The future of AI in software development | Inbal Shani (CPO of GitHub)

Transcription for the video titled "The future of AI in software development | Inbal Shani (CPO of GitHub)".


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Introduction To Inbal

Inbal’s background (00:00)

The user of AI tools to develop software needs to cultivate different thinking. You need to start figuring out how you are using these AI tools to help you be successful. And it's no longer just the actual code writing. It's really about evolving your thinking to the big picture, to the connected experience, to connected systems, which today we find more in the world of more senior developers and less and less in junior developers. When junior developers start, we usually expect them to be able to write simple code. But now, with an AI assistant helping them write code, they can spend more time from the get-go understanding the system, understanding the environment they are building, or understanding the product they are building, which they don't have time for today because they are still learning how to code. Today, my guest is Inbal Shani. Inbal is the Chief Product Officer at GitHub, formerly a General Manager at AWS and at Microsoft, and also a Senior Software Developer at Amazon Robotics. In our conversation, we explore the end state of Copilot and AI-based tooling for software engineers, what the future of software development looks like with AI, what's underhyped and overhyped in AI and software development, what metrics the teams look at to understand if Copilot is doing its job, what mistakes teams and companies make jumping into AI, the design philosophy of Copilot, plus what's helped Inbal most become the leader she is today, and also where GitHub is heading as a product and an organization. With that, I bring you Inbal Shani after a short word from our sponsors. You fell in love with building products for a reason, but sometimes the day-to-day reality is a little different than you imagined. Instead of dreaming up big ideas, talking to customers, and crafting a strategy, you're drowning in spreadsheets and roadmap updates, and you're spending your days basically putting out fires. A better way is possible. Introducing Jira Product Discovery, the new prioritization and roadmapping tool built for product teams by Atlassian. With Jira Product Discovery, you can gather all your product ideas and insights in one place and prioritize confidently, finally replacing those endless spreadsheets. Create and share custom product roadmaps with any stakeholder in seconds. And it's all built on Jira, where your engineering team is already working, so true collaboration is finally possible. Great products are built by great teams, not just engineers. Sales, support, leadership, even Greg from finance. Anyone that you want can contribute ideas, feedback, and insights in Jira Product Discovery for free. No catch. And it's only $10 a month for you. Say goodbye to your spreadsheets and the never-ending alignment efforts. The old way of doing product management is over. Rediscover what's possible with Jira Product Discovery. Try it for free at That's This episode is brought to you by Sanity. Your website is the heart of your growth engine. For that engine to drive big results, you need to be able to move super fast, ship new content, experiment, learn, and iterate. But most content management systems just aren't built for this. Your content teams wrestle with rigid interfaces as they build new pages. You spend endless time copying and pasting across pages and recreating content for other channels and applications. And their ideas for new experiments are squashed when developers can't build them within the constraints of outdated tech. Forward-thinking companies like Figma, Amplitude, Loom, Riot Games, Linear, and more use Sanity to build content growth engines that scale, drive innovation, and accelerate customer acquisition. With Sanity, your team can dream bigger and move faster. As the most powerful headless CMS on the market, you can tailor editorial workflows to match your business. Reuse content seamlessly Thank you. Get started with Sanity's generous free plan. And as a Lennie's podcast listener, you can get a boosted plan with double the monthly usage. Head over to to get started for free. That's Inval, thank you so much for being here and welcome to the podcast.

Discussion On Generative Ai And Its Impact On Developers

Why generative AI is not going to replace developers in the near future (04:17)

Awesome! Thank you for having me. I'm excited to be here. I want to start with just a broad question about where software development is going. It feels like you're at the center of the storm of what is changing in software development. Almost feels like GitHub kicked off the storm with the launch of Copilot. So let me ask, what do you think is overhyped in terms of what is changing in software development? And then what do you think is underhyped? So I joined GitHub as a CPO basically a year ago, two days ago, I celebrated my first year and for me, the initial approach is like, let's look at the platform, let's look at the software development lifecycle and draw some conviction and for me, the biggest conviction is that developer happiness and productivity strongly depend on their environment and dev tooling, and as such, AI is really critical to that mission. When we're looking into what's happening right now with AI, it really became stable stakes on expectations among developers, and it's really central and critical for them to be able to do their job. We know that something like 92% of developers are already using AI tools. But in that landscape, some of the things that are overhyped is that generative AI will replace humans. I don't see that happening in the near future. The way I think about it, you always need that human in the loop because AI cannot replace innovation, right? That creative spark, that creative thinking that is the center of humanity, this will not be replaced by AI, at least not in the near future. If we think about what does that mean, having an AI tool that is successful? You need data.

Why AI-driven testing is underhyped (05:54)

In order to generate data, you need humans using tools, acting with tools, doing something, something. So I think the overhype that is happening right now is that generative AI will replace humanity. It's going to be the solution for everything. And I think it's a tool. If I'm drilling down to the things that are under-hyped right now in the world of software development, I'm starting to hear a little bit more about AI-driven testing, but there's not a lot of mention of it. And if we're thinking about the world of AI, where we're going to generate much code, be much more productive, much more efficient, more lines of code, more applications, then testing is becoming such a critical part of the journey of developing software, and I would like to hear more about that and what companies are thinking about how to use AI to generate a larger suite of testing to be able to validate the work. And by testing, do you mean like unit testing, integration testing, things like that? All of them. Load testing. Mode testing. Structure testing, security testing, penetration testing. Think about that. When you need a human to write all these testing capabilities, you need a lot of humans that need to form kind of different thinking. Can we leverage AI to really generate that testing suite that tests everything that you need from unit testing and functional testing to load testing to performance testing? There's a lot of testing that we do that we don't even consider them as testing. So we don't necessarily spend a lot of time in doing them. But if we're now saying software is at the center of everything, all companies will become a software company in some form or another, there is more code being generated, hence the importance of testing is becoming much more critical. David Pérez- Let's follow that thread. You talked about how you don't see software engineers completely disappearing. What's your just general sense of how software development will be different in like three to five years?

What the next 3 to 5 years will look like (07:48)

What do you think is going to be different? And then also, how do you see the end state of all of this, of co-pilot and tools like this getting better and better and better? Where do you think this ends? For me, and I keep on saying that again and again, co-pilot is a co-pilot. It's not a pilot. You still need the human in the loop. But that means that now the software developer or the user of the AI tools to develop software needs to form a different thinking. One, you need to start thinking more systems, more architecture. You need to start figuring out how you are using these AI tools to help you be successful. And it's no longer just the actual code writing. It's really evolving your thinking to the big picture, to the connected experience, to connected systems. And what we will see in the fullness of time is that developers, one, will adopt much more of that mindset, which today we find in software development. Don't get me wrong. We just find it more in the world of more senior developers and less and less in the junior developers. The junior developers, when they start, usually we expect them to be able to write simple code, but if now there is an AI assistant that is helping them write code, they can spend more time from the get-go understanding the system, understanding the environment that they are building or understanding that product that they're building, which today they don't have time because they are still learning how to code. So I think that's one element. The second element that I expect to see changes is in the world of hardware development, because we know that AI has a big footprint and requires a lot of resources. And in order to be able to meet the scale, to meet the demand, we need to be able to improve our hardware, our CPUs, our GPUs, our compute, and optimization of code. And that's another area that today it's a niche of people that are spending time, and I think it will become much more aligned across the software development lifecycle. Integration with AI is something that will definitely become something that is more normal for every developer. It's today something that we start seeing that revolution, that acceptance that AI tools, I need to figure out how to integrate with AI, I need to know how to use AI, it will become something that is more custom. And we will start seeing that shift from very early on when developers are start shaping their thinking, they will start understanding all these components.

Stats around the use of GitHub Copilot (10:13)

I saw a stat that at Shopify, over a million lines of code in their codebase were written by Copilot, which I don't know if I can do the math, but I imagine that's more than one person would have written in their lifetime. And probably many engineers write in their lifetime. And I think that number reminds us just how fast things are moving and how much is actually changing. You also mentioned 92% of engineers are using AI tools already. Are there any other stats that you can share that might surprise people about the usage of Copilot, growth of Copilot, things along those lines? Yeah, well, we have over 37,000 organizations and more than 1.5 million developers that are using Copilot. Wow. And they're writing code based on our surveys 55% faster. So imagine that you can give a software developer even a few minutes, half an hour back in every given day, how much more productive they're becoming and also as a result, how much happier they're becoming. So we know that in a survey that we have run through our users, 85% of that persisted, felt more confident in their code quality. And also, code reviews were completed 15% faster without, and we know that usually no one likes to do code review. So if we can help accelerate and make developers take their time and doing code reviews, and also if we think about removing frustration, then 88% felt less frustrated and more focused on their ability for coding. And that is just the beginning. We have different examples from our customers. I think Accenture is a recent one that we got where they had 88% of suggested code was retained. So we have a lot of these stats that are coming together to showcase the size and the impact of using Copilot and AI tools for software development. Sam Bracegirdle, Say companies hear these stats of like, oh, we're going to be 25% more efficient.

How Copilot enables engineers to work more efficiently (12:07)

Some people may think we need 25% fewer engineers; we're going to do all the same things. I imagine what you're actually finding is your engineers can do more. So it's not like you need to cut your people. It's that people can be faster and better. No, let me be very clear. You cannot cut your people. You have to have a human in the loop. Copilot is not a pilot. And I keep on saying that sentence again and again. I think the second thing that companies need to realize, and that's another developer experience survey that we have run recently, is that throughout the years, we have added so many tasks to a single software developer. From writing code, testing, interacting with people, collaborating with 21 people every week, going to meetings, waiting on builds, and digging into legacy code, all of that is added to the fact that they need to write code, which is their basic work. So most developers spend less than 25%, some say less than 20% of their time writing code. So if we're able to give them half an hour back, one, they can write more code. Second, they can have a break and take a breath so we don't burn them out and they're happier, third, we give them more time for collaboration and creative thinking, so that sparks innovation. That is not something you want to cut back. This is something you want to be able to give as a gift to your developers, so they're happy with you and you can retain them. They can do their job. They're productive, hence they're happy. David Pérez- I've talked to a bunch of engineers that use Copilot.

Common mistakes when adopting AI into your workflows (13:38)

One thing that surprised me is the most amazing engineers love Copilot. You'd think this would be for junior engineers learning to code better and faster, but 10x engineers say that. Someone told me it made them 50% better and faster too, which is incredible. What mistakes do you see product teams and engineering teams make when they rush to start adopting AI, and integrate AI into their workflows? I think there are a couple of things. The first one is companies expecting a change to happen magically. "Here's a tool, go use it." And it's not always flying the same way across all companies. So investing time in really taking the company through a change management process. The second thing that I'm seeing is that since AI is so hyped right now, there is often the question of, "Oh, what should we do with AI?" So there is that sense that I have to do something with AI because if not, I'm not doing the right thing. Versus the question that for me is a big focus for my product team is what is the problem that we're trying to solve? And how can we leverage AI better to help solve the problem versus what do we do with AI? So it's really working backwards from the customer problem, from what we're trying to solve, and then realizing what are the best tools that we have in order to do that work better and think through that landscape versus, "Oh, AI is here. We have to use it because if not, we're not doing something right." Is there an example of that that comes to mind of someone that did that incorrectly or wasted a lot of time? It's not necessarily examples, but I've been talking to some of our customers and they're coming to us. We heard about AI. "What do you think we should do with AI? And how should we adopt AI?" And can we learn from your experience? And I'm sharing our experience. When we started thinking about AI, it was with the idea to make developers much more productive. And what are some of the things that we've seen? That developers have multiple tasks on them and they don't have enough time to spend on coding. So how can we give them time to spend more on coding and what are some of the coding elements that we can automate? And from that comes the need to, "Okay, let's incorporate OpenAI, let's incorporate ChatGPT, let's connect all of them to help create that tool that helps developers be more productive and is AI-based." So I'm sharing how we went through that journey to think about how to use AI. And that I see a lot of the times light bulbs with the customers like, "Oh, we didn't think about it like that. It's more about we wanted to plaster AI on a lot of things versus I have this workflow and it requires a lot of manual work or it requires a lot of configuration, maybe I can think how to incorporate AI into that and really shorten the time or make it seamless or make it less friction for developers to adopt that tool and so on and so forth. So it's really sharing more how we are thinking about it and helping our customers to understand the same.

How GitHub operationalizes “dogfooding” (16:42)

Along the same lines, you're talking about how you think about it with your teams. It feels like your product teams are probably living in the future of how other product teams will operate because you have access to stuff everyone else doesn't necessarily have access to. They probably adopt these tools a lot faster than the other teams. What are some ways that your product teams operate that other teams may not yet be operating in? In GitHub in general, it's not just the product team. GitHub is GitHub. I'll say we eat our own dog food. And that means that we are the first to try every feature, every capability that we're developing. And it's not just that our engineering team expands across GitHub. So for example, GitHub is running on GitHub. There are several blog posts and talks. Even in the recent universe, we had a specific talk in terms of how GitHub is using GitHub and adopting AI across the software development lifecycle. And the biggest focus for us is if we're saying that GitHub is a developer platform, and it's more than that, it's a software development platform. How are we making sure that all the personas that are taking part in the development of GitHub are also using GitHub? So for example, our finance team is using discussions and posts and PRs and repos to communicate our AR numbers and so on and so forth. We have our legal team using, we have our HR team, if they like it or not, it's a different question. But the idea is that we're using GitHub to run GitHub. And the product team is one of the first early adopters, along with our engineering team. So for example, when we've tested chat and we wanted to see the quantity or the recommendation, the search, this is something that the product team was one of the first users to go and figure it out, is it giving me the right thing that I need to do that, or is it summarizing the PR the way I want to summarize and so on and so forth. Is it giving me the right thing that I need to do? Is it summarizing the PR the way I want to summarize, and so on and so forth. But we are really strong in advocating that we have to be the one testing our tools. If we cannot use them, our customers cannot use them for sure. So nothing is shipped before it spends enough months cooking inside GitHub. So we have to say if this is working for us or not before we put it at the hand of our customers. One of the magical elements of Copilot is how it just kind of fades into the background.

The philosophy behind Copilot (18:46)

You don't have to chat with it. You don't have to tell it anything. It just infers, here's what you're doing. Here's the answer for you. If you want to use it, it's cool. If you don't, it's fine. Is there anything you could share about just the design philosophy of Copilot? Yeah, the design philosophy for Copilot is very much aligned with the working backwards concept that I just mentioned. It's really putting yourself in the shoes of your customers and figuring out what is it that they need? How is that experience going to work for them? If it's an extra tool, and if you need to ask for it, and if you need to ask for it, or if you need to wait for it, then developers will not adopt it. So because we developed it for ourselves first, and we wanted to experiment with that design philosophy, put yourself in the shoes of a developer, how would you like that experience to be? GitHub engineers that were working with the design team and with OpenAI GPT to really figure out how we're going to build that and how it can work to help developers to do their job more efficiently, more productively, improve their collaboration and happiness and so on and so forth. But the grounding principle was that developer needs to want to use these tools. And the more you add friction, the more you add churn, the more you add complexity, developers will not want to use this tool. And the more you add friction, the more you add churn, the more you add complexity, developers will not want to use this tool. We just talked about all the things that the developer needs to do in every given day, now add to that mix, I need to learn how to adopt AI, we knew that it will not fly, so it has to be a seamless integration. It needs to be something that is very intuitive for a developer to use to be successful. Henry Suryawirawan, What are the success metrics for Copilot?

Copilot’s success metrics (20:24)

It's such an interesting problem of measuring efficiency gains and productivity gains for engineers. What metrics do you focus on to tell you this is doing what we want it to be doing? We are in a world that there are no right metrics. There is no one metric to rule them all. It's a combination of the things that you're looking to measure out of adopting AI. You can think about measuring code quality using AI. Can you improve the code quality? Can you improve the security of your code by introducing, for example, our GitHub Advanced Security using AI? So there's a lot of different metrics that if you combine them together are providing you with that developer productivity. And then there's a lot of different metrics that if you combine them together are providing you with that developer productivity. And then there's developer productivity, developer collaboration, time gained, that if you're bringing them together are yielding kind of that developer happiness. The biggest area of focus for us right now is the definition of productivity. Because we have so many users that are coming to GitHub and they're looking for that productivity gain. But one of the things we're very conscious of as we continue evolving Copilot and AI across the software development lifecycle, across all the tools, productivity is not the right metrics against each one of these components when we're implementing AI into GitHub Advanced Security. Writing more secure code is the right element. It's like, how many secrets were we able to prevent from leaking? How many issues in the code were able to detect and find and fix before you ship that? So I think that the world of the metrics for AI is really a big one. We are working a lot with our customers as they're asking the same questions and they're running their own experiments within their companies to figure out what is it that they want to measure. The most easiest one is time. But time is, it's funny what I'm going to say, but time is not quantifiable as a success metrics because you can write really bad code really fast. So it's really about how are we taking time and translating it to efficiency, to productivity, to something that are harder to measure? And then how does that lead to developer happiness, which is another harder matrix to measure? So the combination of all these input metrics leading to eventually developer happiness is what we're focusing on. Yeah, it's so interesting because engineers could end up spending more time coding because they're enjoying it more. They're getting more done. You also like more lines of code isn't a good metric because you don't want to know if those are good lines of code. That's like a classic way to not measure engineers well. So to me, it feels like developer happiness is the ultimate metric. And there's we had Nicole on the podcast who came up with this framework, Dora. That's an interesting way of just measuring developer experience, developer happiness. Right. I think one of the things that we're talking to customers and we're trying to figure out how we articulate is instead of time, can we talk about time to value? So from the moment you put a developer on a task, how long did it take you until you realize the full potential or the full value of that? If it's generating revenue, if it's in adoption, if it's time to market, like you can define your time to value in different ways. But eventually you're investing in AI tools to help your developer to be more productive, to be more efficient, to be more happy, and then easy impacting your time to value, which is something that that's the business side that they're looking to venture. This episode is brought to you by Help Bar by Chameleon, the free in-app universal search solution built for SaaS. Your help content lives outside your app and is scattered in many places, forcing users to waste time hunting for answers. Help Bar solves this. It delivers answers directly inside your app and eliminates context switching. Users can search or ask questions to get AI-generated answers and lists of the most relevant documentation from all of your help sources, including your knowledge base, docs, blog, and video libraries. You can also use Help Bar to navigate your app and launch actions, such as scheduling a meeting or viewing an interactive demo. The best products today use Command-K for in-app search and navigation. Help Bar makes that readily available within your app without engineering or new code. Give users a faster and more delightful self-serve experience that reduces friction and increases in-app engagement. Upgrade your user experience with this modern component and supercharge your product-led motion. Sign up for Help Bar today. It's free and easy to set up in minutes. Check it out at help That's help Coming back to where this all goes. I imagine you've seen all these demos of people like sketching a website, taking a picture, sending it to chat GPT and it builds the website for you.

How Copilot encourages collaboration (24:54)

Yeah. How do you see all of that sort of thing playing into this? Do you see CEOs being like, "Here's the iPad app I want. I'm going to sketch it for you and then just run it through Copilot version 10 and it writes it for you"? I know that you're saying engineers won't be cut, and only increase and become more efficient. But I guess, where do you see that version of it going? Just like, "Here's a sketch, build it for me." I'm thinking about that as a better collaboration tool versus a production tool, because right now, a lot of the time where we see challenges in articulating ideas is the communication, it's the clarity of thoughts. So if we can leverage AI to improve collaboration and you basically put a drawing in front of Copilot and it helps tell the story of what it is that you're asking your developers to do, it shortens a lot of cycles in the back and forth. "So what did you mean? Did you mean the button here or did you want it to think or did you want a data application or did you want a data operating system?" So if Copilot can help refine communication, and it's becoming likely the best collaboration tool that exists in the market, especially in the global world where we are, where we see communication is an ongoing challenge between different personas that are taking part in coming up with an idea. And basically we see AI, and in our view, Copilot, becoming that next natural language conversation. It's that translator that helps bring everyone on the same page because it's creating that universal conversation language, the same that math used to be. Hmm. Reminds me, there's another conversation I was having with an engineer and he was saying how he loves writing those simple functions that Copilot is now doing for him.

What we lose when AI writes code for us (26:37)

And he's like, sad that it's doing it for him. But he doesn't turn that off. But he kind of likes that. That's what I really enjoy. These little things that I could just crack, like a sort function of some sort. I guess, is there anything you think we lose with so much of our code being written for us? I think each developer has their own part that they enjoy doing. And you don't have to use Copilot to work with your simple code. You can use Copilot through a chat experience and use it as an AI assistant and brainstorm. We're giving you a set of tools and we have a scenario in mind how you should use them. But what we're seeing is developers are using them very differently. Everyone is choosing their own flavor on how to use the tools. There is no right and wrong way to use it. It's a tool. It's a language. When, you know, when I was learning how to code, I used to code in C. And then Java became a thing. I'm like, oh, but this is taking away my job because I used to write all these functions and all these libraries and now Java has it all and then Python came, which is another abstraction layer. It's like, okay, so now I don't even need to think about that because Python can do all of that. So it's a matter of how you use the tools and what you use them to do what. There are areas that I will still go and write in C even today. I don't trust someone inverting metrics for me in an efficient way if it needs to run on a very small CPU. So I'll do that myself. On the other hand, there are going to be elements that I'm going to delegate to a higher obstruction tool. And that's exactly the way I think about AI. You pick and choose how you want to use it. There is no global way that is the absolute truth that everyone has to follow. You need to think about how you use your tools to do your job in a way that you're still keeping the things that make you happy. On the other hand, use AI to do the things that you less enjoy. Maybe you like writing functions, but you don't like writing testing. So generate unit testing and so on, and let AI do its job for you while you still focus on the things you enjoy. Are you actually still finding time to code? Are you still writing some C on the side? A little bit. Now I'm mainly playing with my older son. Um, so he has more time to code, but sometimes I enjoy spending time with him and kind of brainstorming and talking about things in architecture. I think recently, a few months back, he did a project on sensors and sensors integration, and he came across FunMon Filter, which was the thing that I used to do many, many years back. And he's like, okay, what is it and how is it done? And then we went through that and I helped him kind of think about the structure and the sensors fusion and all of that. And then sometimes I'm like, okay, move aside. Here you go. This is how you. Turn on Co-Pilot. Mom is Co-Pilot. That's so funny. That reminds me, I was watching a talk of yours and you were training tuning models and LLMs way back in the day before it was very cool. Right.

A retrospective on the generative AI space (29:35)

And so you've kind of been in the space for a long time. Looking back, is there something that you see now of where things were going that you didn't see at the time? I grew up in a world of niche AI where you have to be an expert and you need to be trained for years, understanding how to tune a model, what is the right output, and you needed to understand how to build a simulation to test it, how to ingest data, and focus on optimization. I've seen the evolution of AI throughout the years with all the talking devices and conversational AI, and then it started as a single term; it came up as a multi-term. So I've started seeing that trend of more democratizing AI, but I didn't expect that boom of generative AI that everyone that doesn't even know what AI is, thinks that they need to use AI. I think that was the thing that caught me by surprise because I was so trained that you have to be an expert to use AI. So for me, that was aha moment like, hmm, this is interesting. So what's next? Henry Suryawirawan, Do you have an opinion on whether large language models are the end-all and be-all of where all this goes and just continue to add in compute and data, or do you think there's another, I don't know, something on the horizon that's going to again, completely transform the transformer model of AI?

Reflections On Future Of Ai And Innovation

Inbal’s thoughts on the future of AI (30:47)

I think we have pivoted very hard towards a generative AI, and generalized AI can solve everything. I believe that the future is in scaling back a bit to the niche AI. So we will live in a hybrid world where you still have specific AI models that are solving very specific problems where the generative AI will have its limitations. You know, it's a general-purpose tool, so it can be as good as the datasets, but it also tries to find some statistical recommendation of is it good, is it not good? So it's limited by the training set that it has, and it's trying to be everything for everyone. There are going to be areas where you still need to train models, for example, I spend my time in the aerospace industry and in the automotive industry, I don't see self-driving car models that require so much delicate specific model tuning, high safety regulation, all of that being developed on a chat GPT, you know, I might be wrong, but I think that eventually we will find ourselves in the world of hybrid models and multi-models where there will be several LLM models coming together because each one of them will have their own benefit. There's going to be a niche AI or a more configurable customized model for specific use cases. So that's my prediction of the future, but you know, in 10 years from now, we'll all be smarter. Yeah. The future, hard to predict. Yes. I wanted to ask, so GitHub kind of came up with this whole co-pilot idea.

How to make space for innovative product ideas (32:35)

I think it was when we had Ryan on the podcast talking about how there's a researcher just experimenting with what we can do with a bunch of data, that Co-pilot idea. And then that essentially turned into, "Wow, this is actually working. Let's quickly scale this and launch it." I guess, is there anything you learned from that huge success within GitHub and Microsoft to find other big opportunities? Is there something you do about how you structure product teams or create space for people to experiment with things like this to find the next opportunities? Is there something you do about how you structure product teams or create space for people to experiment with things like this to find the next Co-pilot? It's a lot about that. It's a lot about creating that bandwidth for the team to innovate and experiment and carving that time out, but also being okay that not every experiment is going to be successful, so the ship to learn or the fail forward. These are the philosophies that I believe in. If you don't try, if you don't experiment, you will never innovate. And if you don't fail, you will never learn from your mistakes. You'll never learn from your experience. So you will not progress forward. So really creating that ability to experiment, the same that we have done with Copilot or we've done in GitHub Advanced Security, or even the way GitHub was created, it's all that innovative thinking that the teams have that are thinking about what is that next-generation use case, what is the future developer will need to be happy, to be more productive, to do their job. In a world that software development is changing so fast, you have to create that bandwidth for the next generation, the next innovation, and if you didn't have a chance to see the two-day keynote in the universe, I highly recommend it because we basically shared our next vision for Copilot with Copilot Workspace. We also had Satya Nadella on stage and he was very excited. He's like, "Oh my God, GitHub, if you build that, this is amazing." So you see that we keep on putting this vision forward again and again, at least six months to a year before we even have something in the market, because we are, we're already thinking, we're already innovating, we're already on the next thing. David Pérez- Is there something that you do to allow for that other than just these principles of ship to learn and it's okay to fail?

How GitHub stays on the cutting edge of innovation (34:37)

Is there a way you structure teams or resource teams? Is it like you have a percentage of time to think about other ideas or is it just generally we're okay failing, find time to work on other things? It's a couple of things. One is spend time with customers and learn from your customers because a lot of the innovative ideas are coming basically from conversation with customers because they're sharing with you their frustration, they're sharing with you what they would like to have, they're sharing with you what will be an amazing invention for them. So that's the first thing is creating that mechanism. And we spend a lot of time talking to our customers and also talking to our community, we're fortunate to be the home for all developers and home for all open source, large open source foundation and small open source foundation are running on top of GitHub. So that's another feedback cycle that we have to really gather those ideas and those asks to help us shape the future. And then the second thing is really about the team's pitching idea. It's like, I have this idea, I've been doing some research, here is a paper that describes what is it that I want to do, and then we figure out, okay, when can we fund it? How can we fund it? Can we allocate a specific bandwidth? Can we do a POC? Do we do that in that team? Do we do it as a V team? So we keep it flexible. And we also have a research team, which is GitHub V Next, that basically that's our day job is to think about the next big innovation. But in GitHub, innovation is coming from everywhere. It's coming from our field team that are talking to customers and are implementing different variations of GitHub for customers, and they'll come back with some feedback or an idea. It's coming from the product team, the design team, the engineering team, it comes from everywhere. So if you try to structure innovation, you're losing that organic spark that is humanity. Imagine that you say, someone say you have 15 minutes a day to be creative. I don't think it's the goal. So it's encouraging that thinking more than structuring. Can you talk more about that team that you mentioned?

The GitHub Next team (36:44)

The "what is it called?" The vNext team? The GitHub Next, yes. Next team. Can you talk a bit more about what that team is and how that works? They're basically applied scientists, research scientists that are working together and they're really thinking about three to five years horizon. They're not thinking about the right now. They're thinking about what the future for software development is going to look like. They're writing papers, they're running experiments, they're doing POCs. Their job is to invent the future. But again, it's really a synergy because they're talking to the product, to their engineering, so they're getting ideas from there. They're talking to customers or sharing feedback. So that's our GitHub mix. It's basically our research team. And that's where Copilot emerged out of. Is that right? It's so interesting because there's, I think I talked about this with Ryan. There's so many companies with a team like that. Like Facebook had a famous one, new product experience team. Google had Google, forget what it was called. But none of them have really been successful is my understanding. And this one was, is there anything you think you're doing differently? Is it more science-based and research versus just like product managers trying a bunch of different apps? I don't know. Is there anything you think is key to this team being successful? I think there are two elements for that. One is having the right people with the right mindset on the team, allowing them and giving them the bandwidth and freedom to innovate, and the second thing is that we're focusing on making things real. So we're not keeping them far or in disconnect from the product and engineering. There's a lot of synergy. There is a lot of discussion because what happens in teams that are not successful, at least from what I've seen, one, that the team is becoming basically another university. They write papers, but nothing is coming out of that. So nothing finds its way to production. So if you don't introduce that, how do we take it to production from day zero when you're thinking about a new idea, then it's rare that it will go through that hump cycle and end itself in production. And the second thing that companies tend to use these teams is too much tactical. And here is that we need something in six months, go invent it right now. And then they're basically creating another engineering or product team to think about things that are now in Horizon One. So I think in GitHub, we found the right balance between having the right people, having the right mindset, but also creating that synergy between the future thinking to how we make it real. So we have a strong relationship, especially between the product team and GitHub Next to make sure that these relationships are ongoing and ideas are flowing both ways. Today, you're the chief product officer at GitHub, which is, I think, a dream job for a lot of people.

Advice for early product managers (39:20)

Early product managers and senior product managers want to figure out how to get to the place that you're at today. And I'm curious what you found most helped you build the skills that were necessary to be successful in this role. Was it mentors, executive coaches, just doing the job for a long time? Anything else along those lines? A combination? Tanya Herasmo- Combination? It's never one thing. Because the role of the chief product officer is so broad. You're not just the head of product. And that's the mindset that is now evolving in the industry. A chief product officer is more than just the head of product. They need to have a business thinking. They need to understand their go-to-market strategy, the sales play, and how engineering builds products. It's not just about writing a set of requirements or thinking about the vision and the future. It's about how all these components in the company are operating together to build that product. And some of that you learn from looking into the leaders that you work for and seeing how they're thinking. From my first boss in the aerospace industry, I learned how to think systemically, not just about the problem that I'm solving right now, but how the problem that I'm in charge of solving, that filter that I'm responsible for tuning, is connected to the control system and the ignition system, and how it is eventually being displayed. So that requires me to think from the get-go about the bigger solution, not just my component. So I think that's one. The second thing is really thinking about the people and change management and how you take people with you, because a product manager's role is a very influential role. And a lot of the things you do involves influencing other people. Although everyone thinks that as a product manager, you get to do things, you get to write papers. But you need to influence the engineering team to build the product that you want them to build. You need to influence the revenue team to go with a go-to market and sell the product the way you envision it. So there's a lot of influencing happening. You need to understand how to do that. And building that skillset as a product manager from the get-go is very critical. So for me, it was the fact that I was fortunate enough to do different roles in the industry and learn from different people, but also looking up, looking across, and looking down into the people that I manage and what are the things I could learn from them. What are some of the skills that they have that are not necessarily in my toolbox and how can I adopt them? What does success look like and what are some of the things that make people unhappy? And you don't necessarily want to have these tools in your toolbox. So it's a lot of what's yes and what's no in a combination. Yeah, it's exactly like you said, it's all the things combined. I really liked that last point of just identifying people that are good at something that you want to get better at and just learning from them, watching them, maybe asking them questions about how they're operating. I am trying a new segment of this podcast called Failure Corner.

Inbal’s “biggest learning” from her career (42:17)

And my question to you is, what's the biggest career or product failure of your career and what did you learn from that experience? I'll call it the biggest learning because I'm not a believer in failure. I believe in learnings. And every time you fail, it's a big learning. And I prefer to look at that as opportunities. Um, I think the biggest one was likely early on when I started being a leader and the manager of a team. I'm a very go, go, go person, and I have a lot of energy. And when I come to something and I see all the issues that need to be fixed, it's like, okay, let's take the toolbox out. Let's go fix everything. And that includes driving change. And I've seen that when I started doing that without building that understanding of why we need to make a change or why this is a problem that we need to fix, it meant that I didn't take people with me through that journey. So really analyzing that not everyone appreciates change the way you do. Not everyone has that mindset of the go, go, go; let's go, let's go do it. And really figuring out, taking a step back and assessing what is happening, so you can take the team with you on that journey of changing. That was for me the biggest learning, and likely a lot of the not supportive feedback I got from the team at that time was, "Right, you're moving too fast. Slow down. Explain the why. Why are we doing that? How should we move forward?" Henry Suryawirawan, where did this actually happen? Which company? Which role? In TomTom, when I was leading the location-based services team. And I think for me, it was one, it was the first time I was working in an international company. So really figuring out that my character doesn't always align with every culture that exists. So how am I adjusting to that? And the second thing is that they were very accustomed to working in a specific environment in a specific way. And yes, I was hired to drive change. But one, the team doesn't have to accept that it's your job to drive change. And also, they're not necessarily on board with the fact that you're here to drive change. So how you take them with you through that journey was a big learning. And being able to communicate in a general way of communicating to drive clarity, that was a big learning. That's actually a recurring theme on Failure Corner of the failure stories I'm thinking about is just somebody starting in a company and trying to change too much too quickly. It's very common because we are human beings. We think that if we're coming, we have our experience, we're coming to a new space, and you immediately see all the things that are not working or all the things that need to be fixed. If you're working in the same space for a long time in the same team, you often don't see these gaps anymore because you got used to living with them. So if someone is new, you're coming in, you immediately see all these holes and cracks and you're like, "Okay, here's all the things I need to fix." But you have a team that has been there for a while. So how do you find that bridge to communicate between the things you see and the things that they think are fine? Like, "Why aren't you driving these changes? If it's not broken, don't fix it."

Closing Thoughts

Inbal’s closing thoughts (45:34)

Henry Suryawirawan and Bal, is there anything else you'd like to share before we get to our very exciting lightning round? Balam Kishorelis, For me, it's an exciting moment in time. I've been celebrating my first year in GitHub and my first GitHub universe on stage last week. It was an amazing experience for me to meet our customers, our partners, and talk a lot about the importance of developer experience, our AI-powered platform, and how we're shaping the future. So I'm in a very exciting part of my journey with GitHub. And you don't get here if you don't make some mistakes and you do some learning and you have some successes and then you take the right turn and sometimes you take the wrong turn, but you find your way to a place that makes you happy and feel fulfilled as a product leader. Awesome.

Rapid-Fire Questions

Lightning round (46:19)

We're going to link to that talk in the show notes if people want to watch it. With that, we've reached our very exciting lightning round. Are you ready? I'm ready. What are two or three books that you've recommended most to other people? "Failing Forward" by John Maxwell, "The Flywheel from Good to Great" by Jim Collins, and a recent book that I read that is called "Dare to Lead Like a Girl" by Dahlia Feldheim. That's a new mention on the show. Awesome. What is a favorite recent movie or TV show that you really enjoyed? I like a lot of fantasy and I like a lot of history-based movies and theories. There is a recent one that came up on Netflix, "All the Light We Cannot See". Amazing. So well produced. Really tells an interesting part of the history of World War Two. David Pérez de León- Have you seen "Wheel of Time" on Amazon Prime, if you like fantasy? Yeah. Yes. I love that. It's done. It's good luck. David Pérez de León- Yeah, especially season two is like, wow, it's a lot better. Okay, next question. Do you have a favorite interview question you like to ask candidates when you're interviewing them? I think the first question that I really like to ask is what is the most innovative thing you have done and why do you think it's innovative? And it's really interesting that there are some people that think they need to answer about the biggest invention that they've done and there are some people that are very vulnerable and they talk about something very personal that they have innovated and it talks a lot. It shows a lot of their character. And I think the second question that I like to ask a lot is give me an example in a time where you had a disagreement with your manager, you're in the line with your manager, and then what did you do about it and how did you go? Because it showcases a lot about your character and are you willing to stand your ground and push up when you need to? And then what are the communication skills that you have, your influential skill that you have? Do you have a favorite life motto that you like to share with people often come back to either in work or in life that you find useful? I think it's, "if you don't take risks, you cannot create a future." I forgot who said it. I think it's from one of the animations, Monkey Luffy, if I remember. But it basically summarized the life motto that you have to take risks. You have to experiment. If you feel comfortable, it's not a good thing. You always need to feel uncomfortable to stretch yourself, to go to the next thing. So that's something that I've done. You just heard about my career story from applied scientist to chief product officer, Viettel, like who knew? I love that motto. Hinbal, thank you so much for being here. Two final questions. Where can folks find you online if they want to reach out and follow up on anything? And then how can listeners be useful to you? Lots of interesting sessions and talks from Universe. So if people want to learn more about what is it that we're doing, how we're thinking about AI there, LinkedIn is likely the best social media platform to find me, where I have a chance to talk to a lot of people. These are the best links. And then how can listeners be useful to you? If you can share a similar experience or a story that you think will be helpful for me as I'm thinking about what does that mean being Chief Product Officer for GitHub, any tips and tricks on how you use GitHub so maybe I can learn or share your experience and story so I can learn from your experience as well. Amazing. And Paul, thank you so much for being here. Thank you so much for having me. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at See you in the next episode.

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