Loading summary
Transcript33 lines
- [00:00]
A
You had the developer Zeitgeist in AI the very beginning. How are you repositioning with this agent launch?
- [00:07]
B
Over the last year, it has clearly turned out to be that race between a Copilot, Cursor and Windsurf. We believe we still have the biggest user base across these three. As with every competition, whether it's in sports or elsewhere, you constantly need to keep reinventing yourself. The race is on.
- [00:38]
A
Thomas, welcome to the show.
- [00:40]
B
Thank you for having me.
- [00:41]
A
Great to have you here. Psyched to get to talk to you about GitHub. You just launched GitHub Copilot agents, which is awesome. How do you feel about it?
- [00:49]
B
I feel amazing. It's obviously the moment after the keynote when Everybody in the GitHub team and lots of our friends at Microsoft worked really hard over the past few weeks or days to get everything ready. All the announcement today actually shipped, so there's no wait list this time around and so people can just try using it. And Keynote went well and so we were really happy about that moment. And now the next phase starts and collecting all the feedback and listening what people have to say and hopefully a lot of positive actions.
- [01:22]
A
That's awesome. The place I want to start is I remember Copilot as like the first AI application of this wave of AI, even before ChatGPT, where I was like, wow, this thing is awesome. And Copilot actually quietly has a ton of users. You have like 15 million users or something like that. But when you think about the. Or when I think about the ecosystem of next generation AI coding, like the Cursors or the Windsurfs or the Claude codes or whatever, GitHub doesn't come up as much. So take me through that evolution and sort of where we started with GitHub Copilot and how that ecosystem has evolved and how you see yourself as part of the whole landscape of AI coding tools.
- [02:09]
B
Yeah, absolutely. And you mentioned ChatGPT already. While we were before ChatGPT, we were not before GPT. So GPT2 was kind of like the first model that had an impact. Then GPT3 camera 5 years ago at this event, although we weren't here in Seattle due to Covid, everything was online, but Kevin Scott and Sam Altman had a session talking about Transformers and large language models. And shortly thereafter we got access to GPT3 and we were playing around with it together on a call. Somebody at the keyboard, others were typing, dictating prompts, and we were seeing candidates bytecode in different programming languages. And we had these moments where we're like, holy shit, this actually works and can separate Python from JavaScript. And so we had this original idea for Copilot, which were actually three different concepts. One was code to text, have code explained in natural language. We felt this was cool but not good enough. And many developers would try that and say this isn't working and describe something that doesn't really work.
- [03:15]
A
Like in a chat interface.
- [03:17]
B
Well, yeah, it was like in a chat like interface where you would highlight things and then say, right click and say, explain this to me. But that wasn't good enough. And so we figured, okay, we park that for now. Chat was the second idea. Same idea or conversational coding is what we called it back then, which was in the concept paper. But we felt the model is not there yet and we were very worried about developers trying this out. And then you have a negative response or you see the description or the response in chat doesn't match what you ask and then you stop using it. Right? That's always the danger in developer tools to try something and it's not good enough and then you move on and you always remember that moment instead of what the tool has evolved to. And that kind of like goes back a little bit into the beginning of your question and then auto completion or effectively text to code. Like you write something in the editor and it provides a suggestion of what comes next. And that worked fairly well. And we had a lot of confidence that that use case would work. Because if you think about what developers do every day when they're writing code in editor without auto completion is that they also have imperfect code, either because they're mistyping themselves, I don't remember what the API looked like, or because they took code snippet of Stack overflow, of Reddit, of some blog or what they saw in your channel or some open source project. And more often than not, that code snippet that they're taking from somewhere else and putting in their code isn't perfect. Right? It might have different variable names or it might be for a different version of the library that you're using. And so you still have to make that work. So that was the initial idea behind auto completion, was we can make that work close enough to the real developer workflow to actually provide some real value. And that's how this all started five years ago and ultimately created this market that we are now all in. We have all these developer tools. I've been in developer tools for 15ish years or so. I've been a developer for 30 years. I've never seen anything like that. We had such an explosion of startups in different spaces. And you already mentioned Cursor and Windserve. If you just look at that space of IDE extensions, that's obviously a whole like a dozen or more other startups. There's all the hyperscalers invested into that space, you know, all our traditional competitors like BitBucket and GitLab or Atlassian and GitLab. And so there's this one space of competition and that gives developers a lot of choice and they can pick the tool that like there is things like Bolt New and lovable and vercel v0 or manus at, you know, some part of that spectrum that competing in like you want to go from a prompt straight to to a web application. There's open source tools like Client and woocode and many others that are looking at the evolution of what could that look like. But you have an open source extension and then you bring in your own model and GitHub plays in all of these. And in fact GitHub also has place in continuous integration, continuous deployment. Right. Like we traditionally had lots of competitors in CI CD that were there before we were in CI CD because we added actions only in late 2018 when CICD was already a thing for like a decade or so. We are in the security space where we're competing against companies that are finding secrets or code vulnerabilities dependabot those kind of things. And so as GitHub we've always seen ourselves, as long as we are part of the developer ecosystem where we provide a platform that all these companies integrate with, that all developers come to to collaborate. And now agents and developers collaborate. We are part of the the developer ethos developer ecosystem and that's where we see ourselves. And so Copilot evolved from auto completions to chat. We added voice, we added cli, we looked into customization code search, bringing more context into the prompt to know agents, where we have agent mode and VS code, where you synchronously work with the agent and where we have the coding agent on GitHub, we assign an issue or task to it and then it does the thing in the background. You can actually run 10 or so of them in parallel. And where we look at the pull request and review all that code. And so I think that journey will continue. Where we provide an end to end experience across our platform on GitHub and across the Copilot experience between the IDE and the DevOps lifecycle.
- [07:41]
A
I think maybe what I'm asking is you had the developer Zeitgeist in AI the very beginning and now you're launching this agent, which seems like it's actually really like right at the edge of what all the other kind of all those other companies are doing. But for the last couple years, I feel like personally I just haven't seen that much in my world. I know you guys are shipping tons of stuff in the enterprise that has a huge impact internally. For example, we use GitHub all the time. That's where all of our code is. We have 50 GitHub repositories or something like that. But most people at the company are not using GitHub AI tools. So I'm curious, what do you think happened, what's been going on for the last couple years and how are you repositioning with this agent launch?
- [08:27]
B
Well, look, if you go back a year to last build 2024, at that time those competitors were still in a group of competitors and it was always clear there's going to be a number two and a number three. And with us being the number one that created that market. And we knew that at some point as everything in technology like Windows versus Mac or iPhone versus Android and whatnot, there's always two or three players that define a market. And over the last year, it has clearly turned out to be that race between a Copilot, Cursor and Windsurf. We believe we still have the biggest user base across these three. And as with every competition, whether it's in sports or elsewhere, you constantly need to keep reinventing yourself. Just because you won the championship or in the season this year doesn't mean you're going to win next year. And so I think for us the most important thing is that we're able to move really fast. We shipped over 100 change logs for Copilot just now in 2025, and it's only May. And we're going to keep shipping and we're going to evolve our platform from VS Code, where we announced today that Copilot is going to be open source, but we also have Xcode support and Jetbrains and Eclipse and of course the old school visual studio all the way into our platform. We not only have the coding agent, we have a code review agent, we have an autofix agent that fixes security vulnerabilities and you can actually already stitch them together and have one agent create the pull request and the next agent review the pull request and look at security vulnerabilities and then have the SIE agent monitor your Cloud resources. So I think the race is on and we're excited about the competition because we ultimately believe in developer choice. It's great that there's many tools out there and developers have the choice and they can pick the editor that fits the. Fits them best. And then as you mentioned, they're still coming to GitHub and store their code there and hopefully use Copilot as part of that journey.
- [10:43]
A
Yeah, tell me about the Copilot agent you just launched. Tell me about the architectural and product and UX decisions that you made and why you think it's a better agent solution than maybe Codex or some of the other more popular agents on the market right now.
- [10:59]
B
It all starts with GitHub Actions. And I already mentioned earlier, we brought Actions to market back in 2018.
- [11:06]
A
What are actions for people?
- [11:07]
B
Actions is conceptually, you can understand it as our way of implementing continuous integration, continuous deployment, or simpler. You have a trigger, a new commit, a new issue, a new pull request, or comment on a pull request. And there's lots of these triggers that you can define that then runs a script on a compute instance, aka a virtual machine. And GitHub Actions offers you that integrated into, into GitHub. And so you can define within the repository a workflow file. It's a YAML file. And you say, okay, on every commit, I want to run this build script or this test script and this actions ecosystem. So it's developer automation, if you will. Right? Like you can run something on any, on an event, every time you push.
- [11:58]
A
Code, push code back, run something for.
- [12:00]
B
You, or say this code pull request breaks the tests. And so you're not allowed to merge that back into the main branch. And that ecosystem has now existed for almost seven years and has over 25,000 actions within our marketplace. And so the cool thing about Actions is that you can compose it from other actions. And so there's actions for everything that you can imagine, including for what is worth. Also lots of AI scenarios connecting to model APIs, and a lot of open source projects and enterprise customers are using Actions for their engineering stack for their platform. And so we have that compute layer available and customers are already trusting that compute layer. Right? Because ultimately what happens is that whenever you run actions, you're cloning your source code, your intellectual property, onto something else. Right? A virtual machine. And then you're trusting that that virtual machine exists during the runtime of your script, and then the virtual machine, when the script is done, then the virtual machine gets shut down and everything gets deleted. And so you're not having your source code set outside of your compliance boundary or your permissions, who has access to that code base. So we think that's one of the advantages of our agent, that we're already integrating it into the existing ecosystem where people are already running their CI cd. The second thing is that we believe that the best place for agents to exist is where developers already do their work. And so we already mentioned the ide. With vm, you can just trigger the coding agent from Copilot chat in VS code and say at GitHub, create a pull request to add tests to the method that I just wrote and then that spins it off in the background and it does its thing on GitHub Actions and it opens a draft pull request, describes what the plan is and then commits changes into that pull request. And that's very similar to how you would delegate tasks to one of your co workers. So you're basically taking the exact same workflow that you're already doing with your team and applying it to agents and bringing those two worlds together. And that also means that three years from now you can go back to that pull request. And it doesn't matter whether the pull request came from a human developer or from an agentic developer. What matters is that you have still everything together in one place. It also means that you have a session log that is stored together in the repo. So again, that session log survives as long as you like. You have audit logging, so you know, when the agent started its work and it stopped its work. And lots of enterprise customers, you know, stream that audit log into their threat intelligence so they can kind of like monitor, you know, a lot of the questions we're getting is how do you do, how do I trust the agent? In contrast to CI cd where you have a script and you describe exactly what the job does, right? Like download maybe a library, run tests, you know, upload the test results into the log file. With agents, that process is not described because it's a model that is inherently non deterministic and depending on the time of the day you get a different outcome. And you see that when you're using agent mode and you're following it along in VS code, how you can give it the same task multiple times and it does different things and maybe calls different tools or finds a different file in your code base to get started with. So we believe this integration of the agent in the same workflow that your team is already doing is incredibly powerful because you don't have to relearn how you're reviewing code. You know how you're testing code, how you're proving code to get merged into the into the main branch.
- [15:37]
A
This episode is brought to you by Addio, the AI native CRM built for the next era of companies with Addeo Setup takes minutes. Connect your email and calendar and it instantly builds a CRM that mirrors your business with every contact enriched and organized from the start. From there, Addeo's AI goes to work. It gives you real time intelligence during calls, IT prospects leads with research agents, and it automates your team's most complex workflows. Industry leaders like Union Square Ventures, Flatfile and Modal are already building the future of customer relationships on Adeo. Go to Adeo.com every and get 15% off your first year. That's Attio.com and now back to the show. Do you think at all about the personality or taste or style of the agent? And if so, like, how did you think about shaping that?
- [16:27]
B
Yeah, I think, you know, part of that shaping is the reason that we're supporting multiple models since last year's GitHub universe. We no longer have just the OpenAI models, which we still also offer and a lot of those models got added since Universe and but in addition we also have Anthropic's Claude series, we have Google Gemini, we have bring your own key as you can actually just connect to openrouter or the Anthropic API, put in your key and connect to these models, including deepseek as another example. Because we believe that developers want choice in the same way that they want choice when they're picking the programming language and the open source Library or the JavaScript framework, whether React or Next or whatever favorite framework you have. And the same is true for models, because the models they have benchmark, but the benchmark never tells the whole story. It might be better for one language than another. It might be better or worse for your personal style. And so developers, by trialing and erroring these models, will figure out what's best for them. And some of that is obviously influenced by social media and podcasters and the news and whatnot. You're trying things out because you heard about it somewhere else. But some of it we believe is from experience. You're going to gain more and more experience learning how these models work. And just like you know how you know you're building your craft over the course of your career and you're getting better because you have been in the situation before and you know how you dealt with it then and maybe you learned something last time, and now you're doing it better. The same is also true for these models. You know how Claude 3.7 sonnet behaves. And so you pick that because you know, okay, this is the easiest way I can write these test cases. Now, the other part of that is what we call custom instructions. You know, you have, for all these agentic features, you have a system prompt. Oftentimes, either the prompt is directly provided or leaked, and it's in a GitHub gist already. And then developers can customize that behavior by creating their own prompt files within the repository. And so we support that across all the different surfaces. And so you can customize the model behavior and maybe tell it. I only want responses in German, and the code, of course, in the comments should all be in English, because I'm sharing that with my team. But the interaction with the copilot should be in German and in a professional environment here in the United States, that's probably relatively uncommon because once you work in the tech industry, you kind of get to at least be very proficient in English over time. But if you're early in Korea or if you're in school and you grew up like my kids, with German parents, that may not necessarily be the case, but that still doesn't mean that you don't want to share that code with others. And so we're democratizing access to computer technology, to software development, with these copilots being able to speak any human language, any major human language, not just English. And I think that's dramatically different to the time, you know, in the 90s when I learned coding and when you wanted to engage in the Internet, you had to, you know, have a proficient level of English.
- [19:58]
A
Yeah, one of the. I mean, I think there's a serious advantage to being able to use GitHub's agent inside of GitHub, where you're already collaborating. I feel that pain already. Just like if you're reviewing PRs or submitting PRs, there's a separate interface for an agent, and then you have to go to GitHub to, like, talk about it.
- [20:14]
B
It's.
- [20:14]
A
It sucks. So I'm excited to try this, and it sort of strikes me that GitHub, that's a huge advantage for you, and it's a huge advantage to be used by millions and millions and millions and millions of developers every day. One of the things I'm kind of curious about is how you think about making product decisions when you have to satisfy millions of developers that have a way of doing things that they know how to do it and they like it. Versus there's probably this sort of growing contingent of AI first developers who maybe were technical before, but are really just leaning in or basically writing 90 to 100% of their code in a non enterprise environment. Maybe they eventually will be running a big enterprise, but it's mostly just them in their apartment or in their college dorm just using these tools to write 90 to 100% of their code. With AI, those seem like sort of different profiles of users. And how do you, you have so many big enterprise users and that's such a big part of your business. How do you think about serving both those audiences and doing that? Well.
- [21:23]
B
First of all, I would say while enterprise customers are a big part of our business, from a business perspective, like where the revenue comes from, at the same time the larger part of our user base is the open source developers or those that have some form of free account on GitHub with a public repository, whether that's technically open source under some proper open source license, or whether it's just a hobby project where you want to put the code out there for others to use and you're not caring so much about the thoughts behind open source licensing and whatnot. And both of these audience have historically been equally important to GitHub. In fact, if you go back in the early days of GitHub it was only public repositories first and then private repositories get added and then you can still find those threads. People were begging the founders to actually introduce a payment model and so they could pay for their GitHub account and earn trust that GitHub will survive and doesn't become an ad business and things like that. And so I think for the longest time GitHub actually was very focused on this. Today we call it park led growth on this growing the the developers that love using GitHub and the enterprise came second. And we encoded this within the GitHub culture. One of our most important principles is that we always put the developer first. So we're not putting the enterprise first and we're not putting a foundation first, we're putting the actual developer first. And that actually goes as far as that. Everybody at GitHub, not only the engineers, but every role spanning finance and HR and product management, sales, they all are using GitHub for everything. So we have lots tens of thousands of repositories with all kinds of information that are not necessarily products that we build or microservice services that we run. But it's folks using GitHub issues and GitHub pull requests, our terms of service are in a repo and so you can see every change in the terms of service pull requests. So we're using our product every single day. And I think that really helps everybody in the company to understand what is important to GitHub. Now, what you alluded to is that the developer audience itself is no longer as narrow as it used to be.
- [23:44]
A
That's what I'm trying to say is I think what it means to be a developer is changing in certain ways.
- [23:50]
B
I think it's still writing code at the end of the day, whether you're using AI to generate code or you knew how to write code without AI, and now you're trying to figure out how do I combine wipe coding with writing code manually? Doesn't matter as much, because at the end of the day, the artifact that you're creating is still code. And as soon as your project becomes more and more complex, you gotta have some fundamental understanding of that code. I don't believe in a world where you can create something like GitHub without knowing what the code actually does. Now, this doesn't mean that low code, no code doesn't exist, right? And has actually existed before AI. And so there are going to be scenarios where you just wipe code, a webpage, or using Manus. I met the founders last week and they showed me how they used Manus to create a page to find an office in Tokyo. And it basically visualized all the available offices on a map. And so that was very specific webpage just for them. But that period of time where they're looking for an office in Tokyo. Right. So this kind of like micro app or personal software, I think will play a big role in the way we are interacting with those agents. Because not every answer an agent can give you is going to be effectively visualized with just a text response. Like the agent is actually more powerful. It creates a little bit of piece of software that you can interact with and maybe pick the flight you're trying to book or the office you're trying to find. And so in those scenarios, you might not need the code because it isn't relevant to you, because you're not actually creating software to produce software and sell it, but you're creating software to solve tasks for you. But the counter example is that you can ask ChatGPT to whatever, how many airplanes got sold last year, and it renders you a chart and then you look in the code and you realize it didn't actually Pull the source data from some source, but it just filled the source data into a static variable with some random data. And it might even start tell that to you in the text. But you know how it is. Who reads all that text? Just look at the chart, it looks cool. And so looking at the code, you know, this simple example shows us that it is still important to understand what this technology does. And as soon as you get into like complex software projects where software is your business, you gotta understand what the code does. You gotta have software developers that review this code, you know, approve it before it gets merged. Because otherwise you're going to have security vulnerabilities, you're going to have functional issues. You might actually read a feature in a complex application without noticing it. And ultimately, look, businesses, corporations exist because teams of people are more efficient than a single person. But they exist to sell products and ultimately make a profit for that group of people. And I think AI cannot make all these business decisions for yourself. The team has to do them. They have to set the parameters of the business to ultimately return money to the founders, to the corporation or the shareholders.
- [26:59]
A
I'm really curious, if we're back here a year from now, what are your predictions for where you guys are going to be? Where are you focused? What are you going to ship? What do you think you want to do to win this future of coding with AI race?
- [27:14]
B
I think in a TV show, I don't know, I think it was Star Trek, there was a line, the more things change, the more they stay the same. I feel like it was in Deep Space Nine, but am I misremembering it? And I think on the one side a lot of things will dramatically change and you see that with agent mode or wipe coding as a discipline. Although I think Andrej also recently had a blog post kind of like saying wipe coding maybe wasn't as good as an idea as I originally thought. But we're going to have these coding agents, code review agents, security agents, SIE agents. We're going to have those available to developers and those that are on the forefront of the field and know how to adjust their project to be very efficient with these agents are going to get a lot of work done. At the same time, we still have lots of companies that have code on mainframes, COBOL and things like that. We still have huge code bases in old school languages like C, C, Perl, and those things don't go away. And while there is certain progress on app migration, we showed a Java and a NET upgrade agent today. As well. There's a lot of work to be done before we get into a world where everybody's just working with agents and no longer has to write code in the ide. And so I think the IDE will still be there, it will be more and more agentic. GitHub will still be there, it will be more and more agentic and a lot of folks will still scratch their head and try to find the bug that's preventing them from going home at night, or they go home with a bug in their head and then hopefully by the morning when you had a lot of sleep, you can figure it out. So I think we will definitely see a world where you have agents across the whole developer life cycle. In fact, I think there's going to be a lot of opportunity for small companies to use agents for everything. Designing a feature, prototyping it, doing customer research, using something like deep research to do competitive analysis, maybe even write the business model, implementing the feature, testing the feature, deploying the feature, monitoring the feature. The full stack builder I think will become a thing. But there will be thousands if millions of professional software developers that will still write code like we wrote QL code this year.
- [29:35]
A
Well, thank you so much for your time. It was great to chat with you.
- [29:37]
B
Thank you.
- [29:45]
C
Oh my gosh, folks, you absolutely, positively have to smash that like button and subscribe to AI and I.
- [29:52]
B
Why?
- [29:52]
C
Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge bombs. About ChatGPT Every episode is a rollercoaster of emotions, insights and laughter that will leave you on the edge of your seat craving for more. It's not just a show, it's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor, hit like Smash, subscribe and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely, hopelessly in love with you.