Copilot, Agent Mode, and the New World of Dev Tools with GitHub’s CEO Thomas Dohmke

This week on No Priors, Sarah and Elad talk with GitHub CEO Thomas Dohmke about the rise of AI-powered software development and the success of Copilot. They discuss how Copilot is reshaping the developer workflow, GitHub’s new Agent Mode, and competition in the developer tooling market. They also explore how AI-driven coding impacts software pricing, the future of open source vs. proprietary APIs, and what Copilot’s success means for Microsoft. Plus, Thomas shares insights from his journey growing up in East Berlin and navigating rapidly changing worlds. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ThomasDohmke Show Notes: 0:00 Introduction 0:37 GitHub Copilot’s capabilities 4:12 Will agents replace developers? 6:04 Copilot’s development cycle 8:34 Winning the developer market 10:40 Agent mode 13:25 Where GitHub is headed 16:45 Building for the new challenges of AI 21:50 Dev tools market formation 29:56 Copilot’s broader impact 32:17 How AI changes software pricing 39:16 Open source vs. proprietary APIs 48:01 Growing up in East Berlin

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  Hi listeners and welcome back to No Priors. Today, we're joined by Thomas Dunk, the CEO of GitHub, a platform used by over 150 million developers worldwide to collaborate and build software. As CEO, Thomas has overseen the development of tools like GitHub Copilot. Before becoming CEO, he helped shape GitHub's product strategy, empowered its global expansion, and previously worked at Microsoft. In this episode, we'll talk about the future of software development, the role of AI encoding, open source, and product plans for Copilot. Thomas, welcome to No Priors. Maybe we can start with the meat of it. What is happening with Copilot and the new releases at GitHub recently?

• 0:42Speaker 01

  You're heading straight into it. We're really excited about making Copilot more agentic. A few days ago, we announced the agent mode in Copilot in VS Code. So instead of just chatting with Copilot, getting responses, and then copy and pasting the code into the editor or using autocompletion, the original Copilot feature, you can now work with an agent and it helps you to implement a feature. And when it needs to install like a package, it shows you the command line, terminal, commando, and you can just say, okay, run this. You're still in charge, right? So that's the crucial part of these agents that we have available today. That as the human developer, you still need to be in the loop. But we also showed a teaser of what's about to come in 2025.

• 1:26Speaker 01

  We call this Project Patawan, because it's like a Jata in a Patawan. You gotta have patience and you gotta learn how to use the force. But we think in 2025, we get into a place where you can assign a GitHub issue, a well-defined GitHub issue to Copilot, and then it starts creating a draft pull request and it outlines the plan and then it works through its plan. And you can, similar to how you observe a coworker, you can see how it commits changes into the pull request and you can review this and provide feedback to Copilot. And so Copilot basically graduates from a pair programmer to a peer programmer that becomes a member of your team.

• 2:03Speaker 02

  The obstacles to that right now are some new model advancements? Is it just building out some other core technology? Is it? Just the UI, like what is keeping that from happening right now?

• 2:15Speaker 01

  Yeah, I think the first thing is the model, the full O3 model that's not available yet, that OpenAI showed as part of the ship miss right before the holidays. We're going to see improved reasoning and I think it's as the models get better in reasoning, we're going to get closer to 100% of this V bench, which is that benchmark out of 12 repos, open source Python repos, a team in Princeton identified 2,200 or so issue pull request pairs. Effectively, all the models and agents are measured against. And so that's number one, you know, the model and the agent combination. I think the second piece is just figuring out what's the right user interface flow. If you think about the workflow of a developer, right?

• 2:56Speaker 01

  You have an issue that somebody else filed for you, you know, user product manager or something that you filed yourself. Now, how do you know whether you should assign co-pilot to this, the agent to it? Or whether you need to refine the issue to be more specific, right? It's crucial that the agent is predictable, that you know that this is a task that the agent can solve. If not, then you need to steer it. So sterability is the next thing you need to either, you know, extend the definition or the agent needs to come back to you and ask you additional questions. And then at the end of the process, you want to verify the outcome. And so in our demo, that's why we're thinking the right flow here is actually that the agent works in a pull request, like similar to a human developer with lots of commits.

• 3:39Speaker 01

  And then you can rollback those commits or check them out in your VS code. We saw that with some of the agents that are available is that do I as a developer actually tolerate the agent? Like, is it actually saving my time or is it wasting my time? And the more often you see it wasting your time and just burning compute cycles, the less likely you're going to use it again. And so if you predictable, scalable, you know, verifiable and tolerable. If you get to that for all four criterias to a certain level, I think we're going to see a wide adoption of agents.

• 4:11Speaker 02

  How far away do you think these agents are from being? Sort of the median programmer equivalent. And then how much longer do you think it takes to get to sort of superhuman?

• 4:22Speaker 01

  You know, I thought about this this morning, right? Like what if regardless of what agent you're thinking of a travel agent or a coding agent, or maybe it's an agent that designs your house. The fundamental challenge is actually the same as you as have as a human developer. Right? Like you have this big idea in your head and you can sketch it on a whiteboard, but then you want to start coding and you have to take this bigger idea and break it down into small chunks of work. I think that's the part where we're far away from from agents actually being good enough to take a very rough idea and break it down to small pieces without you as as developer or as architect or even in planning your travel, constantly getting questions back of what decisions you want to make. You know, what database, what cloud?

• 5:06Speaker 01

  Like, imagine you give the agent a task saying, you know, build GitHub or build a mobile app or something like it will just be not specific enough. Right? So that's the system thinking that I think the median developer will not be replaced by agent. And the flip side of that is a lot of, you know, what developers do is just picking up issues and fixing bugs and finding where to fix the bugs, adding a feature that comes from a customer. And then you have to navigate the code base and figure out what files you have to modify. I think there we are going to see dramatic progress over the year. We actually, you know, when we recorded the demo for the Padawan project, we actually had one of our product managers use an issue and the agent create the progress themselves. Right? And so a PM that usually doesn't code and doesn't write code in the code base

• 5:50Speaker 01

  was able to use the agent to create a real progress that was then reviewed by the developer and merged into the code base. So in some ways, we're already there. In other ways, we need to get to the point where you trusted enough that you're using it day in, day out.

• 6:03Sarah Guo?

  I'm sure you guys were doing a bunch of dog fooding before releasing agent mode and Padawan as well. Maybe if we just sort of zoom out from that, from the eval phase, like, can you describe what the overall like development cycle is for Copilot today? Like, how you do planning and make decisions about what to try and how you improve it?

• 6:22Speaker 01

  The industry calls no AI engineering. Which is, you know, we extended the full stack of backend and frontend development with AI development. And so how do we use a new version of a model or new model as we have now the model picker in Copal? We're constantly dealing with multiple models from multiple vendors. How do we integrate that into into our stack? We have, you know, an applied science team that runs evaluations. We have a team that builds out these these benchmarks that the applied science team uses to compare models with each other, but also the teams that build your features like code review agents or the three agents or agent mode users to to validate their work as as part of their test suite, right?

• 7:05Speaker 01

  So it's it's no longer just the the data scientist and the engineer that those roles have, you know, uh, more and more overlap and they're collaborating day in, day out. Um, we do a lot of experimentation with A B testing, where we flight new versions or new fine tuned versions of a model. Um, after the offline test, you know, and online test first with GitHub and Microsoft employees and and then with sets of the population. And then overall, you know, obviously we have a roadmap of features, you know, that we wanna build in a long backlog, not just for Copilot, but all up for GitHub, right? Like GitHub is, you know, turning eighteen this year. I think it's eighteen years that since the founders in late two thousand seven started working on it and then it launched in early two thousand eight.

• 7:50Speaker 01

  And and Microsoft turns fifty, actually, uh, April fourth. Um, and so we have a long backlog of of of customer feedback where we're using Copilot to build those features, you know, and agent mode now, um, you know, to to accelerate our feature delivery. But at the same time, you know, the market is moving so fast. And whether we're meeting with OpenAI or with Anthropic or with Google, we learn about new new model versions. And then our roadmap changes from one day to another, you know. And, you know, I'm sure you guys are seeing that as well, like the market is moving so fast. We're we're literally sitting on an exponential curve of of of innovation that is hard to keep up. And you can't really plan more than than a month or two ahead of time.

• 8:28Sarah Guo?

  What do you think about competition being on that exponential curve? I think like it is wild to think that, uh, you know, sweet agents, as you describe them, like, didn't exist as an idea a year ago. You know, we now have. A market full of folks experimenting with these products. How do you think about winning the like GitHub is obviously a very dominant force overall as is Copilot, but how do you think about, you know, winning the developer over and what they care about in that, you know, changing and competitive market?

• 8:57Speaker 01

  The way we think about winning is that, you know, we care deeply about developers and that's always been, you know, the heart of GitHub is that we put developers first and that we are developers that are building products for developers. You know, we have the saying at GitHub is that we're building GitHub with GitHub on GitHub using GitHub, right? And so everything that we do in the company, including, you know, the our legal terms and our HR policies and our product management, sales, sales enablement, all these functions are in GitHub issues and GitHub discussions and GitHub repos. So I think that's number one, that we deeply care about our own product and we're using it for everything day in, day out. You know, the first thing I do in the morning is open the GitHub app on my mobile phone and then Slack as a lot of our, you know, operations

• 9:42Speaker 01

  company chat runs through Slack. Number two is, you know, you mentioned competition. I mean, it's obviously like they've never seen anything like that in the developer space. It's the most exciting time, I think, for developer tools. And you know, I've been a developer for over 30 years. It's amazing to see the innovation, you know, the news that is coming out every day. And I think that energy, you know, is in the market and innovation driven both on the open source side and on the closed source side, right? Let's not forget, you know, that is not one sided. As much as there's innovation on proprietary models and software, there is as equal amount of innovation in open source and on GitHub. And that energy obviously gravitates into us. I'm a big Formula One fan. Like it's good when there's competition because the races are so much more fun to watch if there's

• 10:27Speaker 01

  multiple teams that can that can win the championship. And I think the same we feel about the competition. It gives us motivation every single day when we wake up to do better, to move faster and to ultimately win with the best product in the market.

• 10:40Sarah Guo?

  You have such like rich data about how people are actually using Copilot. What is surprising you even from the last week or so since Agent Mode was released?

• 10:50Speaker 01

  The thing that always surprised us from the early days was how much code Copilot is writing, you know, some of the folks from Microsoft and GitHub on your podcast in the past and in the early days, you know, soon after we launched Copilot preview, it already wrote like 25% of the code. And remember that meeting where we looked at this in a product review and said, that must be a mistake in the telemetry. Go back and validate that. It can't be true that it's writing 25% of the code because it was just auto-completion. And you know, as cool as that was, at the same time, you know, you it's still made a lot of mistakes in the early days. But it quickly dawned on us that A, the number's true and B, that's just the learned behavior of software developers, right? Like you're

• 11:32Speaker 01

  typing something, you're always reaching the point where you need to look something up. So you go to your browser and you find code on Stack Overflow or on Reddit blogs or on GitHub. And then you copy and paste that and you're modifying it anyway afterwards, right? Like that's the inner loop is always this kind of like you write something, you try it out with the compiler and debugger, and then you keep modifying until you make it work. And that number, you know, then quickly rose to around 50%, depending on the programming language. If you look now, you know, with these agents, it's hard to measure that because you know, when you you can literally go into agent mode and say, I want to, you know, build a snake game in Python and it writes all the code for you,

• 12:14Speaker 01

  right? Like it writes multiple files. So the denominator becomes zero, right? Like it's like infinite percentage because you know, the only thing you wrote was a prompt and the 15 minute demo from two years ago is a one minute demo now. And I think that's, you know, still surprising in many ways that we were already so far ahead on that curve. And then the opposite is also true, right? You can you can get it into a place where, you know, it just keeps rewriting the same file or deletes the whole file because it gets stuck somehow in the logic. And so it grounds us also in the reality is like we're not close to an agent just autonomously passing through all my GitHub issues and

• 12:56Speaker 01

  then fixing all my backlog for me. The only thing I'm really doing is just validating and becoming the code review human for the software development agent, right? Like so we are in this, you know, we're swinging between the excitement of how much it can already do and the reality where it gets stuck in very simple scenarios where it's like you're trying to kind of like figure out the prompt of telling it.

• 13:18Speaker 01

  Just do the one thing and then you just go into the file and change the whatever the background color yourself.

• 13:23Speaker 02

  That makes sense. Outside of a lot of the agentic efforts that you all are doing, and obviously I think that's amongst the most interesting stuff that's happening right now. What are other big areas you want to evolve over the coming few quarters? Like, are there other big thrusts or is it all kind of, it's all in on AI and that should be the focus of the company?

• 13:41Speaker 01

  Well, so far we only talked about, you know, the generics fee agent where you can assign an issue and it generates a progress. But if you actually look in the developer life, the day to day, you know, in most companies, that's maybe two, three hours of your day that you're actually writing code and then you're spending an equal amount of time of reviewing code of your coworkers. And while we don't believe that goes away from a pure security and trust perspective, you always want to have, you know, another human in the loop before you merge code into production. At the same time, we believe code review agents and code review is a big topic where AI can help you, especially when you work, you know, with a distributed team in different time zones where you don't want to wait for,

• 14:23Speaker 01

  you know, the folks on the West Coast to wake up, to get an initial loop of feedback. So I think code review is a big topic for us. And again, you know, the AI part is one piece to that, but the user interface is equally important, like, right? Like, ideally, you get feedback and then you can work with the code review agent on that feedback to loop because you know, won't always get exactly the right feedback to just click accept, accept, accept. You have to have a user interface, you know, cloud environment where you can just open this. If you always have to, you know, clone the repo on your local machine and then install the dependencies, switch to a different branch, you're still, you know, having way too much boilerplate work, right? So moving to a cloud environment where you can just, you know, try out the changes that came from code review and can modify

• 15:07Speaker 01

  them to make them work and have that, you know, fast outer loop in that same realm of security vulnerabilities, which is A, you know, you want, you know, your code scanning not only find vulnerabilities, but also fix them. You know, an even simpler version of that is Linter errors, you know, like code formatting and those kind of things hopefully all go away and just the AI fixes all that. Instead of you going through a hundred Lintar warnings telling you where to put the spaces in the in the parentheses. But also, if you look in in any decent sized software project, it has outdated dependencies, it has lots of, you know, known software vulnerabilities. Hopefully, non-high risk and and a lot of them low risk or where somebody decided that's not actually crucial to fix right now because the code is not reachable or, you know, we have other priorities.

• 15:51Speaker 01

  Having AI to burn down that security backlog will make, you know, both the open source ecosystem and a lot of commercial software projects so much better because it brings that, you know, effort down that every engineering manager swings back and forth between, you know, the tech debt, the legacy code, you know, the security accessibility, European regulation, whatever, right, and the innovation backlog. And there isn't really like a balance between the two. It's just like, what is the most urgent issue, the most, the biggest fire draw? Is it your sales team telling you if we don't get that one feature, we can't sell the product? Or is it the security team telling you, you gotta fix that one issue and otherwise we are going to flack you up to the management chain, right? And so that's, I think, is the AI, the AI side of things.

• 16:34Speaker 01

  But similarly, GitHub as a platform needs to evolve to support or have all the primitives for these agents and the AI to work in in tandem with the human.

• 16:44Sarah Guo?

  Do you think there are problems that people are not addressing yet that emerge from this transition, how software development is done, right? Like, so, for example, you know, you feel like we're somewhere between crossing the tipping point of the majority of code being generated this year to maybe like all of the code in like some cases or some some tasks. How does that change, like, testing or or, you know, the way we should look at technical debt or any of that?

• 17:10Speaker 01

  To be clear, I don't think all of the code is written by AI. I think the way this will work is that we have two layers. We have the machine language layer, you know, which is Python or Ruby or Rust, right? Those are effectively abstractions of the of the chipset, the machine instruction set. And that's the last layer that's deterministic, right? Like, programming language inherently, when I, it does exactly what I want it to do. And then human language is inherently non-deterministic, right? Can all the three of us can say the same sentence and and mean a different thing. And so while we will use human language to describe a lot of the, you know, features and and behaviors that we're going to build. We will still have the programming language layer below that and that we are going back and forth as engineers to to figure out.

• 17:54Speaker 01

  Uh, is the code that was written by AI actually the correct one? Is it the one that, you know, aligns with my cost profile, if you will, as an as an example, right? Like, at the end of the day, we're still running businesses that have to have. Uh, positive profit margins. I think we're going to, as engineers, have both of these layers. Um, and and we're heading into a world of more human language and and and less and less programming language. Um, but at the same time, you know, we are in a world where lots of financial services institutions still run cobot code on mainframes. And we are very far away of just taking that, you know, uh, code that's thirty, forty years old. And just hunting an agent that transforms that magically into a cloud application, right? Like, I think that's coming, but it's like self-driving cars.

• 18:35Speaker 01

  Um, uh, are coming as well, but we don't know when that cut, cut, cut over point actually happens. Where you can have a car without a steering wheel and it drives you everywhere. You know, uh, within, within the country you you live in, right? Like, the, the, it works for Vemo in in San Francisco and it doesn't work for Vemo. Uh, all the way down to SFO to San Jose yet, right? And so the the scope will increase, but we are, we are far away from, I think. Uh, solving all the tech debt and all the legacy code that exists. And so we are still, uh, I think for like a decade or so, uh, at least. Going to have software developers that work in in lots of old school, you know, PHP code and and cobot code and and and all that stuff. While at the extreme other end of the spectrum with web development and and AI, you're going to be able.

• 19:20Speaker 01

  And you're, we are already there, like, you know, just look at a, a ten-year-old, give them, you know, um, a tool like like Copilot or, you know, um, Repilit, Bolt, um, you name it. And and have them type a couple of prompts and have them explore how that works and how they can, similar to, say, with the Fusion Midjourney. Render software themselves and and iterate on that.

• 19:38Sarah Guo?

  You yourself lead a large team of software engineers. As you said, you have more human language and instruction versus machine language. Does it change what you look for or what you want to develop in your own team?

• 19:52Speaker 01

  Well, what you're looking at right now is, I think, this, how do you describe actually your problem specific enough that an agent can pick it up, right? Like, basically, the planning and tracking side of software development, the issue. That's often the biggest challenge that you have as soon as you have a decent team size. Like, 10-person startup has no problem, and most of 10-person startups don't have a product manager. The founder is the product manager, and the rest is just building the stuff. And if you have a problem to solve, you have very short communication paths. If you have a thousand engineers, the biggest problem is, what do you want to build? How do you build it? What did you actually mean when you wrote up this thing? And if you look into that space, there isn't much AI helping you yet. We have been in early phases ourselves with that with Copilot Workspace,

• 20:37Speaker 01

  where we have a spec and a brainstorming agent that basically looks at what you wrote in a GitHub issue, compares it with a code base, and describes you the before and after in human language. And then you can, similar to Notion Doc, just modify that and basically add stuff to the specification. So I think that's going to be a whole set of agentic behavior that we're going to bring into the product management space. Similar for designers, right? Like, today, a lot of designs are hand-thrown in Figma. I think tomorrow, you're going to, as a designer, type effectively the same specification as a product manager, and you have an AI tool render the code for the wireframes and

• 21:18Speaker 01

  then apply grounding out of your design system to make it look like your product, right? And so those disciplines get closer to each other, and the product manager will be able to, if they're good in writing a specification, create the whole change set. And the designer will be able to take over part of the product management role, and the engineer gets closer to these other roles, as if you know if they're good in describing the feature, can take over that part as well. So I think that's where a lot of innovation is going to happen, and in rethinking how the traditional disciplines in the software engineering team are evolving in the coming years, as we have more and more of these agents available, and they're actually good at what they do.

• 21:55Speaker 02

  As you think about these different agents and these different use cases, do you think it's going to be the same company or product that provides all three? Do you think it's going to be one interface? Is it going to be a different interface? I'm sort of curious how you think about the actual flow in terms of very different users in some sense. So there was some overlapping either responsibilities or goals. And what are the set of tools that they interact with? And is it a singular tool? Is it many? Is it one company? Is it many? Where does it launch out of? Like, how do you think about all that stuff?

• 22:26Speaker 01

  One of our strongest belief at GitHub is developer choice. And you know, imagine GitHub as a platform where you had only JavaScript libraries available or only, you know, React available to you. And we would tell you that's the only open source library you need to put in application. Right? Like, there would be a set of users using React, using GitHub because they love React. And the rest would go somewhere else because some other platform would offer them all these other open source components. Right? AI, I think we're going to see the same thing. We're going to see a stack or universe of companies that offer different parts of the software development lifecycle. And developers pick the one that, you know, they like the most, that they have experience with, and that convicted are the future.

• 23:10Speaker 01

  You know, a lot of that is part of a belief system. You know, programming languages in many ways are very similar. And then if you look at the discussion between developers, you have get the feeling they're very different to each other, right? Like, at the end of the day, they're all compiling down to an instruction set that runs on your Apple M4 chip or your Intel CPU or AMD or Nvidia or whatever. Right? So I think we are going to have a stack of different tools. And there's going to be companies that offer, you know, all the tools. Well, not all of them, because you're never going to have all of the developer tools out of one hand anyway. Right? Like, think about GitHub. We are a big platform, but then you still have an editor and an operating system and a container solution and a cloud that doesn't come from GitHub, right?

• 23:55Speaker 01

  Like, you know, we have Hushy Corp, Terraform, or Wolt as an example, or Vercella and NextJS as another example. Like, just like go into any random company in the Bay Area, and they're all going to have a different stack of tools that they have combined because they believe that's the best stack for them at this point. So I think in this AI world, we're going to see the same thing. Going to have choice of different agents, you're already there where you have choice of different models. And some believe the cloud model is better, others believe the OpenAI's model is better. You know, the reality is somewhere in the middle and different scenarios are very better with different models. And I think the same will be true in this agentic future that we're heading into.

• 24:34Speaker 02

  Is that true, given the generalizability that we're seeing? In other words, if you were to remove X percent of the models and you just got stuck with one of the ones you mentioned, up to a point, you'd still be extremely happy, given the relative capabilities we had four or five years ago. Right? In other words, it's a little bit of like, we have so many great options and some things are better than others. But fundamentally, any one of these things would be spectacular by any sort of baseline metric.

• 25:00Speaker 01

  It depends on what end state, you know, we're talking about, right? Like, if the singularity is coming, then none of that matters.

• 25:07Speaker 02

  Five years from now, five years.

• 25:09Speaker 01

  You know, we started Copilot almost five years ago, June 2020.

• 25:12Speaker 02

  And that was what, GPT-3 at that point?

• 25:14Speaker 01

  GPT-3 was really the early experiments. And then we got this model that then eventually became Codex, which was this code-specific, you know, version of the model. And today, that, you know, no longer really exists, right? Like today, everybody sits on top of one of these more powerful base models.

• 25:31Speaker 02

  Yeah, yeah. And that's kind of my point, is to some extent, the generalizability started to take over. And so I'm just a little bit curious how you think about generalizability versus specialization and a five-year time horizon for agents.

• 25:44Speaker 01

  I can see that happening at the model layer. But it's again, like predicting a little bit of, you know, when do we truly have self-driving cars? And, you know, had a Tesla for 10 years with self-driving and autopilot in one form or another. And it still cannot make the left turn into my neighborhood. I can see that future happening, but I don't know when that is. And when the models are basically just all, you know, about equal.

• 26:10Speaker 01

  But I think for software developers, the lowest level only matters until there's differentiation at the higher layer of the stack, right? Like, I think programming language or open source libraries are great examples for that. Because, you know, if you zoom out enough, there All the same, right? Like at the end of the day, you know, whether you're building an app with, you know, Swift or Kotlin or React Native. What does it matter? And like, that's just like the intricacies of software development and the belief system that we have.

• 26:39Speaker 01

  And so I think the differentiation is going to come from both, you know, where the developer gets the most, the best experience and doing their day to day, right? Like where can I, you know, start my morning, pick up, you know, something I want to work on, explore my creativity and get the job done with the least amount of frustration and the highest amount of ROI in terms of what can I ship in? Like software development, you know, over the last thirty years has always, or actually the last fifty years, if you go back, you know, all the way to the nineteen seventies, when, you know, microcomputers came and then all of a sudden you longer had to share mainframe with others, was always about how can I take all my grand ideas that are way bigger than what I can actually achieve as an individual?

• 27:23Speaker 01

  How can I, you know, get that done faster? I don't think we are at the, at the top of that exponential curve. I think there's still a lot to come. The other question you could ask is, when do the CEO of GitHub get to the point where my backlog is empty? And I actually don't believe that that point is ever coming.

• 27:39Speaker 02

  Yeah, there's a super related, interesting question to what you're saying, which is, for how long are humans making decisions on what agents to use? Because if you look at it, there are certain roles, a lot of the ones that you mentioned, developers, designers, et cetera, that have traditionally tended to be a little bit trend based. You know, it's, it's almost mimetic what certain developers were used sometimes. And obviously there's like the dramatically superior products and there's sort of clear choices around certain tooling. And sometimes it just feels like it's kind of cool and so people are using it. Same with programming languages, right? So it's almost an interesting question when the human component of decision making goes out the window. Are the decisions that are made radically different? Because you're, you're getting rid of trendiness, you know, you're not going to use Go, you're just going to use Python or whatever.

• 28:21Speaker 01

  If I look at, you know, my team. How often as a CEO do I have to check in with them to see if what they're building is actually what I thought I want them to build when I gave them a task, right? So it's number one, the human that takes over a task and feature an epic, whatever, still has a loop with other team members to kind of like ensure what they're building is actually the right thing. I don't see a world where we can be specific enough when we give the agent work that it can just do it all by themselves, unless, you know, the unit size is very, very small. The other side of that question, I think, is like, when do we get to the point where all our software is personal software?

• 29:04Speaker 01

  And in fact, I no longer install an app from the App Store. I just, you know, use a natural language interface to build all the apps myself. And so I have basically completely personal software on my personal computer, my smartphone, instead of off the shelf software that is the same for ourselves, where the user interface effectively is completely personalized. And, you know, we have science fiction movies or action movies like Iron Man, right? Like where Java is completely personalized to Tony Stark. And so I think that future, that will happen in the next five years for sure. It's just a question of how good is Java is going to be? And can I just tell it Springback is coming up, same hotel, same family, you know, say, and it books me the trip?

• 29:46Speaker 01

  And the only question I have to confirm is do I do the $5,000 trip?

• 29:50Speaker 02

  One other thing that's been striking about GitHub and Copilot and everything else is the actual business success of all of it, right? And I think it's been quite striking on the earnings calls more recently that have been done. What can you share in terms of business and financial metrics and the impact that Copilot and GitHub more generally are having for Microsoft?

• 30:11Speaker 01

  Not a lot beyond the earnings call.

• 30:14Speaker 02

  Sure.

• 30:14Speaker 01

  I'm trying to remember. I think the last number we shared was some. A few quarters ago, seventy seven thousand organizations using Copilot. And back then, the number of paid users was 1.8 million paid users. We haven't shared an updated number since I can't share that latest number. But I think what's really interesting from these earnings calls, if you look at the number of logos that Satya has called out, it's across the whole spectrum of industries. It's not just cool startups. It's not just financial services institution. It's really every industry that has adopted Copilot. And I don't think there has been a developer tool that has been adopted with such a velocity

• 30:58Speaker 01

  across the whole spectrum of software development in any company size and in any industry. You know, if you think about it, $20 compared to the salary of an average software developer in the United States is like, what, 0.1% if at all? And then we're talking about now 25, 28% productivity gains on the end to end, 55% or higher on the coding task. But as we said earlier, developers do more than just coding. That's an incredible ROI on the dollar spent. And I think that's what is driving this adoption curve. And then any companies now, software company, and they all have the same problem described earlier, they have long backlogs and way too much work.

• 31:41Speaker 01

  And every time, you know, one of the managers goes to their team and asks them, how long does it take to implement a feature? It becomes the Jim Kirk, Scotty joke that, you know, how long does it take to repair the warp drive? And you get an estimate that's outrageously long. And then it becomes a negotiation where the captain sets the deadline instead of the engineer actually estimating what's possible. And I think that's where, you know, a lot of the business success of Copilot is coming from. All the people writing software are frustrated how long it takes, not because they don't think their engineers are good, but just the complexity of building software.

• 32:15Speaker 02

  How much do you think this pricing changes? And I know it's just speculation at this point, when you're actually replacing people. And I know in a lot of industries, it could be legal, it could be accounting, it could be coding. People say, well, eventually this will shift to value-based pricing because eventually instead of just paying 20 bucks a month to make a person more productive, you're actually replacing a person who costs 50 or 100 or $200,000 a year, whatever it is, depending on what their role is, across the different disciplines. So I'm just sort of curious how you think about, is this eventually a rent a programmer and it's priced like a programmer? Does it all get commoditized and eventually something that normally costs a hundred thousand, two hundred thousand, three hundred thousand dollars a year costs you a thousand dollars a year? Like, how do you think about where this market goes?

• 32:55Speaker 01

  I think it's going to be compute based or some unit that's now a derivative of of compute asymmetric.

• 33:03Speaker 02

  So it's gonna be cheap?

• 33:04Speaker 01

  It's going to be cheap in the same way that your dishwasher in your kitchen is not a derivative of what a person would cost you when when doing your dishes every single day. But I think the buyer persona is is not going to be willing to to pay for a machine, you know, whether it's a dishwasher or an agent, a price that's an equivalent of human developer. And and I think that's actually the correct mindset because I I don't believe that the agent is actually replacing the developer. The creative part is still coming from the software developer, the systems thinking. Picking the future always has the the fun part of that. I'm coming back on the podcast a year or two and you're telling me how wrong I was about my predictions.

• 33:45Speaker 01

  But I think there's a lot of decisions that are made in software development that a human has to make. What database, you know, with cloud, a lot of that are a function of the business and how it operates. You know, which cloud you're using is is not necessarily a question of how much the cloud costs. It's a question, it's a strategic decision of, you know, the CTO or the leadership, engineering leadership team. And more and more we see, you know, companies using more than one cloud because they want, don't want to have a dependency on on just one single supplier in the same way that, you know, any random car manufacturer has multiple suppliers for airbags because they don't want to be stuck with, you know, their factory line when when airbags are not deliverable from that one supplier. And so I think, you know, the agents, the price, the price points will certainly go up as as these agents become more powerful.

• 34:30Speaker 01

  You know, we see, we see that with OpenAI, where the highest tier now now costs two hundred dollars for deep research and over one pro model and and clearly people see the value, the value in that. And I think, you know, two years ago, if if we had predicted that, you you, we wouldn't have believed it. You're willing to pay two hundred dollars a month for chat agent because the the flip side of that often is in in software that that people feel like a five dollar subscription for mobile app is a lot of money. And you can just see that when you look into the reviews of of apps that move from a one time payment, you know, to a subscription model of how how many people don't like that model because they feel like software is something that you buy once, like a CD, and then and then you own it.

• 35:12Speaker 01

  Definitely they're going to price increases that will be based on the value that you're getting out of it. Because you know, the other side of that is that human developers are expensive because there's limited supply. Agents will have infinite supply that will only be limited by the amount of compute capacity GPUs available in data centers.

• 35:34Sarah Guo?

  Speaking of that unlock of supply, like we've been talking about, like, what is the, what is the pricing of the code generation? I think there's also a question of just like, what happens to the value of software at all? Like, everybody's been talking about Jevon's paradox for a while. I don't want to ask about that. But maybe something more specific. You're from East Germany. You remember the Trebonne car?

• 35:52Speaker 01

  I do. I had one. Oh, well, my parents had one.

• 35:55Sarah Guo?

  Oh, okay. Right. So you can, you can tell me what it's actually like. But good for, good for you guys, because it was this, it was the okay car, but it was the default car that ended up having this like 10 year waiting list because of the supply constraint with the rest of the world. And then as soon as the wall came down, you, you know, the demand completely collapses.

• 36:11Speaker 01

  Yeah.

• 36:12Sarah Guo?

  Right? Because you have access to the world of cars and, and pricing at least did. I guess one question I'd have for you is, I'm, I'm generally such an optimist about like, the demand for software being very elastic, but I think of that as volume and quality and variation. Are there types of software that you think collapses in value when AI takes away? Like some of the scarcity of engineering?

• 36:36Speaker 01

  You know, the Trebonne, the waitlist was actually, I think, 17 years in the late 80s.

• 36:40Sarah Guo?

  Okay. 17, not 10. Yeah.

• 36:42Speaker 01

  That road, by the way, still exists. Today, it exists in supercars, right? Like often, you can buy a supercar, you know, the top end Porsche 911 R3 or whatever. And then the resale price is higher than the, the new price because you can't get one to go to a dealer. Because at the dealer, you have to buy like 100 Porsches first before you get a slot for, for that exclusive top of the line Porsche or Ferrari is the same thing. And so the Trebonne, actually, the one that my, my dad owned, he sold, I think in 84, 85, to a neighbor at a higher price than, than we bought it because you could shortcut the, the 17 year wait. To get a car and often parents had a subscription, quote unquote, subscription like

• 37:26Speaker 01

  a signed up their kids already for car when the kids were still young. So you could actually get one by the time you reached, you know, adulthood and could do a driver's license. And so, you know, I think we're going to see, coming to your software question, right? Like, we're going to see it going both ways, right? Like, if you think about Copilot, Copilot, you know, costs for businesses twenty dollars per user per month. That's actually almost exactly the same price as you pay for GitHub Enterprise, which is twenty one dollars per user per month, right? And so for storing all your repositories, managing all your issues, your whole software development lifecycle was twenty one dollars per user per month. And then many used to perceive that as a lot of, a lot of money for, you

• 38:09Speaker 01

  know, DevOps. And then we came with Copilot Auto Completion, and that was twenty dollars a month, right? And so all of a sudden, that, that sub-feature of the software development lifecycle, Auto Completion, costs twenty dollar. And then goes back to your last question, right? Like, if there's the value where you get the ROI and you get twenty five percent productivity increases, yeah, you know, you're willing to pay more for something that, you know, probably five years ago, if I told you Auto Completion is going to be that standalone feature to run by AI, that, that costs more than the average selling price for all of GitHub, you would have said, well, that's, that sounds unlikely. I think we're going to see deflation of, of software prices. And so I think it's, it's a mix of both, you

• 38:49Speaker 01

  know, some things we won't pay for anymore. You know, nobody pays for the operating system anymore. And then at the same time, you pay way more than ever for your Netflix subscription and for your office subscription and, and all those kind of things. So I think both of these things will be, will be true at the same time. And it's all about how much value do you get for your business paying for that solution, whether, whether it's doing it yourself or using something that you manage yourself or install on your own server.

• 39:15Sarah Guo?

  GitHub is foundational infrastructure for open source. So I'm sure you have like general opinions about what's happening in the the open source ecosystem. Today, you can use Claude and OpenAI in Copilot and Gemini, but not necessarily open source models right now.

• 39:29Speaker 01

  Correct. So in Copilot, we have Claude, Gemini, and then OpenAI. And OpenAI has different models that I was I was just processing it in my head. Wait, there's more than three models, but it's the four O model and the O one and the O three mini model. In GitHub models, which is our model catalog, we have open source or open rates model like like Lama. As an example, and then all kinds of other models like Mistral, Cohia, Microsoft's five, five, four model. And the model catalog, you know, while it's a separate feature within GitHub, you can add model, add models in Copilot because Copilot has extensions. And so you can actually reach from Copilot into the model catalog. And so if you want to just quickly inference against five, four, you can do that by by using the add models extension in Copilot.

• 40:14Speaker 01

  So that way we have more models than just the the ones that are packaged into Copilot.

• 40:18Sarah Guo?

  I know that. What do you think is the relevance of open source versus the proprietary model APIs for developers in the future?

• 40:26Speaker 01

  The biggest thing I think is that open source is going to drive innovation. And we saw that, you know, with DeepSeek earlier this year. Oh, actually, you know, a couple of weeks ago, it's not that long ago even.

• 40:37Sarah Guo?

  Long year, yeah.

• 40:38Speaker 01

  It's been, it feels like already a half a year has been passed instead of just a month and a half. But I think open source is is going to drive innovation. You know, we saw that with image models like stable diffusion. And now there's the flux model from a startup actually not too far from from my home base in Germany and the Black Forest in Freiburg. Black Forest Labs is actually the company behind Flux. And so we're going to see innovation, I think, you know, on open source models that that drive the other vendors. And this back and forth between the open source ecosystem and the proprietary closed source companies will, I think, accelerate the whole space. You know, DeepSeek is is the most prominent example right now where you can, you know, look into this. The paper is open, the the models are open.

• 41:21Speaker 01

  Some of them are like, you know, fully open source and then the MIT license. Like open weights and you can look at the weights and then the code run it is open source, but the rates itself are under another summer probably lessons and governed by Chinese law and whatnot. And I think that is going to drive innovation and it's going to open up that space and it democratizes access. Because if you just wanna play with a model, you don't have to to run inference against the commercial API. You can just, you know, try it out yourself on your local machine and play with this. And if you think about kids and students and research, that that opens up a huge space. And that's ultimately, you know, what has always been part of our DNA at GitHub.

• 42:05Speaker 01

  Was that a satisfying answer, Sarah?

• 42:07Sarah Guo?

  Yeah, yeah, I I think the most satisfying answer is like somebody wins, right? But I I think that's a very hard thing to predict right now.

• 42:16Speaker 01

  What has won iPhone or Android, which Windows or Linux or Mac or S for that matter? I don't like, I think we like to think about these, you know, binary battles in the tech industry. And the reality is that's not actually how that works and certainly not in in the developer space, right? Like React hasn't won and there's always going to be the next thing, you know? Before React, there was jQuery or whatever, you know, like a library you preferred. I think there's going to be a next programming language after Python and TypeScript and Rust. And Rust in itself, you know, wasn't really a thing five years ago. And then so there's going to be more languages that are probably closer to to human language

• 42:58Speaker 01

  and to to be more specific about the natural language layer in AI and the programming language layer that that converts down to the to CPU or GPU. And so I think there is no no winning, there's always just the, you're playing the infinite game. It's like Minecraft, software, software is like Minecraft and there is no winning in Minecraft. You can win little battles and and they're isolated to a certain sub-challenge or whatever, quests. But ultimately, we're building a bigger and bigger world of software and there's always going to be the next big thing.

• 43:27Sarah Guo?

  That's a funny analogy. If I think about any individual developer, like, there's something people have been saying to me, developers of a particular ilk, right, really strong technical people who are more experienced, not all of them, but like more experienced gremlins, systems developers, often people very attached to Rust. And they'll say, basically, they're worried about the next generation of developers, building the taste and understanding of architectural choices and the trade-offs and corner cases of how a particular implementation can fail, given some shape of data, given their experiences of the actual implementation, right? And so they're worried, you know, obviously, the right thing to do for anybody

• 44:07Sarah Guo?

  who wants to win that next, you know, level of Minecraft in 2025 is like, use AI aggressively, learn to use it. But like, does that concern from this segment of, I'm sure you've heard it, does that concern resonate with you at all? Like, can you foster the requisite depth of understanding of engineering at an abstract level when we're not writing the code? Or is it like a silly, silly concern?

• 44:30Speaker 01

  I wouldn't call it silly, because obviously, you know, there's some truth to that, right? It's easy, you know, to cheat at a programming exercise or advent of code and those kind of things. As these AI models get better, these competitions of who's the best hacker or coder are going to have to move to a whole different level, where you assume that the developer is using AI to solve the challenges, because otherwise, it's going to be made too easy. If you think about the next generation of developers, you know, maybe not 2025, but 2035, like, look, you know, and you mentioned, you know, me growing up in East Germany, and then the war fell, and I bought a Commodore 64, but there had no internet, and so I bought

• 45:10Speaker 01

  books and magazines, and that was it, right? Like, there was no forum I could go to and ask questions. I could go to, I went to computer club every Wednesday or so, until nobody there had anything to say anymore that I didn't know already, right? If you take that and compare it to today, the kids of today and those that want to learn coding have an infinite amount of knowledge available to them. And you know what, also an infinite amount of patience, because you know Copilot doesn't run out of patience, parents do, I am one. And so it's incredibly democratizing to have AI available if you want to learn coding. Your parents don't have to have any technical background.

• 45:53Speaker 01

  All you really need is an internet connection on your mobile phone. And one of these co-pilots or chat GPTs or whatever you prefer. And you can start asking coding questions and you can ask about Boolean logic and about systems thinking. And you can go infinitely deep on any of those questions and traverse to other topics as you like, right? And so I think, you know, we're going to see, you know, a new generation of humans that, you know, grow up with the technology. For them, it's just natural to leverage their personal assistant, their personal set of agents. You know, they call it the orchestra of agents and you're the conductor of that orchestra of agents.

• 46:33Speaker 01

  And they know how to do that. And so they can achieve in the same amount of time so much more than we could, you know, in the last, in the last thirty years. And I think that's incredibly exciting because like, again, like, find me a developer that doesn't have this big idea of that computer game or software system or feature that they always wanted to build and don't have the time for. Like my engineers talk much more about being overcommitted and burned out and not having enough time for all the things I'm asking for. The customer is asking for and the security team is asking for. And so I think that's, that's just where we're heading and where, how this is going to be super exciting. Both actually in open source as well, right? It was open source sustainability is another big topic that we could probably spend another hour on.

• 47:18Speaker 01

  And in any kind of software that people want to build.

• 47:21Sarah Guo?

  I definitely agree with that, you know, excitement and optimism. I think about my three kids and like, like what they would be able to learn at what pace with the, you know, the AI resources that people will have. And I'm incredibly jealous. I'm like, I could be much better as an engineer, so much faster with, as you said, the infinite patience and understanding of today's models. By the way, I was very lucky. My parents are both engineers, right? But, you know, it's a very human dynamic where I'd ask a question and my dad would be like, it's logic, Sarah. I'm like, oh no.

• 47:57Sarah Guo?

  Can I ask you, you know, maybe a more personal question to close? Like East Berlin, you know, you have this unique experience of this really rapid technological change after reunification. Do you think that informs at all how you think about like the speed of the current AI transition and how like users and human beings will react to it?

• 48:16Speaker 01

  I always wanted to believe that a lot of my life has been, you know, defined by that one moment of change in nineteen eighty-nine. And, you know, I remember, you know, the night when the wall fell or when it was announced that the wall would be opened and it was a Thursday night and then Friday was normal school, Saturday was still school as well, a half day in school. And I think I was one of four kids that that showed up in my class and then they sent us home and then and then and we actually crossed over to West Berlin. I think the thing that is important for that generation of kids that lived through that change is that they can no longer return to their childhood. You know, home, home is gone. Like, you know, there isn't like that store in the corner that's the same as it was like forty years ago.

• 48:59Speaker 01

  And the schools are gone, the system is gone, the traditions, all that resolved into, you know, that new world. And so it's a bit like when you're moving from one country to another, which then I, you know, did ten years ago as well to move when Microsoft bought my company. Once you have done that step in your life, you gain a whole new perspective on things. And I think that's, you know, unification in nineteen ninety and then, you know, through the steps of my life, including, you know, to become the GitHub CEO through, you know, random decisions or at the time they felt random. This is how I got here and this is how I look forward and I'm optimistic about the future while recognizing my past and taking, you know, some of those experiences when

• 49:43Speaker 01

  I talk with you guys and reflect on what it was like in the nineties to program on the Commodore sixty-four, before and after the internet, right? Before and after open source, before and after the cloud, before and after mobile. And now we have before and after AI and there's no looking back. Like the the future will be that we have AI for almost everything we do in our lives if we want to, you know? You can still always throw your your cellphone into the corner and enjoy a day without without the internet.

• 50:09Sarah Guo?

  This has been great, Thomas. Thanks so much for having the conversation.

• 50:11Speaker 01

  Thank you so much for having me.

• 50:12Speaker 02

  It was great to connect. I really appreciate the time and everything else.

• 50:16Sarah Guo?

  Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.