Lenny's Podcast

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/

Ryan J. SalvaguestLenny Rachitskyhost

Sep 3, 20221h 4mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

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.

IDEAS WORTH REMEMBERING

5 ideas

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.

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.

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.

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.g., tweets, Hacker News) to confirm that the inline experience felt “magical” and worth scaling.

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.

WORDS WORTH SAVING

5 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

Origins and technical foundations of GitHub Copilot and CodexStructure and role of GitHub’s R&D team (GitHub Next) and horizon planningTransitioning a moonshot prototype into a production product within a large companyEthical, legal, and responsible-AI considerations in AI code generationImpact of AI on developer productivity, learning, and the future of codingOperational and scaling challenges (compute constraints, community expectations)Portfolio management: balancing bold bets vs. incremental product investments

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