GitHub 的 Agent 时代:14x 提交、2亿开发者与 Copilot 的下一幕——Kyle Daigle
Agent 驱动的活动既是 GitHub 最强的需求信号,也是近期最大的执行风险。 swyx 提到,GitHub 2025年完成10亿次提交,到4月时已达到每周2.75亿次;若仅按线性增长,年化约140亿次,而 GitHub 嘉宾表示增速仍在加快。GitHub 如今“一个月完成的事情超过去年一整年”,这既是“艰难时期”,也是它经历过最令人兴奋的阶段。
基础设施的应对需要重写架构,而不是普通的容量扩张。 Agent 驱动的 PR 增长带来更多构建任务,Actions 对 CPU 的需求大幅上升;内部称为 MySQL 1 的数据库仍承载着大量权限管理;更大的 monorepo 也在改变平均 push 和 PR 的规模。GitHub 嘉宾将新的扩展方向称为“对角线式”扩展——既不是纵向,也不是横向——并预计随着一系列量级跃迁式修复落地,未来3个月可用性事故会“越来越少”。
Copilot 的战略重心已从代码补全转向覆盖软件生命周期的通用 Agent 运行时。 GitHub 最初押注通用微调和按客户微调,但基础模型快速进步,很快让这条路线失去了意义。GitHub 的应对是打造共享 SDK 和 harness,覆盖 Copilot 桌面应用、CLI、云端 Agent、安全修复、Issue 分诊、文档检查和并行任务执行。
企业 AI 的差异化正从模型访问能力转向上下文与权限问题。 最有价值的工作流会回溯 PR、网页帖子、Obsidian、Teams transcript、Slack 和邮件,再据此建议未来几天的行动。Work IQ 和 Foundry IQ 的价值,在于连接企业已有的数据存储,同时保留企业级安全与合规控制: “一切归根结底都只是上下文”。
小型、可组合的 skills 正证明比庞大的单体 Agent 工作流更持久。 GitHub 内部团队正在“终结这些庞大、漂亮、完美的 skill 时代”,转而共享狭窄能力,再根据不同角色重新组合。同一个总结能力,面向市场、分析师、客户或销售时必须给出不同判断,否则用户马上会问:“这是 AI 做的吗?”
AI 会不成比例地放大那些兼具技术模式识别能力与其他领域积累的领导者。 GitHub 嘉宾描述了如何“周六同时启动15个 Agent”,陪孩子打 lacrosse,再把多年商业知识用于收入规划或产品创作。AI 消除了低价值的幻灯片制作,却不会消除首席幕僚角色:这些岗位“不会全部消失,只会改变”,转向人与人的连接、协调和判断。
Agent 化开发的真正瓶颈可能是信任,而不是代码生成。 如果一个 Agent 写 PR、另一个 Agent 审核, “信任就有点分散”;技术验证仍无法回答人类、企业和监管机构是否愿意放心接受结果。因此,GitHub 更倾向于可配置的声誉启发式规则,以及 sponsorship 这类有成本的信号,而不是统一规则,同时承认 stars、账号年龄和贡献数量都可以被操纵。
Microsoft 对 OpenClaw 的兴趣,显示出一项从企业上下文延伸至操作系统和算力的平台押注。 好用的个人 Agent 需要访问计算机,但职场版本还需要操作系统级沙箱,让员工能够“使用那个 claw 而不至于被解雇”。机会在于为 Windows、云端沙箱、推理、CPU 和芯片提供可复用的平台组件,而不是让每个团队重新打造同一个顶层 Agent 产品。
1. Thomas Dohmke 的技术根基如今横跨 GitHub 运营与 Microsoft 市场营销
Dohmke 13年前以开发者身份加入 GitHub,负责构建 webhooks、API 和平台层,之后带领工程团队直到约2018年。他还在2018年10月的 Universe 大会上帮助发布了第一版 GitHub Actions,随后转向更深层的业务领导岗位。他提到的收购后新增项目包括 Semmle、Dependabot、Pull Panda 和 npm。
他如今同时担任 GitHub COO 和 Microsoft 开发者 CMO。二者之间的连接点是“翻译”:开发者和企业买家需要“完全不同的两套信息”,而公司自身则必须在被 Microsoft 收购后保留 GitHub 独特的运营文化。
按 Dohmke 的说法,写代码从来不是最难的部分:“问题一直都是人。”他的公开理念也由此而来——“讲真话、保持真实、向人们展示如何使用”,然后让产品自己说话。
2. GitHub 用 AI 回看过去,再规划未来
Dohmke 重新高频提交代码,并不是要回到传统应用开发,而是为了连接散落在各处的信息。他的核心模式是“少一点向前构建”,多一点“向后的递归循环”:重建发生过什么,判断哪些做法奏效,再调整接下来3到4天的安排。
一套工作流会检查当天的 PR、公开帖子、过去3个月的工作、Obsidian 笔记、通过 Work IQ MCP server 获取的 Teams transcript,以及 Slack。只有在拼完整段历史之后,它才会生成本周真正的对外沟通计划。
要把这套方法推广到约3,000名员工,GitHub 设定了一条规则:“没人需要改变自己的工作方式。”GitHub 提供 CLI 和共享 skills,让它们读取员工已经在使用的工具——主要是 GitHub、Teams、邮件和 Slack——而不是要求大家再采用一个新的目的地应用。
对一家全球远程办公的公司来说,这些回顾报告能够找回人们在办公室之外容易错过的信息。结论可以自动发布到 GitHub issues 或 discussions 中;Dohmke 打开笔记本电脑时,Copilot 桌面应用、GitHub Actions 和 Agent 工作流也会持续运行。
3. 原子化 skills 正在取代脆弱的超级工作流
GitHub 内部正逐步远离复杂的端到端 skill。GitHub 嘉宾认为,“几周过去,几个月过去,事情会变化”,紧耦合的超级 skill 很难修复;更好的抽象是“我们正在使用的乐高积木”,根据需要再组装说明书。
swyx 提出,输入不必严格匹配,只要大致包含即可;他还建议用编排型 skills 调用更窄的原子能力。嘉宾表示认同,同时强调,即便是看似通用的总结能力,也会随着受众不同而改变含义。
给分析师 briefing 准备的总结,不会等同于客户会议、营销计划或传播审查所需的总结。正在出现的复杂性,是原子能力与职业语境组成的矩阵;正是这些小规模组合,决定了输出是可信,还是会引发一句“这是 AI 做的吗?”
与传统插件基础设施不同,一个 skill 在给出糟糕回答后可以直接打开,用“英文单词”修正。这种可编辑性是一个“神奇”的构建模块,但团队仍需摸索哪些指令能够稳定地产生想要的判断。
4. AI 让前开发者重返创作,但不会抹去人的工作
swyx 问,这是否是曾经的开发者转型高管后的黄金时代。答案聚焦于模式识别和解决问题:技术熟练度很重要,但更大的优势,是把它与10年以上的商业、运营或行业知识结合起来。
这个判断也适用于拥有非线性经历的开发者——学过其他专业、转过职业,或曾离开软件行业后又回归的人。有了 AI,嘉宾可以“周六同时启动15个 Agent,你知道的,那时我的孩子们正在打 lacrosse”,重新找回创造某个东西、再展示给另一个人的最初魔力。
在年度收入规划期间,他汇总内部上下文,搭建了一个由 SQLite 支持的小型应用,并生成整套演示文稿,全程没有手动编辑幻灯片。他刻意把 skill 控制得很克制,让结果“在人类层面显得很糟”,但 CRO、CFO 及其团队都没有追问这是 AI 生成的;产出来源根本不重要。
swyx 反驳称,明显由 AI 制作的幻灯片也可以诚实地提示潜在错误。更大的结论仍然成立:首席幕僚不再需要花数小时排版 deck,但连接人员、识别应参加的会议和协调出行仍然有价值——正如这个岗位曾从拆信转向处理邮件。
5. GitHub 的供应链战略以兼容性换取标准化
当 swyx 质疑 Actions 是否是 GitHub 最大的安全问题时,Kyle Daigle 表示,仓库中的代码本身“可能”才是更大的来源。Actions 最初由 GitHub Services 执行用户任意编写的 Ruby 代码,后来逐步强化容器化;但未锁定版本、跟随
v1或 “latest” 的 workflow 仍保留类似依赖的风险。Pages 的旧版本也是另一个独立于 Actions 执行代码的基础设施案例。GitHub 正在底层使用 Azure 算力支持其新 Dev Compute 服务的部分功能,让小型 VM 能够快速启动,以执行容器化工具调用。
收购 npm 的首要目的,是维持并扩展这个“支撑互联网运行”的基础设施。GitHub 投资了后端和 manifest 工作,并收紧了2FA与 token 政策,但每一项安全改进也可能因为打破积累约15年的契约,给开发者制造一场“暴雪假”。
swyx 提议用 Agent 检查、裁剪并 vendor 的源代码取代 package。GitHub 嘉宾认可小型依赖的价值,认为这是“旧事重提”,但拒绝把它视为完整方案:Agent 也可能被说服某段代码是安全的,因此仍然需要静态分析、运行时测试,以及 GitHub Advanced Security 或 Socket 等工具。
6. Agent 化 PR 将验证问题变成信任问题
GitHub 历来允许社区先形成实践,再将其固化为平台默认设置。当“80%的 PR”来自 Agent 时,这种做法变得更难,因为 prompt 请求、更丰富的 artifacts 和替代性的 review 流程都只能解决问题的一部分。
讨论最终指向同一个诊断: “我们归根结底是在把信任编码下来。”当一个 Agent 写代码、另一个 Agent 审核、再由人检查结果时,额外证据可以提高验证质量,但“信任就有点分散”;而最终接受往往仍建立在某个已知之人的批准上。
swyx 预计,当机器的安全性可以被量化证明高于人类开发者时,市场会出现拐点。GitHub 嘉宾以 Waymo 作比较:他愿意信任 Waymo 到可以看手机,却不信任其他自动驾驶系统,即使仍然盯着路面;统计学证明与人的安全感以不同速度成熟,跨国监管环境下尤其如此。
维护者背书体系可能让一个社区满意,却在另一个社区失效。讨论认为 sponsorship 是更强、且有成本的支持表达,但更倾向可配置的 Agent 化启发式规则——已接受的 PR、关联的社交历史、项目特定规则——因为统一门槛只会教会攻击者养账号、刷 stars、制造贡献记录。更广义的信任体系,最终可能涉及某种形式的人类数字身份。
7. 2亿开发者扩大 GitHub 市场,也让信号变得更模糊
Mario Rodriguez 承认 stars 和提交数量都是被动且可操纵的信号,GitHub 的反滥用应对仍有一部分是“打地鼠”,如今则获得 AI 辅助。但他认为,stars 的爆发式增长也反映了真实的时代情绪,而不只是买来的热度。
GitHub 的开发者数量已超过2亿,swyx 因此反驳说,这代表的是拥有账号的人,不一定是开发者。Rodriguez 认为这种区分属于过早设门槛:如果一个人把想法变成了可运行的代码——即便借助 AI——那他就已经踏上了同一段通往数据库、bug 和更深技术理解的旅程。
Spark 等实验强化了 GitHub 的一项平台原则:GitHub 可以加一层低代码外观,但“绝不会”隐藏代码。Rodriguez 希望软件创作像更换电灯开关一样,任何人都能尝试,但不会假装每个人都应该打开配电箱。
8. Agent 活动让 GitHub 进入最好、也最艰难的时代
当被问及 GitHub 面对的是轻松时刻还是困难时刻时,Mario Rodriguez 回答“艰难时期”,随后又称这是他记忆中“最好、最令人兴奋的时期”。过去足以支撑年度 Octoverse 报告的活动,如今增长太快,以至于 GitHub 现在一个月完成的事情超过过去一整年。
swyx 给出了最醒目的对比:GitHub 2025年完成10亿次提交,到4月时已达到每周2.75亿次;如果按线性增长,年化速度将达到140亿次。GitHub 嘉宾表示增速“仍在加快”,而 swyx 追问,为什么其他系统经历14倍增长,却没有出现类似宕机。
Thomas Dohmke 表示,故障正在新的地方出现:并非历史上不可靠的 webhooks,后者已经重写;问题在于跨越大量对象的新型权限组合。GitHub 内部也会感受到每次宕机,因为员工使用的就是同一个 github.com,而不是受保护的私有版本——这就是“吃自己的狗粮”的承诺。
他没有为超常需求提供豁免:“这些都不是借口。我们必须把工作做好。”GitHub 想恢复的是过去那种社区契约:发布技术解释,并在修复完成后由工程师说明究竟出了什么问题。
9. GitHub 必须沿对角线扩展,而不只是向上或向外扩展
Actions 是主要压力点,因为更多 Agent 带来更多 PR,更多 PR 产生更多构建任务,更多构建任务又需要 CPU。GitHub 仍然需要 GPU,但 CPU 容量已经成为明确约束,推动其从自有数据中心扩展到更多 Azure 和云端算力。
大范围宕机往往可以追溯到权限问题,其中许多仍然触及内部称为 MySQL 1 的数据库。GitHub 多年来一直利用 Vitess 等技术将其中的组件拆出,Shale 就诞生于这项工作。
工作负载形态也逆转了此前的假设。行业过去趋向于更多、更小的仓库;如今 GitHub 看到更多大型 monorepo,其中有时包含巨大的 blob,而平均 push 和 PR 的规模也不再符合旧服务的预期。因此,Git 基础设施和任务排队系统需要重新设计,而不只是调参。
历史上,GitHub 可以通过更大的数据库纵向扩展,或通过增加服务器横向扩展。Dohmke 表示,在算力约束和 payload 变化之下,二者都不够,运行了10年或15年的服务必须进行“对角线式”重写。最近几周情况已经有所改善,他预计随着实质性修复带来量级跃迁,未来3个月事故会减少。
10. Copilot 正成为覆盖 SDLC 的共享 Agent 运行时
Copilot 成功发布代码补全功能后,GitHub 花了约1年至1年半推进微调,包括训练更强的通用模型和按客户定制的模型。随后,下一代基础模型到来,竞争性编程工具加速,而这些模型“让我们有点失去了先手”。
GitHub 的答案,是让 coding agent、CLI、桌面应用和云端 Agent 共用同一套 SDK 与 harness。Daigle 强调了模型选择、bring-your-own-key 支持、改进后的 post-training、特定语言模型,以及如今由他每天使用的 Copilot 应用。
产品边界如今延伸至安全修复、每一个新进入的 issue、文档漂移、部署和并行任务编排。开发者不再需要先提交 issue 来保存一个想法再开始工作,而可能直接告诉 Agent:“直接把这个做出来。”成熟项目仍会同时使用 issues 和 Agent。
GitHub 的独特野心,是最终让系统表现得像 Kyle、swyx 或某个特定团队,依靠规则、记忆、依赖关系和积累的实践来工作。Daigle 承认,完整个性化仍是“一个开放的研究问题”,尤其是团队方法本身会有意变化时。
11. Daigle 认为,编程仍缺少的产品形态是环境式上下文
当 swyx 问 Agent 是否已经覆盖 SDLC 的每个环节时,Daigle 称这一前提“极度短视”。即便是后台运行的 coding agent,通常也会因为普通的安全与治理原因,丢失代码之外的一切信息,尽管软件本来就依赖规格、邮件、团队讨论、业务优先级和当前需求。
他想要的产品形态是“真正的环境式 AI”:在实现功能时,系统应当知道所有相关文档和对话,并将团队的偏好、判断和积累的专业能力带入代码。OpenClaw 的有趣之处,在于它连接了一个人在意的数据源,而不只是为编程任务提供另一个启动器。
swyx 表示,他欢迎 Agent 提醒自己喝水,但不希望 Agent 改变汽车路线。Daigle 另行说明,他并不是在宣称 AGI,而是认为系统应该记住用户已经反复表达过的偏好。
12. Microsoft 正在重建工作安全型 Agent 底层的平台
Microsoft 为 OpenClaw 配置专门领导团队,反映出这个 Agent 已经具备系统人格化特征:理解某个人,访问其资源,并操作计算机。在工作场景中,这要求更好的 Windows 和操作系统级沙箱:“我必须能够使用那个 claw,而不会被解雇。”
Microsoft 嘉宾认为,Microsoft 应该提供可复用的平台组件,而不是让所有人重复构建同一个顶层产品。这意味着参与开源,把操作系统重新设计为能够服务更多 Agent 用户,并将需求一路追溯到云端沙箱、CPU、推理和 Azure 的芯片层面——“我们已经进入物理学问题”。
实际目标是,“我周末如何构建,工作时就应该如何构建”,即使是在 Fortune 100 或 Fortune 500 公司内部。Work IQ 和 Foundry IQ 作为上下文引擎连接现有数据存储,同时保留安全与合规控制,让企业 AI 真正有用,而不会因为信息泄露立刻被关停。
swyx 观察到,GitHub Copilot 与 Microsoft Work IQ 团队应该坐在一起,因为双方共享同一个上下文问题;嘉宾表示,这种协作实际上已经持续了数月。他建议在 Build 大会上问 Satya:2到3年后他认为哪些事情会成为现实,以及为什么 Microsoft 对上下文、推理和 Agent 工作的整合最终会产生回报。
Cargo
I just find that the folks who came from a different career, went to school for something else, went off and did this random thing, and then became a software developer—or were a developer, did a random thing, and came back—learning that extra set of information, learning those extra skills, and now having the power of AI, where I can crank up 15 agents on Saturday while my kids are doing lacrosse, that's really powerful. I think it gets me back to that feeling of creation, and it's very hard to replicate that in most other senses.
Before we get into today's episode, I just have a small message for listeners. Thank you. We would not be able to bring you the AI engineering, science, and entertainment content that you so clearly want if you didn't choose to also click in and tune into our content. We've been approached by sponsors on an almost daily basis. But fortunately, enough of you actually subscribe to us to keep all this sustainable without ads and we want to keep it that way. But I just have one favor to ask all of you. The single most powerful, completely free thing you can do is to click that subscribe button. It's the only thing I'll ever ask of you. And it means absolutely everything to me and my team that works so hard to bring the Inspace to you each and every week. If you do it, I promise you we'll never stop working to make the show even better. Now, let's get into it.
Shawn Wang
Okay, we're here with Cargo, CEO of GitHub. Welcome.
Thomas Dohmke
Yeah, thanks for having me.
Shawn Wang
You're not just the CEO of GitHub. People know you as that.
Thomas Dohmke
Yeah.
Shawn Wang
You have a new role.
Thomas Dohmke
Yeah, so I have an expanded role now. I mean, we've been working—I've been working—at GitHub for 13 years and doing all things developer. I joined as a developer myself, and now I'm also responsible as the CMO of Developer for Microsoft. All the learnings and passion for developers, how we work with them, how we communicate, and how we bring our products to market—we're also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product, or would like to, have a similar experience to what they've had with GitHub over the years.
It's a very different role in some ways, but it's also just building on the experience that I've had at GitHub: tell the truth, be authentic, show people how to use it, and then let the products speak for themselves. I'm not just doing that with GitHub, but with all of Microsoft.
Shawn Wang
Yeah. We'll be releasing this in conjunction with Build. You have lots of stuff planned, and we can touch on that whenever it's appropriate.
I think one of the interesting things is that I rarely meet a COO who's also a CMO. I think you're very outward-facing, and you're very confident publicly. That's rare. Do you actually view yourself as COO? What's your thing?
Thomas Dohmke
I think for me, it's been funny. The titles have always felt a little strange to me. I joined GitHub as a developer, you know. I wrote so much of the—
Shawn Wang
Let's bring that up.
Thomas Dohmke
Yeah.
Shawn Wang
Yeah, you wrote the backend.
Thomas Dohmke
Yeah. I was going through some old photos when folks were talking about how things were being built and how others would build GitHub. I built webhooks and worked with teams building the API. I built the platform layer—anything that integrated with GitHub. Up until really 2018, I built or ran the engineering teams, and that's where the beginning of my passion always was: helping people build things and deliver them to their customers.
Being a developer building for developers was always super unique. As my role expanded, it became my ability to talk to not just developers, but also enterprise customers and business leaders, and have this translation layer. Through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in 2008, but all that expertise of running remote teams and doing it well became this sort of bigger role, ultimately turning into the COO role: How do we operate GitHub in the way that GitHub has always operated after the Microsoft acquisition?
For me, I still code. I love coding, but the problem has always been people. It's a much harder problem to support our own employees, and it's a harder problem to communicate to developers and enterprise buyers what we're building and why it matters, because those are 2 very different messages. Getting to work in the mix of COO, CMO, and also just being a developer is what's kept me at GitHub for so long.
Shawn Wang
Apparently, your commits have gone up.
Thomas Dohmke
Yeah.
Shawn Wang
What's this? What's going on?
Thomas Dohmke
You called me out pretty aggressively. As you can imagine, you can see my normal era of being a developer in the 2013–2014 era, then moving into management and ultimately the COO role. I think what you see there is me really getting back to coding thanks to AI.
Similar to connecting problems between how to market, how to operate a business, and how to code, I find that building agents and workflows that connect very disparate problems is what's driving this. Some of it is writing software, but a lot of it is connecting a ton of different data sources to help me out. That is completely me really diving in on the AI side and trying out our tools and everyone else's tools.
For me, building for the nontechnical leader—though I'm technical—and figuring out how we're able to use these tools for more than just the simple call-and-response that I think a lot of nontechnical employees do when they're told, "You have to use AI," is really important. Everyone uses ChatGPT, Copilot, Claude, or whatever, but I want to get into how this is going to help me out.
It's not the simple example of "I need to write a blog post." It's helping people find workflows like, "I need you to go through all the PRs today. I need you to go through everything that we've posted online. I need you to go through what we did over the last 3 months. Go through all of my Obsidian notes for any mentions of this, then go through my transcripts at work."
We use Teams, so I can use Work IQ, call that MCP server, grab all the transcripts, go through all of Slack, and then build me out the plan of what this week's messaging actually was. That's something that was impossible before. For me, what most of this launch is actually about is less building forward. It's actually a recursive loop backward.
I'm always looking at what happened first. Go back through the week and tell me what we did, what worked, and what didn't work. Then tell me, in the next 3 or 4 days, what would you tweak based on this sort of looking backward and then looking ahead a little bit? I find that to be so much more valuable, especially for nontechnical people, because LLMs are very good at that retrospection—finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or a short period of time.
It's all a bunch of apps that I've built and launched, like a bunch of internal tools that I use. I use the new GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it's running workflows for me. It's just a ton of different stuff, and of course it all ends up on GitHub.
Shawn Wang
Of course, that's where stuff is hosted. Man, there's so much to ask you. I was going to leave the "How do you run a company with AI?" thing to the end, but I have to ask one thing. You said you're looking back at the week and understanding what happened. When you say "we"—
Thomas Dohmke
That's 3,000 people.
Shawn Wang
Yeah.
Thomas Dohmke
Yeah.
Shawn Wang
How?
Thomas Dohmke
When we started rolling out AI internally beyond engineering, one of the things I was really passionate about was that we had to do this in a way where no one had to change how they work. I don't want to have to teach you a tool, and I don't want to have to teach you something new. We tried out a few tools, but most of them didn't work because I had to get you on board and teach you how to use them.
What we've actually ended up doing is building a set of skills internally. We each have our own set of skills, and we've been distributing the CLI even to the nontechnical folks. We're effectively just giving it access to read everything that we're writing. For us, that's usually GitHub, Teams, email, and Slack—Teams for video chat, generally speaking.
Shawn Wang
Teams and Slack?
Thomas Dohmke
Yeah, we use Teams for video communication, but we don't use it for chat. We use GitHub for a long, long history, right? We always talk about ChatOps, and everything is built into Slack—every command, every flow—
Shawn Wang
Even though you've been acquired for, I don't know, 8 years now, you still use that?
Thomas Dohmke
Yeah, it's a purpose-built tool for us.
Shawn Wang
And I think the reality is that moving off of it would be so bluntly expensive, simply because all the tooling is baked in with that paradigm. They both have their pros and cons, but they don't work the same way at all.
Thomas Dohmke
Yeah. I mean, we still use a bunch of different tools because they're the purpose-built tools that we need.
Shawn Wang
But the same doesn't go for the rest of Microsoft, presumably. Various teams operate in various ways. I think it just matters what you're trying to do. Yeah, yeah.
Thomas Dohmke
But we do work across every tool that we use. By giving everyone access to all of that context in the new Work IQ MCP server, which is quite cool if you live in the M365 world, I can ask it all these backwards-facing questions, and it's incredibly important for our teams that are working remotely.
There's a lot of stuff you miss when you're not in an office, and we are spread out all over the world. So most of that is looking back, and then we post either automatically into GitHub issues or discussions these sorts of findings or our industry reports—what's happening this morning, today, or yesterday.
A little automation gets run. We'll use the app, or we might use GitHub Actions with our agentic workflows, just to go do that run. Then we push it into GitHub and keep having a conversation. Usually, for us, it's about looking back and looking forward on the nontechnical side.
Then, of course, for a lot of those folks, it's also building an app, pushing it to GitHub Pages, or pushing it somewhere to host it. It's just enabling everyone with that power of, "It's going to take me a week to figure this out." Instead, we're going, "I built a skill. Let's put it into a repo. We'll all share that skill together," and then we'll use the CLI—or now the app—just to run it.
Shawn Wang
How do you manage the bloat of skills like everyone has their thing and they're trying to promote it to the rest of their peers in your org? And obviously whoever becomes a skill influencer internally becomes an AI leader, right, of sorts. I assume you have those. And I assume it's a mess like
Cargo
I think the reality is there's two pieces. First is I think that we're ending the era of these massive, beautiful, perfect skills that are just not any of those things. What we found is that we're really talking about incredibly micro skills that are just doing one thing for us very, very well, versus a skill that's going to do that full report. It's usually a single skill that's going to identify the most important marketing information given any MCP server. It's less about stitching a bunch of tools together and having it produce this mega output, because then weeks go by, months go by, things change, and you want to tweak your mega skill and you're screwed. Now we're really just talking about the Legos we're using and letting the instruction book be something we're all putting together.
Shawn Wang
Anyway, so um yeah, this is one of those like download anything, transcribe anything, and then you can string together the atomic skills that do one thing well into some kind of orchestration skill that calls other skills. I assume—does that match?
Thomas Dohmke
Yeah, I think so. I think that the—
Shawn Wang
Summarize anything.
Thomas Dohmke
Totally. For me, summarizing something—I do communications and PR, analyst relations and marketing, and customer activities—my approach to summarizing everything is very different for each one of those contexts. If I'm summarizing something for an analyst, that's very different from how I'm probably going to summarize something for a customer meeting or an engagement.
That's the difference when we're talking about the tools I might use on Saturday, or the skills I might use on a Saturday when it's just for Kyle. Those have an atomic, actual tool underneath—or maybe a skill—and then Kyle cares about X.
But when we're talking about work and enabling the marketers and communicators, it's the atomic: this is what good summarization is, and then this is what I care about for marketing, for communications, or whatever. I think that's the interesting matrix problem when we go from a developer set of concerns to all kinds of different professions. What that word means to me is different from what it means to you, and different from what it means to the analyst or the salesperson.
And that's where I think the matrix mess is that we're just starting to find. It's not these mega-skills; they're all just slight permutations, but those permutations are really important. It's the difference between someone reading this and going, "Did AI make this?" Or, "This makes total sense, and I would expect this when I'm giving a briefing to Gartner," or whatever else.
Yeah. I think the beauty of it maybe is that you don't have to be that careful about what goes in there. It doesn't have to exactly fit, as long as it's roughly contained in there. I used to complain about plug-in hell—when you have a framework and then 100 things that you need to integrate. GitHub used to be bloated with all these things, and now we don't need them anymore.
Yeah.
Now you just use skills.
Yeah. Oh, and I think the most magical thing is that I can also crack it open. I could change how the plug-in is coded, or I could do that now with AI, but I think there's something more magical about getting a response back and being able to say, "That's not right." Then you just crack the skill open and type English words. It's different.
That building block is very unique. Once I get everyone to understand how to best make those changes to get the most power out of them—
Is there a common framing among your peer group of people like you for something I'm feeling, which is true? Is this a golden age for former developers who are now in leadership? You can wield the tools, you would know the right words, and you're maybe not too close to the details, which doesn't matter. But you're more effective than someone who doesn't come from that background.
I think the secret has always been your ability to identify patterns and solve problems. For folks like myself who don't code day to day anymore, that has made me successful as a developer, made me successful as COO, and now as CMO.
Now that I have access to Git and can write code, I'm applying that pattern-finding and problem-solving ability. I know enough about how to say, "I want to make an app, and I don't want to break out of jail or create something that's not going to be able to work, be deployed, or scale."
That ability to apply all that additional business knowledge and still code is what makes that so interesting to me. It's slightly different from some of the other technical leaders who became business leaders and are now going back to their apps and updating them. Good for them. But I think the much more interesting thing is, "Now I have this whole new set of expertise over 10-plus years. Why not take that and use that as a developer with these AI tools?"
So I definitely think that makes me more powerful, but I think that's true for every developer as well. Most of the developer friends I still have also have some other underlying skill and passion. There are really talented, very linear computer science software developers, absolutely.
I just find that the folks who came from a different career, went to school for something else, went off and did this random thing and then became a software developer—or were a developer, did a random thing, and came back—are learning that extra set of information and those extra skills. Now they have the power of AI, where I can crank up 15 agents on Saturday while my kids are playing lacrosse.
That’s really powerful, and it gets me back to that feeling of creation. It’s very hard to replicate that in most other senses. That first time you build an app, click it, and show someone—that’s magical. Being able to do that not just in code, but across all kinds of different assets, is huge.
We were doing our revenue planning. Every year, we talk about what it’s going to look like for the next year, and, of course, there are slideshows everywhere talking about what we’re going to talk about and what the narrative is. As you said, I thought, “I could probably just build something to do this,” so I wouldn’t have to build the whole spreadsheet and pass it to my team. We went through this process, I got all the information, and used the skills I mentioned.
I built a little app that made it easier to look at some of the information in a SQLite database. I ultimately built this entire presentation without touching any of it. I thought, “I’m just going to present this to our CRO, the CFO, and their teams without mentioning that I built it with AI.” I built a skill to make it look very much not AI-driven—not pretty, but clearly not AI. Don’t do anything interesting; just go exactly through it.
It used my notes from Obsidian and all the context I mentioned before, including the plans, and it never came up once that it was AI-generated.
Yeah.
Never once. Exactly. It didn’t matter. Now I can take that tool and say, “Look, I don’t want you to go build slideshows. They’re just helping us share information with each other. If this thing can do it with a little bit of crafting from you, and then we can look at it together, awesome.” There’s no value in all that extra work.
I think the ability to make it look humanly bad and build a little app to manipulate the data is part of that upside for developers who are now in leadership roles. The thing that I feel like I said before—that’s all a people problem. I know if you’ve used Cowork to build a slide deck unless you spent a bunch of time to not—
Okay. Well, I think there’s a certain charm to just being blatantly AI. You’re honest about the fact that there may be mistakes here that I cannot vouch for. How much value is there? But anyway, the real question I want to ask is: You were a chief of staff to Thomas Dohmke, and in the pre-AI world, that job would have involved preparing slides for me and all that. Now you do it yourself?
Yeah. I still have a chief of staff, because the difference is sort of the discussion every time we have some sort of technology evolution. It’s not that the jobs or the roles all go away; they just change.
I don’t have someone spending all their time building out slides for me in presentations, because I don’t need that anymore. Now I need a person who’s able to find all the different connections between humans in those discussions and help me figure out, “Okay, I should be meeting with this group and this team. They have an opportunity, and I’m going to be in San Francisco today and Seattle tomorrow.” Those human-connection aspects are still incredibly valuable and have always been a big part of the chief of staff role.
Chiefs of staff aren’t opening up letters to process anymore; they’re doing email. It’s the same thing. Now they’re not building as many presentations because they have the ability to have AI take that on for them and share it with me. Great—let’s keep moving, because it’s allowing us to go faster and make better decisions more quickly.
Yeah. Awesome. We can dive into more productivity insights as we go. I did want to do a brief history of GitHub. We started here, and you were also involved in the npm acquisition. I wanted to touch on that, and then more recently, bring us up to the present day, where we’re having uptime issues—which, transparently, you’ve already addressed publicly, but we’ll discuss on the podcast. Did I miss anything? Any other major highlights? Obviously, it’s a lot of years to cover.
Yeah. I think one highlight was that, right before the acquisition closed in 2018, I got to launch the first version of GitHub Actions at GitHub Universe.
They’re that young?
Yeah. It was October of 2018, I think. I was an engineering leader on that project and got to launch it. Then we did acquisitions of npm, as you said, Semmle, Dependabot, and Pull Panda—a whole bunch of things.
Pull Panda, right?
Yeah. Pull Panda did well on developer experience. That was the big shift after the acquisition: I joined the sort of business side.
I need to press you on some of these things, because you were there, right? How often do I get to talk to someone who was there? Is Actions the number-one source of security issues on GitHub?
Oh, shit. I think the number-one source of security issues is probably the literal code in everyone’s underlying repositories. I would say, further back than that—if you remember, I had this graph; I didn’t say this before—this is ultimately webhooks.
Yes.
If you look at the graph, it says Hookshot, Hookshot FE for front end, and then it says GitHub Services. Back in the old days, we had a repository that was Ruby code. You could write any Ruby code in there, and we would execute that on your behalf as a service. If you were trying to integrate with something, we would run it for you.
Of course, there were no containers, because it was 2014. There was some isolation, obviously, but it was mostly separation at the server level. That’s an example, as is the very old version of Pages, which ran on its own containerization infrastructure, not on Actions. Pages is an all-time great product; it powers the internet to some degree.
Those were places where, clearly, there were no issues, to my knowledge. But I was looking at those things and thinking, “Okay, we can’t be running arbitrary Ruby code on everyone’s behalf.” We containerized all of that into Actions. Now the containerization is really good. Most folks aren’t pinning it to a particular SHA, so that’s a big source of pain for people if they’re doing something similar to dependency management—just using `v1`, `newest`, or `latest`.
That journey—from “we’re just going to run all this arbitrary code and it’ll basically be okay” to where we are now—has led to really good containerization. We have a new underlying agent-containerization service. We’re using it under the hood through Azure compute for some parts of the new Dev Compute service.
With Dev Compute, you can run things really fast and spin up small VMs very quickly. When you’re doing a tool call, you can just do it containerized.
Exactly. Yeah. I mean, look, that grows into the full software development life cycle. Code hosting was just the start, and it’s grown beyond that. Let’s talk about npm, because I think that’s also a very major point in the industry. I do think it was looking for a home and was kind of struggling as a business, right? I don’t know how you characterize that whole acquisition and how it—
Mario Rodriguez
Yeah. When we were talking to the team, I think the big thing for both of us was to find a way to keep npm—which was basically powering the internet then, and way more so now, to some degree—running, keep it going, and continue to scale. It was having scaling problems. If I recall, back at that time they were doing some rewrites.
I mean, that’s cute compared to now.
Yeah, well, that’s the thing. When I’m talking to folks now, there are so many more underlying uses of npm than there were back when we had them join GitHub.
But that was ultimately the goal. It was really like, okay, we used to have Pages; we have the world’s code. Let’s make sure that we can keep npm running well for the world.
We put a bunch of time and investment into fixing some of the underlying backend changes, some of which we talked about, like the manifest work, et cetera. And now we’re really trying to bring the security posture of npm up to speed.
But it is a unique challenge in that every move we make to make it more secure will break a lot of people. Security is paramount, and we take it very seriously. Anytime we have a problem with GitHub where we make a change that makes us more secure but hurts people, there’s a snow day for developers, or a really bad fire that they have to go put out.
We’ve changed the 2FA policies, and we’ve changed the way the tokens work. When we find tokens that have been exposed or potentially exposed, we invalidate them.
I love that feature of GitHub that creates issues.
But that’s the thing: we’re trying to push the community forward without necessarily doing something that is going to break the contract that’s been in place for 15 years—or close to it—on npm.
Yeah, I think so. Now we’re talking about open source and publishing. I think there’s something here with what people are calling “slop forks,” which I think Malte from Vercel is doing.
Yeah.
Part of me thinks, well, the way to get past any vulnerability is: let’s just get rid of the concept of npm. We only publish source code, and anytime you want to import it, you have your coding agent look at it and then adapt whatever subset you’re going to use into your code—vendor it, but as an AI-vendored dependency. Is that realistic? I don’t know. Will that solve all our security issues? I don’t know.
Mario Rodriguez
I mean, I don’t think it will solve them. Mitch Hashimoto was just talking about this today, and in some ways, it’s all things old or new again. Absolutely, vendoring everything—I remember in 2013—
Yeah, we must return.
That’s what I mean. We were vendoring everything. We were having actual discussions around, “Should we take this full thing? Why is this so big? We only need this one file.”
So I do think there’s something true there, where either taking only what you need or having the dependencies get incredibly small over time will help to some degree. But it’s not going to solve the fundamental problem, I don’t think, because of the vulnerabilities. An agent looking at them can be convinced in a million different ways that something is secure or not and pull it in.
We can do static code analysis or runtime testing to say whether the code works or not. That is the step that needs to continue to be invested in. The question is just how much scope it should have: is it this enormous project that I’m pulling down, or should it be this smaller piece?
Either way, most companies are running some amount of security checking on the packages that they’re bringing in or vendoring. I think that won’t change. That’s what Advanced Security does to some degree. Socket does to some degree. Everyone is doing a piece of that.
How we each do that, especially when we’re talking to enterprise customers, is very different. No one wants one single way to do it. I think that’s always been GitHub’s unique position in the world.
I talk a lot to maintainers, and I talk a lot to folks about this. We rarely start a process or a practice and push it onto the community. We usually wait for the sort of RFC process, socially or literally, with everyone agreeing, and then we’ll cement something in.
If it's your role in GitHub, we don't want to shape the whole thing; we want it to be figured out. But how do you balance that role in the industry to keep everything as secure as possible, make sure you're not going to be compromised as a human because that's usually how it all happens, and not create a process or lock us into a flow that you're not going to like, or Mitchell's not going to like, or other open source projects aren't going to like? That's always been a tricky balance for us. I think that's something we haven't talked about enough. We're not going to be able to fix everything for everyone in a way that everyone is going to like, so tell us what is working. We hear from him and we'll fix up just like we do all other kinds of maintainers.
But that process between making those types of improvements and being more secure, and creating—I forget what he calls it. It’s not the proof process, not the claims process. Do you know what I’m talking about? His projects have a way for you to vouch.
Thank you. Yeah, he has a vouch system for saying, “Hey, you should accept my PRs.”
I just built this into GitHub. I don’t know.
Well, see, that’s the thing. You say that, and Mitch and his community really like this. Then I’ll go talk to other maintainers globally, and they’re like, “No, this doesn’t work for me.”
Yeah.
Mario Rodriguez
That is the tension, but also the beauty of GitHub, depending on which way you look at it. We want to help maintainers, so we create all these tools to let you have more control over how much you take in from AI and PRs. But you can also use this project. If it takes off and becomes the de facto standard, then we probably wouldn’t enforce it, but we would add it in, because that’s the flow we tend to follow.
Yeah, a lot of people don’t know the history of the pull request.
Sure.
That’s something that GitHub standardized, basically.
Mario Rodriguez
Yeah. It was a very messy process beforehand, and now we have the benefit of it being the process. Now we have to figure out the next best process, what adaptations need to change, and what a pull request looks like when 80% of your PRs are just coming from your agents and not from other developers.
Do you like the prompt request idea from Peter?
I think each idea has its merits. I’m not trying to say anything good or bad, but I feel like I’ve seen versions of this. We have Entire, Thomas’s startup, taking all the assets of what you’ve built and putting them in. I think that has great ideas. There are all these various permutations of the PR flow.
But I think the reason there’s not a single answer is that ultimately we’re trying to codify trust. We’re trying to say, “Okay, if Shawn reviews this, I’m going to trust it because you’re Shawn, or you’re the senior developer, or you’re the whatever.”
Right now, when we’re working in a flow where an agent writes code, another agent reviews code, and then Kyle goes and looks at it, the trust is diffuse. Most of the tools we’re talking about are focused more on verification flows. We have more assets to look at, so I can probably say whether this is a good PR or not. But that still doesn’t solve the human problem of looking at a PR and wanting to know whether you can trust it.
We still tend to use human signals for that.
Mitchell approving it, or Kyle approving it, or whatever.
So I think that’s why most of these options haven’t really solved it. Ultimately, it’s a social problem. It’s a human problem to review it and agree, or you fully trust the tool and imbue that tool with full trust, which I think in some cases absolutely exists.
So, in the same way that there will be a tipping point in society when we don’t allow humans to drive anymore—
Because machines are measurably better than humans.
I’m looking for that tipping point, right? Waymo is ridiculously expensive. Someday we’ll have Waymo on a desktop. I don’t know. What does that change in the equation?
I took a Waymo here, and I was on my phone, not looking around at all. There are other self-driving vehicles that I would not trust while staring at the road. I think that trust is something that is—
Is this a Zoox thing?
I think that is both. I think that is both, you know, like—
There’s Zoox in this robotaxi.
Alessio Fanelli
Well, I mean, depending on what level of self-driving, my point is that I think part of that is a mixture of verifiable proof—how many accidents, how much data, and so on—and the human aspect of how I feel when I'm in this car, what it tells me, etc. That's why I think some of these AI tools tend to imbue me with more of that feeling of trust, even if the data says this is 100% accurate. I feel like it takes more time for us to decide, “Should I trust this or not?”
That's in the soft sense of startups with high-agency weekend projects and open source. Then there are enterprises and regulated industries and everything else, and that is an even harder problem to solve because even when it is fully verified, not only do you have to have trust from the humans on the team, you probably have to have trust from multinational governments around the world and regulating agencies. Until we tip over to your point on the human EQ side of it—“I feel okay, this feels okay, I've been proven enough”—the ball won't start to roll a lot faster. We'll end up getting to, “Okay, we can trust this and feel good about it,” in the most difficult cases.
You know, if human trust is the thing that matters, I feel like GitHub, as the developer social network, could maybe do more there. Vouching is one system, but we have star counts and contributor rights, and that's it. I feel like there should be more in that space. I don't know if there are any other design decisions that could help there.
Mario Rodriguez
I mean, I think that one of the places that we don't really expose right now in this sort of way is some degree of hard trust and support. For me, Sponsors is a good example of that. It costs you something to prove that you believe in my project and trust me to some degree, or want to support me at the very least.
Alessio Fanelli
Okay, so payments for open source. Why not?
Mario Rodriguez
As we keep moving forward, there's more and more projects where I'm adding more and more dollars into Sponsors personally because I want to support them. I also know enough of their work that I want to support them, even though I've probably never met them in person.
The thing that I don't love about stars or commit counts or anything else is that, ultimately, even with all of the various de-spamming, deduplication, and anti-abuse work that we do, these are not active social signals. They're passive ones that are ultimately gamifiable. You may trust me, but another open source maintainer may not, and on what heuristic should you be trusting me? I think that's kind of where some of our thinking is right now: What signal from me is most important to you?
If you can define that, potentially, honestly, in an agentic workflow, that's what we see some of these open source projects do. You have GitHub Actions, and then you have an agentic workflow that's calling AI, and you're setting these rules: If Kyle has submitted and gotten accepted PRs across any given project, has a social handle tied to his account in GitHub, and that social account is older than a certain amount of time—really complex measures that matter to you, because most open source projects have that heuristic built into their heads, if not written down in the contributing guidelines—you could take that and apply it, and then just say, “Oh, we're not going to accept this PR.”
Building something that is malleable to everyone's needs is a little bit better than going, “Hmm, this account's too young,” because what happens? The attackers just go and create a multitude of accounts and wait until they age up. “It needs to have a certain amount of stars.” That's how star inflation happens. “It needs to have a certain amount of repositories with PRs.” They all just create repositories and submit PRs to each other, and then they come in and do something nefarious. It's hard to find the measure.
I think we're looking more at how we can provide you tools so you can choose what's best for you. Of course, we'll give you some standards, but the trust vector gets down to some version of human digital ID. Everyone's been talking about, “How do I prove that it's me on the internet?”
Shawn Wang
Give me your eyeballs. Exactly. I've got to keep moving on topics, but obviously I could go all day on this because I've been involved in GitHub and open source my entire professional career.
Stars are very superficial. Everyone knows it. But I think time to 100,000 stars is the fastest I've ever seen, right? People just reached that in, I don't know, months. At the same time, I don't trust it. How many of these are real or bought or whatever? I don't know how to ask this, but what can we do about it? Are stars broken? Are stars fine?
Mario Rodriguez
I think that there's 2 pieces. Obviously, we're constantly trying to find ways in which our users are producing spam, which I would include as only doing star gamification. When we find them, we pluck them out.
Shawn Wang
Like a whack-a-mole.
Mario Rodriguez
It's 100% like a whack-a-mole, now powered by AI to be helpful. But I think more so what I'm seeing is that a lot of the fastest time to X tends to be because we're now inviting so many more people into software development on GitHub that the zeitgeist is just swarming.
Shawn Wang
It's not just developers.
Mario Rodriguez
It's not you and I. However you want to define what a developer is, it's not just folks who have been coding for a very long time. It's folks who may have started coding or only joined in since the AI era. What's the latest Octoverse number? I know 80 million was my last memory for the number of developers on GitHub.
Shawn Wang
Oh, we're over 200 million now.
Mario Rodriguez
Yeah, yeah, yeah. Over 200 million developers now.
Shawn Wang
But it's not developers, right? It's people with a GitHub account.
Mario Rodriguez
This is the biggest debate that everyone loves to have at GitHub at this point. From my perspective, I think there's clearly a difference between a professional enterprise developer and developers. But I think the idea that we should be splitting hairs or segmenting developers in the early era of software development is not worth our time.
Shawn Wang
You get into gatekeeping.
Mario Rodriguez
100%, because I wasn't a developer when I started writing code. I cloned the thing 7 years before I learned to code, and then I wrote about my learning-to-code journey, and people called me a fraud because I had a GitHub account.
Shawn Wang
And I'm like, “Well, no, I just use GitHub.”
Mario Rodriguez
I mean, I remember that. I remember those sets of posts, and that's why I fight very clearly on the line of this: If you create code, if you have an idea and turn it into some way of saying, “I'm going to run it and use the app right now,” you may still use AI in that moment, but that's okay. At some point, you're going to do the next thing. You're going to have to learn about this database, fix a bug, whatever. We're all on some same journey.
Those people are also hearing about the great new agent skill package or a new CLI tool or a new whatever, and those projects are going up because you want to be a part of this moment, just like I wanted to be a part of the Ruby community when Ruby was popping off when I started becoming a developer. Now I can just click the star button.
So, yes, there's clearly some amount of spamming and gamification that we're working against, but I really think we're just seeing this whole new cohort of folks who are moving from technology to technology because they're not working on a 20-year-old software application. They're working on a side app that they built on the weekend for their friends or for their new idea or whatever. That's how you see these enormous charts going up and to the right with stars.
Alessio Fanelli
I think something that's remarkable is the persistence that GitHub extends to those folks. Usually, when I see platforms go into a new audience, they usually have to have a second platform with a different name that wraps the main platform. Somehow GitHub has been able to persist and extend, and it's friendly and whatever. It's nice.
Mario Rodriguez
That's partially why I think, as we've tried to move into more low-code things, we started working on GitHub Spark as a way to build an app and run it. The reality is that anytime we try to put even a veneer on top of it, we still always show you the code. That's kind of a tenet.
We're never going to hide the code from you, ever. That's the whole point. However, I think what we learned with things like Spark is that, for most developers, the real value of Spark is an easy runtime. You may have a runtime or a host that you're going to use for that, or you just build something and run it, but the package that makes it even simpler isn't really needed for folks who are trying to build software and not just an app, which is a slightly different goal.
I want to get you in. I want to get you comfortable. I think the best thing for me, as someone who did not traditionally come into software development way, way, way back, is that I want anyone to be able to breach that chasm and not be in—I don't know. I feel like we're still in an era of “STEM, STEM, STEM.” I've got a 12-year-old and an 8-year-old, and it's like, we have to get them into STEM over and over.
I do the things that good parents do. I was like, “Oh, we want to do coding.” “Yes, I want to do coding. Do coding classes.” But now they're just not afraid of doing software. That's honestly the thing that's kept me at GitHub for so long. Anyone should be able to go and build a thing, just like I can go change a light switch in my house. I'm not going to go into the breaker box because I'll probably kill myself, but I can go change that light switch.
Everyone should be able to say, “This freaking app doesn't do what I want. I want it to work like this.” That, I think, is what's kept us all connected with GitHub through the years, during the easiest of times and during the hard times, because of that opportunity. We're the home for all developers, and we want everyone to be able to have that feeling we've had: “I had an idea, I created it, and here it is.”
Shawn Wang
Here it is. All right, I'm going to try to ask more spicy questions. Great. Is it an easy time now or a hard time?
Mario Rodriguez
At GitHub? Yes. I mean, it's a hard time, and it's also the best and most exciting time that I think I can remember at GitHub. I was just with my team, and I said, “This is also the best and most exciting time that I think I can remember at GitHub.”
We talk about Octoverse reports, and usually we do an Octoverse report once a year. We look at the numbers and say, “Oh, my goodness.” I was at Universe in October saying, “This is the fastest year of growth that we've ever had,” and now we're doing more in a month than we did in a year last year.
Shawn Wang
Best of times, worst of times—never.
Shawn Wang
You're talking about PRs—
Mario Rodriguez
Commits, PRs, kind of—you name it. By roughly every measure that we're looking at, there's some amount of growth that's much, much bigger. And that is breaking our system in new ways, not old ways.
Webhooks were notoriously unreliable over the years. Whose fault is that?
Shawn Wang
Whose fault is that?
Mario Rodriguez
Not mine anymore, but for a period of time, I'm sure you could pull up a tweet that said, “It was me. I'm sorry.” But now that got rewritten at scale, and it's still working without problems today.
Thomas Dohmke
What we're finding now isn't just the simple stuff that folks on Twitter or on the internet are sometimes saying, like, “Hey, why is this like this?” Sure, there are absolutely silly problems that shouldn't exist. But now we're talking about unique, novel permission problems that happen only at a scale across all different objects, where we have to rewrite the underlying system.
There are problems that caught us off guard. I think I said that the growth is astronomical, but we're also making such material progress. I'm excited about what's going to be possible once we've reimagined the underlying foundation layer, or at least pieces of it—not just all of us, but all the new people who are becoming developers, and all of their agents and all the tools working together.
That will still happen in that GitHub tool, that GitHub community. But it's a hard day anytime we can't give you what you're looking for. We have the same problem internally. We operate through GitHub.com, of course, and we have backups when things go down, but we feel it, too. If it's not working, it's not working for us.
That's the promise of dogfooding for GitHub. It's always been true: we're using the same tool you're using. We're not using a super-secret version. We need it to be great for us, for our customers, of course, and for open source—and now with an exponential growth of agents doing it, too.
I wanted to lay out for audio listeners who maybe haven't seen your tweets or whatever: 1 billion commits in 2025, and now 275 million per week, on pace for 14 billion this year. If growth remains linear, is that still the pace?
Yeah, it's speeding up. It's still speeding up. Exactly. This was in April.
All right. So basically, you have 14x growth, year on year. I think that's a scaling issue. I'm going to try to steelman this thing, right? People have experienced 14x growth. They haven't had your downtime. Can we dig into that? What broke? What are we doing to fix it? Anything for the community to reassure them?
Yeah. There are a couple of different places where we've seen growth issues. Some of them, which is why we're pushing hard on more CPUs, are in Actions in particular. More tools, more agents, and more PRs mean more builds. More builds need more CPUs, so we're expanding through not just our data center but, as we were talking about, moving to Azure and adding additional cloud compute because we simply need more CPUs.
It's not as much GPUs. We definitely need GPUs, too, but now CPUs are becoming a factor underneath the hood when it comes to some of the underlying services. We've been breaking up our database infrastructure over the years so that we have more logical separation between the various services.
The place where we continue to have pain is permissioning. Many of our permissioning layers sit in a database that we internally call MySQL 1. Old Hubbers will know what I'm talking about. We've been pulling things out of MySQL 1 for many years because we use Vitess and other technologies, so Shale was born from this.
And so, part of it is that I think there are some other places where more projects appear to be moving toward monorepos, whereas we were going the other direction for many years in the industry. Repos were smaller, but there were more of them. Now we're seeing the opposite: repos are bigger. It's not that there are fewer of them, per se, because there's new growth; we're just seeing many more big repos.
Big monorepos have always had a unique performance problem because each one is slightly different, particularly if the underlying blobs are incredibly big inside the repos. We've been doing a ton of work that most people probably haven't experienced unless they're in this situation with a monorepo. But that Git infrastructure layer improvement does help the overall system, because many of the improvements that make monorepos work better make all repo infrastructure work better.
I can keep going down the line. Another thing is that we're changing how we do—I'll just say—job queuing, for lack of a better explanation, changing the underlying technologies there.
I spent 2 years being a job-queuing guy.
So it's a little bit piece by piece. It's mostly because, as we built it, we built everything in a way that assumed, I guess, in some ways, that the size of the pipe of work was going to remain the same. There were just going to be more people coming through each of those pipes.
But instead, now, in places where a git push was generally a certain size, for example, that's no longer true.
Oh, yeah. You know, even the average online commit—
Same thing with PRs. A PR is the same thing, and we've talked about optimizing that and making changes. There were technology choices that did not work there; it got slow, it wasn't fast, and it didn't do what users wanted.
So we've been ripping that all out and saying, “Okay, that's just not right. Let's stop putting good money after bad and do it the right way.”
So, there are a lot of things. It’s not quite like when I’ve experienced scale at GitHub historically. It’s almost always been 1 of 2 options that we’ve used: vertical scaling, particularly with databases, or horizontal scaling. We just have more people using the service, so we add more servers and rack them in our data center or use them in a cloud.
Now we’re sort of in a diagonal where vertical scaling doesn’t really work anymore. Horizontal scaling doesn’t work either, because we all have CPU or GPU constraints in the world now. We have to crack open services that have been running for 10 or 15 years and say, “Okay, the rules of this service have legitimately changed, and now we have to rewrite them.” None of this is an excuse. We have to do the work. We have to make it better.
Alessio Fanelli
I mean, actually, as an infra guy, this is one of the most fascinating scaling challenges I’ve ever seen.
Thomas Dohmke
That’s the thing that’s hard. When we weren’t talking about it publicly, I came out and said, “Hey, I just want to explain what’s going on.” Part of it comes from a very old GitHub ethos, which is: it’s our uptime; it’s down. I know you’re a developer, so you’re inclined to want to understand more about what’s going on, but at the same time, us saying, “This service didn’t perform the way we expected, and now we have to go change it”—we weren’t trying to hide anything from you in that. That’s our problem, because you expect us to be up.
I think that’s really baked into the core origins of GitHub. What we’re trying to do as a team now is all that work, but also talk about it more. We want to share more technical details, write these blogs and posts, and get the engineers who built it to tell you, after they’ve finished the work, “This is what we did.” I think that’s the contract we want to bring back to the community: we’re still very serious about what we’re doing. We just haven’t been telling you about each piece.
So, let’s do that. We’re going to keep building this and scaling it in a way that supports whatever the next exponential growth is going to be—if it’s not 14, then it’s 30 or 50 or whatever.
Alessio Fanelli
Yeah. First of all, fantastic answer.
Thomas Dohmke
I apologize in advance if any of that is slightly incorrect, simply because I’m still in the weeds with this, but it’s not my day-to-day. That’s the thing: we’re all looking at it to that level. Obviously, if people want to help, they can join.
Alessio Fanelli
Absolutely. I think that’s good. I think people also just want to know: when are you through the thick of it? Have we identified all the issues? Is this just never-ending? Is Git broken? Do we have to change the Git protocol? How much is breaking? It’s been a while.
I think people do want to know what the path back is to the reliability that everyone expects out of GitHub.
Thomas Dohmke
Yeah. Our availability in the recent few weeks has been much better than the 3 weeks before that, or the 3 weeks before that, and so forth. A lot of these improvements are still very much paying off for us. We’re still working on the database piece that I mentioned, and that just takes a little bit of time to get fixed up.
My answer in my head was, “Call YouTube.”
So, YouTube ultimately—
They also use Vitess.
They also use Vitess, but I think some of it is that our move to get additional compute will alleviate a fair amount of this, particularly on the Actions side. A lot of the underlying outages are actually related to Actions.
I’ll tell you that Actions is the root of all evil.
I mean, it has its pros in that it’s the core compute layer for either CI or side projects.
Actions.
Thomas Dohmke
No, I don’t know.
I mean, I pay a lot for compute, right?
Yeah. Actions is definitely a piece of the overall business, but we also give away so many minutes as part of our entitlements that everyone’s using it. We talk about it as CI/CD, but the reality is people use it for CI/CD, various processing, and automation. Part of it is also that compute piece, which is alleviating some of our availability issues.
My abuse of Actions—I’ve been scraping with it every day.
Thank you for your service.
But this is also how I track Actions uptime.
Sure.
So, anyway—
Thomas Dohmke
Some of it is going to be that. I would say that each month, over the next 3 months, you’re going to see fewer and fewer moments where we have an availability problem or things go down. It’s not that the growth has stopped. We’re still experiencing faster growth than ever before. It’s just that the underlying improvements we’ve been hard at work on are finally paying off.
These improvements take time. It’s less about incremental improvements, where you make a small change and get a big output. It’s now material change that takes a bit of time, and then you see a step change in our availability.
There’s a thing we used to do at Amazon—I don’t know if it’s still a thing—like automated software verification, simulation, load testing, and all that. At this point, you have a whole map of GitHub. You can assume whatever growth rates you care about on whatever dimensions and run it through the system. I feel like there’s a way to have a systems model of GitHub and see what breaks, but obviously I’m not that close to the problems.
Yeah, totally. I would say that’s been the journey and the work happening since November. October was when we even said, “Look at the growth,” and then you start to see the chart really pick up. It’s like, “Oh, we tested it at N amount of scale, and now it’s at N cubed, maybe, in some vectors.” So now we have to build it that way and make sure it can handle all of that scale.
Shawn Wang
Let’s talk Copilot. How many original creators of Copilot are there?
Oh, jeez.
Shawn Wang
I count around 12.
I forget the number of people who were on the original GitHub Copilot team. There was a bigger group.
Shawn Wang
It’s Alex.
Alex worked on it. Ugo worked on it. There are a bunch of people.
Shawn Wang
And their entire management line.
It was enormously successful in its day. I think the last number I heard was that Mario came to my conference and talked about the $100 million mark. I think most recently it was $300 million, but I might be out of date there.
Shawn Wang
I don’t think we’ve shared the dollar amounts.
What’s the state of Copilot? As a concept, it’s obviously been brought into more of Microsoft, but what’s its state at GitHub?
One of the challenges we had with Copilot is that we came out of the gate with code completion. It was super great and powerful. What we initially worked on after that first year or year and a half was fine-tuning, because our customers and the industry as a whole were talking about how to get more correct or better performance out of this.
We were working on a whole bunch of efforts to fine-tune larger and larger code completions, next-edit suggestions, and so on. We were fine-tuning one model for overall use, as well as fine-tuning per customer for customers that wanted that as a service.
Around that time, the next generation of models arrived. That’s also when all these other AI coding tools came to be, because the models really sped up. Everyone would ask, “What happened to GitHub Copilot? There’s all this time.” I would say that we were in an era of thinking, “We want to improve everyone’s results, so let’s focus on fine-tuning, because that will give us better results.” Then the models got better.
Since then, we’ve been on this journey of saying, “Of course we have great code completion, and we’ve invested a ton in better underlying models.” We’ve post-trained better next-edit suggestions and post-trained language-specific models. All the stuff that sits in the ether of GitHub Copilot is not just code completion anymore. We now have a single underlying SDK and harness for our coding agent, Copilot itself, the new CLI, the new desktop app, and cloud agents that all use the same SDK.
And so there was this moment of really trying to figure out what our customers want, with models sherlocking us a little bit, and then asking, okay, what does everyone ultimately need? What we think is that it's not solely about code generation. It's really about having the ability to use these coding-agent-based harnesses or runtimes across not just the coding experience—where I'm going to send a bunch of tasks out, or use Fleet to break up a single task, or use Autopilot—but also elsewhere.
How do I do that for all of my security remediation? How do I do that for every GitHub issue that comes in? Just stick a coding agent on it to see if it's possible. How do I go through my repository, see all of my documentation, and extract, okay, this doesn't actually match?
That amount of AI coding-agent automation is a big part of what we see when we're looking at, okay, we're still going through a similar but very different flow. It's all happening at the same time. There's not really the same, “I'm going to create an issue to track my idea of building this”; you're probably just going to go do it. You're going to say, “Hey, just build this.”
There are still tons of open issues and projects that are using agents like Peter and OpenClaw to be able to sic all of his agents on that infrastructure layer. A really great coding experience that allows you to handle the multiplexing aspect is what we've built and are still building with GitHub Copilot.
For folks who haven't really used GitHub Copilot since the thing that got them excited about it, I really encourage you to look at, especially, the GitHub Copilot app. That's my new daily driver. Obviously, if you prefer the CLI, you can also use the CLI and all the models, including the bring-your-own-key side of it. We're still improving our own models and using those too.
It's just a very different experience, but I think that broader sense of software development—and how coding agents can help throughout, not just writing the code or even verifying it or deploying it—is where we have this unique angle. The other side is the context piece.
Shawn Wang
Oh, God.
We're still—it's one of those things where I think the final thing that will let me ultimately feel complete at GitHub is when we have this ability for GitHub to act like Kyle wants it to act, or Shawn, or whatever. We all codify that in rules and memory and everything else.
Shawn Wang
And that's an open research problem, right?
100%. But if we can even just do it where my team, without me having to codify everything—and as our methods shift on purpose—we're able to have that full experience and all the understanding of what's happening in my dependencies or open source, that feels like a big place for us to continue to provide something really unique and valuable with GitHub Copilot.
Shawn Wang
Is there a form factor that we haven't explored? I think we did code completion. Then we did, broadly, what we'll call an agentic IDE, which Cursor famously popularized. Now it's all about agent orchestration, background agents, whatever, whatever. Then there's security review. I feel like everyone has just thrown agents at everything. The entire SDLC is covered with agents.
Are we at the end of history here, basically? Is it just refinements from here on out?
I think that we're all still in such a hyper-myopic era of AI that the reality is, for various boring security and governance reasons, at least for most people's work, why is my coding agent—even if it's all background agents running in the background—not losing all the context that's available to it across everything that I'm doing outside of coding?
Shawn Wang
Yeah.
I think the most interesting thing to me in AI is actual ambient AI, not an inserted assistant name or something. I've tried just about every AI Pin and tool, and they don't work the way that I'm looking for them to work because they're just trying to capture, codify, and recall.
The thing that I'm looking for goes back to the very beginning: I'm looking to build out the next version of webhooks or implement a new feature, and for it to know every spec doc, every email, the conversations that I've had online, everything about how this could be implemented, and be able to use that as part of its decision-making. None of these tools are ultimately doing this.
I think it's as if software development were a single-lane task where it only needs a developer: once I write the perfect code, we'll be done here. But that's just never been true. It's all the context of the other team members, what the business is doing, and what's popular right now.
I think that's this huge opportunity for us to go much broader than really excellent coding agents, and that is honestly why OpenClaw has been so interesting: it's connecting to all the data sources that Kyle, the human, cares about. Now my question is, okay, how can I take all that and use it every day as a software developer, connected together, and not just have a new way to kick off a coding agent?
Shawn Wang
And that's where we're at. We're saying, “Okay, I'm going to go use this CLI under the hood or this SDK.” But that's not what I'm talking about. I'm talking about having a conversation with you: it downloads the podcast and realizes, “Oh, Kyle sounds like Kyle needs this app or this thing.”
Exactly. That level of connectivity is where we still have a ton of ways to go in software, because when we have that red thread and want to pull that idea, it can not only use the perfect way to write that code, but also all of the taste and judgment calls and expertise that I've earned, or that we've earned as a group, and use it as part of the actual implementation.
Shawn Wang
Yeah. The extreme of it is AI runs your life, right? I think there's a scary inversion of control in the way that I literally mean it and in the way that developers mean it in terms of frameworks like the Hollywood Principle: “Don't call me; I'll call you.”
Yeah.
Shawn Wang
At some point there's an inversion of control where you stop telling the AI what to do; AI tells you what to do. That's a little scary, but maybe better. Nat Friedman shared this at a Stripe event, talking about how OpenClaw was like—he connected OpenClaw to his cameras, and it was watching and directed his Uber.
Yeah.
Shawn Wang
There's a degree of this where I was like, I actually would love OpenClaw to tell me to drink water. I don't know that I want it changing where my car goes, but I do think that's kind of what I'm talking about: it needs to have so much more information at its disposal for it to be helpful to me.
I still don't think we're anywhere near talking about AGI. I'm just talking about every time I have to tell you something I care about that I've said a dozen times, it should be able to know that, codify that, or gain access to it. The dreaming ideas are an attempt to do some version of this, but I think there's a much more proactive angle that will help software developers if we can test that out a bit more.
Shawn Wang
Yeah, yeah, yeah. The other thing about OpenClaw that reminded me is Microsoft has a CVP dedicated to OpenClaw.
Yeah.
Shawn Wang
Why?
Because you don't think they should.
Shawn Wang
I don't know. I think CVP is a high title.
Guest
Yeah, yeah.
Why is this so important? Microsoft doesn't even own OpenClaw.
We're talking a lot more about this at Microsoft Build this year too. I think the main thing is that what OpenClaw has done is made this connection for people to have access to the resources that they have access to and be able to do things for them in a way that previously people were trying to codify into their own agents.
When you think about it in the work context, wouldn't it be great to have a Claw-like object that I could run on my work device, that had access to my work assets, and worked well on Windows? That's what that would look like.
I think that OpenClaw has become a personification of a valuable agent that understands me because it has access to all of my information and it can use a computer. Thus, it can do a lot more than just a task-oriented process or a chat tool.
That's a big part of the goal of Build, right? We're at Build this year trying to take a very different approach. It's unapologetically aimed at developers. We're trying to show the bigger investment, not just to say, “Hey, like you said, why do you have a CVP of OpenClaw?”
One of the problems that we have is that if you install our agents not on a Mac mini or a hosted device, but on a personal device or a work device, we need better sandboxing at the OS level.
I need to be able to use that OpenClaw and not get fired. Microsoft is like, “Okay, great. Let’s do that, too.” And then it’s, “Okay, well, where should I be able to talk to this agent? Should each of us just have an OpenClaw available to us at work?”
Probably.
Guest
And so there you go, continuing to contribute a ton to the open-source project, too. Microsoft, I think, as I’ve gotten more and more information, there’s so much investment in the open-source projects themselves that, for whatever reason, there’s this—I think those teams don’t want to come off as taking any credit or getting any recognition. But so many of these core contributors on these teams are full-time just pushing into open-source projects.
I think that kind of shows the difference between, well, why are we looking so hard at something like OpenClaw? Why are we looking at sandboxing on Windows? Why are we looking at cloud versions of sandboxing? Why are we looking at these things? Because ultimately, we need more platform components. We don’t need everyone to be building the same exact top-line product over and over and over again. If we’re building for builders, that requires us to give you all these components and tell you what they are, how they work, and why you should be interested, versus only delivering that single vertical over and over and over again.
Yeah. I think maybe one way of framing it is that Microsoft is the original operating systems company, and here’s the new operating system for AI.
Yeah, yeah, yeah. I mean, I think that we’re also in an era where we need to help build that bridge. All joking aside, operating systems need to look different than they looked 5 years ago because it’s not just you using them anymore. That’s changed the whole idea. It’s not, “Okay, my OpenClaw is going to create a user account.” It doesn’t work like that.
Just like all of us, we all have to look much, much more deeply in the stack, all the way down to the silicon layer in Azure, and be like, “Okay, well, what do we need now?” Because the workloads are different. It’s not just, “Okay, we need more inference.” It’s, “Okay, well, what type of inference do we need? What type of compute do we need to run these agents or run these agentic flows?” It’s a really interesting, multilayer problem, versus software in the last 5 or 6 years. We’re all going to our events and kind of saying a version of the same thing: “SaaS product has new SaaS thing. It’s the best SaaS thing ever.”
It was boring for a while.
And so now it’s like, “Oh my goodness, we’re at physics. We’re at physics problems.”
Yeah. I mean, we’re now trying to make room-temperature superconductors.
Still.
Yep, yep. That’s never going away. No, I think that’s a really good overview of everything. Have we left anything unsaid that you wanted to really get out there that we should cover?
Guest
Yeah, I’m really excited for folks to check out the announcements we have at Build. You can go look at them online and take a look. I’m hoping that it’s driving a degree of curiosity and interest, because there’s such a big shift that we’re making at Microsoft for developers.
If you’re a daily driver of a Mac device or a Linux device and you’re like, “Okay, I don’t use Windows,” there are improvements being made that I think are going to surprise folks, just to be like, “Oh, they really want to do that.” I’m talking about developers. I’m not talking about, “I play video games on the weekends on my Windows computer.” I’m talking about my daily driver, all the way from that to, “Okay, well, what is it like to build an agent or build an app and deploy it and run it at work in particular?”
I think that is a big piece of it. I talk all the time with the team about how I build on the weekend should be how I build at work. But if you’re working in a Fortune 100 or Fortune 500, you’re probably not building an app and then shipping it to some service. You’ve got to go through security and compliance. How can we move just as fast at work? That’s something that we have a bunch of different offerings for, to give you that same sort of agility and power but in the work context.
And then I will tell you, as I’ve mentioned a couple of times, it’s very freaking cool: if you are in the Microsoft 365 world in any way, check out Work IQ and Foundry IQ. These little—oversimplifying it—context engines are wildly good. We’ve given them to our developers at GitHub; we’ve given them to employees at GitHub. We’ve used these tools to be able to just ask questions around everything that you have in your work context, and with Foundry IQ, be able to do the same exact thing across all your existing stores. Don’t move to new tools; just connect them in. It’s surprisingly powerful.
You’re still not going to get fired, and IT isn’t going to turn it off because it’s leaking all this private information. That’s the trick that I think is sometimes getting lost when we’re talking about all these great new platforms, because I can use them. I’m like, “Oh, this is super powerful.”
Oh, and I can’t use it.
And it’s not because I’m at work at GitHub. It’s not because I’m not allowed; it’s because they can’t do all the things that large, complicated companies need. And so whether it be, like I said, just the interesting daily-driver curiosity all the way through to, “Oh my gosh, I can go use this at work tomorrow, potentially,” and have that context layer, have that intelligence, it’s a huge shift.
And so, check it out. I’d love to hear feedback. I’m not shy on social: what’s working, what’s not? Hopefully, we surprise folks a little bit.
What I’m hearing—I mean, first of all, I think that’s a great pitch. What I’m hearing, actually, is that you should put the Work IQ people next to the Copilot people because the exact context problem that you named, they’ve solved enough for you to do your job, which is nuts.
So, the thing that we are—literally, that’s what has been happening the last several months.
I already forecasted you.
It’s, like, totally—because you’re totally right. The code and the code asset problem is a little bit unique, but otherwise, yeah, we’re all working with each other now. It’s all just context.
Exactly. Yeah, yeah. Amazing. Great. I’m going to be there. I’m going to be doing a couple of sessions there. I’m going to be interviewing Satya. I know when I first started the pod, though, I had Jeff Dean on. Jeff is in the Hall of Fame of people I want to meet someday. Satya’s on there, so what should I ask Satya?
I think the best question to ask is what he thinks is true in 2 or 3 years from now. It seems like such a throwaway question, but ultimately, the way that he is looking at this AI problem—inference problem, token problem, and how we’re actually going to be working—I think you can see some of the recent shifts that have been happening inside Microsoft to drive us to a place where it’s not 4, 5, 6, 7, 8 different things. It’s not a lack of context everywhere, but why is this approach in 2 years going to pay off? Because that, I think—
Wow, that’s a bold question. Okay, I’ll ask it. I’ll say, “Prompted by you,” but absolutely. It’s a bold question because I think there’s a lot of doubt, to be honest, externally. And so, yes, I want a straight answer from him on that. I think it would reassure a lot of people and honestly give me a lot of food for writing.
Thank you so much for spending your time. Thank you for doing what you do. As a co— you don’t need to be the external face, but because you are authoritative, because you have so much background with GitHub, and because it’s so authentic, we on the outside feel it. So thank you for that.
Of course, I appreciate it. Thank you so much, Shawn.