No Priors Ep. 139 | With Snowflake CEO Sridhar Ramaswamy

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. That's right. Doing search still. You're now 18 months into being CEO of Snowflake. It has been a very eventful 18 months.

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 and it took the world by storm. Now obviously they had the story IPO the biggest software IPO of that 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 and that was a little bit of honestly the reason why Frank voluntarily pushed for the change because he felt preciently that we are headed into a time that was just a lot more tumultuous from a product perspective and he wanted someone that was product first to be in charge of the company and the last 18 months have really been about embracing that wave of change and if you look back to what's happened in the last you know two years it's crazy how much change has happened with respect to AI how it's become common place 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 our go to market overall I think that transformation has been pretty amazing to watch but you know times can be difficult last year uh 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 ridden through that um and are now at the cutting edge of data and AI for enterprises. It's been an amazing journey to have gone through.

Yeah, the first 6 months were uh 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% year on year uh 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 that feature and there were like seven to 10 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. You're just the winning cloud data warehouse. Yeah. Drive a truck through that. But on the other hand, if uh 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. organized into different product areas like AI or the core warehousing analytics product. Um 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 uh 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 uh 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 and I think um it's that kind of clear product insight into what value we add that is setting up the stage for the earlier parts of the year or even what we are about to talk about today.

I mean first of all when it came to AI as I said we had to look hard at ourselves. Early last year we uh actually went down the path of creating foundation models. We created a credible model. This was early last year, but we also quickly realized that our ability to compete with the likes of OpenAI or Anthropic was going to be really hard. We simply did not have the capital to be able to invest meaningfully in things in things like that. So we pivoted away from that into much more of a how does AI massively accelerate what can be done um with data that is in snowflake and uh over time that can become a reason to bring more data into snowflake which is the phase that we are in now but uh a lot of our AI product strategy was actually quite humble it didn't say we are going to rethink everything it said enormous number of customers um you know something like half of the qualifying Fortune 2000 companies on the planet are Snowflake customers. They have their most valuable data on Snowflake. What does AI mean for that? And so we systematically invested in the components whether it was search or whether it was text to SQL in ways that added on value to the things that people were already doing um with Snowflake. And SI is an agentic platform but it's actually an opinionated agentic platform. A lot of agentic platforms for example from the CSPs will basically say oh you can bring in data from anywhere. You can imagine any kind of workflow that you want to do and the one agent will rule them all which is nice in theory but in practice when you have an infinity of things that you can do it's also hard to figure out what you should actually do. Snowflake Intelligence is very focused on how do you create value from data whether it's structured or unstructured a whole lot faster and uh so the kind of use cases that got us really excited honestly internal ones were things like if we were to take all of the different dashboards that we used in sales and put it into one single interface what could that be we had done two three versions of this but eventually that culminated in this internal product we call it Raven but it's basically the sales data assistant Then we started working with early customers whether it is a Cisco or a Fanatics or the USA bobsled team to figure out what does this all mean for them. And the theme again is get away from the inflexibility of things like dashboards. A dashboard is a 2D view of a complex surface. It just has no easy answers to the many questions that any reasonable person you or I is going to have off of that. So we wanted to create something that freed people of the 2D style of thinking was much more flexible in what it gave people access to but also knew its place. This is not a general purpose agentic platform to do it all. This is an agentic platform that lets people realize value from data faster and is a great foundation for people to get value from data like really quick in a meaningful way. I think having this sort of an opinionated framework for AI has been super helpful for us.

Yeah. So, we should show a demo of snowflake intelligence uh to you. But uh yes, it's an interactive interface. You can ask questions. There are a set of canned questions uh to make sure that you know you don't have block when it comes to being able to ask uh questions. You can ask it to say, "Hey, what data sets do you have access to? What kind of questions can you answer?" It'll do a perfectly reasonable job of that. And our aspiration was for this product to be used by every single employee in the company. So it's not for people who can write SQL. It's not for people that can write SQL. We wanted it to be enough of a daily use product for every single person. There is not a single customer meeting that I'm going to have without quickly checking up on what's the latest with this customer. And so uh Raven that I talked about our sales data assistant absolutely has things like what's our relationship with the customer what kind of contract have they signed what is their consumption looking like but also things like what are the most recent conversations that we have had with them what came out of these are there any outstanding tickling issues and so it is a lot of that and uh like many good products it there's breadth there's value driven to many many people within a But on the other hand, we don't pretend it's a BI dashboard. There are more things that you can do with a Tableau or a Sigma than you can do with SI. But that's not the goal because this product also lets you do a bunch of things that you could not easily do in a dashboard and is really meant for any business user. And we place a lot of trust um and we place a lot of emphasis in all our AI products on trust. Meaning we I tell people we need to think of AI the same way we think about software engineering, which is there's a right and there's a wrong. Okay, it cannot be this mode of like yolo AI. You can get some good answers, some terrible answers. It's your problem. And so we very much emphasize you need an eval for every single new thing that you're going to launch. If you want to change the underlying model, you need to be able to quickly verify that you didn't blow up on the things that you are already doing. And so we want it to be the trustworthy product for every employee, which is actually a new thing for us by the way because Snowflake for pretty much all of its history has always been used by the data team to slap a dashboard on top which then gets exposed to end users. This is a very different motion. This is why we are working on things like identity provider integration so that you don't have to set up Snowflake accounts for each of the many users you're going to have in your company. We are also by the fact that there is subscription fatigue and so snowflake intelligence is very much a consumption product. People pay for what they consume and we are experimenting with a bunch of things there in terms of how do we drive broad and deep adoption without having people worry about runaway costs and things like that.

Back to my point about execution, I tend to be like completely emotionless about where the where where the strongest current is. On the other hand, it's absurd if we think we are SAP or Salesforce. We are not. there are, you know, somebody managing a hundred billion dollar supply chain ecosystem with a complicated software provider isn't saying, "Hey, I'm going to use SI and I don't need that." That's really not the goal. But on the other hand, I think the line between what an agentic system like this is going to be and what like pure software is going to be will be um absolutely is going to be blurry. And I can imagine a lot of easy use cases like my sales team has to go update Salesforce quite often, you know, because we force them to update when whenever there's use case transition and stuff like that. Can that be done with APIs? Absolutely. Should you be able to file a vacation on top of workday using our HR agent? I would say that's sort of a reasonable thing. And so we very much take this approach of be opportunistic but again operate from a position of value and strength and not just on naked ambition because I think that doesn't work out. But if on the other hand you focus on how do you like what does value creation mean? What do these users really want? I think that tends to be much more durable.

Change is hard. You have to acknowledge that and uh driving behavioral changes from lots of people is incredibly difficult. We were measured about how we rolled out changes. Uh for example, among the first changes were leadership and alignment changes and clearer accountability. That happened within a few quarters because you're not dealing with as many people. Yes, you organize the teams under them. But I would say that change also what we then call the war room or the pod model of product and engineering and uh our go to market functions will all work together. That was again an early change and it was done with small groups of people without necessarily disrupting lots of people. I would say other things for example rolling out coding agents to our engineers uh that was a project not everybody wants to do it. Some people are skeptical some people are not. I'm a big fan of uh combining bottoms up with tops down approaches. The example with coding agents is that Benoit, our wonderful founder who fell in love with uh coding agents did more to drive coding agent adoption with the engineers and any number of words from me. You sort of have to find the right people, find the champions. Um my take is that every large organization has these forward-thinking, curious, I'm going to work over the weekends to figure out how to do something kind of people. Mhm. Um you need to find them, you need to encourage them, you need to elevate them and use that to drive change. Top down change can be helpful but it really needs to come you know from a bottoms up perspective. We've rolled out coding agents to all of uh our solution engineers and they are excited because that just dramatically lowered the amount of time it takes to create a demo. Usually our demos used to be canned and they were not always customizable to uh a particular customer. Um but we can now be like okay we know the kind of data we think Elad and Sarah are going to have um as part of their podcast let's create a demo with you know synthetic data sets just for that I think that's the kind of ability that um that that we have gotten changes heart.

I think these things are accretive um They add on ways uh they add on to things in ways that you don't always appreciate or like then or ever. It is what it is. I always um you know complain to my family about the 10 years that I spent doing research and getting a PhD. Um I was like that was a waste of time but not really. It um for example doing a PhD teaches you to focus on ideas teaches you to focus on how do you convey them crisply. you often spend like enormous amounts of time writing four line abstracts, but it actually turns out that's incredibly powerful to be able to convey ideas in an easy way. NEA is among the hardest and most heartbreaking experiences of my life. It is what it is. Sometimes you are too early. But on the other hand, I probably learned more about hustling, took success far less for granted. um learned more about social or marketing or any of these other things that you kind of take for granted if you're at Google. At Google, whatever you did, my first um launch at Google, which was entirely my work for 3 months, one person, okay, was covered by the New York Times. Okay. Yeah. Um and so you just have immediate scale with anything. Yeah. Distribution, your ideas, your products. Um doing a startup real, you know, makes you realize that that's actually special. Um, and so I think, you know, I bring quite a lot of that when it comes to what does it take to hustle? What does it take to win? Honestly, I think both the Google and the NEA experiences make me uh, you know, somebody that's just a lot more grateful for my job. We talked earlier about how you have to deal with like a bunch of stuff that you don't really want to deal with when it comes to like doing something big that you like. I'm a lot more gracious about that because it's just it is just such a privilege. um to be at a place like Snowflake to be having the kind of impact that uh we have. I remember what you told me after summit you said something along the lines of thank you for inviting me to your um rock concert. There was a line that was more than two blocks long of people waiting to get into Javit Center in New York for this little conference that we were doing. A lot more grateful for things like that. There's nothing ordinary about it.