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

I was fortunate to be part of another trillion dollar company where a product that I was personally leading, search ads, went from $1.6 billion in revenue in '03 to making $100 billion the year that I left, 2018. Eric Schmidt, our CEO at the time, made me and a few other folks write a hundred billion revenue plan. This is like 2007, '08. We all thought that this was the funniest thing ever because the idea that one company could make $100 billion just sounded so preposterous.

There's no question that we have to move fast. You have had pretty good quarters, 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?

Excited to be here. Thank you, Molly.

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 Meta was here before. I think only partly.

We do. Okay, they say we do. I'm not that much of a snowboarder, so they must know better.

There's a lot of snowboards. There's a lot of snow gear. We are a very blue snow.

To be honest, 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 like fun calculations without spending a whole lot of time by asking ChatGPT questions. If you ask ChatGPT questions like, 'Hey, how big does my sail have to be in space? If I have to use solar power to power my 1 megawatt space data center I want to run,' it's really big. So, 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 sound that attractive, but who knows? We went from data centers on the moon to data centers in space, and I think the next evolution might be data centers on Earth. What do you think?

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?

Okay. What is pretty remarkable about this moment is that there is more demand for AI and AI products than people can reasonably produce. That's the reason why there is so much investment in things. Similarly, people are doing calculations like if every human being had a ChatGPT-like product that they could use, how much inference capacity will you need? And what's also cool about this moment is that the previous generations of chips are still getting used because those chips are becoming super useful for running smaller models and people are getting utility out of that for simple applications like voice 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, new models, the frontier models are letting people do stuff 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.

It's a great question and my humble answer is I don't think anyone really knows. It is early and it is useful to draw from history about similar kinds of large changes. And if you look back, even things like industrialization, which one tends to think of as like some discrete thing that just happened within a few years in I don't know 1800s something like that, actually is a phenomenon that started closer to the 16th century and just kept going for a very, very, very long time. I think AI represents the beginnings of us being able to run truly thinking algorithms to industrialize thought in a very, very big way. And I think it's very much the beginning and I think there are lots and lots of applications for things like this. Everything as I said from being able to pretty much create for example what's called a classifier or a sentiment detector. You have a piece of text. Is this text angry or happy? Being able to do problems like that without really needing to know any programming. Two, 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. 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 interpret it, 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 partially assist how we as humans think in every sphere. I think it's just super early.

You used an important word here right, which is accruing. I think we should first start with where is value being created at scale. Value is being created by all the users of ChatGPT, nearly a billion of them, and it is getting created in completely surprising ways. My wife and I moved into a new home. We don't know anything about it. I'm trying to reprogram the gate because I can't get it to open. 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 and said, 'Hey, how do I program this thing?' Tells me what the model is, what button I'm supposed to press, what I'm supposed to do on my remote. Huge amounts of value being created right there. Value is also being created with all of the people that are using coding agents to write software faster. Absolutely. There's a huge amount of value being created there. In the world of enterprise, big value is being created in areas like customer support 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 in things like Snowflake Intelligence where my sales team, 5,000 plus people, rather than use a series of dashboards to get 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 terms of where is value accruing, definitely value very firmly is accruing with Nvidia. Look at their market cap. It's accruing with the hyperscalers. They simply cannot build enough data centers, buy enough chips, and satisfy all of their demands. And value is accruing in areas like the model makers themselves as in their valuations are going up. But I think this is where one has to then think about the second and the third order effects of will open source models catch up, how much of a lead, a moat as it were, will the model makers have. It's very clear that it's not all that easy to make chips like Nvidia. It is very clear that running operations as large as what the hyperscalers run for renting out compute, that's not that easy. It's very hard for a fourth or fifth hyperscaler to come up. I think beyond that, it is clear 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.

I think there's still an open question about things like the moats that traditional application providers have. I think the jury is out. I think they have absolutely a lot of value, 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. There's just an application for every single niche that you can think of and there is an amount of fatigue in the enterprise, men trying to manage 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 life cycle. So at Snowflake for example, absolutely we want to be there for our customers at every stage of the data life cycle so that they don't have to stitch together lots of complicated 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.

Tech companies are often ahead when it comes to using AI. It makes sense that Snowflake is using AI because we are a tech company. I think we have seen very good results with customers like ServiceNow, with DocuSign, with Zoom, these are all folks that are well-versed in tech. Unsurprisingly, the place where AI is making a big difference is in different kinds of financial services companies. Now there are different categories of financial services companies subject to different amounts of regulations. Asset management managers are less heavily regulated compared to banks and so they tend to be leading when it comes to adopting 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 provide software for asset managers, fully embrace AI. Their CIO, Wax, is really literal about agents. Yes, he has cool names like Taya that he created on top of Snowflake Intelligence that do a lot of 24/7 support for their various customers. We are also pleasantly 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. Imagine cases like this one. A lot of medical providers 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, that would be a project. Right now it's a SQL query that you can run that's going to rip through all of these notes and kind of give you an effective answer. So we are seeing a lot of adoption. I'm often shocked that even now when I go visit doctors, they are very happy and proud about taking notes on their phone with spoken voice, which is pretty cool. So that's a sector that's actually leaning in pretty heavily.

Snowflake at its core is a data platform. We make it easy for you to bring data typically from other systems of record and be able to analyze them. 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 that data into Snowflake. So we are fundamentally about making data easier to analyze and when we think about AI, we think of AI as a massive accelerant to this data cycle. 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 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. AI is an extension of our data platform capabilities. We define our mission as a company to be to let every enterprise realize its full potential through the use of data and AI. And so that's very much what we aspire to. A surprising side effect of AI and Snowflake is AI is making it much easier for our customers to set up Snowflake to be able to do things like configure agents. It's very meta. You want to create agents on data, agents on top of Snowflake because it can let end users get to data faster, 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 can massively accelerate this very painful process of bringing in data, stitching it together, and moving it from raw to consumable insights.

I'm very pleasantly surprised by how quickly a pretty large team in dozens of countries has adapted to a very new environment of operating. Two years ago, AI was a nice to have, but no one thought that it would affect their day-to-day life. And yet what we expect for example from I expect our sales team to be able to show off Snowflake Intelligence ideally on their phone every single day. That was not something that I thought would happen all that quickly. Similarly, solution engineers went from people that clicked things on screen and maybe wrote a SQL query or two to actually being really good and proficient with coding agents in order to deploy 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, I guess we'll figure out what we do next week and act week on week. I think that's been a very pleasant surprise for what I said started as a database company. Companies don't change that quickly, but I think the company has transformed itself very, very quickly. Doesn't come easy. It is hard. But I think that's been very positive.

Change is always hard. Okay. I think Snowflake, through an act of sheer conception from Benoit and Thierry, the founders of Snowflake, created a product that was years ahead of what anyone else had imagined. And that made Snowflake a very, very confident company. Two, three years ago it was clear that data was going to be affected in a very massive way by AI and so two years ago there were 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, but in a number of these situations you have to try and you have to prove yourself and I think that's been positive but it's also been very hard. And right now for example getting the whole company to think iteratively because the way of deploying products today is completely different from what we did at the beginning of last year. 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 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. It's a completely different way of developing product. Making changes like that to how people think, to how people act every day is really hard. It's hard for me because I went from 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 this is a whole new world. And if I don't pay attention for three months, 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.

That's right.

Well, Mike is still on our board. Still as opinionated as ever. Love that. You know, Mike is a technologist, super passionate about where the world is going, super passionate about the opportunity in front of us. This is a moment where it feels very much like a company has only two outcomes. It's either going to become a trillion dollar behemoth or disappear without a trace. And so he is super passionate about the power and impact that things like AI are going to have. And so we have lots of fun discussions in that very room. That's our boardroom.

You can pretend to be Mike and ask Mike questions.

Um, there's a formula. I'm joking. I think the answer is honestly compound growth. Okay. Compounded growth. I was fortunate to be part of another trillion dollar company where a product that I was personally leading, search ads, went from a billion and a half, $1.6 billion in revenue in '03, the day I started as an engineer in that team, to making a hundred billion dollars the year that I left, 2018. And so I've watched that transition. And in fact, funny story, Eric Schmidt, our CEO at the time, made me and a few other folks write a hundred billion dollar revenue plan. This is like 2007, '08. We all thought that this was the funniest thing ever because the idea that one company could make a hundred billion dollars just sounded so preposterous. But the magic of Google is 35% odd growth compounded over and over again. And so that's what it takes to become a hundred billion dollar revenue company, to become a trillion dollar company. Those things are better off as kind of visions. I tell our team here, you're a great company. We want to be an iconic company. And 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 to increase RPM, which is how much revenue we made per thousand queries, by 5% every quarter. Every single quarter during my time there, we took that as the objective. And that's the reason why you get to have these compounded gains of 35%. You do the math. 1.35 raised to the power of like 12 or 15, very big number. That's what it takes for Snowflake to be an iconic company. But you need to leave it as like that distant north star over there and focus on getting stuff done today.