GitHub首席执行官称AI编程将"长期存在"

本节目由ServiceNow赞助支持。我们致力于让人们从事他们真正想做的充实工作。因此这则广告是由真人撰写并朗读，而非AI。你知道人们不想做什么吗？那些无聊的琐碎工作。

Support comes from ServiceNow. We're for people doing the fulfilling work they actually wanna do. That's why this ad was written and read by a real person and not AI. You know what people don't wanna do? Boring busy work.

现在通过ServiceNow平台内置的AI代理，您可以自动化企业各个角落（IT、人力资源等）数百万重复性任务，让员工专注于他们想从事的工作。这就是让人工智能代理为人类服务。轮到您行动了，请访问servicenow.com。

Now with AI agents built into the ServiceNow platform, you can automate millions of repetitive tasks in every corner of your business, IT, HR, and more, so your people can focus on the work that they wanna do. That's putting AI agents to work for people. It's your turn. Visit servicenow.com.

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，那些故事会成为多年内部笑话。而现在又多了一个爱上它的理由——为让全车人都开心的鸡肉三明治停车。Culver's新推出的系列，无论是酥脆、香辣还是烤制款，均采用100%全白肉鸡胸，配以清凉爽脆的生菜、熟番茄、 creamy蛋黄酱和完美脆嫩的新鲜腌黄瓜，夹在烤制的布里欧面包中。搭配清爽的雪碧，这将成为值得铭记的午餐停留站。

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.

这类餐食能让兴致持续高涨，安全带也愿多系一会儿。无论您前往何处，都请停驻这个让所有人欢聚的站点。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.

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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CIPIC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 CIPIC protected. Investing involves risk, including loss of principal.

证券交易通过Robinhood Financial LLC会员CIPIC账户提供，其为注册经纪交易商。

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

欢迎收听《解码器》。我是Alex Heath，本周四的特邀主持兼The Verge副主编。本周是我们的AI编程主题周。您刚收听了我的朋友Casey Newton对Cursor CEO Michael Cherrell的访谈，现在我将与GitHub CEO Thomas Dunka展开对话。从许多方面来说，GitHub Copilot引爆了我们正在经历的这场AI编程热潮。

Welcome to Decoder. This is Alex Heath, your Thursday episode guest host and deputy editor at The Verge. It's AI coding week here at Decoder. You just heard my friend Casey Newton's interview with the CEO behind Cursor, Michael Cherrell, and now I've got a conversation with GitHub CEO, Thomas Dunka. In many ways, GitHub copilot set off the current AI coding boom we're all living in.

但自Thomas一年前做客节目以来，氛围编程(vibe coding)的兴起已将焦点转向Cursor和Windsurf等新平台。您将在对话中听到，Thomas对行业竞争和GitHub在未来软件开发中的角色有着深刻思考。现在正是进行这场对话的好时机。Thomas对行业走向有着鸟瞰视角。我想了解他认为软件工程师的角色将如何随AI持续演变，以及何时我才能安心尝试氛围编程。

But since Thomas was on the show a year ago, the rise of vibe coding has shifted the buzz to newer platforms like Cursor and Windsurf. As you'll hear in our conversation, Thomas is thinking a lot about the competition and GitHub's role in the future of software development. It's a good time to be having this conversation. Thomas has a bird's eye view of where the industry is headed. I wanted to know how he thinks the role of a software engineer will keep changing with AI and when I'll feel comfortable vibe coding myself.

毕竟这是《解码器》节目，若不询问他作为微软旗下独立公司运营GitHub的现状就太遗憾了。好了，GitHub CEO Thomas Dumpka的访谈现在开始。Thomas，非常感谢您做客节目。

This is Decoder after all, so I couldn't have him on the show without asking what life is like these days running GitHub as its own company within Microsoft. Okay. GitHub CEO, Thomas Dumpka. Here we go. Thomas, thanks so much for being on the show.

自从一年前你和Nely最后一次做客《解码器》以来，我有太多要和你跟进的话题。但首先，我认为去年至今最大的转变就是这股我们《The Verge》和《解码器》节目里不断讨论的'氛围编程'浪潮。作为一个从未编写过任何软件的人，我理解的氛围编程就是未来某天我能完全不需懂代码就能创建应用或网站。这让我非常兴奋，但感觉我们还没完全达到那个阶段。你认为行业何时能发展到让我这种小白真正从零开始'氛围编程'的程度？

I have a lot to catch up on with you since a year ago when you were last on Decoder with Nely. First though, I think since last year, the biggest shift has been this rise of vibe coding that we're talking about nonstop at The Verge and on Decoder. And when I think about vibe coding as someone who has never engineered any software in my life is the moment where I'll be able to create an app or a website without needing to understand coding at all. That to me is very exciting, but it doesn't feel like we're quite there yet. Do you have a sense of when the industry will get to that point where someone like me can literally Vibe code something from scratch?

这完全取决于你要构建什么。比如小型项目我们已经实现了——你完全可以用GitHub Spark或Lovable这类工具做出贪吃蛇游戏或《Pong》这样的小型应用。但复杂度提升后，就需要系统架构设计、数据库等专业知识了。

It all depends what you're going to build. Right? Like, I think we are there for small things. I challenge you to use something like GitHub Spark or Lovable, and you can probably build, like, a snake game or Pong or like little scoped apps, and I think we're already at that point. And then as you get more complexity, alright, it requires you to have certain systems understanding, you know, architecture design, databases, and all those kind of things.

我们正越来越接近这样的未来：网络编程平台会提供更多我们称之为'基础元件'的功能。去年十月GitHub Universe发布的GitHub Spark最初只能创建基于JavaScript的前端应用，现在已能生成全栈应用——包含后端、数据库，还能对接AI模型。短短九个月就实现了巨大进化，这类平台的能力将呈指数级提升。但核心挑战在于：从零创建是一回事，而修改现有系统、定位功能模块和测试用例则是完全不同的命题。

I think we're getting closer and closer to a point where you get more of these, as we call them, primitives, supported by the web coding platform. So if you take something like GitHub Spark, and when we launched that at GitHub Universe, last October it was only creating front end applications, something that runs in your browser with a language called JavaScript, so it could do things that work in a browser, it had a little bit of back end storage. Now the version of GitHub Spark actually generates a full stack application, so it also has, you know, a back end and a and a database, and it can connect to AI models and those kind of things. And so it got more and more complex over, like, what, nine months or so, and I think we will see exponential improvements to what these wide coding platforms can generate. The challenge I think will remain that it's one thing to create something from scratch, it's a totally different thing to take an existing software system and modify that and figure out where in the code base, which functionality, and which test cases.

随着大多数软件经过多年迭代变得日益复杂——往往由数百甚至数千名工程师共同开发——我认为AI要替代系统专家解决所有用例还非常遥远。

And as most software applications, you know, over the years become bigger, bigger, and more complex as sometimes hundreds if not thousands of engineers have worked on that, I think we are really far away from AI being able to solve all these use cases that an expert in a system can solve.

这正好引出你最近那篇采访开发者的博文。我很好奇这些开发者的构成——他们不全是你的团队成员吧？具体是哪些类型的开发者？

That's a good segue to this blog post you recently published where you spoke to a bunch of developers. I'd be curious to know what kind of developers they were, but you interviewed a bunch of developers and kinda took their temperature on the state of AI coding and where they're at with it and what they see coming. What kind of developers did you talk to? These weren't all just people on your team. Right?

都是外部开发者吗？

These were external developers?

对，都是外部开发者。七年前我刚加入GitHub时就发现：凭借GitHub的品牌影响力，很容易找到愿意接受30分钟访谈的业内人士。这次受访者虽都与GitHub有关联或愿意提供反馈，但确实涵盖了各类开发者。

Yeah. It's external developers. Yeah. Folks that were recording, you know, when I joined GitHub seven years ago, was the first eye opening moment is that when you're at GitHub, given the GitHub brand and then how widely known GitHub is in the industry, it's really easy to find people that are willing to jump on a thirty minute interview and answer some questions. So it was a range of developers that are certainly affiliated with GitHub or have an interest to provide feedback to us, but otherwise, it was a spectrum of folks.

你得到的最重要反馈是什么？能否结合软件工程师角色正在经历的变革来谈谈——受访者如何看待当前及未来一年内这个职业的演变？

What was the top feedback you got? And I'd like to connect that too to the role of the software engineer and how it's changing and what you basically heard from people about how they think the role of being an engineer is changing right now and then how it's gonna change over the next year.

简而言之：AI将永久改变开发行业。多数受访者已意识到这点并正在适应新的工作方式。今年早些时候我在博文中称之为'开发者奥德赛'——从打卡机到个人电脑彻底改变了开发效率，就像我母亲那个年代还需要预约大型机时间，而突然每个极客桌上都有了能直接编程的PC。

If I wanna summarize it really quickly, AI is here to stay, and I think most developers that we interviewed and most developers in the in the industry, they have realized that the profession of a software developer is going to change. It's already changing through the use of AI, and as such, you know, the majority of them are in the process of adjusting to that new way of working. And I had another blog post earlier this year where I described this as the odyssey of developers. We move from, you know, punch cards and mainframes to the personal computer, drastically changed the inner loop of how fast I can dry out something. Because before that, I had to book time on a mainframe, I remember going to my mom's office, she had punch cards on her desk, and then all of sudden everybody had a PC on their desk, at least the nerds had them, and they could just start coding.

我们从汇编语言、Basic、Pascal进化到Python、Go和Ruby；从没有互联网到能调用数百万开源库；从自建服务器到云计算。开发者始终在抽象阶梯上攀升——如今构建全栈应用的人可能根本不清楚运行程序的CPU参数，除非是游戏开发者也很少接触汇编语言。由于代码量在过去五十年呈指数增长（人类最不擅长的就是指数曲线），我们需要这些工具来稍微压平曲线，让我们能继续驾驭如今数十亿行代码的复杂系统。

And then we went from assembly language and basic and Pascal and to Python, Go, and Ruby. We went from no internet to millions of open source libraries that I can just pull into my application from my own servers to the cloud. They've always moved up the abstraction ladder, and today a developer that builds a full stack application likely does not actually know what CPU is running that application and RAM and gigahertz and what have you, the technical specification of that CPU. And unless they're a game developer, they're likely not doing assembler either. So we're naturally up that stack because the amount of code that we're managing has grown exponentially over the last fifty years and will keep growing exponentially, humans are really bad on exponential curves and so we need these tools to you know at least for us flatten the curve, not make it flat but like make it a little bit less steep so we can actually still handle these complex applications with you know now billions of lines of code.

参与本文讨论的开发者中有一半表示，他们认为两年内90%的代码将由AI编写——这个观点我也从其他人那里听过。Anthropic的CEO达里奥·阿马德最近提出了更激进的时间表，他认为三到六个月就能实现。我很好奇你在这个辩论中持什么立场？你认为距离'几乎所有生产环境代码都部分或全部由AI生成'的世界还有多远？

Half of the developers you spoke to for this post said that they believe that within two years, 90% of all code will be written by AI, which is the thing I've heard from others. Dario Amade, the CEO of Anthropic, he recently had an even more aggressive timeline. He said he thinks we'll be there in three to six months. I'm curious where do you fall on that debate? Where do you see the timelines for a world where pretty much all code in production was at least generated in part or fully by AI?

每当讨论这个话题时，我首先想到的是：必须承认我们现有技术栈90%的代码本就来自他人——也就是全球数百万开源开发者。无论是Birch的网页、后端还是其他部分，我敢说90%都是开源的。如果统计代码行数，90%来自开源库、开源操作系统、编程语言和依赖项等。工程团队实际编写的只占10%。GitHub如此，微软的朋友们也是如此。

The first thing that always comes to my mind when we have this conversation is that we need to acknowledge that 90% of our stack today is already written by somebody else, aka millions of open source developers around the world. If you look at the Birch web page and the back end and all that, I'm sure it's 90% open source. Like, if you count the lines of code, 90% of those are from open source libraries, open source operating system, programming languages, dependencies, all that. And the team, the engineering team is running 10%. Same same for us at GitHub and and even, you know, the same for our friends at Microsoft.

明白吗？我们早已身处这样的世界：专注于定义业务价值的顶层薄代码层，同时吸纳全球数百万开发者的成果来加速创新、保障生态安全。AI也将如此——它将编写九段代码，而我仍能专注于八小时工作制里写自己那段。这不意味着我不再编码，而是意味着我的产出将提升十倍代码量和十倍功能特性。

Right? So we're already in a world where focusing on the thin layer on top that defines what makes our business, while we're pulling in the work of millions of developers from around the world to accelerate and to innovate what have you, to secure the ecosystem. And the same will happen with AI, that AI will write nine pieces of code so I can still focus on my eight hour workday and have my one piece of code. So it doesn't mean that I stop writing code. It just means that now I have 10 times as much code and 10 times as much functionality and features as I could produce on my own.

AI就像开源一样是效率放大器。关于时间线，我认为不同团队和公司会呈现巨大差异，这不仅取决于他们使用AI的意愿，更取决于其软件架构设计——是否能让AI代理充分利用基础设施。比如拥有设计系统的公司，当要求AI添加功能时，它不会发明新的CSS样式，而是复用现有组件，使产出符合产品设计语言。可以想象，未来会出现全新的应用架构方式，让AI能像搭乐高积木般组合新功能，这将极大影响企业达到'90%代码由AI编写'目标的速度。

It's an amplifier in the same way that open source is an amplifier. On timeline I think we're going to see a huge spectrum of teams and companies and depending on how they adopt AI, not only because they're willing to use AI, but also because they have designed the company, they have designed their software, their infrastructure in such a way that AI agents can leverage that infrastructure. Right? Like, for example, they have a design system, so whenever you want to ask the agent to add a feature, it doesn't invent new cascading style sheets and what have you, instead it uses a component so all the features the agent builds actually look aligned with the design language of your product. And so you can imagine there's a whole new way of architecture and applications that make it much easier for an agent to use those Lego blocks to compose new features, and I think that will heavily influence how fast a company can get to the point that 90% of code is written by AI.

反观另一端，许多公司仍在使用COBOL大型机、PHP/Perl遗留代码。GitHub自身也有与公司同龄的Ruby on Rails单体架构——这不是我作为CEO能简单决定拆分的，这往往涉及工程与理念的双重讨论。从遗留系统转型到云、数字化再到AI，这些企业所需时间远长于那些前沿公司——后者可能已有90%甚至更高比例的AI生成代码。

And then vice versa, there's obviously lots of companies still that have COBOL code running on mainframes and that have PHP and Perl and all the legacy spaghetti code that sits around with. Here at GitHub we have Ruby on Rails monolith that is now as old as GitHub is and it's not that I can just make the decision as CEO to say, split down the monolith and we will have nice modern architecture, let alone that often is a philosophical discussion as much as it is an engineering discussion. But getting out of this legacy state, going through the cloud transformation, digital transformation, now the AI transformation, that will take organizations way longer than those that can be on the cutting edge where 90 or even more percent of the code is written by AI.

所以听起来你并没有明确的时间表。

So it sounds like you don't have a timeline.

如今已有许多实践者在博客中记录：如果你清楚目标并使用Cloud Code等工具从零开始，AI可以编写100%代码。作者只需修改人类语言编写的指令文件来指导Cloud Code构建应用。按此定义，这些应用的所有代码都来自AI，作者仅提供规范说明。当生成代码不完善时，你可以直接修改，但更合理的做法是调整指令文件，保持系统完整性继续让Cloud Code编写。

Like today, lots of folks already out there that are, you know, documenting that on on their blog posts is, like, if you know what you're doing and you started from scratch with something like Cloud Code, you can have it write 100% of the code. And the only thing the author was doing was modifying these in human language written files to then instruct Cloud Code to build their applications. And so that application is, by definition, a 100% of the code is written AI and all the author is still writing is instructions, specification. And whenever the source code that was generated was not functional, you of course can just go in and modify that or you can say, no, no, I don't wanna do that because then I'm breaking my system. Instead, I'm trying to figure out how do I change my instruction files to then, you know, keep going with Cloud Code writing on my code.

说实话我有点后悔问这个问题。CEO们纷纷宣称'X%代码由AI编写'的趋势让我困扰。你的老板萨提亚说微软20-30%代码由AI生成，桑达尔·皮查伊、马克·贝尼奥夫也都抛出过数据。

Yeah. I'm kinda mad at myself honestly for even asking you about this because this trend that's happening of CEOs saying, you know, random percent of our code is made by AI actually really frustrates me. Your boss, Satya Nadella, has said actually that 20 to 30% of the of your code at at Microsoft is written by by AI. And, you know, Sundar Pachai has put out a stat. Mark Benioff put out a stat.

扎克伯格也公布过数据。但我始终在思考：这些真是优质代码吗？是我们希望持续增加的代码吗？最近Stack Overflow的开发者调研显示——你应该看过——虽然超80%开发者表示正在或计划使用AI工具...

Zuck has put out a stat. But I've been wondering, is this actually good code? Is this code that is something you want over time, to increase? Because I was looking at this recent Stack Overflow survey where they interviewed a bunch of developers. I'm sure you've seen it.

但约半数开发者明确表示不信任AI编程工具的准确性。66%受访者最大的挫败感在于：AI生成的代码总存在瑕疵，导致需要大量调试时间，最终AI编程反而降低了效率。我想知道，这种现象在GitHub整体生态和你们内部是普遍存在，还是相对孤立的情况？

And it was really interesting because the vast majority of them were saying that they either used or planned to use AI tools over the next year. It was, like, over 80%. Yep. But I thought something really interesting was that about half of them said they really distrust the accuracy of these AI coding tools, and that their biggest frustration, which was cited by, I think, 66% of the survey, was that the code that it spits out is just not quite right, which often leads to them having to spend a lot of time debugging and that AI coding is actually more time consuming than productivity gaining. And I'm wondering, do you see that across, you know, everything that GitHub does and also inside GitHub, or do you think this is more more isolated?

这似乎是个大问题——没错，AI生成的代码数量在增长，但这些代码质量可能并不高。

It seems like a big problem that, yes, AI code output is on the rise, but maybe it's not actually good code.

是啊。你现在是不是因为我没给出具体百分比而生气？

Yeah. And now are you mad that I didn't give you a percentage?

我确实有点恼火，因为我刚才只顾着追问百分比数据。但看着笔记我突然意识到：人们本就对这类百分比问题有争议。我们讨论的方向对吗？虽然各公司AI代码产出量在增长，高管们也借此标榜成绩——但我们关注代码质量了吗？我想听听你如何看待这场辩论。

Well, I was mad that I was actually just asking you for percentages when I'm, like, looking at my notes here and going, well, actually, like, people take issue with these percentage questions. Is this even the right way to be talking about this? Because, yes, AI code output is going up across companies and COs are using it to to to brag about their efforts, but are we focusing on good code? I'm curious how what you make of that debate.

在我看来这两者其实相辅相成。百分比显然是种营销手段，具体90%、80%还是95%根本不重要。关键在于软件开发行业正在变革——我们正在向技术栈上游迁移，从逐行理解代码转变为在规范说明与AI之间切换。人类语言天生具有不确定性，我们说同一句话可能表达不同含义，更不用说不同语言描述事物的结构差异了。

Those two, from my perspective, actually go together. And I think the percentage, obviously, is a bit of a marketing instrument, and what really doesn't matter, but it's 90%, 80%, or or 95%, whatever. I think what matters is that it's clear that the profession of software development is changing, and that we're moving up the stack, and we're moving from understanding every single line of code to switching back and forth between specification AI. Human language is inherently nondeterministic. We can both say the same sentence and mean different things, let alone that different languages have different constructs of describing these things.

编程语言本质上是处理器（CPU/GPU等）的抽象层，归根结底只是描述晶体管如何从0切换到1的另一种方式。我们仍需要传统编程，因为机器始终运行在CPU上——这点不会改变。

Programming language is an abstraction of the processor, right, of the CPU or GPU, what have you. As such, it's nothing else than describing how the transistors ultimately are flipping from zero to one. Right? And we are going to have both because the machines are running on CPUs. That's not going to change.

但我们的思考是基于人类语言的。所以核心问题是：我们想写更多自然语言还是更多代码？这不是AI能替你决定的，而是开发者自己的选择。我认为未来的创作自由在于：我可以选择当场手写代码，也可以调用AI代理。

But we're thinking in human language. Okay? So then the question really is, do we want to write more human language or do we want to write more code? And that's not AI determines that for you. That's a decision that you make as a developer, and I think this is going to be the creative freedom for me as a developer to say, want to write code because I know what I'm doing in this moment, or I want to use an AI agent.

具体到代理工具，首要考量是效率：用代理更快还是亲自动手更快？如果我三秒就能搞定，让代理处理反而浪费时间和资源。就像我们发短信时，总不会用ChatGPT来代写回复吧？

And in fact, if you think then about agents, the first question really is is, is it faster to use an agent or is it faster to do it myself? And if I know I can do it myself in, like, three seconds, I'm time, energy, you know, resources if I ask the agent to do that. It's like, you know, I hope when we're texting, you know, using ChatGPT to write the response to my text message.

我用消费级代理时经常有这种体验——比如让它订酒店，折腾一小时结果还不理想，还不如我自己动手效率更高。

I have many reactions like this when I'm using consumer agents where it's like, yes, you can go off and book me the hotel or whatever, but it takes you an hour and it's not really right at the end. I should just go do it myself. It's more productive for me to do it myself.

就连邮件/iMessage的AI摘要功能，至少对我来说，常常不如直接看原文前三行有用。第二个问题是：如果AI编写了90%的代码（即产量是我的九倍），我该如何审查这些代码的质量/安全性/规范？是否也需要AI辅助审查？否则人类开发者将面临代理工具的海量输出——就像遭受分布式拒绝服务攻击(DDoS)那样。

And even, you know, the notification summaries in Mail or Outlook or Apple iMessage are often, for me at least, less helpful than just reading the first three lines of text. Right? Yeah. Then the second question is, okay, so now AI is writing all this code, and if AI is writing 90% of the code, as in nine times as much code as I am writing, am I reviewing all that code for, you know, quality, security, coding standards, or do I find ways for AI to help me with that as well? Because ultimately, we're going to DDoS the human developer population with the agent population as those agents sorry, DDoS distributed denial of service attack.

这些代理永不休眠，周末也不休息，还能并行工作。看看OpenAI的代码工具：每个任务默认生成四个变体，你选择最优解的过程其实在帮它们强化学习——但理论上完全可以生成40甚至400个变体。

Yeah. As these agents don't sleep, they don't take time off over the weekend, and you can't run them in parallel. If you look at OpenAI codecs, they're already generating four variants of every task that you're giving, and then you're and you're effectively helping them with their reinforcement learning by picking the one that you like most. But you could not only generate four, you could generate 40 of of 400. Right?

然后你可以有另一个代理来检查代码质量并评分，这样就能在代理之间形成反馈循环。实际上我认为我们正达到一个阶段——代理生成的代码质量终将超越人类，因为你可以无限扩展运行规模，从而发现人类因时间有限而无法察觉的漏洞。但核心挑战依然存在：我们是否信任这些代码？说到这里，其实我们又回到了GitHub的初衷。GitHub正是为此而生的。

And then you could have another agent that looks at the quality of their code and rates that quality, and then you have a feedback loop between those agents. It could be actually I think we're getting to the point that agents will generate always better quality than a human can generate because you can just run this at infinite scale and as such find all the bugs that a human wouldn't find because they don't have the time to do that. But the challenge will remain as do we trust that code? And there, you know, we're actually coming back to GitHub. GitHub was built exactly for that reason.

它专为人与人协作而构建。明白吗？GitHub的核心逻辑就是：我有个开源项目，你fork后添加改进，通过pull request反馈给我。关键不在于直接合并代码，而在于我会审查代码并给出反馈，比如'Alex这个功能很棒，但能否重构这部分？'有时语气可能更尖锐些。最终确认没问题才会合并。

It was built for human to human collaboration. Right? GitHub is all about me having an open source project, you forking that project, you like something or you want to add something, you send me back what we call a pull request, and then it doesn't just go into my codebase, but instead I review that code and maybe give you feedback and say, hey, Alex, this is cool, but how about you rewrite this? And sometimes the tone is a little bit harsher on that as well. And then at some point, decide, okay, this is cool, let's merge it.

这本质上是人类协作的质量控制机制。当然，同样的流程也适用于人机协作——只是当代理数量激增至数千时，就需要重构这套体系。我认为未来开发者工具的最大分水岭在于：如何在代理生成海量代码的同时，确保人类仍能掌控生产环境中的关键环节——无论是客户数据处理、计费系统还是其他核心功能——保证代码质量与安全性。未来几年的真正挑战不是'代理能写多少代码'，而是'我能将多少代理生成的代码投入生产系统？'

That is actually quality control of human to human collaboration, and of course, you can use the exact same process for human to agent collaboration, except if you now have thousands of agents, you have to rethink that approach, I think that's going to be the biggest differentiator for developer tools, those who figure out how we can have agents generate so much more code than humans, and have the humans still be in control and make sure that what goes into production and handles customer data and billing processes and what have you, that that code is actually functional and has quality and doesn't have any vulnerabilities. That is the challenge of the years to come, is not how much code is agents writing, it's how much code from agents can I actually accept into my production system?

听起来你预见的开发者未来，就是整天监控代理行为、审批pull request、审查代码但基本不亲自编写，可能只给出高层级指令，实质上退出具体编码流程。你是说这将成为普遍趋势吗？

It sounds like you're saying the future of a developer is someone who is just looking at how all these agents are behaving all day long and accepting pull requests or rejecting them or reviewing code, but not really writing it, maybe giving very high level instructions, but really stepping back from the actual process of coding. Is that what you're saying is gonna happen broadly?

我认为某种程度上确实会如此。我们从白盒编程就已看到端倪——现在你只需写提示词就能生成代码。图像领域同样如此：比如用ChatGPT生成宫崎骏风格图像时，你并不编写底层代码，只是提供指令，模型会完成剩余工作。

I think I think to some degree this will happen. We started this with white coding, and white coding is already happening. All you do is, you know, write a prompt. It's actually also happening with image models. If you, you know, use a chat gpg, create like a Studio Ghibli image, for example, you're not writing the code to that, and you're just giving the instructions, and then something in the model that does that for you.

有个技巧是上传现有图像，让模型将其解构为JSON数据结构，修改后再作为提示词生成新图像。这就像开发者在操作，但全程不接触代码。因此未来会出现专门运用模型和代理来构建验证系统的开发者群体。

And in fact, one of the hacks is that you can upload an existing image and have it decompose the image into like a JSON file, like a data structure, and then you modify the data structure and use that as the prompt to generate an image that has something removed or something modified on that image. Right? So you're kinda like acting like a developer, but you're not writing any code. You're just having the model do that. So I think there is going to be a class of developers that will use models and agents to build and verify systems.

当然也会存在仍热爱编码的开发者，他们将工作拆解——把测试用例、文档等枯燥任务交给AI代理，腾出时间专注创造性工作。关键在于如何分配八小时工作制：是探索创新还是完成琐事？理想状态应是两者结合。事实上，当前大小企业的开发者日均编码时间通常不足四小时。

And I think there's going to be a class of developers that also still love coding and that are mixing up their workday with offloading some work to AI agents, like writing test cases, documentation, you know, offloading the stuff that they don't want do so they have time for the stuff they actually love doing. Because the question then becomes, how do you spend your eight hours a day? What is it actually that lets you, you know, explore your creativity and and ship innovation and so on versus just getting stuff done. And I think it needs to be a mix of both. Most developers today, in small and large companies, code maybe four hours a day, sometimes even less.

其余时间都消耗在会议、邮件、代码评审和服务器维护上。因此多数人仍会保留部分时间用于编写代码或生成代码的指令——后者本质上仍是编程。就像从汇编语言过渡到高级语言和开源库时，没人会怀念手写汇编的痛苦。

The rest of the day they do all kinds of other things. They are in meetings and they write emails and review other people's code and update servers. There's lots of other things that developers also have to do. So I think most of them will preserve a certain amount of time during the day where they write code or instructions to generate code, which will feel like coding. In fact, if you think about it, I brought this example earlier, if you move from assembly language to high level programming languages and open source libraries, right, I haven't seen many people that are sad that they no longer have to write assembly.

汇编语言并未消失，只是编译器帮我们将高级语言转译为汇编。同理，AI模型可视为新型编译器：将自然语言指令编译为编程语言，再由传统编译器转为汇编。理论上你的代码就是自然语言本身。

They can. It's not like assembly isn't there anymore. It's just that we have a compiler that takes my programming languages and compiles it down into assembly. Now you could see the AI model effectively like a compiler. It takes my human language instruction and compiles it down into programming language, then the real compiler takes that into assembly.

但目前所有模型仍存在幻觉问题，生成的代码可能偏离预期。比如我现场演示的贪吃蛇游戏，每次生成的版本都不同。正因如此，我们尚不能完全信任自然语言到编程语言的转换——就像当年我们成功剥离了汇编层那样。只要存在这两个层级，我们就需要在确定性与非确定性层面间持续切换，保持编码能力。

Your code actually would be the human language, right? Like, you wouldn't ever have to look at the programming language, except that all the models today still have hallucinations, and they still write code that doesn't exactly do what I wanted. Or maybe I do it ask it 10 times and each of the 10 times it writes different version of that code, and you can actually see that when you watch people write coding live on stage. The snake game that I have, you know, used as a demo looks different every single time, And so we don't trust the human language conversion into programming language enough to actually cut out that layer, but we were able to cut out the assembly layer and we're good about this. And I think as long as we have those two layers, we are always going to also write code and and learn coding and and go back and forth between the deterministic and the nondeterministic layer.

由于大语言模型本质上是非确定性的且会编造内容，这实际上是功能而非缺陷。它们的架构设计就是如此，旨在产生幻觉般的输出。我能否回到对话初衷——像我这样不懂编码、无法区分编程语言差异的人，能否可靠地信任自己构建的东西？那些需要调用不同API、连接数据库、涉及网络功能的应用，是否都能通过自然语言实现？若真能达成，这将意义重大，对世界产生深远影响。

Because LLMs are inherently nondeterministic and and do make things up, it's actually a function, not a bug. It's how they're architected, they're designed to hallucinate. Is there ever going to be a point where I can get to what I was starting this conversation with where someone like me who has no understanding of coding, who can't tell you the difference between languages can reliably trust that something I'm building that requires access to different APIs, a database, web, all that can be built through natural language. Because if we ever get there, that seems big. That seems like it will have profound impacts on the world.

但如你所说，或许这项技术本质上就无法实现这个目标。

But like you said, maybe it's just inherently not in this technology to be able to get there.

只要将范围限制在一定程度内，我们就能实现目标，而且这个范围会不断扩大。比如现在你让AI代理生成图表时，它实际是编写Python脚本再执行渲染。在JGPGs中你可以查看该脚本，但多数用户根本不需要理解代码就能查看图表，对吧？

We'll get there as long as the scope is, you know, limited to a certain degree, and that scope will increase, I think. I think you're going to be able to do certain things where you're in if you will, if you talk today to an AI agent and let it do, you know, render a chart for you, what that actually does is write a Python script and then uses Python script to render the chart. And you can, in JGPGs, go and say, show me that Python script. And I think most users never need to understand that Python code to look at the chart. Right?

这类用例将快速增长。同时专业软件系统会变得异常复杂——因为开发者现在能在相同时间内实现更多功能。过去他们需要编写Python脚本生成图表，而未来我们眼中的尖端软件，其复杂程度将远超人类开发者当前的想象。

So and those use cases, I think, will increase at at a rapid pace. But then also what we consider as professional software systems, the products that professional software developers write will become so much more complex. Right? Because it is now then easier for professional software developers to build more functionality in the same amount of time where in the past they would have built that Python script to render a chart, if that makes sense. What we consider now today kind of like state of the art software will be so much more complex than what we can imagine a human developer can write.

事实上这种趋势早有先例：如今iPhone的性能远超90年代初的Commodore 64。当年Commodore开发者看iPhone应用的编写方式，就像我们现在看AI般觉得不可思议。按这个逻辑，我们终将实现目标，但专业开发者仍会像魔术师般，因为他们能构建极其复杂的应用。

And in fact, that has always been true also with what my iPhone can do today is unbelievably more powerful than what a Commodore 64 could do in the early nineteen nineties. Right? And that developer that was building for the Commodore 64 would look at the way of how iPhone apps are written and think about that as magic in the same way that we we see AI as magic. And I think if you think about it that way, we will get to the point, but we will also get, at the same time, have the professional developer feel still like a magician because they can write such complex applications.

短暂休息后马上回来。

We'll be right back after this short break.

本节由Adio赞助。Adio是专为新时代企业打造的AI原生客户关系管理系统，功能强大且能适配任何数据结构。同步邮箱日历后，所有业务关系将实时可视化呈现，并支持生成可定制报告。

Support for this show comes from Adio. Adio is an AI native customer relationship management system built specifically for the next era of companies. It's extremely powerful, adapts to your unique data structures, and scales with any business model. They say setting up Adio takes less than a minute, and in seconds of syncing your emails and calendar, you'll see all your relationships in a fully fledged platform, all enriched with actionable data. Adio can enrich your business with real time, customizable reports featuring valuable data points.

最棒的是能创建AI自动化流程，用研究代理处理复杂业务，让你专注公司建设。现访问addio.com/decoder可享首年85折优惠。

The best part is you can build AI powered automations and use its research agent to tackle some of your most complex processes, so you can focus on what matters most: building your company. Join leaders like Flat File, Replicate, Modal, and more. You can go to addio.com/decoder, and you'll get 15% off your first year. That's atti0.com/decoder.

Fox Creative。

Fox Creative.

这是Adobe的广告内容。我们房地产咨询公司需要持续开拓客户。这个行业注重第一印象，必须展现专业性与个人风格。我的职责就是确保团队拥有快速制作出众内容的工具。

This is advertiser content from Adobe. Our real estate consulting business is always marketing to new clients. Real estate is still about relationships, and first impressions matter. We need to project credibility, style, and a personal touch. It's my job to make sure our team has the tools they need to create standout content quickly and easily.

这就是为什么我决定使用Adobe Express。从名片到社交媒体，Adobe Express的模板让我们的团队看起来既专业又符合品牌调性。

That's why I decided to use Adobe Express. From business cards to social media, the templates in Adobe Express keep our team looking polished and on brand.

作为营销团队的创意人员，我一直在寻找让内容脱颖而出的方法。Adobe Express的生成式AI对企业安全，因此我能制作出连法务部门都认可的图形和视频。

As a creative on the marketing team, I'm always looking for ways to make our content break through the noise. The generative AI in Adobe Express is safe for business, so I can create graphics and videos that even our lawyers love.

从赢得新客户到推动项目完成，Adobe Express助我们开创新局面。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。

From landing new clients to taking projects over the finish line, Adobe Express helps us break new ground. Adobe Express, the quick and easy app to create on brand content. Learn more at adobe.com/express/business.

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，或许还能邂逅其他惊喜。

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，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也有瑜伽装备，或许能帮你找到内心平静。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。

Sierra, let's get moving.

我们继续。谈谈竞争吧。其实你们已经提到过几个GitHub Copilot的竞品，这说明你们愿意讨论这个话题。编程领域可以说是当前AI世界竞争最激烈的角落——至少这是我的观察。现在并购频发，初创公司不断被收购。

We're back. Let's talk about competition. You've actually already mentioned several competitors to GitHub Copilot in this conversation, so that tells me you're open to talking about it. Coding, I think, is safe to say the fiercest competitive corner of the AI world right now, at least that's my read of it. You've got a lot of deals happening, startups being sold.

事实上，史上增长最快的公司中有不少是AI编程公司：Cursor、Lovable，还有我们聊过的Vibe编程应用。GitHub Copilot作为AI编程助手确实是这个领域的先驱，现在规模依然庞大。但我特别好奇的是，自从你上次做客Decoder节目这一年来，你是否认同这个观点：GitHub Copilot似乎在硅谷这个痴迷此类技术的圈子里失去了心智占有率？当然你们用户基数仍然庞大，但你是否感受到这种压力，促使你们必须加快产品迭代？以及你如何看待今年竞争格局的变化？

Some of the fastest growing companies of all time, in fact, are AI coding companies, Cursor, Lovable, the Vibe coding app we've talked about already in this conversation. GitHub Copilot was really the first to this space in terms of being an an AI coding assistant and still huge obviously. But I'm curious, like, especially in the year that's lapsed since you've been on Decoder last, I'm wondering if you agree with the sentiment that GitHub Copilot seems to have lost mindshare, at least in the Silicon Valley niche that that is obsessed with this stuff and and talking about it every day. I'm sure you still have a ton of users, but I'm curious if you feel that and if you feel like that's a pressure on you to evolve the product faster, and yeah, how you look at how competition has changed this year.

我认为人工智能整体市场、大语言模型以及这些模型的可能性正在飞速发展。我从业多年从未见过这样的技术浪潮。你说得对，AI代码生成正处于这场创新的最前沿。逻辑上想想就明白——因为这些公司本身也在雇佣开发者，所以大家天然有动力让编程变得更简单，这意味着能更快创新。如果我的编码工具能让开发效率比竞争对手快5%，我一定会采用它。

I think the overall market of artificial intelligence, large language models, and now what is possible with these models, is moving at a really rapid pace. I've never seen anything like that tech, and I've been around for a while. And you're right, AI code generation is at the forefront of of that innovation. You know, logically, if you think about it, because all these companies are also employing developers, and so there's an inherent motivation for everybody to make coding easier because that means I can innovate faster. And if I have, a coding tool that lets my developers move 5% faster than my competitor, I will use that.

自从我上次做客Decoder节目以来，这个市场的发展速度加快了，我想坦诚地说，这太不可思议了。作为一名长期开发者，我从未见过软件开发工具领域涌现如此多的创新。作为GitHub，你们始终理解我们是生态系统中既竞争又合作的一部分，无论是在AI代码生成还是CICD领域——比如应用程序构建方式上，我们不会替开发者决定该选用哪个开源库。如果GitHub只提供JavaScript生态却让用户去别处使用Python，大多数人根本不会选择我们。就像没人会看永远只有一支队伍赢球的体育比赛。

Since I was on last time at Decoder, that market has accelerated, and I want to be really open to this, this is amazing. Like I've been a developer for so long and I've never seen so much innovation in software development tools. And as GitHub you've always understood us as a part of an ecosystem that is both competing and partnering with companies in our space, whether it's in AI cogeneration or whether it's in CICD, like how applications are built, they're not making decisions of what open source library you prefer. Most people wouldn't use GitHub if it would only offer you the JavaScript ecosystem, but would tell you to go somewhere else to use Python. And nobody wants to watch sports if there's only one team winning at all.

我不同意关于我们失去心智份额的说法。我认为有得亦有失，有时甚至每周都能看到开发者在社交平台X上发文，说因为我们的夜间构建和月度发布版本新增了某些功能，他们又转回VS Code了。这值得我们整个行业引以为豪。虽然Copilot从单一模型转向多模型选择的步伐较慢，但我们在去年十月实现了突破。我们新增了智能体支持，构建了MCP（模型上下文协议）集成，现在所有编程智能体都在使用我们与Topic合作搭建的GitHub MCP服务器。

Where I don't agree with you is that we lost mindshare. I think we, have won some and lost others, and sometimes that's on a week by week basis that you see folks posting their X that now they're back on Versus Code because the latest release we're doing nightly builds and monthly releases has something they want. That's something we should, as an industry, be really proud of. Copilot was late on moving from a single model to multi model choice, but we got there last year in October. You know, we added agentic support, we built MCP, model context protocol integration, and now all these coding agents are using the GitHub MCP server that we have built in partnership with Topic.

这就是一场竞赛。微软财报显示，GitHub Copilot目前拥有2000万用户，企业用户季度环比增长75%，《财富》100强中90家都在使用。这些数据我都可以详细列举。

So it's a race. It's a race where, you know, we just announced in Microsoft earnings that we now have 20,000,000 users on on GitHub Copilot, 75% quarter over quarter growth in in enterprise usage, you know, 90 of the Fortune 100. I can give you all the stats.

等等，关于用户数我必须要问清楚。托马斯，这个用户数统计方式有点蹊跷——这是累计用户总数吧？

Hold on. On the user number, I gotta ask you about this user number. Yeah. Thomas, there's something funny going on with this user number. This is lifetime users.

这是指曾经使用过的用户？还是月活？日活？能否提供更细化的数据？

This is users that have ever used it, or is this monthly? Is this daily? Can you give me a little more granular detail here?

这是2000万已启用Copilot的GitHub用户数。统计口径与1.5亿账户总数一致。另外Visual Studio和VS Code有5000万用户，这能让你了解我们在各类IDE中的用户激活程度。

It's 20,000,000 users that have 20,000,000 GitHub users that have Copilot enabled. Enabled. So it matches the 150,000,000 accounts number, same same way of measuring it. And we have 50,000,000 users on 50 five zero million users on Visual Studio and Visual Studio Code, so that gives you an idea of, you know, how far we are in in activating, you know, the the users on all IDs.

你在公布新用户数的推文中写道：'企业的真正价值从不体现在风口浪尖，而在于压力测试下的韧性'。后面还有更耐人寻味的一句：'即便面临诸多限制，我们证明了一点坚韧就能赢得比赛'。作为GitHub CEO，你们应该没什么约束才对，这里说的限制具体指什么？

This post of yours where you announced that that new user number stat, you wrote, the true measure of a company is never drawn during its hype wave, but by its resilience when pressure tested. And then you wrote something even more interesting I thought where you said, even with all the constraints we faced, we've proved that a little grit wins the game. What constraints were you referring to there in that post? Because you're you're the CEO of GitHub. I would think that you guys are not really constrained.

你们还保持模型中立性，而微软其他部门目前似乎仍与OpenAI紧密绑定。但你们似乎能自由选择模型，在微软体系内相对独立。所以你当时指的是哪些约束？

You're also model agnostic, whereas the rest of Microsoft seems much more still connected to OpenAI at least for now. But it seems like you're able to work with whatever model you want. You're still a a separate part of Microsoft relative to the other parts. So what constraints were you talking about?

任何规模的企业都会受限。预算、员工编制就是最直接的约束。盲目增加人手反而会陷入'人月神话'的陷阱——团队扩张有时会导致效率降低而非提升。无论是10人初创公司、3000人的GitHub，还是约20万人的微软集团...

Any size of company is constrained. Right? That's the definition of of budgets and employees or headcount, what have you. And you can just always add more people, then you run into the mythical person months that adding more people to a team actually slows you down and doesn't accelerate you. As such, you know, whether you're a 10 person startup or a 3,000 person GitHub or, you know, 200,000 or so person Microsoft, you

都

have

做决策时，苹果公司有句名言：每一声‘是’背后有一千个‘不’。我们的一个现实约束是，待办清单里堆积着无数项目，其中许多需求甚至早于AI时代——来自GitHub issues或GitHub projects的用户反馈，各种功能请求应有尽有。实际上有个经典笑话：每当收到新反馈，总能发现几个月甚至几年前就有人提过相同想法。对吧？

to make decisions, and Apple famously said, you know, a thousand no's for every yes. And so one constraint is that, of course, we have endless, you know, number of items in our backlog, and many of the backlog items that that Gitp has stem from a time before AI. You know, customer feedback, people wanting a feature in GitHub issues or GitHub projects, you name it. And in fact, know, there is a running joke that for every customer feedback item we get, there's already one there from months or months or years ago where somebody else had that same idea. Right?

这很自然，因为我们的用户和团队都是开发者，总有人会提出已有想法或改进建议。比如X上就常有这样的群体：昨天还在骂GitHub某个默认设置愚蠢至极，今天却称其为划时代决策。我们正身处信息过载的世界，核心挑战是从海量信号中精准筛选——在这个连行业都看不清AI代码生成（乃至整个AI领域）两三年后形态的时代快速行动。若诚实面对，ChatGPT堪称2020年代的‘网景时刻’，而我们现在就像身处1995年：已有亚马逊和谷歌，但还没出现Facebook、Shopify和iPhone。

Like, Naturally, because our customer base are developers and we're developers, there's always going to be somebody having an idea that was already there or telling us how to do something better, and then have a group on X that says, well that was a stupid decision for GitHub to make it default and now they're saying this is the best decision ever. And so we are living in that world where we have way too many input signals and we got the constraints, the grid is to pick the right ones, where you're really moving fast and where nobody in the industry is actually knowing what AI cogeneration or AI in general looks like in in two or three years. I think that if we're all honest to ourselves, that is true. We're like, if Chek GPT was the Netscape moment of the twenty twenties, then we are like in 1995 now where we have an Amazon and a Google, but we have not seen a Facebook, not seen a Shopify, and not seen the iPhone. Right?

在1995年，预测这些事物的出现简直天方夜谭，但如今我们确信这就是未来。GitHub自创立起就活在这样的世界里：当年超前地利用Git构建了尚不叫DevOps的体系，最终成为最大开发者平台。我们不断在‘自主创新’与‘行业合作’间摇摆，而Copilot的成功证明了我们的战略判断——尽管作为CEO，我也曾做出过偏离航向的决策。

Like, in 1995, it would have been preposterous to predict that those things were happening, and you actually have the confidence that that is true. And I think that's the world that GitHub has been living in ever since its founding. It was ahead of its time leveraging Git to build what nobody called DevOps back then, but what is ultimately now the largest developer platform. Then it was swinging back and forth of what is the next big innovation and what do others in industry are doing and where do they actually prefer to partner instead of building it themselves. And I think with Copilot we have navigated that well and we had our moments where this wasn't working and a strategic decision that ultimately I as CEO made wasn't the one that pulled us in the right direction to win this space.

如今到2025年8月，我们仍保持领先地位，依然是市场领导者。对此我们深感自豪，这些辛勤耕耘的硕果让我们倍感欣慰。

And now I'd say in August 2025 we're still ahead of the curve and and we're still the leader of the market. And as such, we're really proud and and happy about, you know, the the fruits of the hard work that we're earning.

说到合作与否，编程已成为牵动AI领域巨额交易的热点。比如谷歌与Windsurf——这个势头强劲的AI编程工具本要卖给OpenAI，交易却因故终止，这才有了微软的介入。

Speaking of partnering or not, you know, coding has become a thing that affects huge transactions that people read about as it relates to AI. And I'm thinking about Google and Windsurf. Windsurf was this, you know, still is very ascendant AI coding tool. And they were gonna sell to OpenAI. That deal got called off, and that's where, you know, Microsoft comes in here.

虽然你们是GitHub，但这一切都有关联。我好奇的是：那次交易流产的核心原因，是担心Windsurf的IP会通过微软-OpenAI合作关系泄露，这种竞争格局最终导致谷歌直接挖走了Windsurf团队。为什么这个IP保护问题能扼杀这场本将震动硅谷、价值数十亿的AI编程领域重磅交易？从你的立场看——虽然你可能不主导此事——但为何如此关键？

And I know you're GitHub, but it's all kind of related. And I'm curious, you know, a big thing with that and why Google ultimately ended up hiring that team from Windsurf is that the company wasn't able to sell to OpenAI because there was a concern that the IP from Windsurf was not going to be shielded from Microsoft, that there were competitive dynamics at play given the Microsoft OpenAI connection. So I'm wondering why was that such an important issue that it actually ended up killing a huge deal that would have been a huge deal for not just the AI coding space, but Silicon Valley in general. This would have been a multibillion dollar sale to OpenAI. And I'm curious why from where you said, I know you you probably weren't driving that, but why?

为什么这个问题如此重要？

Why why was that such an important issue?

这个问题该问他们——我本可以拒绝回答。显然，我的立场存在利益冲突，也确实不了解内幕细节。

You have to ask the question, and I I can refuse to answer the question. No. Look. You know, obviously, I have a conflict of interest of even involved in in in these conversations as such. I I don't have any background knowledge I can I can share with you on this?

但这恰恰说明：开发者将被AI取代的恐惧毫无根据。事实正相反——像Vint Cerf团队或Meta这样的顶尖人才，签约价已堪比职业篮球运动员，有些人甚至雇佣经纪人谈判合同。这难道不令人振奋吗？至于具体内幕，该由掌握文件的人来解答。

But that actually shows you though is something else, which is the fear that developers are soon going to be replaced by AI is actually not justified. In fact, the opposite is true, and teams like the founders and core team behind Vint Cerf or other parts of the industry, Meta comes to mind, get offers that match professional basketball players. Yeah. Some of them even have agents helping them to land a new contract, and that should be something that we should be excited about. How we get to that point is a good question to explore for somebody who has access to all the documents, I don't.

作为深耕开发者工具领域的从业者，我认为这现象极其振奋：顶尖开发者的市场价值持续攀升，这将激励更多从游戏少年转型的年轻人加入——若局势如我所愿，未来我们需要的开发者数量将远超今日。

But I think as a developer that have been in this industry building developer tools and using them myself, that's freaking exciting because it shows you that the market value of those at the top of their game keeps growing and it hopefully also motivates kids, teenagers, those that are often first into gaming and then want to build their own stuff, still get into a profession. Because I do believe that we will need more developers than ever if this all plays out the way I hope it does.

让我们具体谈谈Cursor，我认为它确实已经接过了接力棒，至少在硅谷的早期采用者群体中，它被视为当前最热门的AI编程工具。他们破解了什么？他们看到了什么洞见，使得在用户心智份额上能如此迅速领先？我知道GitHub Copilot规模仍更大，但他们增长非常快。而且，他们的CEO最近还和Casey Newton一起上了Dakota节目，他提到你们是激励他们创立公司的灵感来源。

Let's turn to Cursor specifically, which I think has really taken the mantle as, at least in the Silicon Valley, really early adopter set like the hottest AI coding tool right now. What have they cracked? What was it that they saw, the insight they had that you think got them such a quick lead in terms of just mind share awareness? I know GitHub Copilot is still bigger, but they're growing very fast. And, you know, the CEO was actually on Dakota recently with Casey Newton, and he he cited you all as as inspiration for, you know, them starting the company.

但他们破解了哪些你们没发现的点？你们又是如何回应的？

But what have what did they crack that you didn't see, and and how are you responding?

Cursor的突破在于意识到不仅是将AI集成到IDE中，而是要改变IDE本身，设计出可称为AI原生的 workflow。他们真正思考的是：当AI成为默认选项而非附加功能时，开发者将如何工作？他们率先采用Cloud Sonnet 3.5，在理念上也超前——主张多种模型各司其职，让开发者自主选择最适合的模型，而非由我们做决定。这与我一年前和Eli交流时的理念形成鲜明对比。

What Cursor cracked was to realize that it's not just about adding AI into the IDE, but about changing the IDE itself and designing what you might call AI native workflows. It was really like thinking about how will developers work if AI is the default and and not an add on. They were the first Cloud Sonnet 3.5, so they were also ahead of the curve of saying, okay, multiple models play a role, let's give developers the choice and pick the model that works best for them instead of us making the choice, which was our philosophy a year ago when I talked with Eli. That's two different philosophies. Right?

我们曾运行大量评估套件，筛选出我们认为最优的模型。但这并非二元选择——不同编程语言、测试用例、生成功能代码或测试代码等场景的评分各异，需要综合判断哪个模型整体更优。但GitHub最清楚的是：开发者工具的历史证明，提供选择权永远是最佳策略。让开发者根据经验（或者说技艺）自主选择，才是王道。

Like, we ran lots of eval suites, we looked at these models, and then we picked the one that we believe was best. But that's not a binary choice because each programming language and each test case and generating functional code or testing code and so on has a different score, and so then you have different scores for different roles, and then you make a judgment call, okay, is overall a better model. Right? Well, but we know from the history of developer tools, in fact, we at GitHub know this very well, is that the best thing you can always offer to a developer is choice, and have them pick the model that they think is right. Then they will know, you know, from the experience or you may call it craft, which to pick.

这彻底改变了市场格局。如今想在AI编程领域竞争，必须满足：提供多模型选项、拥有开发者公认的'最佳'模型、支持用户自带模型、在IDE内运行代理节点、并能将该节点卸载到云端（我们称为Copilot编码代理，已集成到GitHub平台）。作为F1车迷，我常以此类比：冠军车队新赛季可能落后，但他们会将此视为重新思考策略的契机。Copilot在2024年中后期经历类似过程，而到2025年8月的现在，我们已能重新夺冠。

And and I think that has, you know, changed the market. Today, you cannot compete in AI coding if you're not offering multiple models, if you're not having the best models, quote, unquote, the best perceived by the majority of developers, If you don't offer them to bring their own models, if you don't have an agent node that runs within the IDE, if you can't take that agent node and offload it into the cloud, what we call the Copilot coding agent, that is actually integrated into the GitHub platform. And so I'm a big Formula one fan, I like to always think about the world like that. There's teams that win whole seasons and then the next season they're behind, and they're not taking that as in, well, shit, we lost and we'll never win again, but they're taking that as an inspiration to say, okay, we need to rethink of how we are doing these things. And I think that's what happened to Copilot in in mid late twenty twenty four, and, you know, I think now where we are in August 2025, we can win races again.

并非每场比赛都能赢，对手亦然。但我们在AI代码生成领域持续创新，同时也追赶上了其他竞争者的优势。

Not every race and neither does the competition win every race, but we certainly have innovated in AI code generation ourselves while also, you know, caught up to what has made others faster.

这个领域确实充满变数，正如你所说——工具竞争力高度依赖模型。领先工具的模型选择、定价策略，以及模型本身的排名都在快速变化。即使身处行业，要跟上所有模型发布也几乎不可能。比如LAMA 3表现不错，但LAMA 4就逊色不少。

This space does seem so dynamic because of that point you brought up, which is that it is so dependent on the models. Whatever tool is currently leading, the models they're using, their way that they price for those models, and the models themselves who is leading changes a lot. It's almost impossible even being in it to keep up with all of these model releases. You know, LAMA three was a good model. You know, LAMA four, not really as much.

目前Anthropic似乎是编程模型的王者，但可能几个月后OpenAI又会反超。这种变化如何影响应用层？

Like, Anthropic right now seems to be the king of coding. Maybe that changes. Maybe it becomes OpenAI in a few months. How does that change the app layer? Right?

这个领域的快速迭代令人印象深刻。现在断言你们彻底落败或Cursor永久领先为时过早。考虑到你们与Anthropic的深度合作（你提到的MCP服务器，以及通过GitHub Copilot访问Cloud），我一直想问：为什么Anthropic在编程领域如此出色？

So it's I I appreciate how, you know, quickly the space is changing. So I think to say that you all are for sure lost or cursor's for sure won is way too early. But as I think about Anthropic, and you've done a lot of work with them. You mentioned the MCP server, and and, obviously, you can access Cloud through GitHub Copilot. A thing I've been asking people is why is Anthropic so good at coding?

他们的秘密配方是什么？Dario最近在科技播客上避谈这个问题，但这确实是价值万亿的疑问。作为密切合作方，你认为他们能在编程领域独占鳌头的原因是什么？

What is the secret sauce they have? You know, Dario was on the the big technology podcast recently, and he he did not wanna talk about why they're so good at coding. But it's the billion I mean, god, it's maybe the trillion dollar question is is why is it such a good model at that? I'd be curious as someone who who works with them closely, why why do you think they've gotten such a lead in coding specifically?

让我先回到你问题的第一部分，稍后我再解释Anthropic的优势。在科技领域，我们有种观念认为一方胜出必然意味着另一方失败，但这并不成立——想想Windows与Mac OS、iPhone与Android的关系。开发者工具领域更是如此，否则今天就不会有数十种编程语言共存，人们也不会固守各自认为的最佳选择。看看GitHub Copilot的竞争对手如Cursor、Lovable、Windsurf、Alt、Vercel等，这些工具的用户实际把源代码存在哪里呢？

Let me, you know, for a second go back to the first part of your question, and I come to Anthrop why Anthropic is good in a minute. I think, a, in tech we have this notion that for somebody to win, somebody else is to lose, that has not been true, you know, for Windows and Mac OS. That has not been true for iPhone and Android and many other technologies. In fact, in developer tools that never was been has been true because if if so, we wouldn't have, you know, the dozens and dozens of programming languages today, and everybody would have moved to what is perceived as the best one. And if you look at, you know, the competitors of of GitHub Copilot, like Cursor, Lovable, Windsurf, Alt, Vercel, etcetera, well, where do those people that use these tools actually store their source code?

在GitHub上。他们在哪里管理项目和问题？在GitHub。在哪里运行持续集成？还是在GitHub。甚至很多竞争对手的模型推理服务就运行在Azure上，不是吗？

On GitHub. Where do they manage their issues and projects on GitHub? Where do they run their CICD on GitHub? You know where many of those competitors actually run their model inference on Azure. Right?

因此我们同属一个生态系统。微软始终是平台公司，GitHub也是。我们既与这些公司竞争，也受益于它们推动的软件生态扩张。毕竟软件开发规模从未停止增长。

And so as such, we are part of an ecosystem. As Microsoft, we have always been a platform company. GitHub has always been a platform company. And so we can both be competing and we can benefiting from these companies driving the size of the overall software ecosystem. Because let's face it, software development has only ever grown.

这个领域从未萎缩过。开发者数量持续增加，看看股市上科技公司的表现——它们经常跑赢大盘。当竞争对手成功时，我们同样获益。

It hasn't really detracted. It's not getting smaller. The number of developers are getting bigger, the value generated from look at the tech companies, you know, on the stock exchange on any given day. They're often outperforming the market. If our competitors are winning, we are winning too.

我认为讨论这个领域的竞争时，保持这种思维方式至关重要。

And I think that's the mindset that is really important to keep in mind when we talk about these these battles in this space.

这就是超大规模企业的优势，Thomas——竞争对手的成功就是你的成功。不是所有企业都能这么说。

That's the benefit of being a hyperscaler, Thomas, is that when the competitors are winning, you're winning too. Not everyone can say that.

确实。我们称之为差异化优势。具体到CloudSonnet，如果你相信YouTuber、博主和Simon Wilson等专家的评价，它的核心竞争力在于工具调用能力——模型能为智能体模式选择合适工具。这说明训练不再局限于编程语言和代码，还包括智能体应掌握的工具使用方式。

Sure. Let's call it differentiation. Mhmm. And then on topic, you know, specifically CloudSonnet, where CloudSonnet, you know, if you believe YouTubers and bloggers and experts like Simon Wilson, and where really it outcompetes other models is tool use, like the ability of the model to pick the right tool for the next step of the agent mode. And that shows you it's no longer just about the training in programming languages, code and whatnot, it's also training in what are the tools that the agent should use and some of the fashion as a developer should use.

如果某一步工具调用失败（其他模型在这方面不如CloudSonic），整个智能体模式就会崩溃。想象它需要安装JavaScript依赖包——若这一步失败，就算拥有世界顶级训练数据也无济于事。

And if that fails in a step, and you see that with some other models where the tool usage is just not as good as CloudSonic, then your agent mode just falls apart. Right? Because it can go through the chain of thought and actually, imagine it needs to install an NPM package like a JavaScript dependency. If that doesn't work, then the agent can have the best training set in the world. If it cannot achieve that one thing, the whole thing no longer works.

Anthropic凭借更优的Evolve Suite测试套件获得先发优势，其他公司正在追赶。有趣的是，等这期内容发布时，局势可能又变了——这就是这个行业

I think that's where Anthropic had an early insight of a better Evolve Suite, better testing that led them to have this head start, and others are trying to catch up. But the funny thing is we are recording this, and then by the time it actually gets published, maybe that road has already changed again. That's that's the fun part of

的魅力所在。暗示下可能随本期节目发布的GPT-5...观众们已经举手了，我们得再进段广告。

that industry. Alluding to GPT five, which, may be coming when this episode comes out. You Audiences, hands are in the air. We need to take another short break.

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欢迎回来。在休息前，托马斯和我正在讨论GitHub的竞争对手。这给了我绝佳时机询问微软与OpenAI的合作进展。我们已经涵盖了所有主要竞争者。最后我想说——我知道您不便多谈，因为这些都在商洽中——但微软正与OpenAI商讨企业关系下一篇章，因为OpenAI正从非营利转向营利机构，而微软作为大股东目前拥有IP独家使用权。

Welcome back. Right before the break, Thomas and I were talking about GitHub's competition. That gave me the perfect window to ask about how things are going with Microsoft and OpenAI. We've touched on pretty much all the main competitors. I do wanna end just with I I know you can't say much because this is all being discussed, but Microsoft is currently talking with OpenAI about what the next kind of chapter of the corporate relationship looks like because OpenAI is moving from a nonprofit to a for profit and Microsoft, you know, is obviously a huge shareholder and and currently has exclusive access to the IP.

作为GitHub CEO，您希望OpenAI与微软的关系如何发展？从您的立场看，这个关系中哪些要素至关重要？您期待看到什么？

I'm curious, you know, as the CEO of GitHub, how would you like to see the OpenAI Microsoft relationship continue? Like, what do you think is important for where you sit for that relationship, and what would you like to see?

对GitHub和所有开发者而言，关键是保持健康的关系——双方互利共赢。正是这种合作催生了最初的Copilot：OpenAI开发了GPT-3和Codex模型，微软则贡献了基础设施及多年负责任AI规模化经验。想想最初的Copilot，它本身就是杰作——每次按键都在大语言模型上运行推理，这在当时前所未闻，且要支撑数百万开发者即时使用。GitHub、微软与OpenAI的创新开辟了这个如今竞争者云集的市场，未来一个月可能还会涌现更多。我相信这种合作关系将继续通过新模型和更高效的运行方式催生创新。

For GitHub and for all developers, it's always going to be important that it's a healthy relationship, one where both states are partnering with each other, you know, that's how the original copilot came to be, for partnership between OpenAI, who had developed GP three and then the codex model, Microsoft who had not only the infrastructure, but also years of experience and responsible AI scaling these processes. Because look, know, if you think about the original Copilot, it was a masterpiece on its own because on every single key we're running inference against a large language model. That was unheard of at that point in time, and at that scale of millions of developers in very short amount of time. And so that innovation between GitHub, Microsoft, and OpenAI created this market that we're now in and that has all these competitors and probably another dozen to come in the next month. And I think that partnership going forward will create new such innovation through new models and through new ways of running these models more efficiently.

关于AI能耗、成本与毛利率的讨论已有很多。GitHub始终以开发者优先为宗旨，我们专注于人与人协作，现在正转向人机协作，未来将是智能体间协作。这三个层次将相互叠加——OpenAI凭借ChatGPT拥有当今最大AI平台。确保开发者通过ChatGPT生成的内容符合我们称为'控制系统'的安全合规企业标准，这对开发者采纳工具并将其融入工作流至关重要。

I mean, lot has been written about energy consumption of AI and cost and gross margins and those kind of things. And through GitHub, which always has put the developer first, that's what we are, you know, breathing. It has always been about human to human collaboration and they're moving into human to agent collaboration and more likely into agent to agent collaboration. And all these three layers will stack on top of each other, and OpenAI has the largest, with ChatGPT, largest AI platform today. And so partnering with them, making sure the content that developers generate in ChatGPT with the OpenAI codecs complies with the same, we call it the control system, the same security compliance enterprise company standards, is going to be crucial for developers to adopt these tools and integrate them into their workflows.

我认为AI的终极战场将在平台层展开。当所有智能体都无比强大时，真正的竞争点在于如何将其整合到其他业务流程中——对我们而言，自然是开发生命周期。

And I think the ultimate battle in AI is going to be fought at the platform layer. Because if all agents are insanely powerful, then what you really care about is how can you integrate those into your other business processes, for us, obviously, that's the developer life cycle.

有道理。那么，我们最后讨论几个关于GitHub和微软的具体问题。自上次公布年度经常性收入数据已过去一年，我记得是20亿美元。为何没有更新？

Makes sense. Well, let's end on some GitHub Microsoft specific questions. It's been a year since your last annual revenue run rate stat. I believe it was 2,000,000,000. Why no update?

现在的数字是多少？

What's what's the current number?

投资者关系团队决定了财报中披露的内容。按照惯例，我只能告知财报脚本中的信息，这次我们决定不公开具体数字。

Investor relations team has made the decision what's being disclosed in earnings and what's not. So as per usual, I can only tell you what's in the earnings script, and this time we made the decision to not publish the number.

好吧。我猜应该比这个高。我猜超过20亿美元了。

Okay. I'm assuming it's higher than that. I'm assuming it's higher than 2,000,000,000.

我们公布了其他令人印象深刻的数字，对微软整个第四季度的业绩感到非常满意。

We published other we have published other impressive numbers, and we're really happy about the q four results of of all of Microsoft.

Neil，去年我问过类似问题，估计会得到同样的回答。但大家最关心的是：Copilot是否为GitHub盈利？这重要吗？是否在考虑范围内？

Neil, I tried this one last year, and I'm expecting to get a similar response. But Yeah. A big question that we have is is does Copilot make money for GitHub? Does that even matter? Is that even a consideration?

还是说，由于你们作为微软内部相对独立的组织自主运营损益表，目前它是在盈利，还是为抢占市场份额的成本中心？

Or because you're running effectively your own p and l to a degree as a as a separate org within Microsoft, like, is this actually making money or is it a is it a cost center right now to to gain market share?

没错。GitHub有自己的损益表，我们依此接受考核。微软业务始终追求盈利，只是时间框架不同——尤其当赌注很大时。更恰当的问题是：在整个客户合同生命周期（LTV）中能否盈利？对此我深信微软股东和管理层会对GitHub的业绩满意。

You're right. GitHub is has its own p and l, and as such, we are measured against the the goals of that p and l. You know, I think in general if you look at Microsoft as a business, there's always going to be the desire for these units to make money. The time frame is different and often you know the bets are really big, And as such, think the better question is, is it going to make money over over the lifetime of of of the customer contract, so called LTV? And on that, I'm very confident that Microsoft's shareholders and and Microsoft's leadership team is going to be happy about GitHub's business results.

我们在2018年以75亿美元收购GitHub，当时其年收入仅2亿美元。去年公布的20亿美元意味着七年增长十倍，这个数字还在持续增长。既然Cursor的业务数据已公开，而Copilot规模更大，你可以推算我们的数字。微软对这次收购极为满意，我个人将其列为微软史上最佳交易前三。

Look, know, we bought GitHub in 2018, so now, you know, a little bit over seven years ago for $7,500,000,000 at a time when the last revenue number 200,000,000 a year before that, and you mentioned last year we published 2,000,000,000, so the revenue has 10 times increased in seven years, and it's safe to assume that that number keeps growing. You you mentioned the the business numbers from Cursor, given that, you know, we are very open about that we believe Copilot is bigger than Cursor, you can, you know, forecast what our number might be. Yeah. And so such, you know, we we are really really happy as Microsoft about this acquisition. I I I personally put it into the top three of deals that Microsoft has ever made.

我相信萨提亚·纳德拉和艾米·胡德会同意我的观点。我们以可持续的方式发展平台，没有抛弃开源——用直白的话说，我们没搞砸。GitHub依然是开发者生态中最受喜爱的品牌。

I think, you know, Satya Nadella and Amy Hood would agree with me on that one. And we are really happy about that we have grown the platform in a sustainable way because we haven't left open source behind. You know, I may say we haven't fucked it up. GitHub is still GitHub. GitHub is still like the most beloved brand in the developer ecosystem.

它已被全球几乎所有企业公司采用。并非所有公司都为所有开发者配备，但大多数公司已为部分开发者部署。在这个AI时代，我们实现了自我革新——正如我们曾用Git构建GitHub，现在我们运用大语言模型、GPE-3与OpenAI共同打造了史上首个Copilot，并引领了这一市场。

It is adopted by almost any enterprise company in the world. Not all of them have it for all the developers, but most of them have it for some of the developers. And it has, in that age of AI, reinvented itself. We have reinvented itself because just as we used use Git to build GitHub, we used large language models and GPE-three together with OpenAI to build the first ever copilot and to lead that that market.

嗯，这个回答很全面。抱歉，我需要消化很多信息。那么你们是否设定了某个重大转折目标？比如Copilot这类产品目前或许尚未盈利，但未来会在某个节点爆发式增长？

Yeah. That's a good response. I'm sorry. There was a lot for me to unpack there. So is there a is there a big goal where you all see this all flipping and this becomes super profitable if it's not right now, like the the Copilot stuff?

是市场规模达到临界点？还是与其他微软产品形成捆绑效应？你们究竟预见了怎样的'如果这样，就会那样'的发展逻辑，让这一切投入都物有所值？

Is it is there is it the market gets to a certain size or there's bundling that happens with other Microsoft products? Or what is the if this, then that that you're seeing that makes this all worth it?

我们有个类似摩尔定律的规律——或许现在该叫黄氏定律——GPU正变得更快更便宜。就像你多次提到的硅谷，现在有十几家初创公司试图发明更高效的Transformer处理芯片。但我相信规模效应。十五年前人们同样质疑过贝索斯，如今看看超大规模云服务商的盈利水平。我们有足够的耐心等待。

We have a version of Moore's Law, maybe it's Jensen's Law now or whatever, where the GPUs get faster and cheaper. That's like, you know, you mentioned the Valley a bunch of times. There's like, what, a dozen startups that are trying to invent processing units for transformers that do that more efficiently, but I believe in scale. You could have asked the same question to Jeff Bezos, like about fifteen years ago and so, and then look at where the hyperscalers are today in terms of profitability. And so I think we will have the necessary patience.

我们将持续优化效率，提示词缓存技术已对智能体产生巨大影响。商业模式的演进是必然的，我们的开发者也会因AI更高效。这不仅关乎毛利率优化，更关乎重构公司内部的软件开发方式。我坚信那些仍在怀疑AI的企业，终将在创新竞赛中掉队。

We will drive efficiencies, caching, prompt caching plays already a huge role for agents. We will see an evolution of business models for sure. And we will see our own developers being more productive with AI. And as such, it's not only that you're optimizing gross margins, whatever those may be, but you're also optimizing, you know, the way the software is built in our own company. I actually believe, strongly believe that those companies that are still doubting the use of AI, they're going to put themselves in a position that ultimately gets them fall behind in innovation.

不用AI的人应该感到严重FOMO（错失恐惧症）——同行都在用AI让开发效率提升10%、20%。别小看20%，考虑到软件工程师的培养成本、项目切换损耗，这已是巨大增益。任何未将AI用于提升内部流程（不仅是开发，还包括客服、销售、潜在客户开发等）的企业，最终都将丧失竞争力——届时成本结构是否健康已无关紧要。

Whoever is not using AI should have massive FOMO, fear of missed opportunity, that everybody else in their space, in their industry is already on AI and have their developers 10% more productive, 20%. And look, know, these numbers are always like, why is it only 20%? Couldn't it be 3020% is a massive productivity gain compared to, you know, what a software developer costs, how long it costs them to go through higher education to ramp up in a company, what have you, to move from one project to another. And as such, whoever is not using internally AI to make their own processes more efficient, not only for developers, for everything else, support is another great example, sales, lead generation, all these things, are going to ultimately fall behind and whether they have positive cost margins or not won't matter to them. What would matter is that they're no longer being able to compete in the in the market.

这让我想到微软某高管最近的内部分享备忘录，要求管理者将员工使用内部AI工具的情况纳入绩效评估，明确表示'这不再是可选项，而是所有岗位、所有层级的核心能力'。您应该理解人们对此变革的焦虑——无论是习惯既定工作模式的老员工，还是被变革浪潮淹没的职场新人。如果我是微软的中级工程师，肯定会担心自己是否会被淘汰，公司又能否准确评估这种转变？

That brings me to a memo one of your colleagues recently wrote internally at Microsoft where it was instructing managers to evaluate employee performance based on the use of internal AI tools, saying it's, quote, no longer optional. It's core to every role and every level. I'm sure you can appreciate the angst that people feel about this shift. People who are farther along in their careers and feel like they're set in their ways or even younger people who are just coming in and are overwhelmed by all the change. I feel everyone's overwhelmed and if you're a mid level engineer at Microsoft, you're probably wondering, am I gonna get left behind in this shift and is the company actually gonna be able to even evaluate this correctly?

作为GitHub CEO，您认为微软管理层真有可量化的方法来测定AI带来的内部效率提升吗？员工能切实感受到这种评估的客观性吗？

Do you feel as the CEO of of GitHub, do you feel that the management team at Microsoft has a actual measurable way to determine productivity gains internally with AI and if that's actually going to be something that employees can can know in a in a real way?

备忘录其实更 nuanced（ nuanced）。它强调的是在'Connect'（员工与管理者对话机制）中讨论AI学习和应用。GitHub也有类似流程：员工需总结年度成果、改进空间、未来计划及个人成长。我认为到2025年，要求员工反思AI使用情况完全合理——是否用了GitHub Copilot？是否用Teams Copilot做会议纪要？如果没有，原因是什么？

The memo was a bit more nuanced. The memo talked about AI learning, AI usage in what we call the connect, which is a conversation between the employee and the manager. And at GitHub, have a similar process, And it's about the employee, you know, writing up what they have achieved in the last year, you know, what could they have done better to have more impact, what are they going to do in the next year, and how they're growing. That's kinda like the framework. And I think in 2025, it's totally fair game to say you should reflect on your AI usage, and you should reflect what did you learn about AI, did you use GitHub Copilot or Microsoft Copilot, Teams Copilot to summarize a meeting, and if not, why not?

然后管理者会给予反馈——这常成为双向学习机会。我就遇到过下属AI应用比我熟练的情况。微软文化核心在于'mindset'（思维模式）：承认现有方法存在优化空间。没有人天生具备所有能力，但每个人都拥有自我提升的潜能。

And then the manager, you know, provides their feedback on that and and what they have learned, which often also is a good learning experience for the manager to say, oh, the employee is actually ahead of me in in AI usage. Like, I certainly have had those moments where somebody showed me how they did something, I'm like, oh, you know, didn't realize that that actually works. And I think that process is key to Microsoft culture, and, you know, the term that was coined around this is called mindset. Having the mindset of saying, okay, I did something and there's a way of doing this better. I wasn't born with all the capabilities I have, I was born with the capability to improve myself.

我认为，如果你在那个背景下看那份备忘录，它实际上完美契合了微软的哲学。另外我想特别对GitHub说的是，公司里没有任何GitHub员工可以不使用GitHub，无论你在公司担任什么职能。所以不仅是开发者和产品经理，人力资源、财务、法务等所有行政职能部门，技术职能部门，销售职能部门，他们都在使用GitHub。我绝不允许有人推脱说'抱歉我不想用GitHub'。如果雇主有这种要求，我认为这很公平，毕竟还有成千上万其他科技公司可供选择。

And I think in that, if you see that memo in that context, it actually perfectly aligns with Microsoft's philosophy. The other thing I would say, specifically to GitHub, is there is no GitHub employee that cannot use GitHub, no matter what function you have in the company. So it's not only developers and product managers, it's also HR and finance and legal and all G and A, all the G and A functions, the technical functions, all the sales functions, they're all using GitHub. There is no world where I would allow for somebody to say, well, sorry, I I don't wanna use GitHub. And I think, you know, that's fair game if the employers want that, and then there's tens of thousands of other tech companies out there where they can have that.

但让每位GitHub员工使用GitHub是我们企业文化的一部分，同样地，让每个人都使用Copilot和AI也应当成为文化。这并不意味着我们会像从不根据提交了多少拉取请求或写了多少Git评论来评估员工那样，去统计你今天用AI写了多少行代码——因为这些指标容易被操纵。但如果你用我们的工具来构建我们的工具，这本身就体现了与我们的文化相符的思维方式。

But it's part of our company culture that everybody at GitHub uses GitHub, and it should be part of our company culture that everybody uses Copilot and AI. That doesn't mean that we're looking at how many lines of code you have written today with AI in the same way that we never looked at an employee and evaluated them based on how many pull requests have they filed or how many Git comments have they done, because these metrics are easily gamified. Right? But it shows a mindset that aligns with our culture if you're using our tools to build our tools.

好的，这个回答很有见地。我想以这个宏大的话题作为结尾——关于通用人工智能（AGI）和超级智能的争论，以及如何实现它。扎克伯格等人曾提出，AI编程可能是实现路径，因为这将使AI能够自我监督地构建和维护其他AI，而赢得AI编程竞赛的公司很可能率先实现超级智能（无论你如何定义它）。如果你对超级智能有定义，我很想听听。

Okay. That's that's a good answer. I I want to end on it's kind of a heady topic. You can take it whatever direction you want, but this debate about AGI, superintelligence, how we get there, a thing I've heard Zuckerberg has mentioned this, others have mentioned this is that AI coding is actually the path because it gets you to self supervised AIs building and maintaining other AIs and that the company that wins in AI coding will probably get to superintelligence, whatever you wanna call it. If you have a definition of superintelligence, I'd actually love to hear it.

即AI编程可能是实现超级智能的途径。我很好奇你是否认同这个观点？或者你有不同的见解？托马斯，请分享你对超级智能的定义。

But that AI coding will actually be the way we get there. I'm curious if you agree with that or if you don't and if you actually have a definition of superintelligence. Please share it, Thomas.

我个人对超级智能没有明确定义，因为我认为这些定义除非被写入合同或作为营销手段，否则意义不大。你可以宣称自己率先实现了超级智能，下周就会有人反驳说他们才是真正的第一。这些术语就像弹性目标在不断变化。我认为关键节点（无论你称之为AGI还是ASI）是AI能够自我改进和提升——比如模型从GPT-4自主演进到GPT-5而无需人类干预。当AI像孩子般成长，比如从会听懂笑话到能讲笑话时，那就是我们该谈论AGI的时刻。

I don't have a definition of superintelligence myself because I think ultimately those definitions don't matter much other than either they are encoded in contracts or like a nice marketing instrument, and you can go on stage and say we are the first to have superintelligence, and then next week somebody comes and says, no, no, no, we are the first that have actual superintelligence. These terms are like stretch goals and they are constantly changing. I think the crucial moment, you may call it AGI or ASI, is that the AI can improve itself and and make itself better. The the model you're basically jumping from g p d four to five without, you know, humans in the loop. I think when we get to that stage where all of a sudden AI behaves like a kiddo, where your four year old moves from laughing about a joke to telling a joke, I think that's when we will talk about AGI.

那一刻我们会宣告它已来临——当出现能够自我完善并持续进化的事物时。这才是人类真正认可的智能。不是像' trivia pursuit'游戏那样仅凭知识储备，而是具备持续演进的能力。

That's the moment when we will say it has happened, and there is now a thing that is able to improve itself and keep going. That's what we as humans really consider intelligence. Not what you know just because you've been in trivial pursuit. It doesn't mean you're the smartest person in the world. It just means that you have a good mapping between the questions and the answers.

这种不断进化与自我提升的能力，我认为才是我们应该采用的界定标准。

But the the ability to constantly evolve and improve yourself, I would I think that's that's the definition that we should use.

'像孩子般行为'这个比喻很新颖，我第一次听说。托马斯，非常感谢你的时间。

When it acts like a kiddo. I like that. I haven't heard that one yet. Thomas, thanks so much for your time.

谢谢亚历克斯，这次对话非常愉快。

Thank you, Alex. Really enjoyed the conversation.

再次感谢托马斯参与节目，也感谢各位听众。如果想分享对本集的看法或建议后续话题，请致信decoder@theverge.com。我们还有TikTok和Instagram账号@decoderpod，欢迎关注。

Thanks again to Thomas for joining the show, and thank you for tuning in. If you'd like to let us know what you thought about this episode or what else you'd like us to cover, drop us a line. You can email us at decoder@theverge.com. We also have a TikTok and an Instagram. Check those out at decoder pod.

如果你喜欢《解码器》节目，请分享给你的朋友们，并在你收听播客的平台订阅。如果还未订阅，别忘了订阅The Verge，这样你就能阅读我们所有的报道和新闻通讯，包括我主笔的《命令行》栏目。《解码器》是The Verge出品，隶属于Vox Media播客网络。我们的制作人是凯特·考克斯和尼克·斯塔特，本期节目由赞德·亚当斯编辑。

If you like decoder, please share it with your friends and subscribe wherever you get your podcasts. And if you haven't already, don't forget to subscribe to The Verge, which gets you access to all of our stories and newsletters, including the one I author called Command Line. Decoder is a production of The Verge and is part of the Vox Media Podcast Network. Our producers are Kate Cox and Nick Statt. This episode was edited by Xander Adams.

《解码器》节目的音乐由Breakmaster Cylinder创作。下次见。

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