India need not confine itself to only open or private AI source model: Kyle Daigle, Global COO, GitHub

How concerned are you that global trade frictions could increase the cost of essential hardware, disrupt supply chains, or create enough economic uncertainty to dampen overall enterprise tech spending?

 As we work through constraints, software developers continue to impress us with innovation. and I don’t think that’s going to change. I don’t see any particular disruption in terms of supply chain. For us, given the digital open-source ecosystem, as long as developers can continue to work and be able to do that with their existing devices and access the Internet, then we can continue to collaborate with each other globally.

At the AI Action Summit in Paris, 58 countries signed a joint statement emphasising ethical AI development. However, the US and the UK abstained from voting. How do you view this split in AI policy frameworks, and what challenges does it pose for Big Tech?

I don’t see any particular challenges as such. For over 10 years, we’ve been talking about data privacy as a major issue. Several countries have varying policies, and they will do what’s right for their citizens and as part of the global community. We’re committed to fulfilling our obligations and we’ll continue to advocate and support developers. As technology is moving quickly, we want to make sure that everyone has all the right information and that we’re protecting open-source.

India is developing its own LLM platform; should it rely on an open-source model or a proprietary one?

I don’t believe it’s an either-or situation for India. There’s a variety of models available. For instance, Llama just released two new models over the weekend. In the future, we’re all using a variety of models. We’re using models that are fine-tuned for specific use cases and public models that are low parameter or very fast or have large context windows. By creating an open-source model and putting it into the world, you are inviting collaboration, but there are some use cases where an incredibly powerful model can’t be open-source. Driving the experimentation and learning from it is the most important thing, and if you’re stuck on which do I choose, you won’t actually experiment and show the world what you have learnt. I think it depends on the use cases but over time I expect more open source models because the benefits of being able to work together outweigh the benefits of only working in private.

Is that how GitHub is balancing its proprietary with the foundational idea of open-source as well?

 We give the developers the freedom to choose. If we tell a developer what to use, even if it was the best tool in the world, they will say no because developers want choice. So as of last November, we are offering access to whatever model they want. Last Friday, we allowed people to bring their own model and connect it with other models. Some of them are open-source, some of them are proprietary, some are mix of both.

How has AI changed things for developers?

In some ways you could say it hasn’t changed much but then we’ve been unlocking more of the power of an individual developer to not just produce more code but solve problems faster, get their answers faster to review other people’s code faster, with fewer errors. So all of that time has been a very synchronous journey with AI. I do think we’re at precipice of another big change with the introduction of agent of workflow where an autonomous or semi-autonomous system can talk to all of your tools. It’s going to do more of its own thinking and operations on its own.  

Is there any particular way in which the young developer should now orient themselves in the age?

I think it’s very important to still learn how to code. My 11 year old is learning math on paper with a pencil even though, he’s going to get handed a calculator in middle school. That’s a version of what AI is providing software. The second thing is communication. We’re going to be spending a lot more time talking about the problems with our customers, colleagues and it’s important that we be clear and concise because if we’re aimed in the wrong direction, we’re just going to get to the wrong place quickly. Also, you can’t get stuck on a single model, programming language, technology. You have to be continuously learning. So, the difference for these developers when it comes to growth mindset versus 30 years ago is you can’t get stuck on a single piece of tech. You have to always be ingesting the new stuff, otherwise you get left behind because it’s moving so much more quickly than it ever has.

There’s also this thought going around that AI is going to take the jobs of the very developers who made them. Should developers be worrying about their jobs?

No. If I was churning butter manually and a machinery shows up so that I don’t need to churn the butter, it’s not that my job disappears but that I now work with this machine. There’s also not one butter in the entire world with the same exact recipe. So the software developer is the chef and the machinist in this scenario. Humans are an incredibly important part of software development. Access to AI and learning how to code will create more developers, not all of them will be professional developers. It will create more jobs because we’re simply lowering the barrier to entry. I feel there will be more jobs for these developers as well because if we look at the IT services industry, it’s slated to hit I think it was $500-900 billion. It’s an enormous amount of growth in the next few years. 

Has DeepSeek changed the AI landscape in terms of affordability and GPU requirement for AI development?

It has demonstrated that creating models that are more efficient ultimately generates more demand. It was a huge influx of demand, not just for one model, but for all models after that because it demonstrated a way of both running and potentially training models more efficiently. I think the big change is, there’s more interest across all models.

India is expected to surpass US in terms of developers on GitHub platform by 2027. Is this projection on track? How is GitHub looking to translate that into higher revenue growth from India? Are you expanding your operations here?

 I’m currently focused on how do we talk to the IT services companies like Infosys and Cognizant and others on how they’re adopting AI and then in talking with them, we immediately start talking about universities like we give Copilot for each students. So, how do we work with the universities, incorporate AI in the classroom? So we’re investing heavily on the education side and then just in open source, there’s an enormous amount of open source maintainers that are sold.

So are you trying to adapt your offerings in any way, specifically to India? Do you see any unique opportunities here?

 Over the past like two years, we’ve been showing demos for Copilot, where you can use it in one’s mother tongue. You can use it in many many different other languages and it has led us to explore in partnership with Microsoft how to train additional models so that way while the software is generally always in English.

Has India lost the AI race to China after DeepSeek?

We’re so early in the AI race that predicting any winner right now is myopic. There’s much more opportunity to create new models, powerful models, small models, big parameters, small parameter models that India is well positioned with its developers to join in and share with the world and maybe surprise the world again with something new and powerful.

You’ve often highlighted the productivity gains from AI tools. What metrics or feedback are you tracking internally at GitHub to quantify this impact? What are the potential downsides or new challenges like code quality that GitHub is actively working to mitigate?

What we generally find is that code creation is just one statistic. How much time it takes to review the code tends to go down when you use AI code, quality tends to go up when you use AI code, security tends to go up when you use AI. For every customer the big stack that moves is different because it matches that organisation’s culture. We focus on the end-to-end of the software lifecycle and ensuring that we’re showing the value there. I think the downside of AI is that it magnifies your existing team’s culture and so everyone is looking for the magic fix, not just in AI, but in business too. What it does is show where the gap is in team operations and you have to invest in that to get the full value out of these tools.

Looking ahead 5 years, what do you envision as the next big disruption or opportunity in software development that GitHub is preparing for — beyond the current wave of generative AI?

It’s likely to be in how much of the operations of technology is being handled autonomously. I think there’s going to be a degree of monitoring and application like getting user feedback and having the AI, whether it be our current LLMs or the stateless models or new models, go operate on that fact more autonomously, while leaving the creative, active, software more to the development side. So hopefully there’s far less pages and phone calls in the middle of the night for a developer in the future when a website goes down because models within GitHub will be able to take that off the backs of developers.

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