GitHub CEO Thomas Dohmke on Building Copilot, and the the Future of Software Development

27:20We were developing autocompletion and then chat. And then with chat, we also announced something that we called Copilot X, which the idea was we're bringing AI features into every part of the developer lifecycle. We're bringing Copilot wherever developers are. So, you know, while we added chat to the editor, we also added, you know, a little copilot I can into the input field where you write your commit message. And that might be trivial, right? Like, everybody can write a commit message. But it also means I reduce my mental workload and I reduce kind of like the bias that I have for the work that created myself, right?

27:55Like, for me, everything is obvious that I just did for the last hour. But for you, when you want to review my commit or my pull request, it's not as obvious. And so having an AI described that in a neutral form, kind of like in an outset, I'm describing what I just did is incredibly useful and it just keeps me in my flow. And we added it to the debugger. We added it to many different parts of the lifecycle already. And with Copial Enterprise, we bundled it into a higher price product that allows enterprises to customize Copial it based on their institutional knowledge. And Enterprise here means really any company that has gone for more than a few weeks, because they're all immediately built institutional knowledge, right?

28:38How we work as a team, how is our coding practices, these are the libraries and languages that we use. And so, unless your student at university that gets to have the free range of technologies available, at least when the professor allows that, every time you join a company or join a different project, you have to ramp up again on how they are doing things. And so, co -bought enterprise, let's companies customize the co -pilot to their institutional knowledge and it makes it really easy for me to join that company Because I can now ask them questions without you judging me like you know imagine I would you know join here My first day with like why is Thomas asking all these questions?

29:18Should he already know all of the activities? You know been in professional life for a long time That's the challenge that we have in we join companies and we have the anxiety in our head that we can't ask too many questions before Steph says what the hell Right? And so that's I think the power of co -pilot enterprise, that's the power of bringing co -pilot into every part of the developer life cycle and ultimately into every part of our lives. You've also mentioned that agents are one of the most important things for GitHub. Maybe just to set the stage, how would you describe an agent versus a co -pilot?

29:49And can you give us a teaser for what types of agenteic capabilities we should expect come into co -pilot soon? Yeah, I would say, you know, co -pilot in agents is kind of like the same thing. And an agent is using a model to get a task done, right? Effectively, it's looping with a model to solve something for you. And a co -pilot is an agent of agents, and it has multiple features available to it. You know, if you think about auto completion, well, it's an agent that takes every keystroke you did. And the context that you have, and you added a sensor to model inference, that gets the response back, it might pick the best prediction, and then it shows it to you.

30:27So we are going to see more of these agents that take over more of our tasks. One of them that I'm most excited about is autofix. The very works is so you submit a pull request and traditionally some security scanning feature that you have integrated into your pipeline, find security vulnerabilities, SQL injection or cross -site scripting. Well, that's great, except now I cost more work for myself. It's kind of like you have a Wumba, but instead of vacuuming your house, it just shows you where the dirt is. And then you have to go and vacuum yourself on that position. So now with autofix, we actually only showing you security vulnerability.

31:05We're also giving you the fix and that uses the AI model together with the vulnerability and the description and the code to basically solve that vulnerability for you. And the initial results are really impressive. with some customers, we see that we can burn through like 75, 80 % of their open alerts. Everybody has those alerts. And if you don't have any alerts right now, I bet you you have them by Monday. That's the challenge in this world of software security. Everything is around us moving so fast. There's always a new version of open source library. There's a new version of Linux or a new Windows patch.

31:40There's a new device coming along the way or new NVIDIA GPU. And so we constantly are behind just keeping our applications up to date, you know, to the standard that is expected by our customers. And at the same time, we have to build all that innovation and all that. Yeah. Super interesting. Close stuff. What else do you think is missing in the product roadmap? Like if you could wave a magic wand, like what else is there to build that you're really excited about? We think there's still a lot of work to do on these agents. I think there's a lot of agents you can think about. We talked a little bit, or you mentioned workspace before.

32:20What co -pilot workspace does it provides different agents to get you from idea to your pull request, to get you from idea to the code. And the first one is the spec agent and what that actually does, it helps you with your thought process. So you know, you write down an idea, implement some feature. And it looks at your existing code base, and it basically helps you then to reframe that idea. Now, that's not only useful for developers, it's actually useful for product manager, right? Because it may might tell you, well, Thomas, this idea is no way you can describe that with a single sentence and a developer cannot implement that in just a single ticket.

32:54It needs to be an epic or multiple different user stories, right? And then the next step is the plan agent that, you know, helps to figure out where to make the changes in the code base. And again, you can kind of see here, but there's a lot of other benefits you get from that because it helps you to understand the code base. Because most code bases that are older than a few days have hundreds of thousands of files. And as developers, we have to navigate all those files. And even if you have been in a code base for a long time, you still might miss out that one file that you haven't touched for a while.

33:28And you have to add a config statement there. So it helps you understanding the code base and then the implement agent helps you implementing the code change. And every step in that way, you're still in charge. You can, you know, with natural language, modify the bullet points of each of these agents. And then obviously you can modify the code at the end. And so if you look at just these three agents, you can easily start thinking about other agents that you might have along the way, right? For example, the one that estimates the size of a ticket, the story pointing agent as one example. Or another one is that, you know, once you have implemented the file, Well, now you want to build Run and Debug the file.

34:06And maybe you have an agent in the future that will just automatically fix any bugs that they introduced by the previous agents. And so I think we're going to have more of these building blocks, Lego blocks, if available to us. And in fact, if you look at Lego, they have way more types of pieces today than they used to have. Trusting models are much more complex. and then you can buy NASA rockets and whatnot, they need different pieces for that. And I think that's kind of like the same way we should think about co -pilots. They will have more of these building blocks that enable us. In addition, to more powerful models and mix of models, these building blocks will enable us to do more.

34:48Increasing modularity, interesting. Where does your ambition for GitHub take you, or maybe even with GitHub co -pilot specifically take you, as you think about what you alluded to from the perspective of deepening where you can go with just the software engineer, to also expanding into potentially different personas, PMs, you mentioned maybe an SRE, maybe a security engineer. Where does that, you know, the breath also take you? Yeah. I mean, first of all, I think all those roles today are already collaborating on GitHub. In fact, GitHub had always that mantra that we are building GitHub on GitHub.

35:21And it's sometimes, We have pushed it a bit too far, but today, most of GitHub employees, we call them Hubbers. Hubbers are engaging on GitHub and GitHub discussions and GitHub requests. Our legal documentation is all on GitHub, which if you think about it's actually much better than managing red lines in Word. Because you have a version history and you can see who made what changed it. In fact, you can see soon a future where maybe your legal document is explained by a co -pilot in human language, you know, in actually understandable human language, not the lawyer language, right? And so, so we're using GitHub, you know, to our, our company, but yeah, we, we, it's, we are all the developers and all the supporting functions collaborate on a project.

36:05So that's, I think number one, number two is we want to democratize access to software development. And, you know, I recently gave a TED Talk and I talked about that. I think our goal is to get to 1 billion software developers on the world. Now, it doesn't mean 1 billion professional software developers, although that might not be a bad thing, necessarily, given the demand is still very high. It's sometimes hard to find qualified software developers. But it's really about democratizing access to writing software on these devices that are with us. You know, our mobile phones are, you know, a really important part of our lives today.

36:43you can't really imagine a live, an urban knife for sure without a mobile phone. And so then also being able to write the applications or little scripts or just using natural language to control the phone, I think is incredibly empowering. And so bringing that into 10 % of the world's population by 2030 is so assuming that then 10 billion inhabitants on this planet, I think it's going to create a better world. And it's going to unlock a creativity everywhere. and hopefully, you know, we see cool venture backed startups in India and in Brazil, and maybe, you know, the next big tech company is coming from one of those countries instead of the US West Coast.

37:25We hope so. Maybe zooming out of GitHub Copilot to broader GitHub itself, GitHub Copilot itself is driving so much innovation within GitHub. But what are some of the other key initiatives that you're leading across GitHub overall as well? Yeah, we already talked a little bit about, you know, autofix and security. I think security is securing software supply chain is, is dear near to our hearts. Um, there is, there is no future of, you know, human progress and with software, if you're not also able to secure the supply chain, you know, today, you know, there's this XKCD comic, um, uh, of the internet's infrastructure.

37:59And there's this one building block that says, you know, the guy in the map, the braska maintaining this one library alone, right? That actually is funny, that is a reflection of the software world today. So we have to make it sustainable for those maintenance to build that software and keep it joyful. And then also we have to make sure that all these building blocks that are on our stacks that have become system critical to be secure. And so we're investing a lot in platform security and application security and of course in our security products. and I think that's going to be crucial combined with co -pilot and AI to not only create all these work items, but also enable developers to burn them down to fix all these issues.

38:42Yeah. I want to zoom out from GitHub, her second and just talk about how you see the future of AI and coding overall. Like you mentioned, there's been a billion casual developers around the world. Like, well, what will it look like? Will everybody be kind of coding applications for themselves to use? Will there be some number of professional developers who are super developers? Like, have you read in the world looks when you so kind of democratize the craft of coding? I mean, I think it's an incredibly creative world in a world where you're not dependent on, you know, when you're a kid on your parents having technical knowledge or your school having a teacher that knows how to do this thing.

39:23So we're going to have much more access to those that interested in learning about this. I mean, it's easy today to take a sheet of paper and paint something and every restaurant, at least in this country, gives you crayons, if you come with kits and a coloring sheet. Life saver. Life saver. Or you have your mobile phone and they use your mobile phone. You know, it's easy to learn in the sense of accessible to learn a musical instrument. And I think it should be easy to learn coding. And so, first of all, I think that should be something we are excited about and not concerned that we are inflating the number of developers because just because kids learn it doesn't mean they want to become a developer.

40:03I think there's still a world where people want to do something else in software. But then if you think about many other professions, physicists, for example, they use a lot of software. The first image of a black hole was rendered by the help of Open Source Project. The Mars helicopter, run on open source. And so, yeah, it's space, space engineering, but they're using software and they're building software. And so the professional itself is everywhere. Every company is a software company. Banks are software companies. Energy providers are software companies. Farmers are software companies predicting what seed to plant is here, what the weather is going to be like, what was the soil quality from last year and things like that.

40:45But of course, you know, that the hobby scenario isn't is also important. Like, you know, a text season is over here in the United States, but that doesn't mean that I couldn't think about next year to automate a lot of that. If I only had an AI agent that does all that work for me, you know, and then downloads all the PDFs and extracts all the numbers. And I don't think we're too far away from that. And you know, I'm now in software for almost 30 years or almost 30 years. And as a professional software developer, and I don't have a lot of time to code. I have a company in Toronto, I have podcasts to give.

41:22And things. But the problem with today is you find an hour on a weekend and you have a project. And the first 20 minutes you're spending with updating everything to whatever you missed. and the burden is actually, the fun is gone to certain degree because the burden of maintaining software is so high. So having something available to you that gets you quickly into the hobby and out of the hobby or out of that task, I think, is incredibly empowering and brings the brings the fun back. And you know, that's where the Lego comparison is so useful, right? Because Lego is just incredibly accessible.

41:58And even if you, the best Lego is the one where you don't have of instructions. They just have a table full with Lego brakes, even in random colors, right? And then have this excitement of play and even professional workers at their off sites or workshops often have little gadgets or bricks on the table so you have your fingers do things while you're thinking. So I think that's where that word is leading us and we will have more access to the technology. We have more people that can build software that doesn't mean that they're taking jobs away from professional software developers. On what timeframes do you think we'll have coding agents that are as good as maybe the average professional software developer and and you know there's the legend of the 10X software engineer like at what point do you think AI will be as good as the 10X software engineer in capability?

42:50You know the trick in that question is as good as and what does that mean? So I think you know is a model today able to write better code than the average developer. If it is prompted in the right way or given the right context, I'd say we are already there on average. Because often, the model just knows more about that whole space than I as a human do. And you see that the students, if they have to implement a conversion from binary to decimals and that, and they might write 100 lines of code and then they go and ask co -pilot how to do that and they get probably an open source library and one line of code and then you can kind of say, well, I'm not allowed to use open source.

43:34And then you would probably still get a better code than they would. And I think the tools, the same as tool for the professional software developer, because look, we're not perfect. We are human and I think that's part of our nature, that's part of creativity. Now, that's the key thing though that the model is not creative. And the model cannot, today, the model cannot make decisions for us. Or if it does make decision, it doesn't actually take all the constraints into account. Like if you think about software development, other than writing code, which I think that fun part, it often means I take a very complex problem and you break it, decompose the problem into small building blocks.

44:14And the block size is increasing over time. It used to be, all the completion used to be just the next word, and then it was maybe a full command and it's multiple lines of code and maybe it's whole files in the future. But along that decomposition process, you still have to make a lot of technical decisions what database am I using and you know, you probably know better how many database startups Sequoia has invested in and how many infrastructure startups and how many serverless startups and obviously there's all the incumbents in all those spaces. So there's a thousand or ten thousand decisions to be made and the engineer is the system's thinker that is making those decisions or the team of engineers and companies.

44:54I think, you know, we have been building houses, you know, as humans for thousands of years. And if you have ever built a house, it's still not a solved problem. But yeah. But what at what point do you think that creativity in that systems thinking gets built into co -pilot? Or do you think it never does? I mean, you know, it's a bit of predicting the future. I don't know if I would say never, never, never say that's a dangerous thing on a podcast, you know, you invite me back in three years and say, well, Thomas, you know, last time we asked you about this and Kelly has happened since no, I think, you know, we'll see where the research goes and where the monogamy goes in kind of critical thinking and systems thinking and those kind of questions are also learning, you know, learning, you mentioned your two -year -old, your two -year -old and my kids, they learn, you know, as they mimic the humans around them, they have so good at learning language and especially in young Asian or they can learn multiple languages, mine speak English and German because we speak German at home and they don't have an accent in English, well they have an American accent but they don't have the German accent right, they don't have an accent in German either.

46:04And I think this show is like that the human brain is still so much more advanced than the transformer models and the diffusion models and the other types of models that we have to image recognition and whatnot that we have today. And you know, it remains to be seen if we can kind of like add that sentience piece to it. But today I'm not seeing it and I haven't seen any research that telling me that that's coming anytime soon. Mm -hmm. That's a really clear, Dwayne. Switching gears a little bit, I mean, I'd love to hear your thoughts on the overall ecosystem of startups right now. AI Code Gen is the hottest category with a lot of ambitious founders.

46:40and there are so many different attempts that they're taking, whether it's folks who are trying to build a better model, folks who are trying to build a better IDE, folks who are trying to build kind of an all -in full -stack engineer as an agent. And you know, but the big elephant in the room is GitHub Copilot and all the adjacent products that you have around it with Copilot workspace, with Copilot Enterprise, with autofix and owning the S code as well, amongst many other things. How do you think about what white space there is for existing or sorry for new founders? And if you were a founder yourself, trying to build a space, what would you do?

47:20I mean, I love developer tools. So I probably still do developer tools. And I'm not sure I would worry too much about what's white space that's taken by incumbents because that can't change quickly. When Github started SourceForge, was the big elephant in the room, and SourceForge had all the open source projects, and then came Gith and Gith accepted that space, and then the founders of Github took Gith and built Github, and all of a sudden, that elephant in the room was no longer the elephant. And I think it is often fun to compete in the same space, and we love competition because it pushes us forward.

47:55But it's boring to do a race or a game if you don't have an opponent and if you don't have other teams in the league who would watch the Super Bowl if there's only one winning team every year. So I think, I don't think competition should hold you back as a founder from going into that space. I think the software development space is wide open and there's lots of problems to solve. There's lots of problems in different industries and categories to solve. We haven't really solved modernization of source code. Cobalt runs still on mainframes and you can use a co -pilot and we are heavily looking into that and because it's such a pain point for many financial service institutions.

48:39You know, the credit cards, your bank account, Wallsweed all that runs still, Cobalt I haven't met a single bank that doesn't run some cobalt on some mainframe. What would you do with co -pilot and cobalt? So today you can explain that which is often helpful because the cobalt was written 60 years ago and so the people that wrote that code are retired. Yeah exactly. Would you rewrite all the code with co -pilot? Then you can ask it to write unit tests because nobody would unit test in the 60s and 70s either. Let alone that there were unit testing frameworks for those languages. just like keep in mind, you know, the 60s that was before hard drives, right?

49:17Like, you know, before before we had, you know, personal computers, it was a very different world back then. And of course, those companies have done work to modernize to certain degree, but it's far behind Azure software development. So you can support that transformation process today, but there is, you're not at the point where you can just click a button and you have a transform. The same is true, you know, for many more modern languages, you know, there's large PHP code basis, there's lots of Java out there. There's lots of optimization that we can take to just make our existing stacks more efficient where agents can help.

49:52There's lots of things to improve in every part of the software development lifecycle and in this ecosystem around us. GitHub, you know, is I like to think about GitHub as one planet in this universe of software development tools and there's smaller planets around us and then equally or almost equal size planets in our space and we consider them partners and we're happy that they're there. Yeah, so interesting. Agents are just pulling that thread a little bit. It's also an area of excitement for us. And I think you have a lot of the benefit of owning so much of the data and everything you can do on the post training side.

50:30Opening I itself is also getting better and better with each new model class as are or every other model company out there. Would you ever want to invest into building your own agents, maybe from scratch, by building your own agentic models, or just to partner with some of the others out there? Me as GitHub CEO, or the rest of your business. Yes. Or me as a agent investor. You as GitHub CEO. Yeah, I think we are probably doing both. We are going to, we are always, you know, in a way we always have done both things as we have invested into our own things, the thing that we consider as core part of our platform and of our offering, part of our primitives.

51:14And we have partners, partners with companies. Just earlier this week, we announced the partnership with JFROG, which covers spidery artifacts, scanning of containers and those kind of things. And they are obviously very naturally our space. We have the source code and we'll do a source code value, either compile it into binaries or you combine it with binaries before you deploy it into the cloud. And so there's a natural value chain there where we have things where we invested ourselves, things like releases that you see on GitHub and packages, we own NPM, the largest package service tree in the world for the JavaScript ecosystem.

51:53And NuGet, who are partners at Microsoft, the largest .net, where I just see in the world. But that doesn't mean that we cannot also partner with the J -Frog to ultimately enable that secure software supply chain that I mentioned earlier and that I think is crucial and that Figma, you know, verse sell like there's so many other companies in our space that will play part in the lifecycle and I don't see where somebody covers all of that and can convince every developer that using all these tools is better than picking, you know, what what analysts might call best of breed. And like the tool that I consider the best is all subjective anyway, right?

52:34Like it's all those, does it meet the expectations of the developer in the environment? Yeah, makes sense. That's great advice for startup founders and I think really encouraging. What about for incumbents? I think you are such a beacon of hope for incumbent companies. I think Satsya said in the last earnings call that GitHub is now growing 40 % every year. or thanks to the co -pilot acceleration. And so like, and to your points earlier, like you feel like you're a young startup again, like walking into Sequoia offices. Like what advice do you have for the Sony incumbents that are trying to reinvent themselves around AI?

53:09What advice would you give those folks? Sajja actually said 45%. So we're really excited about that. Okay. All up on our revenue equal 45%. You know, I got an email from somebody last week who was like looking at GitHub, I'm losing the belief that large companies is cannot move as fast as startup can. And I think the key ingredient on this is radical photo -cookers. And basically focusing on a few small things just because you have 1 ,000 engineers or 50 ,000 engineers doesn't mean you can do it all. That's just a false assumption. And I think it's in a way misleading as you manage larger teams.

53:46You're losing the unit of side team size that shows you how much you can actually get done and how much friction you have in the system if all these teams work on different things. So I think focus, you know, lots of notes on all the ideas that people have around you and customers, that's I think the key piece to move really fast. And then obviously taking in some strategic bets and strategy means you're thinking about it as how do I differentiate from others, you know, what makes me specific, It makes me special in this market, but it lets me charge the prices. I'm going to charge and not fight, you know, race to the bottom.

54:26And I think that's this kind of like thinking about, okay, you know, in software, we like to think about, well, if I add just three more features, then I'm going to win the space until you realize, well, everybody else can also add those three features. There's almost nothing in GitHub itself as a platform that you couldn't rebuild with significant investment and time. But I think it's really hard to mimic our culture. It's really hard to mimic our experience, our obsession about developers, and ultimately our focus and the way we're approaching these things. I guess very tactically, do you recommend staffing a Tiger team to get to the product market fit on AI?

55:05Do you recommend pulling half your engineers off, whatever they're working on, and like, hey, you guys are the AI team now. We gotta go bigger, we'll go home. like, tactically, how do you work when you're losing? Well, as an incumbent, you always have the challenge that you have to sustain whatever the business is you're in. And so you can just pull everybody into a new topic, unless you're willing, you know, to disappoint all the existing customers, you know, no enterprise business has not made promises to enterprise customers of what's coming next on their roadmap. You know, lots of conferences, including our own is, is, you know, stuff they're shipping right now and stuff they're announcing for the next six months.

55:40And if you're not delivering on these announcement, your customer base is not going to be happy. And so you can't really make that drastic move right away. And so we love this idea of a Tiger team or an incubation team. We call it GitHub Next. And when we set that team up, and we actually thought they're going to work on projects that are like five years out, or in the horizon three, as space. And then it turned out, well, it was more like six months ahead of the curve. The future comes really fast on us. And so as that future then comes fast, you really also need to move fast and handing things from the incubation team over to your mainline engineering team.

56:18And then it's all about, okay, so we're funding AI because we're seeing the traction there. And that means we're leaving some of our previous bets in, you know, keep the lights on mode KTO or, you know, saying goodbye to these ideas and shut them down. I think that's the hard part of the strategic pivot. And that's easier for a startup. At least it often looks easier from the outset when a startup pivots. Like there's lots of startups on the wall downstairs that have gone through that as well. But obviously, internally, it's also very hard. Emotionally, you're tight to ideas. You remember the workshop under the Sequoia trees where you had those ideas.

56:59And then six months later, you're realizing we never got to product market fit. And the time is now to move to something else. So that is the same for large companies as well. Small companies, except that small companies have more of a forcing function to give up and move to something else. The innovation and success that you've created around GitHub Nex is amazing. How does a kernel of an idea start with in GitHub Nex? How do you then resource and invest into it? And then how do you, at what point and what's the process in which you decide whether or not something should be continued did Mestan or shut down entirely?

57:36So I think this start is always the employee, the hub that has an idea and we have lots of ideas in the company. We do hack weeks, hack -a -tons, passion projects, whatever you want to call it, 20 % time. And then the next team specifically, there's lots of demo meetings, demos and not memos. It's much more useful just to show a working prototype. It's so easy these days to just use some design system, react components and still, or figman and stitch something together, even easier with co -pilot. And then it's strategic decisions that the leaders of these teams, all the way up to me, need to make many ways very similar to any other creative industry.

58:24You know, like at Disney or Pixar, they have to decide what movies to produce and which ones are probably not going to gain traction. And part of that is customer research, talking with developers. And that then keeps going as we go through the initial idea and the first prototype and then going into a technical preview. And then the preview is all about that 5 -wheel, that feedback loop with the people using it. And quickly you see, are they trying it out and then they are churning or are they keeping the energy high and then keeping the ideas flowing is great, you know, any project if people just sending you more ideas and more feedback.

59:03And I think that's then the decision whether we are keeping that project and making it a main project or whether we are deciding okay, the X -ray rate is over, we learned a lot and we're moving to the X -ray button. And of course there's commercial aspects as well. Maybe outside of the world of code generation and anything in your current purview, what else are you excited about in the world of AI in the span of one, five or 10 years. Well, outside of developers. You cheated a little bit on me. I mean, I think in the context of one year, within the space of developers and outside of the space of developers, I'm excited about agents.

59:42And we're still very early in this journey. I think we have high expectations of what the agents could be. Maybe too high expectations to some degree. And so things will probably take a little bit longer. but I think in the next year or so we're going to see more of these help us within the chat interface and out that of the chat interface that will will solve tasks for us, you know like I look forward, you know, to that travel agent that often gets demoed by big companies to actually materialize and it can just go into my chat interface and say, you know, I want to have beach vacation over spring break and then figure out, you know, when spring break actually is because that's all on the internet and And you know what my name is and what my family's names are on their birth dates and their passport numbers.

1:00:26And so I don't have to enter that into a cumbersome interface anymore. It should be the price points. And probably we're going to the same hotel as every year anyway. And so I think that those travel agents or those kind of agents are going to happen in the next year plus I think five years is my national language vision and unlocking the world's knowledge, including software developers to everybody in any language, any human language and maybe that's even happening sooner. It's 10 years is hard, 10 years is so far away. But I think that's the AI of things, the material, the mechanical world of AI.

1:01:06So many things that we do in life, you need to grab something, you need to push something and go and check into any hotel. there's an AI involved once you get your own key. And maybe the elevator has some kind of AI because you push a button what you want to go to and then it's also to take elevator C and so you no longer push the button within the elevator. That's an optimization problem in itself. But I think there's so many physical things in life, you know, and we are still not at really self -driving cars. We have a dishwasher, but I still have to put the dishes into the dishwasher and out of the dishwasher, it's better with nine and 12 -year -olds and over two year olds.

1:01:46And there's so many other things in life where I think whether it's a robot or some other form of physical AI is going to take over some of the things that the consider is towards. We're really excited to hear that. And I think we have a very similar view of what will happen for one, five, and 10 years from now. Who do you admire most in the world of AI? I think I admire those that are building new stuff AI and software. Those that have a dream of what they could build. I think those that obsess about a problem and not about the technology, we're talking a lot about AI, but at the end of the day, it's what's the problem I was solving for the world.

1:02:32There's lots of biotech companies that use AI to try to cure diabetes or cancer. I'm sure there's companies trying to sort of climate change with technology. And I think those builders, the founders, those that have big ideas and that can change the world, those other ones I admire the most. And often when I meet with them in their offices or on calls, I'm like, this is so cool. And obviously, as GitHub, we are enabling a small part of that. And so we feel free to be the part of that journey. and we're really excited about building more for them. I think you're enabling a huge wave of new AI companies, especially those that are born open source.

1:03:19It's amazing to see. What advice do you have for the founders listening in the audience who are building in AI today? I mean, what are you, Sonia, ask, I think focus is everything. Like it's so easy to get lost in the all the ideas that you can put on a whiteboard. And that's the danger of a whiteboard that it has covers, you know, so much space where you can put ideas But focus ultimately is everything, you know, finding very quick market validation Often on the developer space that means going product -led growth Enterprise growth can come later, but like from my experience like There's nothing then, you know, a few excited developers spreading the word about your product even though the big revenue number then later comes from big enterprises buying you.

1:04:05But the reverse is often much harder of going enterprise first and you might find excited people there as well. But the feedback loop is just so different. So it's focused trying to find that flywheel, the product market fit, you know, and then I think the other one is to think big. I think it's like, like, it's easy to find smaller ideas on top of, you know, model today that get, you know, commoditized tomorrow. And so you have to think forward in that, you know, 10 years is a long period of time, but that's, you know, a good period of time for startup to build something that is actually meaningful in this world.

1:04:42And so you have to think big, you have to, you know, create a vision that may be much larger than the MVP, you know, the first thing, the prototype that you're building right now. And I think that's really hard as a founder to draft out that vision. And then decompose right and go back to that small problem that you can solve right now. So we'll close with some rapid buyer questions. One more answer. One more answer. One more answer. You change the blue. It's good. OK, say one more answer. One sentence if you need it. No, I say. I'm going to answer this. Yes. OK, let's go. OK. Well, anyone, meaningfully, disrupts Nvidia and AI chips in the next call it 5 to 10 year.

1:05:21Yes. In what year will we pass the 50 % threshold for sweet agents? 2025. And what about 90 %? 2028. We'll get hub primarily. There was too pessimistic. That last one. B. We can hold you to it. We'll get hub primarily be using open or close -source models in the next five years. Both. Both. Where does the majority of value accrue in AI? Models, compute, infra, applications. A cost of whole stack. That in high -end, so it was one word. Is systems thinking and creativity going to get baked into the models in the next five years? Maybe. And will there be a new consensus architecture beyond the transformer in five years?

1:06:13Of course. Yes. Wow. Why would you think the other way around? Like that's said that it is a much easier bet to think that there will be a new architecture because there was other architectures before Transformers and like that doesn't mean it replaces Transformers. You know your cell phones do as a CPU, even though GPUs are the hot commodity right now. And so I think yeah, there will be new architectures and they might be bigger than Transformers today. So interesting. I mean that would bring a lot of new oxygen for the builders and the ecosystem and a lot of things that we have to get reworked, rebuilt, re -architected.

1:06:47Yeah. Amazing. Thomas, thank you so much for joining us today. It's been a wonderful digging into the history of GitHub, the birth of GitHub Copilot, and the ambition that you have going forward as well. Thank you. Yeah, thank you so much for having me. It was so fun to talk to you both. Thank you. Thank you. Nice, thank you.