Snowflake vs Databricks: The AI Data War | CEO of $SNOW

[00:00] I was fortunate to be part of another trillion dollar company where a product that I was personally leading, Search Ads, spent from $1.6 billion in revenue in 2003 to making $100 billion the year that I left. Eric Schmidt, our CEO at the time, made me and a few other folks write a $100 billion revenue plan. This was like 2007, 2008. We all thought that this was the funniest thing ever. [00:30] found itself to the past. We're in a bit of a wartime environment and one of your competitors recently raised, I think it was a Series Q, rumors of IPOs coming around. How do you view your strategic positioning against someone fast moving like that? There's no question that we have to move fast. We have had pretty good quarter, but bluntly, we should be asking ourselves why we are not growing a whole lot faster. What's the biggest secret you can tell me right now? [01:00] you [01:03] Siddharth, welcome to Sorcery. Excited to be here. Thank you, Ma'am. Thank you for having us in your glorious snowflake palace. It is a nice office, but I'm always very quick to point out that we subleased it. It's not as expensive as it looks. And Meadow was here before? Meadow was here before. I think only partly. I also heard that there are rumors you have a secret [01:29] Ski slope.

[01:33] We do? Okay, they say we do. I'm not that much of a snowboarder, so they must know better. So I also noticed that there's only snowboards. [01:42] here. There's a lot of snowboards, there's a lot of snow gear, we are a very blue snow. You're just good with any kind of, yeah. [01:49] Light blue. So since you are the data expert, [01:53] I want to know [01:55] what's happening with data centers in space? [01:58] To be honest, [02:00] I'm not convinced that I understand the economics of that thing or whether the economics are really going to work. If you went and do, as you know, you can do a lot of fun things [02:12] calculations without spending a lot of time by asking chat GPT questions. [02:16] If you ask Jai GPD questions like, "Hey, how big does my sail have to be in space if I have to use solar power?" [02:25] to power my 1 megawatt [02:28] space data center I want to run. It's really big. [02:32] So [02:32] I'm not completely sure of how this thing is going to work out. Maybe the same people that want to send it will also send like a little nuclear reactor, which does not. [02:41] sound that attractive but [02:43] We went from data centers on the moon to data centers in space. [02:49] And [02:51] I think the next evolution might be data centers on Earth. What do you think? We have them. [02:57] And it's precisely because we can't get enough power [03:01] in countries like ours that you have all of these other ideas coming.

[03:05] Wonderful. So part of what's driving that is people think that it's because of SpaceX's IPO and they're going to need extra money and that kind of thing. What do you think? [03:15] I think it's more to do with influence capacity. What is pretty remarkable about this moment is that there is more demand for AI and AI products. [03:29] than people can reasonably produce. [03:31] That's the reason why there is so much investment in things. Similarly, people are doing calculations like if every human being [03:40] had a ChatGPT-like product that they could use. [03:44] How much inference capacity will you need? [03:46] And what's also cool about this moment is that the... [03:50] previous generations of chips. [03:52] are still getting use. [03:54] because [03:55] those chips are super useful for running smaller models, and people are getting utility out of that for simple applications like Weiss transcription or doing classification on large volumes of text. So the remarkable thing about the current moment is that old models are useful, old chips are useful, [04:15] new models, the frontier models, are letting people do stuff [04:19] with things like coding agents that they could not dream of doing before. So there's a lot of demand for the new stuff. So it's a time of plenty. In turn, that is leading to people feeling very optimistic about just having more AI capacity. That's what's driving the boom. That's great. I guess to take a step back a little bit further, where are we exactly in the AI super cycle? It's a great question.

[04:45] And my humble answers, I don't think anyone really knows. [04:49] It is early. [04:51] And it is useful to... [04:55] draft from history about [04:58] similar kinds of large changes. [05:02] And if you look back, [05:04] even things like industrialization. [05:06] which one tends to think of as like [05:09] some discrete thing that just happened within a few years in I don't know 1800s something like that [05:16] actually is a phenomenon that started [05:18] closer to the 16th century. [05:20] and just kept going for a very, very, very long time. I think AI represents the beginnings of [05:27] us being able [05:29] Duran truly [05:32] thinking algorithms to industrialize thought in a very, very big way. And I think it's very much the beginning. [05:39] And I think there are lots and lots of applications for things like this. Everything, as I said, from being able to [05:46] pretty much [05:49] create, for example, what's called a classifier or a sentiment detector. You have a piece of text, is this text angry or happy? [05:55] Um, [05:57] being able to do problems like that without really needing to know any programming. [06:02] 2 [06:03] Let me think about a complex analysis that I am going to do when I'm given a new piece of information. We get revenue information every single day. [06:11] We often want to know, why did revenue go up? Did it go down? Is there something else? These are all things that a human with expertise in the area needed to be able to do. Now we can begin to write down some of these things. In plain English, have models interpreted

[06:25] go take action on that behalf. So I think we are very much in the early cycles of how do you actually use AI to [06:35] partially assist how we as humans think in every sphere, I think it's just super early. [06:42] Another way to kind of ask this question is where the value is accruing. So in 2025, value really accrued to a lot of models. [06:50] hardware, [06:51] Where do you see the value accruing in the next year and [06:54] afterwards. [06:56] You used an important word here, right, which is accruing. [07:00] I think we should first start with where is value being created at scale. [07:07] Value is being created. [07:10] by all the users of ChatGPT, nearly a billion of them. [07:14] and it is [07:17] getting created in completely surprising ways. [07:21] my wife and I moved into a new home. [07:25] we don't know anything about it, [07:27] I'm trying to reprogram the gate because I can't get it to open. [07:32] And normally it's like a super painful process where you like brush off things, try to see what the model name is, go inside, look for the manual, hope you can understand it, come back, try and program it. Instead, I took a picture with ChatGPT. [07:45] and said, "Hey, how do I program this thing?" [07:48] tells me what the model is, what button I'm supposed to press, what I'm supposed to do on my remote. [07:53] huge amounts of value being created right there.

[07:56] values also being created. [07:58] with all of the people that are using coding agents to write software faster. [08:03] Absolutely, there's a huge amount of value being created there. [08:07] In the world of enterprise, big value is being created in areas like customer support. [08:14] interactions that are particularly amenable to an AI model saying, I'm going to go take the context of hundreds or thousands of conversations that have happened before, and I'm going to use them to drive and help new conversations. So there are these kinds of applications that are driving the bulk of where value is being created on the AI side. We are also beginning to see the power of AI [08:44] team, 5,000 plus people, rather than use a series of dashboards to get our data, they just ask questions. Before I show up for a customer meeting, it's much easier for me to ask like a six sentence question about what do I want to learn about this customer than it is for me to somehow stitch that together with eight different dashboards. So these are all places where value is being created. In [09:08] accruing [09:10] Definitely, value very firmly is accruing with NVIDIA, [09:17] Just look at their market cap. It's accruing with the hyperscalers. [09:21] They simply cannot build enough data centers, buy enough chips, and satisfy all of their demands. And...

[09:31] value is accruing in areas like the model makers themselves, as in their valuations, are going up. [09:40] But I think this is where one has to then think about [09:45] the second and the third order effects of will open source models catch up? How much of a lead, a moat, as it were, [09:55] will the model makers have. [09:57] It's very clear that it's not all that easy to make chips like NVIDIA. [10:01] It is very clear that running operations as large as what the hyperscalers run for renting out compute [10:07] That's not that easy. It's very hard for a fourth, a fifth hyperscaler to come up. I think beyond that, [10:14] It is clear. [10:16] that OpenAI and Anthropic have created a ton of value in creating these frontier models that are better than anyone else's. But I think an open question there is how quickly will others catch up? I think that's part of what is quite open right now. [10:29] In 2025, over 75, like 70 to 75%. [10:35] of the gains in the stock market were from AI and AI adjacent companies. [10:41] around $6 trillion. Yeah. [10:45] And so given that those were mostly NVIDIA, Meta, [10:49] big, big name stocks, alphabet. [10:52] Um, [10:53] Some people are pointing to the application layer, some of the categories that you just mentioned. [10:58] Are there any categories within that that you think are underrated?

[11:02] I think there's still an open question about [11:07] things like the mods that [11:09] application, traditional application providers have. I think the jury is out. [11:14] I think they have absolutely a lot of value. [11:17] but there is also going to be a lot of disruption for many applications as we know it. What happened in the first decade of the cloud computing revolution is that things like enterprise SaaS software had a Cambrian explosion. [11:33] There's just an application for every single niche that you can think of. And there is an amount of fatigue in the enterprise when trying to manage it. [11:41] several hundred applications, their own licensing models and things like that. I think even in the world of data, there was a very large explosion of different kinds of tools for every stage of the data lifecycle. So at Snowflake, for example, absolutely, we want to be there for our customers at every stage of the data lifecycle so that they don't have to stitch together lots of complicated tools. [12:07] individual applications in order to get some job done. I think what AI does to the application space is very much something that is open. I think there's lots of opportunity and lots of disruption. [12:21] Of your customer set, who have you seen just outperform in their embrace of AI? Obviously, they're using you with data and increasing their capabilities. So what is a good

[12:34] case study there. Tech companies. [12:36] are often ahead when it comes to using AI. It makes sense that Snowflake is using AI. [12:42] because we are a tech company. [12:44] I think there we have seen very good results with customers like ServiceNow, with DocuSign, with Zoom. These are all folks that... [12:55] like well-versed in tech. [12:58] unsurprisingly, the place where AI is making a big difference, [13:04] is in different kinds of financial services companies. Now, there are different categories of financial services companies subject to different amounts of regulations. Asset managers are less heavily regulated compared to banks. [13:18] And so they tend to be [13:20] leading when it comes to adopting [13:23] new tools because it can make a real difference. They are always trying to create that little bit of alpha because they manage assets on behalf of other people. So we have published case studies of companies like TS Imagine, which provides software for asset managers to fully embrace AI. Their CIO waxes really lyrical about agents, yes, yes, cool names like Taya that he created on top [13:53] their various customers. [13:54] um, [13:56] We are also [13:58] pleasantly [13:59] surprised by how much the healthcare sector is adopting AI. They drown in a sea of data, and so a lot of those companies are actually adopting AI because it can make a real difference.

[14:12] Imagine cases like this one, a lot of medical providers [14:17] have a huge amount of clinical data. Let's say notes written by a doctor whenever you go visit them. And previously, if you wanted to answer questions like, what fraction of people had flu-like symptoms in a particular month, [14:33] That'll be a project. [14:35] Right now, it's a SQL query that you can run that's going to rip through all of these nodes and kind of give you an effective answer. So we are seeing a lot of adoption. I'm often shocked that... [14:45] even now when I go visit doctors, they are very happy and proud about taking notes on their phone, [14:53] with the spoken with spoken voice, which is pretty cool. So that's a sector that's actually leaning in pretty heavily. Sorcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in three venture backed startups in the US. Nearly 40% of startups fail because they run out of cash. Brex is literally built to help founders avoid that. Unlike traditional banks that let your money sit idle, shipping away at it with fees, [15:23] Their all-in-one solution combines checking, treasury, and FDIC protection into one powerful account. You can send and receive money globally at lightning speeds, get 20 times the standard FDIC coverage through their partner banks, and even high yield from day one. With same day and even same hour liquidity, access your funds anytime. Companies like Scale AI, DoorDash, Service Titan, HIMSS, Anthropic, Flexport, Robinhood, and Plaid trust and use bricks.

[15:53] Start today at brex.com slash sorcery. That's B-R-E-X dot com slash sorcery. So what is Snowflake's role in the AI super cycle? Snowflake at its core. [16:05] is a data platform. [16:07] We make it easy for you to bring data typically from [16:12] other systems of record [16:15] and be able to analyze them. [16:18] And so people do all kinds of analysis. For example, in our own case, we use Salesforce as our CRM solution, but we bring that data into Snowflake so we can figure out what's the rate at which we are acquiring new use cases. What are characteristics of new customer logos that we are winning? We bring all of the data into Snowflake. So we are fundamentally about make data easier to analyze. [16:48] And when we think about AI, [16:50] We think of AI as a massive accelerant to this data cycle. [16:55] So what this means is instead of bringing in data into Snowflake and going through what is a fairly painful and laborious process of setting up dashboards, of basically needing an army of analysts in order to provide you with an insight, we can provide you with a conversational interface. [17:12] to this data that lets you ask questions in natural language about any aspect of the data that you have in Snowflake and be able to provide that for you. [17:22] We see AI as an extension of our data platform capabilities.

[17:28] define our mission as a company to let every enterprise realize its full potential [17:36] through the use of data in AI. [17:37] And so that's very much what we aspire to. A surprising side effect of AI on Snowflake is... [17:45] AI is making it much easier for our customers [17:49] to set up Snowflake, to be able to do things like configure agents. It's very meta. [17:54] You want to create agents on, data agents on top of Snowflake because it can let end users get to data faster. [18:01] But the analyst or administrator can use AI now to make the process of setting up that agent be a whole lot faster. That's something that I'm really excited about because I think that coding agents... [18:14] can massively accelerate this very painful process of bringing in data, stitching it together, and moving it from raw [18:23] to consumable insights. [18:27] Coming into this role as CEO, you've been here almost over two years now. What has been the biggest surprise for you leading through one of the most volatile environments? I'm very pleasantly surprised by how quickly a pretty large team [18:45] in dozens of countries. [18:47] has adapted to a very new environment of operating. [18:52] Two years ago, [18:54] AI wasn't nice to have. [18:56] but no one thought that it would affect their day-to-day life.

[18:59] And yet, what we expect, for example, from I expect our sales team, [19:04] to be able to show off Snowflake Intelligence, ideally on their phone. [19:08] every single day. [19:10] That was not something that [19:12] I thought would happen all that quickly. Similarly, [19:15] Solution engineers went from people that clicked things on screen [19:19] and maybe wrote a SQL query or two, to actually being really good and proficient with coding agents in order to deploy [19:27] actual solutions to problems for our customers. Similarly, our engineers have gone from, we are a database company, we make 24 month plans. Database companies love making long plans to [19:41] I guess we'll figure out what we do next week. [19:44] act week on week. I think [19:46] That's been a very pleasant surprise for what I said. It started as a database company. Companies don't change that quickly, but I think the company has transformed itself very, very quickly. It doesn't come easy. It is hard, but I think that's been very positive. [20:02] And personally for you, what has that been like? How has your leadership changed? Is it harder? Is it... [20:08] you know, [20:09] easier than you would imagine. [20:12] Change is always hard. [20:14] I think [20:15] we [20:18] Snowflake [20:21] through an act of sheer conception from Benoît and theory, the founders. [20:25] of Snowflake created a product that was years ahead of what anyone else had imagined.

[20:33] and [20:35] That made Snowflake a very, very confident company. [20:39] two, three years ago. [20:40] it was clear [20:42] that data was going to be affected in a very massive way by AI. [20:46] And so two years ago, [20:49] There are doubts. Hey, will we make this transition? Can we also be good at AI? This feels like something that we hadn't done as well on. [20:58] but [20:59] In a number of these situations, [21:03] you have to try and you have to prove yourself. [21:07] And I think that's been very, that's been positive, but it's also been very hard. [21:13] And right now, for example, getting the whole company to think iteratively. [21:20] because [21:21] the way of deploying products today, [21:24] is completely different from what we did at the beginning of last year. [21:28] At the beginning of last year, if you ever wanted a product to get deployed, you needed to have a perfect web UI that would walk people through how to configure something, help them with mistakes. You'd have to write long manuals for how you go about configuring something. Today, the expectation is that... [21:44] There's going to be a smart person that knows how to use coding agents, is very comfortable doing it, that can just deal with APIs, which are super abstract concepts. It's how you talk to some server, but they can get a lot of work done. [21:56] It's a completely different way of developing product, making changes like that to how people think.

[22:03] to how people act every day. [22:06] is really hard. [22:07] It's hard for me. [22:09] because I went from [22:12] somebody that had a pretty good grasp of how tech worked, of how cloud computing worked, of what was fast and what was slow, and what are qualities to look for in engineers and leaders to [22:24] This is a whole new world. [22:26] And if I don't pay attention for three months, [22:28] I'm really behind when it comes to what is possible with AI. It's honestly, I think it's a little bit of a terrifying moment for every technologist that there is, because there is a lot of Alice in Wonderland here, which is you have to run as fast as you can to kind of stay at the same place. [22:48] Turing is training the next generation of AI with tasks that require real expertise and real world judgment. That's why companies like NVIDIA, Anthropic, Salesforce, and Gemini partner with Turing. Turing builds realistic reinforcement learning environments and data systems based on real operational traces. The kind of infrastructure frontier labs need to train superintelligence. Visit Turing.com slash S-O-U-R-C-E-R-Y. [23:18] to tech background. [23:20] Snowflake was incubated and created out of Sutter Hill. That's right. How involved is Sutter Hill to this day? [23:27] Well, Spice are still on our board. [23:30] Still as opinionated as ever. Love that. [23:33] Um,

[23:34] Mike is a technologist, super passionate about where the world is going. [23:39] Super passionate about the opportunity in front of us. [23:43] This is the moment where [23:46] it feels very much like [23:49] a company has only two outcomes. [23:51] It's either is going to become a trillion dollar behemoth, [23:54] or disappear without a trace. [23:56] And so he is super passionate about the power and impact that things like AI are going to have. [24:06] And so we have lots of fun discussions in that very room. That's our boardroom. [24:11] Wow. Should we go in there? Should we interview in the boardroom? You can pretend to be Mike and ask Mike-like questions. All sit at one end of the table, you sit at the other. No pressure. [24:24] And then so [24:26] I guess... [24:27] You brought it up. How are you going to become a trillion dollar company? [24:31] There's a formula. I'm joking. I think... [24:37] The answer is honestly compound growth. [24:39] Okay. [24:40] compounded growth. I was fortunate to be part of [24:46] another trillion dollar company. [24:48] where a product that I was personally leading, search ads, [24:51] than from [24:53] a billion and a half, $1.6 billion in revenue in 2003, [24:58] the day I started as an engineer in the team, [25:01] to [25:02] making $100 billion the year that I left, 2018.

[25:07] And so I've watched that transition. [25:10] In fact, funny story, Eric Schmidt, our CEO at the time, made me and a few other folks write a $100 billion revenue plan. This is like 2007-2008. We all thought that this was the funniest thing ever. [25:26] Because the idea that one company could make a hundred billion dollars just sounded so preposterous. [25:33] But the magic of Google is [25:35] 35% odd growth compounded over and over again. [25:40] And so that's what it takes. [25:42] to become a hundred billion dollar revenue company, to become a trillion dollar company. [25:48] on [25:49] those things are better off as kind of [25:53] visions I tell our team here we're a great company we want to be an iconic company. [25:59] And, uh, [26:00] That requires a lot of hard work year in and year out. For pretty much the entirety of my stay at Google, we took what's called an OKR, an objective, [26:11] to increase RPM, which is how much revenue we made for 1,000 queries, by 5% every quarter. [26:19] Every single quarter during my time there, we took that as the objective. [26:24] And that's the reason why you get to have these compounded gains of 35%. [26:29] You do the math, 1.35. [26:32] raised to the power of like 12 or 15. [26:35] very big number. That's what it takes for Snowflake to be an iconic company. But you need to leave it as like,

[26:42] the distant North Star over there? [26:45] and focus on getting stuff done today. [26:47] I'm really curious, like, because we're in such a competitive environment, [26:53] You alluded to this a bit. [26:56] like culturally it's totally different in even from like the beginning of last year. So how do you keep the teams motivated and [27:05] kind of, uh, [27:07] able to weather the storm with you as it changes so much. You have to embrace change. [27:12] And you also have to find people. There are a class of people. [27:17] Let's thrive under change. [27:19] you have to make sure that they are in positions of being able to have an impact. [27:25] When we wanted to roll our coding agents, for example, to our solutions team, we identified 35, 40 odd people. [27:33] that were naturally inclined [27:36] to want to go learn, to want to go tinker. [27:39] You hold them as exemplars to the rest of the team. [27:43] and [27:44] help spread the message, help spread change through. [27:49] winning over [27:50] iconic people, [27:51] with extra effort makes a huge difference. [27:54] I joke to people that I could scream from the rooftops to all my engineers about how they really need to use coding agents. [28:02] It's not going to have that much impact. They're going to go like, what does he know about? [28:06] Software engineering. [28:07] On the other hand, Benoit, [28:09] our iconic founder, [28:11] Fallen La. [28:12] with coding agents.

[28:13] And Benoit is a truly religious figure. When he believes in something, trust me, you're going to hear about it. And every engineer that he met, [28:21] heard about the impact that [28:24] these coding agents had on Benoit's own [28:27] day to day [28:29] work. [28:30] that had a huge impact. So you also need to be strategic about who do you have representing change. [28:36] and the more you can have naturally influential people represent change, [28:41] the easier it's going to go. [28:43] mentioned a couple characteristics for talent what are the key traits you look for [28:48] I think that's a good thing. [28:49] In terms of... [28:51] Like... [28:51] qualities that I look for in people that I want to bring into the team. [28:57] I look for, I mean you need a certain amount of subject matter expertise. Before our conversation I talked to you about [29:05] how hard it is to just become skilled at something. [29:08] Sometimes you have to spend 10 plus years to get like really good at something. [29:11] Stuff is hard. [29:13] And after a certain age, you cannot get good at something. It's like... [29:17] I'm not going to become a concert pianist like ever. It's just gone. But even when you have the background to get really good at something, [29:25] you need to be spending a lot of time at it. So I look for drive. [29:30] I also look for malleability. [29:31] I'll often ask people, tell me things that you have changed [29:35] personally in your life. Everybody will give you a good story about what they did with work. [29:40] because they know that that question is coming. And they say, "Oh, Sridhar, I did this project to make these people more efficient, and I use this chatbot over here." So I'll practice answers.

[29:50] I don't like them. [29:52] I'll ask them questions like, tell me how you changed yourself. [29:56] Like, show me that you're malleable in how you think, in a professional setting, in a personal setting. To me, the combination of drive and malleability. [30:05] those are the prized qualities that set the [30:11] the truly amazing people apart from everyone else, especially at a moment like this. My kids are two years apart. [30:19] And [30:22] When they were growing up, even in their teenage years, you could tell the difference between the older one and the younger one. The younger one always used voice to ask questions. [30:30] off of then chatbots like Google Home, while the older one just like never quite wanted to do that. There are all these age characteristics that go on, but the truly amazing people, they like break through all of these kinds of just learned behaviors and adapt and change. So drive. [30:50] willingness to change. [30:52] One of the iconic figures of Snowblake [30:55] is Frank Sweetman. What are the biggest lessons you learned from him? Frank [31:00] has [31:03] decisiveness, [31:05] and clarity of thinking [31:07] that just strikes you. [31:10] in a regular public company, [31:13] The way this transition would have happened from him to me, for example, would be I would have gotten appointed as COO. I'd be told to do something. It would take two, three more years. And then Frank would change over from CEO to like executive chairman. He would still be running, but not really be running.

[31:31] But his attitude was, [31:33] This is a pivotal moment. [31:35] when it comes to Snowflake, its role in the data ecosystem, the changes that are being [31:42] brought on us by AI. [31:45] He went to the board and basically said, if we are going to bet on this guy, [31:50] We just bet on this guy. [31:51] No [31:52] No halfway. [31:54] I think that [31:56] clarity of thinking, [31:58] is incredible. And you find that with [32:01] Honestly, all great leaders [32:04] They are very good at reductive thinking. [32:08] can be dangerous, but [32:11] to truly cut down all the noise and say, this is the core problem that we need to solve. [32:17] and to have [32:19] a vision. [32:20] to have determination about how we are going to solve it. [32:24] There are very few people. [32:25] like Frank when it came to this. [32:28] I have a lot to thank him in terms of setting me up. And then... [32:34] you [32:35] even recognizing. [32:37] that a big change was needed for Snowflake the company. I won't say that, I will never say, [32:43] that we are like, [32:45] confident about getting through the AI era because no company has any business being confident about getting through the AI era. [32:51] But I feel like the amount of change that we've been able to make in the past two years is remarkable. That's because of people like Frank who saw what was coming and said a different way of looking at things as needed. And it takes a special leader to say, you know, I think someone else should be in charge. There's just so many qualities to admire about him.

[33:12] Some of you may not have heard this yet, but our sponsor Public just launched something called Generated Assets, and it brings AI into investing in a way I've honestly never seen before. Here's how it works. You type in an idea like AI-powered supply chain companies with positive free cash flow, or defense tech companies growing revenue over 25% year over year. Public's AI then dispatches a swarm of agents that scan every single US stock, evaluates them, and instantly builds a custom index around your thesis. [33:42] why each stock is included. And before you invest, you can even backtest your idea against the S&P 500. So you're making decisions with real context, not just guessing. And beyond generated assets, Public lets you invest in stocks, bonds, options, crypto, all in one place. They'll even give you an uncapped 1% match when you transfer your investments over from another platform. If you want to build a portfolio that actually reflects your thesis, visit public.com slash sorcery. [34:08] Paid for by public investing. Full disclosures in the description. [34:12] He's also seen as a bit of a wartime CEO and leader. [34:16] we're [34:17] in a bit of a wartime environment. And one of your competitors recently raised, I think it was their Series Q might have been, I don't know, very late round. Rumors of IPOs coming around. How do you view your strategic positioning against someone fast moving like that? There's no question that we have to move fast. [34:36] And [34:38] We have had pretty good quarters. [34:41] But bluntly, we should be thinking about, we should be asking ourselves why we are not growing

[34:46] a whole lot faster because the opportunity is there. The hyperscalers who are massive compared to us, [34:51] often are showing us how. [34:53] And so I think there is very much that competitive spirit in me. [34:57] In Christian, our head of product, in Vivek, who runs engineering, or Mike, who runs sales. Absolutely, I think we need to do more faster. [35:06] That's that sense of urgency and opportunity that I do feel. On the other hand, with private companies, there is... [35:17] Like, [35:18] selective metrics. Not everything is subject to scrutiny the same way that a public company has scrutiny. But I feel very good about where Snowflake is as a company when it comes to supporting enterprises. [35:34] We were talking to the [35:38] to the head of a really important financial services company right again in that room this morning, [35:45] and [35:46] He was saying being enterprise grade in everything that we do. [35:52] where [35:53] We provide for disaster recovery, we provide for excellent governance, we provide a whole slew of things that are needed to be truly enterprise ready. That's our heritage. We are very good at doing that. Do we need to combine that and be faster moving and seize the opportunity? [36:09] 100%. [36:11] What companies do you think are going to fail or struggle in this next phase? Every generation of Silicon Valley companies

[36:17] is smarter. [36:18] than the previous one. [36:20] We all learn. [36:22] And [36:24] Everybody understood. [36:25] How [36:28] Yahoo let go of something that was incredibly important search. [36:31] And sort of that led to kind of its eventual downfall. [36:36] we all know for example the story of Friendster. [36:40] And so there's a lot of or how [36:45] Firefox and Netscape [36:47] had a brief moment in the sun and sort of peered out after that. [36:52] So I think every [36:56] Every company in this generation learns and tries to adapt from stuff that has not worked. [37:03] I think there are companies [37:05] that are [37:07] What's the right way to put it? [37:09] that are well optimized when it comes to [37:12] squeezing value. [37:14] that are taking out a lot of value from their customers. [37:19] And then you have AI products that are fundamentally cheaper and better. [37:25] that produces a real dilemma that cannot be solved with strategy. [37:30] If there are a set of companies, for example, that rely [37:34] heavily. [37:35] on basically people power. They charge by the hour. They charge a lot of money. They make a lot of money. [37:41] And now you're confronted with a technology like coding agents that can literally get jobs done for a tenth of the time.

[37:49] And so embracing something like that [37:53] That's a lot harder. [37:55] I would say it is companies that have these kinds of structural problems. [38:00] that are going to find it hard to navigate. [38:04] If it's a matter of just pure product velocity, I think people are now kind of, they understand that they cannot get disrupted when it comes to adopting technology. I think it's more of if somebody is very heavy in a seat-based licensing model. [38:20] and someone else shows up that has a better product and is also cheaper, that's a very dangerous combination. I think those are the companies that are going to struggle. [38:29] Thank you for having me in your wonderful Silicon Valley AI hub. This is an amazing space. We have lots of startups here. It's it's a fun space. [38:37] - There are rumors that [38:39] You live in monk mode. [38:43] What does that mean? [38:46] I sometimes say that to simply say [38:50] That's... [38:51] We all have to make choices. [38:54] to be good at what we do. [38:56] So I spend a lot of time working. I spend time in the gym. [39:01] and I spend time with my family. [39:03] Mm-hmm. [39:04] That's it. [39:05] I don't really have much by way of hobbies. I don't watch any TV. [39:10] No binge watching Netflix. Okay. [39:13] So what's your form of entertainment? [39:17] Work? My life. Okay. That's great. So no founder mode, full force monk mode. That might be the theme of the year. Sure. It's working. It's making choices. Well, congratulations. I'm being happy with them.

[39:30] And being happy with them. And being happy with the choices. [39:34] Well, one of your recent choices was acquiring Observe. Yes. Can you talk to me about this? What happened? [39:41] Well, [39:44] Lots and lots of systems produce-- [39:46] even more data than before. [39:49] keeping track of it, making sure that things are running well, [39:52] is a big data problem. [39:54] and Observe is actually a partner. We've been working with them for many years. They're built right on top of Snowflake. [40:01] And we are very excited to expand [40:05] Snowflake's core platform offerings to also include observable [40:10] and we are headed into the world of agents. [40:12] that are going to be spewing out even more telemetry information about are they working well or are they not working well. [40:19] lots of complicated pieces interacting with each other. [40:22] So we think... [40:24] that it's going to be a great offering for our customers. [40:27] I'm also super excited [40:29] by having agentic [40:32] platforms like Snowflake Intelligence, which is our data agent platform, [40:36] on top of the observed data. [40:38] So if you are an on-call engineer, [40:42] again, ask a set of questions and the agent underneath will figure out which are the different sources it should talk to. [40:49] and come back with some theories about why you're having [40:52] either these performance issues or your software is kind of glitchy, [40:56] all of that stuff. [40:58] is [40:58] all going to come together with observance snowflake

[41:01] So we think it's going to be a great acquisition, both for the Observe team [41:05] and for snowflake. [41:07] And as somebody who came into Snowflake VR and acquisition, [41:10] I'm super excited. [41:12] as well. [41:12] What did you learn from your acquisition and how are you making this onboarding process? [41:33] Our acquisition went pretty smoothly. It's a little bit different from Observe in that we were more [41:39] like tech and talent? [41:41] rather than a running product, observe as a running product, it's a business, [41:45] Um. [41:46] To me, the most important thing to... [41:48] Remember? [41:49] if you are an acquired company, [41:51] is that [41:52] you have to become one with your new home. [41:56] you need to align yourself [41:59] Um. [41:59] with [42:01] what your new home, in this case, Snowflake wants. [42:03] On the other hand, [42:06] As the CEO of Snowflake, [42:07] I have to recognize that what this startup, what Observe has created [42:12] is magic. [42:13] really hard. [42:15] to create products that are working well. [42:17] People often underestimate it because [42:19] you know, we see so many successful companies, but to me, [42:23] a working product. [42:24] is pure magic. [42:26] Frank, my predecessor, used to say [42:28] a working product or working startup is like a life force.

[42:31] You just can't create it. It's very, very special. [42:34] And so we're going to try everything that we can do [42:37] to keep that team together. Jeremy is going to report to me directly. [42:41] and you're going to empower him to scale that business as rapidly as he can, [42:45] benefiting [42:47] from things like close access to Snowflake engineers, the scale of the platform, they'd no longer have to [42:52] pay snowflake for using snowflake? Do you think things like that are going to be beneficial? [42:57] They're trying to strike the right elements. [42:59] between [43:01] give them give them that support but also give them that independence [43:05] pendants. [43:06] for them to truly thrive. [43:09] Amazing. [43:10] Well, another... [43:12] large moment in the markets. Okay, so [43:15] Snowflake was one of the largest software IPOs of all time. If not, it was. [43:20] We're coming into the area of [43:22] trillion dollar IPOs. What advice do you give to these companies? [43:32] We had a very big IPO, but we also had a huge run up after the IPO. At some point, we were valued at more than $100 billion. [43:41] making less than a billion dollars over it. [43:43] Oh wow. [43:44] I thank God. [43:46] You know, the one piece of advice that I would have [43:50] Would we do? [43:51] nudge things one doesn't [43:54] have powers to shape the market, but one can nudge it and hold. [43:58] to make sure that valuations are same. [44:00] What happens is this is human nature.

[44:03] we all have loss aversion. [44:06] If somebody thinks that they are worth a thousand dollars [44:09] And suddenly they are [44:10] only worth $700 the next day. [44:13] They feel depressed. [44:14] Yeah. And they don't think-- [44:16] $[redacted address] to be. Right. They just go like, hey, I lost 30%. [44:21] And so those kinds of things can be hugely distracting inside a company. [44:25] So steering the same valuations is, I think, [44:29] an important part of every CEO's job. [44:32] Um, [44:33] And what this also does, the thing that people underestimate, [44:37] is the IPO process also [44:41] If done right, can produce a set of long-term investors, [44:44] who stay with you forever. [44:46] And that gives your public stock just a lot of stability. [44:50] Otherwise you can end up in situations where [44:52] There are a lot of little retail buyers. There are a lot of hedge funds that go in and out. [44:56] and that can make your stock very, very volatile. [44:59] So planning for sensible IPOs, [45:02] is something that all of these companies need to think [45:05] hard above [45:07] so that it is a [45:08] relatively smooth trajectory. [45:10] What's your relationship with your investor base? [45:14] Can we talk to them often? Yeah. [45:16] We do callbacks with a lot of our key investors [45:21] I go visit them once or twice a year. [45:24] Um... [45:25] These are all... [45:26] One of the things that I realized very quickly [45:29] after I came to Snowflake. This is even before I became CEO. [45:33] is that a lot of

[45:36] all our customers. [45:37] typically [45:39] CIOs or CDOs within our customers [45:42] They're not... [45:43] just buying snowflakes. [45:44] They're in fact putting their careers on snow. [45:48] I met this one person [45:50] that had a [45:52] failed migration? [45:54] meaning they were starting on a migration, [45:56] it needed to finish within two years or they had [46:00] to go renew some other large contract, did not finish in two years. [46:04] And so they had to go explain to the board that this was a failed project and [46:08] a bunch of money was wasted. [46:10] you could see the real fear. [46:12] in their eyes, even as they were telling me and Frank this story. [46:16] that's when it hits you. [46:18] these people are bidding their lives [46:20] on their professional lives. [46:22] on snowflakes. [46:23] I think-- [46:25] Similarly, [46:26] investors. [46:28] They're typically investing [46:29] other people's money, sometimes your money and my money, [46:32] Um, [46:33] and they have a responsibility. [46:35] So these are folks that are betting on the company [46:38] betting on the management team. [46:41] to be good stewards of their investments. [46:44] And so we think it is only natural. [46:47] that you go back. [46:48] And-- [46:49] you just... [46:50] You give them... [46:51] Again, all publicly available information [46:54] but you give it to them, you talk to them, [46:56] you treat them like the constituency that they are. [46:59] That just makes it-- [47:01] Human? [47:01] it makes the relationship a lot smoother and a lot more pretty. [47:05] What's the biggest secret you can tell me right now?

[47:07] *laughs* [47:08] I have no secrets. What you see is what you get. Okay. Awesome. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.bc, where we deliver a once a week top deals and tech headlines email, and also go deeper on our podcast interviews. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.