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

2026-02-25 · Source: Lenny's Newsletter · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, extended

Summary

Chintan Thurakia, Senior Director of Engineering at Coinbase, details strategies for driving AI adoption within a 1,000+ engineer organization, transforming skepticism into a "superpower" for increased velocity. He emphasizes the critical role of a hands-on, conviction-driven leader who demonstrates AI's practical benefits, particularly in automating "toil" tasks like unit testing and PR creation. Coinbase achieved significant results, including a 10x reduction in PR review cycle time from 150 hours to 15 hours, and a 3-4x increase in PR volume during "surges." The approach involved using tools like Cursor for data-driven analysis of AI usage, identifying power users, and creating targeted guidance. Additionally, an in-house tool called Cloudbot was developed to automate the feedback-to-feature cycle, translating live user feedback into Linear tickets and then into ready-to-review pull requests within seconds, significantly compressing development timelines.

Key takeaway

For engineering leaders aiming to scale AI adoption, your direct involvement and demonstration of AI's practical benefits are crucial. Focus on automating repetitive, low-value tasks like unit test generation and PR descriptions to quickly show engineers how AI enhances their work. Utilize data from AI tools to understand usage patterns, identify power users, and create targeted playbooks to guide your team's progression, ultimately accelerating development cycles and fostering a culture of rapid iteration.

Key insights

Driving AI adoption in large engineering organizations requires hands-on leadership, automating toil, and data-driven user analysis.

Principles

• AI adoption is an "adapt or die" imperative for engineering teams.
• Leadership must "show, not tell" the value of AI tools.
• Focus AI on eliminating "shit work" to gain engineer buy-in.

Method

Analyze AI tool usage data (e.g., Cursor analytics) to identify user cohorts and power users. Generate targeted guidance and playbooks to help users advance, fostering a gamified approach to skill development and adoption.

In practice

• Conduct "PR speed runs" to demonstrate AI's immediate impact.
• Automate feedback-to-feature workflows using custom agents.
• Integrate AI tools into existing platforms like Slack and Linear.

Topics

• AI Adoption
• Developer Productivity
• AI Agents
• Feedback Automation
• Engineering Leadership

Best for: Director of AI/ML, MLOps Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Lenny's Newsletter.