对话Cursor CEO：AI编程的未来

家庭公路旅行总有些特别之处。摇下车窗，调高音乐，零食散落各处，那些故事会变成多年流传的内部笑话。而现在又多了一个让人喜爱的理由——为了一份能让全车人开心的鸡肉三明治而停车。卡尔弗的新品系列，无论是酥脆、香辣还是烤制的，都采用100%全白肉鸡胸，搭配清爽脆生的生菜、熟透的番茄、丝滑的美乃滋和恰到好处的脆嫩腌黄瓜，全部夹在烤过的布里欧修面包里。配上一杯冰爽的雪碧，这顿午餐停留绝对值得纪念。

There's just something about family road trips. Windows down, music up, snacks everywhere, and stories that turn into inside jokes for years. And now there's one more thing to love, pulling over for a chicken sandwich that makes the whole car happy. Culver's new lineup, crispy, spicy, or grilled, is made with 100% whole white meat chicken breast, topped with cool, crunchy lettuce, ripe tomato, creamy mayo, and perfectly crisp fresh pickles all in a toasted brioche bun. Pair your favorite with a refreshing cold Sprite, and you've got a lunch stop worth remembering.

这样的美食能让心情持续高涨，安全带也愿意多系一会儿。所以无论你去往何方，找个能让所有人欢聚的停靠站。查找附近餐厅或在线订购请访问culver's.com。

It's the kind of meal that keeps spirits high and seat belts buckled up a little longer. So wherever you're headed, make a stop that brings everyone together. Find a restaurant near you or order online at culver's.com.

本节目由Robinhood赞助。在一个平台上管理投资组合岂不是很棒？通过Robinhood，你不仅能交易个股和ETF，还能以低成本无缝买卖加密货币。一站式完成所有交易。立即登录Robinhood开始吧！

Support for this show comes from Robinhood. Wouldn't it be great to manage your portfolio on one platform? With Robinhood, not only can you trade individual stocks and ETFs, you can also seamlessly buy and sell crypto at low costs. Trade all in one place. Get started now on Robinhood!

加密货币交易存在重大风险。加密货币交易通过Robinhood Crypto LLC账户提供。Robinhood Crypto持有纽约州金融服务部颁发的虚拟货币业务活动许可证。通过Robinhood Crypto持有的加密货币不受FDIC保险或民事保护。投资涉及风险，包括本金损失。

Trading crypto involves significant risk. Crypto trading is offered through an account with Robinhood Crypto LLC. Robinhood Crypto is licensed to engage in virtual currency business activity by the New York State Department of Financial Services. Crypto held through Robinhood Crypto is not FDIC insured or civic protected. Investing involves risk, including loss of principal.

证券交易通过Robinhood Financial LLC账户提供，该公司是CIPIC成员，注册经纪交易商。

Securities trading is offered through an account with Robinhood Financial LLC, member CIPIC, a registered broker dealer.

随着营销渠道的激增，内容需求呈爆炸式增长。但借助Adobe Express，每个人都能制作符合品牌又脱颖而出的内容。无需设计师也能生成图像、改写文本并创造特效——这就是商业安全的生成式AI的魅力所在。企业各部门团队将兴奋地协作创建精彩演示、视频、社交帖子、传单等。

As marketing channels have multiplied, the demand for content has skyrocketed. But everyone can make content that's on brand and stands out with Adobe Express. You don't have to be a designer to generate images, rewrite text, and create effects. That's the beauty of generative AI that's commercially safe. Teams all across your business will be psyched to collaborate and create amazing presentations, videos, social posts, flyers, and more.

认识Adobe Express——快速轻松创建品牌内容的应用程序。了解更多请访问adobe.com/express/business。

Meet Adobe Express, the quick and easy app to create on brand content. Learn more at adobe.com/express/business.

大家好，欢迎收听《解码器》。我是Casey Newton，《平台家》新闻简报创始人兼主编，也是《硬分叉》播客的联合主持人。在Neelai休育儿假期间，我将客串主持接下来几期节目。恭喜Neelai。

Hello, and welcome to Decoder. This is Casey Newton. I'm the founder and editor of the platformer newsletter and cohost of the Hardfork podcast. And I'll be guest hosting over the next few episodes of Decoder while Neelai is out on rental leave. Congratulations, Neelai.

我对我们为听众准备的节目感到非常兴奋。如果你关注过我的作品，尤其是我在The Verge担任记者时，就会知道我是个十足的生产力控。我热爱生产力应用，无论是待办清单、协作工具还是AI应用。我认为最好的生产力应用是将技术进步转化为人类进步的方式——而且它们还很有趣。

And I'm very excited for what we have planned for you all. If you follow my work at all, particularly when I was a reporter at the verge, you'll know that I'm a total productivity nerd. I love productivity apps, whether it is a to do or some kind of collaborative app or something making use of AI. At their best, I think productivity apps are the way we turn technological advancement into human progress. And, also, they're fun.

我喜欢尝试新软件，每个新工具都承载着'这终将成就我梦想中的工作流程'的希望。多年来我用过许多这类程序，但很少有机会与开发者对话。因此在《解码器》节目中，我特别想与生产力领域最知名、最有趣公司的创始人聊聊他们的产品如何帮助我们高效工作。这便引出了今天的嘉宾——AnySphere首席执行官Michael Truel。你可能没听过AnySphere，但想必知道其旗舰产品Cursor AI。

I like trying new software, and every new tool brings with it the hope that this is the one that is finally going to complete the setup of my dreams. Over the years, I've used a lot of these programs, but I rarely get a chance to talk to the people who make them. So for my Decoder episodes, I really wanted to talk to the people behind some of the biggest and most interesting companies in productivity about what they're building and how they can help us get things done. And that brings me to my guest today, Michael Truel, the CEO of AnySphere. You may not have heard of AnySphere, but I bet you've heard the name of its flagship product, Cursor AI.

Cursor是一个集成了Anthropic、OpenAI等公司生成式AI模型的自动化编程平台，旨在协助编写代码。在此我需要暂停并做出本集声明：我的男友就职于Anthropic。但Cursor被内置在程序员称之为集成开发环境（IDE）的标准版本中，其CursorTab技术能在你编写时自动补全代码行。如今Cursor已成为全球最受欢迎且增长最快的AI产品之一。而Michael从MIT毕业后仅三年前联合创立的AnySphere公司，正逐渐成为后ChatGPT时代最成功的初创企业传奇之一。

Cursor is an automated programming platform that integrates with generative AI models from Anthropic, OpenAI, and others to help you write code. I guess there I will pause and give my disclosure for the episode, which is that my boyfriend works at Anthropic. But Cursor is built into a standard version of what programmers call an integrated development environment or IDE with technology like CursorTab, which auto completes lines of code as you write them. And Cursor has become one of the most popular and fastest growing AI products in the world. And Any Sphere, the company that Michael cofounded just three years ago after graduating from MIT, is now shaping up to be one of the biggest startup success stories of the post ChatGPT era.

为此我专访了Michael，探讨Cursor的运作原理及AI编程为何能获得惊人普及。正如你将听到Michael解释的，这个领域在过去几年已发生巨变。如今在旧金山，科技公司高管和员工经常向我提及他们使用Cursor的频繁程度和喜爱之情。听着，许多人担忧AI会取代他们的工作——这种担忧我认为完全合理。

So I sat down with Michael to talk about Cursor, how it works, and why coding with AI has seen such incredible adoption. As you'll hear Michael explain, this entire field has changed a lot over the past few years. And now here in San Francisco, tech executives and employees are regularly telling me about how much they are using and liking Cursor. So look. A lot of people are worried that AI could take their jobs, and rightly so, I would argue.

但Michael认为失业潮不会源于像他开发的这类工具的基础进步。湾区许多人认为超级智能AI会一夜之间重塑世界，让Cursor等产品失去意义，但Michael相信变革会缓慢得多。我还想请教Michael关于'氛围编程'现象，这让业余爱好者即使毫无经验也能用Cursor尝试构建自己的软件。Michael告诉我这虽非Cursor的主要用户群，却是编程领域更广泛变革的一部分，他坚信我们对AI潜力的探索才刚刚开始。现在有请AnySphere首席执行官Michael Truel。

But you'll hear Michael say that job losses are not going to come from simple advances in tools like the one that he's making. Lots of people in the Bay Area think that super intelligent AI is gonna remake the world overnight, making products like Cursor Pointless, but Michael believes that change is gonna come much more slowly. I also wanted to ask Michael about the phenomenon of vibe coding, which lets amateurs use tools like Cursor to experiment with building software of their own even if they've never built anything before. That's not Cursor's primary audience, Michael told me, but it is part of this broader shift in programming, and he's convinced that we're only scratching the surface of how much AI can really do here. So AnySphere CEO, Michael Truel, here we go.

Michael Truell，您作为Cursor AI母公司AnySphere的联合创始人兼CEO，欢迎来到《解码器》节目。感谢邀请。请问Cursor是什么？它有哪些功能？

Michael Truell, you are the cofounder and CEO of AnySphere, the parent company of Cursor AI. Welcome to Decoder. Thank you for having me. What is Cursor? What does it do?

它的目标用户是谁？

Who is it for?

我们打造Cursor的初衷是成为最佳的软件开发方式，特别是与AI协作编程的最佳途径。对于非技术人员，现阶段可以将其理解为强化版文字处理器——工程师们构建软件时其实在进行大量写作，他们在类似文字处理器的界面中编辑数百万行逻辑代码。Cursor能极大提升这类工作效率，尤其是结合AI时。目前Cursor主要通过两种方式实现：

Our intention with Cursor is to be the best way to build software, and in particular, the best way to code with AI. For people who are nontechnical, I think the best way to think about Cursor as it exists today is think of, like, a really souped up word processor, where the way engineers build software is they're actually doing a lot of writing. They're sitting in something that looks like a word processor, and they're editing millions of lines of logic, so things that don't look like language. And Cursor helps them do that work way more efficiently, especially with AI. And there's kind of two different ways Cursor does that right now.

其一是Cursor会观察你的工作流程，尝试预测你在平台内即将进行的操作。这种自动补全形态在编程中比文字处理更强大，因为编程时常存在未来20分钟工作完全可预测的情况，而写作时计算机很难预判作者下一步要写什么——系统里根本没有足够的信息来推断他们后续的

One is Cursor is kind of watching you do your work, and it's trying to predict the next set of things you're gonna do within Cursor. So this is this is the autocomplete form factor, and that can be really souped up in programming compared to writing. Because unlike writing, it's actually there there are oftentimes when you're programming where the next twenty minutes of your work are entirely predictable. Whereas with writing, it's a little hard to get a sense of what a writer is actually gonna put down on the page. There isn't really enough information in the computer to understand the next set of things they're

操作内容。

gonna do.

另一种方式是用户逐渐将Cursor当作结对编程伙伴来委派任务，就像与人类搭档协作那样，把小型任务交给Cursor让它端到端地完成。

And then the other way that people work with cursor is they're kind of increasingly delegating to cursor like they're working with a pair programmer, working with another human. So they're handing off small tasks to Cursor and having Cursor kind of go end to end on them.

稍后我们会深入探讨产品细节。但首先，请谈谈这一切的起源。您创立AnySphere时最初从事计算机辅助设计软件，是如何转向Cursor的？

We'll we'll dig a little deeper into the product in a moment. But first, let's talk about how all of this started. When you founded AnySphere, you were working on computer aided design software. How did you get from there to Cursor?

我和我的联合创始人都有着长期编程的背景，我们从事AI工作的时间几乎与编程生涯一样长。我们中有人曾在大科技公司开发推荐系统，有人长期从事计算机视觉研究，还有人致力于开发能从极少量数据中学习的机器学习算法，更有一位曾参与开发类似谷歌的竞品，运用了LLM技术前身的机器学习方法。

My cofounders and I, we come from backgrounds where we've been programming for a while, and we've also been working on AI for almost as long as we've been programming. And so, you know, one of my cofounders one of us had worked on, like, recommendation systems in big tech. Another one of us had worked on computer vision research for a long time. You know, another one of us had worked on trying to make machine learning algorithms that could learn from very, very, very little data. You know, another one had worked on, like, competitor to Google using the antecedents or the, you know, the things that came before LLM technology in machine learning.

我们深耕AI领域多年，同时也是经验丰富的工程师，热爱编程。2021年有两个时刻让我们特别振奋：一是首次使用真正实用的AI产品，二是看到大量研究表明即使创意枯竭，只要扩大模型规模并增加训练数据，AI仍会持续进步。这让我们萌生了创业公式——选择一个知识工作领域，打造该领域的最佳产品，随着AI变革逐步实现工作方式转型。

But worked on AI for a long time, had been engineers also for a long time and loved programming. And in 2021, there were two moments that really excited us. One was using some of the first really useful AI products. Then another was kind of this body of litter literature that was showing that AI was gonna get better even if we kind of ran out of ideas by making the models bigger and training them on more data. That got us really excited in, like, kind of a formula for creating a company, which was you pick an area of knowledge work, and you build the best product for that area of knowledge work, like the place where you do your work as AI starts to change.

若能做好这项工作并获得大量用户，就能观察AI的实际助益与局限——哪些地方需要人类反复修正AI输出，哪些环节仍需完全人工操作。这些洞察能推动产品迭代，进而促进底层机器学习技术进步。随着技术成熟，或许能开辟出一条重塑知识工作未来的道路，成为推动核心技术发展的先锋。正是这个创业公式吸引了我们。

And then hopefully, you you do that job well and you get lots of people to use your thing. And then if you do, you can see where AI is helping them, and you can see where AI is not helping them, and where the human just has to correct the AI a bunch or just just do work without any AI help. And then you can use that to then make the product better and kinda push the underlying ML technology forward. And then that can maybe get you into a path where, yeah, you can really start to build, like, the the future of knowledge work as this tech gets more mature and be kind of the one to push push the underlying technology to. So we got kind of interested in that formula for making a company.

虽然我们最热爱的知识工作是计算机开发，但最初却选择了另一个领域——计算机辅助设计，试图帮助机械工程师。这个决定相当冒险，因为我们四人都不具备机械工程背景。尽管有相关领域的朋友，也曾涉足机器人技术，但这并非我们专长。选择该领域是因为当时已有许多人致力于用AI提升程序员效率。

And the craft that we really love, the knowledge work that we really love, which was building things on computers, we actually didn't touch it first. We went and we worked on a different area, which was, as you know, computer aided design, it was trying to help mechanical engineers, which was a very ill fated decision because none of the four of us are mechanical engineers. And we had friends who were interested in the area. We had worked on robotics in the past, but it wasn't really our specialty. And it was because it seemed like there were bunch of other people working on trying to help make programmers more productive as AI got better.

在机械工程领域摸索约六个月后，我们回归了编程领域。部分源于对编程的热爱，部分因为那些看似占领市场的产品虽实用，但发展方向与我们不同，且缺乏必要的雄心。于是我们决定打造最好的AI编程工具——Cursor由此诞生。

But after, you know, six or so months of working on the mechanical engineering side of things, we got pulled back to working on programming. And part of that was just our love for the space. Part of that too was just it seemed like the the people who we thought had the space covered, they were building useful things, but they weren't really pointed in the same direction. And they didn't really seem to be approaching the space with, like, the requisite ambition. And so, yeah, we decided to to build, you know, the best way to code with AI, and that's that's where Cursor started.

我了解到你们早期使用的AI工具包括比ChatGPT早一年面世的GitHub Copilot。你们最初对Copilot有何反应？它如何影响了你们的产品构想？

I have read that one of the AI tools that you used early on was GitHub Copilot, which came out about a year before ChatGPT. What was your initial reaction to Copilot, and how did it influence what you wanted to build?

Copilot令人惊艳，对我们影响深远。这是首个以AI为核心且真正实用的产品。作为长期关注AI的研究者，我们曾遗憾地发现AI技术多停留在实验室或玩具阶段。此前AI对我们生活的实质影响基本仅限于推荐系统——新闻推送、YouTube算法之类。

Copilot was awesome. Copilot was a really, really big influence, and it was the first product that we used that had AI really at its core that we found useful. One of the the sad things to us, you know, as people who had been working on AI and interested in AI for a while, was that it was very much stuff that was just, like, kind of in the lab or in the toy stage. It felt like, for us, the the only real way AI had had touched our lives as consumers was mostly recommendation systems. Right?

Copilot确实是首个以AI为核心且极具实用价值的产品，给了我们巨大启发。当时我们正考虑是否投身学术界，而Copilot的出现证明：是时候将这些系统应用到现实世界了。即便在2021年，它还存在明显缺陷，代码输出不能完全信任，但依然令人振奋。值得注意的是，这不仅是首个实用的AI产品，更是我们多年来采用过的最具变革性的开发工具。

You know, the news feeds of the world, YouTube algorithms, things like that. And so GitHub Copilot was yeah. It was the first product where AI was really, really at the core that was that was useful. And so that was a big inspiration. And at the time, we were considering, you know, should we try to pursue careers in academia?

Copilot的存在验证了在现实世界开发这类系统的时机已经成熟。虽然2021年时产品还存在明显瑕疵，代码输出可靠性有限，但其突破性意义毋庸置疑。另一个关键点是，这不仅是首个实用的AI产品，更是我们这些极致优化过编程环境（当时甚至使用Vim这种硬核文本编辑器）的开发者多年来见过的最革命性的开发流程改进。

Copilot kind of was this, existence proof that, no, actually, it was, you know, time to work on these systems out in the real world. And, you know, even back then in 2021, there were some rough edges. There were some places, you know, the product was wrong in really obvious ways, and you couldn't completely trust its code output. But it was, nonetheless, really, really exciting. And another thing to note too is apart from being the first useful AI product, it was the most useful new dev tool that we had adopted in a really long time.

我们这群人曾精心配置编程环境，深度定制文本编辑器，当时甚至在使用极客向的Vim编辑器。Copilot不仅是我们使用的首个实用AI产品，更是多年来体验过的最具突破性的开发流程革新。

And we were people that had kind of optimized our our setups as as programmers and had kind of modded out our our text editors and things like that. And we're using this, like, crazy kind of text editor called called Vim at the time. And it was yeah. Yeah. Not not just the first useful AI product that we'd used, but also the most useful dev flow we had used in a really long time.

这很有趣。所以你们团队算是软件爱好者？喜欢使用软件，热衷于寻找能提升效率的软件工具。我觉得这种特质让你们特别适合解决像Cursor正在攻克的那类问题。

That's interesting. So so you guys sort of like software. You like using software. You like trying to find a software that makes you more productive. I feel like that probably made you well suited to tackle a problem like the one Cursor's trying to solve.

确实。我认为关注所用工具的特性对我们很有帮助。实际上我们联合创始人团队内部也存在不同程度的工具偏好——其中一位简直就是典型早期采用者原型，总是第一个尝试新浏览器，第一个涉足各类新工具领域，而其他几位则相对保守些。

Yeah. I think caring about the tools we use was helpful. And I think that there are actually kind of different degrees of that on our Co Founding team. One of my Co Founders in particular is the straight out of central casting early adopter who is the first one on these new browsers, first one on kind of the new category of everything. A couple of us are a little bit more laggards.

这种多元化的观点实际上在某些产品决策中给了我们很大帮助。

And so I think actually kind of having maybe that diversity of opinions has helped us in in some of product decisions we've made.

你把ChrisOver描述成强化版文字处理器，而软件工程师会称之为集成开发环境(IDE)。开发者从八十年代就开始使用IDE，但最近AI实验室推出了像OpenAI的Codex或Cloud Code这类可直接在终端运行的工具。用户为什么要选择Cursor而非这些方案呢？

So you described ChrisOver as kind of like a souped up word processor. Software engineers, I think, would call it an integrated development environment or IDE. And developers have been using IDEs since the eighties. But recently, AI Labs have released tools like OpenAI's codex or a Cloud Code that can run directly in a terminal. Why might someone use Cursor over those options?

那些都是优秀工具。但我们的核心追求是——虽然起步于IDE/文本编辑器——真正想实现的是彻底改变编程形态的未来：或许开发者甚至无需查看代码就能开发专业级软件。我们要把编程从阅读数百万行逻辑和晦涩语言的现状，转变为仅需指定最小化意图就能构建所需软件的新范式。

Both of those are really useful tools. The thing we care about being so I think we we start as this IDE. We start as this text editor. And what we really care about getting to is to a world where programming has completely changed, and in particular, you know, a world where you can develop professional grade software perhaps without even really looking at the code. And it's that that kind of future programming and, like, changing it from this weird like, you're reading these millions of lines of logic and these, like, esoteric programming languages to getting you to a world where you can build software by just specifying the minimal intent necessary to kind of, to build the software you want.

想象你只需向计算机传达最精简的信息需求，它就能自动填补所有空白。当今编程是项极其耗费人力和时间的工作——实现简单描述的功能需要数千小时、庞大团队和大量工作，特别是在专业层面。我们正致力于发明这种新型编程范式，这始于编辑器，但终将超越编辑器。

You know? You can tell, the computer the the shortest amount of information it needs to really get you, and it can fill in all of the gaps. And, yeah, programming today is this intensely labor intensive, time intensive thing where to do things that are pretty simple to describe, to get them to actually work and show up on a computer, it takes many thousands of hours and really large teams and lots of work, especially at professional scale. So that's where we want to get to is kind of inventing that new form of programming. I think that that starts as an editor, and then that starts to evolve.

我们已处于这个进程中：当前Cursor支持用户与智能体一对一协作，使用TAP系统。未来编程将越来越像向多个并行助手委派工作，我们需要构建相应的产品体验——使其高效可控，无需逐行审查代码就能理解助手们的工作成果。虽然编程效率工具领域竞争激烈，但终端UI的局限性在于其表达力和控制力有限。

And so we're already kind of in the midst of that, where right now Cursor is this place where you can work one on one with an agent, and you can work with our TAP system. And then increasingly, we're getting into a world where more and more programming looks like starting to delegate your work to a bunch of helpers in parallel. And there's a product experience to be built for making that great and productive and understanding what all of these parallel helpers are doing for you, being able to intervene in the places where it's helpful, understanding their work when they come back to you at a level that's not having to read every single line of code. Yeah, I think that there's a competitive environment with a bunch of tools that are interested in programming productivity. One of the things that's limiting about, just a terminal UI is that you have only so much expressiveness in the terminal and control over the UI.

我们始终认为代码自动化需要双管齐下：既要构建程序员工作的'玻璃窗'界面，又要探索新型工作方式。这既需要UI创新，也需要底层技术突破。与终端工具相比，我们的核心差异首先是UI控制维度——其次我们在模型层做了大量超越演示级别的改进，特别是在AI产品的响应速度、稳定性和准确性调校方面。

From the very start, we've thought that the solution to automating code, replacing it with something better, is this kind of two pronged thing where you need to build, like, the pane of glass where programmers do their work, and you need to discover what the work looks like. You need to build the UI, and then you also need to build the underlying technology. And so one one thing that would distinguish us between, you know, some terminal tools is just the degree of control you have over the UI. Another thing too is we've done a lot of work on the model layer, on improving going beyond just having things that show up well on on a demo level. And there's a lot of work on AI products to dial in the speed and the robustness and the accuracy of them.

对我们而言，关键创新在于构建了与API模型协同工作的模型组合体系——每次调用Cursor智能体时，实际上是由API模型和定制模型共同完成的。某些功能（比如卓越的自动补全）完全由定制模型驱动，这也是我们区别于其他方案的重要特征。

And for us, one important product lever there has been building kind of an ensemble of models that work with the API models to improve their abilities. And so every time you kind of call out to an agent and cursor, it's like this set of models that some of them are API, some of them are custom. And then also for some form factor or for some of the features, it's entirely custom, for instance, like the the Supeb auto complete. And so that's also one thing that has kind of distinguished us from from other solutions.

没错。我们来聊聊这些专有模型——它们似乎是你们成功的重要推手。当ChatGPT和OpenAI API刚发布时，我们看到大量被戏称为'API套壳'的初创公司涌现又迅速消失，对吧？

Yeah. Let let's talk a bit about these proprietary models. They seem to be fueling a lot of your success. When Chappi GPT and the OpenAI API first got released, we saw a lot of startups come out that quickly were dismissed as just wrappers for an API. Right?

你基本上是在别人的API之上尝试构建一些东西。Cursor最初也是类似的方式，利用他人的API来打造产品。此后你们逐步构建自己的底层。能否详细说说你们正在构建的内容，以及如何希望这能让你们区别于那些纯粹的封装型公司？

You're just sort of trying to build something on top of somebody else's API. And Cursor started in a similar way where it was using other folks' APIs in order to to create its product. Since then, you're building on top. Say a bit more about what you're building and how you're hoping it kind of sets you apart from those sort of pure wrapper companies.

我认为在讨论模型层面之前需要加个注脚——'封装'这个词源于AI产品刚兴起时，当时人们只有有限时间让产品更具深度。而现在我们处于产品严重过剩的阶段。即便你仅基于API模型开发，比如我们专注的软件开发生命周期领域或其他平行领域，我认为在这些基础上仍能构建非常深度的产品。因此对某些领域而言，'封装'这个说法已经有些过时了。

I think that also, like, one one asterisk before getting into the model side of things, I think that the the wrapper term came from the very start of when people were building AI products, when there was only so much time to kind of make the products a bit deeper. And now I think, you know, we're at a point where there's a ton of product overhang. And so even if you're just building with the API models, I think that there's and, you know, lots of areas our area of, you know, working on the software development life cycle, but in other parallel areas too. I think there are very, very deep products to be built on top of those things. And it's and, you know, and that sounds like the wrapper term for at least some some areas is is a little bit dated.

但从模型层面来说，我们最初就想打造一个用户量庞大的产品。规模带来的好处是你能清晰看到AI在哪些场景真正帮助用户，哪些场景无效或被纠正。这对提升AI实用性至关重要。例如我们的Tab模型每天处理超10亿次调用，堪称全球生成生产代码量最大的语言模型之一，目前已经迭代到第四或第五代。

But on the model level, you know, from the very start, we wanted to build a product that, you know, got a lot of people using it. One of the benefits you get from that scale is, you can see where AI is helping people, and you can see where AI is not helping people and where it gets corrected. And that's a really, really important input to making AI more useful for people. And so at this point, you know, for instance, with our our tab model, which does over a billion model calls per day, so one of the large language models that writes actually almost some of the most production code in the world. And we're also on our fourth or fifth generation of it.

这个模型通过产品数据训练而成——观察AI的实际帮助场景与失效场景，预测其潜在价值。这需要大量专用基础设施和顶尖人才。比如参与开发的Jacob，他早在GitHub Copilot之前就创造了首个编程自动补全产品TabNine，也是首批百万token上下文窗口模型的构建者，在提升模型信息理解能力方面贡献卓著。

That is trained using product data of seeing where AI is helping people, seeing where it isn't, seeing what in the places where it trying to predict how it can help humans. And also requires a ton of infrastructure, specialty talent to be able to make those models really good. You know, for instance, one of the people who has worked on those models with us is Jacob, who built actually the kind of GitHub Copilot before GitHub Copilot, which was Tab nine, which was the first kind of programming autocomplete product. He is also one of the people who built one of the first million token context window models, and so has done a lot of work on making models understand more and more and more information. But yeah, specialty talent and specialty infrastructure to do that work.

在曲折探索Cursor的过程中，我们早期开发CAD和其他项目的经历反而帮助很大——我的联合创始人曾深度钻研过机器学习基础设施和建模。虽然我们原计划要很久后才自建模型，但实际进展远超预期。

And one of the in our ambling kind of windy way to working on Cursor, I think one of the things that that really did help us was when we were working on CAD and also in some of our explorations before. My co founders had to dig very deep into kind of the ML infrastructure and modeling side of things. And so when we actually set out to work on Cursor, we thought it'd be a long time before we started to do our own modeling as a a product lover, but it it happened much sooner than we expected.

最近我与某科技巨头CTO共进晚餐时询问工程师们爱用的编程工具。他透露定期调研结果显示：当Cursor作为试用工具提供时，工程师们竟恐慌地发消息'求你们别停用Cursor'——他们已产生重度依赖。能否谈谈为何程序员觉得这类工具带来了职业史上的分水岭时刻？Cursor究竟如何改变了工程师的日常工作？

Recently, I had dinner with the CTO of a big tech company, and I asked him about what coding tools were popular with his engineers. And he told me that he actually regularly surveys them on this question, And they had cursor available as, like, a a trial, it was labeled. And he said he was getting these panic messages from engineers saying, please tell us you're not about to take away cursor because they've become so dependent on it. Can you give us a sense of why for programmers, this has kind of felt like a before and after moment in the history of the profession? What is it that tools like Cursor are making so different in the lives of these engineers day to day?

我认为

I think that

我们距离技术天花板还很遥远，离'编程被更好方式取代'的世界更远。但现有产品和模型已能为程序员承担大量工作。这项技术特别适合编程有几个原因：编程本质是文本处理——这正是当前AI最擅长的模态；网络上有海量编程数据；开源代码资源丰富；而且编程具有可验证性。

we're just already at a point where the you know, we are far, far, far from the ceiling of where things can go and far, far, far from a world where, you know, much of coding has been replaced with something better. But, you know, I just already at this point, these products and these models can do a lot for programmers and already taking on, you know, quite a bit of work. And I think that the technology is especially good for programming for a few reasons. You know, one is that programming is text based, and that is the modality that that the field has figured out perhaps the most. There's a lot of programming data on the Internet too.

推动进步的核心引擎曾是'训练模型预测网络下个词'并扩大模型规模，虽然这个引擎仍有潜力，但其主要使命已经完成。

There's a lot of open source code. Programming is also pretty verifiable too. And so one of the important engines progress has been training models to predict the next word on the Internet and making those models bigger. That engine of progress, you know, has largely run its course. There's still more to do there.

接力棒现在传到了强化学习手中——就像2010年代中期人类教会计算机精通围棋和Dota等游戏那样。我们正在让语言模型具备任务执行能力，并通过'游戏化'机制持续优化。编程特别适合这种模式：你可以编写代码→运行→验证输出是否符合预期，形成完美的强化学习闭环。

But the next thing that's kind of picked up the torch in making models better has been reinforcement learning. So it's been basically teaching models to play games, kind of similar to how in the mid twenty tens, you know, humanity figured out how to make computers really good at playing Go and playing Dota and other video games. We're kind of getting to a level of of language models where they can do tasks and you can set up games for them to get even better at those tasks. And programming is great for that because you can run it, write the code, and then you can run it. And then you can see the output and see if it's actually what you want.

因此我认为这项技术有很多特点使其特别适合编程。是的，我想这是目前最前沿的应用场景之一，这项技术正在全球范围内部署，人们正从中获得实际价值。

And so I think there's a lot about the technology that makes it especially good for programming. And, yeah, it's just, you know, I think one of the use cases that's the furthest ahead in kind of deploying this tech out to the world and people finding real value from it.

是的。我的感觉是，如果我过去需要每天工作八小时，现在可能接近五到六小时。这是其中的一部分原因吗？

Yeah. I mean, my sense is maybe if I used to have to work eight hours a day, now it's maybe closer to five or six. Is that part of it?

没错。从某种意义上说，我认为在某些公司，过去需要八小时完成的工作现在确实可以缩短到五到六小时。这是真实存在的，虽然并非所有公司都如此。但我想指出的是，程序员实际上并没有减少工作时间。我认为这很大程度上是因为软件开发具有极强的弹性。

Yes. In the sense that I think that the productivity gains of what would have taken you eight hours before in some companies now actually can take you five or six hours. I think that that is real, not across all companies, but it is really real in some companies. But I think that the the thing I would nitpick on there is I I don't think programmers are actually just working, you know, or or shortening the hours that they're working. And I think a lot of that is because there is just a ton of elasticity with software.

我认为非技术人员或非职业程序员很容易低估专业规模编程的低效程度。这主要是因为编程是隐形的。比如在Salesforce这样的公司，程序员面对的是数千万行代码、数百万个文件构成的现有逻辑体系。每次需要修改时，他们必须处理这个庞大而笨重的代码库。

And I think it's really easy for people who are nontechnical, or just don't program professionally to underrate how inefficient programming is at a professional scale. And a lot of that is because programming is kind of invisible. You know, what a programmer is doing at, you know, a company like Salesforce is there are just tens of millions of lines, many millions of files of existing logic that describes how their software works. And anytime they have to make a change to that, they have to, like, take that ball of mud, that mass of things. It's it's very unwieldy, and they need to edit it.

这就是为什么很多人会对某些软件发布周期如此缓慢感到震惊。所以是的，我认为确实存在真实的效率提升，但目前可能并没有减少程序员的工作时长。

That's why I think that it's, it's kind of shocking to many people that, you know, some software release cycles are are so slow. But so, yes, I I think that, you know, there are real productivity gains. I think that it's probably not reducing, the number of hours that programmers are working right now.

我们需要短暂休息一下，马上回来。

We need to take a quick break. We'll be right back.

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我们继续与AnySphere首席执行官Michael Chewel对话。此前我们讨论了他们公司的产品Cursor AI如何被专业程序员采用。但现在我想探讨另一个应用场景——氛围编程（vibe coding）。您提到非技术人员，虽然Cursor主要用户是专业程序员，但今年出现的'氛围编程'一词描述的正是业余程序员（甚至完全新手）借助Cursor等工具实现的操作。

We're back with AnySphere CEO, Michael Chewel. Before the break, we were discussing how his company's product, Cursor AI, is being adopted by professional coders. But now I wanted to ask about a different use case, vibe coding. Well, you mentioned nontechnical people. Cursor is used by a lot of professional programmers, but this year saw the coining of the term vibe coding to describe what more amateur programmers can do, sometimes even complete novices, and often with tools like Cursor.

氛围编程在Cursor的应用规模有多大？您认为氛围编程的未来会如何发展？

How big is the Vibe coding use case at Cursor, and what do you think is the future of Vibe coding?

我们的核心目标是帮助以开发软件为生的人群。目前这主要指工程师群体，这也是我们的主要应用场景。有趣的是，当你专注于这个场景并通过它推动技术进步，让程序员在更高抽象层级工作时，软件开发的准入门槛也随之降低——这正是我们非常期待的方向。

So our main goal is to help people who build software for a living. Right now, that means that means engineers. And so that's that's our main use case. It's been interesting to see as you focus on that use case and use the understandings you get from that use case to kinda push the tech forward and you hop programmers up more and more levels of abstraction, how it then also makes things more accessible. And that's that's something that we're really excited about.

我认为最终状态下，软件开发将变得极其平易近人。人们不再需要大量编程语言和编译器方面的经验。当然，在任何人能开发专业级软件之前仍有大量工作要做。但已经能看到设计师在专业环境中从零启动项目原型，非技术人员为专业软件项目提交小补丁、错误修复或功能修改——这些现象非常有意思。

And I think in the end state, I do think that building software is gonna be way more accessible. You're not gonna have have to have tons of experience on understanding, programming languages and compilers. And I think there's still a bunch more work to do before anyone can build kind of professional grade software. That said, it's been really cool seeing people spin up projects and prototypes from scratch, designers in professional settings doing that. It's been really interesting to see nontechnical people contribute small patches and, you know, bug fixes or small feature changes to professional software projects already.

这就是氛围编程的应用场景。虽非公司主要盈利点，但随着专注提升专业开发者天花板的过程中，这个场景的规模将会持续扩大。

And that's kind of the the Vibe coding use case. Not our main use case, not where the company makes most of its money, but one that I think will become bigger and bigger as you push the ceiling of focusing on professional developers.

我很好奇您对其市场需求怎么看。我明白这不是业务重点，人们也热衷讨论这个话题。我的意思是...

I'm curious I'm curious what you think of as, like, the demand for it, though. Like, I I understand. It's not it's not your focus of the business. And I you know, people like to talk about it. I think people you know, I mean, look.

对于从未开发过软件的人来说，这感觉很酷

It feels cool to to have never built software before,

以及所有的

and all of

突然间，不知不觉中，你已经为自己创建了一个小型的待办事项应用之类的工具。

a sudden, next thing you know, you've actually created a little to do list app for yourself or something.

我个人在这方面可能与一些同事看法不同。就当前世界现状而言，我认为那五种编程用例大致可分为两类：一类是娱乐性质的，即你主要出于个人兴趣或爱好做这些事情；另一类则更偏向专业领域，比如设计师制作原型，或是服务客户的员工为专业代码库贡献错误修复。我与其他同事的分歧点在于：有些人极度热衷于终端用户编程、一次性应用和个性化软件，认为每个人都应该完全构建自己的工具。我觉得这确实很酷。

I probably differ from some of my colleagues on this personally. As the world as it exists right now, I do think that, like, kind of the two buckets of that five coding use case, one is there's, an entertainment bucket of you're you're doing these things mostly for for, like, personal, enjoyment or hobbies. And then there's also a bucket that's that's more professional, and I think that that's like designers doing prototypes or that's people that work to serve customers contributing back bug fixes to a professional code base. And the the way in which I probably differ from some of the people I work with is there's a group of people who are really, really, really interested in end user programming and throwaway apps and personalized software where kind of everyone, yeah, like, builds entirely builds their own tools. I think that that's really cool.

我认为实现这种可能性非常酷，而且很多非技术人员也会对此感兴趣。但我仍然认为，即使到了人人都能在计算机上构建东西的时代，大多数用例仍将由全球约5%的少数群体来满足——这些极度关注工具并构建工具的人，而其他人更多是使用这些工具，因为人们对这类事物的兴趣本就存在差异。所以目前商业层面上，我认为更多氛围编程（vibe coding）属于MidJourney这类娱乐范畴，有些人会短暂感兴趣后便搁置；另一部分则属于专业领域，比如那些以软件开发为生但目前不直接编码的人。

I think enabling that is really cool. And I think a lot of people who aren't technical will be interested in doing that. But I still think even if you get to a world where anyone can build things on computers, there's gonna be I think most of the use cases will still be served by like a small minority of, you know, 5% of the world that's carrying a ton about the tools and building them, and that everyone, will more use those tools because I just think that the interest in that stuff really differs amongst the population. But so yeah, right now commercially, I think that a of the more vibe coding stuff falls more into like a mid journey camp or like an entertainment camp, but it's something that some people get interested in for a bit and then kind of put it aside. And then, you know, some of it is in this professional camp of people that, you know, work on software for a living but but don't code right now.

我觉得你是对的，因为我在传统公司工作时，每当引入新软件总会引发不满。这就是我认为大多数人不会成为专业氛围程序员（pro vibe coder）的例证。不过我个人喜欢软件，所以对氛围编程很好奇。或许两三代Cursor迭代之后，我就能给自己做出些实用工具了。

I think you're right because when I worked at more traditional companies, whenever a new piece of software was introduced, everyone would get upset. So that's my case for most people not becoming, like, sort of, you know, pro pro vibe coders. I like software, though. So I'm I'm I'm vibe code curious. You know, maybe two or three generations from now in Cursor, I'll be able to make myself something useful.

你之前提到人们使用Cursor主要有两种方式：一种是查看代码时让工具辅助自动补全，另一种是直接交给任务后离开，回来查看构建结果。你最近告诉Ben Thompson，未来6到12个月内可能实现专业软件工程师20%-25%的工作属于后者——完全交由计算机端到端完成。这个数字最近有更新吗？你认为最终比例能提升到多高？

You mentioned earlier that there are these kind of two main ways that people use cursor. There is the I'm looking at code and you're helping me auto complete things, and then there is the I'm gonna give you a task and walk away and come back and and see what you've built. You told Ben Thompson recently that over the course of the next six months or a year, you think you can get to a place where maybe 20 or 25% of a professional software engineer's job might be the latter use case of just handing off work to the computer and and having the computer do the work end to end. Any updates to that number in the last month or so? And how high do you think that number can scale ultimately?

这类预测非常困难。阻碍达到100%的因素之一，是模型需要学会理解整个代码库和组织语境等新事物，并从错误中学习。目前领域内仍缺乏完美解决方案。两个候选方案：一是扩大所谓的'上下文窗口'（即大语言模型能处理的固定文本/图像范围），但存在上限；

I think these things are really hard to predict. I think some of the things that are blocking you from getting to a 100%, one is having the models learn new things, like understand an entire code base, understand the context of an organization. But yeah, learn from their mistakes and really learn new things. I still think that the field doesn't have an amazing solution for that. The two candidate solutions are one is you make the quote, unquote context windows longer, which is these large language models, they see they have like a fixed window of text or images that they can see, and then there's a limit to that.

二是通过训练模型来学习新事物——每次需要新能力时收集训练数据并融入模型。这两种方案都有重大缺陷。人类大脑会持续吸收新知识并保留（尽管部分记忆会消退），而模型出厂后其知识就固定了。因此持续学习的第一方案是极大化上下文窗口；

And outside of that, you know, it's just the the model that came off the assembly line and then that new kind of information that's put into the model's head, which is very different from humans because humans are going through the world and, like, you know, your brain is changing all the time. You're getting new things. That's kind of persists with you. And, like, obviously, you know, some memories fade away, but but it persists with you somewhat. But so candidate solution number one to the continual learning problem is just make the context windows really big.

第二方案则是反复训练模型。但两者都存在明显问题。我认为机器学习领域真正具有范式突破意义的重大创意产出率其实很低——尽管过去五年进展飞速，但像取代长上下文/上下文学习与微调的新型持续学习方式这类创意，平均可能每三年才出现一次。多模态发展也需要时间，这对编程很重要，因为你需要能点击按钮实际交互式使用软件输出。

Candidate solution number two is train the models. And so every time you want them to learn a new thing or a new capability, you go and collect some training data on that, and then you throw it into the the models mix. And both of those have big issues, I think. But that's that's one thing that's stopping you. And, you know, I think that the rate of really consequential ideas in ML that are kind of like new paradigm shifts is pretty low industry wide, even though that the, you know, the rate of progress has been, you know, really fast over the past five years.

因此要实现这种持续学习方式的突破还需要时间。多模态技术同样如此，它对编程至关重要——因为你希望能操作软件，点击按钮实际使用输出结果。

And so ideas of the form of replacing long context or in context learning and fine tuning with some other way of continual learning, I don't think that the field actually has an amazing track record of generating lots of ideas like that. I think it's sort of ideas on the rate of maybe one every three years. So I think that will take some time. I think the multimodal stuff will take time too. The reason that's important for programming is you wanna play with the software, and you wanna be able to click buttons and actually, yeah, use the output.

你希望能使用带图形界面的工具来辅助软件开发。比如，像Datadog这样的可观测性解决方案对于理解如何改进专业软件至关重要。这感觉是必要的。这些模型现在能连贯工作数分钟甚至数小时，但要像人类那样持续数周处理任务则是另一回事。

You wanna be able to use tools also to help you make software, tools that have GUIs. So for instance, you know, observability solutions like Datadog are important for, understanding how you can improve a professional piece of software. So that's that feels like it's needed. These models also, they can work coherently for minutes at a time, now even hours in some cases. But it's a different thing to work on a task for for the equivalent of a human's weeks.

因此，仅从架构角度看，验证我们能否在如此长的时间序列中保持连贯性就很有趣，我认为这会很棘手。虽然存在诸多技术障碍阻碍实现100%自动化，还有许多未知的未知因素，但乐观估计，一年左右我们可能实现将高级文本指令转化为代码库修改，覆盖现今过半编程工作。

And so just even architecturally, knowing if we're gonna be coherent over sequences that long will be interesting to see, and that, I think, will be tricky. But there are all of these, you know, all all of these technical blockers to getting to something that's a 100%, and there's many more that you could list, and there are also many unknown unknowns. And I think that in a year or so, even with just playing the game of going from a high level text instruction to changes throughout a code base playing that really well, I think if in the bull case, you know, you could probably do over half of of programming as it exists today.

是的。我看到Meter发布的研究显示AI模型持续工作时长正以惊人速度翻倍。你指出的障碍很重要，但宏观来看任务时长确实在显著提升。

Yeah. Yeah. I I see these studies that Meter puts out where they look at the average length of time that a software or that an AI model can do, and it does keep doubling at this really impressive rate. So I think the the hurdles that you identify are super important. But when you pull back, it does seem like length of task is really improving.

毕竟人类通常不会处理超长离散任务。因此大语言模型完成全天工作量正变得更容易想象。

And, ultimately, you know, humans don't tend to work on discrete tasks that are all that long. So it I do think it's getting easier for people to imagine an LLM putting in a full day's work.

预测这些确实困难。自动驾驶发展史或许能提供参考——它虽取得巨大进步，比如旧金山已有商用自动驾驶汽车。

Yeah. I I think that just, like, forecasting these things is tricky. And one, like, related field that can maybe be telling of how things will evolve here is just kind of the history of self driving, which obviously has made, you know, leaps at the bounds of advancements. And, you know, in San Francisco, we we there there are way most. There are commercial self driving cars.

据我所知特斯拉也有重大改进。但2017年人们认为自动驾驶一年内就能落地，如今仍存在重大障碍。相比当前领域讨论的其他目标，这似乎是个天花板更低的任务。

My understanding is Tesla's also made big improvements. But, you know, I remember back in 2017 when people thought, you know, self driving was gonna be done and deployed within a year, and obviously, there are still still big barriers to getting it out into the world. And that feels like a you know, as hard and as very distracting is, it does feel like a a much lower ceiling task than some of the stuff the field's talking about right now. So we will see.

有意思。我本想探讨时间线问题，不过稍后再问。现在请回答几个经典解码问题，Michael。

Yeah. Interesting. This I I do wanna sort of ask you about timeline stuff, but I'm gonna I'm gonna wait till a little bit later. Alright. Let me now ask you some of the famous decoder questions, Michael.

n eSpirit目前规模如何？有多少员工？

How how big is n eSpirit today? How many employees do you have?

我们现在大约有150人。

We're roughly a 150 people right now.

好的。对于公司未来规模，你更倾向大规模团队还是精干灵活的小团队？

Okay. And when you think about, like, how big you want the company to be, are you somebody who envisions very big workforce, or do you sort of like the smaller, nimbler nimbler team?

我们确实青睐更敏捷的团队。我认为其中的关键在于，我们希望根据当前处理的工作范围保持团队的灵活性，但这仍意味着未来几年团队规模会大幅扩张。不过我在想，是否有可能建立一个蓬勃发展的科技公司，从事真正重要的工作，而团队规模上限控制在约2000人左右，类似《纽约时报》的体量。我们很期待验证这种模式是否可行。

We do like the nimbler team. And I think the caveat there is, like, we wanna keep the team nimbler for the scope of work that we're tackling, but that will still mean growing the team a lot over the next couple of years. But, yeah, I wonder if it will be possible to build, you know, a thriving technology company that do does really important work with, you know, a maximum team size of maybe 2,000 people or something like that. You know, something of the size of the The New York Times. And, you know, we're excited to see if that is if that is possible.

但毫无疑问，我们需要在当前员工数量的基础上大幅扩充团队。

But, definitely, we need to grow a lot from from our current headcount.

你们的组织架构是怎样的？你们有几位联合创始人，如何划分职责？

What is your org chart like? You have a a few cofounders. How do you all divvy up your responsibilities?

公司最大的两个板块是工程部门和研发相关领域（广义上的研发），以及市场推广和客户服务板块。这家公司从多位联合创始人和强大创始团队的配置中获益良多。我们采取分头负责的方式——特别要提到创始团队中有批杰出人才，他们在早期市场开拓方面做出了非凡贡献，这部分成绩几乎完全归功于特定的创始成员。

The two biggest areas of the org are engineering and the research side of things, like r and d generally, and then the go to market side of things, so serving customers. This is a company that has really benefited from having set of co founders and a a big, very capable founding team. And so there's a lot of, across that scope kind of dividing and conquering. In particular, I think that there's a really important set of people on the founding team who have done, phenomenal work in in building out that early part of the go to market side of things. And a lot of that is just entirely people on the founding team is kind of entirely credited to, like, a a subset of it.

因此业务上存在大量分工协作。同时我认为，当聚焦到技术层面时，四位联合创始人会高度集中精力，几乎将所有资源都倾斜到这个业务板块。我们很幸运处在一个能开发出极具价值产品的时代，而最关键的制胜点就是打造该领域最出色的产品。这使得我们能在保持其他部门相对精简的同时（尤其是相对于工程研发部门的比例），依然实现快速成长。

And so there's a lot of dividing and conquering across the business. At the same time, I think actually amongst once you, like, zoom into the technical side of things, there's, like, an intense focus from the four cofounders on that and really putting kind of all eggs in in one basket of that side of the business. And so I think that we're lucky enough to be in a time where there are really, really useful products to build in our space. And I think that the highest order bid, the thing you cannot mess up, is having the best product in the space. And so we've been able to be relatively lean in other parts of the business, especially relative to our scale, but also as a ratio to to engineering and research and still be able to grow really far.

你个人最喜欢主导哪块业务？哪些工作你会亲力亲为，如果别人接手你会介意？

What part of the business do you like keeping for yourself? Like, where do you like getting your hands dirty, and would you be mad if someone tried to take it away from you?

我投入大量时间参与团队建设，我们认为招聘至关重要，尤其是对IC（独立贡献者）的招聘。是的，独立贡献者。

I spend a lot of time trying to help how I can in in growing the team, and we think hiring is incredibly important, and especially the hiring of ICs. Individual contributors. Yeah. Yeah. Individual contributors.

科技公司衰败的一种模式是：最优秀的独立贡献者开始感到被边缘化，认为失去对公司的影响力，人才密度随之下降。在技术领域，无论管理层多么优秀，如果实际执行者不够出色，能取得的成就终归有限——管理层的发挥空间其实存在天花板。因此我通过亲自参与招聘来贡献力量。事实上前75名员工都是联合创始人亲自招聘的，当时我们甚至没有专职招聘人员。

I think that one way technology companies die is that the best ICs start to feel disengaged, like they don't have control over the company, and the talent density lowers. And then I think that if you're working on technology, like, no matter how good kind of the management layer is, if just you don't you have less than excellent people doing the real work, I think there's only only so much you can do. Like, I think that the dynamic range of what management can do is is is kind of limited. And so I'd like to help how I can by spending a a bunch of time on hiring. And we actually got to maybe 75 people just with the co founders hiring and without hiring functional recruiters.

现在我们有优秀的招聘团队协助，与紧密合作的招聘专员配合。我在这方面投入大量时间，同时也会参与工程和产品板块的工作，这两个是重点领域。当然还有一长串次要事务需要处理。

And so now, you know, have have fantastic people helping us hiring. I have people on the recruiting side of things that work with us closely. Spend a bunch of time on that and then try to help how I can on the engineering and and product side of things. And those are the two biggest areas of focus. And then there's a long list of long tail things.

确实如此。

And yeah.

没错。你还相当年轻。我想你才25岁，却已经需要做出许多重大决策，比如融资、收购，还有你刚完成的所有招聘决定。你如何尝试做决策？是否有固定的框架，还是全凭临时发挥？

Right. You're fairly young. I think you're 25 and have had to make a lot of really big decisions about raising money, making acquisitions, all those hiring decisions that you just made. How do you try to make decisions? Do you have a framework that you use, or is everything ad hoc?

是的。我不确定是否存在单一框架。我认为，我们常用的一些有效方法是尽可能在组织上下层级间收集意见。这不仅适用于我个人，也适用于公司所有决策——逐渐确立明确的直接责任人（DRI），同时让多方参与提供决策参考。

Yeah. I'm not sure there's one framework. I think that, you know, some pretty common devices that help us is we try to do our best to to farm from descent kind of all up and down the group, the org. And this is not just for me. It's try try to do it for kind of all decisions in the company of having increasingly like a very clear DRI and then lots of people who are kind of inputs to the decision.

每个决策都相当独特。其他众所周知的实用方法包括评估决策的风险等级和可逆性。特别是在我们这样高速发展的垂直领域，你能投入的时间和获取的信息本就有限。此外，清晰传达决策内容也是个好方法，它能迫使你理清自己的思考逻辑。

Every decision is pretty unique. I think that other devices that are well known, that are helpful, are kind of understanding how high stakes the decision is and how reversible it is. I think that, especially when, you know, you're in a vertical like ours with the speed that it's moving, there's just a limit on the amount of time and the amount of information you can gather on each thing. And then, you know, other devices like, you know, clearly communicating the decision and using that as a way to kind of force clarity for how how you thought thought it through.

我们需要短暂休息一下，马上回来。

We need to take another quick break. We'll be right back.

本节目由OpenPhone赞助。作为企业主，公司的电话系统应便于客户与您沟通。这本是理所当然的事，但现实往往并非如此。OpenPhone是一款能简化和扩展客户通讯的商业电话系统，通过手机或电脑应用实现团队共享号码、协作处理客户来电和短信。

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若您已有其他服务商的号码，OpenPhone将免费为您转移。OpenPhone——不错过任何来电，不流失任何客户。本节目亦获Framer支持。

And if you have existing numbers with another service, OpenPhone will port them over at no extra charge. OpenPhone. No missed calls. No missed customers. Support for the show comes from Framer.

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条款与限制可能适用。

Rules and restrictions may apply.

在Sierra，发现顶级品牌运动装备的超值优惠，比如高品质健步鞋，或许还能带来更多惊喜发现。

At Sierra, discover great deals on top brand workout gear, like high quality walking shoes, which might lead to another discovery.

四万步了宝贝，现在谁才是赢家啊凯伦？

40,000 steps, baby. Who's on top now, Karen?

你把办公室步数挑战玩过头了。不过别担心，Sierra还有瑜伽装备，或许能帮你找到内心的平静。以意想不到的低价探索顶级品牌。

You've taken the office step challenge a step too far. Don't worry, though. Sierra also has yoga gear. It might be a good place to find your zen. Discover top brands at unexpectedly low prices.

Sierra，让我们动起来吧。

Sierra, let's get moving.

我们继续与AnySphere首席执行官迈克尔·特鲁尔对话。刚才讨论了核心解码器问题，现在我想多谈谈招聘。特别是AI人才争夺战如何影响像AnySphere这样的公司。既然你提到了，我们就深入聊聊招聘。有传言说OpenAI曾考虑收购你们，鉴于扎克伯格近期的疯狂挖角，我不得不问——马克邀请你去他在太浩湖的别墅了吗？

We're back with AnySphere CEO, Michael Truel. We just covered the core decoder questions, but now I wanted to talk a bit more about hiring. In particular, how the AI talent wars are affecting a company like AnySphere. Well, let's talk a little bit more about hiring since you brought it up. There has been talk that OpenAI had considered acquiring you, and I have to ask, given his recent spending spree, has Mark Zuckerberg invited you to his house in Tahoe?

没有。没有。

No. No.

没有？他...他没带着两亿美金签约奖金来找你说'迈克尔不如加入我们？我们正在打造超级智能'？

No? He he's not he's not coming around with his $200,000,000 signing bonuses saying, Michael, why don't you kinda come over here? We're building super intelligence.

对我们来说这像是毕生的事业。很幸运能拥有这样的技术阵容、创始团队配置、加入我们的伙伴，以及产品的发展态势——这一切让我们有能力执行'自动化编程'这个雄心勃勃的目标。时间会证明我们是否能成功。但作为长期从事编程和AI工作的人，能用AI重塑编程、帮助人们实现各种数字创造，感觉就像是我们的天职。这应该也是这项技术最棒的商业应用之一。

This is this for us is is kind of life's work territory. So, yeah, feel really lucky to be, you know, have the technology lineup, kind of the initial founding team lineup, the people that have decided to join us, and the way things have gone on the product to have the pieces in place to execute on this ambitious goal of automating programming. And time will tell if we're gonna be the ones to do that. But as people who have been programming for a long time and working on AI for almost as much, being able to reinvent programming and help people build whatever they wanna build on computers with AI kind of feels feels perfect for us. And know, it feels like one of the best commercial applications of this technology too.

所以我认为如果在这个领域取得成功，也能大幅推动其他垂直领域和行业的发展。所以...不。

So I think that if you can succeed in that, you can also push the field forward in big ways, you know, for for other verticals and other industries too. And so no.

听起来你们真的很想保持独立。最近Meta的招聘狂潮是否明显增加了你们的招聘难度？

Yeah. It sounds like you really wanna stay independent. Yep. Has Meta's recent hiring spree made it noticeably harder for you to recruit lately?

不，其实不是。研究团队尽量保持较小规模。我的意思是，相对于所从事的工作，整个公司规模都算小，尤其是研究团队。人们在招聘决策上各有不同的考量方式。

No. I not really. The research team, try to keep fairly small. I mean, the whole whole company is kind of small relative to what it's doing, but the research team especially. People think through hiring decisions in in different ways.

而且，你知道，我们提供的环境最吸引那些想加入特别小团队的人。专注于用AI解决现实世界的问题。我也想待在一个结合了两者的地方——我们是个有点奇怪的公司。你提到了API模型团队开发的某些产品，但我觉得我们像是介于基础模型实验室和普通软件公司之间的实验性企业，既要精通产品开发，又要擅长模型研发，让两者相互促进。

And, you know, what we have to offer is most appealing to people who wanna be a part of an especially small team. Working on something focused, kind of solving problems with AI out in the real world. I wanna be in a place too that marries like, we're kind of this weird company. You you, you know, talked about some some products that are being made by some of the the great folks that work on, like, kind of the API models. But I think that we're like this weird experimenting company that's smack dab in between the Foundation Model Labs and normal software companies, where we try to be really excellent at both the product side of things and the model side of things under one roof and have those feed into each other.

所以我们吸引的是特定类型的ML研究员或工程师。对他们来说，参与这种事业本身比某些其他因素更重要。

And so we appeal to, I think, a a certain type of ML researcher or ML engineer. And for them, I think it's kind of about being being part of this and a little bit less, you know, like some of the other things.

最后一个招聘问题。本周有报道称，你从Claude Code挖来的两位负责人在加入Cursor几周后又回去了。能谈谈发生了什么吗？

One last hiring question. It was reported this week that two folks who used to run Claude Code that you'd recruited to come over to Chrisser left back after a couple weeks. Can you speak at all to what happened there?

Kat和Boris非常优秀，他们在Cloud Code还有很多未竟事业。据我了解，他们就是那个项目的灵魂人物，那是他们的心血。作为花了三年半时间从零打造产品的人，我理解这种归属感。他们还有很多想实现的，对此充满热情，所以决定留下。是的。

Kat and Boris are are awesome, and I think that they have a lot left to do on Cloud Code. And they're really, you know, as I understand it, just the people behind that, and and that is their creation. And as someone who's been working on something for three and a half years from inception, you know, I kind of understand the ownership that comes with that. And they have a lot left to do, and they were they were excited about that, and so they've decided to stay. Yeah.

明白了。你提到Cursor处于大型实验室和其他使用你们软件的初创公司之间的特殊位置。在招聘时你会如何描述Cursor的文化？

Cool. It seems like you're you know, you were mentioning this interesting position that you sit in in between the the big labs and other startup companies who are, you know, using your software. How do you describe Cursor's culture when you're recruiting people?

可能不出所料，我们对流程和层级制度持怀疑态度。虽然随着目标越来越宏大，需要更多协调，但在公司当前规模下，我们尽量保持精简。这是个崇尚知识诚实的团队，大家可以毫无压力地公开批评和反馈工作。

Perhaps unsurprisingly, we are process skeptical and kind of hierarchy skeptical. And so, you know, we need to as we do more and more ambitious things, like, more and more coordination is required. But for, like, a certain level of thing you know, the scope of the company, we try to be, like, pretty light on each of those. I think it's a very intellectually honest group. It feels very low stakes to criticize things and just be be open very publicly about about feedback on work.

团队充满求知欲。大家从事这份工作的终极目标是实现编程自动化。与工作生活平衡无关——因为我们希望在这里既能平衡生活又能做出卓越成果。这里没人把工作当成'仅仅是一份工作'，大家都对此充满激情，现在正是打造技术的特殊时期。

It's a very intellectually curious group. You know, I think that people are interested in doing this work, you know, for the end goal of automating programming. And separate from any work life balance things, because we want this to be a place that's all of us work life balance can do can do great work. It's a place where I think no one really treats it like, quote, unquote, you know, so far, like, just just a job. Like, they're really, really excited about this, and I think it's kind of a special time to be building technology.

所以应该寻找不把工作当'仅仅是一份工作'的角色。我们非常专注且低调。由于对外沟通很少（这点急需改进），外界大多只知道Cursor是那个快速增长的产品，了解些顶级指标数据。而内部我们认为，招聘既要雄心勃勃又要谦逊低调、头脑清醒的人很重要，因为外界噪音太多了。

And so one should try to seek out a role where you can don't treat it like just a job. I think it's very focused and understated. Like, I think from the outside, partially because of how little communication we do with the outside world, and we need to get much better at that. I think mostly people know Cursor as, oh, that thing that grew really fast and kind of know about, you know, top level metrics and things like that for just, like, how how fast the adoption has been. And internally, we've thought that it's really important to hire people who are while while they might be very ambitious, are are very humble and pretty understated and pretty focused and level headed because there's noise left and right.

我认为保持清晰聚焦、埋头苦干对团队成员的幸福感和执行力都至关重要。是的，这些就是描述当前团队的一些特质。

And I think that, yeah, just having kind of clear focus and putting your head down is actually really, really important for people being happy in this space and also just for the execution of the team. Yeah. Those are those are some things to describe the the current group. So

你提到与外界沟通的问题。我认为，Cursor的历史基本上就是一部取悦客户的历史。但最近你们确实有过一次定价方式的调整，引发了用户强烈不满。当时你们从固定费用转向了更多基于使用量的定价模式，有些人因未察觉而超出了使用限额。从这次经历中你们学到了什么？

You you mentioned communicating with the outside world. I think, you know, your cursor's history is is is mostly just a history of delighting its customers. But you did have this moment recently where you changed the way you price things, and and folks got pretty mad. And, basically, you just moved from a set fee to more usage based pricing, and some people ran over their limits without realizing it. What what did you learn from that experience?

是的。我们从中吸取了很多教训，也有很多需要改进的地方。先说明一下，Cursor的定价模式从创立之初就基本如此：用户订阅后，会在订阅期内获得一定次数的AI使用额度。随着功能迭代，定价结构也在演变。

Yeah. I think that there was a lot to learn from that and a lot on our end that we we need to improve on. To set the stage show, the way, Cursor pricing has worked even, you know, back when when Cursor first started is by and large, you sign sign up for a subscription, and then you get an allotment of a certain number of times you can use the AI over the course of, you know, your subscription term. And the the pricing evolved. You know, features were added.

功能调整会影响这个限额——有时用户可以选择支付额外费用来突破限制，有时则不行。与此同时，单次使用AI带来的价值与底层成本也发生了巨大变化。一个重要转变是：现在当用户调用AI时，其工作时间已经从秒级延长到分钟级乃至小时级，这个增长速度非常快。

Features were changed kind of like up and down that that limit has or like, you know, there are there are different ways, like, you have been able to pay down that limit or not pay down that limit over time. And what's happened in parallel is kind of using the AI once, what that means, the value that gives people and the underlying costs, in some cases, has changed a lot. One big switch there for us is that increasingly when you, quote, unquote, use the AI, the AI is working for longer and longer and longer. And so you called out that, that chart that you've seen where, you know, it's showing the kind of max time an AI can work, and it's gone from, you know, seconds to minutes to hours at this point, and it's gone up very fast. We're kind of front lines of that, where now when you ask the AI to go do something or answer a question, it can work for a very, very, very long time.

这彻底改变了AI能提供的价值——从解答简单编程问题到为你编写300行代码。相应的底层成本结构也变了，尤其是成本波动幅度而非中位数。我们打包推出了一系列定价调整，最受关注的是从按请求次数计费转向按实际计算资源消耗计费。需要澄清的是，基于使用量的定价本就是Cursor原有体系的重要组成部分。

And that changes the value it can give to you. You can go from just asking a simple programming question to having it write 300 lines of code for you, that also changes the underlying costs. And in particular, less the median and more the variance of those costs. So, yeah, we bundled together a series of of pricing changes, and the one that, garnered the most attention was switching from a world where kind of the the monthly allotment is in requests to it's, you know, in the underlying compute that you're spending. And one one thing to knit on on what you said is that actually usage based had been a big component of of Cursor before.

随着用户对AI的使用频率持续攀升，他们开始触及使用上限，我们本意是提供突破限制的弹性方案。这次改革调整的是用量计费的结构框架，从按请求次数转为按计算资源计量。当然，这个变更本可以传达得更清晰明了。

Because over the life of Cursor, people have just used AI more and more and more and more. Then, you know, they started running out of limits, we wanted to give people a way to kind of burst past that. What this this didn't is it changed kind of like the structure of also how that usage pricing worked, it's not on a request basis. It's on the underlying compute basis. And definitely, that could have been, communicated legions better.

这次经历让我们获益良多，也暴露出许多未来需要改进的环节。

There's a lot we learned from from that experience and a lot we need to show up on in the future.

确实。对消费者来说，理解用量计价模式尤其困难，因为他们习惯了Spotify和Netflix那种每月支付10到20美元就能无限畅用的模式。但AI的经济模型完全不同。

Yeah. I think it's it's hard for consumers in particular to understand usage based pricing because they're used to Spotify and Netflix where they pay their

没错。

Yep.

（继续）这种'自助餐式'的商业模式在AI领域根本行不通。

10 or $20 a month, and they and it's sort of all you can eat. But the economics of AI just don't really work that way.

我们这个领域的发展会很有趣。就消费者聊天应用市场而言，至少目前为止...（思考）观察用户平均计算资源消耗随时间变化的曲线会很有意思。不过如果过去18个月这个曲线相对平稳，我也不会感到意外。虽然我不掌握内部数据，但从模型规模角度看，现在确实能在缩小模型体积的同时保持同等智能水平。专业用户使用的模型体积可能反而随时间在缩小。

It will be interesting to see how things play out in our space in particular. So I think that for, like, the consumer chat app market, so far, at least, there's been yeah. I I it would be interesting to see how the the curves of just, how compute per user over time has gone up. But I wouldn't be that surprised if it's been pretty flat over the past, you know, eighteen months or so, where the original Cheap d four, don't I'm not privy to any inside information, but it seems like there have been big gains from a model size perspective where you can actually miniaturize, models and get the same level of intelligence. And so I think that the model that most professional users are using in something like AttacheevT has actually maybe gone smaller over time.

计算资源使用量确实下降了。但在我们领域，我认为对单个用户而言，计算需求很可能会上升。存在这样一种可能性：令牌成本下降速度不够快，最终会变得更像AWS的计费模式，而非按席位的生产力软件定价——这点仍有待观察。但需要强调的是，我们坚信给予用户选择权极其重要。因此，我们既想成为追求极致体验者的首选——只要你愿意调高所有参数，获得最优质（也最昂贵）的AI编程体验；

The compute usage has gone down. But in our space, yeah, I think that there's just for one user, I think the compute is probably gonna go up. And there's a world in which the token costs don't go down fast enough, and it starts to become a little bit more like AWS costs and a little bit less like per seat productivity software and still remains to be seen. But, one thing to note is that we do think it's really, really, really important to offer users choice. And so we wanna be the best way to code with AI if you just wanna turn on all the dials and just get the best, most expensive experience.

同时也想成为预算明确者的最佳选择——如果你只想支付固定订阅费，获得该价位能提供的最优服务。即便是20美元的专业个人计划，绝大多数用户也从未触及月度限额，自然不会收到要求开启用量计费的提示。

We also wanna be the best way to code with AI if you wanna just pay for a predictable subscription and get the best thing that that, you know, price can offer you. Even for the, you know, the main individual plan, the $20 pro plan, vast majority of those users don't hit their monthly limits. And so don't get aren't hit with a, you know, a message saying you need to turn on usage pricing or not.

我就是这类AI用户——从没触过限额，反而让我觉得自己该多用用。你知道的，我正努力...

That's the kind of AI user I am. I never hit my it makes me feel like I need to be using it more. You know? I I I'm trying to There

前5%的用户与中位数用户之间存在巨大差异。有些人对AI的依赖程度确实超乎寻常。

is a really, really big difference difference between between the top 5% and, you know, a median a median user. So some people are very, very, very AI for it.

最后几个问题——我想了解你对AGI的信仰程度。早先谈话时，你指出了构建更先进系统时面临的实际技术难题，这些都是AI领域尚未解决的：比如扩展上下文窗口、赋予系统类人学习能力等。然而业内许多人坚信2027-2028年世界将彻底改变。

Well, coming to my last couple of questions here, I I wanna try to get at how AGI pilled you are. Because when we were talking earlier, you're sort of identifying all these very real technical problems in building more advanced systems that are just truly unsolved problems in AI. You know, the the size of the context when you get giving these systems longer memory, helping them learn the way that a human might be able to learn. We we don't know how to do that yet. And yet there are lots of folks in the industry who believe that by 2027, 2028, the world looks very, very different.

在'巨变即将发生'与'这是持续数十年的渐进过程'的光谱上，你如何定位自己的观点？

So where do you sort of plot yourself on the spectrum of people who think that everything is absolutely about to change and we're sort of at the start of a process that's gonna take decades?

我们押注于'混沌中期'——既认同这将耗费数十年，也坚信AI将成为超越性的技术革命（可能比过往任何变革都更深远）。创办Cursor时很有趣，我们收到两种截然相反的反馈——

I think we're kind of this bet on the the messy middle where we do think it's going to take decades. We do think that, nonetheless, AI is gonna be this transformational technological shift for the world, bigger than, you know, maybe yeah. Just, you know, a very, very, very big technological shift. And, when we started working on Cursor, it was funny. We would get these kind of two dual responses.

其中一种观点现在逐渐式微（随着首批触达数十亿用户的AI产品出现）。但在2022年初，我们常听到：'为什么搞AI？感觉没什么可做的'；另一派来自密切关注AI的亲友同事则会问：'为什么要开发特定应用（无论是CAD还是编程）？AGI会在某年（比如2024-2025）颠覆这一切'。

And I think one is now increasingly falling out of favor just, you know, with the rise of the first AI products that really reached billions of people. But, early twenty two, we would get kind of two reactions. One reaction was, why are you working on AI? You know, I'm not sure that there's really much to do there. The other reaction that we would get, because we we did have close friends and colleagues who are who are very interested in AI, is why are you working on, you know, insert x application, whether it be CAD or whether it be programming specifically.

我们认为真相存在于'锯齿状顶峰'的中间道路。审视AI发展历程，真正推动进步的突破性创意其实寥寥（尽管细节填充工作浩如烟海）。尽管过去十五年深度学习领域人才济济，但具有重大影响力的原创思想增长速率并未显著提升。

You know, AGI is gonna wipe all of this stuff out in, you know, y years. You know, maybe it's 2024, 2025. We think it's this middle road of this jagged peak where if you actually peek under the hood at what's driven AI progress so far, again, I think that there's been a a few ideas that have really worked. There's been lots of details to fill in between, but there have been a few really, really important ideas. I think that despite the number of people that have worked on deep learning over the past decade and a half, the rate of idea generation in the field, like really, really consequential idea generation in the field, hasn't budged that much.

我们仍需攻克大量实质性的技术难题。人们容易拟人化这些模型，看到它们在部分任务上展现超凡能力后，就假定其能胜任一切。但我坚信这是条充满锯齿的攀登之路，需要数十年渐进发展。

And I think that there are lots of real technical problems that we need to grapple with. And so I think that there's like this urge to anthropomorphize these models and, see them be amazing and human level or super superhuman at some things and then think that, you know, they will just be great at everything. And I I really think it's this this very jagged peak. And so I think it's gonna take decades. I think it's gonna be progressive.

我认为，我们对Cursor最雄心勃勃的期望之一是，如果成功实现编程自动化并打造出卓越产品，就能仅凭最小必要意图在计算机上构建事物。或许这一成功及实现过程中需探索的技术，也能普遍推动AI进步。我思考的类比实验是：若身处2000或1999年想推动AI发展，最佳选择之一就是打造类似Google的项目使其成功，并将其研发成果开放给世界。某种程度上，我们正在尝试做的正是这类事情。

I think that, one of our most ambitious hopes with Cursor is if we are to succeed in automating programming and building an amazing product here, that makes it so you can build things on computers just with the minimal intent necessary. Maybe the success of that and the techniques that we need to figure out in doing that can also be helpful for pushing AI for progress forward in general. And I think that the experiment to play back here is if you were in 2000 or 1999 and you wanted to push forward AI, one of the best things you could do is work on something that looks like Google and make that successful and make that R and D available to the world. And so, you know, in some ways, one of the ways, at least, I I think about what we're doing is trying to do that.

但好吧。听起来你认为不会仅靠一次大规模参数训练就突然觉醒出机器之神？

But okay. So it sounds like you don't think that there's just gonna be one big new training run with, like, a lot more parameters, and we're gonna wake up to a machine god.

时间会证明一切。我猜测确实如此。保持健康怀疑态度很重要——我们对这些事物的认知终究有限。但我的最佳推测是：这会比预期耗时更久，但最终仍将引发重大变革。

You know, time will tell. My guess yeah. And I think it's important to, you know, have healthy skepticism about how much you can know with these things. But, my best guess is that, it will take longer than that yet also still be this this big transformational thing.

好的。最后个问题：今天我们多次谈及预测的困难性，所以不会要求你疯狂预测五年后的Cursor。但展望两年后，你希望实现哪些当前尚未完善的功能？

Alright. Well, last question here. We've talked a couple of times today about how hard predictions are in general. So I'm not gonna ask you to do something crazy like predict what Cursor is gonna look like five years from now. But when you think about it maybe two years from now, what do you hope it's doing that it isn't quite doing yet?

短期内我们期待这样的场景：能将越来越多工作委托给高效'人工助手'，并打造极致愉悦的协作体验来协调这些智能体。另一个长期关注但具风险的想法是：当更多工作委派给AI时，你会面临是否逐行审查代码的困境——在专业场景中，完全无视代码或逐行检查都不可行。

I think a bunch of things. So I think in the short term, we're excited about a world where you can delegate more and more work to kind of very fast, helpful helpful humans, and you can build a really amazing experience for making that work delightful and, orchestrating work amongst amongst these agents. Another idea that we've been or I've been interested in for a long time, which is a bit risky, is, you know, I think that if you can get to a world where you're delegating more and more work to the AI, you'll start to run into an issue, which is, do you look at the code? And are you reading everything line by line, or are you just kind of ignoring the code wholesale? And I think that neither closing your eyes and ignoring the code entirely in a professional setting or reading everything line by line will really work.

因此需要中间方案。编程语言可能向更高抽象度和非形式化演进——本质上编程语言只是程序员精确传达指令的界面，也是理解软件运行逻辑的窗口。未来编程语言或将高度压缩，从百万行代码缩减至数十万行。

And so I think you'll need this this middle ground. And I think that that could look like the evolution of programming languages to be higher level and to be less formal. And, you know, all that a programming language really is is it's a UI for you as a programmer to specify, you know, exactly what you want the computer to do. And it's also a way for you to look at and read exactly how the software works right now. And, yeah, I think that there's a world where programming languages will evolve to be much higher level, more compressed instead of millions of lines, know, hundreds of thousands of lines of code.

未来构建软件的重要方式可能是阅读、指向并编辑这种高阶语言。这触及公司核心理念：模型侧需大量工作（领域会解决部分，我们负责部分），但终极目标是解决'如何将思维具现为屏幕内容'的界面难题。

And, you know, I think that, for a while, an important way you build software is you could, read and point at and edit that kind of higher level programming language. And I think that this also kind of gets at a bigger idea that's behind the company of there's all this work to do on the model side of things. The field's going to do some of that. We're going to try to do some of that. But then the end state of what we wanna do is also this UI problem of how do we get the stuff that's in your head onto the screen.

仅通过聊天框构建软件的愿景虽强大（这种极简界面能走很远），但非终极形态。专业软件开发需要更强控制力——既要能精调像素级细节，也要能剖析逻辑并精细编辑。

And I think that the the vision of you just entirely built software by typing into a chat box is is powerful. Like, I think that that's a really simple UI. You can get very far with that. But I don't think it can be the end state. You need you need more control, when you're building professional software.

这需要重构新型界面，当前解决方案就是编程语言本身。我认为它们必将持续进化。

And so you need to be able to kind of point at, you know, different elements on the screen and be able to, you know, dive into the tiniest detail and change a few pixels. You also need to be able to point at parts of the logic and understand exactly how the software works and be able to edit something very, very fine grained. That requires rethinking new UIs for these things, and the UI for that right now is programming languages. And so I think that they're they're gonna evolve.

好的。你们正在推进诸多迷人项目。Michael，感谢做客Coder节目。

Alright. Well, a lot of fascinating things that you're working on. Michael, thank you for coming on to Coder.

谢谢邀请我。

Thank you for having me.

感谢迈克尔抽空与我交谈，也感谢各位的收听。希望你们喜欢这期节目。如果想告诉我们你对本节目的看法或希望我们探讨的其他话题，欢迎来信。你可以发邮件至Decoder@TheVerge.com联系团队，他们真的会阅读每一封邮件。

Thank you to Michael for taking the time to speak with me, and thank you for tuning in. I hope you liked it. If you'd like to let us know what you thought about this show or what else you'd like us to cover, drop us a line. You can email the team at Decoder@TheVerge.com. They really do read every email.

或者你可以直接在Threads或Blue Sky上联系我。我在Threads上的账号是crumbler，在Blue Sky上是Casey Newton.b sky.social。不太顺口对吧？Decoder还有TikTok和Instagram账号，你可以在DecoderPod找到我们。

Or you can hit me up directly on threads or blue sky. I'm at crumbler on threads, and I'm at Casey Newton dot b sky dot social. Not very catchy, is it? Decoder also has a TikTok and an Instagram. You can check those out at DecoderPod.

这些平台都很有趣。如果你喜欢Decoder，请分享给朋友并在你获取播客的地方订阅我们。Decoder是The Verge出品，隶属于Vox Media播客网络。节目由Kate Cox和Nick Statt制作，Ursa Wright负责剪辑。

They're a lot of fun. And if you like Decoder, please share it with your friends and subscribe wherever you get your podcasts. Decoder is a production of The Verge and is part of the Vox Media Podcast Network. Decoder is produced by Kate Cox and Nick Statt. The show is edited by Ursa Wright.

Decoder的片头音乐由Breakmaster Cylinder创作。下次见。

The decoder music is by Breakmaster Cylinder. See you next time.