Meet Snowflake Intelligence: A Personalized Enterprise Intelligence Agent with Sridhar Ramaswamy

**Sarah Guo** (0:06)
Hi listeners, welcome back to No Priors. Today, I'm here with Sridhar Ramaswamy, the CEO of Snowflake, the former founder of Neva and the SVP of Google Ads. We will talk about his first 18 months of being CEO, the incredible execution over that time in shifting a company at scale to being AI first, where the enterprise ROI is, and what happens to the cloud service providers and the ads model in the age of AI. Welcome, Sridhar.

**Sridhar Ramaswamy** (0:35)
Sridhar, I'm really excited to be back.

**Sarah Guo** (0:37)
Well, it's a pleasure to talk to you as an old friend and colleague. The last time we spoke, you were on the entrepreneurial journey.

**Sridhar Ramaswamy** (0:44)
That's right.

**Sarah Guo** (0:44)
Doing search still. You're now 18 months into being CEO of Snowflake. It has been a very eventful 18 months. Tell us a little bit just about the journey from, you know, taking the mantle from Frank to the first few months to where you guys are today. I think the market has reacted in many ways. Most recently, incredibly well to the execution. But it's been a journey.

**Sridhar Ramaswamy** (1:09)
That's right. That's right. Snowflake has always been an amazing product company. The original product that Benoit and Thierry conceived of 10 plus years ago was many years ahead of its time. It took the world by storm.
Obviously, they had the storied IPO, the biggest software IPO at that time. I think what happened was the company was a little slow to reacting to changes from things like machine learning and AI. That was a little bit of honestly the reason why Frank voluntarily pushed for the change because he felt presciently that we are headed into a time that was just a lot more tumultuous from a product perspective. He wanted someone that was product first to be in charge of the company. The last 18 months have really been about embracing that wave of change. If you look back to what's happened in the last two years, it's crazy. How much change has happened with respect to AI, how it's become commonplace every day in all of our lives, and then the speed at which things are still getting driven through. I think the really amazing thing about Snowflake is the company embraced this change, transformed itself, and then showed that not only can we do it from a product perspective, which one could have expected, but we also done significant things to retool our marketing or go to market overall. I think that transformation has been pretty amazing to watch. But times can be difficult. Last year, there were a lot of doubters, but there were a lot of us who believed both in the value that Snowflake was already creating. And the reason I took this job was because I talked to a whole lot of customers before I became CEO. They all loved Snowflake. And that was a big motivation for me to take this job. So I think we have sort of successfully written through that and are now at the cutting edge of data and AI for enterprises. It's been an amazing journey to have gone through.

**Sarah Guo** (3:14)
Walk me through just some of the orientation, prioritization you did in the first six months and then a little bit more about the long-term vision here.

**Sridhar Ramaswamy** (3:25)
The first six months were a lot of tactical changes, which primarily around accountability. Like every company that goes through essentially a rocket ship phase of growth, growing at 100 plus percent year on year, Snowflake had basically specialized at every layer possible. And there was a very long distance between the engineer that did a feature and the customer that made use of the feature. And there were like seven to ten layers of teams that were involved. That works fine when you have perfect product market fit and you're trying to optimize for every function.

**Sarah Guo** (4:00)
You're just the winning cloud data warehouse. Yeah, drive a truck through that.

**Sridhar Ramaswamy** (4:04)
But on the other hand, if you're working in the world of AI where we can barely tell what's going to come out next month, forget next year, this is the wrong structure to have. So we did a lot of organizing by different areas, making sure that there were accountable people. For example, in product engineering, that was among the first changes. Organize into different product areas like AI or the core warehousing analytics product. But then we also wanted a straight line over to our go-to-market team. So we created these specialized teams that work closely with product and engineering and marketing to take these new products to market. And that was a lot of the early phase of Snowflake with an emphasis towards faster iteration. This is something that I've believed in all my life, which is speed wins. Ability to iterate always trumps carefully laid out strategies. Yes, you shouldn't do dumb things, but on the other hand, realizing any kind of gain requires a lot of iteration. So we made a number of changes on that side, both with respect to how quickly we created products, but also how quickly we iterated with customers. And I would also say we took a little bit of time to find sort of our sweet spot in this AI space. As you know, that itself has evolved a lot. We are not a CSP. We are not a foundation lab. So what are we? And there was that discovery of ourselves as the AI data cloud as opposed to the data cloud.