No More Hustleporn: Snowflake's Frank Slootman on being relentlessly confrontational
We pulled out the highlights from Snowflake CEO Frank Slootman's recent interview with Elad Gil and Sara Guo. Transcription and light editing by Anthropic's Claude, curation by Yiren Lu :-)
Highlights:
Sara Guo: 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 it's where investor friends will direct them. What advice would you have for people choosing that company in terms of the things you can't change?
Frank Slootman: It's a great question. I get 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, don't go working for some consulting firm out of school. Try to get a real job in the real economy, building real products, selling real products. You really need to feel what it's like to sort of be in the drivers seat of 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 disappearing, because that's the real world, and a lot of people choose jobs that are very removed from the real world. And I don't think that's helpful for people's development and their careers.
Sara Guo: So one of the core messages in Amp It 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?
Frank Slootman: Well, I know you guys have been to a California DMV before. You see a lack of urgency. This is what naturally happens to human beings. It's innate. People will slow down to a glacial pace unless leaders are driving tempo, pace, intensity, and urgency. Leaders need to set high focus, high intensity, high preoccupation. People sometimes ask me, what's the message of your book? It's to amplify urgency. There's an enormous amount of room right under your nose. You have opportunities to take it up in the next meeting, podcast, email or Slack message. You can push urgency, standards, and alignment. It's an easy message, but requires mental energy to bring to every instance of the day. There's a lot of companies with inexperienced CEOs, who just hire people and wait for greatness. They don't realize they must relentlessly drive every second, interaction, and seek confrontation. CEO jobs are confrontational, not human nature. We naturally avoid confrontation. A founder CEO once had a CFO fire people because it was so hard. They lacked the disposition but enterprise needs it.
Sara Guo: That fully resonates. But another piece that strikes me is people are afraid 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.
Frank Slootman: Well, if they leave, they should leave. This is a great thing, culture sorts and sifts. You attract the right ones and you start losing the wrong ones. So it's actually quite perfect if 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 around you. Otherwise you're correct. You have nothing but conflict.
I mean, I remember having people after two weeks just said, you know what? I can't take the pace and intensity of this place anymore. It wasn't me personally. It was like everybody was like that. They were all calling people out and driving these expectations they weren't used to, and they wanted to go home at 04:00 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.
Culture can be incredibly helpful to a company, but culture is not a general thing. There's no such thing as general goodness. I mean, a 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. It depends on the type of leadership you have and type of business you have and where you are in your journey and all this kind of stuff. But culture is a very powerful thing, because if you don't fill the void, somebody else is going to.
So what we wanted to do with Streamlit is to bring it inside Snowflake. We call it Streamlit in Snowflake. The reason is that you need to have that hardcore, trusted, sanctioned 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.
Full Transcript:
Sara Guo: Our guest today needs no introduction. Frank Slutman is the legendary threetime CEO of Data Domain ServiceNow 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.
Frank Slootman: Absolutely good to see you too.
Sara Guo: 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 and in college? And how did you end up in product management and computing in the US?
Frank Slootman: 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 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, I would say. And definitely 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 something to prove to the world or whoever. Those are all useful things.
Obviously, I ended up in the US because I think the US 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 that all around us, right? People that come from all over the world here because they have far greater opportunity than 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 having had that opportunity. I always tell younger people, it's very important where you decide to be. Don't just go where your friends are.
Sara Guo: To the point of choosing the right place, yes. And thank you, America. My parents were also immigrants. You talk about being on the right elevator and some of the companies you worked at weren't the hottest companies at the time when you joined. Tell us about those choices.
Frank Slootman: I just use the analogy of the elevator because there's this aspect of opportunity and circumstance that 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 I can will my way to anything. That's not true. The 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. You have to make very careful choices because if you combine good choices with great execution, you get the perfect cocktail for opportunities, for future opportunities, and for having a successful sequence of experiences. So it matters a whole lot.
Sara Guo: 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 it's where investor friends will direct them. What advice would you have for people choosing that company in terms of the things you can't change?
Frank Slootman: It's a great question. I get 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, don't go working for some consulting firm out of school. Try to get a real job in the real economy, building real products, selling real products. You really need to feel what it's like to sort of be in the drivers seat of 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 disappearing, because that's the real world, and a lot of people choose jobs that are very removed from the real world. And I don't think that's helpful for people's development and their careers.
Sara Guo: How do you think about company versus industry versus role? Often when I talk to people as well, I kind of advocate for 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?
Frank Slootman: 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 being an immigrant in this country, beggars couldn't be choosers. And I figured, look, I'll get in there and I'll make my way from there.
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 get into.
In hindsight, I was right, because once I got in there, you spent two years doing typical M&A stuff and all the presentations for boards and all this kind of stuff. But then after that, they pretty much gave me whatever I wanted to do, was fine with them. And from there, I made my way.
Elad Gil: You had three just amazing CEO jobs, right? So I believe you took Data Domain from less than 3 million in revenue through an IPO and a $2 billion acquisition by EMC. At ServiceNow, you took it from 75 million in revenue through an IPO into, I think, 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, when you joined, Data Domain had an academic co-founder. It didn't really have a product that was commercially scalable. Snowflake was growing but was spending a lot of cash. So what are the commonalities between those different experiences? And more generally, what kind of drives you? What do you have to prove? You already had accomplished so much by the time you got to Snowflake. How do you keep going?
Frank Slootman: So let me first sort of correct the record on Data Domain. They had no revenue, no customers, nothing. There were 15 people there. And when we first started to assert the product, it had 1 usable space. Just imagine that, okay? Now, it was a while ago, and it ran 30 megabytes a second. So it was useless for 99.9% of applications. So we're like, what are we going to do now?
Sara Guo: Why did you take the job?
Frank Slootman: I'll tell you why I took the job. First of all, 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, don't hold out. Do not go for a second or third string deal. You need to have really good investors. We were a startup one out of hundreds at the time. I'd be walking the halls of companies like NEA and Greylock, and people looked at me, who are you? What company is that? Okay? We were a no-name, and we were lectured on other companies that, in hindsight, ended up being no name. So, I mean, 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 I found a little bit of fit. I remember meeting with a CIO company that has been acquired since by EMC, and they were testing the product. And the guy said to me, he said, he said, that little product of yours was a real hero here on Friday, and tell me more, do tell. But he explained that they had their email database backed up on our device and they had a massive corruption email databases, as happened back then, that's not common anymore. And it was 04:00 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 on disk. And by 07:00 that evening, they were going home.
Obviously, you don't need to be a rocket scientist to figure out that use case. You can sell a few times more, right? So we stayed alive and we did do that $3 million that first year, but I still remember doing the very first contract with like a $5,000 service deal with Stanford University. And they bitched and complained the whole way. I'm like, well, this is going to be a great business.
Sara Guo: 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 that'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.
Frank Slootman: I get an awful lot of inbound questions. 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'll become a full-time job. So I'm like, look, I'll write. And by the way, there's a main book, Tape Sucks, self published with Homebrew, and it's a very dense book. Even though it doesn't have that many pages, I don't spend a lot of time waxing poetic or having a lot of platitudes.
Frank Slootman: These books all have had different reasons. The last book that I wrote, I didn't want to write it, okay? Denise Pearson, our CMO, really 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. But I did have a ghostwriter who just went through and said, look, you need examples here or nobody will understand this outside your business, all that kind of commentary, and explained this better.
Frank Slootman: The net of the reason why I wrote Amp It Up was 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. What's the secret sauce? 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. 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?
Sara Guo: So one of the core messages in Amp It 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?
Frank Slootman: Well, I know you guys have been to a California DMV before. You see a lack of urgency. This is what naturally happens to human beings. It's innate. People will slow down to a glacial pace unless leaders are driving tempo, pace, intensity, and urgency. Leaders need to set high focus, high intensity, high preoccupation. People sometimes ask me, what's the message of your book? It's to amplify urgency. There's an enormous amount of room right under your nose. You have opportunities to take it up in the next meeting, podcast, email or Slack message. You can push urgency, standards, and alignment. It's an easy message, but requires mental energy to bring to every instance of the day. There's a lot of companies with inexperienced CEOs, who just hire people and wait for greatness. They don't realize they must relentlessly drive every second, interaction, and seek confrontation. CEO jobs are confrontational, not human nature. We naturally avoid confrontation. A founder CEO once had a CFO fire people because it was so hard. They lacked the disposition but enterprise needs it.
Sara Guo: That makes a ton of sense. And you talked about, in the book, the importance of also setting those high standards, not just the urgency, but really pushing people outside of their comfort zone. Can you talk a little bit about how do you determine what are the right standards and how do you continue pushing them higher over time?
Frank Slootman: That is the art of leadership. That is the art of running a company. Instilling standards, making them clear, making them crisp, making them aligned, making them higher over time and holding people accountable to meet and exceed those standards. The minute you let up on that, the company will slow down to their natural level, which is a lower level than you want. So those standards determine the level of ambition, determination, excellence in the company. And the minute you stop pushing, you lose it. You'll lose it.
Sara Guo: That fully resonates. But another piece that strikes me is people are afraid 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.
Frank Slootman: Well, if they leave, they should leave. This is a great thing, culture sorts and sifts. You attract the right ones and you start losing the wrong ones. So it's actually quite perfect if 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 around you. Otherwise you're correct. You have nothing but conflict.
I mean, I remember having people after two weeks just said, you know what? I can't take the pace and intensity of this place anymore. It wasn't me personally. It was like everybody was like that. They were all calling people out and driving these expectations they weren't used to, and they wanted to go home at 04:00 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.
Culture can be incredibly helpful to a company, but culture is not a general thing. There's no such thing as general goodness. I mean, a 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. It depends on the type of leadership you have and type of business you have and where you are in your journey and all this kind of stuff. But culture is a very powerful thing, because if you don't fill the void, somebody else is going to.
Sara Guo: I want to switch over to talking about Snowflake and then what's going on in AI. Can you just give our listeners Snowflake 101? What is the 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 in AI.
Frank Slootman: Our founders probably would argue immediately with you that they were never a warehouse in play. They sort of want to forgive me. There's a reason for it because they were dealing with semi structured data right from the get-go. And the workload types were more than just batch analytical, which is mostly associated with data warehouse and purely structured data. So there was always a broader scope and focus. Our founders were two French guys, longtime Oracle CTOs and technologists, architects. They were responsible for taking Oracle from departmental level to enterprise level. Things like parallel SQL were all things that came from them.
So they left and they wanted to reimagine database management for cloud computing. They didn't want to carry technology forward. Building a database or data platform for cloud computing was very different than just taking a Postgres SQL kernel forward and hacking it up for the cloud. They did some really breakthrough things. Most notably, the separation of storage and compute. Back in the day, you couldn't buy one without the other. Whereas in the cloud, you can commandeer compute and storage independent of each other. It became a consumption model. Not right away, but over time. Today it's by the machine second or compute second.
The other thing they did is they took the control plane out of the cluster itself. The clusters are now all stateless. You can run tons of them concurrently. Running jobs concurrently is another huge thing because in data warehousing, you had to beg for a 2-3 AM time slot three months from now because the cluster was consumed very quickly. Now there's no limit. I'm not creating demand, I'm just enabling it. The architecture does that. I can also provision workloads either for economy or blistering fast performance. This opened up the demand in that legacy marketplace.
We started migrating massive Teradata plants. We're still in the early innings of that because it's not easy to move those platforms. Tons of Hadoop, which we used to call big data. Now old data is big, so that descriptor doesn't make too much sense anymore. Tons of Oracle, SQL Server - that's what we've been doing. When I started, the positioning or core message was, this is the data warehouse built for the cloud. I'm like, we're not going to stick with that because you taint yourself with a brush, pretty soon you can't get it off you, which is pretty much what happened to us. 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 or industry.
Cloud data management platforms, we are seeking to become full spectrum workload capable. From the most batch analytical to the most streaming online transactional, massive scale and extremely low latency. 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. Historically the data has always been pumped around to go to the work. You get massive siloing of the data. The siloing prevents you from fully exploiting the potential that lies within your data because there's no walls that exist between them.
The notion of a data cloud is a new data strategy element. I've said it to CEOs of large banks, don't go recycling your world in the cloud. You end up with the same set of problems you have right now and your data science, ML, AI teams are going to be very frustrated trying to overlay and blend that data. We're trying to create an unfettered data universe, data orbit that's much bigger than your enterprise, because this is really an ecosystem.
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 can have a data cloud in a single public cloud platform. By definition you can't. That's really the strategy. Things have taken off a lot, but there have been multiple iterations in the journey of Snowflake, started off just moving legacy systems to the cloud and taking advantage of the elasticity and the economics and the provisioning. But now it's much more broadly workload capable and that's a journey that goes on.
It's no longer a database world. Historically a database was just a platform with standard interfaces like ODBC and JDBC that the application used to access the data. Now it's like, well wait a second, we don't want to operate that way anymore because you're breaching the governance perimeter. The application needs to execute inside the perimeter of the platform, not outside. So we have a programmability platform called Snowpark. That's where all the applications live. We have a native application framework. So now you're looking at a very different platform environment, very different layered stack than historically what we've had in the on premise stack that we've grown up with.
Sara Guo: That's really great background. And obviously Snifflink 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 doing 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 Chat GPT came out about six months ago, and then GPT four 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.
Frank Slootman: Yeah, first of all, large language models are about language, okay? No surprise. And it's a huge deal because I was taught Cobol basics when I was in school, and Cobal stood for Common Business Oriented Language. Well, there was nothing common or business oriented about it. There was extremely cryptic syntax and all that, but compared to assembler and machine code, it was amazingly comprehensible. It's all relative. In the 80s, we had SQL, which was back then also positioned as something that ordinary people could use to query data. So this is all about how and what is your relationship with data. And over the years, that has evolved, but it's been immensely frustrating for people to get 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 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 ordinary person, if you're literate, maybe you're not even literate, you can just talk. You can get value from data. Wow. So it is an incredibly big deal.
But 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 structured proprietary data. And they're not planning trips to Yellowstone. They're going to ask really hard questions. Like in insurance, for example, they may say we had disproportionate bodily injury claims in Florida and the surrounding states didn't have it. A, what explains that? B, are we going to have it again next quarter? And C, what do we do about it? Do we stop underwriting? We change our pricing? Believe me, you're not going to get the answer to that question from generative AI.
So you got to sort of separate the issues of text to SQL and all that, which I think are incredibly valuable from going to structured proprietary data because that's a very different realm. So 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 Geico and Progressive and Liberty Mutual and all these people, telemberty data is number one through ten for them. Telematic 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 in terms of 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 level of and by the way, that's not even AI. That's just machine learning really data driven and that's already in broad use in other insurance companies. That is sort of where this is all going.
I need to be able to ask questions that analysts might take weeks and months or bring in McKinsey or Bain or whoever to go and study problems. Right. The systems will be able to start giving you insight into those kinds of questions. That's really where we live. Proprietary structured enterprise data. That's a totally different realm. And by the way, you couple that with language models and having natural language capabilities yeah, that's pretty powerful story. Reminds me of Marvell movies. That's a nice model. But imagine the medical we have diagnostic models and we have all these different levels of intelligence that we can build. As long as they have the data, they're going to be insanely lightning fast providing insight.
We recently acquired this company called Neva. I'm very excited about bringing their expertise into the company because they're search experts and I'm a search addict. I mean, 25 years ago. I wish I had had search earlier on in my life because it's such a huge thing. I just can't help myself. Search is so addicting because it lets you explore everything that's known and ever been written or published or opinionated about and process all that information. The problem with search is it has no context, right? It just matches on strings. If you search on snowflake, 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. Context is really the name of the game in the world of data, right. One attribute can make a data attribute go from mundane to high octane because of the context that it creates. And search needs context to become stateful. Chat and search may eventually become one natural language conversation.
So you combine that with having these new levels of intelligence specific to industries or just subject matters. I think that's really where there's a world of opportunity waiting to unfold still. I'm certain that it will.
Sara Guo: Yes. Nevo is a dear former portfolio company. Do you imagine that the snowflake interface for users changes a great deal over the next 5-10 years in terms of supporting more natural language or a broader user set?
Frank Slootman: Yeah, both of those things. I think there still will be a future for BI companies. Business intelligence or the Tableaus, Lookers of the world and dashboarding is done for a number of reasons. Sometimes it's just basically providing data in a 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 ad hoc based now. A lot of it is. And for ad hoc, nothing is going to be better than natural language.
At least I'm already using it. We push Salesforce data into what we call Snowhouse. That's our internal Snowflake database. That's where we push everything into and it's just incredibly easy to use. Already commonly available services and have a conversational relationship with that data. "Are my two top reps in this country or that market or this industry?" Now it spits it out in a fraction of a second, but a beautiful graph attached to it and all of that. It's very addicting 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 way more value than they ever imagined from the data and it will change how products get used. I mean, BI will not be the same. I think I see that as severely affected by this evolution.
Sara Guo:You made another acquisition of a company called Streamlit that I think we're also both familiar with. Can you talk about the rationale for that?
Frank Slootman: Streamlit is a company that does visualization and animation for Python applications, 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 understand what it is and how it works. But Streamlit 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 and animation.
The reason that we acquired Streamlit is that we have to have visualization and animation. By the way, this also touches the world of BI because a lot of people use Streamlit 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 Streamlit is to bring it inside Snowflake. We call it Streamlit in Snowflake. The reason is that you need to have that hardcore, trusted, sanctioned 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 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 making Python non-porous, and it was an enormous effort to do that. But you go to large financial institutions, 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 a 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 a world of AI, if you don't have highly organized, optimized, sanctioned and trusted data, what do you want your models to do? Just kind of train on a data lake? I call it a landfill. You have no idea what the hell is in there. Everybody dumps their stuff in there. You're going to go train on that? It's just absurdity.
So having highly organized, optimized, sanctioned data, it's a prerequisite for all, and people publish what they call data products. I'm sure you've heard that term before, a data products. Essentially, I've taken data 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 trusted, organized, and optimized. And then also that the business can get in trouble with it either because the data is no good or because they're breaching all kinds of security and compliance aspects of using data.
So Streamlit is really important to us. The great thing about it, it's an open source project. So many people out there are reaching for when they want to publish something, and we're like, okay, we're going to bring that inside the enterprise perimeter and make it high trust.
Sara Guo: 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 broadly more online analytics, other workloads applications that sit inside Snowflake with unified data. What are the biggest challenges you guys face in making that vision come true? Is it convincing people to move to 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 enterprise architecture overall.
Frank Slootman: Yeah, but we are rewriting anyways because of our migration to cloud. It's like the most disruptive thing ever. And yeah, look, when I was at ServiceNow, we basically had an on premise architecture that we hosted in the cloud. And by the way, I'm not being unduly critical here because it was very useful that we were a single tenant platform. It 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. All the federal business that we had at Service Now was all on premise oracle because 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. Federal is a very small part of our business because we're in the process for years and years and years to meet those standards. It's very hard, right? But we are a pure cloud implementation. We can't run on premise. I get asked that by people. I can't even conceive of it the way Snowflake works because of commandeers resources. It's not a machine centric platform.
It is a big change, there's no doubt. And as I said earlier, 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 bunkered 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, 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 adds no value to what you do because you're not a data management expert. It's just a utility function for you. But then 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 process 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, that says, hey, we're going to fully replatform on Snowflake because in the world of supply chain management, that's really important because we need to have visibility across all the entities that make up a supply chain. You can only do that when you have a single data universe and when you have all these containers, it's impossible. That's why supply chain management has never been platformed because the data problem was unsolvable. Literally the other thing is the supply chain management. They run these extremely demanding analytical processes, right? And they run many times per minute, per hour and they are very commanding of resources. Right? So again, this is where our style of computing is 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 to concurrently.
So all these new architectural things are lending themselves, really to use cases that have been there for generation. But supply chain management is an email spreadsheet business. I mean, they're still living in the world of Microsoft 30 years ago. That's insane, right? Because it's one of those use cases that should have been extremely optimized, but it isn't. So yeah, you're going to be doing replatforming, 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.
Sara Guo: 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 thrusts that you see coming in terms of data?
Frank Slootman: Data is going to redefine whole industries. Okay? And that's what I find the most interesting. The reason I say that is, first of all, 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 making medicine predictive, for example, because everybody knows healthcare is economically not viable at the scale that we need to deliver it in. So data can make it predictive and prescriptive. Right? If we have enough data, we can tell who is at risk for what disease, when and what they need to do. All data driven. This is not somebody's opinion. Data doesn't have opinions, okay? 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 healthcare will become much more effective, obviously, because you don't need 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 us, 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. It takes on average twelve years to bring a drug to market. Well then you 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 data is far more important to how the economics and how the industry functions than people still realize.
Elad Gil: How do your investments in R and D reflect this or what are the big areas of thrust that you have right now from an R and D perspective?
Frank Slootman: The hardest part for us is 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, we'll just pump the data over here and now we're back to fragmenting and siloing the data. So if we have the data, we have to enable the workload. 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, that's incredibly important because a lot of cybersecurity companies, there are partners of ours, they are running on the data cloud because they couldn't sell to their customers yet another database container. Customers didn't want it. They said, look, they'll bring the data here and then we can combine it with all these other data sources vulnerability and then our analysts can search one data universe instead of 15 of them and try in their head to figure out what does it all mean and do something with it.
Sara Guo: Yeah, I'm definitely seeing 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 enriched functionality on top of that or access to that data in ways that are really performant and combined with what the company is trying to do more broadly. So I think that's been a really great innovation for the industry.
Sara Guo: 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?
Frank Slootman: Not really. 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. When I was at ServiceNow, I always felt that it was not an equitable relationship that we had with our customers because oftentimes 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 all their users all this time. How is that equitable?
Frank Slootman: 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. Is that painful? Sometimes, yes. I talked to the CIO of a bank last week and he said, my bank's growing 3%, Snowflake is growing 22% and that can't go on forever. The CFO gets in there and he goes, he starts calling bullshit on everybody and saying like, hey people. They basically say, this is the size of your breadbox. Live with it, you're not going to get a new contract. And then people need to go back to the drawing board and go, okay, it's a very fine grained thing because you can go into Snowflake workload and 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 if you're in a SaaS subscription model, they got to wait for their next row before they can start cutting up a limp here.
Frank Slootman: Whereas 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.
Sara Guo: Yeah, 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 there are 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 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 prioritizing in both good times and bad times. How should people think through managing differently in this changing economic environment or what are the first things people should do?
Frank Slootman: I see all these layoffs with Amazon and Meta and Google and all this kind of stuff. We don't do layoffs because we don't wait until there is a huge headwind. 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. 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 sort of evolved over the years where it's just 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 the other way until we get a crisis and then we start ripping out tens of thousands of people. I just don't think that's fair as well as effective.
Right. 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 how you run things 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. By the way, people are not used to living in downturns. When you've been around longer, it's like, hey, they come around, okay? That's part of life.
And by the way, let's double down, triple down, put our game face on, put our boots on. We're into fight now. This is actually going to be a lot. I will say to you, this is going to be a lot of fun. This is where it really happens. Right? So in other words, you can get up for us. We need these amp things up. That's what you're doing. People are growing up and like, oh, they only 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 and just get a lease on reality and say, look, this is part of life.
Do I have to start rethinking everything because economically things are now different? To some degree, yes. I mean, we're scrutinizing productivity much harder in sales organizations. We might be a little bit quicker on the trigger, all that kind of stuff. For startups, obviously, 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 data domain, 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 in recent years. People have not 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 because that's how you stay alive. Fundraising is oxygen for a company.
Elad Gil: Yeah. Basically, I think gravity turned back on and everybody's like realizing it.
Frank Slootman: Yeah.
Sara Guo: Frank, this is a great conversation. Is there anything that we missed that you think would be useful or interesting to talk about?
Frank Slootman: Well, we've already talked about amping things up, and that's always when we have conversations like this, and a lot of people are listening to it. I'm trying to get people to say, in my next meeting, my next message, my next encounter, my next situation, I'm going to amp it up because it's just a choice that you make. And don't be afraid that people will react poorly to it. Good people will actually love it, and especially if you're in a leadership role. This is really what people want. They want to inject energy, focus, intensity, and quality so that the whole place starts to feel exciting. It's much easier to live in an energized environment than one that's devoid of energy.
Sara Guo: I love it. It's a very courageous message. Thanks for doing this, Frank.