Snowflake CEO Sridhar Ramaswamy reports 37% revenue growth and says AI is industrializing software at every layer of the enterprise

Speaker 2: Team Up next. We need an elephant. Snowflake. Bye tomorrow. We got the CEO with us here live on TBPN, Sridhar Ramaswamy. Welcome Stack. To TBPN. How are you doing? Thank you so much for joining us. And congratulations on the fantastic success. Break it down for us. What's working? Is there anything that's not working? It seems like everything is going really well. How are you doing? Hi. Am I live? Yes. Live. We're live. Welcome to TVPN. Thank you so much for hopping on. Oh, thank you.

Speaker 6: We had a pretty amazing quarter, dollars 1,490,000,000, 37% year on year. Our AI products, Coco and CoWork, getting really broad adoption. We feel very good overall. But this is also a time of intense competition. There's the hyperscalers, but also the foundation labs. So, we think there's lots of business to be had, but a ton of change to keep up with, both for our product teams and for our go to market teams. So, yeah, it's a day after earnings are done and it's back to work, pedal to the metal.

Speaker 2: How much time are you spending trying to identify, like a venture capitalist, the next company that's going to be a major Snowflake customer. Because I I feel like the big labs, you identified them, they're obviously huge consumers of data, huge businesses there. Yeah. But it feels like every time we talk to somebody on the show, there's a new business that's getting venture funding. They're getting users. They're getting revenue. And I can I can tell that they're just generating a ton of data? They're ramping ramping revenue way faster than any company. They're probably ramping data two orders of magnitude faster than they're ramping revenue even. So what does that look like for you and for Snowflake?

Speaker 6: Yeah, we spend a lot of time both with our own innovation, what are great new ideas to be had at this moment because so much is possible. AI is industrializing software. It's much easier to create it than it was before. So, I spent a lot of time with the product and engineering teams, back to basics innovation. We also meet a lot of startups and companies, both established ones, talking to them about the opportunity that is possible with AI, talking to them about what we have done with our own sales teams, but also brand new startup. I met a start up, I think it was the day before yesterday, barely three months out, but they have dozens of customers all doing reinforcement learning on their platform. We were talking about how we could better partner together. So, a good amount of time spent on both sides of the cycle. But what I think is unique and cool is how quickly ideas go from it's just an idea to prototype to it's a product feature to getting adoption with a lot of customers. Yes.

Speaker 2: With all of the growth, I imagine that there's new challenges. Where are you going to be focused on unbottlenecking the organization? Are you going to be hiring, putting more AI, as you mentioned, in the hands of even an even broader swath of the organization, even though I imagine everyone has access to some level of token budget. But what does the growth side of the business, what are you trying to unlock in the next quarter or the next year?

Speaker 6: For the majority of the company, the biggest mind shift mindset shift that they need to go through is scale is not just about people any longer. What AI makes possible is stuff that needed individuals, people to go and do can largely be automated. Yeah. It's much more about the judgment. What's the work that you're trying to get done? What do you how do you want that you know, how do you want that done? And then, setting up a framework by which things can get stamped out. But, that's a pretty tough change for an org or a set of people, even you and me, that have traditionally looked at organization size as an important barometer of growth, and that's something that we are stressing with the entirety of the Snowflake team. I expect some functions to grow, but these are typically the ones that do things like interact with the external world. Sure. So, account executives, I can see that growing for customers that are spending, but for a lot of other functions, including in engineering, there is so much leverage that you can get by being at the forefront of what is possible with agentic AI. We continue to hire folks, especially young folks who know of no other world, because they bring a perspective that is fresh and they know how to go from zero to 100 straight off the bat. But, a lot more focus is on how do we become effective as a company. And, a lot of it is also about how do we set up new efforts and give them space to experiment so that they can find that magic in a bottle. Yeah. AI is not going to help us create great new products just like that. There's an amount of experimentation to figure it, and giving space to small teams to execute is among the biggest challenges that we need to make sure that we solve. Cocoa, for example, it's a breakout success, but for much of its existence, it never had more than five people. And even now, the team that works on Cocoa is tiny, but that's the kind of impact that is possible today. Are internal

Speaker 2: meetings underrated? Do would you expect that in a world where AI can do more of the work but lacks some of the context and taste and decision making and deciding what to do. If you woke up and you said, Wow, we're you know, as a company, Snowflake is having 10%, 20% more internal meetings between various members of the organization. Would that sit well with you? Because there's been a long history of, oh, internal meetings are such a waste of time. You can be very cumbersome if everyone's calendar is just full. That's just layers and layers of management. But is there some world where that's actually the correct way to be running a business of this scale in this era?

Speaker 6: I think bringing people along is an important function of every company. I wish I could tell you that we've solved this information exchange problem perfectly. There are a number of things that we are working on. We have an internal enterprise brain project, like many other companies do, in order to capture all of the relevant knowledge about the different things that are happening within the company, so that the right piece of information gets to the right person. So, I do think of projects like that as greatly enhancing internal communication. Remember, in a regular company, people go to meetings so that they don't feel left out, which is something that I really discourage people from doing. My attitude is like, if I have nothing to contribute to a meeting, I should read the notes. I really should not have to go to that meeting, but I would say it's a work in progress to make sure that we do a good job of transmitting internal information. It also has more profound implications. I think things like how many reports a manager should have needs to be dramatically different in the age of AI. Of course, making sure that people feel happy and motivated about their work, that's always going to be the role of a good manager, but, you know, information about what exactly did you get done last week, that's the kind of stuff that AI can facilitate a lot. It's a work in progress. I truly hope, you know, I've not measured it recently. You bring up a good point. We should go look at it. I hope we are not spending more time with internal meetings,

Speaker 1: but we should also not pretend that communication is a solved problem, even in the world of AI. Yeah. How are you thinking about your own internal AI spend and budgeting heading into next year? I think a lot of companies planned to spend a bunch of time planning next year, then there was a capability jump, and they blew through their budgets more quickly. I think as a leader, you have to assume there's gonna be capabilities jumps, which may mean that you spend more, but at the same time, there's a lot of drive for efficiency, cheaper models, open source, etcetera. But how are you planning around token spend looking forward?

Speaker 6: My take overall is that the money that we are spending on AI tokens is well worth the cost. We continuously optimize. Absolutely. We don't tell people to token max or do dumb things like that. It is about driving real results, impact as it were. And, when our cost does go up, we have a good team that focuses on optimization, everything from what's the default model to can you create task graphs where the simpler aspects of solving a problem are handled by models that are not quite as expensive? And my take is that we gain a lot by having people embrace the technology and feel like it can make a real difference to their job. I feel pretty good about optimizing. We also practice what we preach, where we have things like the AI gateway that's meant to make model access more efficient. We are absolutely experimenting with open rate models because they can be a good vector for lowering costs. Because we also control the harness, which is COCO, a lot of our internal teams use, there's a lot of instrumentation that we have for what exactly are people doing, how can we come up with better ways for doing the same thing. Our support and our SRE teams, the folks that keep Snowflake up, they spend a lot of money on tokens. But on the other hand, if they are running through tens of thousands of alerts that are coming in every day, it makes sense to go there and optimize because that's very leveraged work that can benefit everybody. So, yes, we are planning for it, but AI token cost is not the top thing on my mind. Creating great products, getting our customers to adopt them, having the framework by which these things become more self correcting, that's kind of how I think about it.

Speaker 1: Based on all your experience in the enterprise, how do you think the model routing landscape will evolve? This seems like something that a lot of big companies are excited about you know, getting a slice of that market. You saw Stripe's deal with OpenRouter focusing more on developers. Ramp, our partner, has a product everybody wants to be in the token flow, but how do you think that it'll evolve?

Speaker 6: That's not the highest value creation point for Snowflake as a company. We have products like Cowork that can literally get deployed to every employee in a company. So, we spend a lot of time thinking about what does it mean for an enterprise sales team to be AI built, to be operating at the edge of what is possible with AI. And we are seeing deployments of Cowork go to thousands of users within companies. It's operating at a much higher level than simply model routing. I think it's a good infrastructure capability, but I focus a lot more on things like how do we get every data engineer within a company to be using Cocoa, our builder product, or how do we get large fractions of employees within companies to be using co work? What are high leverage, high value projects that we could be doing for customers? Our customers have always trusted Snowflake with all of their most important data and focusing on how we can drive real business outcomes is where a lot of our energy is at. Absolutely. The Gateway is a good product, and it's not just model routing. We are also offering things like access to tools, MCP tools, as it were, that provide governed access to lots of different applications within an enterprise. We are also actively experimenting with, is there a security solution around agent trajectories to make sure that people are not misusing models? So, there's a slew of these things that are there at the infrastructure layer, but I think our bigger price is in delivering value for our customers.

Speaker 2: Last question. There's a lot of M and A news. Obviously, it's a very exciting time. You spent fifteen years at Google, very acquisitive company. How do you think about M and A? What makes for a successful acquisition? How is it unique at Snowflake when you consider it, when you don't?

Speaker 6: Our strength is as a data platform. People trust us with their most important data. People trust us with helping them get insights and drive actions from their most important data. My primary lens is, will a company, being part of Snowflake, accelerate that mission? We are thrilled that we bought Natoma because MCP is increasingly really, really important for Snowflake and all of our customers because it provides the real time context of everything that's happening in your and my life, whether it's Slack or email, right in the harness. And that was a great acquisition to make. That's the kind of lens that we bring, which is how does something being part of Snowflake accelerate both the company that we buy but also the larger mission of Snowflake as the AI and data platform for every enterprise that there is. Thank you so much for coming on this Yeah, great update. Great quarter. Congratulations on the quarter. Yeah, Thank to you the so much. Can't wait to talk to you Thanks again for having me. Have a great day. Cheers. Bye. Take it.