The role of AI in new product development | Ryan J. Salva (VP of Product at GitHub)

Ryan J. Salva is the VP of Product at GitHub, where he led the incubation and launch of Copilot, which uses OpenAI to suggest code and entire functions in real time, right from your editor, and is changing the way we build software. Ryan is an experienced developer and product manager, with over a decade of experience working for Microsoft before moving to lead the GitHub product team. In today’s episode, he shares how Copilot got its start, how it moved from prototype to live product, and how he structures R&D teams within larger companies. He also discusses the ethical questions surrounding AI use and how to build a successful product team, and shares the inside story of the development of Copilot. Find the full transcript here: https://www.lennyspodcast.com/the-role-of-ai-in-new-product-development-ryan-j-salva-vp-of-product-at-github-copilot/#transcript — Where to find Ryan J. Salva: • Twitter: https://twitter.com/ryanjsalva • LinkedIn : https://www.linkedin.com/in/ryanjsalva/ • Website: http://www.ryanjsalva.com/ — Where to find Lenny: • Newsletter: https://www.lennysnewsletter.com • Twitter: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ — Thank you to our wonderful sponsors for making this episode possible: • Amplitude: https://amplitude.com/ • Athletic Greens: https://athleticgreens.com/lenny • Modern Treasury: https://www.moderntreasury.com/ — Referenced: • GitHub Copilot: https://github.com/features/copilot • Make It So: Interaction Design Lessons from Science Fiction: https://www.amazon.com/Make-So-Interaction-Lessons-Science/dp/1933820985 • Brief Interviews with Hideous Men: https://www.amazon.com/Brief-Interviews-Hideous-Foster-Wallace/dp/0316925195 • The Memory Palace podcast: https://thememorypalace.us/ • Arrival: https://www.hulu.com/movie/arrival-6ec67b11-b282-4383-85ac-38c4731b40e4 • Oege De Moor’s LinkedIn: https://www.linkedin.com/in/oegedemoor/ — In this episode, we cover: [00:00] Ryan’s background and how he became involved in development [10:46] What is GitHub Copilot? [14:44] How GitHub Copilot can be utilized for education [17:46] How GitHub incorporated AI models with computer languages [27:24] Project horizons: delegating tasks based on confidence levels [30:39] How to put together a development team for “moonshots” [35:22] When and how to transition your R&D team smoothly [38:28] Dealing with ethical issues surrounding AI [44:40] The future of AI in development [48:48] Challenges with scaling Copilot [54:23] Allocating your energy as products scale [58:17] Lightning round — Production and marketing: https://penname.co/

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

• 0:00Speaker 00

  We had actually created a snapshot of GitHub's public code for what we call the Arctic Code Vault, right? Essentially, this is up in like way in the northlands of Finland. There's a seed vault. And we're like, you know what? Like seed vaults are really there to preserve the diversity of the world's flora in seeds in case of some crazy, either natural or man-made disaster. But another really important asset to the world is our code, our open source. Like this represents actually a lot of the collective, uh, well, certainly software, if not like intelligence of kind of like the modern world, right?

• 0:43Speaker 00

  And so we had put this snapshot of public repositories on these like, it's like silver film that would be preserved for thousands of years in this Arctic Code Vault. Well, we took that same data snapshot and we brought it to our friends over at OpenAI to see like, okay, what can we do with these large language models built on public code? Well, it turns out we can do some pretty cool things.

• 1:09Lenny Rachitsky?

  Ryan Salva is VP of Product at GitHub, where amongst other projects, he incubated and launched GitHub Copilot, which in my opinion is one of the most magical products that you'll come across. If you haven't heard of it, it uses OpenAI's machine learning engine to autocomplete code for engineers in real time as they're coding. And I think it's one of the biggest advances in product development and productivity that we've seen in a while. I'm always really curious how a big product like this starts, gets buy-in, builds momentum, and then launches, especially at a big company like Microsoft, and especially a product like Copilot that has surprising ethics challenges, scaling challenges, business model questions.

• 1:54Lenny Rachitsky?

  Also, this came out of a small R and D team that GitHub has. And it's so interesting to hear what Ryan has learned about incubating big bets within a large company and then taking them from prototype to Microsoft scale. Ryan is also just super interesting as a human. He's got a very non-traditional background, and so I am excited for you to hear this conversation. And so with that, I bring you Ryan Salva.

• 2:22Lenny Rachitsky?

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• 3:06Lenny Rachitsky?

  Just visit amplitude.com to get started.

• 3:10Lenny Rachitsky?

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• 3:54Lenny Rachitsky?

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• 4:40Lenny Rachitsky?

  Ryan, welcome to the podcast.

• 4:42Speaker 00

  Thank you, my friend. I am genuinely very excited to be here. Lovely to geek out with you for a little while.

• 4:48Lenny Rachitsky?

  I'm excited as well. We were chatting briefly before we started recording and you mentioned a little bit about your your background, which is really unique for someone that is leading product at GitHub. And so could you just share like what you studied in school and then briefly just how that led to your career in product management?

• 5:06Speaker 00

  Wow, you're gonna you're gonna make me remember all the way back to to school. Okay. Yeah, so back in school, I like, I was not a classic software engineering CS major. I studied the kind of esoteric answer is philosophy of aesthetics and 20th century critical theory. The easier access answer is philosophy and English. But primarily, it was really about like, how do we as people communicate with each other? How do we express ourselves through creativity? We like, you know, as as humans, since the dawn of time, have been painting on cave walls and dancing around the fire and writing stories and novels and and singing to each other.

• 5:52Speaker 00

  And I was just really interested in how we kind of convey our experience of the world to others. And so I kind of, I got started in software development and product management because I wanted to be in the business of creativity. And like, we're at a really, really unique time in human history where like, we actually get to witness the advent of a brand new medium, like software development and the worlds that it creates wasn't possible. And maybe 50, 60 years ago now. And, you know, if I'd been born in the 1700s, I probably would have been the guy making, I don't know, new colors of paint and paintbrushes. But I wasn't. I was born kind of at the turn of the 21st century.

• 6:36Speaker 00

  And so I work in engineering. That's what I've been doing for the last about a little bit more than 20 years now. Working sometimes in startups, some of them, other people, some of them my own. About 10 years at Microsoft and now three years at GitHub.

• 6:51Lenny Rachitsky?

  Amazing. I didn't know that was a job to make new paint colors for paintbrushes. Is there a color you would come up with?

• 6:58Speaker 00

  Oh, man. A very, you know, it so happens that like yellow, I think, I think I would do it like a really like, like vibrant gold sunshine yellow if I was, if I was, you know, in that business.

• 7:13Lenny Rachitsky?

  Very positive, happy. I love it. That could be a new GitHub brand color. So today, you're VP of Product at GitHub. And before that, you were a super senior product leader at Microsoft. And I'm always curious how that transition happens when you move from just like a long time senior product leader at a larger company to taking on something like this that was an acquisition. And so I'm curious, what made you decide to kind of take this leap? And then just, is there anything interesting about the machination that went into just making that transition and figuring that out?

• 7:45Speaker 00

  Yeah, yeah, it's a good question. You know, so like I said, I was, I was working on development tools and developer services when I was there at Microsoft. Specifically, I was leading product for what they call one engineering system. It's essentially like the shared developer infrastructure for all Microsoft products like Windows and Office and Azure and things like that. As well as a kind of Microsoft DevOps solution called Azure DevOps. And when the acquisition happened, like it was clear that so much of the energy, so much of the focus and the innovation that was going to be happening around developer tools and services was going to be happening around GitHub.

• 8:30Speaker 00

  I mean, that's where, that's where the community is creating. That's where people are learning. That's where so much of the, the mind share of just the development community is focused. And like I said, I'm, I'm motivated. What I care about is helping people create. And it was very clear to me that there was no place that I could have a larger impact than working at GitHub. And so I really took that opportunity to make the transition out of, you know, a little bit more kind of enterprise focused, internal role at Microsoft.

• 9:05Speaker 00

  To get going where I could work on everything from, I don't know, AI technology like Copilot to a, you know, cloud hosted development environments like Code Spaces, repos, which literally like every single developer on the planet is like participating in some way and get have repos in the typical year. And so that was like, that was what I wanted to accomplish is just like, how do I get more connected to the community, especially the community outside of what Microsoft could reach on it on its own. And the, the decision to move as well, I think was, was really focused

• 9:45Speaker 00

  not just on what GitHub was and maybe is at the time, but what GitHub also can be. I mean, GitHub has more than a decade, nearly a decade and a half of history of bringing developers to together to collaborate on code through repositories. But in the last few years, we've, you know, really expanded that portfolio to include so many different parts of the developer lifecycle. Again, I talked there about Code Spaces and Copilot, but it's also actions for CICD and advanced security. Like, you know, as, as developers, we are so much more than just where we put our code. There's a whole part of the tool chain there. And to get to an opportunity to work

• 10:30Speaker 00

  on so many V1 products, like that is creation itself, right? To be able to, to build an entirely new product, get it out to market, test it, iterate on it, and really feed on the energy that's coming back from the community.

• 10:46Lenny Rachitsky?

  Awesome. There's definitely a lot of energy coming out of GitHub. And what I want to spend most of our time chatting about is a product that your team helped launch at Incubate, which is GitHub Copilot, which in my, just from my outsider perspective, feels like one of the biggest advances in software development in, I don't know, a decade, maybe more. And it's definitely one of the most magical products out there. And your team and you kind of led the incubation and launch of the Copilot. And so I'd love to spend most of our time chatting through that. And the first question, okay, cool. So my first question, just for folks that don't know a lot about Copilot is just like, what is it? Can you just kind of briefly describe what Copilot is?

• 11:26Speaker 00

  Yeah, sure. So.

• 11:29Speaker 00

  Developers for the last 20 years or more have had essentially simple and tele- and tele-sense autocomplete. You hit the period and you get the next variable that might come up. It's, you know, it's helpful for moving a little bit faster through your code, helpful sometimes for remembering, you know, what the particular syntax might look like for a method or a function. Copilot is essentially that magnified by many lines of code. It is multi-line autocomplete that is fundamentally powered by an AI model called Codex, which is a derivative of another kind of one that you might be familiar with, GPT-3.

• 12:14Speaker 00

  Now, when you are in the editor, could be VS Code, it could be IntelliJ, it could be them, essentially as you are typing, Copilot will provide suggestions, usually in kind of this like italicized gray text that is really, to your point, kind of magical what it's able to infer based upon the variables around it, the class names, the method names around it, your comments. Copilot kind of infers what you intend to create and then hopefully does a pretty good job at nailing it by providing scaffolding code template that you can then riff on.

• 12:55Speaker 00

  Now, what we tend to find is that developers love it. They really like enjoy it. They kind of find themselves getting a little addicted to it because it helps them stay in the flow, right? Like as developers, we love to be in that place. I love to be in that place where I'm creating things, where I'm focusing on some, some products, some pieces of software that I'm going to give to, you know, my customers, my users. The, I don't know, the labor of remembering, you know, what's the order of a parameters that need to come into a particular API? Or, hey, what's the particular syntax of this thing that's supposed to do? Or, oh, I've got to create a bunch of like.

• 13:37Speaker 00

  Dummy data that is days of the week or months in the year like that's just labor. It's not creating, it's just typing. Copilot helps developers stay in the flow by bringing all of that information into the editor, preventing them from having to go check out documentation or watch a tutorial or go to Stack Overflow and you know, either find an answer or worse have to ask a question and wait for an answer. It just brings all of that into the editor and gives the developer often multiple suggestions that they can choose from and just kind of pick and choose what is the, what is the right solution to solve the problem for the thing they're trying to create.

• 14:21Lenny Rachitsky?

  Awesome. What I'm most curious about, and we're going to spend time on this, is just like how a product like this comes to be at a larger company. But before we get into that, what's like the craziest story of someone using Copilot to write code? And I'll, I'll share one real quick. I was watching some YouTube videos to prepare for this chat. And one guy, maybe this is the touring test of AI writing code as he used Copilot to center divs.

• 14:44Speaker 00

  Yeah, yeah.

• 14:45Lenny Rachitsky?

  He's like, wow, this did it right. And then another guy, he's like an instructor of code. On, he makes YouTube videos teaching people how to code and he's like, Copilot just gives you the answer immediately. And so I can't make these videos as easily. I have to like turn it off so that it doesn't just give it away. And so I'm curious, yeah, what have you seen?

• 15:02Speaker 00

  There, I think there are so many of those. I mean, I'll, I'll just kind of give a couple of recent ones that, that I've heard. So I was talking to one developer who was, he's actually an educator. And he's teaching kids how to code, usually like, like kind of high school age, right? So 16, 15, that kind of thing. And you know, his experience matches my own, which is that.

• 15:31Speaker 00

  Many of us, we learn to code best by, not by like arbitrary exercises, but by actually building something that's going to be useful, right? Solving problems. And so what he does is he matches small businesses and medium sized businesses who need to build internal tools with essentially classes of students, like a group of maybe six or eight students. And then gives those students Copilot and says, here, small business, medium sized business, you know, group of of students, go build this internal tool for this business. And Copilot is essentially kind of whispering in the student's ear, metaphorically speaking. Hey, you know, here's how you solve this problem. Here's how you do this.

• 16:16Speaker 00

  And students build not only the kind of the tool, the software that the business needs, and then get to put that on their resume and their, you know, application for college and university, but they also get to learn by kind of using the tools that likely are going to be part of the the core DNA of the developer tool chain two, three, four years from now as AI starts to permeate our entire stack. So that was a pretty cool recent one that I that I talked to.

• 16:47Lenny Rachitsky?

  That is very cool. I didn't think about just education lever here, just like making so much easier to learn to code, not even just building code.

• 16:55Speaker 00

  Yeah, I mean, and that's the thing, like Copilot is particularly good, not just at taking away some of the effort, but often, you know, there's learning a new language. And then there's also just wading into a code base that you're not necessarily familiar with, right? Like, I mean, heck, sometimes I don't recognize some of the code that I wrote six months ago or a year ago. It feels like I'm wading into new territory. But maybe, maybe you need to fix a bug in an app that you don't often touch. Wading into that codebase is kind of like learning and creating a mental map for that codebase. One of the, you know, really magical pieces of Copilot here is that that AI is collecting context of the application that you're going into. And so it can help

• 17:40Speaker 00

  you build that mental map and learn the codebase, even if it's a language that you're already familiar with.

• 17:46Lenny Rachitsky?

  Awesome. Okay, so going back to the beginning of Copilot and how it started, I'm always curious how a project that ends up being a huge deal to a larger company begins, and especially how it builds momentum, how it gets buy-in, and then just kind of gets out the door. And so you talk about just the original seed of this idea, like who did it come from? Who had the original vision? How did this idea emerge and build momentum? Where you put resources into it?

• 18:12Speaker 00

  Yeah, yeah. Oh, wow. What a, what a long and, I don't know, depending upon your point of view, assorted or exciting story that is. So yeah, so Microsoft and OpenAI have been collaborating for, for quite a while now on large language models, making its way into kind of all different experiments and different parts of both Microsoft's kind of software portfolio, as well as just kind of helping OpenAI by providing, you know, like the compute necessary. It takes massive amounts of compute to train these models. And they were mostly large language models. And so a couple of years ago now,

• 18:52Speaker 00

  it kind of dawned on us that, well, language models aren't just English and Spanish and German and Korean and Japanese, right? But Python and JavaScript and Java and C sharp and closure, all of these are languages too. In fact, they're kind of nice from an AI perspective, because they're relatively constrained in terms of their semantics, right? The, the number of words, I put that in scare quotes, as it were, that can be expressed in Python, for example, is much smaller than the English language, which has all sorts of different grammar rules and nouns and verbs, adjectives, adverbs.

• 19:35Speaker 00

  And so we started to see what it would be like to actually bring code to these large language models. And the way that I actually got introduced to it is kind of funny. Microsoft and OpenAI had this idea. And at the time, one of the teams that I was responsible for was GitHub's infrastructure team, the team responsible for our data centers, our reliability, our uptime. And we noticed one day that we were getting hammered, I mean, absolutely hammered with a tremendous amount of clone requests. And like, we're like, oh, my gosh, is this like a denial of service attack? How are we going to respond to this? What's

• 20:18Speaker 00

  going to happen? And we figured out pretty quickly that it was actually OpenAI. They were cloning all of our repositories to harvest kind of the data out of GitHub. I mean, like, it's totally legit practice, but like, you know, it does have a real consequence. And we were able to step in and mitigate it very quickly. There was not a reliability, kind of an uptime incident there. But we're like, hey, y'all, like, cool, love this thing. Let's see if we can get that data to you in a more responsible way, in a way that's packaged a little bit more to meet your needs. And so what we did is, just like the year before that, we had actually created a snapshot of GitHub's

• 21:01Speaker 00

  public code for what we call the Arctic Code Vault, right? Essentially, this is up in like, way in the nortlands of Finland. There's a seed vault. And we're like, you know what? Like, seed vaults are really there to preserve the diversity of the world's flora in seeds, in case of some crazy, either natural or manmade disaster. But another really important asset to the world is our code, our open source. Like, this represents actually a lot of the collective, well, certainly software, if not like, intelligence of kind of like, the modern world, right? And so, we had put this snapshot of public repositories

• 21:45Speaker 00

  on these like, this like, silver film that would be preserved for thousands of years in this Arctic Code Vault. Well, we took that same data snapshot, and we brought it to our friends over at OpenAI to see like, okay, what can we do with these large language models built on public code? Well, it turns out we can do some pretty cool things, just like a translation tool that goes from English to Spanish, Spanish to German. You can also go from English to Python or Python to C sharp. And like, okay, this is cool. We, you know, we can start to get not only translation, but a little bit of predictive text here as well. And we're all, I think,

• 22:28Speaker 00

  fairly already familiar with predictive text already in our code editors as in TELUSense. But in, I don't know, you go to your favorite word processor and chances are that you've got some kind of predictive text happening there as well. And we started experimenting with different user experiences, right? Do we want it so that you, I don't know, right click and get a little side panel that comes up with a bunch of different options for things that you might want here? That was nice because it would give you like whole functions, but it's kind of out of the, and it's out of the cursor, right? And you had to really, even if you weren't switching over to a different window, you still had

• 23:09Speaker 00

  to switch over to a different panel, which itself was a little bit distracting. And we eventually came to this idea of inline autocomplete. And we were able to, with the kind of partnership of some of our friends over on the Microsoft side of things, partner with our friends in Visual Studio Code, be like, hey, there's not really an extensibility yet in your editor for this multiline autocomplete, but we've got an idea for how this might work. You know, played around with kind of the, you know, the actual presentation of it. What should the keystrokes be? What should the presentation layer be? The gray italicized text seemed to be a good way of indicating that it was ephemeral, as it

• 23:54Speaker 00

  were. And pretty early on, we landed on this user experience that is co-pilot, as most developers experience it today. That was now, I want to say that was at least 16 months ago, 14, 16 months ago.

• 24:13Speaker 00

  Since then, we brought it to developers.

• 24:16Lenny Rachitsky?

  Just to double click on that. So you're saying just like less than a year and a half ago, this kind of really started as a project and now it's out to the world.

• 24:25Speaker 00

  That is exactly right. That's exactly right. Yeah. So it's about a year and a half ago.

• 24:30Lenny Rachitsky?

  That's insane. What uh, what was that period between OpenAI almost taking down GitHub to, I guess that point?

• 24:38Speaker 00

  So the period in between kind of OpenAI uh, almost taking down GitHub and then us really arriving at the kind of the user experience. Now, part of that was frankly, a lot of really smart researchers at OpenAI experimenting and doing what like only world-class AI researchers can do. It was a lot of them experimenting, occasionally asking for updates to the data set, tossing back to us a model that we might play with and tinker around with. And these models have like literally thousands of parameters that you can pass to them. So when you're really thinking about kind of GPT-3 and Codex and then the transition from that to something like Copilot,

• 25:25Speaker 00

  it was not just like the model, creating the model is one thing, but then figuring out how to use the model in terms of what parameters do you want to kind of like adjust for, what do you want to optimize for in terms of like, a great example of this is performance, right? When you're in a code editor, you don't necessarily want to type, type, type and then have to wait one second, two seconds, three seconds to get a suggestion back when your entire goal is to stay in the flow. And so we would run experiments to see like how many milliseconds are the right amount such that a developer doesn't feel like they're being interrupted by Copilot and a suggestion.

• 26:06Lenny Rachitsky?

  What's the answer to that?

• 26:08Speaker 00

  It seems like right now it's around 200 milliseconds. So depending upon where you're in the world, your latency can go up or down a little bit from there, but it seems like the sweet spot is somewhere around 200 milliseconds.

• 26:20Lenny Rachitsky?

  Get to know.

• 26:21Speaker 00

  And uh, we also experimented quite a bit. So it's not just about the, the model, but it's also about what you feed the model. How do you prompt the model to return back a useful response?

• 26:34Speaker 00

  And so this kind of began a journey of experimentation for what we call prompt crafting.

• 26:40Lenny Rachitsky?

  So going back to the way this started, it sounds like basically it was kind of this fortunate accident where OpenAI just did some that you didn't expect. And then somebody within this like PhD group that you described is like, oh, wow, maybe we could do something really good with this. Is that kind of how it began?

• 26:57Speaker 00

  That's fairly accurate. Yeah. I mean, you know, we had a model that really like was amazingly good, like a step level change in actual like intelligence, right? And then marrying that up against a like a really good use case that actually changes developers' fundamental experience of the creation process, the creative process.

• 27:24Lenny Rachitsky?

  Was there, was there kind of a point at which it was clear to you or leadership in general, like we should double down on this thing, go big? Where this smaller team was working on this idea and then they're like, oh, wow, this is going to work. Or is it always like, we will bet on this thing. This is such a big and great idea. We're going to invest resources for sure from the beginning.

• 27:45Speaker 00

  Yeah. Yeah. So the, the original team that was working on Copilot at GitHub was, you know, the team that we call GitHub Next. And essentially their job is to work on second and third horizon projects. What some folks might call moonshots, right? Things that we never really expect to work in the next one or two years, but might three, five years down the line actually turn into something meaningful.

• 28:17Lenny Rachitsky?

  Is there a concrete definition of horizon two and three? Is it like number of years out, like, like Amazon style?

• 28:23Speaker 00

  Not necessarily a concrete definition. Like for me, I usually ballpark it as first horizon is the next year. Second horizon, the next three years, third horizon, next five years. But we generally think of it more as like a measure of ambiguity and confidence level more than calendar dates.

• 28:47Lenny Rachitsky?

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• 29:32Lenny Rachitsky?

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• 29:57Speaker 00

  Yeah, it is, I would say it is not necessarily a Microsoft thing, but it's definitely at GitHub, how we have really contextualized it. Not to say that there aren't teams at Microsoft who might also kind of use kind of that methodology, but where we've been really maybe explicit or intentional about it is at GitHub, where we've actually ring-fenced a team to think about that Horizon 2 and Horizon 3 work and kept them separate from, you know, EPD, EPD here being engineering, product, and design, the folks who are working on building, you know, productized, operational, like, products that we bring to market, and we either give away or monetize in some way.

• 30:39Lenny Rachitsky?

  This is so interesting. There's a lot of companies that have these sorts of R and D groups. Your product experience team at Facebook and Google has one. I'm not sure how many successes have come out of these teams from what I've seen and I'm curious. What have you, and clearly you had a huge success as far as I can tell so far. Is there anything you've learned about how to do this? Where you invest in these big moonshots within a larger company?

• 31:04Speaker 00

  Yeah. I mean, I think like the first step is to invest in it. Like the first step is really like hire really smart people, attract smart people and give them the opportunity to be creative. Don't expect any, anything out of them that is going to turn into a moneymaker or something that is going to be beholden to fundamentals around security, privacy, uptime, you know, accessibility, all that groupie kind of stuff upfront. They need space to create an experiment. And also, when you do get to, you know, a place where like that team

• 31:43Speaker 00

  has an idea that is clearly connected to like a representative set of customers who have a genuine problem. And there is signal with at least medium confidence that this solution, whatever it is, solves it in a novel way. That's the time to start thinking about, okay, like, let's actually put a little bit of,

• 32:09Speaker 00

  I'm going to call this market testing. It's nothing so formal as market testing. It's really just like, let's start to actually bring prototypes of this in front of more and more customers to kind of test it out and see, hey, is this, is this actually solving a problem for you? Is this something that you would use? And this is where the transition between Next and EPD at GitHub really, you know, really started. And this is actually where my role in the, in the product lifecycle kind of really started to increase. You know, I had kind of been in tight connection and kind of been monitoring the work and, and kind of like consulting a little bit with the Next team prior to that.

• 32:51Speaker 00

  But it was that moment when we identified that, okay, this is actually something real. Customers are saying, developers are saying, this is magical. This does something extraordinary that I could not do on my own. That we started to think about, okay, how do we transition this over? And so from there, we really did is like, okay, we think we've got a hit here. We think we've got something that we can actually, that we can actually bring to developers. And so we made an intentional decision to take some of the researchers who were in the next team. And for a finite period of time, move them over to create a new EPD squad. Right?

• 33:36Speaker 00

  We want them to be researchers, but we need to do knowledge transfer and we need to actually kind of provide the seed for a team that could eventually operationalize and productize. And that kind of began the technical preview where we started to invite tens of thousands, then hundreds of thousands to the technical preview. And in that technical preview, when we started to see like crazy, you know, mind blown emoji tweets and like threads on Hacker News about people getting really, really excited about it. That's how we knew it was time to start scaling. And it was time to really start thinking about how do we do hiring?

• 34:16Speaker 00

  So that we can build in some insulation around these researchers so that they can eventually go back to GitHub Next to do what they do best, which is be innovative and creative and think about the next moonshot. That process that took, well, we're actually still kind of at the tail end of it now. Here we are, you know, like I said, roughly a year and a half after kind of the initial kind of creation of the of the product, having gone through technical preview, have achieved general availability. We've now hired in a team around them and the researchers starting actually as early as last month have started to gradually move back over to GitHub Next.

• 35:00Speaker 00

  And a EPD squad, multiple EPD squads actually are now taking the product forward and starting to respond to customer feedback to think about, okay, how do we now as a product team carry this roadmap forward? From an idea that originated in.

• 35:21Speaker 00

  Get up next.

• 35:22Lenny Rachitsky?

  I love that insight of bringing the people along and not just kind of like, cool, we'll take it from here. If you were to build a team like this again somewhere, let's kind of R and D Horizon three or two team.

• 35:33Speaker 00

  Yeah.

• 35:34Lenny Rachitsky?

  Is there anything else you would do differently? Anything, any lessons you take away from this experience? For maybe founders that are, or PMs working at larger companies that are like, hey, we should have something like this. Is there anything else that you find is important for making something like this successful?

• 35:49Speaker 00

  The criteria for moving researchers back into their, their R and D team, whatever that happens to be for your organization. That can't be based on a calendar. It needs to be based on a replacement in seat. Who's actually doing the job and has picked up all of the skills necessary. And only then can the researcher move back. So make sure that you've got continuity of expertise and skillsets and domain familiarity. Before you move over, I feel like we've, we've, we've managed that pretty well today.

• 36:28Speaker 00

  As well, it's critical that the team who is taking over from the R and D shop feels like they have control over their own future. You can't really delegate roadmap to an R and D team. The team who's responsible for maintaining the product, for building the product, who has the closest feedback with the end customer. They're the ones who really need to own and, and feel like, you know, they control the roadmap. And so making sure that you're not outsourcing innovation exclusively to an R and D team. But that is happening within the product team as they take ownership of the idea and over kind of the use case and the customer.

• 37:13Speaker 00

  Last I would say here is really that engineering fundamentals in a lot of ways are the contracts that differentiate an R and D team from a operational product team.

• 37:28Speaker 00

  And bringing that fundamentals process into it is going to feel candidly a little bit unnatural to the researchers. And that takes therefore a little bit of cultural change management for everyone to just kind of like adapt their way of working and understand that we're graduating from an experiment and a research project to a, you know, an operational product. And often because those researchers are, you know, they're the first wave that come over. They're the, they're the seed of the project. It's going to feel a little bit unnatural to them, but they probably won't have all the right skillsets in order to make that transition.

• 38:08Speaker 00

  And so making sure that you've got a good mix of engineers who are comfortable maintaining a service as well as, you know, engineers and researchers who are really thinking about what, what is the idea that we've created? What is the new thing that we've brought to market and can bring that vision to it?

• 38:27Lenny Rachitsky?

  Yeah, I can totally see the, the, the challenge that comes from, this was my thing. I've been working on this, like, what are you guys doing to this project? Where is this going? Where, I'm not sure, I'm feeling, and then, and then there's all these new asks that are coming at you. Like, oh my God, this was so much fun and now I have to scale this freaking thing.

• 38:45Speaker 00

  Well, and I mean, this is the, the best problem in the world to have. Talk about kind of customer asked, like for Copilot in particular, the amount of, of chatter, the amount of customer feedback that was coming in, especially for us with AI. I mean, the world is still figuring out AI candidly. Like, I mean, didn't, we're getting a lot, lot better at it, especially in the last couple of years, you know, with things like Dolly and Copilot. But it brings with it not only engineering challenges, but also frankly ethical challenges. Right? Like, you know, and, and legal challenges, like making sense of how we actually, what, what our expectations are of AI.

• 39:29Speaker 00

  And if AI produces something that is offensive.

• 39:34Speaker 00

  Who was at fault? You know, our, our stance on it, what we ended up coming to is actually the, the framing of Copilot as an AI pair programmer, I think is a useful one. You know, pair programmer, I suspect most of your listeners will know, but like a pair programmer is usually two developers sitting side by side, working on a problem together. One's at the keyboard, the other one's kind of like, you know, helping them talk through it, talk through the ideas and, you know, make corrections, that kind of thing. Well, if Copilot is your AI pair programmer and they're whispering crazy stuff into your ear and then bringing politics into it, or gender identity into it, or I don't know,

• 40:17Speaker 00

  whatever other, you know, they're spouting off slang and slander and all that kind of stuff, you're probably not going to be able to focus on your work, right?

• 40:26Lenny Rachitsky?

  Right.

• 40:27Speaker 00

  It's gonna be really distracting. And so really kind of coming down to some principles about what is the use case we're trying to solve, what is appropriate, I put this in scare quotes, behavior of the AI kind of bot sitting side by side with you, helped us kind of create some principles or some, some guidelines for the developer experience that we wanted to create.

• 40:52Lenny Rachitsky?

  I love that, just kind of creating like a persona of the thing to help you inform how to, how the behavior of the thing should work. How do you work through these challenges? Is it like discussions with you and the legal team? And I don't know, like these ethical things are really tricky, I imagine. How does, how do you approach something like that as a product team?

• 41:09Speaker 00

  It is conversations with a very, very wide cast of characters. It's, this product in particular, I probably spent more time with legal than any other products that I've ever kind of been responsible for. All wonderful, creative people, but it's not just legal. It is also, you know, privacy and security champions. It is frankly developers, like the people who are using it, like listening to them, like, hey, what works here, what doesn't work for you here? Why is this offensive? Why is it not offensive? When we, when we started out, I'll continue on the example of the crazy pair programmer whispering crazy things in your ear.

• 41:50Speaker 00

  When we first started out, we didn't really have any filter on copilot whatsoever in the very, very, very early days. And then eventually we're like, okay, it needs to be slightly more controlled experience. We need to edit out, you know, some of the most egregious stuff. And so we introduced a simple block list of words. And these block lists are always fraught with peril, like, you know, which words are okay, which words not okay. All of a sudden we become editors and that's just of like language and that's kind of a scary place to be. I'm not comfortable with it at least, but like at a certain level, it has to be done because otherwise you're going to create a bad developer experience.

• 42:32Lenny Rachitsky?

  Yeah.

• 42:33Speaker 00

  And, and so, you know, often we would get feedback from developers of like, hey, this particular word was blocked and like that it was blocked either was offensive to me or prevented me from being able to kind of get good value out of the product.

• 42:51Lenny Rachitsky?

  Oh, man.

• 42:51Speaker 00

  And so always kind of like dancing the dance of like editorial content. We're actually at a place now where we're able to partner with the Azure Department of Responsible AI. And they've created some really extraordinary models that help detect, I'll call it sentiment, for lack of a better word, basically when there is something that is patently offensive. Because there are some words that in some contexts may be offensive and in some contexts may be totally reasonable, right? Especially, especially in when you get into software for medical kind of scenarios, right?

• 43:34Speaker 00

  And so being able to start to shift a little bit, to shift a little bit, to focus or to rely on AI models that can also do a better job than we could with crude or simple block lists is maybe another proof point, both of how AI as a solution for common development problems is getting way better. It's solving more parts of our stack or filling in for more parts of our stack and how, at least in our case, we were pretty fortunate to be able to deliver on or depend on a parent company's contributions to solve a real acute problem that GitHub probably could not have solved on our own.

• 44:16Lenny Rachitsky?

  I never thought that Copilot would be like, you would have to worry about it saying things that are, that are crazy. So that is wild that you guys have to deal with that. And wasn't it, wasn't it Microsoft that had that bot that got turned really negative? And

• 44:30Speaker 00

  It was.

• 44:31Lenny Rachitsky?

  So there's

• 44:31Speaker 00

  I wonder if it's named Talia or something like that.

• 44:35Lenny Rachitsky?

  Yeah, something like that.

• 44:36Speaker 00

  Yeah, something like that. Yeah. Yeah. We don't want another one of those incidences.

• 44:39Lenny Rachitsky?

  Yeah. Wow. What that makes me think about is your, your team is kind of at the forefront of AI in this applied way. And I'm curious what your thinking is on just like where this goes for developers, especially like I saw a stat that maybe 40% of people's code is now written by Copilot. I don't know if that's right, but like, is the vision in the future becomes something like 90? Where do you see this all going?

• 45:00Speaker 00

  Yeah, yeah. And so just to put a fine point on that stat, it is 40% is specifically for Python developers. It candidly, it varies depending upon the language. As you might imagine, some languages have better representation in kind of the public domain than others. And usually both the volume and the diversity of training data correlates with the quality of suggestions, which is then represented by either the number of lines written or the acceptance rate, or you know, any one of a number of other metrics.

• 45:34Lenny Rachitsky?

  Awesome.

• 45:35Speaker 00

  In aggregate. Yeah, yeah, totally. We see it range anywhere from the upper twenties to the forties

• 45:40Lenny Rachitsky?

  Got it.

• 45:41Speaker 00

  Across all the different languages.

• 45:42Lenny Rachitsky?

  I, as a, just to throw this out there, as a not great engineer, I used to be an engineer for about ten years. I welcome our AI overlords writing all my code. And so I'm excited for this to do more and more. And yes, I'm curious where you think, where you think this goes.

• 45:57Speaker 00

  It, it does. It like, it enables even mediocre developers like myself to be able to do some pretty amazing things.

• 46:05Lenny Rachitsky?

  All right.

• 46:05Speaker 00

  But where is it going? Like, so first, I think, I hope it's obvious to most developers that AI is going to infuse pretty much our entire development stack in the not so distant future. Copilot is really just kind of the very tip of the spear for a lot of innovations. And, you know, better managing maybe our build queues or helping to, here's a great one. You know, I don't know about you, but often the comments that I get with commit messages and PRs aren't super great.

• 46:47Speaker 00

  It kind of, you know, it puts a lot of effort onto the code reviewer to go figure out what the developer was actually trying to do. What if AI could summarize all of your changes with your pull request? And you just have to, as the contributing developer, just review it to make sure it's accurate, send it on its way. And you don't have to put an extra effort for that. There are lots and lots of different opportunities for AI to essentially be able to take some of the drudgery out of our work so that we can focus on creative acts. Like what I hear from developers and what I experienced myself is that Copilot kind of forces me

• 47:29Speaker 00

  to think a little bit more about what are the design patterns I'm trying to create? What is the end user experience or the outcomes that I'm trying to drive with my code? And then I can kind of rely on Copilot to scaffold out a lot of that so that I can focus on more creative work. And that is really what, what I hope for our industry.

• 47:53Speaker 00

  Five, 10 years from now is that not only will we be inviting more developers or more people to become developers, right? By, you know, essentially providing a layer of abstraction a little bit, or at least a little bit of a kind of a hand in development. But that also the really experienced developers are focusing on much larger problems and focusing on outcomes and creativity rather than really kind of low level, kind of difficult rote memorization of things like syntax or ordering of parameters and the like.

• 48:32Lenny Rachitsky?

  Great. Yeah. And like, if nothing else, it'll keep people from just having a tab stack overflow, copy and pasting every function that they're trying to figure out.

• 48:40Speaker 00

  Yeah, I know. Like, I want Stack Overflow to stay in business, but I would mind a little bit less context switching myself.

• 48:48Lenny Rachitsky?

  So in the experience of scaling this thing, what would you say has been the biggest challenge, either technologically or even operationally? Just kind of scaling it to a real product that people are paying for?

• 48:59Speaker 00

  Yeah, so there's, there's a few dimensions of that. One is a problem that's very much of, of our time and the world, namely that supply chains have been disrupted dramatically over the course of the last years. And it turns out that Copilot for both training and operating the models requires some very rare and unique GPUs that there's not a lot of global supply of. And so part of it is just like, can we get enough hardware in order to run these things? We've actually earmarked quite a bit of capacity

• 49:41Speaker 00

  and we are greedy, greedy, greedy for more capacity globally. As soon as we can produce those chips and get them in data centers, we do it. So that's, that's been one kind of unique challenge.

• 49:52Lenny Rachitsky?

  Wow.

• 49:53Speaker 00

  I would also say here that, you know, Operationally, another challenge has been, how do we create a model that the community really feels like ownership over, right? And like the dialogue that's had to happen as we brought an AI tool to market, especially one that is trained on public code, right? Has required a lot of dialogue between us and our community. And every good product manager should be spending as much of their time as possible with their customers, with their, you know, potential customers.

• 50:34Speaker 00

  Copilot in particular has been a more complicated kind of rollout because we as a kind of, as an industry, as a society, are still figuring out how to make sense of it. And so the amount of kind of give and take between developers and us as a product team has really required us to scale up more of the product team than it has the engineering team.

• 51:02Lenny Rachitsky?

  Interesting. And that's, and why is that?

• 51:04Speaker 00

  So it's a couple of different reasons. I mean, one, like I said, we are trained on public code and like not all of the community is really sure, like, when is it okay to train a model on public code? When is it not okay to train a model on public code? Is Copilot producing secure suggestions? Is Copilot producing buggy suggestions? Like, there's a lot of doubts. There's a lot of very healthy skepticism. And I actually, I mean that genuinely. I want people to be skeptical of Copilot. We, we owe it to ourselves as a community to be skeptical of any AI. Because just like there's great potential for benefit, there's also great potential for harm.

• 51:46Speaker 00

  How like people keeping us accountable, like, how are you preventing things like model poisoning, right? Like, is there going to be a new attack vector that we just haven't really thought of yet around AI that might, you know, produce negative consequences? We think that we've done a really good and responsible job of that. By making sure that first, we're very clear that Copilot is not a replacement for a developer. It will never be like, we do not want Copilot auto generating code where a thinking, reasoning, breathing human being is not on the other side of that keyboard, making reasoned decisions.

• 52:30Speaker 00

  We do not want Copilot to replace any other part of the stack, whether it is static analysis tools, or your unit tests, or whatever kind of like measures you're putting in today to make sure that your humans produce good quality code. We want you to keep all of those same systems in place to make sure that humans who are leveraging tools like Copilot continue to produce that good quality code. But there's a lot of, at the same time, like anxiety of like, where is AI stack? Is AI, you know, eventually going to be, this is back to your question about where will we be five, 10 years from now? Will it be writing 90% of the code? We don't want Copilot to be that, we don't want it to replace anything.

• 53:15Speaker 00

  We want it to augment. The idea here is really that AI is an enabler for developers to focus on the creative work, to stay in the flow, to be able to move faster, right? And kind of working through those anxieties, working through that healthy skepticism, takes conversation. It takes dialogue. And that takes us, you know, on the product side, having kind of that guided conversation with the community.

• 53:50Lenny Rachitsky?

  It feels like, connects back to your, your education back in the day, philosophy and literature, and how convenient is that?

• 53:57Speaker 00

  It often feels very connect. I mean, like certainly the education side of things taught me that the importance of dialogue, the importance of skepticism is valuable in so much more than esoteric armchair ponderings. It's actually applicable to the real world.

• 54:16Lenny Rachitsky?

  Maybe a final question before we get to our very exciting lightning round.

• 54:20Speaker 00

  Ooh.

• 54:23Lenny Rachitsky?

  So just looking back at this whole experience of one, just building, incubating, launching this big, bold bet within a big company. You can go in either direction, either just like any lessons on just like taking a bold bet versus incremental wins and how you think about investing in these two kind of categories, or just within a large company, a lesson of just how to build something like this, like a massive new product from just like a seed of an idea to a large new business line potentially.

• 54:50Speaker 00

  Yeah, so as a, both a product manager and a portfolio manager of multiple products, and I'm responsible for multiple product lines at GitHub, like the allocation of time, of focus, energy and resources becomes a really challenging question, the answer to which isn't always the same depending upon the time, kind of world circumstances, organizational circumstances, technology circumstances. As a general rule, as a general principle, I certainly try to make sure that we're always reserving some capacity for bold, audacious,

• 55:33Speaker 00

  experimental research projects. You can kind of think of those like those really uncertain bets as being five to 10% of the team's capacity. About 25, maybe 30% of the team's capacity should generally be on just operations, like how do we keep our in-market products?

• 55:58Speaker 00

  Meeting customer expectations. And then the remainder of it, what is that about 60% or so? Is really on incremental progress for our end market products. You know, how do we make iterative improvements? And continue to actually realize payoff for the larger bets that we made, you know, one, two, three, four years back. Right? And from a rough kind of like distribution, that's generally how I run kind of my larger teams. That works when you have larger teams though. At startups, you know, where we were pretty much only a big bet.

• 56:40Speaker 00

  Uh, obviously your percentages get very different and it becomes a matter of you're all in for that one proverbial lottery ticket. Right?

• 56:50Lenny Rachitsky?

  Awesome. Thanks for sharing that. I was gonna ask you the, the percentages that you recommend. And so thank you for getting to that. So with that, we've gone to our very exciting lightning round. I'm just gonna ask you five questions briefly and just whatever comes to mind, whatever answer you have. Let's, sure, let's do it. Sound good. Okay. Uh, what are, what are two or three books that you recommend most to other people?

• 57:13Speaker 00

  Oh, good question. So one of them is a book on user experience called Make It So. Uh, it's a reference back to Star Trek. And the idea here is essentially that, uh, user experiences that are presented to us in sci-fi often make their way into our everyday, you know, products and tools, you know, 20, 30 years down the line. It is a great eye-opening, illuminating, and just really fun book. So that's one. And then completely different take, I'll go outside of tech and I'll just do like entertainment value.

• 57:55Speaker 00

  There's a David Foster Wallace book called Brief Interviews With Hideous Men that I love. It's a, it's a collection of short stories. And essentially what it is, is it is like, if you're watching a movie and like the villain gets their opportunity to like have their big speech, which kind of explains why they are who they are. Right? And makes them maybe a little bit vulnerable in that moment. It's that speech like 10 times over for different hideous people, terrible, terrible people. So in interesting read, I recommend it.

• 58:32Lenny Rachitsky?

  I love that. It reminds me of this book that is the interior design of dictators. And they show you their like homes of like Saddam Hussein, Hitler and all these guys.

• 58:42Speaker 00

  Dude, oh my gosh, that's awesome. I gotta find that one. You'll have to, you'll have to send it to me.

• 58:47Lenny Rachitsky?

  It's like a found one at an old bookstore, like used bookstore. So I don't know where they're they're around anymore, but well, I'll find it. Okay. Second question. What's a favorite other podcast that you like to listen to or recommend if there's any?

• 59:02Speaker 00

  Oh, God, there's so many. I am, uh, I, I consume hundreds of hours of podcasts every month. It is crazy. Yeah, I am. So I can choose many. I'll give you just one. So, um, the Memory Palace with Nate DeBaio is an excellent storytelling podcast. He does about 20 minute vignettes, usually selected from kind of American history. He also was the artist in residence at, uh, one of the museums in Washington DC. And if you're ever at, I think it's the American History Museum or something like that. If you're ever there, you can go to like different rooms in the museum

• 59:48Speaker 00

  and he'll tell you stories about the kind of the objects or the rooms that you see there. It's a magical experience recommended to anyone.

• 59:56Lenny Rachitsky?

  Wow. I love those. What's a recent movie or TV show that you've really enjoyed?

• 1:00:00Speaker 00

  I don't know how this counts as recent, but it's, it's one that I watched recently, which was Arrival. Yeah, that counts. Uh, Arrival. So, uh, movie about ostensibly about aliens, but is really about language and memory. And I found that really, really compelling.

• 1:00:20Lenny Rachitsky?

  Have you read Tiching's books and short stories?

• 1:00:23Speaker 00

  I have not. I have not.

• 1:00:24Lenny Rachitsky?

  Oh, wow. Oh, you would love it. It's like that rival is from one of his stories, I believe, is one of his stories. And there's a whole book of many more short stories by the same guy. And they're amazing.

• 1:00:34Speaker 00

  Brilliant. Okay. Yeah, I've got my, um, my, my weekend cut out for me then.

• 1:00:39Lenny Rachitsky?

  There you go. Just leave work and get to reading.

• 1:00:43Lenny Rachitsky?

  What's a favorite interview question that you like to ask in interviews?

• 1:00:46Speaker 00

  Let's see here. So I'll give you a fun one, more than it is a challenging one. This is kind of my icebreaker interview question, particularly for like more early to mid-career product managers. So I ask them to teach me something new in one minute. And so I'll usually like, I'll pull up my, my phone and I'll like, start the timer. I'll give them a second to think about it and start the timer. And they're graded on three different criteria. So one is completeness. Do they actually finish the lesson inside of one minute? Two is complexity. So, you know, it's one thing if you teach me how to, I don't know, pat my head and rub my stomach at the same time.

• 1:01:28Speaker 00

  It's another thing if you teach me something about 18th century art and its connection to religious trends at the time. And then last is, is really, uh, here, clarity. Oh yeah. Clarity is the last one. Clarity is like, do I actually understand that I learned something by the end of the lesson? Did they convey the idea fully and wholly?

• 1:01:52Lenny Rachitsky?

  I have to ask, what's the most interesting thing somebody has taught you in this question?

• 1:01:56Speaker 00

  Uh, my, my go-to, uh, kind of throwaway answer there about, did they teach me something about 18th century art and its connection to religious trends at the time? Someone taught me that. It was astounding. It was actually a, a university candidate. So someone who was still in, in university and she was from Vanderbilt University.

• 1:02:18Lenny Rachitsky?

  And was that a strong yes hire?

• 1:02:20Speaker 00

  It was an extremely strong yes. She was freaking amazing. I like, yeah, such a, such a smart person.

• 1:02:27Lenny Rachitsky?

  Amazing. Final question. Who else in the industry would you say you most respect as a thought leader or just influence person?

• 1:02:36Speaker 00

  There are many, but I think for today, I would probably, I'd probably.

• 1:02:44Speaker 00

  Beat myself up if I didn't say Ooga Demore. Ooga is the primary researcher who really kind of like is the true innovator for Copilot. He deserves credit for the initial work and is a brilliant technologist and futurist. I really, really respect him a lot.

• 1:03:05Lenny Rachitsky?

  Amazing. Cool call out.

• 1:03:08Lenny Rachitsky?

  Ryan, this has been so fascinating. You guys are at the forefront of so much interesting work. I honestly can't wait for Copilot for my newsletter so that I can do less work. Maybe that'll come someday, but in any case, I'm excited to see where this whole thing goes. Thank you for being here. Two last questions. Where can folks find you online if they're curious to learn more? Reach out and then is there a way that listeners can be useful to you?

• 1:03:32Speaker 00

  Yeah, so easy one. How can folks find me? I am Ryan J. Salva everywhere. Twitter, GitHub, any, you know, pick your, pick your choice. Uh, LinkedIn, Ryan J. Salva. And then how can folks be useful to me? Like, please, like, there is a 60 day free trial of Copilot that is, you know, there for everyone to pick up and use and go try it out. And when you do, post either on Twitter or Hacker News or on discussions, GitHub discussions, your experience, give us the good feedback, give us the bad feedback. I am so hungry to see how people are using it in novel ways

• 1:04:16Speaker 00

  and where they're running up against kind of the rough edges too. Like I said, there's lots of room for us to grow and improve from here, but I'm pretty confident that developers will be pretty freaking amazed at what it's already capable of.

• 1:04:29Lenny Rachitsky?

  Awesome. Thanks for being here, Ryan.

• 1:04:31Speaker 00

  Yeah, dude. Thank you so much. It's been a lot, a lot of fun.

• 1:04:35Lenny Rachitsky?

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