
The role of AI in new product development | Ryan J. Salva (VP of Product at GitHub)
Ryan J. Salva (guest), Lenny Rachitsky (host)
In this episode of Lenny's Podcast, featuring Ryan J. Salva and Lenny Rachitsky, The role of AI in new product development | Ryan J. Salva (VP of Product at GitHub) explores gitHub Copilot: From Moonshot Experiment To AI Pair Programmer Revolution GitHub’s VP of Product, Ryan J. Salva, explains how GitHub Copilot evolved from a speculative R&D experiment into a widely adopted AI coding assistant. The project began when Microsoft and OpenAI trained large language models on a curated snapshot of GitHub’s public code, discovering they could reliably predict and generate multi-line code in real time. Salva details how a small “Next” moonshot team incubated the idea, then handed it off to production product squads while navigating ethical, legal, and UX challenges. He also outlines how AI will increasingly permeate the entire software development lifecycle, augmenting developers’ creativity rather than replacing them.
GitHub Copilot: From Moonshot Experiment To AI Pair Programmer Revolution
GitHub’s VP of Product, Ryan J. Salva, explains how GitHub Copilot evolved from a speculative R&D experiment into a widely adopted AI coding assistant. The project began when Microsoft and OpenAI trained large language models on a curated snapshot of GitHub’s public code, discovering they could reliably predict and generate multi-line code in real time. Salva details how a small “Next” moonshot team incubated the idea, then handed it off to production product squads while navigating ethical, legal, and UX challenges. He also outlines how AI will increasingly permeate the entire software development lifecycle, augmenting developers’ creativity rather than replacing them.
Key Takeaways
Treat AI as an augmenting pair programmer, not a developer replacement.
Copilot is intentionally framed as an “AI pair programmer” that scaffolds code, reduces drudgery, and keeps developers in flow—but always with a human making reasoned decisions and using existing quality safeguards like tests and code review.
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Ring-fence a horizon 2/3 R&D team and protect its freedom.
GitHub Next is explicitly tasked with second- and third-horizon bets, shielded from short-term revenue and operational demands so researchers can explore ideas that may only pay off three to five years out.
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Plan a deliberate transition from research prototype to product teams.
When an R&D idea shows real user value, move researchers temporarily into a new product squad for knowledge transfer, hire around them, then only send them back to R&D once replacement talent is fully up to speed and owns the roadmap.
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Use real user feedback early to validate magic and shape UX.
GitHub iterated on multiple Copilot UX patterns (side panels, inline suggestions, keybindings, timing) and used technical previews plus social chatter (e. ...
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Invest heavily in responsible AI: filters, policies, and sentiment models.
The team progressed from crude blocklists to leveraging Azure’s Responsible AI models to filter offensive or inappropriate content, while engaging legal, privacy, and developer communities to define acceptable behavior for an AI coding assistant.
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Expect non-obvious scaling bottlenecks, especially around compute.
Running and training large code models at scale is constrained by scarce, specialized GPUs and global supply chain issues; product strategy must factor in hardware capacity, not just software demand.
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Balance your portfolio: reserve explicit capacity for bold bets.
Salva roughly allocates ~5–10% of capacity to high-uncertainty research, ~25–30% to operations/maintenance, and the remaining ~60% to incremental improvements, ensuring both reliable products and room for transformative innovations like Copilot.
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Notable Quotes
“We actually get to witness the advent of a brand new medium. If I’d been born in the 1700s, I probably would’ve been making new colors of paint and paintbrushes—but I was born now, so I work in engineering.”
— Ryan J. Salva
“Copilot is essentially IntelliSense magnified by many lines of code—multi-line autocomplete powered by an AI model trained on public code.”
— Ryan J. Salva
“The first step is to invest in R&D: hire really smart people and give them the opportunity to be creative, without expecting them to ship a money-maker in the next year.”
— Ryan J. Salva
“We do not want Copilot auto-generating code where a thinking, reasoning, breathing human being is not on the other side of that keyboard.”
— Ryan J. Salva
“I want people to be skeptical of Copilot. 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.”
— Ryan J. Salva
Questions Answered in This Episode
How should companies decide what percentage of resources to allocate to long-horizon R&D versus incremental product work, especially when they’re still small?
GitHub’s VP of Product, Ryan J. ...
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Where is the ethical line between training AI on public code and respecting the intent and licenses of open-source authors?
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If AI eventually writes the majority of boilerplate code, how should developers be trained and evaluated in this new world?
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What new attack vectors—like model poisoning or subtle bias injection—might emerge as AI tools become embedded throughout the software development lifecycle?
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How can teams outside of big tech (with limited access to rare GPUs and massive datasets) meaningfully participate in and benefit from AI-powered development tools?
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Transcript Preview
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 north lands of Finland. There's a seed vault, and we were 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? And so we had put this snapshot of public repositories on these, like, this, like, silver, uh, 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. (upbeat music)
Ryan Solva 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 auto-complete 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. Also this came out of a small R&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's 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 Solva. (upbeat music) If you're setting up your analytics stack but you're not using Amplitude, what are you doing? Amplitude is the number one most popular analytics solution in the world, used by both big companies like Shopify, Instacart, and Atlassian, and also most tech startups. Amplitude has everything you need, including a powerful and fully self-service analytics product, an experimentation platform, and even an integrated customer data platform to help you understand your users like never before. Give your teams self-service product data to understand your users, drive conversions, and increase engagement, growth, and revenue. Ditch your vanity metrics, trust your data, work smarter, and grow your business. Try Amplitude for free. Just visit amplitude.com to get started. This episode is brought to you by Athletic Greens. I've been hearing about AG1 on basically every podcast that I listen to, like Tim Ferriss and Lex Fridman, and so I finally gave it a shot earlier this year. And it has quickly become a core part of my morning routine, especially on days that I need to go deep on writing or record a podcast like this. Here's three things that I love about AG1. One, with a small scoop that dissolved in water, you're absorbing 75 vitamins, minerals, probiotics, and adaptogens. I kind of like to think of it as a little safety net for my nutrition in case I've missed something in my diet. Two, they treat AG1 like a software product. Apparently they're on their 52nd iteration, and they're constantly evolving it based on the latest science, research studies, and internal testing that they do. And three, it's just one easy thing that I can do every single day to take care of myself. Right now, it's time to reclaim your health and arm your immune system with convenient daily nutrition. It's just one scoop in a cup of water every day, and that's it. There's no need for a million different pills and supplements to look out for your health. Make it easy. Athletic Greens is gonna give you a free one-year supply of immune-supporting vitamin D and five free travel packs with your first purchase. All you have to do is visit athleticgreens.com/lenny. Again, that's athleticgreens.com/lenny to take ownership over your health and pick up the ultimate daily nutritional insurance. (upbeat music) Ryan, welcome to the podcast.
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