Utilizing AI internally to iterate faster and empower smaller teams to upskill w/ Vivek Raghunathan #263

**Jerry Li** (0:00)
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**Vivek Raghunathan** (0:33)
I'll tell you what I did. I told all my leaders, you're taking the week off to get your, all your entire team is taking the week off to upskill themselves. And my attitude is, this is the highest leverage thing you can be doing right now. This is how you make sure you can be relevant for the next decade.
Eisenhower had this urgent, important framework, right? This is the important thing that should not be trumped by something urgent. When people do it, they come back out of that week and they are like, hey, I wish I could do this more often. So where I'm thinking even of doing it like twice a quarter or twice a quarter, just getting to a place that having this is not like a one and done, right? Keeping people on the bleeding edge is a full time job.

**Patrick Gallagher** (1:09)
Hello and welcome to The Engineering Leadership Podcast brought to you by ELC, the engineering leadership community.

**Jerry Li** (1:16)
I'm Jerry Li, founder of ELC.

**Patrick Gallagher** (1:18)
And I'm Patrick Gallagher and we're your hosts. Our show shares the most critical perspectives, habits and examples of great software engineering leaders to help evolve leadership in the tech industry.

**Jerry Li** (1:30)
Hey Vivek, so excited to have you join us today and share your learning and insights and journey, you know, building new teams and adopting AI and then looking to the future, how organizations are going to adapt to this new technology. Last time when we had you mentioned a lot about how the whole engineering team and the org at large, how they are adopting AI. Maybe you can take us on a journey of how do we see stuff like engineering is currently at in terms of adopting AI and building an end-to-end team.

**Vivek Raghunathan** (1:59)
Yeah, first of all, I'm super excited to be with you guys. Thank you so much for having me. Some context, Snowflake Engineering is about 2,500 people, something like that. So slightly different scale from a small startup or a medium size company. I used to be at Google and YouTube a while ago, then did a startup for four and a half years, sold a startup to Snowflake. And so the answers can sometimes be different for different size companies. The approach we have taken very much is one size does not fit all, to quote like Mike Stonebreaker in reverse. I think there's a set of things that is very important early on for us not to feel like we are standardizing too quickly, if you will. The paved paths are still being figured out. So at some level, we have built and are operating in a way that lets us standardize the things that can be standardized, the things that we know to be good and at the same time leave room for experimentation for people to push the frontier of how they operate. I see roughly an arc of four stages of maturity, if you will. The first is people adopting AI tools to write code, right? Just in a loop of software, can they do more of that? The second is, okay, now they're writing more code. How does that change the act of, how does AI change the act of product development itself? Are we in the, you know, yes, they wrote more PRs, but like, are we able to build products in a fundamentally different way? The third is, okay, now we are able to write code in a fundamentally different way. We're able to build products in a fundamentally different way. What does it mean to release those products and operate our systems? I mean, we're ultimately, you know, infrastructure software companies use us on a daily basis to run their entire infrastructure. Can we run our infrastructure in a fundamentally different way, right, in an AI-first way? And then finally, and this is, I think, the thing that is most dear to my heart is what does this mean for how I run the engineering organization and my product counterparts, runs the product and design organization, and what are our roles and responsibilities in this new world, and how do we organize our teams so that we can maximize velocity while still staying very interconnected. We're ultimately still shipping one product to the customer. On each of these paths, we have been pretty good about when we find golden pave paths, we standardize them, we measure them at every level of the organization, and we nudge people towards using those pave paths. At the same time, there's lots of people really pushing the boundaries of how they can write more code, or build products in a fundamentally different way, or operate our systems in a fundamentally different way. And it's important for me that we are able to see what those people are doing. Almost these are our new leaders, right? And how do we elevate them, and how do we empower them to discover new ways for the organization to function. And so we're leaving enough room for that kind of creativity.