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

People are skeptical that large, established, highly technical, highly capable engineering organizations can deploy AI at scale and get any effect. But I think you've proven it's possible. It's not only possible, it's adapt or die. It's just been such a huge superpower for the team. How many engineers are we talking about here? A thousand plus. So we're not messing around here. The company tried to adopt other AI tools, and we saw this uptick in adoption. People opened it up, checked the box, did kind of like a hello world thing, but it didn't stick. My biggest thing is how do I make this damn thing stick? Because there's something here. I do think that it's really important when you're doing this organizational transformation that you have a single person with incredible conviction at the leadership metal. Show the engineers, not just tell. And the worst thing any engine leader could do is just be like, I decree you must use AI. Come on. No one's gonna listen to you. Welcome back to How I AI. I'm Claire Bell, product leader and AI obsessive here on a mission to help you build better with these new tools. Today, we have Chintan Turakhia, senior director of engineering at Coinbase, and he's gonna show us, yes, it is possible to drive AI adoption and higher velocity in an engineering organization of thousands of engineers. He's also gonna show us the new expectations for engineering managers and engineering leaders, which is less meetings and more code. Let's get to it. This episode is brought to you by WorkOS. AI has already changed how we work. Tools are helping teams write better code, analyze customer data, and even handle support tickets automatically. But there's a catch. These tools only work well when they have deep access to company systems. Your copilot needs to see your entire code base. Your chatbot needs to search across internal docs. And for enterprise buyers, that raises serious security concerns. That's why these apps face intense IT scrutiny from day one. To pass, they need secure authentication, access controls, audit logs, the whole suite of enterprise features. Building all that from scratch, it's a massive lift. That's where WorkOS comes in. WorkOS gives you drop in APIs for enterprise features so your app can become enterprise ready and scale up market faster. Think of it like Stripe for enterprise features. OpenAI, Perplexity, and Cursor are already using Work OS to move faster and meet enterprise demands. Join them and hundreds of other industry leaders at workos.com. Start building today. Chintan, thank you so much for joining. What I love about what we're gonna talk about today is we've spent so much time talking about the individual vibe coder or the nontechnical person becoming a software engineer, and still people are skeptical that large, established, highly technical, highly capable engineering organizations can deploy AI at scale and get any effect. There's still so much skepticism, but I think …