Claire Vo · 2 min read · AI & Product
How Coinbase scaled AI to 1,000+ engineers | Chintan Turakhia
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Chintan Turakhia discusses how Coinbase scaled AI across a 1,000+ engineer organization, using AI as a force multiplier to rewrite a self-custody wallet into a consumer social app within months and dramatically cut PR review times from 150 to 15 hours. He also outlines strategies for driving AI adoption, identifying AI power users, building custom AI agents, and measuring impact on engineering velocity.
Key Takeaways
- Leadership conviction and hands-on demonstration are essential to drive AI adoption in large, established engineering organizations.
- The 'speed run' technique (e.g., a surge of PRs from many engineers in a short window) can massively accelerate AI-driven changes at scale.
- Identify and replicate the behaviors of AI power users to diffuse best practices across the team.
- Develop custom AI agents that integrate with existing workflows and tools (Slack, dashboards, live demos) to streamline engineering processes.
- Focus on metrics that truly reflect impact on velocity, such as the cycle from user feedback to shipped features and PR review times.
Excerpt
Chintan Turakhia is Senior Director of Engineering at Coinbase, where he’s led the transformation of a 1,000-plus-engineer organization to embrace AI tools at scale. When tasked with rewriting Coinbase’s 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 Podcasts What you’ll learn: How to drive AI adoption in…