How Databricks Went $1M to $7B+ ARR in 10 Years | Ron Gabrisko, CRO

Speaker A: Ron, welcome to the show.

Speaker B: Yeah, thanks for having me. Excited to be here.

Speaker A: Yeah, this will be. I think this will be a fun conversation. So you joined Databricks before you guys were. You really had a business? I think it was less than a million in revenue. And as of the time we're speaking, I think the public disclosure is 6.9 billion in revenue. And I think actually by the time we publish this, I think it's going to be even higher. So this is going to be pretty fun, going deep on just how you did it, how you got there.

Speaker B: Yeah, it's been, uh, absolutely amazing journey. I mean, we're still growing super, super fast. We say it's top of the second inning, still early days. So it's an amazing business. It's been a great journey.

Speaker A: I love those baseball analogies. Like when you're on an earnings call and the CEO goes super deep, is like, we're still getting in the seats. The game has. We haven't even thrown the first pitch yet.

Speaker B: Uh, well, I'm a baseball player. You know, early days, that's what I wanted to be, a major league baseball player. And.

Speaker A: Oh, really?

Speaker B: Yeah, Now I'm, uh, I sell software, I guess is the closest thing I could get to the big leagues.

Speaker A: What do you think is the kind of like the key to success for databricks? Like, if you just had to sum it up in a couple sentences.

Speaker B: The company's been around 13, I think I've been here 10 and a half years. So, you know, company is less than a million. I think most of that growth has been over those 10 years. But yeah, company's been around, uh, almost that long. So, yeah, I mean, I think the keys to success at databricks, um, I can talk about kind of how we built it in each stage, but overall we made first of all, we have seven of the greatest, smartest founders on the planet, all PhDs from Berkeley. Uh, they were at, like I said, the frontier of data and AI way before AI was even cool. Right. So again, this is back in 2014, 2015, um, and they made some really strategic bets early on. One was on go all in on cloud, one was go all in on open source, and the last was go all in on data and AI. And that wasn't really the vogue back then. And then I would say since then, we have the best engineering and innovation machine. Like our product is the best I've ever sold. It's the best on the planet. Um, and then I think we have the best go to market team on the planet. Right. Like to achieve that kind of growth at this kind of scale is fairly unprecedented. So I think the combination of those two things and obviously great people, great culture, uh, that's been the kind of the keys to success for sure.

Speaker A: Yeah, I want to kind of talk more about all those things, uh, I guess going in order though. So when you talk about making it better on the cloud, that kind of seems obvious today. Why was it such a big deal back 15 years ago?

Speaker B: Well, back then cloud was still pretty early, right? Uh, most of the infrastructure was still on prem. I can remember first kind of when I started, I took all the founders out to Wall Street. I said, hey, if we're going to make a business here, we got to sell to all the big financial services banks. And literally we met with most of them and you know, CIOs. I got CIO meeting. It was crazy. They would like bring like hundreds of people to meet the inventors of Spark and Mateo would be signing autographs and stuff. And uh, that's uh. Yeah, but they were like, we're never going to the cloud. And we were cloud only. Like they're like literally never. I mean, but now, you know, like some of our biggest customers, bunch of these big banks, uh, they're all in on the cloud and it's already happened. But back then, ah, that wasn't a no brainer for sure.

Speaker A: That's kind of wild because it would be like someone telling you today we're not going to use AI, we think it's fake or doesn't work or something. What was the justification on not moving to the cloud? Was it not valuable enough and too

Speaker B: hard or expensive really in any market. Right. It's like security, you know, you got to overcome legacy. So you know, there's a lot of people saying like, oh, the clouds aren't as secure as, you know, our own dedicated security team, which obviously is, is, is false. Right? Like, I mean these clouds have to have better security than any one individual company. Um, so a lot of that is like security governance, regulations. Right. Like a lot of the banks regulated markets, they're regulated. So like, you know, it's similar challenges to early days of AI, right. Um, and once you kind of solve those challenges around security, governance, scalability regulations, things of that nature, the market takes off. And so it was a great bet. Obviously, like, you know, everything's moving to the cloud and um, you know, we could move faster than a lot of the on prem players because they were stuck having to deal with legacy issues. So it was A great bet for sure.

Speaker A: And then with the open source versus closed source, I feel like today like open source is not that crazy. Was it a little bit crazier at the time? Like was it just not really proven yet?

Speaker B: I mean you think of it like back then like, you know, what were the big open source projects, maybe like Hadoop, uh, Linux. And the model for how you monetize it was just support and services, right? And so if you think about one of the ways databricks changed the market too was like, how do you monetize open source? Right? And we built a managed service. We built one of the first managed cloud services for open source. Now a lot of the hyperscalers were using open source to monetize their computer, but not a lot of private companies were doing that back then. And then as we kind of built out this model because we're still open source core everywhere, uh, now we've had many, many projects beyond Spark, Delta, ML Flow on and on, but building a managed service around open source and how do you monetize compute usage, things of that nature is, is the future. But that was fairly new concept back then, um, that I think we kind of developed.

Speaker A: So at the time you had just a bunch of people using the open source product that was databricks and you started to essentially start to get people to pay. So how did you kind of make that transition?

Speaker B: Yeah, no, exactly. So I mean there were literally millions of people using Spark. So Spark was the open source product. Early days. Um, that's why I love the company as an opportunity too. And you know, first days we just said go meet with all the customer, meet with as many customers as possible, that we're using Spark and understand how they're using it, what are their challenges, what would they pay for? Um, I mean it wasn't rocket science, right? And then you look for those kind of trends, right? Like okay, you know, people pay for security, people pay for scalability, so you start adding these features as a pay for on top of the open source. Um, and again we're selling a managed service too. So like uh, early days we were selling to a bunch of the, like Silicon Valley called digital native startups. Uh, with open source they tend to love like building their own stuff, right? But when you go to enterprise they don't necessarily have all the expertise to build large open source projects. So they need partners, they need a managed service to do that. So like our break in enterprise was a really big step in how we uh, how we scaled the company.

Speaker A: So when you Say manage service. That means you do some of the work for the customer. In a sense, you're kind of building some of the features or you're updating things for them. Like how do you guys, how would you describe that for somebody who's never heard that before?

Speaker B: Listen, it comes like all the features are there, right? It's all ready to go. You're not just, you're not having to set it up, uh, you know, each, each particular product, each particular piece, each particular feature, configuring it, I mean obviously it's super configurable but like if you were to try to build something like that from scratch, you'd have to get 20, 30 different open source pieces of software. You'd have to stand them up in different services on whichever cloud you're going to use. You'd have to configure all those things. You'd have to worry about security and scalability and governance and all those things. Databricks comes right out of the box ready to use, which was huge for. We kind of created the data science market. So early days I said we're doing AI. Before AI was cool, people were starting to do machine learning, data science. So we just go straight to the data scientists and they were like, oh this is awesome. I can just upload my data sets and start doing that. I don't need to do a bunch of setup with it and things of that nature. And obviously that evolved to all the things we see today around data engineering and data pipelines and AI and machine learning and all those kind of things. But uh, yeah, creating kind of just an easy to use managed service on top of open source was a big uh, tailwind for us.

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Speaker B: Yeah, no, easiest way to describe it is we take massive amounts of data from many different sources and allow you to do AI and do predictions and analytics on it. So like an example is, you know, I'm sure you use streaming service Netflix or Disney or you know, when you see kind of next best movie and it says, hey, it has 98% probability for you. What they've done is they've taken millions of data points from any other users against all the other things, all the other data you've given them, like movies you've watched or liked or things of that nature. And they're using that to predict your next best movie. And so then we apply that to pretty much every industry. Right? So we find credit card fraud for banks, uh, or how do they cross sell upsell customers, how do you do loan approvals, loan origination, uh, R and D for pharma, like early days, I would say someone will solve cancer with databricks. Lots of companies are doing that in Pharma. So again the problem set is like how can you build models, do predictions, do recommendations, build agents, uh, on data. And the more data you can feed those systems, the more accurate the predictions, the more accurate the models and agents, um, even think. In Today's world of AI, um, obviously everybody uses ChatGPT or pick your favorite LLM, um, Gemini, what have you, um, you know, for an enterprise, how do you make decisions? It's all about like having the context of your data and attaching that to AI. So for us we developed a product called Genie. And so what genie, I actually have it on my phone, I use it to run our business, but it basically has all the context of my business and I can just ask it questions in English and it does all the calculations, all the SQL queries, does all the tech in the background, right? So if I ask it like what are the top 10 customers that might churn in Germany? It'll go build the churn model, it'll go try to understand the churn, you know, make recommendations. Then I'll even make recommendations on how to fix it. Like it's pretty cool. So that's, you know, anyway, high level overview on databricks. Hopefully that helps.

Speaker A: Well, I was going to ask you about genie because I know you said you run your team on databricks, so it's kind of like the user facing, simple to use and access interface.

Speaker B: Yeah, we run all databricks on databricks, right. So like I can predict our revenue within 1 or 2%. You know, I can predict which customers are going to churn. I can predict, you know, which products, you know, are the stickiest, like you know, customer retention, all those things. Um, and then Genie allows me to just do all the research and questions. So like it'll prepare me for customer meeting, it'll tell me everything, know about the account, which products they use, what are their opportunities to grow their revenue, you know, because it'll do other research. Uh, and it basically the interface is similar to any like LLM, right? You just start asking it questions. The difference is it can do all these, it's doing it on your data, right. Versus just generic publicly available data. And then it's able to do calculations, graphs, predictions, launch agents. So it's super sophisticated for enterprise AI versus kind of the, call it, the generic tools that are out there today.

Speaker A: Has that always been there or is that more of a newer ish type of product?

Speaker B: It's brand new. We launched it at Data and AI Summit. Now we literally have millions of users on it if CEOs of banks, CFOs of banks, uh, you know, like, it's perfect to run your business. Like, if you think of traditionally how you run your business, you have some kind of dashboard or analytics tool, and you know, maybe it's updating real time, might be batch. It's definitely not doing predictions for you. It's definitely not allowing to just ask questions and knows all the context, not just from whatever that dashboard was built on, but it was context of all your data. Right. Builds out the ontology of, you know, all your data sources. So it knows where to go look for answers to certain questions. And again, it does calculations, predictions, machine learning models, queries under the hood. So it's, uh, it's. It's the first of its kind and it's state, uh, of the art. It's pretty awesome. Yeah.

Speaker A: So it's kind of like instead of having the guy on the team who you just like, are pinging and like, hey, can you give me the updated numbers? It's just like, you're like, you're like messaging genie, like the DataWorks product.

Speaker B: It's real time because, you know, that is what everybody has. They have an analyst, like, oh, can you go get me this, you know, this answer, etc. And then, you know, it takes a day. And they're like, no, no, change this, add this. You know, then it's a little bit stale. Oh, can you get this week's data versus last week's data? You know, it's all real time, so it's right at your fingertips. Um, you know, I see like a couple other, like, you know, you think about it, we want every business user, like I have, you know, we have huge, like, retailers. Like, the store managers are like, how do I maximize revenue? Like on this particular shelf or with this product or which promotions should I run? Which is going to be dependent on where your store's, you know, located. You know, you're going to have different dynamics based on, you know, kind of your clientele, things of that nature. Um, rental car agencies, like, how do I maximize sales? It'll give you feedback. Oh, your CSAT's low on this. You should probably clean your cars better. You know, what have you. Right? Like, uh, you know, it's, uh, again, it's putting at, uh, a gas station. Uh, uh, one of the big gas station retailers was like, I want to maximize sales of pizza. And they would have like a camera feed and it would actually measure like pizza and pizza pricing. Um, so it's. Yeah, yeah, like, I Said put the power at the fingertips of every single business owner out there in your business. And uh, you know, like I said, uh, that's the future.

Speaker A: That's pretty interesting. So you could. So in this pizza pricing example, the camera, I'm assuming it's like a security camera that they just already had in the store. Is it like, what kind of data does it take in? And then like, how does the pricing of the pizza, Is it like real time pricing or they're just like, hey, it looks like we can charge extra $0.30 based on something like.

Speaker B: No, I think more of it's like, you know, if they run out of pizza, they want to make sure they have pizza there. You know, like, uh, so it's monitoring that stuff. Like I said, it could take video images. It'll also be like, you know, how many pizzas you know should you have that day? Like this managing inventory based on seasonality or maybe other promotions they're running. But uh, again, that's a small example. I mean the idea is like, how do I maximize my entire business? A lot of these gas stations, it's about petroleum sales, but a big part of it's like the retail business, right? It's like all the things you're selling inside the store. How can you maximize the sales of all those things? Um, and that's again going to be independent based on location promotions, you know, other independent factors in data that you'll have access to.

Speaker A: So yeah, so in terms of like databricks versus the market, because I see all the time, every day, maybe it's actually tailed off a bit. But there was a time where like every day there was just like a new AI product that solved all your business problems or whatever, right? Like there's, there's a lot of them that are out there at this point. So, like, what do you think databricks kind of differentiates on? I'm assuming this is a pretty standard question. You actually probably get in when you're

Speaker B: talking to customers and there's all kinds of new models out there, right? Like, I mean, obviously OpenAI and anthropic Heather models, but now there's lots of open source models. I'm sure you've read about them, you know, Kimi and TLM and what have you. Um, it's not really about the strength of the model. Uh, the key for databricks and even the key for a lot of these companies to be successful at AI is all about the context of your data. Like the models are already smart enough. Like, you know, I Ask these models all kinds of questions. They're as smart as I am, if not smarter. Right. And it's all about, like, the context that you provided. And so your ability to connect those models with your proprietary data and your context to your business is really what unlocks the, you know, insights and the outcomes for these companies. And so that's been the key for databricks is just, you know, how we built out our data platform. Again, we were AI first from the beginning, uh, 10 years ago. We were thinking about AI and machine learning before, uh, anyone else. And so we approach the market from that perspective and how we govern data. Like our Unity catalog, Unity AI, uh, Gateway. We serve any model unique catalog. It doesn't just categorize your data, but all your models, all your notebooks, everything else you're doing with your business so that we can automatically understand where's your data, um, what is relevant to answer these questions or build models around it. That's the key to databricks. No other company has that kind of breadth and expertise.

Speaker A: Back when you made this big bet on AI, it was probably not quite as obvious back then. What was the thinking was there, oh, artificial intelligence is going to do what it did. Like, what, what element of this was like, you kind of got lucky, uh, how AI has evolved and like, how much of it was, like, you kind of saw where it was going. Like, how would you kind of talk, uh, through what happened there?

Speaker B: Well, yeah, I think, I mean, part of that was just insightfulness from the founders. I think in the first kind of products and use cases they built, like Spark was just a massive, like, data processing engine. Uh, right. Which meant, like, the more data we could process, uh, the better we could perform versus other products that were out there. Right. And so, like, if you think about it, we went after the data science market first. So people who are doing in a lot of these data science projects, if you think about it, like, if they're doing a huge genome study, they're trying to find a new cancer treatment. So they're taking a bunch of compounds and mapping them against different genomes, mapping them against electronic methods, medical records, and trying to find trends like which compounds should we test to try to accelerate R and D. Well, these genome files are huge, right? And so traditionally they'd be like, okay, let's run one model to try to predict something that might take 24 hours. Well, databricks comes along and now I can run that in a minute. So think about how fast I can accelerate my virtual R and D to Try to find. Yeah. And so, and so that's now reality, right, for every single business is like, how do I take billions if not trillions of data points, analyze them, um, get insights and then make decisions based upon my. Based upon that data for my business. Right. So that's been the key versus, uh, you know, being unable to do that in the past. And so, you know, I think it was insightful and certainly fortuitous to kind of start in that part of the market developing machine learning, data science, all those kind of things. And that's how we kind of came at the market, uh, and entered AI early days.

Speaker A: And LLMs were not, I mean, m. I don't know if we had them or how good they were working back then.

Speaker B: No.

Speaker A: So what was. Was it just. What was the initial kind of like product? Like, like, what was it powered by?

Speaker B: Yeah, again, like, we had notebooks. We still have notebooks. Like data scientists, they're building machine learning models. It's just, it's all code. It's not, hey, I can just use plain English to build these things. So, you know, you had kind of the ChatGPT moment when ChatGPT finally got good enough and consumers started using it. Um, we actually developed, uh, our own model, uh, back then, early days of that, it was actually pretty good. And then we ended up buying a company called Mosaic. Um, I don't know, it was probably three years ago to kind of start doing training for custom models, things of that nature. And so our AI R&D teams, as good as any on the planet. But again, we're really focused on how do we solve these enterprise AI problems and outcomes for specific verticals and businesses versus, I would say the rest of the market is more general intelligence. Um, and for consumers. Right. So that's been a big difference at Databricks. And again, that's our secret sauce. How do you connect the AI to the data? Uh, in the best way, in the most secure and governed way possible to unlock these, uh, insights as well as build agents, automate processes, things of that nature.

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Speaker B: Yeah, no, it's massive. I mean, like I said, I think we're still early days. Like, you know, everybody thinks like, hey, we're automating everything, you know, right now we've solved one use case front to end, which is like, how do you better automate coding? Like, you know, cloud code, Codex, cursor, all those guys you know are going after. How do you automate coding, accelerate software development? Um, and I think tools are really good for that. I mean, obviously we serve all of those through our Unity AI gateway. But how do you automate all these other processes? How do you automate finance? How do you automate things in sales? Like, a lot of the admin work in sales, we do a lot of that automation with GENIE and our, uh, GENIE code and GENIE agents, uh, inside our business and inside of our customers. And I think that's still pretty early days because you have to kind of understand the process, re engineer the process. You're just not lifting and shifting, you know, the same kind of process. So, um, but that's, there's tons of upside. Like, again, like I said, like, we're at very early days on how we do that and that's massive upside for this market.

Speaker A: Do you think it, is it starting to tip in some of these other categories? Like, are you seeing, you know, practical usage? It's like actually you're actually able to use it in the same way engineers are using some of the coding agents.

Speaker B: Yeah, I mean, listen, I think it's like I said, you have to get in there and we're like customer zero for this, right? So we're like, we're an AI company, we're going to automate and use AI in our entire business. And so we bring a lot of those learnings then to our customers. But a lot of this is like, how do you get in there in the expertise? Because again, you have to redesign the process. You can't just, you know, automate the same process. Um, and so, you know, that level of expertise, I mean that's, that's how we use our FTE motion. That's how we're, you know, it's like you look at healthcare like, how do you automate claims? Uh, authorization and claims, payment and claims, you know, uh, you know, find claims, fraud, all that kind of stuff. Like you need to understand the process, teach the agents what to look for, um, make sure the agents are high quality. And then you continually have to make sure that they're doing a good job. Right. And so I think we're still at early days on how we automate all those processes. But I'm starting to see the early wins at a bunch of these customers, at a bunch of databricks customers. So that's super exciting, for sure.

Speaker A: So internally at Databricks, I guess you've, I mean you probably added billions in revenue since kind of the ChatGPT moment. How has it changed? How's using AI kind of changed how you operate the team any day to day or any tactical thing. Somebody listening to this can be like, oh, I'm going to do that too.

Speaker B: Yeah, totally. Um, I mean, listen, I run our entire business on databricks. Like I said, I can get super deep on any one customer. Jeanne will prep me for all my Customer meetings. We're starting to automate a lot of the sales sequences. So anything I can do to automate. Automate, like admin work, like forecasting. How do people enter things in Salesforce? You know, we're a consumption business, so we call them use cases. But anything I can automate to kind of increase productivity and increase sales time, I'm going to automate. And so, and 100% of my sellers have GENIE on their phones. So, like, they can run their accounts, their business, they can do demos for their customers. Like, you know, I'll go to customer dinners and I can pick up the phone and just show them, like, show them. I'm like, hey, here's, uh, let me run Gene. I ran Genie. I had them upload, you know, some, you know, dummy data sets for your business. Like, here's how I, you know, here's how I can find particular opportunities and how you guys are handling claims or loan origination or what have you. So, um, you know, we're going to put AI in everything we do. Um, you know, we use it to. It'll diagnose your account and be like, hey, here's the next best action. Like, it'll know, oh, you have a retailer, you've sold them this use case. You should go sell them XYZ use case. You know, you should go talk to this person and sell them this type of app. So it also makes recommendations, um, for all our salespeople. You know, they can do their own demos. Like I said, GENIE is super easy to use, so it's great to demo for pretty much any user of data or AI. So we're putting it in our entire business. Um, I'd recommend that for everybody, just because that's where the market's going. Um, and, uh, it's been great for us.

Speaker A: Yeah. And I think probably one of the most interesting things about kind of your story with databricks is you originally joined after a conversation with an investor in the company. So how was that conversation? How did it go and what happened?

Speaker B: Yeah, no, I met Ben Horowitz, uh, from a 16Z. I mean, guy's a legend, right? Um, and he's like, hey, I have this little company. It's probably smaller than any company you're looking at. It's called Databricks. But it's got the most upside of any company in my whole portfolio. You know, maybe you can, uh, meet these guys, you know, make, you know, recommend somebody, you know. And I, uh, met the founders and, uh, just, you know, hit it off. I mean, These guys, like I said, are, you know, smarter than anybody on the planet. Their brains work in a different way and they just understood the space. And, um, you know, I thought data and AI was the future and, uh, decided to take a big swing at it. But, uh, yeah, Ben was, he was super funny. He's like, these guys are like, Berkeley vented this great piece of software, greatest piece of software on the planet, gave it away for free. I need somebody to come in and help them build a business out of it. And, uh, yeah, uh, it's a fun story. I developed a great relationship, friendship with Ben as well over the years. And, uh, he's just legendary investor and, uh, it's been awesome for our company.

Speaker A: So I have a question from, uh, one of my friends, uh, Paul Klein. He's the founder of a company called Browser Base. He's a solo founder. And his question, he's like, you got to ask him, how do you put up with seven co founders? Just like, how do you do that even?

Speaker B: So I get that question a bunch, right? Especially from CROs. They're like, I can only handle one founder. Like, how do you handle seven co founders? I think it's a massive advantage, actually, you know what I mean? Because you have like seven true owners of the business. Like, and, you know, lots of, uh, you know, companies are too, like, hey, can I get a founder to speak at my event? Or can I get a, can I meet with a founder? And they're all super technical, right? So it's like, you know, if you're talking to a technical audience, uh, you know, I have seven times as many people to do exact alignment with CTOs and CDOs and things of that nature. So, I mean, obviously I spent a lot of time early days. I think generally engineers are skeptical of sales people. So, uh, I spent a lot of time early days getting to know them, understanding their strategy, how they want to develop the company and the culture and all those kind of things. So, um, that would be my recommendation is, uh, you know, and I think Ali, when Ali took over as CEO, you know, first thing he did is he sat down, he said, teach me sales. Um, I want to learn sales. And, uh, we formed a great partnership together building the company. Like I said from early days. So, um, you know, I would. That's my recommendation is like, dig in. You know, the founders are, you know, the company, especially early days. They're going to set the tone, the culture, everything. So it's been a huge advantage with seven of these guys. Love them to death.

Speaker A: I think doesn't Anthropic has seven co founders too? Maybe that's the lucky number.

Speaker B: I have no idea. Maybe, uh, there's a lot.

Speaker A: It's either seven or eight. I can't remember.

Speaker B: Yeah, no, that's a good idea.

Speaker A: And so you said something interesting. So Ali sat you down and he's like, teach me sales. So how does that conversation go? Typically, when a founder asks you that, what do you walk them through? What do they usually kind of struggle with? How does that journey usually go? How should I approach that if I'm trying to, assuming I'm super smart, just how do I learn more about selling things to people?

Speaker B: Yeah, first off, I would say most early stage engineers are, uh, like, hey, if I just develop the best product and I have the best pricing, like, everybody's gonna buy it. Well, it doesn't really work that way. Right. Like, the power of sales, especially for enterprise, is like, salespeople are gonna teach my customers how to use the product, how to get value out of my product, make them aware of my product, those kind of things. So, I mean, I started on a whiteboard talking about organizations. So, like, you know, listen, when you're selling to startups, there might be one person you need to sell to, the CTO or the foundation founder. But when you sell to enterprises, they make decisions as an organization, right? So you need to understand, like the org chart, who are the players, who are the decision makers, who owns the budget, how are the decisions made, how are the approvals done? So just talk it through. And so now I'd be like, okay, like, who's the decision maker, who's our exec champion? All those kind of things on how you develop relationships.

Speaker A: Like, do you typically just ask people that when you're first meeting them and talking to them? Or is that like, too tacky? Like, you have to like, smooth. Yeah. Like, how do you, how do you figure that stuff out if you're just meeting someone for the first time? Like that?

Speaker B: No, no, no, I wouldn't do that the first time. No. I mean, part of, uh, part of what you're trying to do, I mean, a lot of these sales cycles, like, they'll take six, 12 months or more. Right. So, you know, initially you want to get to know a person and you want to be able to understand. It's asking a lot of questions. Like, sales to me is more about asking smart questions and listening versus, you know, hey, I have the great glitzy pitch. Um, like, it was funny, like when I was interviewing for the Company, they were like, hey, do the pitch. I was like, okay, send me the pitch. And I looked at it and it was super technical, right? It sounds like, yeah, like, people probably

Speaker A: just like, zone out. Like, I don't feel like looking at this, like, flowchart with, like, all these diagrams.

Speaker B: It had a lot of acronyms in there, a lot of speeds and feeds, um, and listen, like, you know. So I had some advice on. Here's how I would pitch data breaks. But I also started with. They were like, okay pitches. And I was like, okay, what kind of company are you? You know, I'm like, how do you use data? How to use AI? What other systems do you use? What are some of your objectives that you want to get out of using AI with data? And they're like, are you going to pitch us? And I'm like, I am. Right? Because it's like, you know, the more. The more information I can gather on what are your challenges, how can I help? How can my product help? Um, the more credible I'm going to be as a salesperson. It's kind of like you go to a doctor if they're just like, yeah, you just need surgery. Like, don't you want to know what's wrong with me first? You know what I mean? So, yeah.

Speaker A: Yeah.

Speaker B: So sales is a lot about, you know, come in prepared, know about the customer, try to do as much research as you can on their challenges and issues and objectives and strategic objectives. Go in high in the Org if you can and, uh, and ask a lot of questions, get to know them, get to know how you can help, and then be very credible and diligent, uh, and responsible and trustworthy on how you follow up. Uh, don't bug. Don't bug them. But, like, if there's specific areas you can help, like, that's what a good salesperson does.

Speaker A: And you kind of have to assume if it. The bigger the company, the longer it's going to take to build that trust and get them to get them to, you know, in some cases they're like, moving their company over onto your product. Like, it's a long process.

Speaker B: Yeah, yeah. And like I said, I mean, there's a lot of technical validation in there, doing POCs and things of that nature. But, you know, start with understanding the problem. What is the, you know, what is the business problem you're. They're trying to solve? How can you help them get there faster or cheaper, less risk? Um, you know, and then, you know, I talked with Ali a lot about Medpic, and you know now he's an expert on all this stuff. Like you know, he's medpic.

Speaker A: I've never heard of Med Pick before.

Speaker B: Yeah, Med Pick's just a process on how you govern sales. It's like uh.

Speaker A: Oh really? Yeah. What is it?

Speaker B: Well, each thing stands for a difference, like metrics. So what metrics are you using to kind of justify like, you know, how do they measure their business? What metrics are you actually changing? Uh, you know, with whatever solution. E is exact sponsor, D is like decision process. Like each one, each letter means something in a process to kind of develop. Uh, it's a pretty well known kind of enterprise sales motion. Um, and then there's a thing called command of the message which is similar, uh, for every company. I mean those are two frameworks I would recommend for anybody that's trying to learn enterprise sales or build enterprise sales. Um, those are probably the two most common that I use and that we use. Um, and then obviously branch off of that. It's got to be custom for your company and your product.

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Speaker B: Uh, companies or salespeople or both?

Speaker A: I mean, maybe it's both. I don't know, maybe it's the same thing. I don't know what's usually the biggest mistake people make.

Speaker B: I mean, I would tell you like the biggest mistake I see in salespeople is they start pitching before they understand. Like I said, like, you know, hey, I'm going to pitch you my product, but I don't really understand anything about you or your challenges. So first thing, get to know somebody, establish rapport and, and ask some questions so you know what might be important to them. Um, from a company perspective. Listen, like, companies are growing faster now than ever. Like AI is a huge unlock, uh, for growing companies. Um, you know, I'm a big advocate of, you know, you need salespeople to grow your company. But the PLG motion has been great for companies like anthropic and stuff like that. Like, they're doing great with PLG. Um, I always say, like, there's kind of four stages of a company. There's kind of 0 to 10 or 20 million where you're just finding product market fit from 20 to 100. You're kind of building a playbook. It's got to be repeatable system, repeatable trends from 100 to a billion. You're starting to expand, uh, internationally into partners and then multiple billions. It's a lot about having the right leaders and, uh, culture and systems and processes and continuing that kind of fast innovation. And how do you continue to run fast. Um, so each one of those stages requires kind of different, I would say, things to grow and so based on each company. Again, when I try to advise companies based on where they're at inside of their market, you know, again, you need, you know, usually you need salespeople to push your product because people don't know either know about your product or know how to use your product. Um, and even in the case where PLG takes off, you want salespeople then to be able to go talk to your enterprise customers. Again, enterprise customers, usually it's people that are buying stuff. If people are buying stuff. You want people to sell stuff. Um, if it's intuitive of how to use, uh, something. Yeah, I mean, let it rip. Like, developers know how to use these coding tools. Like, awesome. They pick out their best one and they go, right, um, you need salespeople to get through security reviews and contracts and things of that nature. But, you know, the actual selling part of it might be like, here are the advantages of my product versus another. But, you know, most products, they need salespeople to teach customers what their product does and how to get value out of their product. And so, you know, I think there's ways to ramp that up in a thoughtful way, depending on which market you're going after.

Speaker A: And I know you guys made a pretty strong bet on we're going to hire technical people to sell the product. Is that generally that seems like if the more technical the product, the more technical the salespeople have to be, because you probably have to help the customers understand it. And you need to know the language to help understand the problems.

Speaker B: I'm assuming, yeah, 1,000%. Like, you know, my salespeople can demo our product themselves. Like we have an amazing pre sales field engineering team. But you know, everyone in my organization should be able to demo. They should know the product. Because honestly, if you have technical buyers, you need technical sellers. If you have technical buyers, technical buyers don't like, you know, non technical sellers. That's the difference, right?

Speaker A: Is it because like they feel like they don't understand what they're even talking about. Like I can't stand this person.

Speaker B: If you don't understand it, that's even worse. But you know, if you don't bring anything to the table, like why am I going to, you know, my time's precious. I'm only going to spend time with you if I'm going to learn something or you're going to help me solve a problem. And so if I'm a technical buyer, you need to bring something to the table that helps me solve my problem or get better or learn something. If you don't, I'm not going to waste my time with you. So for us we have very technical product. Uh, obviously with Genie it's a lot simpler now we're starting to sell to business users, but early days, pretty technical product. We have technical buyers, data engineers, data scientists, data people. Um, you know, they're technical, highly technical. I need technical sellers to be able to have credibility with them. So ah, that's how I would decide kind of the profile and then you know, a big thing like as you're starting to scale organization, you definitely need what's the profile of the seller that's going to make this company really successful. You need to know that like obviously it's not the same every time because you know what you look for. But you, you know, you need to build that profile and that pattern for your company so you can scale it.

Speaker A: When you say profile, so how do I understand what kind of profile I'd want?

Speaker B: Yeah, like I said, like, you know, what are the, what are the main criteria I'm looking for in a seller? For them to be successful here, like you mentioned one of them, they need to be technical. So I look for people that are technical. I look for people that have experience in the data and AI market from certain companies. I also test for like grit and perseverance and hard work. Like I always say, like hard work overcomes talent every single day. Right. Um, you know, we're looking for folks that understand how to sell consumption versus committed contracts. Um, you know, that's just, you see, you know, having some cloud, I like having people that have some startup Experience, like, can they build? Like, do they know how to do things without having a big machine behind them? Uh, I also look for, you know, I avoid people that have a bunch of short stints. Like somebody that's had two, three companies less than two years, like they don't know what tough looks like yet. Every company comes across tough times at some point. So you want people that have seen that before and that are going to dig in and push through that. So, you know, I avoid people with lots of short stints. So. And again, I back channel pretty m. I look at every profile we hire here and you know, I, I'm um, probably one degree of separation from everybody in Silicon Valley, so I can back channel just about anybody. But uh, you know, I'm looking for the best of the best on the planet because I think this is a once in a generation type company.

Speaker A: Yeah, when you, when you talk about like grit, determination, hard working, how do you gauge that before someone's actually done the job? Because they could be like, oh, I work so hard. Like they could probably like set their emails up so it sends at 1am so it looks like they're like working or something. Like, how do you, how would you actually gauge if I'm actually a hard worker? If I'm actually gonna like fight through it?

Speaker B: I mean, listen, it's a little bit subjective. I mean, obviously I'm trying to look for folks that haven't had a bunch of short stints, but I also like to learn about the person, the character, like how they grew up, what kind of challenges have they had to work through in their life, those kind of things, right? Like my dad was a construction worker, my mom was a teacher. I grew up from nothing, right? And so I want to hear somebody's background and understand them as a person too. And then we also give them assignments, right? Like we make them build a business plan, we make them pitch to uh, a CEO. Give me your AI vision, pitch for CEO. We'll give them the deck, but I want to see them pitch it. And then we also make them do a genie demo for their vertical. So we're making them do some work, uh, to show that they really want it because I think you get a job at Databricks, it's a lottery ticket. Uh, um, anyway, uh, that's some of the ways I do it. And then obviously I try to, you know, if I know people that have known them, I'll ask them, right? Like, is this person going to be that kind of, you know, caliber seller again? That's why I look at some of the startup experience too, because I think people in startups work crazy, crazy hard and have, uh, a lot of grit. I mean, certainly we did early days creating this company.

Speaker A: How do you, how do you do that kind of reference check? Because, like, I might be good friends with someone and you text me about him, like, oh, he's awesome. Like, he's so great. You should hire him. Like, what do you, what do you usually look for when you're trying to, like, get through the, the BS and understanding if they actually, you know, what's actually going on?

Speaker B: Well, usually I'm, um, um, checking with somebody I know and I have a relationship with, so they're not gonna, you know, BS me because they value, like,

Speaker A: have your, uh, your relationship is like, above who the reference is at least. Like, value that with you at least.

Speaker B: It's like, hey, if I tell this person's amazing and then they're not amazing, like, like, that's, that's not good. Right? Like, again, I've lots and lots of, you know, again, one degree of separation from most sales leaders. And so, you know, they'll get pretty honest because they're a lot of times asking me too, like, what do you think of this person? They're going to want an honest answer from me too. Right. And again, I'm not looking for like, oh, uh, this person, like, has nothing wrong with them. I'm just trying to understand, like, you know, what are their strengths? What are the areas they need to work on? Um, you know, obviously I'm looking for the best of the best. Um, and then two, I'll ask the question, like, is this the top 1%, 5%, 10% of the people you've worked with? Like, I'm trying to calibrate, like, where are they on the bar? I mean, some people would be like, you know, I text some people like, no, pass. So then, you know, you know what I mean? So I think people, you know, I don't just check. One, I check at least, you know, two, three, depending on the position. But they're, uh, pretty honest because it's a small circle.

Speaker A: Yeah, I feel like you kind of have to know who you're getting a reference from. Like, there was, um, m. Some of the founders I backed, they, they sold their company, made like a decent amount of money. You know, they did really well. And the, the references, it was like some co workers and they're like, I. I hate that guy. He's so mean. Like, he's just always, he's always Bossing us around. And like, because he worked at like a big tech company and he was just trying to like, he sold his, his first company to the big tech company. And uh, he's just, he did not, uh, like the hierarchy and like the meetings about meetings to like, do something. And he was just kind of shipping stuff and people on the, in the company didn't like it because he was just trying to get stuff done. And so some of the references were like, you know, this guy sucks. Like, I don't like working with them. So it was kind of like you're kind of calibrating the reference. They're like, okay, that's actually good in this case because he was the CTO of uh, a new company.

Speaker B: Yeah. You want to know what you're getting into is the thing. Right. So I think all the feedback is good, but you got to kind of. Yeah, I mean, you definitely need to know the source.

Speaker A: Yeah. And so one thing I think you did, um, back when you guys were first kind of like building the sales team, it was pretty technical. You hired like a lot of sales reps. I think the number that I saw was you hired 40 reps in the first quarter. That. Is that true? I mean, that's kind of insane.

Speaker B: Yeah, no, it was a funny story. I would like literally be like, okay, all day Wednesday and Thursday, all I'm doing is interviews, right? And, uh, they bought, they got me. I was like, but you got to give me a break to go to the bathroom at least.

Speaker A: Okay, that's good. At least you get that.

Speaker B: Yeah, exactly. Um, but it was fun. I mean, a lot of those folks were from my network too, right. There were people I trusted. My early thesis was like, spark is everywhere. My first task is to understand, uh, what are they willing to pay for and who can I sell it to? Right? Like, what's the profile of the ideal customer? And so you hire a bunch of people you trust to just go talk to all those open source users and get that information and find those trends. And so, like, you know, I think in today's environment too, like, I mean, with, you know, a lot of funding getting thrown around, like that's the first step. Like if you're trying to sell a product, need to hire, you know, a solid sales leader or at least some salespeople that you trust and go, you know, especially an open source project, like, go talk to a bunch of customers that use the open source and understand what they'll pay for. And uh, you know, am I selling enterprise? Am I selling startups? Um, that's job one. And so, you know, we had, uh, you know, we just raised additional funding. I did a coverage model to just basically be able to cover all the different segments and find out which customers were more likely to buy and what they wanted to buy. And so, uh, we moved pretty fast that first year. I mean, I think we went from less than a million to, I don't know, I forget 1315, but then we went to 50 to 100, 250. You know, obviously now we're whatever 6.9 billion plus and bigger. So, um. But yeah, early days, like, you know, those were known quantities for me and uh, that was my first task is go find out what people will pay for and which segments we can sell to.

Speaker A: So it wasn't like you had no one using the product and you just hired a bunch of people to then go try to get customers. It was, you had, I think you said, millions of people already using it. So it's like, okay, you're almost hiring like, like R and D, customer discovery. Like, go out and figure out what's going on, what are people using it for, and then how are we going to make a business around this? In a way, yeah.

Speaker B: And initially we thought like, hey, we tried plg. We thought customers would just come to us and ask the questions. Like, before I got here, that's what they were trying to do.

Speaker A: And it wasn't working.

Speaker B: It wasn't working? No.

Speaker A: Well, like, what was the big block?

Speaker B: Well, customers in open source, they don't really want to buy anything. You have to ask them what they're willing to buy for. It's a little bit different. Right. Um, like if I'm, you know, I'll ask, uh, you know, support questions once I get my question answered, I'm good to go. Right. So, you know, it's more about like salespeople, open doors. They, you know, they get in front of people. Like, that's part of the PLG is more about people come to you. Sales is more about I'm coming to you. It's the opposite. Right. So it definitely will help open doors and open markets faster. Um, now that's being said if you have a great PLG motion. Our PLG motion was more around open source. It was like we have tons of open source folks coming in the door. We need to go people sell them the thing that we actually monetize and make money with. Right. So, um, I would recommend that for any open source project that has traction for sure. Now, if you have a product and nobody's used it. I mean, yeah, you need a couple salespeople to start going to get your beta customers so that you can, you know, also, you know, develop the product. And you know, Again, that's the 0 to 20 million product market fit. You know, you're going to need a sales team to do that as well. So.

Speaker A: And I know you leveraged, uh, I think it was a 16Z kind of the, a big initial wave of customer introductions. Like how, how did you do that? Because a lot of people will say, oh, use your investors to get customers. Right? Like, sounds super easy. Like, how do you actually do that successfully?

Speaker B: Well, I mean, a 16Z is a little special. Like, I, I think it's the best VC on the planet, by the way. But you know, they build these like basically startup teams which are like pieces of your company. Like, you know, they have, uh, they have sales, they have marketing, they have, you know, like recruiting. Uh, but in this particular case, you know, they have relationships with like the CIOs of some of the biggest enterprises on the planet. Like, they've built that for their portfolio. And so they, you know, they would reach out to like Apple or Cap1 and they would invite the CIO and all their staff to come in for Silicon Valley day and they would show them 10 different portfolio companies. So we would have like 30 minutes come in. You know, they wanted to do a demo, like, tell me about your company and do a demo. And you know, that way like those CIOs and their staff would get to see 10 different amazing startups in one trip, right? And they would do it at like a 16Z's office. So, you know, they'd roll out the red carpet and it's pretty cool because you get to meet Ben Horowitz, Mark Andreessen and stuff like that, right? So they had a pretty good draw. I think that the mistake that startups make because I think those programs existed, some of the investors, and some of them run them better than others. But don't just send any old salesperson. I did most of those early days myself because one, I get the opportunity to pitch my product to the CIO of a big company. You know, that's a big opportunity. And most of the time the CIOs are so thankful that you took the time to do it. They'll give you an opportunity. They give you a POC or they'll give you a small land. And now you're in the door. Now you have a big logo on your, you know, uh, on a land, right? So you know, I would, I would say as a founder, go pitch those CEO, CRO, head of sales. Like make sure you take full advantage of those, um, the, you know, hey, just make some intros to you know, some of your, you know, can you intro me to xyz? I think that's less valuable. I'd want to go spend like I'd rather be, you know, smaller number of companies targeted, um, where I can actually get, you know, a chance to talk to somebody and you know, potentially add some value. So a 16Z grip, great program. We landed a bunch of our big customers that way.

Speaker A: Sounds like an in person event where you get FaceTime that seems to be like the, the most efficient or best way to do it versus just like a, you know, can you forward an email or whatever?

Speaker B: Exactly. Like if you want to ask your investor, I'm sure money investors do these like they have a, uh, you know, a customer day or something like that where you know, they'll bring in 10 portfolio companies and pitch to either a panel of customers or maybe one customer. And this particular one was more of each customer. But you know, that's what I would do. That way you get some face time, get to develop some relationships. You know, again, what you're trying to come out of there is with at least one follow up, one relationship, one poc, something like that. Um, so that's what I would ask for from an investor versus just hey, can you introduce me over the phone or on email?

Speaker A: Do you get invited to a lot of those things now as ah, you know, are you guys considered like a big customer now?

Speaker B: Uh, yeah, no, I mean, you know, now I do a bunch of like kind of reverse, like a lot of investors are like, hey, I have my portfolio coming in, you know, kind of, hey, can you do a fireside chat? Like you know, talk about like because you know you think about a lot of these startups, they're, they're technical. Uh, most of them are technical founders, right? It might be their first company, it might not be, but you know, it's usually a technical led founding team. And they're trying to say like, how do I build that sales go to market engine? It's the number one thing I hear. And so you know, most of the questions I get are, you know, similar questions we're talking about here, right, is like, how do I build that go to market team? How do I find that first sales leader, how do I land those first customers, how do I figure out pricing, how do I build the first comp plan? Like all those kind of things. So a lot of my investors ask us to come in and just talk with their portfolios, their CEOs, um, about those kind of topics.

Speaker A: And when do you decide yes or no on those kind of things? If I'm trying to do stuff like that, do I need to have an enticing pitch of like, hey, you know, it's at a cool event, like it's a cool venue or it's like a nice dinner, or like, how do you usually decide what's worth your time? Like, is the, uh, is the guy on the other end now?

Speaker B: I mean, me personally, like.

Speaker A: Well, I guess I'm kind of like trying to reverse this to where, like if I was trying to like set some of this stuff up, what would I, what should I be offering to like, the both sides of the marketplace in a way of like, making it worth everyone's time?

Speaker B: Yeah, fair enough. Um, I mean, certainly as a portfolio company, I want to have access to customer executives because I want to try to sell to them.

Speaker A: Yeah. And you don't want to feel like you're getting sold to as the customer executive, maybe.

Speaker B: Yeah, correct. And you know, like I said on the customer side, for these investors, it was like, hey, come see the best of Silicon Valley. Right? So they would ask like, hey, what do you want to see? You want to talk about AI? Do you want to talk about security? Like they have portfolio of different categories, right? And so they would kind of cater the agenda and the portfolio companies and they would let them pick like, oh, here are the companies I'm interested in. Um, and so a lot of like, uh, I just hosted a couple big, one big bank and one big healthcare company on the east coast, like brought their whole team because they're going to come to Silicon Valley and San Francisco and they're going to want to see Databricks and OpenAI and Anthropic and you know, Nvidia if they're in town, or the big hyperscalers and you know, if your VCs, Silicon Valley VC, they should be able to help host them and you know, get you a sit down there. So, um, that's. Usually I would try to host them in San Francisco in that kind of way. But that's, that's kind of what a 16Z's model was. And it's pretty interesting work. It worked really well. It helped seed a lot of these portfolio companies into these big customers, which is huge.

Speaker A: So when you say that's what the model was, is it is. Do they not do it anymore or is it different now or.

Speaker B: I'm just saying we're kind of beyond that, like that size. They've kind of moved on to the portfolio companies that are smaller. I mean, obviously we can get a lot of our own meetings now, um, with the CEOs and the CIOs. Right. I mean we're, you know, multiple billions. But you know, for like early mid stage companies, that's huge. But I still, you know, I'll still reach out to investors once in a while and be like, hey, do you know, so and so can we get a meeting with them? Right. If I'm having a tough time getting to somebody. So they can be super helpful. It's definitely something you want to look at when you're raising money.

Speaker A: So when you talked about trying, um, to figure out pricing and some of these, like early customers, like, should I be willing to do like a pilot, give a discount, et cetera, to kind of land some of the initial customers to get things going, or how do you think about just navigating that? Because I mean it always comes up is like, you know, what's, how big is the contract, what does it consist of, the timelines, like how long it is, all that kind of stuff.

Speaker B: I mean Most of our POCs and pilots are all, especially early days. We're all free. Oh really? Yeah, we're not trying to make money off the pilots or the POCs. Like we're trying to prove value for our team and our product. Now you probably want to, uh, like, I mean, a couple of things. One, you don't want that going on for a year. So you know, you want to set some expectations around. One, you know, what are we trying to prove? So what are your success criteria? And then two, what's the, you know, time period that you want to execute? Like, we're going to do this over the next 30 days. And then the third piece is you want to make sure you have at least some kind of executive sponsorship, that it's not just some rogue developer that's like, hey, help me do my project and then you have no chance of selling this. But like, um, you know, those are the things you look at. I mean, again, like, I think you need to prove value before you have an opportunity to ask for money and ask for a purchase. And so that's what a POC or a pilot, in my opinion, is really all about. Um, listen, if you're a bigger company and you have references and you have a brand and you've uh, um, succeeded in a bunch of projects and you have a track record, then you can use a lot of those things. And you may not necessarily need to do a POC or a pilot. But even in those cases, I would fund the POC or pilot if I know I'm going to get an opportunity and a bigger contract at the end. So again, I think for those, I wouldn't make pricing the POC or the pilot, uh, the gating factor. I would just make sure that once you're successful, you have a good chance of actually getting a contract after that. That's the more important point.

Speaker A: Yeah, because I guess that first, whatever you kind of land first, it's probably not the final.

Speaker B: Right.

Speaker A: Like if you're going to get more adoption, if you do a good job, they're going to spend more money whether the initial pilot is paid or not.

Speaker B: Like, yeah, it's usually a small piece. Like it's like small set of users, small use case, one department. You know, again, you're trying to prove value, get your first land. I mean it's basically the land and expand model is land, prove value, build a champion, expand, get all the rest of those use cases in that department, start expanding other departments till you're across the entire enterprise.

Speaker A: So was there, uh, anything as you started to kind of like, you know, climb the ladder, people started to pay you. Databricks really started taking off. Like anything that was kind of surprising or unintuitive, that, I don't know, maybe you got it wrong initially or you kind of changed and just things you wouldn't have expected as things really started to scale up.

Speaker B: Yeah, I mean, that's a great question. There's like 10 answers to that.

Speaker A: Um, okay, I want all of them. That's actually a question from. Actually one of my portfolio company founders asked me. She's like, I want to know this.

Speaker B: I think so. One thing that I noticed is like when we started selling databricks, we were doing like 15k, 18k deals. Right.

Speaker A: This is annual.

Speaker B: Yeah, yeah.

Speaker A: So I was like, that's pretty small. Let's people say, like, especially if you're doing enterprise sales, that's bad.

Speaker B: Or, you know. Exactly.

Speaker A: Could be better.

Speaker B: New rule, no deals less than my monthly Uber bill. Um, so, you know, I think, yeah, but we, but when I would talk to the customers, we're adding tons of value, but just a lot of the use cases, because we're usage based, weren't driving a lot of usage. So then we started to say, okay, how do we capture some of those that value, you know, like there's companies out there like Palantir that'll do value based pricing. We weren't doing that. We were doing straight cloud based pricing. So we, you know, we added like a platform fee, then we added user fees, all these things that try to make our price points higher to match value. Because again, your proper pricing model, price equals value. If your price is over the value, then no one's going to buy it. If the price is under your value, then you're leaving money on the table. So basically in squint, that's what you're looking for.

Speaker A: Is there a number, is there a 10x you need to add 10x more value than you charge or how do you think about it?

Speaker B: Yeah, certainly there's like a payback like if you're, you know. But some of this stuff might be lucid, but I'm going to grow your revenue by billion dollars. Like I can't charge you a percentage of that. Right. Um, so a lot of it's like, you know, how do you compare with alternative, which is competition, or build it yourself. How do you compare with, you know, the lowest cost alternative? You know, those kind of things like uh, that you have to kind of, you know, once you get, you know, again, like pricing's, you know, complicated but you know, find out what's, what's the base unit of value. Right. For us it was like, you know, the clouds were charging on compute and usage and that was kind of the base value unit we were going to use. And then it was just relative to either a cloud service or build it yourself or what have you. But again, we put all these features to try to raise pricing early days. And one thing that wasn't intuitive was once we started charging for users, people would be like restricting people using the product.

Speaker A: Oh, interesting. Which then also drives down usage.

Speaker B: Drives down usage. Exactly. So I was like, okay, let's get rid of user pricing. All the users are free. And all of a sudden usage took off. Right. Because now everybody can use it, everybody could get value out of it. And I don't know if that's entire. Like a lot of people use users and usage. I would say, you know, again, one of the best things we did early days was tie ourselves to consumption and usage because data's growing, queries are growing, number of people that want to ask, queries of the data is growing, number of agents that want to ask queries is growing. Like all those things are tied to usage. And I think, you know, restricting yourself on users, I think user based pricing is A thing of the past, honestly.

Speaker A: Really? Is it just gone today? Like, it just doesn't make sense to do that anymore.

Speaker B: I mean, the companies that are doing user based pricing are, you know, they're under siege because people aren't growing employees by, you know, they might be growing agents. So maybe you're charging agents, but again, I think everyone is or will move to usage based pricing. Even the coding tools, like, early days, they were user based. Um, I remember talking to some of the early folks at Cursor and I was like, you guys need to go usage base. And now that market's just blowing up with usage based pricing, right? Um, yeah. So I mean, you got to pick the base unit and then you need to test out that. Well, one thing was like, I don't think user based pricing is a good idea. I, um, think it limits the amount of value you can capture. Uh, in most software. Um, I mean, I can't say equivocally for everything, um, but I would say attach yourself to something that represents value and that's growing over time. Um, it's been a great business model for us, obviously. Great business model for the clouds, for all the Frontier Labs, et cetera. I mean they're selling tokens, but it's the same thing, it's usage.

Speaker A: Yeah, well, because essentially then the revenue upside is kind of uncapped. Like if you just keep creating more value, you can keep adding more revenue every day.

Speaker B: More data. Ah, in my customers systems, there's more people asking queries and doing analysis and doing predictions. And then there's more agents that are doing the same thing. Um, and so, you know, so then you're like, okay, what are the other pieces of tam? I can go after internationally, go after different markets, different verticals. Like, that's how you start to compound exponentially all those different markets together.

Speaker A: So that was one of the ten other unintuitive things. Like, uh, anything else, like, really stands out is like, man, I wish I knew that.

Speaker B: I mean, first of all, like, just going out to the market, we talked a little bit about it already. But just going out to the market and trying to understand, like, what will people pay for? Um, what are the challenges? Like, you'd be surprised at what customers will tell you when you just ask them, like, hey, you know, we're building this company, like, what are the things that add the most value? What would you pay for? Like, you know, a lot of people want to help. Um, and then once you're going into enterprise, some of the things that you got to think about because a lot of companies will be like, well I'm just going to sell it to the people that you know, want to buy right now. And those people might be a lot of startups or AI companies or tech companies. They're going to be early adopters of technology. But if you want a super valuable company, trillions of dollars, you need to sell the enterprises you need to sell to banks and healthcare companies and retailers and CPG companies. So one thing that early stage companies don't think about are like what are those requirements? What are those requirements around security compliance? So a good idea to understand those things upfront because those will be big gating factors in how you grow your business. And if you don't understand how those affect your product or your service up front, it could be, you know, it could slow you down quite a bit to try to add those things on later. So that wasn't something that, I mean, you know, I said it out loud so it's like, you know, kind of obvious but like go under. If you want to be an enterprise, which I think if you want to build a real business, you need to be, you uh, need to understand how those things are going to affect your growth and how you, how you support them. Um, like you want to sell the federal government, you need to get clearance personnel, you might need to, you know, quarantine those developers. Like there's all kinds of extra requirements on how you do that. You know, think about those things up front. So we missed some of that stuff. But uh, I mean obviously it sounds

Speaker A: like it worked out for you. At the end of the day it did.

Speaker B: We made it, we made it so far.

Speaker A: And so how did, has anything changed about kind of the go to market like philosophy, strategy, structure, like how you sell as the products become more like AI native, generative AI. Like I mean you've always kind of been consumption based because that's kind of one thing I hear a lot about with these like AI native companies. Like you're selling essentially usage versus seats. So when you think about the move from on prem to cloud, there was just like change in what got sold. So you know, the incumbents were kind of disrupted because they couldn't sell the seats and renew it. Renewing the software with just like the, the license and then the shift from cloud into kind of this like usage based AI stuff again they like can't quite sell it because you're used to just selling a seat that no one uses and it's like oh shit. You actually have to like Use the product now and we can't make as much revenue and it just kind of messes up the business. Like how did that kind of go for you guys this whole transition? Or was it, was it not even necessary?

Speaker B: We were always cloud, we were always consumption, right? So like, you know, some of the gating factors for us early days was like, who's in the cloud? Like I would tell our sellers, like if they're not in the cloud yet, don't waste a bunch of time with them because AWS or Microsoft or GCP have to convince them to get in the cloud before we can sell them anything. Right. So, um, you know, but I think one of the, a couple of the things that, I mean, each stage, like I said, has been a development for the go to market team. Like again early on it's, it's more around product market fit and how do you build a playbook? But you know, how do you expand internationally? How do you expand with channels? Like we didn't have partners early days, so building out your partner channel. How do you build out emea, how do you build out apj? You know, those are all developments. Um, and then how do you go multi product? Like most companies start with one product. You know, now we have a pretty massive portfolio of products, right? So like as you start expanding your portfolio of products and trying to expand into additional areas of opportunity, um, you know, that creates new muscles too, right? How do you build out specialist organizations for each one of those new products? Um, you know, and again like, you know, do that all in the world of, you know, how do I AI enable my entire sales team so they're using that every day to just be smarter and more productive. So all those things I think are changing constantly. Like you know, it used to be you do an annual planning cycle, we were doubling or tripling every year. So I do a six month planning cycle. We're still on that at this point, right? Like it's like, you know, splitting territories every six months to make sure you have coverage on customers. So every, every little piece of the, you know, building and operating and running this business at this scale, at this size is, is new. There's, there wasn't a playbook for it. So it's been fun.

Speaker A: Was there anything that um, maybe like messed up initially or biggest unlock in terms of like how you expand internationally and, or how you incorporate new products? Like anything that you guys wish you could have done differently and, or you figured something you're like, oh, this is like this really unlocked it. Once we Once we tweak this thing.

Speaker B: Yeah, I mean, international is one area where I think you have to be a little bit careful on expanding too fast. Um, yeah, because I think you can do a lot of your initial sales, you know, remotely not, you know, you're not going to build multibillion dollar business internationally by selling from Americas, but you can get some of your first lands, all those kind of things. Because you know, when, if you hire the wrong leader or you know the wrong strategy in Amir APJ, you're on a 10, uh, or 20 hour plane flight and a lot of hours to try to fix that. So you know, you need to find the right leader, uh, in each of those markets. And then obviously, you know, in Emea there's, you know, Germany is different than France, different than London, it's different than, you know, South Europe. I mean, each one of those different markets, same thing with, you know, Asia and Japan and you know, India. Each one is a different market, different language, different culture. So you need new leaders for each one of those. So you think you need to have like your playbook pretty set in Americas before you start going big internationally. Not saying you need to be at 100 million, but I would say somewhere between 20 and 100 million. You need to like have a pretty good idea what your sales playbook looks like, uh, who you're selling to, what types of companies, what are the requirements, all those kind of things. And then uh, because you're going to want to translate that into those other markets and I mean having the right leader is part of it. But then, you know, early days I actually took some of my key talent from Americas and I said, hey, I'll pay for you to live in London for a year, teach the new team, um, try to do all this stuff with enablement, but there's still always a lot of kind of tribal knowledge. Um, same thing with apj, like seed it with some of the people that have been successful and have been here a while so they can understand you can kind of, it'll help accelerate that growth. But I do think some companies make the mistake of going international too big too fast and then you spend a lot of money and uh, you know, sometimes it hurts your brand. Like if you have a bunch of people that don't know how to sell your stuff or aren't making customers successful, then it's harder to go address them later. So anyway, that would be my advice. It's just be, uh, be aggressive, but be thoughtful. And when you expand international, so it

Speaker A: sounds like, bring some Local. Some of the local people who have been there really know the product, know edge cases, pain points, et cetera. But then also hire somebody who knows Japan or knows India. Like this is the rule, this is how they do it here. Don't forget that you have to like do this one specific thing that is a custom here that no one else does or something like that.

Speaker B: I mean you're going to want the local leader because they know which great salespeople to hire, but they also know the customers. And then you want some, you know, kind of experience knowledge. And again, the local leader will probably learn it. It'll just take them a year. When you put the, the other experience with them, they're just going to go that much faster in how you build international. So if you can do it, I'd recommend it.

Speaker A: So talking about, um, I guess actual enterprise AI adoption, what are you kind of seeing today? Like what kind of challenges where people having the most success. It seems like you probably have as close to a front row seat as you can get. What's kind of actually going on right now?

Speaker B: Yeah, well, I've literally talked to thousands of customers. I think we did publish numbers like 1.7 billion just in AI in revenue.

Speaker A: 1.7 in AI revenue.

Speaker B: Okay. It's been growing super fast for us. Um, it's every industry again. I think I call it the four Cs of what's important for a lot of these companies. The first we talked a little bit about context, like how do I attach AI to my data. It needs the context of my data to be able to be smart about the decisions and the predictions and things I want to make for my business. Um, the second one is, uh, control. Right. Like I also need it governed. I can't have everyone having access to all the data, uh, especially in the world models and agents. I need to know how these agents are going to use this data, make sure they're not disclosing the data. There's obviously a lot of compliance stuff too in regulated industries. So control is incredibly, uh, important, uh, choice. Like right now we're seeing lots of things around model choice. But cloud choice is also important. I mean we're open source, so you can kind of plug in anything into the databricks platform. But we serve all the models. We think frontier models would be really good for a lot of the really complex tasks and we think open source models will be used for a lot of the other tasks and that market's going to grow super fast. Um, so I think choice is really important. Because if you just get locked into one, you're going to end up spending a lot of money. And that's the last thing is kind of costs have been kind of out the roof on a lot of this stuff. And so how do you govern those costs? We do all this stuff, um, in a product we call Unity, AI Gateway, just to give you a plug. Uh, but anyway, I think cost is the last thing that a lot of these CIOs and CEOs, I mean, they're blowing through their budgets super fast. So how do you put on the right cost? Controls, guardrails, things of that nature? So, I mean, that's what I'm seeing in the market. But again, the use cases are phenomenal. I'm seeing new drugs discovered and accelerated, um, all kinds of financial services, use cases around automating, loan origination, fraud, things of that nature. Um, retailers, how do I maximize revenue with campaigns, promotions, how do I stack my shelves in the right way, how do I do the right distribution, how do I fulfill inventory faster? Um, I mean, again, it's every industry, so market's, uh, growing super, super fast. Uh, again, I think the biggest part is like, you know, the models are super smart, but how do you get the context of enterprise AI data, and how do you connect those things in the best, most efficient, governed way?

Speaker A: What seems to be like the, the biggest challenge that some of these guys are facing? When is it it's expensive, Is it that they don't know what to do?

Speaker B: Well, it's a lot of the expertise. Yeah. Like, I mean, everybody's got an FDE model. We do, too. I mean, I think ours are the best on the planet. But, you know, they need some help. They either need some help from us or from, you know, Frontier Labs or the consulting companies to be able to wire all this stuff together. It's not simple. Right? Um, I mean, we're obviously trying to make it as simple as possible with a bunch of our new tech, but it still requires some expertise, uh, to be able to do that. And I would say the biggest challenge in all of it is everybody recognizes, like, the data is key into how you're doing these enterprise use cases. But the data in a lot of cases is not in the right place yet. Meaning it's, you know, it's all over the place. It's in legacy system or it's in old formats, or it's in proprietary formats, all these kind of things. So getting your data in a good place to be able to attach AI is a tough and complicated problem. And I think databricks is the best on the planet to do that.

Speaker A: Do you think, is there any irony to like AI is doing all this stuff and we can't just say hey AI, figure out how to do this for me. Like it's just kind of, it's kind of funny that it does all this stuff, but then people were still kind of like struggling to use it correctly.

Speaker B: It's getting there. GENIE code now can start building data engineering pipelines for you. It does all the ontologies, so it'll go out and find the data sets and label them. And again you want to do all those things in a governed fashion. We use Unity catalog to do that. But AI is getting smarter about how it helps with data problems. I mean that's the main use case for us for GENIE code, which is part of genie. So uh, it's getting there. We have a data engineering genie, uh, code. We have data science GENIE code. Basically, uh, we'll go develop that stuff and automate it. So it's pretty cool.

Speaker A: When you want, when you say your, your fds, your forward deployed engineers are some of the best on, on the planet. Like what, what makes them so good? Is there like a way, do you like structure them as part of the sales team? Like do they, are they just like, are they using databricks as the product which makes them better? Like, like what makes them so good?

Speaker B: Yeah, I mean, uh, listen, I mean this, you got to start with trying to understand what the customer is trying to accomplish, right? Which is similar to any kind of call it PS or consulting project. But then being a leader and a thought leader in how to solve these problems, like which technologies, what's the best way and most efficient way to do it? What are the downstream effects of that? Because a lot of companies that are using FDEs, um, Palantir uses that model. Lots of people are using that model. One of the things you don't think through is like what's the ongoing upgrading and maintenance of what I build on databricks. We're a data platform, data and AI platform. So we're thinking through like we expect these solutions to develop and evolve over years and years and years. They can't have a huge army trying to upgrade them and maintain them. I know that's probably a great revenue model for some, but for us we're thinking through like how does this thing evolve and grow over time and we allow the actual team there at the customer be able to maintain. Uh, and that's why you need kind of a scalable unified data platform underneath all of this. So um, I'm sure there's lots of talented technical FDEs that can solve these solutions. But how you solve them in the right way where it's scalable, cost efficient and again evolves and grows over time without having to rewire everything, I think those are the keys and that's what makes our team the best on that.

Speaker A: So do they report to engineering typically or do they report to the sales? I've actually always kind of wondered what's the right way to structure that because they're kind of both.

Speaker B: We have a group we call field engineering. So and that's all of our pre sales, we call them solution architects, um, that are doing kind of all our pre sales architecture work. But our FDEs sit inside that organization. So they go deep like on the product. Like those folks are engineers. They can code, they can build products, they build out the pilots, they build out the environments. They're engineers. We call them field engineering. So uh, that does report up into go to market. But that's run by one of our co founders, Arsalan, he's also a PhD guy. So like they're crazy smart. Um, they're awesome.

Speaker A: I have to ask you this question because anyone who's listening at this point, they're probably like ah, you got to ask them about this. So DataBricks is like $188 billion company. When do you guys go public? How do you think about you just raised a couple billion more dollars. You could have done it. When does that actually happen? Because there's some companies that are like, they want to stay private forever it seems like what's the view inside databricks?

Speaker B: It's not a matter of if, it's a matter of when. Um, we run this company like a public company. We report our financials and uh, every quarter we have board meeting where we go through our audit committee and we run this like a public company. So um, I expect we're not here to uh, we're here to build a trillion dollar company. So you know we're uh, I would say we're going public six months of it at a time. But you know like uh, again you know, it's not a matter of if, it's just when. And uh, we're not in a rush. Like I said, we're going to build this, make it a trillion dollar company. So uh, that's what, that's the journey.

Speaker A: I was going to say man, that's like a big goal. But I guess you guys are getting Closer and closer there to where it's like you're actually pretty close. At this point, I'm going to start

Speaker B: saying we're doing multi trillions.

Speaker A: Yeah. You're going to have to adjust your team. Like, ah, come on. Only a 5x from here. Got to shoot higher than that.

Speaker B: Exactly, exactly.

Speaker A: Uh, do you have a favorite, uh, business CEO or founder? You can't say Databricks. Can't say anyone at Databricks. Just maybe or like historical figure from history that you just get a lot of inspiration from.

Speaker B: I love a lot of sports heroes. Um, but I was gonna, I was gonna say LL Cool J, actually. You know, I started a business with LL Cool J way back when. Really?

Speaker A: Okay. I did not know that.

Speaker B: Interesting company, uh, like they did this virtual recording studio, um, where like a kid could record a song from like it was kind of, you know, pre Skype back then. Like a kid could record from LA and New York together. But, uh, you know, guys reinvented himself many, many times, uh, in many different industries. You know, kind of, uh, an icon over many decades. So. And he's good friends, so I thought I'd throw him out there. So.

Speaker A: Interesting. What is he up to today? Because I don't, I don't really follow him that closely.

Speaker B: You know, he, he did his, his show on NCIS Los Angeles. He did a bunch of that kind of. He was doing a bunch of TV stuff, so. Huh.

Speaker A: Uh, yeah. Oh, wow. He did that for like 14 years.

Speaker B: Yeah, exactly.

Speaker A: Oh, wow. Okay. Yeah, that's great. Do, um, you have a favorite athlete then? You said there were some athletes.

Speaker B: Yeah, no, Michael Jordan for sure is my favorite. Like, you know, the goat of basketball, in my opinion. And uh, I don't know, you watch his story, like, uh, there's a book called Relentless out there. The guy that kind of trained Michael Jordan and Kobe Bryant and a lot of those, you know, elite basketball players. It's, ah, it's a good book. Um, so. But yeah, I don't know. Michael Jordan, maybe Walter Payton of Walter Payton. I'm a Bears fan, grew up in Chicago.

Speaker A: I don't actually know the Walter Payton story that much. What's, what's his kind of journey to the NFL and what made him so good?

Speaker B: Yeah, I mean, Walter Payton was like, you know, he's uh, he was on the Bears, who were always like one of the worst teams and they literally, you know, they never had a quarterback, so they would just hand on the ball every time and he would still break records running. He like, would to Train. He would run up hills and stuff like that. Um, and, uh, you know, he ended up dying, I think, of cancer early. But, you know, he always won kind of the. There's. There's an award now called, I think the Walter Payton Award for, like, the best kind of humanitarian in the NFL. But he always gave back, too, right? He always gave back to kids and people who were struggling and things of that nature. So it's kind of made him, uh, a hero of mine, for sure.

Speaker A: Yeah. It's good to remember where he came from. Good to remember to help people along the way. Exactly. Well, Ron, thanks for coming on the show. This is a lot of fun.

Speaker B: Super fun. Uh, thanks for having me. It was, uh, great questions a bunch that I never got before, so appreciate it.

Speaker A: Oh, really? Okay. Do you have any crazy stories, like, anything just crazy that's happening in your life that no one would believe?

Speaker B: I mean, the Ella Cool J story is pretty. Like, a lot of people don't know that. That I started a company with that dude. Um, so that's. That's pretty interesting.

Speaker A: What. What happened with it? Did you dig. What did it. Did you, like, get people using it? Did you sell it?

Speaker B: Yeah, yeah, no, we, you know, we got hundreds of thousands of people using it. We licensed it to, like, Sony and Microsoft and Dolby and a bunch of that stuff, and I think it's still out there, but, you know, obviously, I don't have time to. We never updated the technology, like, obviously the technology. Way more advanced now than. I mean, we did that back in, like, 2013 or something. So it was, like, you know, long time ago. Um, yeah, but it was super fun. You know, I've been on stage with them, like, singing and. Yeah, I've been to, you know, a couple Grammys, uh, where you hosted the Grammys and stuff. It was pretty fun. It's a good time. Different world. Different world then. Tech software, for sure.

Speaker A: Yeah, some software.

Speaker B: Yeah, exactly. I mean, I talk a lot about, like, you know, my upbringing as, uh, you know, I said Midwest kid out of Chicago, dad's a construction worker, mom's a teacher, I think, you know, hard work and hustle, you know, Again, I always wanted to be a major league baseball player. Um, you know, I played in college. I was an all American Big 10 mil honor, but I never kind of made it. But, yeah, I've always kept that kind of hard work and grit and hustle. It's a big cultural thing for me with the sales team here. Actually, um, talk about it a lot, um, because I Think, like, that's where I want the next generation of sellers and business people and people starting companies, entrepreneurs, like, they got to realize, like, this stuff. Like, nothing comes easy. Like, even if it looks like it's easy, nothing comes easy because, you know, uh, it's always. Who outhusted? I always say, I never lost the game, just the clock ran out. If we kept going, I would have figured out a way to go, you know, further than the next person. So, yeah.

Speaker A: What do you think? Like, the thing that kind of separates the people that can do that and can't. Like, is there like a. Is it like an internal drive or, like. Like, what is it that separates people who can and can't do that?

Speaker B: I do. I think it's an internal drive, man. I don't think it's externally motivated. People ask me, like, hey, why don't you retire yet? And I'm like, because one, I'm having a ton of fun and passionate about, we're doing. But I'm not done. Until we make this one of the. If not the greatest company on the planet, I'm going to keep going. You know what I mean? I think it's, uh, yeah, it's internally motivated. It's a drive. And again, from that relentless book, it talks a lot about that. Talks about cleaners, uh, and closers, and what's the difference between the two?

Speaker A: What's the difference? I'm looking up relentless right now. I'll throw a link in the description for people to check out the book.

Speaker B: Did you ever watch the Last Dance with Michael Jordan? It's about.

Speaker A: I didn't watch it.

Speaker B: Bulls, basically.

Speaker A: I've seen it. Yeah, I've heard of it.

Speaker B: Would talk about, like, his will to win. Like, you know, I mean, he almost didn't even need a coach because he needed a coach to just make sure everybody else didn't quit.

Speaker A: Okay.

Speaker B: He was that hard on his teammates. He wanted to win that much. There's one page on there in that book that kind of explains the whole book. Um, it's a lot of those kind of things. Like, when everybody's hitting a panic button, they all turn to you, right? Like, it's, you know, it's about being, you know, the best of the best.

Speaker A: So it's about being. Being that person. When everyone hits the panic buttons, like, who do they go to? Like, you want to be that guy?

Speaker B: Yeah, you want to be that guy? Uh, you want to take the last shot? Like, all those things, you know what I mean? Which is, you know, me and Michael Jordan right. So well cool.

Speaker A: This is this been a lot of fun. Thanks for coming on the show.

Speaker B: Yeah, thanks for uh, it was fun. Great spending a couple hours with you. Appreciate it. Like uh, it was fun. It was awesome. Can't wait to see it, can't wait

Speaker A: to hear it and I hope that you had fun. Thanks again. This episode sponsors Flex Numeral Amplitude merging Monaco if you enjoyed this conversation, please like comment, subscribe and share it with the one friend who's trying to go from 1 million to 7 billion in ARR. Make sure to check out the back catalog of over 100 episodes with investors like Gary Tan from YC and early employees at high growth companies like Will Gabri at Stripe. Tune in over the next few weeks for conversations with Jeff Morris at Chapter One Ventures, Tomer London, co founder of Gusto, and Jamie Simonoff, who founded and sold Race Ring and has since scaled it to over 1 billion in ARR. Inside Amazon if you don't want to miss any of these, subscribe to my newsletter. The Split linked in the description to get each episode plus a transcript emailed directly to your inbox every week. Thanks again for listening. See you next time.