其他事情，也会推动增长加速。但指数增长在初期显得相当缓慢，数字也确实很低，因此最初并没有迅速起飞的感觉。

To me this sounds like build it and they will come actually working. You guys just built an awesome product that you loved yourselves as engineers, you put it out, people just loved it, told everyone about it.

在我看来，这似乎就是“把产品做出来，用户自然会来”的真实案例。你们做出了一款出色的产品；作为工程师，你们自己也很喜欢。随后，你们发布了这款产品，用户同样非常喜欢，还把它推荐给身边的所有人。

It being essentially all just us, the team working on the product, and making the product good in lieu of other things one could spend one's time on. We definitely spent time on tons of other things, for instance, building the team was incredibly important, and doing things like support rotations are very important. But some of the normal things that people would maybe reach for in building the company early on, we really let those fires burn for a long time, especially

我们整个团队基本上都专注于产品，努力把产品做好，而不是把时间花在其他事情上。当然，我们也确实为许多其他事务投入了时间。例如，组建团队极其重要，安排客服支持轮班等工作也非常重要。但对于创业初期通常需要处理的一些常规事项，我们却任由相关问题长期存在，尤其是

when it came to things like sales and marketing. And so just working on the product, and building a product that you like first, your team likes, and then also then adjusting it for some set of users, that can kind of sound simple, but then, as you know, it's hard to do that well. And there are a bunch of different directions one could have run in, a bunch of different product directions.

销售和营销之类的事务。我们只是专注于产品，首先打造一款自己和团队都喜欢的产品，然后再针对某一类用户进行调整。这听起来似乎很简单，但真正做好却很困难。可供选择的方向很多，可以采取的产品路线也很多。

I think focus, and strategically picking the right things to build, and prioritizing effectively is tricky. I think another thing that's tricky about this domain is, it's kind of a new form of product building, where it's very interdisciplinary in that we are something in between a normal software company and then a foundation model company, in that we're developing a product for millions of people, and that side of things has to be excellent, but then also one important

保持专注、从战略上选对要做的事情，并有效确定优先级，是一项棘手的工作。这个领域的另一个难点在于，它代表着一种新的产品开发方式，具有非常明显的跨学科特点，因为我们介于一家普通的软件公司和一家基础模型公司之间。我们正在开发一款面向数百万人的产品，因此产品层面必须做到出色；但与此同时，衡量产品质量的一个重要

dimension of product quality is doing more and more on the science, and doing more and more on the model side of things in places where it makes sense. And so that element of things doing that well too has been tricky. The overall thing would note is maybe some of these things sound simple to specify, but doing them well is hard, and they're a lot of different way you can run in.

维度，是不断深化科学研究，并在合适的领域持续加大模型方面的投入。如何将这部分工作做好，同样很有挑战。总体而言，这些事情说起来可能很简单，但要真正做好却很困难，而且可供选择的方向非常多。

I'm excited to have Andrew Luo joining us today. Andrew is CEO of OneSchema, one of our podcast sponsors. Welcome, Andrew.

很高兴今天邀请到 Andrew Luo。Andrew 是 OneSchema 的 CEO，OneSchema 也是我们播客的赞助商之一。欢迎你，Andrew。

Thanks for having me, Lenny. Great to be here.

谢谢你的邀请，莱尼。很高兴来到这里。

So what is new with OneSchema? I know that you work with some of my favorite companies, like Ramp, and [inaudible 00:30:46], and Watershed. I heard you guys launched a new data intake product that automates the hours of manual work that teams spent importing, and mapping, and integrating CSV in Excel files?

OneSchema 最近有什么新动态？我知道你们与一些我很喜欢的公司合作，例如 Ramp、[听不清 00:30:46] 和 Watershed。我听说你们推出了一款新的数据接入产品，可以自动完成导入、映射及集成 CSV 和 Excel 文件的工作，而这些工作原本需要团队手动花费数小时完成。

Yes. So we just launched the 2.0 of OneSchema FileFeeds. We've rebuilt it from the ground up with AI. We saw so many customers coming to us with teams of data engineers that struggled with the manual work required to clean messy spreadsheets. FileFeeds 2.0 allows non-technical teams to automate the process of transforming CSV and Excel files with just a simple prompt. We support all of the trickiest file integrations, SFTP, S3, and even email.

没错。我们刚刚推出了 OneSchema FileFeeds 2.0，并利用人工智能从零开始对其进行了重构。许多客户向我们反映，他们的数据工程团队一直受困于清理杂乱电子表格所需的大量手动工作。借助 FileFeeds 2.0，非技术团队只需输入一条简单的提示词，就能自动完成 CSV 和 Excel 文件的转换流程。即使是最棘手的文件集成方式，我们也都支持，包括 SFTP、S3，甚至电子邮件。

I can tell you that if my team had to build integrations like this, how nice would it be to take this off our roadmap and instead use something like OneSchema.

如果我的团队不得不构建这类集成，那么将其从我们的路线图中移除，转而使用 OneSchema 这样的产品，无疑会省心很多。

Absolutely, Lenny. We've heard so many horror stories of outages from even just a single bad record, in transactions, employee files, purchase orders, you name it. Debugging these issues is often like finding a needle in a haystack. OneSchema stops any bad data from entering your system, and automatically validates your files, generating error reports with the exact issues in all bad files.

完全正确，莱尼。我们听过太多严重的故障案例：仅仅一条错误记录，就可能导致交易、员工档案、采购订单等各种数据出现问题。调试这些问题往往如同大海捞针。OneSchema 可以阻止任何错误数据进入你的系统，自动验证文件并生成错误报告，准确列出所有问题文件中的具体错误。

I know that importing incorrect data can cause all kinds of pain for your customers and quickly lose their trust. Andrew, thank you so much for joining me. If you want to learn more, head on over to oneschema.co., that's oneschema.co. What is the most counterintuitive thing you've learned so far about building Cursor, building AI products?

导入错误数据会给客户带来各种麻烦，并迅速损害他们的信任。Andrew，非常感谢你的参与。如果想了解更多信息，请前往 oneschema.co.，没错，就是 oneschema.co.。在打造 Cursor 和人工智能产品的过程中，你迄今学到的最反直觉的一点是什么？

I think one thing that's been counterintuitive for us, [inaudible 00:32:14] added a little bit before, but is, we definitely didn't expect to be doing any of our own model development when we started. As mentioned, when we got into this, there were companies that were immediately from the get-go going and just focusing on training model from scratch. And we had done the calculation for what it to train before, and just knew that that was not [inaudible 00:32:36]

对我们而言，有一点非常反直觉，[听不清 00:32:14] 刚才也稍有提及：最初，我们完全没有想到会自行开发模型。前面提到，我们刚进入这个领域时，有些公司从一开始就专注于从零训练模型。我们此前也计算过训练模型需要投入多少资源，因此知道自己不可能 [听不清 00:32:36]

going to be able to do. And also felt a bit like focusing one's attention in the wrong area, because there were lots of amazing models out there, and why develop all this work to replicate what other players had done. Especially on the pre-training side of things, taking a neural network that knows nothing, and then teaching it the whole internet.

完成这件事。同时，我们认为这有些偏离重点，因为市面上已经存在许多优秀的模型，没有必要投入如此多的精力，去重复其他参与者已经完成的工作。尤其是在预训练方面，也就是从一个一无所知的神经网络开始，再向它传授整个互联网的知识。

And so we thought we weren't going to be doing that at all, and it seems clear to us from the start that the existing models, there were lots of things that they could be doing for us that they weren't doing, because there wasn't the right tool built for them. In fact though, we do a ton of model development, and internally, it's a big focus for us on the hiring front, and have assembled a fantastic team there. And it's also been a big win on the product

因此，我们原以为自己根本不会开发模型。我们从一开始也很清楚，现有模型原本可以为我们完成许多工作，却因为缺少适当的工具而未能做到。但事实上，我们进行了大量模型开发，这也成为我们内部招聘的一大重点，并且已经组建了一支非常出色的团队。这些工作也让我们的产品

quality side of things for us. And at this point, every magic moment in Cursor involves a custom model in some way. So that was definitely counterintuitive, and surprising, and it's been a gradual thing, where there was an initial use case for training our own model, where it really didn't make sense to use any of the biggest foundation models. That was incredibly successful, moved to another use case that worked really well, and had been going from there. And one of the helpful

质量得到了显著提升。如今，Cursor 中每一个令人惊艳的时刻，都以某种方式涉及定制模型。因此，这一发展确实既反直觉又出人意料，并且经历了一个循序渐进的过程。最初，有一个场景需要训练我们自己的模型，因为任何大型基础模型都完全不适合这一场景。那次尝试非常成功，于是我们又转向另一个场景，效果同样出色，此后便沿着这条路径持续发展。在进行这类

things in doing this sort of model development is picking your spots carefully, not trying to reinvent the wheel, not trying to focus on places, and maybe where the best foundation models are excellent, but instead kind of focusing on their weaknesses, and how you can complement them.

模型开发时，一项很有帮助的原则是谨慎选择切入点。不要试图重复造轮子，也不要把重点放在最优秀的基础模型已经表现出色的领域，而应专注于它们的短板，思考如何与其形成互补。

I think this is going to be surprising to a lot of people hearing that you have your own models. When people talk about Cursor and all the folks in the space, they would kind of call them GPT wrappers, they're just sitting on top of ChatGPT or Sonnet. What you're saying is that you have your own models, talk about just the stack behind the scenes.

许多人听说你们拥有自己的模型，可能都会感到惊讶。人们谈到 Cursor 和这个领域的其他公司时，往往会称它们为 GPT 套壳产品，认为它们只是构建在 ChatGPT 或 Sonnet 之上。你的意思是，你们拥有自己的模型。能否谈谈幕后的整个技术栈？

Yeah, of course. So we definitely use the biggest foundation models a bunch of different ways, they're really important components of bringing the Cursor experience to people. The places where we use our own models, so sometimes it's to survey a use case that a foundation model wouldn't be able to serve at all for cost or speed reasons. And so one example of that is the autocomplete side of things. And so this can be a little bit tricky for people who don't

当然可以。我们的确会以许多不同的方式大量使用最强大的基础模型，它们是为用户提供 Cursor 体验的关键组成部分。至于使用自有模型的场景，有时是为了满足某种特定需求，而基础模型由于成本或速度方面的原因，完全无法满足这种需求。自动补全就是其中一个例子。对于不写代码的人来说，这一点可能有些难以理解，但

code to understand, but code is this weird form of work, where sometimes, really, the next 5, 10, 20, 30 minutes of your work is entirely predictable from looking over your shoulder.

编程是一种非常特殊的工作形式。有时，只要站在程序员身后看一眼，就完全可以预测他接下来 5、10、20 乃至 30 分钟会做什么。

And I would contrast this with writing. So writing, lots of people are familiar with Gmail's autocomplete, and the different forms of autocomplete that show up when you're trying to post text messages, or emails, or things like that. They can only be so helpful, because often, it's just really not clear what you're going to be writing just by looking at what you've written before. But in code sometimes, when you edit a part of a code base,

相比之下，写作则有所不同。谈到写作，很多人都熟悉 Gmail 的自动补全，也尝试过将其用于撰写短信、电子邮件等内容。这类工具能够提供的帮助其实有限，因为很多时候，仅凭一个人此前写过的内容，确实很难判断他接下来要写什么。但在代码中，当你编辑代码库的某个部分时，

you're going to need to change things, and in other parts of code base, and it's entirely clear how you're going to need to change things. So one core part of Cursor is this really suit to autocomplete experience, where you predict the next set of that you're going to be doing across multiple files, across multiple places within a file. And making models good at that use case, one, there's a speed component of, those models need to be really fast, they need to give you

往往还需要修改代码库中的其他部分，而且通常很容易明确具体需要如何修改。因此，Cursor 的一个核心组成部分，就是这套非常流畅的自动补全体验。它会预测你接下来将在多个文件或同一文件的多个位置执行的一系列操作。要让模型出色地处理这一用例，首先必须解决速度问题：这些模型必须非常快，必须在

a completion within 300 milliseconds. There's also this cost component of, we're running tons, and tons, and tons of molecules, every keystroke, we need to be changing our prediction for what you're going to do next. And then it's also this really specialty use case of, you need models that are really good, not at completing the next token, just a generic tech sequence, but are really good at autocompleting a series of diffs, looking at what's changed within a code

300 毫秒内给出补全结果。其次是成本问题，因为我们需要运行海量的模型调用。每次击键时，我们都需要更新对用户下一步操作的预测。此外，这还是一个高度专业化的用例。我们需要的模型不能只是擅长补全下一个 token，也不能只会处理通用文本序列，而必须真正擅长自动补全一系列差异：先查看代码库中发生了哪些变化，

base, and then creating the next set of things that are going to change, both deleted and added and all of that, and we found a ton of success in training models specifically for that task.

再生成接下来将要发生的一系列变更，包括需要删除和添加的内容。我们发现，专门针对这项任务训练模型取得了巨大成功。

So that's a place where no foundation models are involved, it's kind of our own thing. We don't have a lot of labeling or branding about this in the app, power is a very core part of Cursor. And then another set of places where a user own models are to help things like Sonnet, or Gemini, or GPT, and those sit both on the inputs of those big models, and on the output. On the input side of things, those models are searching throughout a code base,

因此，这一部分不涉及任何基础模型，完全由我们自行完成。我们并未在应用中对此进行太多标注或品牌宣传，但这项能力是 Cursor 极为核心的一部分。自有模型的另一类用途，是辅助 Sonnet、Gemini 或 GPT 等模型，而且这些模型既用于大模型的输入端，也用于输出端。在输入端，这些模型会搜索整个代码库，

try to figure out the parts of a code base to show to one of these big models. You can kind of think about this as a mini Google search that's specifically built for finding the relevant parts of the code base to show one of these big models. And then on the output side of things, we take the sketches of the changes that these models are suggesting, you make with that code base. And then we have models that then fill in the details of, the high level thinking

尝试找出代码库中应当展示给某个大模型的部分。可以将其理解为一个迷你 Google 搜索，专门用于查找代码库中的相关部分，并将其展示给某个大模型。在输出端，我们会获取这些模型针对代码库提出的变更草案，再由我们的模型补全细节。高层思考

is done by the smartest models, they spend a few tokens on doing that, and then these smaller specialty incredibly fast models, coupled with some inference tricks, then take those high level changes and turn them actually into full code diffs. And so it's been super helpful for pushing on quality in places where you need a specialty task, and it's been super helpful for pushing on speed, which is such an important dimension of product quality for us too.

由最智能的模型完成，它们只需使用少量 token。随后，规模更小、专业化程度更高且速度极快的模型，会结合一些推理技巧，将这些高层级变更真正转化为完整的代码差异。在需要处理专业任务的场景中，这种方式对提升质量极有帮助；在提升速度方面同样非常有效，因为对我们而言，速度也是产品质量中极其重要的一个维度。

This is so interesting. I just had Kevin Weil on the podcast, CPO of OpenAI, and he calls this the ensemble of models, that's the same way- Yes.

这非常有意思。我刚刚邀请 OpenAI 的首席产品官 Kevin Weil 做客播客。他将这种方式称为“模型集成”，表达的也是同一种思路。确实如此。

... they work, to use the best feature of each one, and to your point, the cost advantages of using cheaper models. These other models, are they based on Llama and things like that, just open source models that you guys plug into and build on?

也就是利用每个模型最出色的能力。正如你所说，使用成本更低的模型还能带来成本优势。这些其他模型是以 Llama 之类的模型为基础吗？也就是说，你们会接入开源模型，并在此基础上进行开发吗？

Yeah. So again, we try to be very pragmatic about the place that we're going to do this work, and we don't want to reinvent the wheel. And so starting from the very best pre-trained models that exist out there, often open source ones, sometimes in collaboration with these big model providers that don't share their weights out into the world, because the thing we care about last is the ability to read line by line, the matrix of weights that then go to give you a certain output.

是的。我们会非常务实地选择在哪些方面开展这类工作，不希望重复造轮子。因此，我们会从现有最优秀的预训练模型入手，通常是开源模型；有时也会与大型模型提供商合作。后者不会向外界公开模型权重，但我们最不在意的，恰恰是能否逐行读取随后生成特定输出的权重矩阵。

We just care about the ability to train these things, to post-train them. And so by and large, yes, open source models, sometimes working with the closed source providers too to tune things.

我们只关心能否训练这些模型，并对其进行后训练。因此总体而言，我们确实会使用开源模型，有时也会与闭源模型提供商合作，对模型进行调优。

This leads to a discussion that a lot of AI founders always think about and investors, which is moats, and defensibility in AI. So it feels like one is custom models, is a moat in the space. How do you just think about long-term defensibility in the space, knowing there's other folks, as you said, launching constantly trying to eat your lunch?

这就引出了许多人工智能创业者和投资者一直在思考的话题，即人工智能领域的护城河和防御能力。听起来，定制模型似乎正是其中一种，也是这个领域的一道护城河。你如何看待这一领域的长期防御能力？尤其是考虑到，正如你所说，其他公司还在不断推出产品，试图抢走你们的业务。

I think that there are ways to build in inertia and traditional moats, but I think by and large, we're in a space where it is incumbent on us to continue to try to build the best thing, and everyone in this industry. And I truly just think that the ceiling is so high that no matter what entrenchment you build, you can be leapfrogged. And I think that this resembles markets that are maybe a little bit different from normal software markets, normal enterprise markets

我认为，确实可以通过一些方式形成惯性和传统意义上的护城河。但总体而言，在我们所处的领域中，我们必须继续努力打造最好的产品，行业中的所有人也都必须如此。我确实认为，这个领域的上限高得惊人。无论建立起多么深厚的壁垒，都有可能被别人跨越。在我看来，这类市场与过去的常规软件市场和常规企业市场略有不同。

of the past. I think one that comes to mind is the market for search engines at the end of 1999, or at the end of the '90s and beginning of the 2000s. I think another market that comes to mind that resembles this market in many ways, it's actually just the development of the peripheral computer and many computers in the '70s, '80s, '90s. And I think that, yes, in each of those markets, the ceiling was incredibly high, it was possible to swish. You could keep getting value for the

我首先想到的是 1999 年末，也就是 90 年代末到 2000 年代初的搜索引擎市场。我还想到另一个在许多方面与这个市场相似的市场，那就是 70 年代、80 年代和 90 年代外围计算机及众多计算机的发展。我认为，是的，在这些市场中，上限都高得惊人，完全有可能实现飞跃。很长一段时间里，

incremental hour of a smart person's time, the incremental R&D dollar for a really long time, you wouldn't run out of useful things to build. And then in search in particular, not on the computer case, adding distribution was helpful for making the product better too, in that you could tune the algorithms, you could tune the learning based off of the data and the feedback you're getting from users. And I think that all of those dynamics exist in our market too. And so I

每多投入一小时优秀人才的时间、每多投入一美元研发经费，都能持续创造价值，不会很快耗尽值得开发的事物。尤其是在搜索领域，与计算机不同，扩大分发规模也有助于改进产品，因为企业可以根据从用户那里获得的数据和反馈来调整算法、优化学习过程。我认为，我们的市场也具备所有这些特征。因此，

think maybe the sad truth for people like us, but then the amazing truth for the world is, I think that there are many leapfrogs that exist, there's more useful things to build. We're a long way away from where we can compete in 5, 10 years, and it's incumbent in our state to keep that going.

对我们这样的人而言，这或许是一个令人遗憾的事实；但对全世界而言，这却是一个令人振奋的事实：未来还会出现许多跨越式突破，也还有更多有用的事物值得开发。我们距离 5 年、10 年后所能达到的水平还很遥远，而我们现在有责任让这一进程延续下去。

So what I'm hearing, this sounds like a lot more like a consumer sort of moat, where it's just, be the best thing consistently so that people stick with you versus creating lock-in and things like that, where they're just... Like Salesforce, where it's just contracts with the entire company, and you have to use this product.

依我的理解，这听起来更像是一种消费产品式的护城河：始终提供最好的产品，让用户愿意继续使用，而不是通过某种方式锁定用户。例如，像 Salesforce 那样与整家公司签订合同，要求所有人都使用这款产品。

Yeah. I think the important thing to note is, if you're in a space where you run out of useful things to do very quickly, then that's not a great situation to be in. But if you're in a place where big investments, and having more and more great people working on the right path can keep giving you value, then you can get these economies of scale of R&D, and you can deeply work on the technology in the

是的。需要注意的关键一点是，如果一个领域很快就不再有值得做的事情，那么置身其中并不是理想的处境。但如果在你所处的领域，大规模投入资源，并让越来越多优秀的人沿着正确方向努力，都能持续创造价值，那么你就能获得研发上的规模经济，并沿着正确方向深入钻研技术，

right direction, and get to a place where that is defensible. But yes, it is... I think there's a consumer-like tendency to it, and I really think it's just about building the best thing possible.

最终建立起真正的防御能力。不过，它确实具备某种类似消费产品的特性，归根结底，关键在于尽可能打造出最好的产品。

Do you think in the future there's one winner in this space, or do you think it's going to be a world of a number of products like this?

你认为未来这个领域只会有一个赢家，还是会出现多个此类产品并存的局面？

I think the market is just so very big. You asked about the IDE thing early on, and one thing that I think a trip of some people that were thinking about the space is, they looked at the IDE market of the past 10 years, and they said, "Who's making money off of the editors?" It's this super fragmented space where everyone kind of has their own thing, with their own figuration, and there's one company that actually makes money off making great editors,

我认为这个市场极其庞大。你之前问过集成开发环境的问题。在思考这个领域时，一些人很容易陷入一个误区：他们回顾过去 10 年的集成开发环境市场，并提出这样的问题：“谁在依靠编辑器赚钱？”这是一个极其碎片化的领域，几乎每个人都有自己的一套工具和配置，只有一家公司真正依靠打造优秀的编辑器赚钱，

but that company is only so big. And then the conclusion was, it was going to look like that in the future. And I think that the thing that people missed was that there was only so much you could do building an editor in the 2010s for coders, and the company that made money off of editors was doing things like making it easy to navigate around a code base, and doing some error checking and type checking for things, and having good debugging tools.

但这家公司的规模也仅止于此。于是，人们便得出结论，认为未来仍会维持这种局面。但我认为，他们忽略了一点：在 2010 年代，为程序员打造编辑器时，可做的事情非常有限。那家依靠编辑器赚钱的公司所做的，无非是帮助用户轻松浏览代码库，对部分内容进行错误检查和类型检查，并提供易用的调试工具。这些功能当然都非常有用，但我认为，可以为程序员打造的产品，

Which were all very useful, but I think that the set of things you can build for programmers, I think the set of things you can build for knowledge workers in many different areas just goes very far and very deep. The problem in front of all of us is the automation of a lot of busy work and knowledge work, and really changing all the areas of knowledge work in front of us to be much higher level and more productive. So that was a long-winded way to say,

乃至可以为许多不同领域的知识工作者打造的产品，其范围可以非常广，深度也可以非常大。摆在我们所有人面前的问题是，如何将大量繁琐工作和知识工作自动化，并真正改变眼前的所有知识工作领域，使其达到高得多的水平，并获得高得多的效率。归根结底，我认为我们所处的市场确实极其庞大。它比过去那些

I think the market's really, really big that we're in. I think it's much bigger than people have realized than the other building tools for developers in the past. And I think that there will be a bunch of different solutions. I think that there will be one company, to be determined if it's going to be us, but I do think that there will be one company that builds the general tool that builds almost all the world's software, and that will be a very, very generationally

为开发者打造工具的人所意识到的规模大得多。我也认为，未来会出现许多不同的解决方案。我相信会有一家公司——至于是不是我们，还有待观察——打造出一个通用工具，用来构建全世界几乎所有的软件，而这将成为一门规模极其庞大、堪称划时代的生意。不过，我也认为市场上仍会存在一些细分空间，例如专门服务于

big business. But I think that there will be kind of niches you can occupy in doing something for a particular segment of the market, or for a very particular part of the software development life cycle. But the general programming shifts from just writing formal programming languages to something way higher level. This is the application you purchase and use to do that. I think that there will be generally one winner there, and it will be a very big business.

某个特定细分市场，或专注于软件开发生命周期中的某个具体环节。但总体而言，编程将从单纯编写形式化编程语言，转向层次高得多的方式。这就是人们会购买并用于完成这项工作的应用。我认为，这个市场总体上只会有一个赢家，并将形成一门规模极其庞大的生意。这一点很有意思。顺着这个话题继续讨论，Microsoft 其实最早处于这股浪潮的中心，

Juicy. Along those lines, it's interesting that Microsoft was actually at the center of this first, with an amazing product, amazing distribution, Copilot you said was the thing that got you over the hump of, "Wow, there could be something really big here." And it doesn't feel like they're winning, it feels like they're falling behind. What do you think happened there?

既拥有出色的产品，也具备强大的分发能力。你曾说过，Copilot 是让你跨过那道门槛，并意识到“这里可能存在一个非常大的机会”的产品。但现在看来，他们似乎并未胜出，反而正在落后。你认为当时发生了什么？我认为，Copilot 之所以可能没有达到预期——至少到目前为止，没有

I think that there are specific historical reasons why Copilot might not have lived up... So far have lived up to the expectations that some people have for it, and then I think that there are structural reasons. I think the structural reason is... And to be clear, Microsoft, in the Copilot case, obviously a big inspiration for our work, and in general, I think they do lots of awesome things, and we're users of many Microsoft products,

达到一些人对它的预期——背后既有一些具体的历史原因，也有一些结构性原因。先说明一点，Microsoft 的 Copilot 显然给我们的工作带来了很大启发。总体而言，他们完成了许多非常出色的工作，我们也在使用许多 Microsoft 产品。但我认为，这个市场对现有巨头并不是特别有利，因为

but I think that this is a market that's not super friendly to incumbents, in that a market that's friendly to incumbents might be one where there's only so much to do, it kind of gets commoditized fairly quickly, and you can bundle that in with other products, and where the ROI between different products is quite small. And in that case, perhaps it doesn't make sense to buy the innovative solution, it makes sense to just kind of

对现有巨头有利的市场，可能是发挥空间有限、很快就会商品化，而且产品可以与其他产品捆绑销售的市场。在这种市场中，不同产品之间的 ROI 差异也很小。此时，购买创新型解决方案或许并不划算，更合理的选择只是购买与其他产品捆绑销售的产品。另一种可能对

buy the thing that's bundled in with other stuff. Another market that might be particularly helpful for incumbents is one where there's... From the get-go, you have your stuff in one place, and it's really, really excruciatingly hard to switch, and for better or for worse. I think in our case, you can try out different tools, and you can decide which product you think is better. And so that's not super friendly to incumbents, and that's more friendly to whoever you think is going

现有巨头特别有利的市场是：从一开始，用户的所有内容就集中在一个地方，而且迁移极其困难。无论这种情况是好是坏，我认为，我们这个领域并非如此。用户可以尝试不同的工具，再判断哪款产品更好。这对现有厂商并不是特别有利，反而更有利于你认为能够

to have the most innovative product. And then the specific historical reasons, as I understand them are the group of people that worked on the first version of Copilot have, by and large, gone on to do other things at other places. I think it's been a little hard to coordinate among all the different departments and parties that might be involved in making something like this.

打造出最具创新性产品的一方。至于背后的具体历史原因，据我了解，参与开发第一版 Copilot 的那批人基本都已离开，转而从事其他工作。此外，要协调开发这类产品可能涉及的各个部门和各方人员，似乎一直都颇为困难。

I want to come back to Cursor. A question I like to ask everyone that's building a tool like this, if you could sit next to every new user that uses Cursor for the first time, just whisper a couple tips in their ear to be more successful, most successful with Cursor, what would be 1 or 2 tips?

我想回到 Cursor。我很喜欢向每一位开发这类工具的人提出一个问题：如果你能坐在每一位初次使用 Cursor 的新用户身边，低声告诉他们几条使用技巧，帮助他们更顺利地使用 Cursor，并最大限度地发挥它的价值，你会给出哪 1 条或 2 条建议？

I think right now, and we'd want to fix this at a product level, a lot of being successful with Cursor is kind of having a taste for what the models can do, both what complexity of a task they can handle, and how much you need to specify things to that model, but having a taste for the quality of the model, and where its gaps exist, and what it can do and what it can't. And right now, we don't do a good job in the product of educating people around that,

目前，我们也希望从产品层面解决这个问题。要想用好 Cursor，在很大程度上需要对模型的能力形成一定的判断，包括它们能够处理多么复杂的任务，以及需要以多具体的方式向模型说明要求。用户必须能够判断模型的能力水平及其短板，了解它能做什么、不能做什么。目前，我们的产品还不太擅长帮助用户形成这些认识，

and maybe giving people some swim lanes, giving people some guidelines.

也没有为用户划定适当的行动范围，或提供相应的指导原则。

But to develop that taste, would give two tips. So one is, as mentioned before, would bias less toward, trying in one go to tell the model, "Hey, here's exactly what I want you to do." Then seeing the output, and then either being disappointed or accepting the entire thing for an entire big task.

不过，对于如何培养这种判断力，我会给出两条建议。第一条正如之前所说，不要总想着一次性告诉模型：“这就是我具体希望你完成的任务。”随后查看输出结果，要么感到失望，要么全盘接受这个大型任务的所有结果。

Instead what I would do is I would chop things up into bits, and you can spend basically the same amount of time specifying things overall, but chopped up more. So you're specifying a little bit, you're getting a little bit of work, you're specifying a little bit, getting a little bit of work, and not doing as much the, "Let's write a giant thing telling the model exactly what to do." I think that will be a little bit of a recipe for disaster right now.

相反，我会把任务拆分成多个小块。总体而言，说明需求所花费的时间基本相同，只是需要将需求拆分得更细。先说明一小部分需求，让模型完成一小部分工作；然后再说明一小部分需求，让它继续完成下一部分工作。不要总想着写一大段内容，一次性告诉模型具体应该怎么做。在现阶段，这种做法很容易导致灾难性的结果。

And so biasing toward chopping things up. At the same time, and it might make sense to do this on a side project and not on your professional work, I would encourage people to, especially developers who are used to existing workflows for building software, I would encourage people to explicitly try to fall on their face, and try to discover the limits of what these models can do by being ambitious in a safe environment, like perhaps a side project,

因此，应该更倾向于拆分任务。与此同时，或许应该先在业余项目中这样做，而不是直接用于正式工作。我建议开发者，尤其是已经习惯现有软件开发工作流的开发者，有意识地允许自己经历失败，在安全的环境中大胆尝试，从而探索这些模型的能力边界。例如，可以使用业余项目进行尝试，

and trying to kind of go around town, use AI to the fullest. Because a lot of the time, we run into people who haven't given the AI yet a fair shake, and are underestimating its abilities.

放开手脚，尽可能充分地使用人工智能。很多时候，我们遇到的一些人尚未真正公平地检验过人工智能，因此低估了它的能力。

So generally biasing towards chopping things up and making things smaller, but to discover the limits of what you can do there, explicitly just try to go for broke in a safe environment, and get a taste for... You might be surprised in some of the places where the model doesn't break.

总体而言，应该倾向于将任务拆开、细化；但如果想探索模型的能力极限，就应在安全的环境中有意识地放手尝试，从而培养这种判断力。你可能会惊讶地发现，模型在某些方面并不会出现问题。

What I'm essentially hearing is build a gut feeling of what the model can do, and how far it can take an idea versus just kind of guiding it along. And I bet that you need to rebuild this gut every time there's a new model launch, when it's on... I don't know, 4.0 comes out, you have to do this again. Is that generally right?

据我理解，你的核心意思是，需要培养一种直觉，判断模型能够做什么、能够把一个想法推进到什么程度，以及何时只需在一旁加以引导。我猜，每当新模型发布时，你都需要重新培养这种直觉。比如，等 4.0 发布时，你就得再经历一遍。大体上是这样吗？

Yes. For the past few years, it hasn't been as big as I think the first experience people have had with some of these big models. This is also a problem we would hope to solve much better just for users, and take the burden off of them. But each of these things have slightly different quirks and different personalities.

是的。过去几年里，这种变化带来的差别，似乎还没有人们初次体验这些大型模型时那么大。我们也希望为用户更好地解决这个问题，让他们不必承担这种负担。不过，每一代模型都有不同的怪癖和个性。

Along these lines, something that people are always debating tools like Cursor, are they more helpful to junior engineers, or are they more helpful to senior engineers? Do they make senior engineers 10X better? Do they make junior engineers more like senior engineers? Who do you think benefits most today from Cursor?

顺着这个话题，人们一直在争论，Cursor 这类工具究竟对初级工程师的帮助更大，还是对高级工程师的帮助更大。它们能让高级工程师的能力提升 10X 吗？能让初级工程师变得更像高级工程师吗？你认为目前谁从 Cursor 中获益最多？

I think across the board. Both of these cohorts benefit in big ways. It's a little hard to say on the relative ranking. I will say, they fall into different anti-patterns.

我认为所有人都能受益，这两个群体都能从中获得巨大收益。很难判断相对而言谁受益更多。不过，他们会陷入不同的反模式。

The junior engineers we see going a little too wholesale, relying on AI for everything, and we're not yet in a place where you can kind of do that end-to-end on a professional tool, working with tens, hundreds of other people within a long-lived code base. And then the senior engineers... For many folks, it's not true for all, and we actually often... One of the ways these tools are adopted is, there's developer experience teams within companies,

我们看到，初级工程师有些过度依赖人工智能，凡事都交给人工智能。但这项技术目前还无法借助专业工具，真正端到端地完成一切，尤其是在一个长期维护的代码库中与几十人乃至几百人协作时。高级工程师的情况则有所不同，尽管并非所有人都如此。事实上，这类工具通常由公司内部的开发者体验团队推动普及。

often those are staffed by incredibly senior people, because often, those are people who are building tools to make the rest of the engineers within an organization more productive.

这些团队往往由资历极深的人组成，因为他们通常负责构建工具，帮助组织内的其他工程师提高效率。

And we've seen some very, very boundary pushing kind of... We've seen people who are on the front lines of really trying to adopt the technology as much as possible there. But by and large, I would say on average, as a group, the senior engineers underrate what AI can do for them, and stick to their existing workflows. And so the relative ranking is a little hard, I think they fall into different anti-patterns, but they both, by and large, yet get big benefits with these tools.

我们也见过一些敢于突破边界、身处最前沿，并真正努力最大限度采用这项技术的人。但总的来说，高级工程师作为一个群体，平均而言低估了人工智能能够为他们做什么，依然坚持原有的工作流程。因此，很难判断谁受益更多。我认为，这两个群体会陷入不同的反模式，但总体而言，他们都已经从这些工具中获得了巨大收益。

That makes absolute sense. I love that it's two ends of the spectrum, expect too much, don't expect enough. It's like the three bears allegory.

这完全说得通。我很喜欢处于光谱两端的这两种情况：一种期望过高，另一种期望不足，就像“三只熊”的寓言一样。

Yeah.

是的。

Yeah. Okay.

好。

Yeah. Maybe the sort of senior, but not staff, right in the middle.

也许恰好处于中间位置的，是尚未达到 Staff 级别的高级工程师。

Interesting. Okay. Just a couple more questions. What's something that you wish you knew before you got into this role? If you could go back to Michael at the beginning of Cursor, which was not that long ago, and you could give him some advice, what's something that you would tell him?

很有意思。接下来再问几个问题。担任这个角色之前，有什么是你希望自己早已知道的？如果你能回到 Cursor 刚起步时，找到那时的迈克尔——其实距今也没有多久——并给他一些建议，你会告诉他什么？

The tough thing with this is, it feels like so much of the hard-won knowledge is tacit, and a bit hard to communicate verbally. And the sad fact of life feels like for some areas of human endeavor, you kind of do need to fall on your face to... Either need to fall on your face to learn the correct thing, or you need to be around someone who's a great example of excellence in the thing.

这个问题的难点在于，许多来之不易的经验似乎都是隐性的，很难用语言传达。人生令人无奈的一点是，在人类从事的某些领域，你确实必须经历挫折才能学会正确的做法，或者必须待在某个人身边，而他在这件事上堪称卓越典范。

And one area where we have felt this is hiring. I think that we actually were... So we tried to be incredibly patient on the hiring front. It was really important to us that, both for personal reasons and also for, I think actually for the company's strategy, having a world-class group of engineers and researchers to work on Cursor with us was going to be incredibly important. Also, getting people who fit... A certain mix of intellectual

招聘是我们对此深有体会的一个领域。在招聘方面，我们一直努力保持极大的耐心。这一点对我们非常重要，既有个人层面的原因，也有公司的战略考量。组建一支世界一流的工程师和研究人员团队，与我们共同开发 Cursor，将会极其重要。此外，我们还要找到具备求知欲的人。

curiosity and experimentation, because there can be so many new things we need to build.

这些人还需要具备实验精神，因为我们可能需要构建大量全新的事物。

And then also an intellectual honesty, and maybe micro-pessimism, bluntness, because if all the noise, and... Especially as the company's grown, and the business has grown, keeping a level head I think is incredibly important too. But getting the right group of people into the company was the thing that maybe more than anything else, apart from building the product, we really, really fussed over. We actually waited a long time to grow the team because of that.

同时，他们还要具备理性的诚实，或许还需要有微观层面的悲观和直率。面对外界的种种噪音，尤其是随着公司和业务不断发展，保持清醒冷静极其重要。除了打造产品之外，将合适的一群人招进公司，也许是最重要的事情。我们一直在反复斟酌这件事。也正因如此，我们等了很久才开始扩充团队。

And I think that many people you hear hired too fast, think we actually hired too slow to begin with. I think it could have been remedied, I think we could have been better at it.

人们常说自己招人太快，但我认为我们一开始其实招得太慢了。这个问题原本可以解决，我们本可以做得更好。

And the method of recruiting that we ended up eventually falling into and working really well for us, which isn't that novel, of going after people that we think are really world-class, and recruiting them over the course of, in some cases, many years, ended up working for us in the end, but I don't think we were very good at it to begin with. And so I think that there were hard-won lessons around both who was the right profile, who actually made sense in that team,

后来，我们最终摸索出了一套事实证明非常适合我们的招聘方法。这套方法并不新颖：物色那些我们认为真正达到世界一流水平的人才，然后持续招募他们，有时甚至要花费数年。最终，这种方法对我们奏效了，但我们起初并不擅长这样做。我们在许多方面都经历了艰难摸索，包括什么样的人才合适，以及什么样的人真正适合这个团队。

what did greatness look like, and then how to talk with someone about the opportunity, and get them excited if they really weren't looking for anything. There were lots of learnings there about how to do that well, and that took us a bit of time.

我们还需要判断卓越的人才究竟是什么样的，思考该如何向对方介绍这个机会，并在他们原本完全没有求职打算时激发他们的兴趣。如何做好这件事包含许多经验，而我们也花了一段时间才掌握。

What are some of those learnings for folks that are hiring right now? What's something you missed or learned?

对于目前正在招人的人，其中有哪些经验值得分享？你之前忽略了什么，又从中学到了什么？

I think to start with, maybe we actually biased a little bit too much towards looking for people who fit the archetype of well-known school, very young, had done the things that were high credential in those well-known school environments. And actually, I think found... Were lucky early on to find fantastic people who are willing to do this with us who were later careered. I think we should kind of spent a bunch of time on maybe a

一开始，我们可能过度偏向于寻找符合典型画像的人：毕业于知名院校、非常年轻，并且在知名院校中取得过亮眼而有说服力的成绩。但我们很幸运，在早期就找到了非常出色、愿意与我们共同做这件事，而且职业资历更深的人。我们起初可能过度关注了并不合适的人才画像。

little bit the wrong profile to begin with, and part of that was a seniority thing. Part of that was kind of an interest and experience thing too, we have hired people who are excellent, excellent, excellent and very young, but they maybe look in some cases slightly different from being straight out of central casting. Another lesson is just, we very much evolved our interview loop, and so now, we have a hand-rolled set of interview questions, and then core our...

我们在这方面花了很多时间，其中一部分是资历层面的问题，另一部分则涉及兴趣和经验。我们招到过一些非常出色且十分年轻的人，但在某些方面，他们与人们想象中的标准模板式人才略有不同。另一个经验是，我们对整个面试流程进行了大量迭代。现在，我们采用一套自行设计的面试题。除此之外，我们的面试方式还有另一个核心环节。

Core to how we interview too, is actually, we have people onsite for two days, and do a project with us, a work test project. And that has worked really well, that increasingly you're finding that. I think how to learn about what people are interested in, and put our best foot forward, and letting them know about the opportunity when they're really not looking for anything, and have those conversations. There's definitely been... Gotten better at that over time.

面试方式的另一个核心，是让候选人到现场待两天，与我们共同完成一个项目，也就是工作测试项目。这种方式效果非常好，现在越来越多的公司也开始这样做。我们还逐渐学会了如何了解人们对什么感兴趣、如何展现出自己最好的一面，以及如何在对方根本没有求职打算时，让他们了解这个机会并与他们展开交流。我们在这方面做得越来越好了。

Do you have a favorite interview question that you like to ask?

你是否有一个特别喜欢问的面试问题？

I think this two-day work test which we thought would not scale past a few people has had surprising staying power. And the great thing about it is, it lets someone go end-to-end on it like a real project. It's not work that we use, it's canned list of projects. But it gives you two days of seeing a real work product, and it doesn't have to be incredibly time-enhancing other teams from time. You can take the time you

这项为期两天的工作测试，我们原以为团队规模超过几个人后就无法继续采用，没想到它始终很有生命力。它最大的优点是，能够让候选人像完成真实项目一样，从头到尾完整地做一遍。项目成果不会被我们实际采用，而是从一份预先准备好的项目清单中选择。这样一来，你可以用两天时间观察候选人真实的工作成果，而且不一定会占用其他团队成员太多时间。你可以把原本……

would spend in a half day or one day onsite, and you kind of spread it out over those two days, and give someone a lot of time to do work on their projects, and so that can actually help it scale.

半天或一天的现场面试所需的时间被分摊到两天里，同时候选人也有充足的时间完成项目，因此这种方式同样具备扩展性。

It helps to enforce, do you want to be around this person type test, because you are around this person for two days, a bunch of meals with them. We didn't expect that one to stick around, but that has been really, really important to our value to process, and then also important to getting people excited at, especially the very early stages of the company. Because before, people are using the product, and know about it. And when the product is comparatively not

这种方式还有助于验证你是否愿意与对方长期共事，因为你们会相处两天，还会一起吃好几顿饭。我们原本没有想到这种方式会一直保留下来，但它对评估流程确实非常重要，也有助于增强候选人对这一机会的兴趣，尤其是在公司创立初期。过去，大家使用过这款产品，也对它有所了解。而当产品相对没那么

very good, really, the only thing you have going for you is a team of people that some people find special and want to be around. And the two days would give us a chance to just have this person meet us, and in some cases, hopefully get convinced that they want to throw in with us.

好用时，坦率地说，唯一的优势就是有这样一群人。有些候选人会觉得他们很特别，愿意与他们共事。这两天也让候选人有机会真正了解我们，并在某些情况下最终愿意加入我们。

That one was unexpected. Not exactly an interview question, but kind of like a forward interview.

这个答案出乎意料。它不完全算是一个面试问题，更像是一场前置面试。

The ultimate interview question. So just to be very clear about what you're describing, you give them an assignment, like, "Build this feature in our actual code base, work with the team to code it and ship it." Is that roughly right?

这是终极面试题。为了确认我准确理解了你的意思，你会给候选人布置一项任务，例如：“在我们真实的代码库里开发这个功能，与团队一起完成编码并上线。”大致如此吗？

Yes. So we don't use the IP, not shift end-to-end, but it's like a mock... Very often in our code base, "Here's a real mini two-day project. You're going to do it end-to-end." Largely being left alone, there's collaboration too. And then we're a pretty imprisoned company, in almost all cases, it's actually just sitting in office with us too.

是的。我们不会使用候选人产出的知识产权，也不会将成果完整上线，但这类似于一次模拟项目。项目通常会直接在我们的代码库里进行：“这是一个真实的两天迷你项目，你要从头到尾完成它。”候选人大部分时间会独立完成项目，当然也会与团队协作。我们是一家以线下办公为主的公司，因此在几乎所有情况下，候选人都会与我们一起坐在办公室里工作。

And you've been saying that this has scaled to even today, so how big are you guys at this point?

你刚才说，这种方式一直沿用至今。那么，你们现在的规模有多大？

So we are going on 60 people.

我们快有 60 个人了。

So small for the scale and impact. I was thinking it'd be a lot larger than that.

以你们的规模和影响力来看，团队确实很小。我原以为人数会多得多。

Yeah.

是的。

And I imagine the largest percent is engineers?

我猜其中占比最高的是工程师？

Yeah. To be clear, a big part of the work ahead of us is building a group of people that is bigger, and awesome, and can continue to make the product better, and the service we give to customers better. And so you don't plan to stay that small for longer, wouldn't hope so. But part of the reason that that number is small is, the percentage of engineering and research and design is very high within the company, and so many software companies when they have roughly

是的。需要说明的是，我们接下来一项非常重要的工作，就是建立一支规模更大且非常出色的团队，使其能够继续改进产品，并提升我们为客户提供的服务。因此，你们并不打算长期维持这么小的规模，我也希望不会如此。不过，员工人数较少的部分原因在于，公司内部工程、研究和设计人员的占比非常高。许多软件公司如果拥有大约

40 engineers would be over 100 people, because there's lots of operational work, and often, they're very, very sales-led from the get-go, and that's just quite labor-intensive. And here, we started from a place of being incredibly lean in product-led, and we now serve lots of our market customers, and it built that out, but there's much more to do there.

40 名工程师，员工总数往往会超过 100 人，因为还需要承担大量运营工作，而且通常从创立之初就高度依靠销售驱动，需要投入大量人力。我们起步时则极其精简，以产品为驱动力。如今，我们已经服务了许多高端市场客户，也建立了相应的团队和体系，但在这方面仍有许多工作要做。

A question I wanted to ask you, there's so much happening in AI, there's things launching every... There's newsletters, many newsletters, whose entire function is to tell you what is happening in AI every single day. Running a company that's at the center, the white-hot center of this space, how do you stay focused, and how do you help your team stay focused, and heads down, and just build and not get distracted by all these shiny things?

有一个问题我一直想问你：人工智能领域正在发生大量变化，各种产品几乎每天都在发布，还有各种资讯简报，其中许多简报存在的唯一目的，就是每天告诉读者人工智能领域又发生了什么。经营一家身处这个领域核心地带，甚至处于风口中心的公司，你如何保持专注？你又如何帮助团队保持专注、埋头工作，只把产品做好，而不被这些光鲜的新事物分散注意力？

I think hiring is a big part of it, and if you get people with the right attitude.

我认为，招聘是其中非常重要的一环，关键在于找到心态合适的人。

All of this should be asterisked in, I think we're doing well there, I think that we'd probably be doing better there too, and it's something that we should probably talk even more about as a company. But I think that hiring people with the right disposition, people who are less focused on external validation, more focused on building something really great, more focused on doing really high quality work, and people who are just generally level-headed,

我需要为上述说法加上一项说明：我认为我们在这方面做得不错，但仍然可以做得更好。作为一家公司，我们或许也应该更深入地讨论这个问题。关键在于招聘性格和心态合适的人，也就是那些不太在意外界认可，更专注于打造真正优秀的产品，更重视高质量工作，而且整体上沉稳理性的人。

and maybe the highs aren't very high, the lows aren't very low. I think hiring can get you through a lot here, and I think that's actually a learning throughout the company, is that for any... You need process, you need hierarchy, you need lots of things, but for any kind of organizational tool that you're introducing into a company, the result you're looking to get from that tool also... You can go pretty far on hiring people with the right behaviors that you want

这些人不会过度兴奋，也不会过度低落。我认为，做好招聘可以帮助你解决其中的许多问题。这也是我们在全公司范围内获得的一项经验：公司需要流程、层级和许多其他机制，但无论引入哪一种组织管理工具，只要招到行为方式符合预期的人，就能在很大程度上获得你希望通过这些工具实现的结果，

to resolve from that for organizational thing. And the specific example that comes to mind is, we've been able to get away with not a ton of process yet on the engineering front, and I think we need a little bit more process, but for our size, not a ton of process, by hiring people who I think are really excellent. One is hiring people that are level-headed. I think two is just talking about it a lot. I think three is hopefully leading by example.

从而在组织层面实现预期目标。具体来说，到目前为止，我们的工程团队不需要太多流程也能顺利运作。虽然我认为确实需要再增加一些流程，但以我们的规模而言，并不需要太多，因为我们招到了一批非常优秀的人。第一，要招聘沉着理性的人；第二，要经常讨论这件事；第三，要努力以身作则。

And for us personally, we've since 2021, 2022 been professionally working on this, and been working on AI, and we've just seen a sea change of the comings and goings of various technologies and ideas of... If you're to transport yourself back to end of 2021, beginning of 2022, this is GPT-3, InstructGPT doesn't exist, there's no Dolly, there's no stable diffusion. And then we've gone through all of those image technologies existing, ChatGPT and that rise, and GPT-4, all of these

就我们而言，从 2021、2022 年起，我们就一直专业地从事这项工作，持续致力于人工智能，也亲眼见证了各种技术和理念来来去去所带来的巨大变化。如果回到 2021 年底、2022 年初，当时还是 GPT-3，InstructGPT 尚未出现，也没有 Dolly，更没有 stable diffusion。此后，我们一路经历了所有这些图像技术的出现、ChatGPT 的诞生与崛起，以及 GPT-4，还有所有这些

new models, all these different modalities, all the video stuff, and only a very small number of these things really kind of affects the business. So I think we've kind of just built up a little bit of an immune system, and know when an event comes around that actually is really going to matter for us. This dynamic too of there being lots, and lots, and lots of chatter, but then maybe only a few things that really matter, I think has been mirrored in AI over the

新模型、各种不同的模态和所有与视频相关的技术，但其中真正会影响业务的技术其实少之又少。因此，我们多少已经建立了一套免疫系统，能够判断什么样的事件出现时才会真正对我们产生影响。这种现象也很有意思：外界存在海量讨论，但真正重要的可能只有少数几件事。我认为，过去

last decade, where there have been so many papers on deep learning in academia, so many papers on AI in academia, then the amazing thing is there are really a lot of... A lot the progress of AI can be attributed to some very simple elegant ideas that have stayed around, and the vast majority of ideas that have been put out there haven't had staying power, and haven't mattered a ton. And so the dynamic is a little bit mirrored in the evolution of deep learning as a field overall.

十年人工智能的发展也呈现出类似的情况：学术界发表了大量关于深度学习和人工智能的论文。但令人惊叹的是，人工智能取得的许多进展，其实可以归功于一些非常简洁、优雅且经久不衰的理念，而绝大多数被提出的理念都没能经受住时间的考验，也没有产生太大影响。这种现象在某种程度上也反映了深度学习作为一个整体领域的演进。

Last question. What do you think people still most misunderstand, or maybe don't fully grasp about where things are heading with AI in building in the way the world will change?

最后一个问题：对于人工智能将走向何方、它在产品构建中的作用，以及世界将如何改变，人们最容易误解或者仍未完全理解的是什么？

People are still a little bit occupied too much, either end of a spectrum of it's all going to happen very fast, and this is all bluster, and hype, and snake well, and I think we're in the middle of a technology shift that's going to be incredibly consequential. I think it's going to be more consequential than the internet, I think it's going to be more consequential than any shift in tech that we've seen since the advent of computers. And I think it's going to take a while,

人们仍然有些过度关注两个极端：要么认为一切很快就会发生，要么认为这一切不过是虚张声势、炒作和骗局。我认为，我们正处于一场影响极其深远的技术变革之中。它的影响将超过互联网，也将超过自计算机问世以来我们见过的任何技术变革。但我认为，这需要很长时间，

and I think it's going to be a multi-decade thing, and I think many different groups will be consequential in pushing it forward. And to get to a world where computers can increasingly do more, and more, and more for us, there's all of these independent problems that need to be knocked down, and progress needs to be made on them, and some of those are on the science side of things of getting these models to understand different types of data, be faster,

将是一个跨越数十年的进程，也会有许多不同的群体在推动这一进程时发挥重要作用。要实现一个计算机能够不断为我们做更多事情的世界，还有许多彼此独立的问题需要逐一解决并取得进展。其中一些属于科学层面的问题，例如让这些模型理解不同类型的数据，使其变得更快、

cheaper, smarter, conform to the modalities that we care about, take actions in the real world.

更便宜、更智能，能够适应我们关注的模态，并在现实世界中采取行动。

And then some of it's on how we're going to work with them, and what's the experience that a human should actually be seeing and controlling on a computer, and working with these things.

还有一些问题涉及我们将如何与这些技术协作，以及人们在计算机上究竟应该看到什么、控制什么，又该如何与这些技术协同工作。

But I think it's going to take decades. I think that there's going to be lots of amazing work to do. I think that also, one of the most... A pattern of a group that I think will be especially important here, not to talk our own book, but I think is the company that works on automating and augmenting a particular area of knowledge work, builds both the technology under the surface for that, integrating the best parts from providers, sometimes doing it in-house,

但我认为，这需要几十年时间，期间还有大量令人振奋的工作要做。此外，有一类群体将在其中发挥尤为重要的作用。并非想自卖自夸，但我认为，这类群体将致力于在特定的知识工作领域实现自动化、增强人的能力，同时构建底层支撑技术，整合各家供应商最出色的部分，有时也会自行开展内部开发。

and then also builds the product experience for that. I think people who do that, and... We're trying to do it in software, people do that in other areas, I think those folks will be really, really, really consequential. Not just for the end value that users see, but then I think as they get to scale, they'll be really important for pushing forward the technology, because I think they'll be able to build... The most successful of them will be able to build

他们还会围绕这些技术打造产品体验。我们正努力在软件领域这样做，其他领域也有人在进行类似的尝试。我认为，这些人将产生极其深远的影响，不仅因为用户最终能够从中获得价值，也因为随着规模扩大，他们将成为推动技术进步的重要力量。其中最成功的公司将能够打造出

very, very big businesses. So, excited to see the rise of other companies like that in other areas.

规模极其庞大的业务。因此，我很期待看到其他领域也涌现出更多这样的公司。

I know you guys are hiring. For folks that are interested in, "Hey, I want to go work here, and build this sort of stuff." What kind of roles are you looking for right now? Anyone specifically you're trying... Any roles you're most excited about filling ASAP? What should people know if they're curious?

我知道你们正在招聘。对于那些有兴趣来这里工作、打造这类产品的人，你们目前在招聘哪些岗位？有没有特别希望尽快招到人才的岗位？对此感兴趣的人需要了解些什么？

There are so many things that this group of people need to do that we are not get equipped to do.

这支团队需要完成的工作实在太多，而我们目前还没有足够的人手和能力把所有事情都做好。

Generic across the board, first of all, and so if you don't think we have a role for something, maybe you should reach out, that won't actually be the case. And maybe we can actually learn from you, and decide that we need something that we weren't yet aware of. But by and large, I think that two of the most important things for us to do this year are have the best product in the space, and then grow it. And we're kind of in this land grab mode, where almost everyone in the world is

首先，我们在各个方向都开放了招聘。因此，即使你认为我们没有适合你的岗位，或许也应该联系我们，因为事实未必如此。我们也可能从你身上学到一些东西，进而意识到自己需要某种此前尚未察觉的能力。总体而言，我们今年最重要的两件事，一是打造这个领域最好的产品，二是推动产品增长。我们目前正处于抢占市场的阶段，因为全世界几乎所有人

either using no tool like ours, or they're using one that's maybe developing less quickly. So growing Cursor too is a big goal, and I would say, especially always on the hunt for folks who...

要么完全没有使用我们这样的工具，要么使用的是发展速度可能没那么快的产品。因此，推动 Cursor 增长也是一个重要目标。我们尤其希望持续寻找

Excellent engineers, designers, researchers, but then folks all across the business side too.

优秀的工程师、设计师和研究人员，当然也在寻找各类业务人才。

I can't help but ask this question now that you talk about engineers, there's this question of just, "AI's going to write all our code." But everyone's still hiring engineers like crazy. All the foundational models, so many open roles.

既然谈到了工程师，我忍不住要问：人们总说“人工智能会替我们写完所有代码”，但每家公司仍在大举招聘工程师，所有基础模型公司也都有大量空缺岗位。

Yeah. We're not out there tooting the horn of, people can learn to code.

我们并没有到处宣扬人人都能学会编程。

Do you think there's going to be an inflection point of engineering roles start to slow down? I know this is a big question, but just... Do you see engineers being more and more needed across all these companies, or do you think at some point there's all these Cursor agents running building for us?

你认为工程师岗位的增长会在某个拐点开始放缓吗？我知道这个问题很宏大，但还是想知道：这些公司会越来越需要工程师，还是到某个时候，会有大批 Cursor 智能体替我们完成开发工作？

Again, we have the view that there's this both long messy middle of it not jumping to a, just you step back, and you ask for all your stuff to be done, and you have your engineering department. And very much, you want to evolve from programming as it exists today, we want humans to be in the driver's seat, and we think even in the end state, that's giving folks control over everything is really important, and you will need professionals to do that,

还是那句话，我们认为，中间会经历一个漫长而混乱的阶段，而不会直接跃迁到这样一种状态：人们只需退到一旁提出要求，一切就都能自动完成，相当于每个人都拥有自己的工程部门。更确切地说，我们希望推动当今的编程方式不断演进，并让人类始终掌握方向。我们认为，即使到了最终形态，让人们掌控一切仍然非常重要，而这依然需要专业人士

and decide what the software looks like. So both I think that, yes, engineers are definitely needed. I think that engineers will be able to do much more. I think the demand for software is very lasting, which is not the most novel thing, but I think it's kind of crazy to think about how expensive and labor-intensive it is to build things that are pretty simple and easy to specify, or it would look like it to the outside observer, and just

来决定软件应该是什么样子。因此，我认为工程师肯定仍然不可或缺，而且将能够完成更多工作。人们对软件的需求也会长期存在。这虽然不是什么新颖的观点，但有些事看起来相当简单，需求似乎也很容易说清楚，至少在外人看来如此；然而，要真正将其实现，成本之高、人力投入之大令人难以置信，而且目前实施起来

how hard those things are to do right now. All of the stuff that exists right now that's justified by the cost and demand that we have now, if you could bring that down by [inaudible 01:07:56], I think you would have tons, and tons, and tons of more stuff that we could do in our computers, tons more tools. And I've felt this, where... One of my early jobs actually was working for a biotechnology company, and it was building internal tools for them, and the off-the-shelf

依然非常困难。按照目前的成本和需求，现有软件的规模是合理的；但如果能把成本降低[听不清01:07:56]，我们能够开发的东西将大幅增加，计算机中会出现更多功能，也会出现更多工具。我对此深有体会。我早期曾在一家生物技术公司工作，负责为他们开发内部工具。当时市面上的

tools that existed were horrible, and did not fit their use case at all. And then the internal tools I was building, there was definitely a ton of demand there for things that could be built, and that far outstripped just the things that I could build in the time that I was with them.

现成工具非常糟糕，完全不符合他们的使用场景。而我开发的内部工具显然还有大量开发需求，远远超出了我在那家公司任职期间所能完成的范围。

The physics of working on computers are so great, you should be able to basically just move everything around, do everything that you want to do. There's still so much friction, I think that there's much more demand for software than what we can build today with things costing like a blockbuster movie to make simple productivity software. And so I think long into the future, yes, there will actually be more demand for engineers.

在计算机上工作的底层特性非常出色：理论上，人们应该可以随意移动一切，完成任何想做的事。但目前仍然存在太多阻力。人们对软件的需求远远超过我们现在所能构建的软件数量，因为即使开发一款简单的生产力软件，成本也高得如同制作一部商业大片。因此，我认为在遥远的未来，市场对工程师的需求实际上会更大。

Is there anything that we didn't cover that you wanted to mention? Any last nugget wisdom you wanted to leave listeners with? You could also say no, because we've done a lot.

还有什么是我们尚未谈到、但你想提及的吗？最后有什么建议想送给听众吗？当然，也可以没有，毕竟我们已经谈了很多。

We think a lot about how you set up a team to be able to make new stuff, in addition to continuing to improve the stuff that you have right now. And I think if we were to be successful, IDE is going to have to change a ton, [inaudible 01:09:18] looks like is going to have to change a ton going into the future. And if you look around, the companies we respect, there are definitely examples of companies that have continued to really ride the wave of many

我们经常思考，如何组建一支既能创造新事物，又能持续改进现有产品的团队。我认为，如果我们想取得成功，集成开发环境就必须发生巨大变化，[听不清 01:09:18] 的形态未来也必须发生巨大变化。纵观我们尊敬的公司，确实有一些成功经历了多次跨越式变革，

leapfrogs, and continue to actually push the frontier. But they're kind of rare too, it's a hard thing to do. So part of that is just kind of thinking about the thing, and trying to reflect on it in our good days, and the first principle side of things, part of it's also trying to get in and study past examples of greatness here, and that's something that we think about a lot too.

并不断推动前沿发展。但这样的公司十分少见，因为这很难做到。因此，我们一方面需要不断思考这个问题，努力在状态良好时进行反思，并从第一性原理出发；另一方面也要深入研究以往的卓越案例。这是我们经常思考的事情。

Yeah. Yeah. Before we started recording, you had all these books behind you, and I was like, "What's that over there?" It's the history of some old computer company that was influential in a lot of ways that I've never heard of. And I think that says a lot about you of, where a lot of this innovation comes from, is studying the past, and study history, and what's worked and what hasn't. Okay. Where can folks find you online

开始录制前，我看到你身后摆着很多书，便问那边那本书讲的是什么。原来它讲述的是一家早期计算机公司的历史。那家公司在许多方面都影响深远，但我此前从未听说过它。我认为，这很能体现你的特点：许多创新都源于研究过去、研究历史，以及了解哪些做法有效、哪些无效。如果大家想联系你，

if they want to reach out and maybe apply? You said that there may be roles they may not even be aware of, where do they go find that, and then how can listeners be useful to you?

甚至想申请加入你们，应该去哪里找你？你刚才提到，有些职位可能连人们自己都没有意识到适合，那么他们可以去哪里查找相关信息？此外，听众怎样才能帮助你？

Yeah. If folks are interested in working on this stuff, would love to speak, they can find... If they go to cursor.com, they can kind of both find the product and find out how to reach us.

如果有人对这些工作感兴趣，我们非常乐意与他们交流。只需访问 cursor.com，就能找到产品，也能了解如何联系我们。

Easy. Michael, thank you so much for being here. This was incredible.

很简单。迈克尔，非常感谢你来做客。这期节目非常精彩。

It was wonderful. Thank you.

这次交流非常愉快。谢谢。

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

各位，再见。非常感谢大家收听。如果你觉得本期节目很有价值，可以在 Apple Podcasts、Spotify 或你常用的播客应用中订阅。另外，也请考虑为本节目评分或留下评论，这确实有助于其他听众发现这档播客。你可以前往lennyspodcast.com 收听往期节目，或进一步了解本节目。下期再见。