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Original: Lenny Rachitsky · 02/03/2026

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Summary

Chintan Turakhia discusses how Coinbase scaled AI adoption among its 1,000+ engineers, achieving significant efficiency gains in app development.

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Key Insights

“AI as a force multiplier.” — Chintan’s approach to integrating AI tools in the engineering process.
“The PR speed run technique that transformed team adoption.” — Describing a method that significantly improved PR review times.
“Engineering leaders must get hands-on with AI tools to drive adoption.” — Emphasizing the importance of leadership involvement in AI integration.

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Topics

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Full Article

Chintan Turakhia is Senior Director of Engineering at Coinbase, where hes led the transformation of a 1,000-plus-engineer organization to embrace AI tools at scale. When tasked with rewriting Coinbases self-custody wallet into a consumer social app in just six to nine months, Chintan turned to AI as a force multiplier. His team has achieved remarkable efficiency gains, including reducing PR review times from 150 hours to just 15 hours, and dramatically compressing the cycle from user feedback to shipped features.Listen or watch on YouTube, Spotify, or Apple PodcastsWhat youll learn:How to drive AI adoption in large, established engineering organizationsThe speed run technique that got 100 engineers to push 70 PRs in 15 minutesHow to identify and replicate the behaviors of AI power usersWhy engineering leaders must get hands-on with AI tools to drive adoptionHow to build custom AI agents that integrate with your existing workflowsThe metrics that actually matter when measuring AIs impact on engineering velocityHow to compress the cycle from user feedback to shipped featuresBrought to you by:WorkOSMake your app enterprise-ready todayRovoAI that knows your businessIn this episode, we cover:(00:00) Introduction to Chintan(02:38) How Coinbase approached rewriting their app with AI assistance(08:00) The importance of leadership conviction and hands-on demonstration(10:30) The PR speed run technique that transformed team adoption(17:57) Measuring success(19:20) Demo: Real-time feedback-to-feature implementation(23:14) Using Cursor to analyze AI adoption patterns(33:15) Quick recap and appreciation(36:00) Demo: Building a live feedback capture system using AI transcription(40:50) Using custom Slack bots to automate engineering workflows(47:10) Advice for driving AI adoption within your organization(50:00) Personal use case: AI for wine selection based on taste preferences(55:23) Lightning round and final thoughtsTools referenced: Cursor: https://cursor.sh/ Linear: https://linear.app/ Slack: https://slack.com/ ChatGPT: https://chat.openai.com/ Claude: https://claude.ai/ GitHub Copilot: https://github.com/features/copilotOther references: Coinbase: https://www.coinbase.com/ React Native: https://reactnative.dev/ How custom GPTs can make you a better manager | Hilary Gridley (Head of Core Product at Whoop): https://www.lennysnewsletter.com/p/how-custom-gpts-can-make-you-a-better-managerWhere to find Chintan Turakhia:LinkedIn: https://www.linkedin.com/in/chintanturakhia/X: https://x.com/chintanturakhiaBase App (formerly Coinbase Wallet): https://base.app/Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevoProduction and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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Originally published at https://www.lennysnewsletter.com/p/how-coinbase-scaled-ai-to-1000-engineers.