📝 本节摘要：
本节作为访谈的开篇，主持人首先介绍了 Cursor 惊人的增长速度——在发布仅 20 个月后即达到 1 亿美元 ARR（年度经常性收入），估值高达 90 亿美元。CEO Michael Tru 随之阐述了公司的核心愿景：在未来 5 到 10 年内，彻底“取代”传统的编程方式。他指出，当前的编程本质上是在编辑数百万行深奥的形式语言，而 Cursor 的目标是创造一种更高维度的构建方式，让用户只需定义软件的运作逻辑和外观，即可自动生成软件，从而将构建能力通过 AI 放大数倍。
[原文] [Michael Tru]: for us the end goal is to replace coding with something much better I think that this is going to be a decade where just your ability to build will be so magnified If you keep pushing the frontier faster than other people you can get really big gains occurring to you Building a company's hard and so you may as well work on the thing that you're really excited about And so yeah we set off to work on uh the future of code
[译文] [Michael Tru]: 对我们要言，终极目标是用某种更好的东西来取代编程。我认为这将是这样一个十年：你的构建能力将被极大地放大。如果你能比别人更快地推进行业前沿，你就能获得巨大的收益。创业很难，所以你倒不如去做那些你真正感到兴奋的事情。所以是的，我们出发去致力于创造代码的未来。
[原文] [Host]: Welcome back to another episode of How to Build the Future Today I'm joined by Michael Tru co-founder and CEO of Anyphere the company behind Cursor the AI coding platform we all know and love They recently hit a $9 billion valuation and are one of the fastest growing startups of all time reaching a hundred million ARR just 20 months after launching Michael thanks for joining us
[译文] [Host]: 欢迎回到新一集的《如何构建未来》（How to Build the Future）。今天我也邀请到了 Michael Tru，他是 Anyphere 的联合创始人兼 CEO，也就是大家熟知并喜爱的 AI 编程平台 Cursor 的母公司。他们最近达到了 90 亿美元的估值，是史上增长最快的初创公司之一，在发布仅仅 20 个月后就达到了 1 亿美元的年度经常性收入（ARR）。Michael，谢谢你加入我们。
[原文] [Michael Tru]: Thank you for having me Excited to be here
[译文] [Michael Tru]: 谢谢邀请，很高兴来到这里。
[原文] [Host]: You've said the goal of cursor is to actually invent a new type of programming uh where you can just describe what you want and it gets built Talk to me about that
[译文] [Host]: 你曾说过，Cursor 的目标实际上是发明一种新型的编程方式，你只需要描述你想要什么，它就会被构建出来。能跟我聊聊这个吗？
[原文] [Michael Tru]: Yeah the goal with the company is to replace coding with something that's much better Me and my three co-founders we've been programmers for a long time More than anything that's what we are The thing that attracted us to coding is that you get to build things really quickly to do things that are sort of simple to describe Coding requires editing you know millions of lines of kind of esoteric formal programming languages uh requires you know doing lots and lots of labor to actually make things show up on the screen that are kind of simple to describe We think that over the next 5 to 10 years it will be possible to invent a new way to build software that's higher level and more productive that's just still down to defining how you want the software to work and how you want the software to look And so our goal with cursor uh is to get there and you know our path to getting there is to at any given point in time always be the best way to code with AI and then evolve that process uh you know evolve it away from normal programming to something that looks very different
[译文] [Michael Tru]: 是的，公司的目标是用某种更好的东西来取代编程。我和我的三位联合创始人当程序员已经很久了。这比任何标签更能代表我们。吸引我们编程的原因是，你可以非常快速地构建事物，去实现那些描述起来很简单的事情。但（目前的）编程需要编辑数百万行那种深奥的形式化编程语言，需要付出大量的劳动才能让那些描述起来其实很简单的东西真正显示在屏幕上。我们认为，在接下来的 5 到 10 年里，发明一种新的软件构建方式将成为可能，这种方式层级更高、效率更高，它仅仅归结为定义你希望软件如何工作以及你希望软件长什么样。所以我们做 Cursor 的目标就是到达那个终点。而我们到达那里的路径是：在任何一个时间点，都要做成当时用 AI 编程的最好方式，然后进化这个过程，让它逐渐脱离常规的编程，演变成某种看起来截然不同的东西。
请问是否继续输出“章节 2：现状分析——‘氛围编程’与专业工程的鸿沟”？
📝 本节摘要：
在本节中，主持人询问 Cursor 是否已经实现了“描述即构建”的愿景。Michael 坦诚地指出，虽然在小型初创团队或短生命周期的代码中，这种变化已初现端倪，但在专业工程领域仍有很长的路要走。他严厉批评了“氛围编程”（Vibe Coding，即不看代码、不理解逻辑只看结果）在专业环境下的不可行性。他强调，在数百万行代码的系统中，盲目依赖 AI 会带来严重的次生效应。目前的 AI 更多是作为辅助工具，用户仍需耗费大量精力去阅读和审查 AI 生成的代码，而 Cursor 的下一个跨越正是要减少这种“阅读与审查”的负担。
[原文] [Host]: So some people would say that is what we have today You sort of describe what you want and out it comes What would you say to that like are we there yet you know what are the steps to where you really want to go
[译文] [Host]: 那么有些人会说，这不就是我们今天已经拥有的吗？你大概描述一下你想要的，东西就出来了。你会对此怎么说？就像是我们已经到了那个阶段了吗？或者要去到你真正想去的地方还需要哪些步骤？
[原文] [Michael Tru]: we're seeing the first signs of things really changing Um I think you guys are probably on the forefront of it with YC because I think that in smaller code bases with smaller groups of people working on a piece of software that's where you feel the change the most already there we see people kind of stepping up above the code to a higher level of abstraction and just asking uh essentially agents uh and AIS to make all the changes for them
[译文] [Michael Tru]: 我们正看到事情真正发生改变的初步迹象。嗯，我觉得你们 YC 可能正处于这一变革的最前沿，因为我认为在较小的代码库和较少人参与的软件项目中，这种变化的感受最为明显。在那里，我们已经看到人们开始从代码中抽离出来，站到一个更高的抽象层面上，仅仅是要求——本质上是要求智能体（Agents）和 AI——为他们完成所有的修改。
[原文] [Michael Tru]: in the professional world I think there's still a ways to go I think that the whole idea of kind of vibe coding or coding without really looking at the code and understanding it it doesn't really work There are lots of nth order effects You know if you're dealing with millions of lines of code and dozens or hundreds of people working on something over the course of many years uh right now you can't really just avoid thinking about the code
[译文] [Michael Tru]: 在专业领域，我认为还有一段路要走。我觉得那种所谓的“氛围编程”（vibe coding），或者说不真正看代码、不理解代码就进行编程的想法，其实是行不通的。这会有很多次生效应（nth order effects）。你知道，如果你在处理数百万行代码，并且有几十甚至几百人在多年的时间跨度里共同开发，在目前，你真的无法避免去思考代码本身。
[原文] [Michael Tru]: Our primary focus is to help professional programmers to help people who build software for a living In those environments people are more and more using AI to code You know on average we see about people using you know having AI write 40% 50% of the lines of code produced within cursor But it's still a process of you know reading everything that comes out of the AI
[译文] [Michael Tru]: 我们的首要重点是帮助专业程序员，帮助那些以构建软件为生的人。在这些环境中，人们越来越频繁地使用 AI 来编程。你知道，平均而言，我们看到在 Cursor 内部生成的代码中，有 40% 到 50% 是由 AI 编写的。但这仍然是一个你需要去阅读 AI 产出的所有内容的过程。
[原文] [Michael Tru]: And so an important chasm for us to cross as a product will be getting to a place where uh you know we become less of a productivity tool that's helping you look at read write understand code and where the artifact kind of changes and I think for professional developers there's still a ways to go there
[译文] [Michael Tru]: 因此，作为一款产品，我们需要跨越的一个重要鸿沟，就是到达这样一个阶段：我们不再仅仅是一个帮助你查看、阅读、编写和理解代码的生产力工具，而是交付物本身发生某种改变。我认为对于专业开发者来说，要达到那个阶段还有一段路要走。
[原文] [Host]: In your head do you think of it as uh like different tiers there sort of obviously startups are starting out with zero lines of code so that's very easy Is there a point that you're tracking right now where oh well that's when you know just vibe coding it stops working and that's when things sort of become real
[译文] [Host]: 在你脑海中，你是否把它看作是不同的层级？显然初创公司是从零行代码开始的，所以那很容易。你现在是否在追踪某个临界点，比如到了那个点，“氛围编程”就不灵了，事情就变得严肃起来了？
[原文] [Michael Tru]: The vibe coding style of things is definitely not something that we recommend if you're going to have the code stay around for a really long time I think that one of the things that characterizes software development when you're two three person four person startup and you're kind of moving around and trying to figure out what you're doing is often the code is only going to be around for for weeks
[译文] [Michael Tru]: 如果你的代码需要存在很长一段时间，那么我们绝对不推荐“氛围编程”这种风格。我认为，当你是一个两三个人或四个人的初创公司，正在快速迭代试图搞清楚自己在做什么时，软件开发的一个特征往往是：这些代码可能只会被保留几周时间。
请问是否继续输出“章节 3：技术演进——从自动补全到智能体（Agent）”？
📝 本节摘要：
Michael 在本节详细拆解了 AI 编程的两种主要形态：一种是“Tab 键模式”（自动补全/辅助），另一种是“Agent 模式”（任务委派）。他认为未来 6 到 12 个月的关键在于让这两种模式的实用性提升一个数量级，直到专业开发者可以放心地将 25-30% 的工作完全交给 AI 而无需检查。此外，他深入探讨了技术瓶颈，特别是“上下文窗口”（Context Window）的局限性。尽管 Token 容量在增加，但在面对数千万行代码的巨型代码库时，单纯依赖长上下文仍面临成本和注意力机制的挑战，持续学习（Continual Learning）和长程任务的可靠性是实现“超人级”编程智能必须攻克的难关。
[原文] [Michael Tru]: Right now we're in this phase where um AI is kind of operating as a helper for you right so kind of like the main ways in which people are using AI to code they're either delegating tasks to an AI and they're saying "Go do this thing for me Go answer this question for me." Or they have an AI looking over their shoulder and taking over the keyboard every once in a while That's kind of the tap form factor And I think that the game in the next 6 months to a year is to make both of those you know an order of magnitude more useful
[译文] [Michael Tru]: 目前我们正处于 AI 扮演你助手的阶段。人们使用 AI 编程的主要方式大概有两种：要么是将任务委派给 AI，对它说“去帮我做这件事，帮我回答这个问题”；要么是让 AI 看着他们的屏幕，不时接管键盘操作一下，这也就是那种“Tab 键”的形态。我认为未来 6 个月到 1 年的游戏规则，就是让这两种形态的实用性都提升一个数量级。
[原文] [Michael Tru]: coding sometimes is incredibly predictable when you're just looking over someone's shoulder you know the next 10 15 20 minutes of their work Um and so the tab form factor can go very far And then the agent form factor of delegating to another human can go very far too And then I think that once those start to get mature and you know for 25 30% of professional development you can just entirely lean on those end to end without really looking at things Then there will be all of these other things to figure out about how you make that work in the real world
[译文] [Michael Tru]: 当你看着某人工作时，有时候编程是非常可预测的，你知道接下来 10、15 或 20 分钟的工作内容。因此，“Tab 键形态”还有很大的潜力。而那种委派给另一个“人”的“Agent（智能体）形态”也有很大的发展空间。我认为，一旦这些技术开始成熟，比如对于 25% 到 30% 的专业开发工作，你可以完全端到端地依赖它们而无需真正去检查，那时我们就需要去解决所有其他的问题，关于如何让这种模式在现实世界中行得通。
[原文] [Michael Tru]: One way in which you can view LMS is their view you interface with them like a human like a helper Um another way in which you can view LMS is they're kind of an advanc and compiler or interpreter technology It's going to be always helpful if we are a tool to help a human go from an idea in their head to something on the screen to give people uh control over the finest details
[译文] [Michael Tru]: 你可以用一种方式来看待大语言模型（LLMs），即把它们看作像人一样的助手来交互。另一种看待 LLMs 的方式是，它们某种程度上是一种高级的编译器或解释器技术。如果我们作为一个工具，能帮助人类从脑海中的想法转变为屏幕上的东西，并赋予人们对最细微细节的控制权，那将永远是有帮助的。
[原文] [Michael Tru]: Right that's one of the product challenges we have in front of us is you should always be able to move something a few pixels over You should always be able to edit something very specific about the logic... Is it a context window thing you know it sort of makes sense that well once you get past about a million to two million tokens only even in the I feel like the last 100 days did we get a usable 2 million token length Is that naturally one of the places where once your code base reaches a certain size you know you got to use rag it has incomplete context and then it just can't do what uh a human coder could do
[译文] [Michael Tru]: 对，这就是摆在我们面前的产品挑战之一：你应该永远能够把某个东西移动几个像素，你应该永远能够编辑逻辑中非常具体的某一点……这是否是一个上下文窗口（Context Window）的问题？某种程度上讲得通，一旦你超过了大约 100 万到 200 万个 token——我觉得直到最近 100 天我们才真正拥有了可用的 200 万 token 长度——这是否自然而然地成为一个瓶颈？一旦你的代码库达到一定规模，你就必须使用 RAG（检索增强生成），这就导致上下文不完整，然后它就无法做到人类程序员能做的事情。
[原文] [Michael Tru]: Yeah I think that there are a bunch of bottlenecks to agents being human level I think one is context window side of things is definitely uh an issue where you know if you have 10 million lines of code that's you know maybe 100 million tokens um and both having a model that can actually ingest that having it be cost effective and then not just having a model that can physically ingest that into its weights but also one that actually pays attention effectively to that context window is tricky and I think that that's something that the field needs to grapple with
[译文] [Michael Tru]: 是的，我认为要让 Agent 达到人类水平还有一堆瓶颈。我觉得其中之一绝对是上下文窗口方面的问题。你知道，如果你有 1000 万行代码，那大概就是 1 亿个 token。既要有一个模型能真正摄入这么多信息，又要保证成本效益，而且不仅仅是物理上能把它摄入到权重中，还要让模型能有效地在这个上下文窗口中分配注意力，这是非常棘手的。我认为这是整个领域需要去攻克的问题。
[原文] [Michael Tru]: and it's not just a codebased thing there it's also just a continual learning problem of you know knowing knowing the context of the organization and things that have been tried in the past and who your co-workers are and that uh problem of having uh you know a model really continually learn something kind of something that the field I think still doesn't really have a great solution to like it has always been suspected that it will be or for a lot of people have suspected you just make the context window infinite and that ends up working out I think that there's a der of really good long context data uh available to the institutions that are training these models and so I think that that will be tricky but continual learning and long context is definitely a bottleneck to being superhuman
[译文] [Michael Tru]: 而且这不仅仅是代码库的问题，这还是一个持续学习（continual learning）的问题。比如了解组织的背景、过去尝试过的事情、你的同事是谁等等。让一个模型真正能够持续学习这类东西，我认为目前在这个领域还没有很好的解决方案。很多人一直猜想，只要把上下文窗口做到无限大，问题就解决了。但我认为，对于训练这些模型的机构来说，目前非常缺乏高质量的长上下文数据。所以我觉得这会很棘手，但持续学习和长上下文绝对是成为“超人”智能的一个瓶颈。
[原文] [Michael Tru]: It's kind of related but being able to do tasks over very long time horizons and continue making forward progress Going around on the internet there's this amazing chart of progress in the last year or two on the max length of time an AI can make forward progress on a task And it's gone up from you know seconds to I think I I don't know the details of how these numbers are actually gotten but I think someone's claiming some of the latest models it's like an hour
[译文] [Michael Tru]: 这与另一个问题相关，就是能够在非常长的时间跨度内执行任务并持续取得进展。网上流传着一张很棒的图表，展示了过去一两年里 AI 在任务上能持续取得进展的最长时间的变化。这个时间已经从几秒钟增加到了……具体的数字来源细节我不清楚，但我记得有人声称最新的模型已经可以达到像一小时这样的水平了。
请问是否继续输出“章节 4：人类的角色——不可替代的‘品味’与逻辑设计”？
📝 本节摘要：
在本节中，对话转向了未来的交互形态与人类在 AI 时代的独特价值。Michael 指出，单纯的“文本框”交互是低效且不精确的，未来的编程界面将演变为更高级的直接操作（Direct Manipulation）。他随后深入探讨了 AI 目前的短板——缺乏“审美”（Aesthetics），这目前仍需通过强化学习（RL）来“笨拙”地弥补。最重要的是，Michael 提出了“人类编译”（Human Compilation）的概念，即程序员目前花费大量精力将想法翻译成计算机能懂的语法。随着这一步骤被 AI 取代，人类的核心竞争力将回归到“品味”（Taste）——不仅是视觉上的审美，更是对软件逻辑运作方式的判断力。
[原文] [Michael Tru]: And then you know one thing I will note kind of hearkening back to a last response is that even if you had something you could talk to that was human level at coding or faster and better better than a human at coding you know sort of the skill of an entire engineering department I think that the UI of just having a text box asking for a change of the software is imprecise and so even in the limit if you care about humans being able to control what shows up on the screen you'll need a different way for them to interface and so one potential UI there is you know an evolution of programming languages to be something that's higher level another is maybe direct manipulation of the UI right being able to point at things on the screen and say "Oh change this." Or actually kind of finick with the values yourself
[译文] [Michael Tru]: 另外我要指出的一点，稍微回顾一下之前的回答，那就是即使你拥有了一个可以对话的对象，它在编程方面达到了人类水平，或者比人类更快、更好，甚至拥有整个工程部门的技能，我认为仅仅通过一个文本框来要求修改软件，这种用户界面（UI）是不精确的。所以即使在极限情况下，如果你在乎人类能否控制屏幕上显示的内容，你就需要一种不同的交互方式。因此，一种潜在的 UI 可能是编程语言演变成某种更高层级的东西；另一种可能是对 UI 的直接操作（direct manipulation），也就是能够指着屏幕上的东西说“噢，改这个”，或者实际上你可以自己微调那些数值。
[原文] [Host]: Yeah I mean that seems like a a bunch of things that are kind of just nent in the wings right like uh the models don't seem to have a really clear sense for aesthetics for instance And so the idea that maybe this human level designer needs to actually you know be able to they need to be able to see actually
[译文] [Host]: 是的，我的意思是这看起来像是一堆正在幕后萌芽的东西，对吧？比如模型似乎并没有真正清晰的“审美”（aesthetics）感。所以这种想法是，也许这个人类水平的设计师真的需要……你知道，他们真的需要能够“看见”。
[原文] [Michael Tru]: Yeah And it's been interesting seeing them improve at the aesthetic side of things And I think that that's actually like an interesting specific example about how we've hacked around these continual learning problems But our understanding is that you know the way you teach these models to be better at something like aesthetics is not in the way you would a human It is by you know basically collecting a bunch of data doing RL on them Um and that's like how you've taught at that task And that's a a task that enough people care about that you can pay the cost to do all of that and you can go and train and have it into sort of baked into the base model um it's kind of a hack around the continual learning problem
[译文] [Michael Tru]: 是的，看着它们在审美方面有所进步是很有趣的。我认为这实际上是一个有趣的具体案例，展示了我们是如何绕过那些持续学习的问题的。但我们的理解是，教这些模型在审美等方面变得更好的方式，与教人类的方式不同。它基本上是通过收集大量数据并对其进行强化学习（RL）。这就是你在这个任务上教它的方式。而且这是一个足够多的人关心以至于你愿意支付成本去做的任务，你可以去训练并将它“烘焙”进基础模型中，这有点像是在绕过持续学习的问题。
[原文] [Host]: So given this sort of future that everyone's building towards and you're certainly a leader at the forefront of it you know what do you think uh will be irreplaceable or like sort of the essential pieces of being a software engineer in the future
[译文] [Host]: 那么考虑到大家都在构建的这种未来，而你无疑是处于最前沿的领导者，你认为未来作为一名软件工程师，什么将是不可替代的，或者说是最本质的部分？
[原文] [Michael Tru]: we think that one thing that will be irreplaceable is taste So just defining what what do you actually want to build people usually think about this when they're thinking about the visual aspects of software I think it's also there's a taste component to the non-visual aspects of software too about how the logic works
[译文] [Michael Tru]: 我们认为有一件事是不可替代的，那就是“品味”（taste）。也就是定义你到底想要构建什么。人们通常在考虑软件的视觉方面时会想到这一点，但我认为在软件的非视觉方面，关于逻辑如何运作，也有一个“品味”的成分。
[原文] [Michael Tru]: And right now the act of programming kind of bundles up you figuring out how exactly you you want the thing to work like what product you're really defining with the logic that you're writing and the kind of high level taste of the implementation details of how that maps onto a physical computer But then right now a lot of programming is kind of this human compilation that you're doing where you kind of know what you want You could tell it to another human being but you really have to spell it out for the computer because the language that you can you have to describe things to a computer is for normal programming just you know for loops and if statements and variables and methods and u you really have to have to spell it out
[译文] [Michael Tru]: 目前，编程的行为把你“弄清楚你到底希望这东西如何工作”（比如你通过编写逻辑来定义真正的产品是什么）与“实现细节的高级品味”（比如如何将其映射到物理计算机上）捆绑在了一起。但现在，很多编程工作其实是你正在做的一种“人类编译”（human compilation）。你大概知道你想要什么，你可以告诉另一个人，但你必须为计算机详细拼写出来，因为在常规编程中，你能用来向计算机描述事物的语言只是 for 循环、if 语句、变量和方法，你真的必须把每一个细节都写清楚。
[原文] [Michael Tru]: And so I think that more and more of that like human compilation step will go away and computers will be able to kind of fill in the gaps fill in the details But since we you know are a tool that's that's helping you make things happen helping you build things that kind of taste for what what is actually useful for what you want to build I don't think will ever go away
[译文] [Michael Tru]: 所以我认为那种“人类编译”的步骤会越来越多地消失，计算机将能够填补空白、填充细节。但既然我们是一个帮助你实现目标、帮助你构建事物的工具，那种对于“什么才是真正有用的”以及“你想要构建什么”的品味，我认为永远不会消失。
[原文] [Host]: That makes sense I it there's that quote good people will uh help you hit you know this bar but the truly great the truly masterful they uh you know hit a bar that you can't even see Yeah So and that requires taste
[译文] [Host]: 这很有道理。我想起那句话：优秀的人会帮你达到你知道的那个标准，但真正伟大、真正大师级的人，他们能达到一个你甚至看不见的标准。是的，这需要品味。
请问是否继续输出“章节 5：行业影响——释放工程产能与长尾软件的崛起”？
📝 本节摘要：
在本节中，Michael 展望了 AI 编程成熟后的宏观影响。首先是专业工程领域的产能释放，现有的千人规模软件项目往往因“逻辑负重”而进展缓慢，未来构建复杂系统（如分布式训练框架、数据库）的速度将大幅提升。其次，他重点强调了“长尾软件”或“小众软件”的爆发。他以自己早期在一家生物科技公司工作的经历为例，指出许多非科技公司（如制药厂）为了业务需求被迫组建昂贵的软件团队来开发内部工具。随着 AI 降低了构建门槛，这些非核心业务的软件需求将更容易被满足，数字世界的“物理法则”将被进一步放大，让“想要发生的事情”更容易在电脑上实现。
[原文] [Host]: You've called it sort of people need to become logic designers You know what does that mean in terms of you know intent driven programming as this tech matures more and more as we get closer to a world where programming can be automated and can be replaced with a better way of building software
[译文] [Host]: 你曾把这称为人们需要成为“逻辑设计师”。这在“意图驱动编程”方面意味着什么？随着这项技术越来越成熟，当我们越来越接近一个编程可以被自动化、并被更好的软件构建方式所取代的世界时，这意味着什么？
[原文] [Michael Tru]: I think there are a bunch of implications I think one is that you know professional devs will just get so much more productive It's just crazy how slow thousand people software projects move Yeah And 100 people software projects move and real kind of professional software projects move Um and a lot of that comes down to the the weight of the existing logic Just kind of getting the best of you
[译文] [Michael Tru]: 我认为这有一系列的影响。首先，专业开发者将变得更加高效。千人规模的软件项目进展之慢简直令人抓狂，是的，百人规模的项目以及真正的专业软件项目也是如此。这很大程度上归结为现有逻辑的“重量”压垮了你。
[原文] [Michael Tru]: When you're in a new codebase you can start from scratch You can do things very quickly When you change something there's not a bunch of other things that then break that you need to to fix
[译文] [Michael Tru]: 当你是一个新的代码库时，你可以从头开始，你可以非常快地做事。当你修改某样东西时，不会有一堆其他东西因此坏掉需要你去修。
[原文] [Michael Tru]: I think that one of the implications of it will be that you know the next distributed training framework or the you know the next database or the next uh visual design tool will just be way faster to build the next AI model which you know if you talk to the labs largely they're bottlenecked on engineering capacity I think all of that will just just improve a ton
[译文] [Michael Tru]: 我认为其影响之一将是，下一个分布式训练框架、下一个数据库或下一个视觉设计工具，构建速度将会快得多。比如下一个 AI 模型——如果你跟那些实验室聊过，你会知道他们很大程度上受限于工程能力——我认为所有这些都会得到巨大的提升。
[原文] [Michael Tru]: I think that you know one second order effect two will be many more pieces of niche software will exist One of my first jobs actually was working for a biotech company Um and it was a company staffed by wet lab scientists They were developing drugs to to cure cure diseases And I was the first software engineer hired and they were generating massive amounts of chemicals and then putting them through these biological experiments and then they needed a readout to kind of figure out which which chemicals to then pursue further
[译文] [Michael Tru]: 我认为另一个次生效应（second order effect）是，将会出现更多的小众（niche）软件。实际上我的第一份工作是在一家生物科技公司。那是一家由湿实验室科学家组成的公司，他们开发药物来治愈疾病。我是他们雇佣的第一个软件工程师。他们当时生成了大量的化学物质，然后将它们通过生物实验，接着他们需要读数来弄清楚哪些化学物质值得进一步研究。
[原文] [Michael Tru]: And they needed a ton of just internal software development to do that And uh it was amazing both looking at the uh exist existing tools off the shelf just how bad they were and then it was crazy to think that this company for whom software was not their core competency had to go out and do this crazy laborious thing of hiring a real software engineering team and training them up and um having them do internal product development
[译文] [Michael Tru]: 为此他们需要大量的内部软件开发。令人惊讶的是，既看到现成的工具是多么糟糕，又觉得疯狂的是，这家并非以软件为核心竞争力的公司，竟然不得不去做这种极其费力的事情——去雇佣一个真正的软件工程团队，培训他们，让他们做内部产品开发。
[原文] [Michael Tru]: and for companies like uh like that company uh there will just be many more options available to them The physics of digital space already are so great but I think that that's just going to you know get turned up many notches into the future Things that you want to want to happen on computers will then just kind of be able to happen
[译文] [Michael Tru]: 对于像那样的公司来说，未来将会有更多的选择。数字空间的物理法则（physics of digital space）已经很棒了，但我认为在未来这会被提升好几个档次。你在电脑上想要发生的事情，届时就某种程度上能够直接发生。
请问是否继续输出“章节 6：创业起源——从 CAD 模型的失败中学习”？
📝 本节摘要：
本节回顾了 Cursor 团队的创业初期。Michael 讲述了四位联合创始人在 MIT 相识的经历，以及他们最初并非直接涉足编程工具，而是试图为机械工程师开发一款“CAD 版 Copilot”。尽管这一尝试因 3D 模型数据稀缺和技术不成熟而告终，但这段经历让他们积累了宝贵的大模型训练与推理基础设施经验。最终，受到 Scaling Laws（缩放定律）和 GitHub Copilot 的启发，他们决定回归自己最热爱的编程领域，致力于构建代码的未来。
[原文] [Host]: Switching gears like I wanted to hear about the early days of cursor You met your co-founders uh Swale Arvid and Aman um at MIT and this company started in 2022 What drew you together and when did you realize uh this was a team that could build something really ambitious together
[译文] [Host]: 换个话题，我想听听 Cursor 早期的故事。你在 MIT 遇到了你的联合创始人——Suhail、Arvid 和 Aman，而这家公司始于 2022 年。是什么把你们聚在了一起？你是什么时候意识到这是一个可以共同构建真正宏伟事业的团队的？
[原文] [Michael Tru]: i think we had a lot of youthful naivee I think probably unjustified at the time So from the start um we were we were pretty ambitious Cursor came out of an ambitious idea exercise actually for the four of us You know we all found programming fairly young and then some of our first engineering projects actually had to do with AI
[译文] [Michael Tru]: 我觉得我们当时有很多年轻人的天真，可能在那时是毫无根据的。所以从一开始，我们就非常有野心。Cursor 实际上源于我们四个人的一次关于“雄心勃勃的想法”的练习。你知道，我们都很年轻就开始编程了，而且我们最早的一些工程项目实际上都与 AI 有关。
[原文] [Michael Tru]: So one of us worked on um improving the data efficiency of robotic reinforcement learning So teaching robots very quickly to learn new tasks That was one of our early AI projects You know another one of us worked on building actually a competitor to Google um using using neural networks um to try and sort of speedrun building an amazing search engine for the web Um you know others did uh academic work in AI
[译文] [Michael Tru]: 我们其中一个人曾致力于提高机器人强化学习的数据效率，即教机器人非常快速地学习新任务，那是我们早期的 AI 项目之一。另一个人实际上致力于构建 Google 的竞争对手，利用神经网络试图快速构建一个令人惊叹的网络搜索引擎。你知道，其他人则做了 AI 方面的学术工作。
[原文] [Michael Tru]: But uh there were two moments in 2021 that uh got us really excited about building a company that was focused on AI One of them was using the first useful AI products where AI was really at the center and GitHub copilot was honestly the moment where that viscerally we really felt like uh now it was possible to make just uh really useful things with AI and that we shouldn't go to work in a lab to work on these things you know in an academic lab Instead it was time for this stuff to come out into the real world
[译文] [Michael Tru]: 但在 2021 年有两个时刻真正让我们对建立一家专注于 AI 的公司感到兴奋。其中之一是使用了第一批真正以 AI 为中心的实用产品，老实说，GitHub Copilot 就是那个时刻，让我们发自内心地感觉到，现在有可能用 AI 做出真正有用的东西了，而且我们不应该去实验室——你知道，那种学术实验室——里研究这些东西，而是时候让这些东西走向现实世界了。
[原文] [Michael Tru]: The other thing that got us really excited was seeing research come out of OpenAI and other places that showed there were these very predictable um natural laws that showed if you scaled up the data and you scaled up the compute that goes into these models they were just getting better Um and so that meant that even if we ran out of ideas for how to make AI better there were a couple of orders of magnitude of that to to still run
[译文] [Michael Tru]: 另一件让我们非常兴奋的事情是看到 OpenAI 和其他地方发表的研究，表明存在这些非常可预测的自然法则，显示如果你增加数据规模并增加投入到这些模型中的算力，它们就会变得更好。这意味着即使我们在“如何让 AI 变得更好”上创意枯竭了，这个领域仍然有几个数量级的增长空间可以去挖掘。
[原文] [Michael Tru]: From the start we wanted to pick an area of knowledge work and then work on what that knowledge work became as AI got more mature... and so initially we set out to do that for uh you know an area of knowledge work we actually didn't know that well which was mechanical engineering and we worked on a co-pilot for um for computerated design for a
[译文] [Michael Tru]: 从一开始，我们就想选择一个知识工作领域，然后致力于研究随着 AI 变得更加成熟，这种知识工作会变成什么样子……所以最初，我们要着手做的领域实际上是我们并不太了解的知识工作领域，那就是机械工程，我们当时致力于为计算机辅助设计（CAD）开发一个 Copilot。
[原文] [Michael Tru]: And So we were training 3D autocomplete models So helping people who are doing 3D modeling of a part that they want to build in something like Solid Works or um Fusion 360 and trying to predict kind of the next changes to the geometry they were going to make
[译文] [Michael Tru]: 所以我们当时在训练 3D 自动补全模型。帮助那些正在使用 SolidWorks 或 Fusion 360 等软件对想要构建的零件进行 3D 建模的人，并试图预测他们接下来要对几何结构进行的修改。
[原文] [Michael Tru]: And and these were not large language models per se You can do it entirely 3D or what you can do is uh one thread that we worked on for a while is turning it into a language problem where you take the the steps that someone's doing in a CAD system and you kind of turn it into method calls So if they're making a circle you make that a method call and it's just kind of like a list of method calls It's not really programming but it it sort of looks like it
[译文] [Michael Tru]: 这些本质上并不是大语言模型。你可以完全用 3D 方式来做，或者你可以做的是——这也是我们研究了一段时间的一条线索——把它变成一个语言问题：你把某人在 CAD 系统中操作的步骤，转化成方法调用（method calls）。所以如果他们在画一个圆，你就把它变成一个方法调用，这就有点像是一个方法调用的列表。这不完全是编程，但看起来有点像。
[原文] [Michael Tru]: And then we put that aside and that was for a couple of reasons One was we really weren't as excited about mechanical engineering as we were about coding We were all coders The other one was I think that the science back then wasn't yet ready for 3D Like the pre-trained models weren't that good at it There wasn't a lot of data There's orders of magnitude less data of CAD models in the internet than code Um and so it's hard to make a useful model or it was back then hard to make a useful model for that domain
[译文] [Michael Tru]: 后来我们把这个项目搁置了，原因有几个。一个是相比于编程，我们对机械工程真的没有那么兴奋，我们都是程序员。另一个原因是，我认为当时的科学水平还没准备好应对 3D。比如预训练模型在那方面表现并不好，也没有太多的数据。互联网上 CAD 模型的数据量比代码少了好几个数量级。所以很难做出一个有用的模型，或者说在当时那个领域很难做出有用的模型。
[原文] [Host]: Did you end up uh going to sit with I don't know people who used CAD or machinists and people like that
[译文] [Host]: 你们最后有去和那些使用 CAD 的人，或者机械师之类的人坐在一起交流吗？
[原文] [Michael Tru]: so we did we did tons of user interviews and I think we could have done that even better And I think that again on the maybe youthful naive we were operating dayto-day week to week counting tasks by the by the hours And looking back on the time we spent on that I think it would have been better up front to actually just go work at a company that was employing mechanical engineers for 3 weeks just go undercover get a better sense for like the just stalt of just get a job as a as a draft person and then I think that would have been immensely valuable
[译文] [Michael Tru]: 我们做了，我们做了大量的用户访谈，但我认为我们本可以做得更好。再说回那种也许是年轻人的天真，我们当时是日复一日、周复一周地运作，按小时计算任务。回顾我们在那上面花费的时间，我认为如果一开始就直接去一家雇佣机械工程师的公司工作 3 周会更好，就去当个“卧底”，更好地感受那种氛围，直接找一份绘图员的工作，我觉得那样会非常有价值。
[原文] [Host]: So one of the things that's I mean a incredibly brave but also incredibly precient was to take a moment and say actually we don't know enough about CAD you know we need to do something else Was it a a straight beline from the CAD training the CAD models you know sort of recognizing that scaling laws were holding and here was a domain that you know we could go down and then you realized actually we need to do something else Like what was it to actually pivot it to you know what it is today
[译文] [Host]: 所以有一件事，我的意思是既令人难以置信地勇敢，又极具先见之明，就是停下来对自己说：“实际上我们对 CAD 了解得不够多，我们需要做点别的。”这是从训练 CAD 模型直接转过来的吗？你们意识到缩放定律（scaling laws）依然有效，虽然这是一个你们可以深入的领域，但后来意识到实际上需要做点别的？比如究竟是什么让你们转型到了今天这个方向？
[原文] [Michael Tru]: it wasn't a straight line Um we I mean being programmers ourselves and being inspired by products like copilot and uh also papers like the early codex papers I remember at the time one of the things we did to justify to investors that they should kind of like invest in our crazy cat idea is we did the back of the envelope math for what codeex the first coding model costed to train From my memory it only cost about 90k or 100k by our calculations that really surprised surprised investors at the time and was kind of helpful in us getting enough money to to pursue the uh CAD idea where you had to start training immediately
[译文] [Michael Tru]: 这并不是一条直线。我们自己就是程序员，受到了像 Copilot 这样的产品以及像早期 Codex 论文的启发。我记得当时为了向投资者证明他们应该投资我们那个疯狂的 CAD 想法，我们做的一件事就是进行了一些“信封背面的粗略计算”（back of the envelope math），估算 Codex（第一个编程模型）的训练成本。据我记忆，根据我们的计算，成本大概只有 9 万或 10 万美元。这在当时真的让投资者感到惊讶，也对我们拿到足够的钱去追求那个必须立即开始训练的 CAD 想法很有帮助。
[原文] [Michael Tru]: So we always knew about coding We were always excited about it We were always excited about you know how AI was going to change coding We had a little bit of trepidation about going and working on that space because there were so many people already doing it Um and uh we thought copilot was awesome and you know there were dozens of other companies working on it too at the time
[译文] [Michael Tru]: 所以我们一直都知道编程这个领域。我们一直对此很兴奋，我们一直对 AI 将如何改变编程感到兴奋。我们对于进入并致力于这个领域有一点点担忧，因为已经有太多人在做了。我们觉得 Copilot 很棒，而且当时还有几十家其他公司也在做这件事。
[原文] [Michael Tru]: When we decided to put aside CAD which was a little bit of an independent idea that was sort of the science not really working out us not really being excited about that domain The thing that drew us back into coding was our our personal interest
[译文] [Michael Tru]: 当我们决定搁置 CAD 时——这有点像是一个独立的想法，科学上不太行得通，我们也并非真正对那个领域感到兴奋——把我们拉回编程领域的是我们的个人兴趣。
[原文] [Michael Tru]: And the thing that gave us the confidence then to continue with it was one seeing the progress that others had made over the course of you know 9 months or whatever it was Felt like it was a little bit slower than it could have been And then also just sitting down and and thinking that if we were being really consistent with our beliefs in 5 years all of coding was going to flow through these models and the active programming was going to entirely change
[译文] [Michael Tru]: 而当时让我们有信心继续做下去的原因，一是看到了其他人在过去 9 个月（或者是多长时间）里取得的进展，感觉比预期的要慢一点。二是坐下来思考，如果我们的信念始终如一，那么在 5 年内，所有的编程都将通过这些模型进行，编程的行为将彻底改变。
请问是否继续输出“章节 7：战略抉择——坚信缩放定律与‘追随曲线’”？
📝 本节摘要：
在本节中，对话深入探讨了 Cursor 团队的核心战略思想——“追随曲线”（Follow the line）。Michael 解释说，他们不仅仅是基于当下的 AI 能力做产品，而是基于对 Scaling Laws（缩放定律）的坚定信仰，预判 AI 在未来数年内的进化轨迹。主持人提到了 Peter Thiel 的经典逆向思维问题：“你相信什么别人不相信的事？”Michael 回忆了 2022 年那个特殊的节点，虽然当时公众尚未对 AI 狂热，但 GPT-3、Copilot、DALL-E 等早期信号已经让他们确信，一场巨大的变革即将到来，必须提前布局“冰球即将到达的位置”。
[原文] [Michael Tru]: And the thing that gave us the confidence then to continue with it was one seeing the progress that others had made over the course of you know 9 months or whatever it was Felt like it was a little bit slower than it could have been And then also just sitting down and and thinking that if we were being really consistent with our beliefs in 5 years all of coding was going to flow through these models and the active programming was going to entirely change and there were going to be all these jumps you needed both at a product level and at a model level to get there and the ceiling was just just so high and it really didn't seem like the existing players in the space were were aiming for a completely different type of coding didn't seem like they had that ambition like they were really set up to execute on that too That first experience taught us that you building a company is hard and so you may as well work on the thing that you're really excited about And so yeah we set off to work on uh the future of coding
[译文] [Michael Tru]: 当时让我们有信心继续做下去的原因，一是看到了其他人在过去 9 个月（或者是多长时间）里取得的进展，感觉比预期的要慢一点。二是坐下来思考，如果我们要真正贯彻我们的信念，那么在 5 年内，所有的编程都将通过这些模型进行，编程的行为将彻底改变。要达到那个境界，无论是在产品层面还是模型层面，都需要发生一系列的跳跃。而且这个天花板实在是太高了，看起来现有的玩家似乎并没有瞄准一种全新的编程方式，他们似乎没有那样的野心，或者说他们的架构并不适合去执行那样的愿景。那第一次的经历教会了我们，创业很难，所以你倒不如去做那些你真正感到兴奋的事情。所以是的，我们出发去致力于创造代码的未来。
[原文] [Host]: It's uh sounds extra precient in that Sam Alman sat in this chair maybe a year ago and talked about how if you're betting against the models getting smarter that's bad you should always bet that the models are going to get a lot smarter and you know 12 18 24 months later that's been uh only more and more true and then it sounds like you had been taking that bet uh a full 12 months before even that was said
[译文] [Host]: 这听起来格外有先见之明，因为 Sam Altman 大概一年前也坐在这把椅子上说过，如果你赌模型不会变聪明，那是很糟糕的；你应该永远赌模型会变得聪明得多。而在 12、18 或 24 个月后，这一点被证明越来越正确。听起来你们甚至在那番话被说出来的整整 12 个月前，就已经下了那个赌注。
[原文] [Michael Tru]: Yeah we had a phrase back then which was follow the line um and you wanted to always be following the line and planning for where the line was I mean kind of hearkening back to the to the scaling loss of like you know these things are just going to keep getting better and better and better
[译文] [Michael Tru]: 是的，我们当时有一句口头禅叫“追随曲线”（follow the line）。你要始终追随那条曲线，并根据曲线未来的走向来做计划。我的意思是，这有点回溯到缩放定律（scaling laws），就像你知道这些东西只会变得越来越好，越来越好。
[原文] [Host]: The classic Peter Tealism is uh what do you believe that nobody else believes and uh you believe this and you were so right that that's what allowed you to actually go to where the puck was going to be
[译文] [Host]: 经典的彼得·蒂尔主义（Peter Thielism）问题是：“有什么是你相信但别人都不相信的？”而你相信这一点，并且你是如此正确，正是这一点让你能够真正去到“冰球即将到达的位置”（意指预判未来）。
[原文] [Michael Tru]: Yeah I think I think it was one of the things that was helpful and now obviously it's become much more uh in vogue But back then you know 2022 was this crazy uh pivotal year where you start at the beginning of the year no one's really talking about AI I mean GBD3 had happened the year before Copilot had happened Copilot was beta 2021 and then maybe GA 2022 and then it started picking up and you know we still remember all the launches of you know instruct GBT which made GP3 a little bit better It was fine tuning on instructions and then uh Dali in the summer I remember that was kind of the visceral moment that convinced a lot of people who weren't focused on the space to be to pay a bit more attention to it But then there was uh palm and stable diffusion and then you start to get RHF you start to get 3.5 and you have these models getting way better without a big increase in the training cost which was an interesting development
[译文] [Michael Tru]: 是的，我认为那是很有帮助的事情之一。现在这显然变得更加流行了。但在那时候，你知道 2022 年是一个疯狂的关键年份。年初的时候还没有人真正谈论 AI。我的意思是 GPT-3 是一年前出来的，Copilot 也出来了（Copilot 2021 年是 Beta 版，大概 2022 年正式发布），然后它开始升温。我们还记得所有那些发布，比如 InstructGPT，它通过指令微调让 GPT-3 好了一点点。然后是夏天的 DALL-E，我记得那是一个直击人心的时刻，说服了很多原本不关注这个领域的人开始投入更多关注。紧接着是 PaLM 和 Stable Diffusion，然后开始有了 RLHF（原文误作 RHF），开始有了 GPT-3.5。你会发现这些模型在没有大幅增加训练成本的情况下变得好多了，这是一个有趣的发展。
[原文] [Host]: Heard it rumored that to go from GPD3 which you know had existed for a while and didn't you know impress some people but was certainly not the breakout moment chachbt was to chache BT was like a 1% increase in the training costs Oh my god It was you know from fine tuning on instructions RHF you know some other details too
[译文] [Host]: 我听到传闻说，从 GPT-3——你知道它已经存在了一段时间，虽然没能打动某些人，但也绝不是像 ChatGPT 那样的爆发时刻——到 ChatGPT，训练成本只增加了大概 1%？天哪。这是通过指令微调、RLHF（原文误作 RHF）以及其他一些细节实现的。
请问是否继续输出“章节 8：产品哲学——为何必须构建独立编辑器而非插件”？
📝 本节摘要：
在本节中，Michael 揭示了 Cursor 早期最关键、也最受争议的战略决策：构建一个独立的编辑器，而不是仅仅做一个 VS Code 插件。他分享了关于 GitHub Copilot 开发早期的“内幕”故事，指出即便是为了实现“自动补全”这样简单的功能，GitHub 团队也不得不修改 VS Code 的底层代码来支持“幽灵文本”显示。Michael 意识到，如果想要实现更激进的 AI 编程未来，必然需要频繁修改编辑器的核心行为，这是普通插件无法做到的。尽管这一决定最初招致了批评，且团队经历了从“从头自研编辑器”到“基于 VS Code 二次开发”的技术路线调整，但掌握编辑器底层控制权最终成为了他们胜出的关键。
[原文] [Host]: Do you remember are there were there like specific features or product choices that you made because you knew that the uh that the models were going to get not just a little bit smarter but significantly smarter that change specific products or road maps that ended up you know sort of causing you to win cuz you mentioned I mean there were certainly maybe a dozen other companies that were quite good that you know were also in the area
[译文] [Host]: 你是否记得，有没有什么特定的功能或产品选择，是你们因为知道模型不仅会变聪明一点，而是会变得极其聪明才做出的？这些选择改变了具体的产品或路线图，并最终导致你们胜出？因为你提到过，当时肯定还有大概十几家相当不错的公司也在这个领域。
[原文] [Michael Tru]: So one of the product decisions that we made early on that was nonobvious that came from being excited about a bit more of a radical future was not building an extension and was building an editor That was was not obvious to people at the time And yeah that came from a place of thinking all of programming is going to flow through these models It's going to look very different in the future You're going to need a control UI
[译文] [Michael Tru]: 我们早期做出的一个非显而易见的产品决策——这源于我们对一个更激进的未来感到兴奋——就是不做插件（extension），而是构建一个编辑器。这在当时对人们来说并非显而易见。是的，这源于一种想法：所有的编程都将流经这些模型。未来它看起来会非常不同，你需要一个可控的用户界面（UI）。
[原文] [Michael Tru]: It also came too from interesting anecdotes we knew about So we knew we knew a little bit about this the internal inside baseball of building GitHub copilot the first version the the whole building GitHub copilot story from what I understand and you know don't have firsthand knowledge so some of these details might be wrong is pretty interesting where it started from a very solution and search for a problem place of being interested in just taking GB3 and making it useful for for coders and I think it came from uh leadership it came from the CEO of GitHub at the time he just said we need to be doing this and he kind of sent a tiger team off figure out was Matt Freriedman at the time yeah that yeah my understanding is came from Matt and I think they spent almost a year wandering in the desert experimenting with different product ideas
[译文] [Michael Tru]: 这也源于我们知道的一些有趣的轶事。我们了解一点关于构建 GitHub Copilot 第一版及其整个构建过程的“内幕”（inside baseball）。据我所知——你知道我没有一手资料，所以有些细节可能是错的——这非常有趣。它始于一个“拿着锤子找钉子”（solution in search for a problem）的境地，就是有兴趣利用 GPT-3 让它对程序员有用。我认为这来自领导层，来自当时的 GitHub CEO，他只是说我们需要做这个，然后派了一个特别小组（tiger team）去搞定。当时是 Nat Friedman 吗？是的。据我理解是来自 Nat。我想他们大概花了一年时间在“荒漠中游荡”，尝试不同的产品点子。
[原文] [Michael Tru]: and of course they had the these were people really excited about the future of AI They thought immediately can we just automate PR's intent a little before or uh its time and they worked on that for a bit and then decided that was impossible and they tried all these other wacky product ideas until they just hit on the simple thing of of autocomplete
[译文] [Michael Tru]: 当然，这些人对 AI 的未来非常兴奋。他们立刻想到：我们能不能直接自动化 Pull Request（PR）？这种意图有点太超前了。他们研究了一段时间，觉得不可能，然后尝试了所有其他古怪的产品点子，直到他们最终碰到了“自动补全”这个简单的东西。
[原文] [Michael Tru]: But even after they got autocomplete to work um they needed to make changes at the editor level They couldn't do it entirely as an extension They had to go and change things in the mainline VS Code and expose different editor APIs to even just show that ghost text Then there was some from my understanding that was actually kind of hard to do organizationally If you were going to need to change the editor for something as simple as ghostex autocomplete we knew we were going to have to do it a bunch And so that was nonobvious and we got a lot of flack for that
[译文] [Michael Tru]: 但即使在他们让自动补全跑通之后，他们也需要在编辑器层面进行修改。他们无法完全通过插件来实现。他们必须去修改 VS Code 的主线代码，暴露不同的编辑器 API，哪怕只是为了显示那个“幽灵文本”（ghost text，即灰色的补全建议）。据我理解，这在组织上其实很难做到。如果你为了像“幽灵文本”自动补全这么简单的东西都需要修改编辑器，那我们就知道（为了未来的功能）我们将不得不频繁地修改它。所以这是一个非显而易见的决定，我们也因此招致了很多非议（flack）。
[原文] [Michael Tru]: And we actually initially started by building our own editor from scratch Obviously using lots of open source technology but not you know basing it off of VS Code kind of like how browsers are based off of Chromium It was a little bit more akin to building you know all the internal rendering of a browser from scratch and we launched with that and then we then we switched to to basing off of VS code but uh the editor thing was non obvious
[译文] [Michael Tru]: 实际上我们最初是从头开始构建自己的编辑器的。显然使用了很多开源技术，但并不是基于 VS Code，不像浏览器基于 Chromium 那样。它有点更像是从头构建浏览器所有的内部渲染引擎。我们用那个版本发布了，后来我们才切换到基于 VS Code。但在当时，做编辑器这事并非显而易见。
请问是否继续输出“章节 9：增长与指标——穿越‘荒野期’与付费核心用户”？
📝 本节摘要：
在本节中，Michael 分享了 Cursor 在爆发前的沉寂期。在正式爆发前，团队经历了一年左右的“荒野期”，期间虽然产品已发布，但并未立即获得巨大反响。为了打磨产品，他们拒绝了容易误导人的“Demo 导向”开发（即只为了演示视频好看），而是坚持“吃自家狗粮”（Dogfooding），关注速度与可靠性。在关键指标的选择上，Michael 透露他们并不关注传统的 DAU/MAU，而是死磕“付费高频用户”（Paid Power Users）——即每周有 4-5 天都在使用 AI 编程的用户。他认为对于昂贵的生产力工具而言，这才是衡量产品价值的唯一真理。
[原文] [Host]: So cursors out you made a bunch of decisions that turned out to be right When did you know it was going to work
[译文] [Host]: 那么 Cursor 发布了，你们做了一系列后来证明是正确的决定。你是什么时候知道这事儿能成的？
[原文] [Michael Tru]: it took a little bit of time If you'll remember there's this initial year is roughly a year in the wilderness of you know working on something that that was precursor to cursor and the mechanical engineering side of things Uh and then you know there was an initial development period for curser that was fairly small before we released the first version to the public I think that it was from lines of code to first public beta release It was 3 months but then there was this year of iterating in public at very small scale where we had did not have lightning in the bottle Um and it was growing but it was you know the numbers numbers were small
[译文] [Michael Tru]: 这花了一些时间。如果你还记得的话，最初有一年大概是在“荒野”中度过的，我们在做 Cursor 的前身以及机械工程方面的事情。然后 Cursor 经历了一个相当短的初始开发期，我们就向公众发布了第一个版本。我想从写下第一行代码到首次公测发布大概用了 3 个月。但随后是一年在极小规模下的公开迭代，那时我们并没有抓住“瓶中闪电”（指一炮而红）。它在增长，但你知道，数字还很小。
[原文] [Michael Tru]: Dialing in the product at that point took maybe a year of getting all of the details right Then it was only after that initial period of cursor being out for 9 months to a year of working on the underlying product building out the team also not just the product side of things but also starting to get the first versions of custom models behind cursor to power you know underneath cursor um that things started to click and then uh growth started to pick up and then yeah since then it's been uh you know we sort of have a tiger by the tail and if we are to be successful there's a lot of things that we need to continue to execute on in the future
[译文] [Michael Tru]: 在那个阶段，把产品打磨好、把所有细节做对，大概花了一年时间。只有在 Cursor 发布后的这 9 个月到一年的初步阶段之后——我们在底层产品上下功夫，扩充团队，不仅是产品方面，还开始构建支持 Cursor 底层的首批定制模型——事情才开始变得顺手（click），增长才开始提速。从那以后，你知道，我们就有点像是“骑虎难下”（指驾驭着一股巨大的力量），如果我们想要成功，未来还有很多事情需要继续执行下去。
[原文] [Michael Tru]: I think one of the challenges we have and a lot of other companies in parallel spaces have is just the rate at which we need to build the company I think is really fast and I think rules of thumb around don't grow headcount more than 50% or year-over-year iron laws have to yeah have to have to be broken I think interesting
[译文] [Michael Tru]: 我认为我们以及许多平行领域的其他公司面临的挑战之一，就是我们需要建设公司的速度真的非常快。我认为那些关于“人员增长不要超过 50%”或者同比年增长的经验法则、“铁律”，是的，不得不被打破。这很有趣。
[原文] [Host]: um were there like uh sort of true north metrics or things that you and your co-founders were monitoring to figure out like is this working was it you know week-on-week retention or open rate or how did that influence uh what you were working on in a given week
[译文] [Host]: 那么有没有什么“北极星指标”或者你和联合创始人们监测的东西，用来判断这事儿是否行得通？是周留存率、打开率，还是别的什么？这又是如何影响你们某一周的工作内容的？
[原文] [Michael Tru]: so we looked at um all the normal things like retention For us the main activity metric we looked at or the yeah the main topline metric we looked at we we looked at revenue we looked at paid power users measured by are you using the AI four or five days a week out of seven days a week And that was the number we were trying to get up And why was it paid well I think that we're a tool that serves professionals And I also think that to deliver the tool it has real costs And so we care about you get graduating to that paid tier And that's that's where things were sustainable Paid power users That was what we you know it wasn't DAUs MAUs or anything like that It was are you using this every single day for your work That's what we were trying to get up
[译文] [Michael Tru]: 我们确实看了所有常规指标，比如留存率。对我们来说，我们关注的主要活跃度指标，或者说主要的顶层指标，是收入，以及“付费高频用户”（Paid Power Users）。这个指标的定义是：在一周 7 天里，你是否有 4 到 5 天在使用 AI。这就是我们试图提升的数字。为什么是“付费”？我认为我们是一个服务于专业人士的工具。而且我也认为，交付这个工具是有实际成本的。所以我们关心你能否晋升到付费层级，那才是可持续的地方。付费高频用户，这就是我们在意的。不是日活（DAU）、月活（MAU）或类似的东西。而是你是否每天都在为了工作使用它？这就是我们要提升的。
[原文] [Host]: And then once that was the metric I guess did you work backwards from that it's like well we know the segment of people we want to grow and then what do they want or what would prevent people from becoming that
[译文] [Host]: 一旦确定了那个指标，我猜你们是倒推工作的吗？就像是“好吧，我们知道我们要增长哪个人群，那么他们想要什么？或者什么会阻碍人们成为那样的人？”
[原文] [Michael Tru]: i think that building for yourself doesn't work in a lot of spaces For us it did And I think it was actually clarifying uh because one of the siren songs involved in building AI products is optimizing for the demo Mhm uh we were really nervous about optimizing for the demo because with AI it's it's easy to kind of take a couple of examples and put together a video where you know it looks like you have a revolutionary product
[译文] [Michael Tru]: 我认为“为自己构建产品”在很多领域行不通，但对我们来说是行得通的。我认为这实际上让思路更清晰了，因为构建 AI 产品时的一个“海妖之歌”（siren song，指诱惑）就是为了演示（Demo）而优化。嗯，我们对于“为 Demo 优化”感到非常紧张，因为用 AI 很容易拿出几个例子，拼凑成一个视频，让你看起来好像拥有一个革命性的产品。
[原文] [Michael Tru]: and then I think that there's a long line you know there's a lot of work between the version that can result in that great looking demo and then a useful AI product which means kind of dialing in the the speed side of things the reliability side of things the intelligence side of things the product experience side of things for us the kind of main thing that we really acted on was just we reload the editor Our product development uh process early on it was very experimental It was very focused on um kind of like what we understand Apple to be like very focused on dog fooding and usable demos like things we could just immediately start using in the editor internally and then we would look at these metrics to make sure that you know week on week on month we were kind of on the right path
[译文] [Michael Tru]: 然后我认为，从那个能拍出好看 Demo 的版本，到一个
📝 本节摘要：
作为访谈的最终章，Michael 分享了 Cursor 在组织建设和未来竞争上的深刻见解。在招聘方面，他强调“为了快必须先慢”，前 10 名员工必须是精英中的精英，充当公司的“免疫系统”，防止平庸文化的渗透。他还透露了独特的面试流程——包括两天的高强度现场试用，以测试候选人的热情和黑客精神。关于 AI 时代的“护城河”，他将 Cursor 的处境类比为 90 年代末的搜索引擎和 2000 年代的消费电子：巨大的分发量带来数据反馈，从而改进产品（类似搜索）；同时需要不断追求“iPhone 级”的产品突破。最后，他以对未来的乐观展望结束访谈，认为这将是一个人类构建能力被无限放大的十年。
[原文] [Host]: Yeah So earlier you said I mean sometimes you got to break these iron laws around hiring Um when did you decide to break it i mean you know was it just the co-founders and a few people until sort of you know some revenue goal how did you think about the gas pedal did you like sort of feather it and then like once it was clear like you hit your numbers like we're pushing pushing the pedal all the way down
[译文] [Host]: 是的。所以你之前说，有时候你必须打破那些关于招聘的铁律。你是何时决定打破它的？我的意思是，是在只有联合创始人和少数几个人直到达到某个收入目标的时候吗？你是怎么考虑踩油门的？是那种轻轻点着油门，还是说一旦看清数据达标了，就直接把油门踩到底？
[原文] [Michael Tru]: so it was just the co-founders for a long time and then the co-founders and a few people until we got to the point where things were really kind of dialed in and taking off Who were some some of the first hire i mean I assume more engineers but you know so we agonized over the first hires and I think that if you want to go fast on the order of years actually going slow on the order of you know 6 months is super helpful because if you really nail the first 10 people to come into the company they will both accelerate you in the future because when you know the nth person comes in that's you know is thinking about working with you comes in and hangs out with the team they'll just be shocked by the talent density and then really excited to work there
[译文] [Michael Tru]: 很长一段时间只有联合创始人，然后是联合创始人和几个人，直到事情真正调整到位并开始起飞。至于第一批员工是谁——我猜是更多工程师——我们在首批招聘上非常纠结。我认为如果你想在“年”的维度上跑得快，实际上在“6 个月”的维度上慢下来是非常有帮助的。因为如果你真的找对了进入公司的前 10 个人，他们在未来会加速你的发展。因为当第 N 个人——那些考虑和你一起工作的人——进来并和团队相处时，他们会被人才密度所震撼，然后非常兴奋地想在这里工作。
[原文] [Michael Tru]: And then the other reason they can help you go faster in the future is if someone comes in and they're not a great fit these people act as an immune system against that right and they will be kind of keepers of holding the bar really high And so we hired very very very slowly at the start We were able to do that also partially because we had such a big founding team and all the co-founders were technical
[译文] [Michael Tru]: 另一个他们能帮你未来跑得更快的原因是，如果有人进来但不太合适，这些人会充当“免疫系统”来抵御这种情况，对吧？他们会成为保持高标准的守门人。所以我们一开始招聘得非常非常慢。我们要能做到这一点，部分原因也是因为我们有一个庞大的创始团队，而且所有联合创始人都是技术出身。
[原文] [Michael Tru]: But yeah the people we got uh uh are fantastic and are really core to the company today and folks who bled across disciplines where we are this company that needs to be something in between a foundation model lab and a normal software company and the models and product have to work together under one roof and so we had fantastic people who were uh product minded commercially minded but had actually trained models at scale So generalist polymath is really really great at sort of that first 10 people stage
[译文] [Michael Tru]: 但是是的，我们招到的人非常棒，今天也是公司的核心。他们是跨学科的人才。我们这家公司需要在“基础模型实验室”和“普通软件公司”之间找到某种平衡，模型和产品必须在同一个屋檐下协同工作。所以我们拥有了非常棒的人，他们既有产品思维、商业思维，又实际大规模训练过模型。所以在前 10 人的阶段，通才型的博学者（generalist polymath）真的非常棒。
[原文] [Host]: These days I mean everyone's sort of trying to figure out how to deal with this but you know simply because the AI tools are so great it's making it harder at times to even figure out how do you uh evaluate great engineers has that changed over time for you as you know literally your own product has become more and more common do you select for people who are really great at using the AI tools or you know is it really just the you know let's stick with the classics and you know anyone could learn how to use the AI tools
[译文] [Host]: 如今大家都在试图弄清楚如何应对这个问题，但正是因为 AI 工具太棒了，有时反而让人更难弄清楚如何评估优秀的工程师。随着你们自己的产品变得越来越普及，这对你们有改变吗？你们是会筛选那些非常擅长使用 AI 工具的人，还是说就像“让我们坚持经典”，因为任何人都可以学会如何使用 AI 工具？
[原文] [Michael Tru]: So for interviewing we actually still interview people without allowing them to use AI other than autocomplete for our first technical screens Programming without AI is still a really great timeboxed test for skill and intelligence and kind of the the things that you would always want someone on your team to to have um as a programmer But then the other reason is we've hired lots of people who are fantastic programmers who actually have no experience with AI tools and we don't want to unfairly disadvantage them because these tools are so useful So we would much rather hire those people and then teach them on the job to to use these things and also kind of mine the product insights from that beginner's mind of them using the tools for the first time
[译文] [Michael Tru]: 在面试方面，我们在第一轮技术筛选中实际上仍然不允许人们使用除自动补全以外的 AI。不使用 AI 编程仍然是一个很好的限时测试，可以测试技能、智力以及作为程序员你希望团队成员具备的那些素质。但另一个原因是，我们雇佣了很多非常棒的程序员，他们实际上没有任何使用 AI 工具的经验。我们不想让他们处于不公平的劣势，因为这些工具太有用了。所以我们更愿意雇佣这些人，然后在工作中教他们使用这些东西，并从他们第一次使用工具的“初学者心态”中挖掘产品洞察。
[原文] [Host]: Cursor is now worth $9 billion Uh how do you keep the hacker energy alive you know as the team scales and do you still write code and I do Yes It's something that we think about a lot uh because I think that cursor in the future will have to look very different from cursor today One I I think you can do it by hiring the right people So uh the last step of our hiring process is a two-day on-site where you come and you just work on a project with us
[译文] [Host]: Cursor 现在价值 90 亿美元。随着团队规模的扩大，你是如何保持那种黑客精神（hacker energy）的？你还在写代码吗？（Michael: 我还在写，是的。）这是我们经常思考的问题，因为我认为未来的 Cursor 必须与今天的 Cursor 看起来非常不同。首先，我认为你可以通过雇佣正确的人来做到这一点。所以，我们招聘流程的最后一步是一个为期两天的现场试用（on-site），你过来和我们一起做一个项目。
[原文] [Michael Tru]: And so this is after an initial set of technical screens and you're in the office and you're kind of a member of the team and you come to meals with us and uh and work on something and then you demo it at the end and then we ask you questions That gets at energy and excitement and passion for the problem space And usually you're probably not going to be super willing to do that if you're maybe just view it as a job and you're applying to a bunch of of technology companies at the same time So I think a big way to do it is by getting passionate people through the hiring process
[译文] [Michael Tru]: 这是在初步的技术筛选之后。你在办公室里，你就像团队的一员，和我们一起吃饭，一起做点东西，最后你进行演示，然后我们向你提问。这能考察出你对问题空间的能量、兴奋感和激情。通常，如果你只是把它看作一份工作，或者你同时申请了一堆科技公司，你可能不太愿意这么做。所以我认为做到这一点的一个重要方法就是通过招聘流程找到充满激情的人。
[原文] [Host]: So one of the things that I think all startups and maybe all businesses right now are even trying to figure out in the face of uh some of the most impressive and incredible models in the world is what are the moes that are going to actually be durable and usable How do you think about that
[译文] [Host]: 所以我认为所有初创公司，甚至所有企业现在面对世界上那些最令人印象深刻、不可思议的模型时，都在试图弄清楚的一件事是：什么是真正持久且可用的护城河（moats）？你是怎么考虑这个的？
[原文] [Michael Tru]: well I think that the the market that we're in and that others are in resembles markets that you've seen in the past that actually aren't enterprise software markets Um so I think that a lot of enterprise software markets are kind of characterized by well there's sort of a low ceiling for the good core value you can deliver in the product and there's a lot of lock in and the market we're in kind of mirrors search at the end of the 90s where the product ceiling is really high search could get a lot better for a long long period of time
[译文] [Michael Tru]: 嗯，我认为我们所在的市场以及其他人所在的市场，类似于过去见过的那些并非企业软件市场（enterprise software markets）的市场。很多企业软件市场的特征是，产品能提供的核心价值天花板比较低，但有很多锁定效应。而我们所在的市场有点像 90 年代末的搜索市场，产品的天花板非常高，搜索可以在很长很长一段时间内变得好很多。
[原文] [Michael Tru]: one of the things that characterize search and I think also characterize our market is distribution is really helpful for making the product better and so if you have lots of people using your thing you have an atscale business you get a sense of where the product's falling over and where it's doing well and so in search that's seeing you know what are people clicking on what are they bouncing back from what was a good search result what is a bad search result which then feeds into the R&D and then helps them make a better search engine Uh for us it's seeing you know where are people accepting things where are they rejecting things in the places where they accept things and then they correct it later what's what's going on there how could we have been better i think that that will be a really really important driver um to making the product better and kind of the underlying models better in the future
[译文] [Michael Tru]: 搜索的一个特征——我认为也是我们市场的一个特征——是分发（distribution）对于改进产品非常有帮助。如果你有很多人使用你的东西，你有一个成规模的业务，你就能感觉到产品哪里不行，哪里做得好。在搜索中，就是看人们点击了什么，从哪里跳出，什么是好的搜索结果，什么是坏的，这些数据反馈到研发中，帮助他们制造更好的搜索引擎。对我们来说，就是看人们在哪里接受了建议，在哪里拒绝了；在他们接受然后后来又修改的地方，到底发生了什么？我们怎么能做得更好？我认为这将是未来让产品变得更好、让底层模型变得更好的一个非常非常重要的驱动力。
[原文] [Michael Tru]: I think another market to take inspiration from is consumer electronics at the beginning of the 2000s The thing there was getting the iPod moment right and then the iPhone moment right And you know I think the chatbt moment is kind of like the iPod or iPhone moment of our age of if you keep pushing the frontier faster than other people you can get really big gains occurring to you And I think that there are a couple more of those that exist in our space And so it's hard to do but we're really focused on trying to be uh the ones to race toward those the fastest
[译文] [Michael Tru]: 我认为另一个可以汲取灵感的市场是 2000 年代初的消费电子产品。那里的关键是抓住了 iPod 时刻，然后是 iPhone 时刻。我认为 ChatGPT 时刻有点像我们这个时代的 iPod 或 iPhone 时刻。如果你能比别人更快地推进行业前沿，你就能获得巨大的收益。我认为在这个领域还存在好几个这样的时刻。虽然很难做到，但我们真的专注于努力成为最快冲向那些目标的人。
[原文] [Host]: It's 2025 I feel like we're actually even in the opening stages of this age of intelligence What a revolution You know what are you personally most excited about right now
[译文] [Host]: 现在是 2025 年。我觉得我们实际上才刚刚处于这个智能时代的开端。这真是一场革命。你个人现在最兴奋的是什么？
[原文] [Michael Tru]: i think that this is going to be a decade where just your ability to build will be uh so magnified Both people who already that's their living and that's what they do but then I think it'll also become accessible for tons more people What a time to be alive
[译文] [Michael Tru]: 我认为这将是这样一个十年：你的构建能力将被无限放大。无论是对于那些以此为生的人，还是对于更多即将能够通过 AI 构建事物的人来说。这真是一个活着的精彩时代。
[原文] [Host]: Thanks for joining me today
[译文] [Host]: 谢谢你今天加入我们。
[原文] [Michael Tru]: Thank you Thanks for having me
[译文] [Michael Tru]: 谢谢，谢谢邀请。