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

In this episode of Gradient Dissent, Lukas Biewald sits down with Thomas Dohmke, CEO of GitHub, to talk about the future of software engineering in the age of AI. They discuss how GitHub Copilot was built, why agents are reshaping developer workflows, and what it takes to make tools that are not only powerful but also fun. Thomas shares his experience leading GitHub through its $7.5B acquisition by Microsoft, the unexpected ways it accelerated innovation, and why developer happiness is crucial to productivity. They explore what still makes human engineers irreplaceable and how the next generation of developers might grow up coding alongside AI. Follow Thomas Dohmke: https://www.linkedin.com/in/ashtom/ Follow Weights & Biases: https://twitter.com/weights_biases https://www.linkedin.com/company/wandb

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

• 0:02Lukas Biewald?

  You're listening to Graded Descent, a show about making machine learning work in the real world, and I'm your host, Lucas Biewald. All right, today I'm talking with Thomas, the CEO of GitHub. I've obviously been looking forward to this interview for quite a while. GitHub is one of those products that I've used nearly every day of my professional career for over a decade, and I have a real attachment to it. I was really excited after Microsoft bought GitHub and their innovation accelerated. And it's interesting timing for me because if you haven't heard, Weights and Biases was recently acquired by CoreWeave, and GitHub was one of the analogies that I used in my head of a successful acquisition.

• 0:43Lukas Biewald?

  So I talked to him and get advice from him actually on camera about how to make an acquisition successful, but we also get to get into how GitHub Copilot works, why it's worked so well, where it's weak today, and how they think about the future of coding. I hope you enjoy this one.

• 1:02Lukas Biewald?

  All right, so Thomas, thanks for joining us. I guess we were just talking before we started recording about acquisitions, and you've been on both sides. Do you have any advice for me and my team post-acquisition of Weights and Biases?

• 1:19Speaker 01

  I would say stay sane, get some sleep in between the craziness that passes. As you mentioned, I've been both the seller of an office startup. I was the co-founder of a company called Hockey App that we sold to Microsoft in late 2014, and so I know what that was like with my team going through that acquisition until the last moment we were setting before the pod started, that and then everything was signed. We were sitting at dinner and it was an emptiness surrounding us. You feel like you should be more happy than you are in that moment, and then it takes some time to actually process that everything is now changing in your life.

• 2:01Speaker 01

  I've been obviously involved in the GitHub deal in 2018, mostly behind the scenes. And then after we had acquired GitHub, we bought, I think, six or seven small to medium-sized companies. And I remember a number of conversations with the founder CEOs of the company we were acquiring. And the emotions that went through them and I could empathize with them and understand where they're coming from, kind of relating back to my own experience. So it is, it is something, you know, for a founder, CEO, or founder team to sell their company, an experience in life that is probably, you know, hard to compare to anything else.

• 2:44Lukas Biewald?

  Well, I wonder if it was like this for you, but, but, you know, one thing about this transaction is, you know, I couldn't talk about it with anyone for, for quite a long time. And it was also kind of very slow where, you know, it kind of felt done, but then we're kind of working through lots of details. And then there's like lots of regulatory approvals. So, you know, it feels, honestly, to me, like very anticlimactic. And also, I'm kind of like, okay, I've actually been wanting to like, you know, just kind of like work on the future stuff.

• 3:10Speaker 01

  Yep.

• 3:11Lukas Biewald?

  You know, for a long time, but, but couldn't. So it felt very unmomentous, I guess, to me, but it probably felt like a much bigger deal to all the people around me who are kind of just learning about it and going through that.

• 3:23Speaker 01

  Well, and why, and why you can't, during that phase where you cannot tell people that this is happening, then you also have to, you're almost forced into, you know, lying to a certain degree or planning for a future that you know will no longer happen, where you may commit, you know, to, you know, an offsite or a customer event. And then you know, okay, but by the time that actually would materialize, there's like a, like a...

• 3:50Lukas Biewald?

  Nice.

• 3:50Speaker 01

  A mosquito above me. By the time that that actually materializes, the world will look fundamentally different. And then you tell people, and of course, you, you deal with a lot of uncertainty and doubt. And I'm sure you have gotten the question, Lucas, you know, what's going to be my role in the future company? What is going to be your role? You know, and people, the tendency is that they doubt what you're saying is actually the truth, given that as soon as, you know, these deals are closed and you moved, you know, into the new office, either virtually or in reality, you realize that the grass isn't always greener on the other side. And there's, you know, as, as many problems often in the, in the buyer company than, than they were in the seller company.

• 4:34Speaker 01

  So yeah, I hope everything works, works smoothly for, for you, for you and your team. And next time, maybe we talk about the, the life after and what it's now like after everything is fully closed and integrated and so on.

• 4:50Lukas Biewald?

  Yeah, no, I appreciate it. It's a very unfamiliar experience for me, honestly, because I've I've spent, you know, most of my career running companies and I didn't think I was like attached to the, you know, CEO title particularly, but it's, you know, one big like big difference, actually, is like, you know, I can't like really guarantee people that I can control, you know, what what happens. Like, it's a very different kind of conversation. I feel a lot more empathy for the managers that have worked for me in the past. Now, I actually have another experience where, you know, like, you know, I think that's I feel like it's really making more sense to me why people want so much clarity from their their leaders and, you know, fast decision making and things.

• 5:30Lukas Biewald?

  I actually think it would make me better to be on the other side of this, you know, for a period of time.

• 5:37Speaker 01

  Yeah, naturally, you lost a little bit of control, right? Because one way or another, now you have a manager above you or a set of managers and the board and whatnot. And while people might trust you, they haven't got to the point yet where they have seen you reacting to instruction, direction, you know, command, if you will, from from your management chain. And then that certainly is a different life. And I think, you know, in many ways, that is what is so fundamentally different between being a leader in a medium sized to to large company, you know, including me as GitHub CEO, because obviously we're part of Microsoft and I have a management chain within that construct and deal, you know, with the Microsoft Finance Team and HR

• 6:20Speaker 01

  and all these kind of things. Whereas being a founder CEO where you live with a different uncertainty, what does tomorrow look like or what does, you know, the company look like in a year? But there's other than your board, if you have one, there isn't really anyone telling you what to do. And I always felt like as a startup founder, that is the ultimate freedom that you can have, even more so if you have a bootstrap startup where there isn't really a board or venture capitalists or investors or anything like that. But it's not that, you know, you wake up every morning and everything is a happy place. And even more so when you have a product in market and now you have customers and they have support issues and I'm sure you have seen that, that they emailed you

• 7:04Speaker 01

  and says, hey, your team hasn't gotten back to me and I'm really frustrated and I'm paying you so much money. And then you're like, oh, okay, I was planning to have a night, a nice night with my family and now I'm bidding with this customer escalation, right? So.

• 7:16Lukas Biewald?

  Yeah, that is the job. I'm curious, actually, you know, we're immediately going off script a lot of it. But, you know, I mean, I feel like GitHub actually, from the outside, feels like one of the most successful acquisitions that I can even think of. I mean, this is such a softball question, I guess. But what do you, what do you think made the, made the GitHub acquisition so successful? And was it a success? Was it all, was it all that you hoped, you know, having like, kind of thought about it before it happened and now running it?

• 7:46Speaker 01

  Yeah, I mean, I would say the, if you ask around in Microsoft leadership, the answer would clearly be yes, this was one of our most successful acquisitions. I think the other two that are always, you know, listed in a similar realm are LinkedIn, which has, you know, very similar integration model where they have a certain level of independence. Ryan Roslansky is the CEO, you know, they have their own offices, their own brand. If you go to LinkedIn.com, there isn't much Microsoft in terms of branding or anything like that. And it's very much perceived as, as an independent platform within the Microsoft ecosystem. The other one is Minecraft, which was a very different acquisition, also how it started and how it went, went through.

• 8:31Speaker 01

  But similarly, right, the brand is, is very independent. Most kids playing Minecraft, that's probably their first touch point with the Microsoft ecosystem, unless they're playing Minecraft on a PC. But if, you know, if they have it on their iPhone or Android phone or on a tablet, but they're getting handed from their parents, it's not even that. And so you have these three brands and GitHub kind of sits in the middle. It is, it is more of an enterprise brand, right? That's where, you know, we make most of our revenue. But at the same time, it has this recognition from, from consumers. Like my, my favorite example is when I go to the Lego store in the Pacific Northwest,

• 9:13Speaker 01

  naturally a lot of the employees there are students and so they know what GitHub is. And so they are as much a fan of, you know, the company that I'm running as I'm a Lego fan.

• 9:26Speaker 01

  And you don't get that many other enterprise companies. And part of that is obviously that GitHub is, you know, both, you know, an open source company hosting all these open source projects and a company that sells enterprise products to many of the companies that wanna work like an open source project wanna emulate a lot of these practices, or they're sitting, you know, somewhere in the middle that they are both providing open source like your company and have, you know, a part of the stack that is open source. So yeah, we are very happy about the acquisition, you know, revenue has grown. We announced in July of 2024, over two billion run rate.

• 10:10Speaker 01

  And you can look up yourself what the rumors were back in 2018. I don't think that's an officially published number, but it's, you know, significantly up. Acquisitions often have scorecards, so the scorecard on the revenue side is more than green. The scorecard on the growth side is more than green. And with Copilot, you know, a post-acquisition and with the OpenAI investment in 2019, you know, we kind of materialized the best of Microsoft with its cloud and experience and responsible AI and all these kind of pieces. The best of GitHub with our developer-first approach and the OpenAI partnership that gave us access to first GPT-3 and then the Codex model to build, you know, the original Copilot, GitHub Copilot.

• 10:56Lukas Biewald?

  Okay, but so why is it so green? Is it the OpenAI connection? Because I feel like even before that, like one thing that I noticed from the outside is that it seemed like the development actually accelerated post-acquisition. Like that was my impression as a user. I don't know if it felt like that on the inside, but yeah, like what happened there?

• 11:19Speaker 01

  As part of the deal, we actually defined the three acquisition principles or integration principles. The first one was, and I think that's really part of our success, is that above everything, we put the developer first. In many ways, you could argue GitHub has always done that since it was founded. It was founded by hackers that had discovered that Git is cool.

• 11:43Lukas Biewald?

  Thomas, did you know, actually, did I tell you?

• 11:45Speaker 01

  No.

• 11:46Lukas Biewald?

  They worked for me. So I'm actually like user number seven of GitHub or something. I don't know if I ever mentioned that.

• 11:52Speaker 01

  I don't think you did.

• 11:53Lukas Biewald?

  Yeah, yeah, it's funny. I was like there when they started. It was on my, my co-founder is Kat, it's actually that they were hackers.

• 12:00Speaker 01

  Yeah, Chris, Tom, PJ, and then Scott as kind of like the fourth late founder, right? They were, it's really actually fun to go to the Wayback Machine and look at what github.com looked like in the very early days.

• 12:13Lukas Biewald?

  Totally.

• 12:14Speaker 01

  I think many folks have a much more evolved picture in their head of what the UI looked like and what the front page looked like. But the reality was that it was almost like a commit log with like single line updates from Chris, I think mostly saying, yep, we have this new feature, check it out. That was all that was there on the front page. And the UI looked much more basic than it is today. And I think many people kind of like have this 20, I would say 2015, 2016 evolution in their head as that was GitHub was always like. But obviously in the early days of this cloud native web two or whatever you want to call

• 12:54Speaker 01

  that wave of startups, it looked much more, much more simple than today. And so we put developers first, right? And so whether we write blog posts, whether we build products, we have all internal processes, everybody at GitHub, across every roles, HR, finance, legal, product management, they're all using GitHub every single day. I think the only other tool that we're using as much as GitHub is Slack. That's the two tools as a remote first company that we're using very intense swiftly. And but that helps us to evolve the product. The second principle was And that I think is the mistake in many acquisitions. We thought about how is Microsoft accelerating GitHub. Right, but I think in many acquisitions is the other way around.

• 13:39Speaker 01

  You think about how you find synergies between the company that you bought and the company that's buying you. And you're looking at how is the startup accelerating the big company? And we thought about how is Microsoft accelerating GitHub? And I think that's where you see things like GitHub Actions, which was, you know, an effort where we took an existing technology out of Azure DevOps called Azure Pipelines and brought that into GitHub. And it looks like GitHub and it has a different format of defining these workflows. But they're underlying technology and part of the team came over in early 2019 to build what is today GitHub Actions. And so you can kind of track that progress.

• 14:21Speaker 01

  And part of, you know, the success was that we very quickly figured out how can we do joint sales with the Microsoft sales team around the world, which gave us coverage that in GitHub, you know, at the time of the acquisition was about 800 employees could never have. And that helped us to accelerate, but really Microsoft invested into GitHub to let it grow. And I think that is crucial and in many ways, you know, it's almost like a D round or E round or whatever, seriously,

• 14:50Speaker 01

  of an investment. And then last, the last concept was the third one was how does GitHub accelerate Microsoft? We always said that comes later. And we will think about that when we have figured out the other two pieces. And Copilot, I would say, was probably the first one, very intentional about taking our learnings, partnering with Azure, you know, building on top of a joint stack between the GitHub Copilot and all the other Copilots that Microsoft now has, leveraging the responsible AI pieces that Microsoft offered us, but also, you know, contributing back to that. And so that, I think, is what describes the success of GitHub now, almost, yeah, seven years, seven years after we announced the acquisition in 2018.

• 15:33Lukas Biewald?

  And so, I mean, you know, Copilot is this really incredible product. I think it was for a lot of people, you know, the first time really seeing, you know, the power of L M's. Like, I remember, you know, when I first, um, you know, when I first kind of started to use it, just, just like this, oh, my God, you know, feeling of like, kind of seeing like how amazing, you know, this could be. I guess, um, you know, I wonder with that, with that product, like, I feel like there's so much momentum in Silicon Valley about, like, you hear, like, kind of like, you know, Codium, and you hear about, like, kind of Cursor, and it doesn't feel like Copilot has the same, I don't know, like, purchase inside the brains of at least, like, Silicon Valley companies. Like, how do you think about that?

• 16:16Lukas Biewald?

  Like, do you feel like, you know, you're behind in some ways and you're, you're planning to catch up? Or is it like, like, I guess, what is, like, Cursor doing even that's, like, different than what, like, Copilot offers?

• 16:29Speaker 01

  Yeah, I mean, I think, you know, we, we were very early, um, when we started with Copilot in the middle of 2020, when there was almost no A I hype whatsoever.

• 16:41Speaker 01

  G P T three was, you know, available to some nerds and data scientists and people that understood what large language model do. And oftentimes, you know, you had to explain what a transformer is first to a customer before we get to the point of why this might be changing the world. And, um, June 2021 is when we launched the preview of Copilot. And, um, you know, you may say we created that whole market with that one announcement. And it was a very short blog post and a site that really, a landing page that showed how code completion worked for different languages and different examples. And if you, if you travel back in time on Hacker News and other pages, there was equal

• 17:25Speaker 01

  amount of skepticism than excitement. And we started giving people, you know, access to the, to the private preview and You know, some were skeptical and then turned around and you kind of saw that evolution happening of saying, oh, you know, after using it for a while, I actually see the significant impact. And there were, you know, legal challenges and discussions around, you know, fair use on the training side and replicating a code and licenses. And so a lot of the investment in the early days was just scaling out Copilot. I think we had a million users on the waitlist within less than a year. And then in the private preview,

• 18:09Speaker 01

  also waitlist and then you're going through the waitlist into the private preview. And so we also scaled the architecture in that first year. And that was the the public launch, what we call GA, general availability, was June 2022. So even that was before ChatGPT, right? Like, it's this whole time horizon is so compressed with so many things that have happened since then, it's easy to forget that Copilot was not only in preview, it was a GA product before before ChatGPT came out. And I was actually on a trip right after the ChatGPT launch, and I could see it in every customer conversation I had, whether it was in Tokyo, Singapore, and Sydney, that the world had dramatically changed. It went

• 18:53Speaker 01

  from AI is kind of this cool technology, but we're far away from that future and actually being useful to Thomas, tell me more how you build Copilot, how we can adopt Copilot. We had the first case studies where we showed that developers are 55% faster. But ultimately, you know, the the play for for GitHub is that we have the platform. And so when you when you buy a Copilot, you not only get it in VS Code, and in Jackbrains, and in Xcode, and Android Studio and whatnot, you're also getting it in GitHub, and you're getting a code review agent to look at your pull requests. And and so just in earnings, Microsoft

• 19:31Speaker 01

  earnings last week, we published that Copilot has now 15 million users. And Copilot code review agent, just as an example of that platform play, has Over eight million pull requests. And I think that's where we're seeing the future is this holistic workflow where you have Copilot for auto completion. You have chat, you have agent mode and you have MCP integration. And then it continues, you know, in your pull request to review code to fix security vulnerabilities. And with Project Padawan, which we announced earlier this year as, you know, one of the early.

• 20:12Speaker 01

  How do you say this? As one of the projects we're working on to implement a software engineering agent or S V agent.

• 20:20Speaker 01

  You really have a flow where you can go from issue to pull request just with the help of an agent.

• 20:27Lukas Biewald?

  And do you have like a sense? I mean, I think you've thrown out different numbers. A lot of people kind of talk about, you know, productivity gains. I myself wonder how you even possibly really measure that. But do you have any sense on like what like what the speed up is or like like? I guess, what are you even like kind of shooting for with this with these projects?

• 20:49Speaker 01

  We are ultimately shooting for making every software engineering team, starting with ourselves, you know, able to deal with the ever growing number of lines of code and the complexity that comes with that. The problem with, you know, when you compare productivity gains between, you know, without Copilot and with Copilot, the challenge you have is you're never working on the same thing twice. You can do case studies and that's where the 55% came from where, you know, we gave 50 developers a task to do without Copilot and 50 with Copilot and the team with Copilot. And everybody does the same thing, right? And the team with Copilot was 55% faster. But that obviously is a, you know, clinical study, if you will.

• 21:34Speaker 01

  It isn't reflective of the real world. I would never have 100 developers in my team do exactly the same thing. And even the one that works on the same topic, right? On the same subsystem or micro, micro system or whatever. They're not doing the same thing day on, day on. In fact, they're adding more stuff to the existing service. And so the complexity is always rising. And so when you look at productivity gains, you've got to ask what's the baseline and what you're comparing against. And ultimately, the AI, the AI, making AI part of the developer workflow had two impacts. One is that we actually increased the complexity for the developers building this system,

• 22:15Speaker 01

  because full stack now means backend and database and all that infrastructure, frontend, and now you also have AI, which means you have to integrate models and eval them and fine tune or, you know, post-train them and operate them. And then you have things like what OpenAI recently saw that they, you know, have a new version of the model and they tuned it too far in the one direction and they have to roll that back. But even to, if you don't want to wait for customer feedback on that, you have to have really good eval test suites and a really solid, you know, monitoring process, which is not fundamentally different to monitoring your uptime and your exceptions in Sentry and all these kind of things. But, but it adds another complexity layer to our development teams.

• 22:59Speaker 01

  So what AI has done to almost all software engineering companies, because the expectation is now that you're integrating AI into your stack, is that you increase the complexity. On the other side, it has made coding much easier. I think auto-completion is all about flow and staying in flow state. When you're typing a code in VS Code yourself and it predicts the next 10 lines of code, it removes you from the obligation to go into your browser and, you know, search for the, how you use a keyword or how you do this algorithm or, I mean, even simple things, when you work in a Ruby on Rails app and you have a, have a model with a couple of, you know, columns and now go into the controller and instantiate the model,

• 23:44Speaker 01

  just having it, you know, tell you what are the different attributes of that model. And, and some of that, you know, already obviously work with IntelliSense or IntelliCode, just more classic auto-completion, but it really keeps you in that flow state. And oftentimes developers, you know, just can accept code and then it's, it's close enough to what you're trying to achieve to then go, you know, and modify the pieces that are broken, have syntax errors and whatnot. And, and let's face it, that was also true when we copied stack code of Stack Overflow or GitHub repos or from blog posts, right? That code was never actually perfect. You had to take it, you had to, you had to paste it into, into your code and then you had to make it work.

• 24:29Speaker 01

  And so that's where I think auto completion has really improved the flow state. And now with, you know, next edit suggestions, you can just tap, tap, tap, you know, from one suggestion to another. The latest VS code has actually now the auto completions also with syntax highlighting. So you can even pass that a bit more than the original ghost tag. You know, this brings us back to the early days, this UI concept of ghost tags of basically showing you 10 lines of code within the editor that you can autocomplete into. That didn't exist, like we built that in partnership with the VS Code team for the Russian Copilot. Before that, auto completion was a dropdown where you could, you know,

• 25:12Speaker 01

  scroll through the different option from, from the documentation or in TextMate. I think it captured what else it had in that file and created kind of like a DSL out of that. Not a DSL, but like a menu of options. And, and so now if you go into agent mode and three agents and all things like bold, dot new or lovable, you know, Vercels, V0, right? In many ways, if you know how to prompt it, you can actually have it write all the code, right? Like, like V0 as an example, like you, you give it a prompt and it renders you the whole webpage. And behind the scenes, it obviously uses next JS and then you click a single button and it deploys it to Vercel.

• 25:54Speaker 01

  And, and so it was never easier to, to spin up a quick webpage for your, for your wedding or for your birthday invitation or, or even to sell like, you know, like a pop up store, like sell a product real quick. And then you can use prompts to redefine that code. But I think where we are, and so that's super efficient, right? Because you often don't even need to hire developer anymore. And you see a bunch of examples out there where the users of these tools are not actually folks with, with a software engineering background. They just heard about this on, on your podcast elsewhere, that this is cool. And then they're trying it out and similar to how they've used ChatGPT to render an image.

• 26:36Speaker 01

  And, and when the image isn't exactly what you want, you redefine it with, with more prompts. They're doing that with these webpages.

• 26:44Speaker 01

  Where I think the biggest challenge lies is that the majority of actual, you know, big scale software projects, like VS Code is one, GitHub is one, you know, Wraith's and Biese is and others, is that there's the complexity of these projects is so high that today the agents cannot actually tackle those. Much in the same way that, you know, there isn't still a, so far we haven't, you know, gotten a car into the market that you can buy that has no steering wheel. The closest is, you know, Vemo in San Francisco and a bunch of other cities, but it's ring fast to that problem space and has a bunch of extra sensors. I think that comparison applies to agents as well. There are certain scenarios where that works well, but there's a whole big complexity

• 27:24Speaker 01

  out there where it doesn't work well and where the agent or agent mode in VS Code takes a file and adds the two lines you wanted and it removes 5,000 lines or screws them up in some form. And I think that's where it's becoming crucial that developers A, still need to know how to code and B, can make a very educated decision whether they want to use the agent or just go into the file themselves and make that one line change where they know exactly how to do it, where to do it. Like it's, it's almost like a waste of energy and time if you're trying to write a prompt for something that you can write much faster and code yourself. And so having this continuum from having AI infused into the editor with code completion

• 28:07Speaker 01

  to asking coding questions, explain the code, inline chat and those kind of things, all the way to agent mode and autonomous agent, I think those two things all belong together. As developers, we want to move, you know, in both directions, up and down, up and down that spectrum. And if you see a pull request that was created by a S V agent and, and you see a bug in there, what you want to do is go into the comment line, say, you know, use the GitHub CLI to check out that pull request and then just keep working in VS Code or any of your other favorite L E's.

• 28:39Lukas Biewald?

  Do you think, um, do you think that's gonna be true three or four years from now? Like when I, you know, yeah, like what, what, how do you know that, right? Cause you could, I mean, it seems pretty clear, like you could look at the situation and be like, well, it's like getting better and better, like every, every month it seems to be, um, you know, better, why, why aren't there, why, why won't it get better at kind of going into like, you know, specific parts of the code and changing it, which I totally acknowledge you definitely have to do today, especially on like a larger code base, but so many software people are working on it, why, why doesn't that get fixed?

• 29:12Speaker 01

  It's getting better, um, certainly the models are getting better, but, um, in some ways you could argue now in 2025, we're still fighting some of the same problems we were fighting back in 2020 with GPT three and, and codex, um, and where the model just writes code that is a little broken or, you know, mistypes and imports that in Python and now with agent mode it can use tools and it, you know, can fix itself by looking at the, at the error message. But I think fundamentally the part, the role of a software engineer is not just to create new code and new files, it's actually to design an application that is unique, um,

• 29:52Speaker 01

  you know, for the mission, um, in the OKRs, whatever you're describing you. And part of that is that you want to build a successful business, um, that grows revenue and, and returns, uh, profit, um, to the, to the owners or, you know, the shareholders. And, um, I think, you know, that systems thinking, how design, how to design the application, you know, how to decide between MySQL and Postgres, um, or between React and, um, jQuery, or I don't know, that maybe, that maybe is an easy decision, go, go and use React.

• 30:27Lukas Biewald?

  A little bit of David.

• 30:28Speaker 01

  You know what I'm saying? Like, there's, there's, there's a 10,000 choices, um, to make when you're, when you're building, uh, you know, um, an application with decent complexity, um, just like you're up in your building a house, um, and almost no house looks exactly the same as another house and almost no application looks exactly the same. And if That where to, you know, if it can just create code with a, you know, a few prompts, that startup has no value, because then everybody else could also build that, right? And so the complexity will just significantly grow as these agents become more powerful. I think the flip side of that is that while we're here in 2025 and many, if not all of the

• 31:11Speaker 01

  financial services institutions, banks, insurances have still some kind of mainframe cobalt, you know, legacy code, unless you know, the bank was founded in the last 10 years and didn't do any acquisition. But, and so, and that, that journey of managing old code that somebody else has written and figuring out what the developer was intending, how can I refactor this? Are there actual unit tests? And if their unit tests do they cover enough of that?

• 31:42Speaker 01

  That is actually the bigger part of software engineering, right? Like if I switch sides and join your company as an engineer today, my biggest challenge is to understand how this all works. And while I could use an agent to achieve my goal here, how do I validate that what the agent has written is actually correct? And doesn't introduce a new security vulnerability? And or doesn't make the thing so much, so much slower that you need, you know, 10 times the compute to run that platform? I think that back and forth between the agent and its output and the developer is going to stay crucial for many years to come. Let alone that obviously there's also a lot of security risks of accepting code into the codebase without understanding what the code actually does, right?

• 32:27Speaker 01

  This IT calls it zero trust principle. Today, most companies don't allow any developer to push into the main branch. They force a pull request review with a different reviewer, a branch protection, all those kind of things. Run CSD and only if all these things are green, then you can merge it back and deploy it, right?

• 32:48Speaker 01

  That we're doing that because we're worried about attacks. We're worried about, you know, the model. We're going to be worried about the model writing code that injects, you know, a security vulnerability, whether that came from an attacker or whether it came from a, you know, built in vulnerability into the model is effectively irrelevant. We have to deal with that risk and that risk is mitigated by human reviewing that code. And maybe they're using AI for that as well. And I can see that happening, but I think ultimately we gotta have humans understanding code, you gotta have humans designing systems and making decisions of when to use an agent, burn, you know, through GPU cycles to write code that actually generates value for the companies we work for.

• 33:33Lukas Biewald?

  I guess you've written about sort of like this long arc of kind of like leveling up to higher levels of abstraction in the world of software development. I mean, I totally agree, right? Like, I think, you know, like, you know, I sort of felt bad when I was in college for not like going deeper on like C, because that sort of felt like an essential software, you know, skill, but maybe unless you're in certain domains, like less and less, you know, important, you know, over, over time. But I guess you're also talking here about like sort of going in and being able to like, there's sort of an architectural level of kind of almost like a manager, like software architect, you know, mindset of like, you know, like what database should I use here? And how should I kind of like lay out my application?

• 34:15Lukas Biewald?

  But you're also talking about sort of like going in and actually kind of modifying some of the code, or at least like understanding it for, you know, security reasons or reliability reasons. But it also seems like, why wouldn't the software be better than a human at kind of like writing tests and doing like security reviews? Like, why, why doesn't that get automated away?

• 34:37Speaker 01

  It will get, it will get automated, I think, to a high degree. If you, if you look at, you know, having AI writing documentation or unit test cases, it already works to a significant degree. Although we also gotta be real, you know, the Most popular benchmark right now is SW bench, SW bench, which is 2000 pairs of issues and pull requests from a dozen Python repos. And the best agent in SW bench is like at 62, 63%. I think.

• 35:11Lukas Biewald?

  Well, you know, we were at the top of it until recently.

• 35:13Speaker 01

  For a while.

• 35:14Lukas Biewald?

  And I will tell you.

• 35:15Speaker 01

  But 65% of Python, dozen Python repos is like returning with 65% from a math school in in high school. From from a math test in high school. That's not a great result, right? 90% I would I would say we're getting there.

• 35:32Speaker 01

  But even that is like a 10% gap of uncertainty in a business. And just, just, you know, this week, the founders of SW bench built out the multilingual version. And they also announced

• 35:47Speaker 01

  an agent that can generate new test data for those agents. And with this multilingual, I think the numbers go significantly down into the into the 20s to 30s ranges. So we are far away from this agent to actually be, you know, at 100%. Will will that point happen? Of course, in the same way that we are going to see fully self-driving cars. But we don't know when we get to that point because it's not an easy problem to solve. And it's not one that hasn't been solved before. But I wanted to come back to, you know, the fundamental part of your question, which is the, you know, the machine that we're ultimately building software for still runs on a processor that has, you know, built an instruction set

• 36:30Speaker 01

  that is abstracted all the way to the programming language, but not higher than that. The programming language is the last deterministic abstraction layer that basically maps directly into the instructions that the CPU or GPU executes. Natural language is inherently non deterministic. Because you can you can both say the same sentence or describe the same user interface and then build a completely different website with, you know, different design and the different functionality, different databases and so on. And so having that, I think, is going to be the craft of a software engineer is being able to jump between those two abstraction layers and making decisions of when can I describe it in natural language because that's ultimately how we're thinking in our

• 37:14Speaker 01

  brains and how we're thinking, you know, when we wake up in the morning and we have an idea about something you want to build, we're thinking that in English or German or whatever language, you know, you have in your head. And then the challenge in the past was how do I convert that now into programming language? And that's often more frustrating than most folks want to admit because it takes way longer than you thought it might take. And then it's easy enough, you know, to pull in an open source library until you realize, okay, so this actually created a whole new problem in my application. Maybe now you have an dependency that requires you to upgrade to a newer version of Rails or what have you. And so I think, you know, that conversion of being able to take my idea, describe it

• 37:58Speaker 01

  in a few bullets, or even use Cloud 3.7 Sonnet, a super powerful and kind of reason with the model, what my specification looks like. And then you create, you know, a markdown file in your project and then you're feeding that into Copilot Agent Mode to have it then, you know, actually implement that. I think that's the new way of working, but it still requires an engineer, right? Like to kind of do that thought process, you know, figure out the user interface flow, building an amazing product and then jumping down into the coding layer to find, you know, the one line that's broken and that you need to fix to compile the application.

• 38:37Lukas Biewald?

  Do you find with, you know, suddenly all these people using automated systems, that your kind of user metrics are changing a lot like in the last year? Like I would think that, you know, automated code generation would potentially make like a massive increase in the amount of, you know, code getting sent to GitHub. I would think that like, you know, you might get kind of more branching because like, you know, models are like more comfortable, like just, you know, generating pub the poll requests. Are you seeing effects like that in your own data?

• 39:15Speaker 01

  I mean, I think the biggest one we are seeing is that adoption has significantly increased.

• 39:20Lukas Biewald?

  Adoption of.

• 39:21Speaker 01

  Adoption of AI.

• 39:23Lukas Biewald?

  Yeah.

• 39:24Speaker 01

  AI is now, you know, part of the lifecycle. And it's no more a question whether software developers should use AI as part of their, of their day to day workflow. Whether that's in the inner loop, you know, when, when writing on, on my computer and code completion chat agent mode, everything that's kind of like

• 39:44Speaker 01

  synchronous with me and my machine, or whether it's, you know, in the outer loop and pull request reviews, you know, summarizing the pull request, having, you know, the Svi agent build the pull request itself. And so I think it's accepted, you know, best practice now to, to use AI in your workflow. That doesn't mean everybody has adopted it, but I think we are way, way past the chasm and we are now talking about the late majority. If, if not, you know, the, the laggards that are even later. And, and that's where you see, you know, this in very intense competition in the developer tool space.

• 40:23Speaker 01

  I, I would, you know, argue I've never seen anything like that in, in my 30 years, you know, since I started coding on a Commodore 64 in terms of,

• 40:34Speaker 01

  you mentioned a few startups earlier, but it's not only the startups, it's also, you know, all, all the hyperscalers that have AI coding tools. IBM has one and, you know, everybody is playing in that space and, and the extended version of that from, from training models to fine tuning models to having an AI stack all the way to, to coding tools.

• 40:57Speaker 01

  If you will, you know, the, the biggest use case for AI today is software development. And maybe that gets us to the point where then AI, you know, becomes AGI when, you know, the AI can write its own code and improve its own code.

• 41:12Speaker 01

  And so adoption is one thing. I think it's accepted now that AI is part of the developer workflow. How much code is writing is constantly increasing. The models are getting better.

• 41:25Speaker 01

  You know, we have been now shipping a new code completion model every month. And we have an ongoing mid and post-training process to improve, you know, the code completion model with, you know, offline and online testing. And, you know, seeing in those experiments, how much the accepted and retained characters, that's one of the key metrics that we're looking at is like when you get a completion, how many of those accept characters do you accept? And then how do you retain them? And so there's a window where we're looking in the editor, what you're doing and if you're modifying those characters. So that's the metric we're tracking. And based on that metric from offline and online testing, we then decide, is that model flight good enough to become the, what is it now, the main model?

• 42:10Speaker 01

  Which is the code completion model that Copilot is using. And I think that's where, you know, even though code completion, you could argue, is somewhat commoditized now and everybody considers it as a standard feature, there's still a lot of improvement happening based on metrics and making the models more efficient, you know, improving, improving the margins or just the number of GPUs you need for it and ultimately improving the user experience and having them get more accurate code with lower latency.

• 42:39Speaker 01

  And latency, by the way, is, I think, you know, latency in many ways is the core metric for every developer tool. Like, if it isn't fast, you're always going to have an audience of software developers that are going to be not happy. And even with these agents, and I think there's going to be a big problem, both for the synchronous agent on your machine and the asynchronous agent that runs in the cloud is like, I mean, I don't want to watch, I don't want to watch an agent do its work for 15 minutes. What I do is I start it, if it doesn't have any question, it goes running and then I go and do something else. And, but we all know context switching is hard. So how many of those agents can you really start and then go to another branch on your

• 43:24Speaker 01

  computer before this becomes more annoying and destroying your flow than actually helping? So even, you know, that, how do we keep the developer in the flow state? And how do you keep the agents fast enough that it feels fun? I think that's going to be. You know, described that that's going to be describing a lot of innovation that's going to happen in the next year. And that's why you, you know, hear many of the folks in industry saying that with agents, what the software development looks like will fundamentally change over the next six, 12, you know, maybe 24 months.

• 44:03Lukas Biewald?

  Yeah, it's what I guess from where I said, it kind of feels like it's already changed. Like, like, I mean, this is my own personal workflow, I guess. But I find myself using a lot more like agentic code generation than tabs these days, which I would just, I think it's a lot less fun. Actually, like, I kind of like that you're talking about the like fun of coding. Cause the context switching is like horrible, right? It goes from like coding, taking me into a flow state to coding, you know, meaning I have to like fight my brain to stay focused while I like, you know, frustrating like way for the the thing to finish. But I think it's more efficient for me. Like, are you so, I mean, obviously you have to support both these. So like, do you look at how many, how much people are doing in those two different ways of

• 44:47Lukas Biewald?

  of engaging? And am I the unusual one here that I've kind of moved to more of the longer pauses? Are most people just tab completing?

• 44:59Speaker 01

  I mean, I, yes, I mean, most people are still mostly tab completing. I think if that's just the nature of things, right? Like, you're not changing how the majority of developers work overnight. And that there's certainly within the developer and AI coaching space, a whole different S curve of, you know, earlier adopters of agent mode. I think that's still a smaller population than the large majority that had adopted code completion. And let's face it, code completion, that was one of the magic of the original Copilot. You didn't have to adopt anything. And we could have probably announced this and not even mentioned AI. And just say, here's cooler code completion. And it's there. And if you don't like it, just ignore the ghost text or,

• 45:41Speaker 01

  you know, command shift peak, disable Copilot is always fair game if you're annoyed by it as well. Especially when you're working in a file where you might not even want, you know, the content of that. Fire go to to model inference. And so we see an adoption spectrum between, you know, code completion, which has the my highest adoption chat and inline chat. All the way to agent mode and agent mode, you know, I think to some degree is still something that developers have to learn and where there's scenarios where it makes sense to now start vibe coding with the agent. Whereas just, you know, doing it yourself. And Toby Lutke, the Shopify CEO recently had this, you know, memo for the whole company saying AI is now, you know, baseline expectation at Shopify and

• 46:25Speaker 01

  I think the most fun bullet in that memo was if you want more headcount, you first have to show that you can do it with AI and, you know, increase the productivity of the company. And I think that shows you where the thought process is. But the thing, you know, with agent mode is that if it takes too long, as a developer, you're always going to have the question in your head is could I have done that faster myself? And so I think that's a culture change and a mindset change that will, you know, go keep going as the models get faster and more efficient. And and can user behavior shows at what point you want to take over and say, okay, this is good enough, I can do it. And we are going to, you know, see a behavioral change across the population.

• 47:10Speaker 01

  I actually think, you know, a lot of the I weekend coding is fun in the sense that look, you know, my hobby projects, I don't run any unit tests because and I don't put a pole crest against myself either. Right. Because what I really want is I have this idea. I want to write some code. I want to see it light up as an app in my dark or as a webpage. And then I want to feel good about this, right? And I was like, look, look what amazing thing I just created with just a bunch of lines of code and a computer. And that was always the magic for me in software development, right? Like you didn't have to build a factory first and tools and all that. You could get any computer, the earliest, you know, PCs or the Apple two.

• 47:51Speaker 01

  And they had, they had a programming language that was the operating system was basic, even, even on the Altair. Even though that didn't even have a keyboard and a, and a monitor.

• 48:02Speaker 01

  And, and so that I think that dream is still there for many developers. I have an idea and I want to implement something. So now if you can do that and then spin off an agent in the backend to write to the unit tests. And you don't even have to watch that because you can just write the next method. And in the meantime, it has built you, you know, the Fifteen unit tests for that one method. And then we run it and fix it itself. And that happens in the backcourt and you have to watch us. You can just focus on the next method in your class. That I think is keeping you in the flow state. Because you're not going from the thing that is fun, which is really building features and

• 48:36Lukas Biewald?

  Yeah, yeah.

• 48:36Speaker 01

  functionality to the thing that is usually not fun for software developers, which is writing unit tests and then running them and you wait and then, you know, the last one fails and now you're, okay, what did I do wrong here? And why is this one not, you know, meeting the assertion? And I think that's where we're going to see a lot of user interface innovation over the coming month. Spinning off these agents and keeping the developer in the flow state and letting them really do what they want to do.

• 49:07Lukas Biewald?

  Is there like a core guiding principle for you to like make it fun? Because I would think like a lot of CEOs would be like, I don't really care if my bump is having fun. I want them to, you know, be productive here.

• 49:19Speaker 01

  It's certainly, it's like developer happiness is certainly one of our principles. And we definitely believe that if developers are not happy, they're not productive. And if they're not productive, they're not building amazing products. I think those are very strongly correlated to each other. And it goes both ways, right? Like if your tool chain is bad, that means your handset or your NPS is going down and your developers get grumpy about the tool chain and they have to wait forever. And we know that, you know, even at the scale of Microsoft, before I became the GitHub CEO, for some time I ran Microsoft Engineering System Team called VNES. And one of their biggest projects was bring down the build time for the Office Suite from days down to, I think, eight hours is where we ultimately ended.

• 50:04Speaker 01

  And even that-

• 50:05Lukas Biewald?

  Wow, it's eight hours to build the Office Suite?

• 50:08Speaker 01

  Yeah, yeah. I mean, eight hours sounds like a lot, but if you're coming down from like three days, you realize at eight hours is actually good because it means you can, you know, start a build and then take off for the night. And then the next morning you have a fresh build, right?

• 50:20Lukas Biewald?

  Wow.

• 50:20Speaker 01

  If it took days and so paralyzing-

• 50:23Lukas Biewald?

  Wait, so what happens if somebody breaks the build in some cross-functional way? Is it just like down for like a month while people? Like figure out what went wrong or add it?

• 50:31Speaker 01

  Well, I don't know what the answer to that is because I was in the team. But I mean, the, I mean, obviously, you know, at the scale of an office reader of Windows, you have many, many builds running every night. And so one broken build is not slowing everybody down. It's just slowing down that team on that branch or that feature workflow. And then they had to probably spend the day out. But yeah, I mean, if the turn, that's the, that's the latency problem at a really large scale, right? Like your turnaround time is eight hours and you, you're, and we have that actually, you know, in model training now, right? Like model training runs for these really large frontier models takes so long

• 51:13Speaker 01

  that if, if you make a mistake or there's a power outage and that interrupts the training, you have wasted a lot of ultimately, you know, money in form of energy and resources that that is now wasted time. So keeping these, you know, supercomputers or clusters up and running to do the model training in itself is, I think, a crucial, a crucial part of, of, of, of training frontier models. But yeah, so coming back to, you know, this office example, right? So the goal there was just bring build down the build time, which means you had to paralyze the builds over multiple machines, which then raises the question, okay, so how do we do that with dependencies and where do the object files goes, go?

• 51:56Speaker 01

  And what artifact storage do you have that's fast enough? So the storing, uploading the file into an artifact store and then downloading it on another machine for, you know, that next object file compilation isn't actually slowing down the build process. But I think it's these, these kind of like optimization problems that we are going to see in, in a new form in AI, in this AI native workflow. I think the funny thing is actually AI native is, is, is a misnomer, right? Like because it's AI first or AI infused, but nobody is actually AI native yet. We all go on coding. Without AI, right? So we might be cloud native. We grew up with the cloud, although I didn't.

• 52:40Speaker 01

  I think you didn't either. But certainly it became a whole and then you can become a cloud native startup. The first really AI native startups, I think, are yet to come, where it's like totally, totally AI native.

• 52:54Speaker 01

  What we, you know, when we in 10 years look back, say, yeah, this is one and things fundamentally changed. And now companies were just built in a whole different way.

• 53:04Lukas Biewald?

  You know, a conversation that I have all the time, I have a feeling, you know, you might have this too, is like talking to executives that wanna like push their team to use more of these, like, you know, kind of use more code automation. It always seems really baffling to me. Like, I sort of feel like, you know, if it's obviously more productive, like, wouldn't, you know, people adopt it? Like, I'm not gonna tell my engineers, you know, what tools to use, but I'm gonna be frustrated if they're like not as productive as they, as they could be. I mean, do you come across this? Like, I just scratch my head with like, if an engineer wouldn't adopt a tool that would make them like 50% more productive, it just seems really, like ridiculous to me. But, but I'm curious, what are your experiences of this?

• 53:48Lukas Biewald?

  Do you like push, like, using tools internally to try to get your team to be more AI native?

• 53:55Speaker 01

  So we adopted or adapted the Shopify memo in some form within, some modified form within GitHub and made that, you know, similar, or I made similar statements to GitHub that the usage of Copilot and AI tools is, is part of our culture and, you know, mandatory where it makes sense. Obviously, you know, we don't want a salesperson use, use Copilot in the same intensity as an engineer does, but there's many other AI tools, you know, that they should be using as part of their workflow. So that's where I think we're past that point where that is actually a question.

• 54:34Speaker 01

  In the same way that, you know, at GitHub, it is part of every employee to use Git and GitHub. That's not optional. You cannot say, okay, I store my source code, you know? On Azure Blob Storage or Dropbox, anything like that, which you know, it's like you're laughing, but like we also know from telemetry that that still is very prevalent in software development, especially in hobby projects, student projects, or companies are just, you know, doing something else in software development is somewhat, you know, a corner case of what they do, they're out. Like, while Git adoption certainly has grown significantly over the lifetime of GitHub,

• 55:16Speaker 01

  there's also still market to capture for us just on the platform and, you know, moving companies into Git. So, you know, nobody questions whether employees at GitHub should be using Git. I think we're at the point where that is also true for Copilot and AI tools in general.

• 55:33Speaker 01

  But that doesn't mean, you know, that that needs to be a hundred percent of the day. And I think you still need to think about when it makes sense and doesn't make sense. That that decision making, you know, do you do Ctrl+Space on your Mac to open ChatGPT? It's a question, or do you Google it? And, and I think, you know, that is feels somewhat more natural now. But it is, it is not binary. There's a gray area. We could do both. And then often it's driven by your belief system. And I think, you know, the, the question you ask is why do developers not use AI? Even though the metrics are clear, I think part of that is belief system. And kind of like this is how I have always worked.

• 56:16Speaker 01

  And now that I have these autocompletions or the agent mode, I'm forced into a way of working that I don't actually like. And now I have to read more code and understand it than write code. And I have to write prompts, but I don't want to really write prompts. And it's this culture change and ultimately the change of how we do things as humanity that we know takes time. And it's, it's just not done by forcing people into this new way of working. And in fact, I think what we're going to see is a bit of a shift in the performance curve of the employee population. Like, you know, when you do performance rankings. So evaluations and those kind of things, the employees that were good maybe five years ago in that career of working

• 57:01Speaker 01

  may not be at the top of their, you know, whole or class in that new way. And then because adapting to that world, it's, you know, I'm a big F1 fan. And in Formula One, you know, this way it always works is there's new regulations. And then the first two years or so, one team had it figured out early. And so they're ahead of everybody else, read the regulations. And then they're getting closer and closer to each other. And then you have really, really tight races and you have a fantastic season like 2021. And then they're changing their regulations. And now there's new regulations and there's a new way of driving these cars. And all of a sudden, a different team and a different driver can handle this new, you know, a type of car.

• 57:46Speaker 01

  You know, current era's counter effect. But the Vettel area, Sebastian Vettel area was where the diffuser was connected to the exhaust and whatnot, right? So there's different drivers handling those conditions better. And then they win the championship. And I think same is true for developers in this new world of AI. You've got to be able to adapt, learn and be willing to kind of like, you know, evolve your mindset into what is now the state of the art of software development.

• 58:15Lukas Biewald?

  Totally, I totally agree with that. Do you also feel like the skills that someone would need to be a good developer are changing? Like, I sort of feel like maybe more product oriented mindset would probably help in a world where the actual like implementation gets easier.

• 58:35Speaker 01

  The technical details have gone away, right? Like in the 90s, you had to know about the CPU and the cache and the memory and the bus. If you even had a hard drive or cassette tape. And so you had to really be in the intricacies of the hardware architecture you're building for. And that led to things like, you know, the 386 having a turbo switch.

• 58:59Speaker 01

  You know, the turbo switch wasn't to make things faster. It was to slow down the software that was so optimized for the architecture that would run way too fast. And so you couldn't play the games anymore. So you slowed things down because everything was so optimized. And so those developers that know exactly how the PC works and the instruction set of the CPU and maybe write an assembler function to run that certain part faster. They are like, you know, a minority of the of the whole developer ecosystem. Similar for, you know, developers that can write shaders in in computer games, that there's certainly, you know, a demand for that, but the majority of of of full stack developers don't know that skill.

• 59:41Speaker 01

  And and so I think we have always moved up the stack and lost, left something behind intentionally, because the complexity has has grown so much. And so the skill that I think the future developer needs, if not the current developer already, is systems thinking, like taking, you know, this problem that you're getting from your product manager or your boss, your engineering manager, and breaking it down into a small enough chunk that now you have enough of a of a way of taking that abstract problem and converting it into code. Right? That ultimately is what software developers do today. You take a description of a feature and then you're kind of in your head on on with a pen and paper. You're breaking it down into small pieces of work that you can, you know, do during your workday.

• 1:00:24Speaker 01

  And I think that that thinking doesn't go away, but it moves at an even higher layer. And today, you know, if you go into Copilot, ChatGPT, Bolt, whatever, and you tell it to build you GitHub, well, it gives you probably a page that looks like GitHub. Maybe you should try that after the podcast. But it certainly doesn't give you, you know, all the features. And it certainly doesn't give you the scale of of hosting, you know, more than 500 million repos, let alone it doesn't give you the Git stack or the, we call it Git systems, and all the pieces that are part of that application. And, you know, that goes back to AVI in five years at the point where that work was just AI. I doubt it, because like designing, you know, a system like GitHub or or weights and biases

• 1:01:07Speaker 01

  or Corvive or whatever, that that is still requires engineering skills, architecture, architecture, or that's why I like it to summarize it as systems thinking. The the other side is is this notion of a full stack builder, where you actually no longer had a PM and a designer and an engineer and testers are already gone from for most companies anyway. And and and SRE, you know, has Has even merged with software development as part of DevOps roles. But basically, having a single person that can, you know, take an idea and work with, you know, one model or agent to mock out the spec and the designs and then create, you know, the figmas and

• 1:01:49Speaker 01

  probably using some form of design system, the one that we have at GitHub is called Primer. Because then the AI can, you know, compose these components into a page. And then taking it into the business logic and application logic with an agent. Writing all the test cases, you know, writing your actions, deploy script, deploying it to the cloud and have an SIE agent monitor this all. Scale it up and down, you know, look at exceptions, maybe take those exceptions, file a GitHub issue. And then you take, you know, the three agent to implement that fixed. I think that full stack builder is, if it not already exists in some small startups or very innovative companies. Is going to soon, you know, become a role in the company. And

• 1:02:33Lukas Biewald?

  Well, if you're out there, I want to work with you. I'll say that. That sounds like an amazing.

• 1:02:37Speaker 01

  And you're good. So what you're going to have, what you're going to have is like the Venn diagram between what a PM is doing, what an engineer is doing, what a designer is doing. Even a marketer is going to have a much higher overlap. So you can, you know, a really good PM that can write a really good GitHub issue of what a feature should look like and hit exactly the right abstraction level to then assign it to Copilot and then Copilot, you know, spins up GitHub Actions and uses tools and MCP and all these kind of pieces to implement that feature, creates a pull request. And then you review that pull request and that's where, you know, the engineer might come in and say, yep, what the PM created here. But you might have the PM basically building

• 1:03:19Speaker 01

  the whole code change, the diff in the form of a pull request, but they have of an agent. And so the engineer becomes much more of a, you know, a conductor of an orchestra of agent together with, you know, the coworkers in the company. But I do think, and you know, I'm not the only one predicting that, but I do think we are going to see very small companies that have very high valuations. And, you know, in fact, with WhatsApp and Instagram and others, we have already seen examples of that before AI. But we're certainly going to see that. And I think that's that's super exciting. You can kind of build a business in your garage all over again at a much bigger scale.

• 1:03:59Lukas Biewald?

  Yeah, because like not everything is, I mean, I think first of all, like, you know, making something as advanced as GitHub is kind of like on the outer edge of like, you know, what you need to do engineering wise to make something useful. And these systems are getting better, right? Like, I don't know about you, I think we're about the same age. Like, I remember, like, you know, programming, you know, like, really, like, you know, basic computers and like just being excited I can make like, I don't know, like a circle grow in size or something, you know, like I, you know. And, and it's like funny to watch my daughter at like age five, you know, like we like vibe code together in the same, you know, doing the same, it's like the same mindset, I think, like when I was like a little kid doing it with my dad, but like, I mean,

• 1:04:43Lukas Biewald?

  she could make a real web app, you know, like, and it's like, and like, you know, kids have such weird, crazy, like creative ideas. Like, I sort of feel like when, when these, like, you know, when, when teenagers and even younger can start, like, you know, deploying their own, like, really realizing their own ideas, like into things, I just feel like we're gonna get like a real Cambrian explosion of, of applications.

• 1:05:08Speaker 01

  Yeah, I mean, absolutely. By the way, I'm born 78, so.

• 1:05:12Lukas Biewald?

  Yeah, similar, yeah.

• 1:05:15Speaker 01

  I learned coding first in East Germany on, on a, in the geography lab in the late eighties with a RoboTron computer. It was a clone of a Z8 chip, I think, was in there and cassette tape and all that. It was, you know, in my head, I think, you know, it, it, it merges with my early experience on a Commodore 64, which I bought, I think, 1991. And that's where I really learned coding, was I had that at home, connected to TV. And when you say growing a circle, in my head, I'm like, well, drawing a circle, even that, you know, the Commodore 64 didn't come with a graphical user interface, right? It was all text based and then you had to switch into pixel mode and. I don't think there was a pretty fine method to draw a circle and you couldn't

• 1:06:00Speaker 01

  just download an open source library either. And so you bought, I bought magazines and books. And then the listings in the magazines, I will not forget that the listings were not actually listings, they were checksums. And so you had to, you had to mail stamps to the publisher to get a disc back with the, with the program, the app, nobody called it app back then, the program. They decoded those checksums back into the code because you could print the checksums, almost like a QR code in, of the, of the 90s. You could print them much, much more dense than you would, would print code. Right, so you would type those checksums into the Commodore 64, then to decode the checksums into the code, and then you had a library that you could use to, to draw a circle or, or, or do a side-scroller and those kind of things.

• 1:06:45Speaker 01

  And yeah, you know, my, my kids are 12, almost 13 and 10. And so same thing, you know, I actually bought them Copilot before we, we had the Fritia that we have now. I bought them Copilot because they always would come to my desk, show me their, their MacBook with their Python, most of the Pygame stuff and say, here, I have a problem here. Go and fix my bug. And I'm like, well, I'm talking to Lucas right now. Can we do that later? And obviously for them, the answer is no. You know, this is way more, higher priority. And then you give them something like Copilot and now they can highlight their code and ask questions or use agent mode. And I think kids are so much faster in adopting that new way of thinking because it aligns

• 1:07:29Speaker 01

  so closely to how they discover the world and learn, right? Like by asking questions and asking questions. And when you have, the first time actually my kids used AI was, I think it was on their own was Adobe Firefly. And they had seen that somewhere in school and they came home and say, hey, look, we have these puppy images. And I'm like, how did you get these puppy? Oh, we signed up for an Adobe account. And I'm like, what? And I was like, yeah, you type prompt and you get a puppy image and you type another prompt and get a better puppy image. And so they very quickly learn this, okay, so if I don't like what the answer I got, we ask another question. Because that's exactly how they interact with their with their families and their parents, their teachers anyway, right? Like, they keep asking questions, they don't like the answer that you're giving them, they're asking you another questions.

• 1:08:12Speaker 01

  And I think that, but you have to combine that with getting coding and computer science into into every school. And I've been, in fact, you know, on Monday this week, May 5th, in partnership with code.org, more than 250 CEOs signed an open letter to to bring computer education into more schools. Because again, like, okay, so you have an agent that does powerful things, but if you don't understand what it actually creates, if you cannot read the code, if you cannot validate, if you cannot, you know, fix bugs, you're actually giving up, you know, something that that makes us human. In the same way that we're teaching all the kids physics and chemistry, and obviously art and sciences and literacy and math. And that doesn't mean you just become a physicist just because you had physics in school, right?

• 1:08:57Speaker 01

  Like, there's still, you're picking your career path based on your interests and, you know, economic value and those kind of things. But I think it's fundamental for everyone on this planet to understand code and computer science as these devices will only have, the devices and the agents will only have a higher, a play of more stronger and higher importance role in our lives than than they're already playing. Well, that seems like a nice place to end, Thomas. I appreciate your time. This is, this is a lot of fun. Thank you. Thank you so much for having me, and I agree, it was super fun and great conversation.

• 1:09:32Lukas Biewald?

  Thanks so much for listening to this episode of Grading Descent. Please stay tuned for future episodes.