The One-Person, Billion-Dollar Startup? — With Thomas Dohmke

Thomas Dohmke is the CEO of Github. He joins Big Technology Podcast to discuss the state of AI-assisted coding, and whether the rest of the economy can see benefits similar to software engineers using coding 'copilots.' We also discuss how AI assistance can help individual developers build whatever's on their mind, and whether we'll see $1 billion dollar startups built by just one person. Tune in the for the second half where we discuss the next set of AI models, how engineering jobs change when AI produces most of the code, and whether AI will eventually improve itself. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here’s 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

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

• 0:00Alex Kantrowitz?

  The CEO of GitHub takes us to the cutting edge of AI, where the AI is actually writing code for engineers. And he tells us how this might change the economy. And importantly, how its success might translate to the rest of the AI field. All that and more is coming up right after this. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis Chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, Head of Enterprise Business Solutions at Voya Financial, to go inside a large scale agentic CX transformation,

• 0:45Alex Kantrowitz?

  including the business decisions, trade offs and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility and peace of mind. That's how companies like Razor were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:20Alex Kantrowitz?

  Welcome to Big Technology Podcast, a show for cool headed nuanced conversation of the tech world and beyond. We're joined today by the CEO of GitHub, Thomas Donka, who is here to talk to us about the field that is probably being influenced and impacted by generative AI the most. And we're probably going to learn a lot about what this era in the coding world is going to pretend for the rest of the technology industry and industry at large as generative AI gets smarter and spreads across more disciplines. We're also going to talk about how it's going to change our economy by making it easier to build tech products. I'm so excited to have him here. Thomas, welcome.

• 1:58Speaker 01

  Hey, thank you so much for having me.

• 2:00Alex Kantrowitz?

  Thanks for being here. All right. First of all, just a bit of news that we got this week. Elon Musk is boasting about the fact that they now have the most powerful XAI. Now has the most powerful AI training cluster in the world. He says they've got 100,000 liquid cooled H100s, which are Nvidia chips on a single RDMA fabric. And basically, he says that XAI is going to be on par with the rest of the AI field or better by the end of this year. A lot of people that I speak with in the AI industry sort of have this perspective of we know Elon's doing something. We don't really know what he's doing. First of all, I'm curious what you think about this AI cluster.

• 2:40Alex Kantrowitz?

  And second, do you think the rest of the tech world is underestimating Musk when it comes to his ability to build AI?

• 2:47Speaker 01

  Well, it sounds super cool, right? So many super cool chips. I don't think the industry is, I'm not estimating him at all. I think, you know, he's looking at what some open as an open AI and what our other model companies are doing. They're training larger and larger models. I don't think we have reached the scale limits of creating greater, bigger models with more parameters. And I think that's where Elon is looking at well, using all the data that he has through X, you know, through Tesla and other sources and then looking and building even better models.

• 3:20Alex Kantrowitz?

  And are you of the belief that sort of the more compute and the more data that we throw at these models, the better they're going to get? You talked about the scaling law. You believe that's going to apply to a certain extent or where do you feel this nets out?

• 3:36Speaker 01

  I think so. I think we are not at that scale limit yet. I think we can build see smarter models with more knowledge, with more reasoning capabilities, multi-step or chain of thought, I think is what OpenAI calls it. And there's a lot of problems that you can't solve with Copilot or ChatGPT today, even in just the coding world where things are fairly limited in, you know, output. And there's lots of more creative work to be done. So I think larger models will. Help us to build cooler stuff.

• 4:07Alex Kantrowitz?

  And so you mentioned OpenAI. You're the CEO of GitHub. GitHub is a partner, is a subsidiary of Microsoft. Microsoft is a partner of OpenAI. So you're using the OpenAI technology. And what you've done with it is quite fascinating. Effectively, what you've done is built this Copilot application, which is, the way I look at it, is an autocomplete for engineers. The same way that when we're in Gmail and Google auto completes our sentences, you're auto completing what engineers are writing. And from your internal studies, you found that using GitHub Copilot increases the productivity of an engineer by 55%. Or what I've heard is that, from you, the way that you say it,

• 4:49Alex Kantrowitz?

  is that basically engineers can complete their projects 55% faster than other engineers that aren't using this AI-assisted Copilot for code. Is that right?

• 5:00Speaker 01

  Yeah, so maybe let me time travel you back about four years. It was June 2020 and we got access to GPT-3, the previous major model release that OpenAI did at a time when almost nobody outside the tech world was talking about AI. Or I don't think the term generative AI was a big thing back then. And we played with that model. It was 2020, so we were all on, you know, a call. Everybody was in lockdown and we saw that the model was able to generate code. That's the thing that engineers write. And we were surprised how good it actually was. In fact, you know, we fed about 200 or so coding exercises, you know, things that engineers do when they apply for a job into the model.

• 5:45Speaker 01

  And even, you know, four years ago, it was able to solve 92% of those. Right? And that was the, that aha moment, you know, the light bulb went on and, and we built the first Copilot. And all it really does is it helps developers. In the editor, you know, the app where they're writing the code to think ahead, to predict what's the next word, the next line, maybe multiple lines if it's a more complex algorithm. And if you think about, you know, other than the AI capabilities themselves, what really is crucial here is that you have an assistant on your side that prevents you from losing the flow. You no longer have to context switch between the editor and your browser.

• 6:26Speaker 01

  And, you know, engineering browsers look the same as your all browsers as well, like lots of tabs open, you know, the thing that I wanted to buy in the vacation I'm planning and the world news. And there's always, you know, something else going on, on Hacker News and whatnot. And so with this context switching, you know, between your editor and your browser, where you look things up, where you look, you know, for algorithms or documentation and things like that, that is made easier with Copilot. We can just stay where you are. You get somebody, you know, the AI thinking ahead of you. And if you don't like it, you can always, you know, just keep typing or edit what you got out of Copilot.

• 7:02Alex Kantrowitz?

  Right. So you're basically in the editor, you're typing, you're coding, and the AI is suggesting code, similar like the way that if you're like writing a sentence, like you might get it on Gmail. So one of the things, I've spoken with CEOs who have, who are like, we have an AI strategy. We need to implement the AI strategy. And they always talk about how code is the first place they're trying to do it. But then I'm also, I also asked them, like, are you seeing the impact? And up until recently, I was sort of being met by a shrug, sort of saying, yeah, our engineers are using it, but I don't see our engineering team, you know, increasing that much. Or, you know, some, some are just like rejecting the suggestion, some just prefer to do it the old way.

• 7:43Alex Kantrowitz?

  So I'm curious what you're seeing on your end, like on the aggregate, is there uptake in the Copilot? And are you really seeing the change in organizations here?

• 7:53Speaker 01

  Absolutely, and you already mentioned the fifty-five percent, you know, that came out of a case study where we asked fifty developers with and without Copilot to solve a coding problem. And the group with Copilot was fifty-five percent faster and about ten percent more successful than the group without Copilot. Now, obviously, if you go into companies, they don't, you know, have a hundred engineers do exactly the same thing, and you can compare both groups against each other. Most engineering managers and leaders have better ways of investing, you know, all their manpower. And so we look at real customers, we look at how often do developers accept the code that they're getting from Copilot, and how often do they press the tab key and getting the job faster done than without it.

• 8:37Speaker 01

  And the number we see there is thirty-five percent on average. Some are a little bit lower, some are a little bit higher depending on what programming language they use. But that shows you that, you know, in a third of the time, when a developer sees a suggestion, they also accept the suggestion. I think that already gives you an idea of how much code is written with Copilot. We have Copilot deployed now in more than fifty thousand organizations with more than one point eight million paid seats. And the companies like Infosys and Mercedes-Benz, Ernst & Young, PWC, but also the cloud natives, MercadoLibre, PTC, HelloFresh, Orderdesk.

• 9:18Speaker 01

  If you look at those companies, they're all writing millions of lines of code that is coming from the AI and no longer from the developer. The developer's still there, and the developer is controlling, you know, the Copilot, he or she is the pilot that is, you know, flying the plane. But the Copilot is providing lots of code that the developers can just accept and as such get the job done faster.

• 9:42Alex Kantrowitz?

  And so are these organizations building more? Like, have they told you? Because that's the one thing that I say, like, all right, so like, you have, like, let's say, one-third of this code accepted by engineers. But I also wonder, like, is that leading practically to more stuff being built? Or in the other side of it, is it leading companies to, like, not have to hire as many engineers because their group that they currently have can get more done? What can you tell us about that?

• 10:10Speaker 01

  So let me give you an example, Accenture, for example, they saw an eighty four percent increase in successful builds with the help of Copilot and they saw eighty eight percent retention of the suggested code. You know, so and most of the developers told us, you know, loud and clear in service that Copilot allows them to stay in the flow, you know, allows them to spend less effort on repetitive tasks. Ernst & Young, you know, had a hundred fifty developers, right, commit more than one point two million lines of code with the help of Copilot. So we see, you know, these statistics all over the place where companies are measuring over a period of time, an increase in productivity, an increase

• 10:50Speaker 01

  in developer happiness, and ultimately an increase in output, right? The build that gets deployed to a server is what Copilot is helping developers to deploy more.

• 11:05Alex Kantrowitz?

  And are they seeing like a specific like ROI there? Like, are they like, all right, so it's one thing if you put more code in the project, but we all know that that doesn't necessarily mean like it's going to be a better product or that it's sort of getting a better return. So what can you tell us about that?

• 11:20Speaker 01

  So we, you know, at GitHub and at Microsoft, we obviously also use Copilot ourselves and we see, for one, from developers, from developer surveys, we do, you know, developer experience surveys every month or so, and we see that developers are clearly more satisfied through the use of AI. They feel happy in their job and, you know, a lot of friends in the industry, they're like, I would pay, you know, twenty dollars just for making my developers happy and the rest doesn't matter, it will come by itself. But if you look at the overall, you know, metrics, engineering metrics, we see more throughput. We see basically more ideas, you know, more work items in a planning system taken and shipped, you know, to the to the customer and as such, you know, the throughput.

• 12:04Speaker 01

  Of software developers is increasing through through Copilot. But the the challenge, you know, of these questions always is is how do you compare teams against each other? Given that the thing that developers are building is a really creative task, you know, most feature requests, most bug reports, most security vulnerabilities, all these things are not easily easily comparable to the next task and the next task. That's why you see software projects always often, not always, often being behind on on on schedule because it's really hard to estimate how long work time work takes and then it's also harder to estimate. So how much did you actually save? If you didn't know how long it would take in the first place, it's also harder to to figure out how much you save.

• 12:46Speaker 01

  But I'd say from our own data at GitHub and from customer evidence, we clearly see, you know, in that range that I mentioned, thirty thirty five percent of productivity gains.

• 12:56Alex Kantrowitz?

  Yeah, but I also would say you could probably more easily measure it than you're letting it on because letting on because you could say within an organization, how many of our projects were behind schedule before Copilot and how many of them are behind schedule with Copilot and then the delta is your increased improvement.

• 13:13Speaker 01

  Well, I think that's true in the sense that, you know, if you would do the same thing all over again, you would be able to measure this, but as the complexity of your software system grows and as your developers, you know, constantly have to tackle new challenges, it is it is somewhat challenging to compare, you know, the feature you shipped in two months with the months that shipped two months ago.

• 13:36Alex Kantrowitz?

  Right. Or the other way I would do it is maybe say you're all building the same, you're on the same project, building on the same timeline. What time do you get out of work? If you're getting out of work at five and now you're getting out at three, then I would say thumbs up, this thing is working. But if you're getting out at five still, then it might be a question there. Is that a stupid test?

• 13:58Speaker 01

  I think, I think, you know, the, if you look at, you know, I've been working in software for over twenty years. I think, you know, the, the best indication you have is just ask the developers if, if they feel that, you know, a tool that was put into their workflow is actually helping them. And, you know, let's, let's face it, most of us hate when somebody comes and says IT, you know, it's like, ah, you know, here's this new tool and you have to use it, it's, it's prescribed by the company. You know, don't touch my, my system, I know what I'm doing is, is kind of standard response. And, you know, what I've seen with Copilot ever since the first ship, the preview three years ago, is that developers are excited about it.

• 14:38Speaker 01

  Even those that were skeptical at first, you know, after a few days of using it, have seen the magic. You know, they, we often see on, on social media that people are saying, I didn't believe it until I used it. And then all of a sudden it understood what I'm trying to do. And, and part of that is, you know, it's not only predicting the next word, it's actually taking everything in the far above the cursor, below the cursor, you know, adjacent tabs. It, it takes what you have already in account. And as such, it knows, you know, how you're writing code and what you're trying to achieve. And, and so developers after using it are like, um, uh, happy about, you know, having this AI available to them. And it doesn't even feel like AI, right?

• 15:18Speaker 01

  It's just a tool that is in, in your editor while you're, while you're doing your work.

• 15:24Alex Kantrowitz?

  Okay, I'm buying your argument that this is something that is helping engineers within companies. And then another thing, like, so let's say, let's continue saying that this is something that is helping and increasing efficiency by the numbers that you're putting forth. You also have this other idea that it's not just within companies, but also that it can empower individuals to build basically anything their mind can think of because the coding becomes that much more efficient to do. You did a TED Talk recently and you gave this example of wanting to build an app that just sort of logs every flight that you've taken. And no one would ever build that type of app because like the amount of time it would take

• 16:05Alex Kantrowitz?

  and the ROI is questionable, but if you want to build it for yourself, you said you were able to do it. In the time that it would take you to finish a glass of wine, now even if you're an extremely slow drinker, that's a pretty fast amount of time to build that type of application. So talk a little bit about what you think this means on an individual level. We've talked, we've heard even in the tech world about like, is there going to be one person that builds a billion dollar company, right? And so I'm curious what you think about this and if this enables that.

• 16:37Speaker 01

  The first thing that comes to mind is when you think about individuals trying to code is that they're often squeezed that into their day. You know, they have, you know, another thing that they're working on, whether it's, you know, me as CEO of GitHub, being on podcasts or having meetings with my team or planning the next quarter, or it's developers that do hobby projects in between all their, you know, work projects and their personal lives. And so you're constantly in this off and on switch where you have to context a switch from what you were working on before to the new thing. And with the help of Copilot and specifically the chat functionality, you can easily get back into

• 17:19Speaker 01

  the flow, into the thing you were working on. You can ask questions. You can even ask questions about what did you build yesterday? Like describe that code again to me from yesterday. And what I said at TED is you can do that in the language that you grew up with. You know, for me it was German. And obviously many people speak English, but also many people on this planet do not speak English. And when they want to build something, what they don't want to do first is to learn a language that they're not speaking every single day. And so it democratizes that access. Even most of us, think about a six to seven year old that wants to build something cool, you know, and is fascinated by computers as many kids are. Maybe they have already seen some computer games and then now they

• 18:01Speaker 01

  want to go and build a little game. I showed at a different conference, Snake Game, you know, the game that was on the Nokia phones back in the day where you control the snake and it eats an apple and the tail gets longer and longer and then to prevent not eating your own tail. You can do that by just asking a couple of questions into the chat functionality. Gives you all the code and you can copy and paste that, you know, into the editor and then you can keep asking questions iterating with the AI and you don't have to, you know, figure it all out by yourself. Um, you, you don't have to read a book first and you don't have to, you know, uh, find the right webpage where this is all described. And even then, if you find, you know, that webpage with the code examples,

• 18:41Speaker 01

  you still have to make that work, um, while, while you have, um, Copilot helping you, um, every step of the way. So I think there's a future coming soon where you can create any software you can imagine with just a few prompts written in, in human language. And as such, you know, you're building, you know, um, your own software ecosystem in the same way that, you know, we're designing our living rooms or uh, building uh, Lego sets and whatnot, right? We're exploring our creativity in so many ways as humans.

• 19:11Alex Kantrowitz?

  Yeah, I think the line you said is the floodgates of nerditude have swung wide open. I like that.

• 19:17Speaker 01

  Yeah, because you know, like, uh, there's so many other things, right? Like, um, I mentioned Lego already. Obviously, if you want to play a music instrument, all you do is buy the music instrument and these days probably watch a couple of Youtubes, you use, um, uh, or, or find a webpage and you can start playing. Nobody is stopping you. It might not sound great. Um, but as long as you do that in your home, um, or on your own computer, um, you, you have, um, all the creativity that you have as a human available to you. And now you have also the tool that helps you, um, um, through that process.

• 19:49Alex Kantrowitz?

  Right. So the example of the one billion dollar value company created by one person, do you see that as something that's feasible?

• 19:59Speaker 01

  Think so. I think that, you know, that, uh, you know, I've seen a bunch of examples where small companies like Instagram comes to mind, you know, that, that started really small and, and by the time they got acquired, they were still very small. WhatsApp, um, is a similar example. And so I think, you know, uh, that, that can exist. Um, the question is a single person company, how they're also managing uh, support and accounting and all the other things that, that are outside of creativity. And maybe a Copilot also helps them with that, um, you know, answering support questions. But I think there's, you know, it's more fun if you have a smaller team, uh, of, of people available that adds those things.

• 20:35Alex Kantrowitz?

  Less lucrative.

• 20:39Alex Kantrowitz?

  So let me let me read you this example that I saw on Reddit of a coder that was talking a little bit about how they've worked with generative AI to build. So they say, it's mind blowing how quick I can move now. They're using Sonnet 3.5, which is an anthropic model. I'm pretty sure I could implement copies of the technical parts of the most popular apps in the App Store 10 times as fast as I could before large language models. I still need to make architectural and infrastructure decisions, but stuff like programming, the functionality is literally 10 times faster right now. And this is the process that they use. The first thing they do is they think hard about the feature and probably discuss it with Claude. The second thing they do is write a basic spec for the feature. It's

• 21:21Alex Kantrowitz?

  just a few sentences and bullet points and also iterate with Claude on the spec. And then they are sure to provide Claude with all the relevant context and ask for the implementation, the code. So basically what they're doing here is brainstorming an app with Claude, specking out an app with Claude, and then having Claude code it. I mean, that is that is remarkable. Is this something that we're going to see be more common?

• 21:47Speaker 01

  I think we're going to see it at a smaller scale. For small projects, you can probably, you know, get there even without a lot of computer science, computer engineering knowledge. For larger projects, I think the step missing is the architect, you know, the software engineering expert that that knows which database to pick, you know, which cloud provider, how to make the app, you know, scale from 10 users to 10 million users. And the model can kind of help you with that by giving you options, right? We have all seen that when you ask ChatGPT a question, an open ended question, it gives you options and explains to you kind of like how to get there. But to navigate then this, you know, tree of information, you still have to have, you know, subject meta expertise. I don't

• 22:32Speaker 01

  think that goes away, but I think, you know, if you have a well-defined task that you can describe, you know, to a certain level, to a model or to a whole system like Copilot, you will, you will have that agent, if you want to call it like that, do the job for you to 90%, you know, of what you expect. You know, the flip side of that is if you think about when you work with other people, whether it's on software or other projects, it's like. How long can you have a person go by themselves when you give them a task? Until they're going so far off track of what you actually wanted to achieve, whether it's what you described to them. Right, like more often than not, we need the feedback loop as as humans.

• 23:17Speaker 01

  We we can't work in isolation for too long until we're either completely off track or we come back, you know, uh, with a work result that that uh isn't isn't really uh what what the what the person that or manager or you know, or customer expected us to do. And and and and I think this is where, you know, we have the boundaries of these models of the human can do that. Because ultimately, you know, the customer can't describe it, um, or the manager can't describe it to the level of degree that you can actually fulfill all the all the requirements. Then a model can do that uh by themselves as well. That's why we believe the human needs to be in the center. The human needs to be

• 23:54Alex Kantrowitz?

  Right.

• 23:55Speaker 01

  involved at the step of the way to make sure that we're not getting into the wrong direction.

• 24:00Alex Kantrowitz?

  But this is exactly what the person is describing, that they're not only asking Claude to write the code, but they're dialoguing with the the AI bot about the different spec and the decisions and how to, you know, set up the components and things like this. And then it builds only at the last step.

• 24:18Speaker 01

  You know, I called this, uh, I don't know what it was. I called it the second brain. It's it's kind of like we have an extra, uh, you know, outside of our brain memory chip that gives us all the information that we can store ourselves. And even if, you know, we have a lot of things that we learn, uh, you know, in in university and high school and and and even before that, that we can't really uh store, um, and and we often forget these things. And um, and so the AI is helpful and to to retrieve this information again. Um, you just need to know how to ask the right question and and then work with you. That's that's why we ultimately called it Copilot. It it it helps you, you know, to um have fun with the things that you want to work on and it takes, you know, uh, over

• 25:03Speaker 01

  the boilerplate, as we call it, encoding, you know, the stuff that that surrounds all the creative part of the process.

• 25:10Alex Kantrowitz?

  Right. Now with Gmail, so Gmail allows me to write emails within within the Gmail application and will suggest some text for me as I write. Sometimes I accept it, sometimes I don't. Uh, but also like I could just go to Claude and ask Claude to write the email. So I'm thinking from your in your circumstances, like, is there a Is there a reason why people should be using like the Copilot within GitHub as opposed to like having this conversation with let's say an anthropic bot and just having that write the code and then dumping it into the code editor?

• 25:42Speaker 01

  By the way, you can also use the AI to summarize the email. So it's better you the one side writes the email with AI and the other side summarizes the email with the AI at which point you can ask the question, why not just send the prompt to the other person and save the time on all the, you know, friendliness and the salutation and whatnot that we put into emails because it's.

• 26:05Alex Kantrowitz?

  Or just become proficient at writing concisely, but I think as a journalist, you know, I know that that part of the world is not gonna, is more difficult to proselytize than others. Okay, sorry, go ahead.

• 26:18Speaker 01

  Yeah, you know, and I think to some degree that will happen. And so to some degree, we have so much information around us now that the summary is good enough. If you only want to read the headline and the summary and not dive into 10,000 word article because it ultimately means you have more time for other things. We're all, you know, dealing with limited attention, limited lifetime ultimately. And so if I can shortcut some of these things, that means I have more time for other things. Coming back to your question now, why not use the generic chatbot? The power of Copilot is that it lives now in the work environment of the developer. So yeah, you can copy and paste everything that you see in front of you into a generic

• 27:02Speaker 01

  chatbot and have it give you an answer, but it's much, much more powerful to have the chatbots sit within your environment where it knows, you know, what files are open, it knows what you wrote, you know, before that, it can look at JSON tabs, it can even look, you know, in developer, in the developer world, at the output, the debug output is what we call that, and the console and error messages and those kind of things. And so it has much more context available that helps it to answer the question, you know, within the specific context of the project you're working on. You know, a very simple example is that it knows, you know, whether you like your, like

• 27:43Speaker 01

  your variable names with camel case or capitalized and or whether you write in German or English, we're just looking at the context of your file.

• 27:54Alex Kantrowitz?

  One question for you. So basically, you can write the prompt of what you want, and the code will be developed on the backend. Is there going to come a point where we're not going to need code at all to build? Because like, if you can just prompt, then why do we really need a B in the code?

• 28:11Speaker 01

  You might say we're already at that point to some degree where, you know, when you go and ask ChatGPT a question, you get an answer. And when you ask a question to plot the chart, for example, or do a mathematical calculation, it actually generates a Python script that then, you know, plots that data into a chart and it shows you the chart. And it still shows you that step where you see in between the Python script. But they could as well hide that and you just see the chart output, right? Like, in many ways, ChatGPT is giving you an answer without you ever having to worry how that was generated. And so, yes, we're going to see computer systems where large language models are just one building block in addition to code.

• 28:56Speaker 01

  Or maybe it's multiple language models and image models and, you know, time series models and whatnot, plus code combined to generate all the output that the developer or the user expects.

• 29:09Alex Kantrowitz?

  Do we still need engineers then to code?

• 29:12Speaker 01

  Yeah, because, well, first of all, you know, there's billions of lines of code out there that still have to be maintained. You know, one of the examples I'd like to get is that most banks are still running COBOL code. That's a program in English from, invented in the late 50s when Eisenhower was the president.

• 29:25Alex Kantrowitz?

  Right, but this is maintenance. I'm talking about like to build new things. Like, is there going to come a time where we're just going to have prompters instead of coders?

• 29:33Speaker 01

  Almost, most developers work on an existing code basis. So I wouldn't, I would push back a little bit on maintenance. We're building on top of an existing world. I think there's, developers have always moved up the abstraction that, you know, we used to build it ourselves. Then it came to the internet. And we used to start sharing software components, so called open source. You know, nowadays, most applications are sitting on a stack of thousand components. Already and you're building the 10% layer on top of that. Now that 10% layer, you know, might, you know, at some point be written uh in 80% by AI or replaced by AI, but that means you have more time for the remaining 20% uh on top of that. The pile is getting always bigger and

• 30:16Speaker 01

  the developers are still going to have enough um work, you know, carved out for them. In fact, I'd say, you know, AI has created more work uh for developers because now somebody has to build all these AI systems and we're not at all at a point where we can just, you know, have an AI engineer, quote unquote, uh do the job of of a hu- human like that doesn't exist. And even if it exists, it's it works great on a demo. Um uh but it doesn't doesn't actually do any real work.

• 30:45Alex Kantrowitz?

  Okay, so you're in a very interesting spot because you're you're running GitHub and GitHub is part of Microsoft. And GitHub with Copilot might be the perfect company to implement generative AI because it's one of those things where like there's usually a right answer to the question. There's libraries and libraries of code uh stored on your platform effectively. That makes it not easy, but more straightforward to train on. And when people are writing, the large language model can rely on that history to predict or to suggest what the next bit of code should be. So it's almost like the perfectly suit, the most perfectly suited discipline to

• 31:25Alex Kantrowitz?

  use large language models for is code. And in fact, if you looked at the discussion of generative AI recently, there's been a lot of discussion of how it hasn't really proved its economic value outside of coding. Now, that's the bull, that's the bear case. But anyway, I'm throwing it out there for point of discussion. The question that I have and lots of people have is, is this now something that what you're seeing in your field where it improves the employee's effectiveness by 55%, makes them happier, allows them to do more and build more? Is that generalizable to other fields? And if so, if you think that it's the case, then why? Because that's the bet that Microsoft is making, right? It's not just coding, it's everything.

• 32:09Speaker 01

  You know, I think we have forgotten how many things AI already does for us, or you know, we are not realizing it, you know, the image recognition in my car.

• 32:20Alex Kantrowitz?

  No, no, no, no. But that is, we're talking about generative AI in particular. So we can go on for days about how AI has been, you know, for feed ranking and computer vision, fine, but the big moment right now is all about generative. And generative has been something that GitHub has ridden with Copilot to this amazing moment. But that's the question, is that type of technology in particular transferable elsewhere?

• 32:46Speaker 01

  I think one scenario that comes to mind that we are already using at GitHub is support. And so, you know, if you look at our support system today, you actually find the GitHub support Copilot that tries to help you before you, you know, submit your ticket to a human. And we actually see, and it generates answers. So it's generative AI, it uses the same large language models to stay within the scope of your question. And we see that the number of tickets that get solved that way is above 50%. So we know 50% of those questions that go through the support Copilot gets solved by the support Copilot and do not get submitted into a human. And so such it makes, you know, supporting our developers, our customers,

• 33:32Speaker 01

  more efficient for us as a company. So I'd say, you know, that's definitely another scenario where we see the efficiency gains for us as a company. Similarly, you know, we have an internal tool called Octobot, you know, like Octocat, our logo that I have on my T-shirt. And it helps, you know, our folks internally to solve IT problems. And our IT team is getting no more than three hours per IT supporter back through that internal tool. By just by, you know, helping employees, you know, to solve their own IT issues instead of having to talk to a human first. And it's all, you know, along the same ways, which are along the same lines,

• 34:14Speaker 01

  and which is like generating text, you know, that helps you to solve the task that you have a problem with, whether that's in support and IT, or that's in coding, to focus on the things that you're really getting value out.

• 34:31Alex Kantrowitz?

  Okay, I want to talk about what the next set of models might bring, but let's take a break before we do that. So we'll be back right after this to talk about the next set of models and a bunch of other stuff. So stay tuned, we'll be back right after this. This episode is brought to you by App Point. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data and most teams have no single way to see it, secure it, and prove it's under control. That's exactly what App Point does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate. Your data, your cloud, and the agents acting on your behalf.

• 35:17Alex Kantrowitz?

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• 36:00Alex Kantrowitz?

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• 36:43Alex Kantrowitz?

  And we're back here with GitHub CEO Thomas Donke. We're talking about everything that AI can do and sort of how the advances might help propel not just coding, but everything else forward. All right, so here's what I'm hearing about the next set of models that are coming. And I'm talking about like the GPT fives and the Claude fours and whatever it might be. That the one thing that I'm hearing is that they're going to be much, much better for coding. And I'm curious to hear your perspective on how much further there is to go for these models to be able to handle code and what you think even, you know, these models getting even better at coding might pretend for, it might pretend for what you're seeing with the developer community today.

• 37:27Speaker 01

  I believe, you know, one of the things these neural models will be able to help with is what we call agentic abilities, solving multi-step tasks. One classic example in software today in small and big companies is that you don't have to go for too long until you have tech debt, until you have code that is old and needs to be maintained, that needs to be updated, that needs to be scanned for security vulnerabilities. And if you look into the backlogs of most engineers today, on the one side, they have all the innovation, you know, all the cool stuff they want to work on. And on the other side, they have all the maintenance tasks. And one of them is burning down security vulnerabilities that have, you know, stacked up over time.

• 38:11Speaker 01

  And so we think, you know, the next generation of models will be able, will be helping with burning down these security vulnerabilities. In fact, you know, we already have a feature in market that we call AutoFix that helps with known issues and burns those down. But it already only works right now in a single file. And just you have more powerful models, you can do that across multiple files, basically solving the issue not just in one place, but in multiple places.

• 38:41Alex Kantrowitz?

  And how do you teach like a model to be able to do that? Like, you know, obviously reasoning, agentic stuff, as I've been talked about, is breaking stuff down to its component parts and then learning how to work on it one by one. I mean, it seems sort of antithetical to the way that LLMs work today. Not antithetical, but very different, which is that they just kind of like take a prompt and then just spit back a bunch of information.

• 39:04Speaker 01

  You know, you described it yourself earlier, which is like this multi-step is that in the first step, you get an answer from the model that describes the solution. And then what you typically do when you reason with a model is you ask it about more about the first step and then about the second step. And so you're drilling yourself down into this tree of different steps. And I think as we move forward, agents will be able to do that to a certain degree themselves. And the tricky part is then to figure out when do I have to come back to the human and ask the question that I need the pilot to make the decision and not have the co-pilot basically go down the wrong path.

• 39:46Alex Kantrowitz?

  Okay, this is sort of a controversial question in the AI world, but I'm going to ask it to you. Do you think we're going to get to the point where, I mean, we're talking right now about effectively AI taking the reins and starting to build and then coming back to the human? Do you think we're going to get to a point where AI is going to just improve itself?

• 40:03Speaker 01

  It seems like a bit of a trick question, I'd say. You know, obviously, we have seen AlphaGo and AlphaFold that already happening. The AI has improved itself, right? And has learned to play Go and then got as good as the best Go players and in fact got better. And what we also saw then afterwards is that the best Go players figured out how to still beat the model. And so even though there was like a period of time when everybody was kind of like depressed that now that the game is ruined, the best players figured out they can still beat AlphaGo. And so I think, you know, there's definitely going to be problems that AI will be able to solve for us. As I mentioned, you know, burning down security vulnerabilities as an example. And I think most folks will be very happy about that because then gives them more time

• 40:47Speaker 01

  to work on stuff that they actually want to work on instead of doing the same security vulnerability fix over and over again in multiple files. You know, is the AI going to get to the singularity? I don't know. And I think, you know, if I know the answer and how to get there, I probably, you know, I built that company myself.

• 41:07Speaker 01

  But, you know, I'm joking a little bit, but like, I think, you know, we will see if, we'll see over the next few years if AI can not only do what it is instructed by humans, but can actually get to a place where it can create itself in the sense of, you know, not getting an instruction first and can kind of like, you know, produce ideas. Today, you know, while it may appear that a cloud of chat GPT is generating stuff, at the end of the day, it's just predicting the next word, right? The next word after that, it has no consciousness because it cannot say no to you. It can only predict an answer that says, I don't want to predict the answer. But you know, it still gives you an output.

• 41:51Speaker 01

  It can be silent if you if you will.

• 41:54Alex Kantrowitz?

  Right, and look, it wasn't a trick question. Like, the question is not can the model sort of learn to get better as it goes, right? Which is still following the model. Like, the question is, like, the basic design of these AI programs. Can AI learn to make them even better? Like, can AI be able to take a GPT four and turn it into GPT five? Right, that's the real question. You think that's gonna happen?

• 42:17Speaker 01

  It's a, I think it's like predicting the future and I don't know if I could, if I can. I don't know. I haven't seen, you know, I haven't seen any indication that that's possible today. But you know, maybe I'm on your podcast again in three years. And you see Thomas.

• 42:35Alex Kantrowitz?

  The AI is interviewing you.

• 42:37Speaker 01

  It has happened, maybe the AI is going to do a podcast between the two of us. No, but like, look, you know, all, if you look at the technology today, it's super powerful. It helps developers and support agents and IT employees to achieve their job faster, which ultimately means, you know, they have more time for other things in life. And I think that's remarkable. And I would, I'm not too worried about, you know, AI taking over these jobs and replacing them with a fully automated employee.

• 43:09Alex Kantrowitz?

  Now, another thing that I find interesting is this sort of constraint on computing. And I was looking at your Twitter and I saw that you recently praised the fundraising of a company called Etched, which has built chips that are purpose-built for inference, which is effectively running these AI models, which are extremely expensive to run now. But it's much cheaper to run on an Etched chips. How important do you think hardware innovation is for these technologies to be able to be cost-effective and grow to the point that sort of the industry is betting on? And then on that note, what do you think about Etched?

• 43:47Speaker 01

  It's incredibly exciting that we have silicon companies in Silicon Valley again. I think that's number one, less innovation in silicon. And there's not only Edge, there's, you know, a bunch of companies that are going in the same direction. And I think it's just fascinating to see after, you know, we believe that Moore's law is over and there's no more innovation in chips. We are back, you know, to a world where there's innovation across the whole stack. We talked a lot about models and we talked about Copilot and we talked about agents, which, you know, is going up the stack, but there's also innovation going down the stack, you know, from the model to the data center, all the way down to the chip.

• 44:27Speaker 01

  And I think, you know, we are going to see much more on that in the coming years. And the cost to run inference will come down with these specialized chips. The models themselves become more efficient. You know, GPT four oh mini was, you know, announced last week, which is much faster and much more efficient. And I think, you know, we are going to have innovation on the top end where bigger models come out and do more stuff. And we are going to have innovation on the efficiency side where the functionality that, you know, a few years ago required, you know, more GPUs and more time is now done, you know, much faster. And I think if you, it's incredibly important to have that because the faster you get the

• 45:11Speaker 01

  response, the faster you're able to iterate. Whether you do it manually, you know, while you're asking questions or whether you're doing that automatically in a Copilot where you need to do multiple steps to generate code or, you know, in Copilot, we're always generating 10 responses. You can actually see them in your editor if you open the side panel. So because we then want to pick the best one for the context you're working on and you can cycle through those, right? So if you can get those 10 faster, you actually probably get the higher acceptance rate from the developer because they saw the suggestion before they kept typing whatever they were typing.

• 45:45Alex Kantrowitz?

  Yeah, and you just mentioned that OpenAI has reduced the cost to use GPT four oh. I got a question I asked, like, what should I ask you? And Alex Willam from TechCrunch, he asked me, he's like, why is GitHub Copilot so cheap when the perceived value is so high? Why not add a zero? What do you think?

• 46:03Speaker 01

  We're really happy about the price point. I think that's the balance with every price point and every new product to find between mass adoption and the value you're getting out of the product. You know, $10 for individuals and $19 for employees in the company per month is a great price for all these productivity gains. It has allowed us to grow to 1.8 million paid seats and we're really happy about the competitiveness of that price point.

• 46:37Alex Kantrowitz?

  Are we going to get to a point where most of the code that's being generated is generated by AI and the developers are basically auditors of that code?

• 46:45Speaker 01

  I believe so, yeah. I said actually two years ago at a conference that my prediction back then was 80% of code is going to be written by AI in five years. So I guess three years to go for that to become true. Last year, we already said that on average, 46% of code is written by Copilot in those files where it's enabled. And for some languages, it's over 60%. Again, I don't think that's a bad thing. I think it's a great thing because it means that developers have more time to write the thing that actually matters, the thing that is creative, that's the thing that's new, this thing has differentiated, and they don't have to write all the boilerplate anymore.

• 47:21Alex Kantrowitz?

  And then, all right, last question for you. You said that you have 100 million users on GitHub today. You think that you're going to get to a billion with this. So I'm curious like why you think AI is going to drive so many people to start coding? And then what does that mean for a broader economy?

• 47:39Speaker 01

  I believe that today, the biggest adoption blocker is the complexity of the technology, the complexity of a language that is not the language that we learn and use every single day when we communicate. And programming languages are great because they're deterministic. You know, the same thing has the same output every time you write it. Um. But it's hard to learn. Uh. It's hard to learn uh when you're a kid. Um. Uh. It's much harder to learn than playing an instrument um or drawing an image um. Because you have to learn the thing first before you can produce anything. And then you still have to develop your craft and and do it over and over again. Uh. To actually get good at it. And I think AI is going to accelerate that massively. And. You know, one uh one billion developers um by twenty thirty or so is.

• 48:24Speaker 01

  A little bit under ten percent of the population, depending on where world's population is going. Um. That's actually a low number if you think about it, because um we all use computers every single day, yet we are not able to create the thing. Most people are not able to create the thing that runs on those computers. Um. And I think, you know, most people are able to go to home depot and and buy a screwdriver and and put a screw in a wall. And I think that's it's it's just going to be a fundamental skill of humans um to to to be able to control the computer and um create something on them. Whether then they use that and become a professional software developer that makes money by doing so. That's a very different question in the same way that not everybody that has, you know, some.

• 49:04Speaker 01

  Uh uh skills at home, well, on on home improvement uh is becoming a professional contractor, professional um. Uh uh musician and professional artist, right? Like those things are are decoupled. And I think for our economy, it means that we have a much higher um literacy in uh. Uh in computer engineering and computer science uh in software ultimately. And that means we will go be able to solve more problems. Because ultimately, we strongly believe at GitHub that most human progress is going to be achieved with the help of software. And um without that software, without software developer, we're not going to, you know, climb the the evolution ladder.

• 49:40Alex Kantrowitz?

  So people used to say, learn to code to people who lost their jobs first as a helpful suggestion and then as kind of an insult. And then they started to wonder. Maybe they shouldn't be learning the code because that's going to be taken over by AI. But your pers- your stance on this is no, we're still gonna need. The coders.

• 49:59Speaker 01

  And we still have code. Look, look, the AI and the, you know, Copal is not going to replace the code. The code is just lower in the abstraction level in the same way that, you know, your, your chip in your computer still has an instruction set, you know, used to do punch cards and then we had assembly language. Now they go into very technical stuff, but you know, the, the chip at the end of the day is still, you know, lots of little switches, that switch between zeros and ones. That doesn't go away. It just moves into, into a layer where it doesn't bother us as much and it doesn't, you know, keep us from building the things we, we want to build. And I think that's the, the true power of generative AI.

• 50:37Alex Kantrowitz?

  Very cool. Well, I think you should release this app. It's a flight tracker app that you worked on. I would definitely.

• 50:43Speaker 01

  It looks, you know, it's, it's, it's looks horrible. It's one purpose. I know every time, you know, I flew yesterday, I, I know whether I've been, been on that specific plane, you know, it has every plane has the tail, tail numbers, like a license plate. And so it can kind of track, oh, I've been on this flight on that exact plane before. Um, but it looks horrible. It's kind of like asking me to, to go on stage with Taylor Swift and sing and do it with her. I wouldn't, I wouldn't do that either, even though I sing in the shower. Right. And that's, I think that kind of directly well describes like the intention here is like, one thing is the freedom of being creative and the other one is being so good that you can, can become a professional.

• 51:24Alex Kantrowitz?

  Right. Well, Tomas, look, you're right. The, the gates of nerditude have swung wide open and I'm totally into it. Thanks so much for joining. Great to see you.

• 51:32Speaker 01

  Thank you so much. It was super fun.

• 51:34Alex Kantrowitz?

  Awesome. All right, everybody. Thank you so much for listening. We'll be back on Friday, breaking down the news with Rajan Roy and we'll see you next time on Big Technology Podcast.