Snowflake CEO Sridhar Ramaswamy on AI-accelerated migrations, 75% growth in customer use cases, and why it's a bad time for 3-year plans

Speaker 1: Large financial institutions.

Speaker 2: Who knows? Think she

Speaker 1: she likes those.

Speaker 2: Well, we have our next guest in the waiting room, Sridhar Ramaswamy from Snowflake. He is the CEO. We're very pleased to be joined by him. Welcome to show. Thank you so much for taking the time. How are you doing?

Speaker 9: I'm well. Thank you for having me.

Speaker 2: Can imagine you're doing well. The the company is doing fantastically. I am interested to hear no. I mean, obviously, there's been an emotional roller coaster, but I'm more interested in the data that you're seeing that shows acceleration, so much promise, so much opportunity in this particular business.

Speaker 9: Yeah. Data is the foundation for insights. I worked in a data company. I worked in the search ads team at Google, where great data was the flywheel that made it into a great business. That's what we aspire to do with each and every one of our customers. AI has done a couple of things. It's made the act of bringing data into Snowflake doing just a whole lot easier. Pipelines that used to take weeks to set up or migrations that used to take years now have dropped to days and months, which is pretty remarkable. But people also realize how much more power they can get by having great conversational access to their data. And now with things like Snowflake co work, how we can actually solve pretty meaningful business problems in an interactive way, it's that it's that loop that is compounding for us that drives a lot of our growth.

Speaker 2: Is has there been any movement for Snowflake? Whenever I hear the story of a a a data lake, a properly managed data warehouse as an entrepreneur, even in a small business, I think that sounds amazing. That sounds like the dream. Then I see the companies that, you know, actually have the resources to onboard be, you know, immensely large, and obviously, that's fantastic for your business. Yep. But I'm interested in in what the future looks like. Is there a world where a 10 person start up with AI agents and and these pipelines that can be built much faster can actually set up their and then not have to go through a cumbersome migration in the future.

Speaker 9: Yeah. We are seeing that happen in front of our eyes with Snowflake's own data team. Yeah. You know, they, like with every other company, used years long. You ask them for a feature, they'd be like, Here, take a ticket. Come back, you know, in a few quarters. What they've been able to do with Agentic AI is grind through those kinds of backlogs very quickly. Absolutely. Data and agents are going to make it much easier to set up these things and have them evolve as they go along. I'll give you examples from the public. We rewrote our, both our support teams and our SRE team's software. These are the folks that take care of different kinds of problems that we get into an, and the net result of that by agents investigating the problem. And if you see enough instances of a problem that you have not seen, you go figure it out, go write a tool for it, it's now in a self healing loop. I increasingly imagine that more and more operations get into that kind of a mindset where there are very competent agents, sets of recipes you can call them skills, there'll be some other name tomorrow that take care of problems, but it's it is a self learning loop. That's what AI makes possible.

Speaker 2: Got it. Where are we in the path to, like, fully automated migrations, fully automated new integrations? It feels like every new model release were like, this is the one. This is the one. The nontechnical CEO is gonna be doing the job now. He's just gonna say, hey. We we have two systems. Go fix it. Go integrate it. Get it into Snowflake. It's gonna happen. And then reality sets in and there's not just token costs and cost of that to to delegating to AI, but also just the real world of of other decisions that need to be made that might be outside of the context. So what are the the the remaining challenges that come up when you're building a new integration or or working on a migration?

Speaker 9: Yeah. I think the timelines are definitely shrinking Mhmm. When it comes to legacy migrating off of legacy platforms, the on prem ones. Yeah. We used to think of them, the biggest ones, as running into, say, like, three odd years. And these strike terror into every company's heart because it's three years of, like, really anxious development work and waiting for results and hoping things don't go wrong. To give you very, concrete numbers, we now feel a lot more confident that we can tackle really tough migrations and have them finish in a couple quarters. That's a big deal. It's going from 12 down to one or two. And the migration team, which work on top of agentic harnesses that we create, are confident that the technical parts of these problems will be solved by the end of the year. But there is the change management. There is all of the other applications that are running on the old system that you have to validate on the new system and ensure that there is business continuity. That kind of change management is still going to be nontrivial. But the technical parts, the act of, doing the migration or writing new software, these are the things that AI is absolutely driving down.

Speaker 2: What is the biggest

Speaker 9: By the way, I actually go check out every feature my, team puts out using our coding agents. Mhmm. That's already a thing. And, honestly, it's not that hard if you have a modestly technical background.

Speaker 2: Yeah. Yeah. No. That makes sense. What is the biggest lesson that you or the company have taken forward from Frank Slootman's era?

Speaker 9: Frank was always about amazing go to market execution. Yeah. And we remember that. Okay. And, in fact, our sales teams

Speaker 2: Yeah.

Speaker 9: Are incredibly well versed now in our products and in AI as a whole because we created products that could work for them. Mhmm. And having an effective go to market motion is the real strength of Snowflake. Again, to make this super concrete, the number of, use cases these are new basically within customers, that our account executives won, went up by some 75%, year on year. That's the power of sort of using AI to get work done faster. And, that is a tradition that will that that will continue. What we have added on focus that comes from a product first person like me. AI in sort of a generic way or about worry about things like what, you know, what can it do. But I think sometimes what we forget is if you have a well built system that uses, like, the latest it's actually an incredible delight. All kinds of information that you would want. I was at a conference recently. Every piece of information that I wanted to know about a customer that I was having a conversation with, I would look up even live if I didn't know that. I would do it in front of them and show them the power of Snowflake's AI. A big part of our success over the past couple of years has been internalizing this. AI is a different way to think about product and product delivery, and the companies that are going to succeed are the ones that truly internalize that. That there is a big chasm between how we did it with web pages and clicking on things to these agentic flows that can truly solve problems.

Speaker 2: Then how is the sales the role of the sales rep changing, the sales associate changing? Are you looking for any different skills or are you doubling down on what worked in the pre AgenTik AI era?

Speaker 9: First of all, a lot more familiarity with Snowflake is a must.

Speaker 2: Sure.

Speaker 9: Because these folks now have access to products that show off what Snowflake is about. Yeah. I expect every sales rep to have co work on their phone and be able to show off the actual impact of having a product like that with pretty access to information to each and every one of our customers. When it comes to our solution engineers, these are the technical presales people. Used to be that we'd have six demos total for all of Snowflake, and maybe they would do a little bit of customization and say, hey, here's what Snowflake can do. Now pretty much all of them are capable of creating a custom demo tailor made for a specific customer in a vertical, in a way that makes sense to them, that will really illustrate the power of Snowflake. It's that kind of empathy that they can use the technology that we create to drive. I would say it's changed in a really, really big way because a lot more possible, but the bar is also a lot higher in terms of what you're expected to bring to the table.

Speaker 2: Can you walk me through the partnership with Amazon Web Services? I I I'm sure you compete in some categories, but how did this come together? What does it mean for the future of your business?

Speaker 9: Yeah. We work with the hyperscalers. AWS is the biggest partner. Yeah. And, you know, we work with them on a number of different levels. We work on product integration.

Speaker 2: Sure.

Speaker 9: You know, buy a lot of capacity from them, a lot of GPUs that our inference team runs runs models on. But where we really shine is in joint go to market. AWS is an incredibly customer first company. And if a customer says, hey. I want AWS plus Snowflake, they lean into it. They bring us. And we really focus on how do we solve problems jointly, for customers. And there's a deep, deep relationship both at the account level going all the way up to the CEO level that ensures that problems that come up in practice get solved very quickly, it's it's the kind of partnership that one only dreams of. It's incredibly effective in practice.

Speaker 1: What's been your what's been your strategy for trying to predict AI progress? Because we have people on the show every single day, somebody from a lab, somebody that's an investor, customers of the labs, things like that. Everyone has a different opinion. You have to weight them differently based on their incentives. But you're running a massive company that's leveraging these tools, but at the same time, you wanna understand what the capability will be one year out, two years out, and it feels harder than ever to actually be able to predict that.

Speaker 9: I think this is a is a really good question and one that to deal with. I don't think the software industry as a whole has internalized that this precious commodity that we all loved creating by hand, which

Speaker 6: is

Speaker 9: software, the cost of creating new software is going to keep going down. I tell people that the best analogy that I can give for them is to go back to, let's say, the 2 thousands when the cost of creating and distributing new content truly dropped precipitously. And you have to work through what is the implication of something like this. For example, I think simply getting deployed into an enterprise and being part of a workflow there is no longer guarantee that things will going to things are going to keep continuing. Because switching is also easier, I I would say that's the first thing. Every every company that's in the world of creating software as a big part of their business needs to think through what is the durable value that they are creating. I think it's a really hard question to internalize, and so we spend a lot of time just thinking about it. The second one, and this is this gets to the heart of your question, and I honestly just have a which is that this is a bad time to be making two and three year plans. Any time things are getting better or worse by 30% every month, all the human ability to predict these things, which are exponential in nature, is, is, you know, it just doesn't it doesn't really work. I joke to people that at this, point in time, we should treat coding agents the same way we treat traffic. Any person is always going to go get, look at Google Maps first before they go somewhere because it's an all seeing agents are sort of becoming like that when it comes to when it comes to software. And the more you have that open mentality that more things might be possible with a new model release than was possible before, the more you'll be pleasantly surprised and you'll be ready to take advantage of it. And so I stress that childlike quality in figuring out what is possible. Obviously, you know, there are nuances to how do you put in work in such a way that even if the model got better, you'll still be able to take advantage of what you have done before. There are practical problems like that. But the biggest thing that I ask for is that open mindedness to what is possible today because a lot different from what was possible, say, a month ago.

Speaker 2: Are you positioning Snowflake as a company that's in the token path or in the token stream? I don't know if you've heard this phrase that's sort of new coinage for a position in the AI value chain. And it feels like you're firmly in token stream, in the token path. But I'm wondering about what you think about that category. Is that narrowing you? Is that valuable to communicate to customers and investors? What do you think of the phrase like in the token path?

Speaker 9: It's a useful way to think about how value is going to get created. Mhmm. Remember, back in the web era, we talked about, you know, number of visits, number of unique visitors.

Speaker 2: Mhmm.

Speaker 9: These were the things, are weekly active versus monthly active. These were the abstractions that we used, to ground the numbers that various people would report in a reality. You can think of the token path as existing along this path that is using the power and knowledge that these models have, and creating value for customers. And, both in the data where our coding agent called Coco comes into play, where we want everyone that wants to get value from data to be using it to create all of the artifacts in that path, but also in the consumption path, where, our product is Snowflake Cowork, where I want every employee in every company to be able to access the valuable data that is in Snowflake, but also in other applications via things like MCP, to be using co work. And so we very much think about can we be in this path of coding up the things that are going to drive consumption and creating value, but also in the path of how does an end user query enterprise data, take an action on that data, and also creating value. So we very much think about this.

Speaker 2: So my my my last question is, you said it's a bad time to be making two and three year plans. How far are you looking into the future? Do you have a one year plan, a three month plan? What

Speaker 1: Well, as a public company,

Speaker 9: you you

Speaker 2: do have have to have forecast. But but but more qualitatively, like, what are you gunning for this year or over whatever time horizon you think is appropriate at this point in time?

Speaker 9: Our overall strategy is very clear. It's just what I described. Yeah. Anytime anyone wants to do something with data, I want them to use, agent tools that are created by Snowflake Mhmm. Cocoa, for example

Speaker 7: Yeah.

Speaker 9: To be able to do that. We wanna have the best data platform in the world so that when and whenever someone thinks, hey, I need a historical view of this, or I need an OLTP database to do this, they think of Snowflake. They can use any coding agent, not just our own, to be able to, to be able to do that. Similarly, we think a lot about how can we make sure that we sell co work to CROs and demonstrate what we have done internally within Snowflake, just transform our own sales organization. So we think a lot about how do we take our customers through the same journey that we have been through in terms of how can AI make a difference to the business. There'll be a lot of details along the way, but that's the path that we are headed in, AI leveraging the full power of data.

Speaker 2: Well, congratulations. Thank you so much for taking the time to