No Priors: Artificial Intelligence | Technology | Startups - Bringing AI to the Data Cloud, with Snowflake's CEO Frank Slootman

Episode Date: June 29, 2023

Frank Slootman, CEO of Snowflake Computing, joins Sarah Guo and Elad Gil this week on No Priors. Before scaling Snowflake to its blockbuster IPO and beyond, Frank was also the CEO from early to scale for landmark enterprise companies ServiceNow and Data Domain. Frank grew up in the Netherlands and is also the author of three books: Amp It Up, Rise of the Data Cloud, and Tape Sucks. In this episode, our hosts talk with Frank about the opportunity for generative AI in the enterprise, why Snowflake isn't really a data warehousing company, their acquisitions of Neeva and Streamlit, apps within Snowflake, and how AI relates to traditional analytics and BI. He also talks about his personal journey, why it's always a good time to do performance management, and why most leaders struggle to raise the bar for performance. ** No Priors is taking a summer break! The podcast will be back with new episodes in three weeks. Join us on July 20th for a conversation with Devi Parikh, Research Director in Generative AI at Meta. ** No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode. Show Links: Forbes: How CEO-For-Hire Frank Slootman Turned Snowflake Into Software’s Biggest-Ever IPO Amp It Up: Leading for Hypergrowth by Raising Expectations, Increasing Urgency, and Elevating Intensity Rise of the Data Cloud (Audible Audio Edition): Frank Slootman, Steve Hamm, Zach Hoffman, Snowflake: Books TAPE SUCKS: Inside Data Domain, A Silicon Valley Growth Story eBook : Slootman, Frank: Kindle Store Frank Slootman’s LinkedIn Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @SnowflakeDB Show Notes: [00:06] - Frank’s Insights on Career Success as a three-time CEO [12:42] - The message of his book Amp It Up [25:01] - Future of Natural Language and Data [36:29] - Data Management and Industry Transformation Future [45:13] - Managing Resources in Changing Economic Environment [50:09] - Amping Up Energy and Intensity Amid Economic Headwinds

Our guest today needs no introduction. Frank Slutman is the legendary three-time CEO of Data Domain, Service Now, and Snowflake, and one of the most looked up to leaders in technology for his relentless execution. We're excited to talk to him about what's on the horizon for Snowflake and how he looks at the AI opportunity. Frank, good to see you. Thanks for being here. Absolutely.

Good to see you, sir. Let's start with just a little bit of personal background. You have had an amazing journey. You grew up in Holland. You're the first person in your family to go to college. What were you like as a kid in college? And how did you end up in product management and computing in the U.S.? Yeah, that's kind of a big, wide-ranging question.

I sometimes have to go back and figure out what was the method to the madness because sometimes your life looks like a random walk. In other words, it's just a series of events that kind of go from one to the other. But, you know, I was always a relatively focused, disciplined kid, if I were to describe myself in almost any realm, whether it was school or sports or any of those things. It's just the nature of the beast, you know, I would say. And, you know, definitely, you know, a bit of a chip on my shoulder, which I generally like in people, by the way. You need to have a reason to get up in the morning and have some to prove to the world or whoever. those are all useful things.

Obviously, I ended up in the U.S. because I think the U.S. is obviously a much better, maybe not obvious, but it's obvious to me that it's a much better canvas for people like me. And obviously, we see it all around this, right? People that come from all over the world here because they have far greater opportunity and they would have where they came from. And it certainly is true for me. I mean, there's no doubt that I would have done where I came from what I've done here.

So I'm very grateful, you know, having had that opportunity. I always tell younger people, you know, it's very important where you decide to be. Don't just go where your friends are. To the point of choosing the right place, be it geography, yes, and thank you, America. My parents are also immigrants. You talk about being on the right elevator, and some of the companies you worked at, you know, weren't the hottest companies at the time when you joined. Like, tell us about those choices.

I just usually analogy to the elevator because there's this aspects of opportunity. and in circumstances you can't change. It is what it is. And you're going to be subject to it for better or for worse. And therefore, you need to choose carefully. Some people think that, you know, I can wheel my way to anything. That's not true, right? So your choices you make, like we just said, where are you going to be, what industry you're going to be, what company you're going to be, what people you're going to be with are all very formative.

And so you have to make, you know, very careful choices. Because if you combine good choices, you know, with great execution, you know, you get the perfect cocktail for opportunities, for future opportunities and for having a successful sequence of experiences. So it matters a whole lot. A lot and I talk to a lot of people joining entrepreneurial ventures and they're always trying to figure out where to go. That is often where their friends go and sometimes is where investor friends will direct them. What advice would you have for people choosing that company in terms of? of the things you can't change. You know, it's a great question.

I got asked a couple of times a year to speak to graduating classes at really prominent business schools and all that sort of thing. And they always ask me, is there one message that you have for the graduating class? I'm like, well, you know, don't go working for some consulting firm, you know, out of school, right? Try to get a real job into real economy, building real products, selling real products. He says you really need to feel what it's like, you know, to sort of be in the drive in the economy as opposed to, I'm just eating out of somebody else's trough. And I kind of sit on the vessel and glide along, and I'm feeling good about myself. But you haven't really touched

the real economy yet. And I really wish that for people early on in their careers to sort of feel the heat of competition and also the cold winds of threat of markets that are, you know, disappearing because that's the real world. And a lot of people choose jobs that are very removed from the real world. I don't think that's helpful for people's development in their careers. How do you think about company versus industry versus role? You know, often when I talk to people as well, I kind of advocate for the choose the right industry and then choose the best company in the industry and the role is secondary. Do you think that holds true or how would you suggest that people actually find their way? Yeah, I totally agree with that. I think the role is not

that important. You'll have many roles, okay? And roles come and go. And my first job, I took a role I really didn't want, but, you know, being an immigrant in this country, beggars couldn't be choosers. And I had to, I figured, look, I'll get in there and I'll make my way from there. You know, I was in a corporate planning group of like six people attached to the CEO of a large computer company. I was about as far removed from the real world as I could be. And I didn't want that, but that's all I could, you know, get into. These were the heydays of affirmative action.

We didn't have a lot of picks. So, in hindsight, I was. it was right because, you know, once I got in there, you know, you spent two years doing typical MBA stuff, you know, M&A and all the presentations for boards and all this kind of stuff. But then after that, they pretty much gave me, you know, whatever I wanted to do was fine with them. And from there, you know, I made my way. You've had three just amazing CEO jobs, right? So I believe you took data domain from less than three million in revenue through an IPO and a $2 billion acquisition by UMC.

At Service Now, you took it from 75-minute revenue through an IPO and I think won a $1.4 or $1.5 billion of revenue. And then Snowflake, of course, has just been an amazing run. And it's one of the really seminal companies in the data world. How do you go from step one to step two with all these things? And in particular, you know, in joint data domain, had an academic co-founder. I didn't have a product that was commercially scalable yet. Service Now, you really turbocharged. Snowflake was growing, but, you know, was spending a lot of cash.

So, A, what are the commonalities between those different experiences? And more generally, what kind of drives you? Like, what do you have to prove? You already had accomplished so much by the time you got to Snowflake. How do you keep going? So let me first sort of correct the record on day or the main. They had no revenue, no customers, nothing. There were 15 people there.

And when we first started to, you know, assert the products, it had one terabyte of usable space. Just imagine that. Okay, no, it was a one. You know, and it ran 30 megabytes, you know, a second. So it was useless for 99.9% of applications. So we're like, what are we going to do now? Why did you take the job? Well, I didn't know that.

You know, I'll tell you why I took the job. First of all, you know, I got rejected numerous times for CEO opportunities. And the ones that they were interested in were like second and third string. And I know people really cautioned me at that time. hold out, you know, do not go for a second, third string, you know, deal. You need to have really good investors. You know, we were a startup one out of hundreds at the time. You know, I'd be walking to halls of NEA and Greylock and people look to me, who are you? What company is that? Oh, oh, okay. We were a no name and we were lectured on, you know, on other companies

that in hindsight ended up being no name. So, I mean, it's almost, it's almost legendary how data domain just manifested itself. And by the way, I live for that kind of drama. It was great. But we didn't have product market fit. We just didn't. And, you know, I found a little bit of fit. I remember, you know, meeting with a CIO company that has been acquired since by EMC.

And they were testing the products. And the guy said to me, he said, you know, he said, that little product of yours was a real hero here on Friday. And I'm like, tell me more. Do tell. But he explained that, you know, they had their email database, you know, backed up on our device. And they had a mass of corruption email databases as that's happened back to end. That's not common anymore. And it was 4 o'clock on the Friday afternoon.

And they're like, oh, my God, we're going to be recovering from tape here all weekend long. We'll be sleeping on cots, blah, blah, blah. And then they remembered, oh, we have a backup home disk. And by 7 o'clock that evening they were going home. And obviously, you don't need to be a rocket scientist. figure out that is a use case you can sell a few times more, right? So we stayed alive and we did do that $3 million stuff for a year, but I still remember doing the very first contract with like a $5,000 service deal with Stanford University and they bitch and complained the whole way. I'm like,

well, this is going to be a great business. You know, one of my favorite books, which I think is really a hidden gem in terms of go-to-market and sales and startups is tape sucks. And I think you get into very great tactical advice. It's lacking from a lot of other books. Like you get into different channel strategies and whether you should do them and partnerships and other things that I just don't think are addressed very well in a lot of business books. And you've now written three books. And we can come back to the question in terms of, you know, what continues to drive you and all the rest? What drives you to actually share knowledge that way and write a book? It looks like with almost every formative experience that you've had. You know, I get an awful lot of

inbound questions, you know, can we have coffee? Can you speak here? Can you do this? Can you do that? And I'm like, I really can't because it's just like, it'll become a full-time job. So I'm like, look, I'll write a, and by the way, the domain book, the tape sucks. You know, I was self-published, was homebrew. And it's a very dense book, even though it doesn't have that many pages. You know, I don't spend a lot of time, you know, waxing poetic or having a lot of platitudes. That's sort of difference between my writing and then everybody else's. There's no fill. There's no fill. Everything, it's super dense. Everything that I write is, I find meaningful and worthwhile sharing. But it's really, look, these books all have had different reasons, okay? The last book that I wrote, I didn't want to write it. Okay, Denise Pearson, our CMO, really, you know, pushed me to write it. And she also made it easy for me to write it because I had a lot of help along the way. I wrote every word of it, okay?

In other words, it's not a, but I did have a ghost writer who just went through. It's not lucky. You need examples here? or nobody will understand this outside of your business, you know, all that kind of commentary and explain this better. And so he helped me just make the book more consumable rather than this very narrow audience that we normally deal with. But the net of the reason why I wrote Amper Up was, you know,

people said, hey, just like you just said, you've had three very successful experiences, different times, different markets, different technologies, different competitors, blah, blah, blah. You know, what's the secret sauce? I mean, Americans always think there's a formula that can be extracted And if I just have my hands on that, I can just do it too, right? It's an immediate gratification type of thing. And the book is really the answer to the question of, what do you guys do?

What do you think explains the success in these companies? It's my answer. It's not that I'm trying to sell that to people at all. I don't care whether you agree with me or not. I'm just telling you what my best guess, my best take is on the answer to that question, right? People sometimes go like, well, I don't agree with this. I don't care. I mean, yeah, I did kill customer success at every company I've been in.

I think it's the biggest bullshit thing that goes on in Silicon Valley. It doesn't mean that I need you to agree with me. I'm just telling you what it is, right? So one of the core messages in Amped Up is about the importance of urgency. And you talk a lot about how to create it. I guess maybe a more difficult question is, why do you think a bunch of CEOs and leaders don't push for more urgency or higher standards? Well, I know you guys have been.

to a California DMV before. You want to see a lack of urgency? You know, this is what naturally happens to human beings. It's innate. We slow down to a glacial pace, and unless there are people who are going to drive tempo and pace and intensity and urgency, that's what leaders need to do because people naturally slow down.

They're like, well, I need to be here anyways. And they're sort of their mind just wandering off on their next vacation or what they're going to do on the weekend. And it's like, you know, you need to set. you know, high focus, high intensity, high preoccupation, you know, with what we're doing? I mean, people sometimes ask me, what's the message of your book? I'm like, read the title, okay? Because that is the message.

Look, there is an X factor. There's an enormous amount of room in the margin that is right under your notes, okay? And you have the opportunity to take it up in the next meeting, in the next podcast, in the next email, in the next Slack message. you can take it up, you know, you can push the urgency, you can push the standards, right? You can push the alignment, right? You have all these opportunities. Are you taking them? It's an easy message, but it's really hard to have the mental energy to bring that to

every single instance of the day, right? And that's the message of the book. There's a lot of room there. There's a ton of room there, and people don't realize it because, you know, I've seen companies where, you know, EFCC, I'm CEOs, they just think, all right, hired a bunch of people, and I sit back and wait for greatness. They have no idea that they have to relentlessly drive, you know, every second of the day, every interaction, and seek the confrontation because, you know, CEO jobs are insanely

confrontational, which is not human nature. We don't like it. We are naturally confrontational. We avoid it. I mean, I had a founder CEO once, you know, every time somebody had to get fired, you know, he had a CFO do it. And you stayed home that day because it's just so hard, right? And it's like, I don't have the disposition for it.

We understand that. But there are people in the enterprise that have to do that stuff, okay? That fully resonates. But another piece that strikes me is people are afraid, right? That they don't have the right people that they'll lose in the talent marketplace. If they push hard enough, their people will leave, right? How would you respond to that? Well, if they leave, they should leave.

Okay. I mean, this is a great thing. You know, culture shorts and sifts. You attract the right ones, and you start losing the wrong ones. So it's actually quite perfect. And people are leaving. They're just not your DNA. They're not your blood type.

And by the way, you need to create your blood type, you know, around you. Otherwise, you're correct. You have nothing but conflict. I mean, I remember having people, after two weeks, they said, you know what, I can't take the pace and intensity this place anymore. It wasn't me personally. It was like everybody was like that. You know, they were all, you know, calling people out and driving these expectations.

they weren't used to, and they wanted to go home at 4 p.m. and pick up the kids from school. I'm like, well, you need to go back to HP and sleep in your cubicle. This is not the place for you. So you need to, like culture can be incredibly helpful, you know, to a company. But culture is not a general thing. There's not such a thing as general goodness. I mean, the culture needs to really enable your mission, right? And whatever enables your mission effectively is a good culture.

There's no universal culture. That's good. You know, it depends on, you know, the type of leadership you have and the type of business you have and, you know, where you are in your journey and all this kind of stuff. But, you know, culture is a very powerful thing because if you don't, if you don't fill the void, somebody else is going to, you know. I want to switch over to talking about Snowflake and then what's going on in AI. Can you just give our listeners a sort of Snowflake 101? You know, what is the sort of scale and core innovation and use case of Snowflake today? and we can talk about how the company has been evolving from warehousing to cloud, the data cloud and application platform and AI after that. Yeah, our founders probably would argue immediately with you that they were never a warehousing play.

So they sort of want to forgive me. Yeah, you're forgiven. But there's a reason for it because, you know, they were dealing with semi-structured data right from the get-go and sort of the workload types were more than just sort of back. analytical, you know, type of stuff, which is mostly associated with data warehouse, and that's also purely structured data. So there was always a broader scope and focus. But our founders were two French guys, long time, you know, Oracle, CTO, technologists, architects, they were really responsible for picking Oracle from the departmental level. You probably can't remember that far back, but Oracle at one point at time was a departmental platform to the enterprise platform

that it became. So things like Parallel SQL, you know, were all things that came, you know, from them. So they left and, you know, they wanted to reimagine database management, you know, for lack of a better word, for cloud computing. In other words, they didn't want to carry technology forward or as little as they could. They wanted to reimagine. So, you know, building a database or a data platform, whatever you want to call it, for cloud computing, was very different than just sort of taking a Postgres sequel kernel forward and kind of hacking it up for the cloud.

I'm being very unflattering here, but there's plenty of people that have done that. So they did some really breakthrough things, you know, most notably that most people know is the separation of storage and compute. I mean, back in the day, people may not remember this, but, you know, I mean, you bought storage and computing combination. You couldn't buy one without the other. whereas in the world of cloud, you can commandeer compute and storage independent of each other. And of course, it became a

consumption model. Not right away, by the way. That was sort of an evolution. And obviously today is by the machine second or compute second. But once in a point of time, it was by the node and it was by the machine hour and all that. Now it's so incredibly fine-grained and granular.

That is completely different. But the other thing that they did is they took the control plane out of the cluster itself. So the clusters are now all stateless. You know, in other words, they're clueless, which is great because you can run tons of them, you know, concurrently, right? So there's not one master. The master lives outside of the clusters. So running jobs concurrently is another huge thing because in the world of data warehousing, just to use that word again, Sarah.

I mean, the reality was, you know, you had to beg for 2.30 a.m. time slot three months from now because, you know, the cluster was consumed very quickly, very easily. Now it's like, there's no limit. So this is what I often tell investors, it's like, I'm not creating the demand. I'm just enabling it's okay. It's so pent up. It's insane, right? And the architecture does that, right?

And then I could also provision workloads either for economy, in other words, we run the cheapest possible, or get running for performance, blistering fast. And you could make these optimizations and choices. So this is beautiful stuff, right? Because we just opened up the demand in that legacy marketplace. And then, of course, we started migrating, you know, teradata databases. I mean, massive teradata plants.

And by the way, I mean, we're still in the early innings of that because it's not easy to move those platforms at all. But, you know, a ton of Hadoop, of course, which is sort of the, you know, what we used to call big data. And now old data is big, so that the scriptor doesn't make, you know, too much sense anymore. You know, and old Cloudera and on and on, on tons of oracles, SQL Server. I mean, so that's what we've been doing. But, you know, when I started, you know, the tagline, if you will, the positioning or core message was this is the data warehouse built for the cloud. That was Snowflx message. And I'm like, okay, when I'm going to stick with that?

Because, you know, you taint yourself with a brush pretty soon. You can't get it off of you, which is pretty much what happened to us. I mean, you just started on. Like, so here we go. Again, I have an allergic reaction every time I hear data warehousing because to me, it's just a type of workload now. It's no longer a market, it's no longer an industry where, and, you know, cloud data management platforms, you know, are, and certainly we are, you know, we're seeking to become full spectrum workload capable, meaning from the most batch analytical to the most streaming, online, transactional, you know, massive, you know, scale and extremely low latency from what you're used to and in OTP type of environments. And the reason is, we don't want, the whole premise behind the data cloud is that the work comes to the data. The data does not go to the work.

Now, why does that matter? You know, because historically, the data has always been pumped around to go to the work. Well, you get massive siloing of the data. You don't even have to work at it. You're going to get siloing, you know, whether you try or not, because you have a new app, you get a new silo. You know, because it comes to its own database, right? And the siloing prevents you from really fully exploiting the potential, that lies within your data because there's now walls that exist between them.

So the notion of a data cloud is kind of a really new data strategy element in the mix. And we advocate really hard. I mean, I said to CEO of the large banks. I says, don't go re-silowing your world in the cloud. You end up with the same sort of problems you have right now. And your data science, ML, AI, etc., teams are going to be very frustrated. you know, trying to overlay and blend that data and fine-tune and train and do all these fancy things we do now, you know, with data. So, you know, we're trying to create an unfettered

data universe, data orbit. That's much bigger than your enterprise, by the way, because this is really an ecosystem, right? You have data providers, you know, in the world of, you know, financial services, you know, got faxed and Bloomberg and S&P and all these tanks. So in hedge fund, they have Hundreds and hundreds, you know, data flows, you know, coming in. So you really need to think of data management as a much broader orbit than just your enterprise. And so in the world of artificial intelligence or general intelligence around data, the ability to mobilize data, you really need to have a data cloud strategy. That's also why we are multi-cloud capable because we don't think, you know, you can have a data cloud in a single public, on a single public cloud platform. By definition, you can't, right?

So, that's really the strategy, and obviously things have taken off a lot, but there have been multiple iterations in the journey, you know, of Snowflake. I mean, started off, just moving legacy, you know, systems for the cloud and taking advantage of the elasticity and the economics and the provisioning and all these things. But now it's much more broadly workload capable, and that's a journey that goes on and out. The other thing that has changed is no longer a database world. You know, historically, a database was just, you know, a platform that was self-contained, and it had standard interfaces like ODBC and JDBC that the application used to access the data. Now it's like, well, wait a second. You know, we don't operate that way anymore because you're breaching the governance perimeter.

So the application needs to execute inside the perimeter of the platform, not outside. So we have a programmability platform called Snow Park. Okay. And that's where, you know, all the applications live. have a native application framework, all these kinds of things. So now you're looking at a very different platform environment, very different layers stacked them historically what we've had in the on-premise stack that we've grown up with, certainly I grew up with.

So that's kind of as short a story as I can tell you. That's really great background. And obviously, Snipik has accomplished amazing things and really become central now to the enterprise data world and ecosystem. How do you think about what's shifting in AI? because I think we went from a world where we had almost like this older version of AI models, CNNs and RNNs and things like that, where people during old school natural language processing or other things. And then more recently, we've had this big breakthrough wave of generative AI.

And it felt like the starting gun for that to some extent was really when ChatGPT came out about six months ago. And then GPT4 came out maybe three months ago. And then suddenly everybody started building applications against this. How has that been showing up or has that been showing up yet in terms of the AI use cases that you see? in the enterprise or your customer requests or has anything really shifted yet in terms of the broader enterprise ecosystem that you deal with, just given that often it takes six months for an enterprise to plan something if it's a very large business. And so I feel like the last few months have just, the last two quarters have just been a lot

of big companies kind of planning against what to do. Yeah, you know, first of all, large language models are about language. Okay. No surprise. But, and it's a huge deal because, you know, I will. It was taught the basics of Cobol when I was in school, and, you know, Copel stood for common business-oriented language. Well, there was nothing common or business-oriented about it. It was extremely cryptic syntax and all that.

But compared to assembler and machine code, it was amazingly, you know, the syntax was amazingly comprehensible. So it's all relative. You know, in the 80s, we had SQL, which was back then, you know, also positioned as something that mere mortals could use to query data. This is all about how and what is your relationship with data, right? And over the years, that has evolved, but it's been immensely frustrating, you know, for people to get, you know, access to data in the form that they want, and there's a lot of ad hoc, and there's a lot of standardized reporting and dashboarding, all this kind of stuff. But it's been difficult.

So, you know, going to natural language is like the last mile here. And that is an enormous thing. I mean, the effect on demand will be just enormous because every more. mortal. If you're semi-literate, maybe you're not even literate, you can just talk, you know. You can get value from data. Wow. You know, so it is an incredibly, you know, big deal. But, you know, the generative aspect in terms of content generation, that's very cool when you're trying to plan a trip to Yellowstone. But when you're in the enterprise, you're dealing with structure proprietary data. And, you know, they're not planning trips to Yellowstone. They're going to, you know, they're going to ask really hard questions. Like an insurance, for example, they may say, You know, we had disproportionate, you know, bodily injury claims in Florida and the surrounding states didn't have it, you know, A, what explains that? B, we're going to have it again next quarter and C, what do we do about? Do we stop underwriting and we change our pricing, blah, blah, blah, blah, blah. Believe me, you're not going to get the answer to that question out of the large language model.

So you got to sort of separate the issues of, you know, text to speakable and all of that, you know, which I think are incredibly valuable. from going to structure proprietary data, because that's a very different realm. So, you know, the way I'm trying to think about it right now is, yeah, we have language models, but we're going to see all kinds of other models. We're going to see business models, okay, because the question I just asked, you need to understand business models. I mean, one of the big things that just to stick with insurance for a second, one of the biggest things in insurance in a specific type of insurance, like auto insurance, auto insurance

is Geco and Progressive and Liberty Mutual and all these people. So, you know, telemody data is number one through 10 for them. Okay, telemary data is the device you get in your car, and it knows when you're speeding and all this kind of stuff. And by the way, that's how they now price risk, and they're capable of lowering their prices yet increasing their profits because of their extremely sophisticated and refined use of that data. That data is extremely predictive, you know, in terms of, you know, what the claims are

going to be. And it's the difference between winners and losers and people who make money and people who don't make money. So that's that level of, and by the way, that's not even AI. That's just, you know, machine learning. They're really data driven. And that's already in broad use in other insurance companies. That is sort of, you know, where this is all going. And I need to be able to ask questions that analysts might take weeks and months, you know, to bring in McKinsey or Bain or whoever, you know, to get and study, you know, problems, right?

the systems will be able to start giving you insight into those kinds of questions. That's really where we live, you know, proprietary, structured enterprise data. That's a totally different realm, you know, and, you know, and by the way, you couple that with language models and, you know, I have a natural language. Yeah, that's pretty powerful. So you're making of Pirates and Marvell movies, you know, the way he and the retro systems. That's a nice model. But I imagine in medical, we have diagnostic models, you know, and we have all these different, you know, levels of intelligence that we can build, as long as they have the data, I mean, they're going to be insanely lightning fast, providing insight.

We acquired this company called NEVA, you know, very recently. I'm very excited about bringing the expertise into the company because, you know, they're search experts. And I'm a search junkie. I mean, 25 years ago, I mean, I wish I'd had searched earlier on in my life because it's such a huge thing. You know, I just can't help myself. I'm always. And search is so addicting because it lets you start to explore everything that's known and ever been written or published or opinioneered about and sort of process all that information. But the problem with searches, it has no context, right? It just matches on strengths. And, you know, if you search on Snowflick, you might get the company, you might get the weather, you might get the social phenomenon because it doesn't know.

It just knows the word. And it's incredibly, and so enrichment. and context is really the name of the game in the world of data, right? We always like to say one attribute can make a data attribute go from being mundane to being high octane because of the context that it creates all of a sudden becomes wildly insightful and impactful and impactful and predictive and all these kinds of things. So, you know, in order for search, you know, to get that context and become stateful is those are going to be enormous step forward.

And, you know, chat and search, you know, it all becomes one natural. language conversation after a while. So you combine that, you know, with having these new levels of intelligence specific to industries or just subject matters. You know, I think that's really where there's a world of opportunity waiting to unfold still. I'm certain that it will, you know. Yes, you know, Enivo is a dear former portfolio company.

Do you imagine that the snowflake, like, interface for users, changed. is a great deal over the next, you know, five, ten years in terms of like supporting more natural language or a broader user set. Yeah, both of those things. You know, I think that there still will be a future for, for BI companies, business intelligence, sort of teblos, lookers, a world. And, you know, dashboarding is done for a number of reasons. Sometimes it's just, you know, basically providing data in the consumable format. But it's also done because it's a way to basically tell people, this is how I want you to look at the data. This is how I want you to understand. So there is sort of a guiding element to dashboarding. Not all analysis is at-hawk-based.

Now, a lot of it is. And, you know, for ad hoc, you know, nothing is going to be better than the natural language. At least, I'm already using it. You know, we push Salesforce data into what we call Snowhouse. That's our internal snowflake data that's we push everything into. And it's just incredibly easy to use already commonly available services and have a conversational relationship with that data. You know, are my two top reps in this country or debt market or this industry? No, it spits it out in a fraction of a second. But a beautiful graph attached to it and all of that. So it's very dicting because it's just like search, right?

You just keep going and going and going and it becomes like a whole journey. So, yeah, I definitely democratize access. Anybody semi-literate will be able to get, you know, way more value than they ever imagined from the data. And it will change, you know, how products get used. I mean, BI will not be the same. I think I see that that's severely affected by this evolution, you know. You made another acquisition of a company called Streamlet that I think we're also both familiar with. Can you talk about the rationale for that?

Streamlet is a company that does visualization, animation, you know, for Python applications, but specifically in the world of machine learning. The problem with machine learning is, if you're not a programmer, it's pretty damn hard to consume, you know, what it is and how it works. But Streamlet is almost reflexively reached for by Python programmers to basically make a machine learning model consumable by a general business user. You can manipulate the variables and it just redraws everything. Visualization animation, and the reason that we acquire Streamlet is,

we have to have visualization and animation. And by the way, this also touches the world of BI because a lot of people use streamlet for the same reason that they would use BI type of products, but this is just much more specific to all kinds of reporting and use cases and dashboarding. So what we wanted to do with Streamlet is to bring it inside Snowflake. We call it Streamlit in Snowflake. And the reason is you need to have that hardcore trusted sanctions governance perimeter

because otherwise people will not allow the business to use these kind of applications. Governance is a really big deal because the data needs to be sanctioned and trusted. And the business should not be able to get in trouble with the data. And that's really what we try to do with Snowflake. We are a hardcore enterprise-grade platform, and it's really hard. I mean, you can bring Python to your data in two weeks' time. But the problem is, you know, people are downloading libraries every couple of weeks to their heart's content, and people have no idea what kind of risks they are exposed

to in terms of exfiltration and all that. We spent two years, you know, making Python non-porous, and it was an enormous effort to do that. But, you know, you go to a large financial institute. And we're not going to let Python anywhere near our core data. It's just not even a conversation. And we're like, well, we're going to do it in a way that the people that use Python, there are many, obviously, but they can do it in the way that they don't violate and create exposures to the enterprise.

So that's really the role that we play. We talk about governance a lot. We talk about data quality a lot. And we get into this conversation. I don't know how many times a day. Because in the world of AI, if you don't have highly organized, optimized, sanctioned and trusted data, what do you want, you know, your models to do, just kind of train on a data lake? I call it a landfill, you know, I mean, you have no idea what the hell is in

there. You know, everybody dumps their stuff in there. You're going to go train on that. It's just absurdity. So you're having highly organized, optimized sanction data is really, it's a prerequisite for old, and people publish what they call data products. I'm sure you've heard that term before. A data products essentially, you know, I've taken data, you know, out of a lake, and I've created into a trusted, optimized, understood object that I can now give to the business and stand behind. That's really the role of the chief data officer to make the data, you know, trusted, organized and optimized, and then also that the business can get in trouble, you know, whether the individual data is no good or because they're breaching all kinds of security

and compliance, you know, aspects of using data. So that's, streamless is really important to us. The great thing about it, it's an open source project. So, you know, people, so many people out there are, reaching for when they want to publish something. And, you know, we're like, okay, we're going to bring that inside the enterprise perimeter and make it high trust. I go back to sort of the journey you described from not just a data warehouse, but only data warehouse as a first workload to, you know, broadly, you know, more online analytics, other workloads, applications that sit

inside Snowflake with, you know, unified data. What are the, what are the biggest challenges you guys face in making that vision come true? Is it convincing people to, like, move to, you know, customers to an entirely new architecture? Is it building the ecosystem? Is it just supporting the workloads? Because it's a very big rewrite of sort of enterprise architecture overall. Yeah, but it's, you know, we are rewriting anyways

because we have our migration to cloud. It's like the most disruptive thing ever. And yeah, look, you know, when I was at service now, we basically had an on-premise architecture that we hosted in the cloud. And by the way, I'm not being unduly critical here. I mean, because it was very useful that we were, you know, a single-tenant platform and had all kinds of advantages. And we were able to manage it really well through massive standardization and things like that. I'll give you an example.

You know, all the federal business that we had at ServiceNow was all on-premise Oracle because, you know, you could not get in there with a cloud-hosted solution. By the way, you still can't. I mean, the certifications on federal are so insanely demanding. You know, federal is a very small part of our business because we've spent, we're in the process for years and years and years to meet those standards. It's very, very hard, right? But we are a pure cloud implementation. We can't run on premise. I get asked that by people, you know, like, I mean, I can't even conceive of it, you know, the way snowflake works, right?

because it commandeers, you know, resources. It's not a, it's not a machine-centric platform, you know. So it's, it is a big change. There's no doubt. And as I said earlier, you know, we fight the siloing of data because we're that kind of a company. From a data strategy standpoint, we really tell people, you need a different data strategy for the cloud. Do not continue with what you've been doing because you've created a massively proliferated

the bunker silo world, and it will not serve you in the world of AI and machine learning and any level of data science. If you want to drive intelligence from data, you're going to be in a world hurt if you keep siloing the data. And we tell that to application developers to ISVs and said, look, don't have your own data container, okay, because instinctively application developers, oh, I want to have my own data layer hanging underneath it. I'm like, you know what, you're going to hate it because, A, it has no value to what you do

because you're not a data management expert. It's just a utility function, you know, for you. But then, you know, you're another silo, and the customer is now frustrated because they're going to start pushing that data into Snowflake. And now we have pipelines and ETL processes and all this kind of stuff and latency issues, governance issues, all this kind of stuff.

So we just announced that this relationship with blue yonder for example. It said, hey, we're going to fully re-platform, you know, on Snowflake. Because in the world of supply chain management, that's really important because we need to have visibility, you know, across all the entities that make up a supply chain.

You only do that when you have a single data universe, when you have all these containers, it's impossible. That's why supply chain management has never been platform because the data problem was unsolvable, literally, you know? And the other thing is the supply chain management. I mean, they run these extremely demanding analytical processes, right? And they run many, many times, you know, per minute, per hour. and they are very, very commanding of resources, right?

So, again, this is where, you know, our style of computing is very, very desirable, right? Because I can run the process. I can run them as fast as I need to. I can run as many as I want concurrently. So all these new architectural things are lending themselves really to use cases that have been there for generation. But, you know, supply chain management is an email spreadsheet business. I mean, they're still living in a world of Microsoft 30 years ago. That's insane, right?

because it's one of those use cases that it should have been extremely optimized, but it isn't, right? So, yeah, you're going to be doing re-platforming, re-architecting, and reimagining. That's what we did. Snowflake is a reimagination of data management

for cloud computing. But as we get through our journey, it's looking more and more different than what it used to look. You mentioned some very large-scale evolutions in terms of just the data world there. What are some of the other future directions that you're most excited about

or the big thrust that you see coming in terms of data? Data is going to redefine whole industries, okay? And that's what I find the most interesting. And the reason I say that is, first of all, you know, nine out of ten conversations I have with customers are not technology and architecture and all that and migrations. It's about industry use cases. It's about call centers.

It's about, you know, making medicine predictive, you know, for example, because everybody knows, you know, health care is economically, you know, not viable at the scale that we need to deliver it. And so data can make us, you know, predictive and prescriptive, right? We can, if we have enough data, you know, we can tell who is at risk for what disease, when, and what they need to do. All data driven. This is not, well, this is not somebody's opinion.

The data just, data doesn't have opinions, okay? It just, that's what it is. And it gives you the accuracy to go with it, the more depth and breadth of data that you have, the more debt certain that stuff becomes. But this is how health care will become much more effective, obviously, because you no longer reacting to disease and symptoms, but you're getting ahead of it. And every healthcare institution that we talk to and they're a customer of ours,

this is where they want to go, this is where they need to go. They don't want to treat disease. They want to prevent it and they want to anticipate it. So it will change healthcare as an industry. But I just mentioned auto insurance. This is a similar type of example. in the world of pharma, you know, it takes on average 12 years to, you know, to bring a drug to market.

Well, then you've got five years left before your patent runs out. What if I could compress that by one, two or three years? Now you've changed the economics of the entire industry, right? So, you know, data is far more important to how the economics and how the industry functions than people still realize, you know. How do your investments in R&D reflect this? or what are the big areas of thrust that you have right now from an R&D perspective? The hardest part, you know, for us is, you know, I have to massively enable,

we have to massively enable this platform to be incredibly broadly and capable, not just broadly, but also in depth, because if it doesn't do what people need to do or it doesn't do it well, they're going to say like, well, forget it, well, just pump the data over here. And now we're back to, you know, fragmenting and siloing the data. So if we have the data, we have to enable the work. workloads, okay? We have to. And that's really hard. That's really hard. You mentioned some of the workload types, but we do things like global search, okay? Because in the world of cybersecurity, you know, that's incredibly important because a lot of cybersecurity companies that, you know,

they are partners of ours. They are running on the data club. They don't, they, because they couldn't sell to their customers yet another database container. Customer didn't want it. They said, look, bring the data here and then we can combine it with all these other data sources, you know, vulnerability all the, and then, you know, our analysts can search one day in the universe instead of 15 of them and try in their head to figure out what does it all mean and do something with it. Yeah, I'm definitely saying a lot of people right now building in terms of Snowflake apps so that they can just maintain the data locally within a snowflake instance for a customer, but then provide enrich functionality on top of that or access to that data in ways that are

really perform in and combine with what the, you know, with what the company is trying to do more broadly. So I think that's been a really great innovation for the industry. I guess one last question is just around the macro shift. So obviously we've gone from a zero interest rate environment where everybody was just buying software like crazy to a world where people are cutting SaaS budgets increasingly, they're rethinking spend. Does the macro environment change your point of view

on consumption or credit-based pricing or how you think about the pricing and economic model in this new regime? Yeah, not really. You know, we have different stakeholders that have different opinions on this. Investors, of course, love it when you have customers over a barrel and you can keep a gun to their head and they're going to pay you no matter what. I don't particularly like that. You know, when I was at service now, you know, I always felt that it was not an equitable relationship that we had with our customers.

Because oftentimes, you know, they would sign up with us for many millions of dollars and it took them nine months to even get in production. They were paying for older users all this time. How is that equitable? So one of the things that I really liked about Snowflake and Cloud Computing and consumption models and the elasticity is that, you pay for what you use. It's a utility model. And, you know, is that painful sometimes? Yes. I mean, I talked to the CIO of a bank last week and he said, you know, my bank's growing 3%.

Snowflake's growing 22%. You know, and it's that can't go on forever. You know, the CFO gets in there and he goes, they starts calling bullshit on everybody and saying, like, hey, people, you know, they basically say, this is the size of your breadbox, live with it, you're not going to get a new contract, but it's, and then people need to go back to the drawing board, they're like, okay, it's a very fine-grained thing because you can go into Snowflake Worklaws, say, okay, I'm going to downgrade the provision on this. I'm going to run this less frequently.

I'm going to change the retention period on data. You can do all these things to lower your consumption of storage and compute. Does that hurt us sometimes? Yes, but it's a value to the customer, because. you know, if you're in a SaaS subscription model, they've got to wait for their next drill before they can start cutting of a limp here versus with us.

You can do it in near real time. Investors don't like it. I understand because they love it on the way up. They just hate it on the way down. Absolutely. I guess related to that, a lot of the people who tune into no priors are people who are running their own companies right now,

and they're at different stages. We have everything from early stage startup CEOs to executives at larger companies, researchers, engineers, et cetera. And one of the big questions of their mind right now is how to manage differently through this, you know, economic downturn or the shift in spend or the shift in the macro environment. You obviously are known as a CEO who is very good at making tough choices

and, you know, prioritizing in both good times and bad times. How should people think through managing differently in this changing economic environment? What are the first things people should do? You know, I mean, I see all these layoffs, you know, with Amazon and meta and Google and all this kind of stuff. And we don't do layoffs because we don't wait. until there is a, you know, huge hatwind. We're always pruning the tree, so to speak, right?

So we don't have to do it as some massive event that is super unsettling. You know, management of resources is something that should be happening on a daily basis, not just performance, but also, you know, bringing supply and demand in sync with each other, alignment that should be happening constantly. But the culture has sort of evolved over the years where it's just, unfathomable, if that's a word, where you just, they can't conceive of being so confrontational that we're going to take somebody out of a job, so we just look to the other way until we get a crisis and then we started ripping out, you know, tens of thousands of people. I just don't

think that's fair as well as effective, right? I mean, so this is the reason my world doesn't change all that much because I was already doing it. So these are just more sort of management practices and ways of thinking about, you know, how you run things, you know, rather than, oh, gosh, we have economics that way now. We need to change everything we're doing. No, you don't. You just need to run things, you know, like you always, by the way, people are not used to living in downturns, you know, when you've been around longer, it's like, hey,

they come around, okay, this is part of life. And by the way, let's, you know, let's double down, triple down, put our game phase on, put our boots on, you know, we're into fight now. This is actually going to be a lot, I will say, because it's going to be a lot of fun. This is where the chip fight really happens, right? So, in other words, you can get up. for us. You know, we need these amp things up. That's what you're doing. People are growing up and like, oh, they only know, you know, that the trees grow into the heavens. Trees don't grow

into the heavens, okay? They don't. So everybody needs to grow up a little bit, you know, and just get a leash on reality and say, look, this is part of life, you know. Do I actually start rethinking everything? Because economically things are now, you know, different. To some degree, yes. I mean, we're scrutinizing productivity much harder in sales organizations, you It might be a little bit quicker on the trigger. All that kind of stuff for startups, obviously, you know, raising money is a whole different ballgame and you guys are in that world. So they definitely need to think harder. I mean, when I was a day at the main, we would basically run the company from one fundraising milestone to another.

That's how it was back then. That hasn't been the way it's been. I mean, you know, in recent years, people have never had to raise money or run businesses that way to prepare themselves for a fundraising milestone. They've never done it before. Well, you should, you know, because that's how you stay alive. You know, I mean, fundraising is oxygen for a company, you know? Yeah, basically, I think gravity turned back on and everybody's like... Exactly.

Realizing it. Yeah. Frank, this is a great conversation. Is there anything that we miss that you think would be useful or interesting to talk about? Well, we've already talked about amping things up, and that's always the, you know, when we have conversation like this and a lot of people are listening to it, I just, I just, I just, I'm trying to get people to say, you know, my next meeting, my next message, my next encounter, my next situation, I'm going to hamper it up because it's just a choice that you make. And, you know, don't be afraid, you know, that people will react poorly to it.

They won't. The good people will actually love it. And especially if you're in the leadership role and who isn't, you know, this is really what people want. They want to inject energy and focus and intensity and quality so that the whole place starts to feel, you know, exciting, you know, and it's not like, oh, it's 4 o'clock or 5 o'clock or whatever. No, right, it's much easier to live in an energized environment than one that's devoid of energy, you know? I love it. That's a very, it's a very courageous message. Thanks for doing this, Frank. You're back. Thanks a lot.