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How Coinbase scaled AI to 1,000+ engineers | Chintan Turakhia

| 12 products mentioned
How I AI How I AI host
Watch on YouTube ai adoption at scale engineering leadership developer productivity ai tooling implementation organizational transformation feedback loops velocity acceleration

Chintan Turakhia, Senior Director of Engineering at Coinbase, demonstrates how to scale AI adoption across a 1,000+ engineer organization by combining hands-on leadership, practical tooling, and cultural shifts that prioritize velocity over process. Rather than mandating AI use, Turakhia shows how to build conviction through personal experimentation, create feedback loops that highlight wins, and compress feedback-to-feature cycles by orders of magnitude—proving that large, technically sophisticated teams can unlock significant efficiency gains through AI tooling. The episode explores both engineering-focused use cases (like feedback capture and automated PR generation) and the organizational dynamics required to make AI adoption stick at scale.

Key takeaways
  • • Leadership conviction paired with hands-on execution is essential for driving organizational AI adoption; leaders must personally use and experiment with tools before expecting broad team adoption, not simply decree mandates.
  • • Focus AI adoption efforts on eliminating toil and papercuts—like unit tests, linting, and review cycles—rather than high-stakes features, which builds engineer buy-in faster and demonstrates tangible value.
  • • Use shared Slack channels (like "cursor wins" and "wins/losses") to make AI successes visible and viral within the organization, which drives adoption faster than isolated tool access.
  • • Reduce PR review cycle time by 10x (from 150 hours to 15 hours) through AI-assisted review and automated feedback loops, fundamentally changing how engineering teams operate.
  • • Build internal AI agents to capture live user feedback (audio/video) and automatically generate tickets and PRs, compressing the time from customer feedback to deployed fix from days to minutes.
  • • Measure AI adoption effectiveness by tracking time from ticket to user-facing change rather than vanity metrics like lines of code, which better reflects actual business impact.

Recommendations (5)

Cursor
Cursor uses

"I was in Cursor every single day, every single hour of the day. And I was like, how do I make this work, right?"

Chintan Turakhia · ▶ 7:30

Linear
Linear uses

"Linear is a incredible tool. It's doing some triaging. But the thing I want to now hop over to is we're going to just create the PR."

Chintan Turakhia · ▶ 40:56

Slack
Slack uses

"most of our company uses Slack. We're all in Slack and Slack, you know, I'm like strong believer it's just a bunch of humans pretending to be systems"

Chintan Turakhia · ▶ 43:50

Claude
Claude uses

"I love Opus High. I also love plan mode because it gives you a chance to like see what it's thinking through."

Chintan Turakhia · ▶ 21:49

ChatGPT
ChatGPT uses

"I've just started taking a picture of it and then throw it into ChatGPT and say create the calendar invites"

Chintan Turakhia · ▶ 50:30

Mentioned (7)

React Native
React Native "we're using React Native but we made a lot of decisions for a self-custody wallet but to become a..." ▶ 4:34
GitHub Copilot "the company tried to adopt other AI tools like GitHub Copilot and we saw this like uptick in adop..." ▶ 6:41
DataDog
DataDog "it goes off into all the MCPs like DataDog Sentry Amplitude um our internal Snowflake databases etc" ▶ 46:51
Sentry
Sentry "it goes off into all the MCPs like DataDog Sentry Amplitude um our internal Snowflake databases etc" ▶ 46:53
Amplitude
Amplitude "it goes off into all the MCPs like DataDog Sentry Amplitude um our internal Snowflake databases etc" ▶ 46:55
Snowflake
Snowflake "it goes off into all the MCPs like DataDog Sentry Amplitude um our internal Snowflake databases etc" ▶ 46:56
Gemini
Gemini "Claude, if I'm using like Claude Opus 4.5 high, like, okay, I'm going to stop using you, Claude, ..." ▶ 57:09

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