The Exchanges

Q Has that changed in the age of AI? Are they looking for something different? What, what, how has the urgency changed or the discussion changed with BIOS?

A I think urgency has changed significantly, right? Like, um, we used to joke about this that in the, you know, Databricks has been a data and AI company now for what, like, 13 years at this point, and we used to have to, we would scream the data part, we'd whisper AI, because AI people were like, that means like self-driving cars and iRobot, who cares? And so everybody wanted to do it, but if the answer was, look, I want to bring in my data and it's helping me replace my data warehouse or my data engineering system to be able to get good analytics and fun. Now what's happened is everybody's like AI is a top line imperative. I need to use AI. I want to drive all of it. And they immediately run into the problem of you'll hear different words. Like I have data silos. I need an ontology. I need a semantic layer. I need context. Right. And what they've realized is what's actually really, really hard for in order to get AI work, I have to break down all those different silos. I have to understand, make my data high quality and be able to be accessible to agents, not human to really transform this. So the urgency of I need to kind of get my data state in order and governance around it and context has gone through the roof because people realize without that, they can't actually get AI to work in any meaningful way.

AI assessment note: “I think urgency has changed significantly, right?”

Q classily great at going into a room and saying, we will solve your problem for putting your data on the cloud. We were just going to solve the problem. What's that Databricks pitch today? So they have this anxiety. They want to get ready for agents. They want to get their data. What, how are you, what's the pitch that the big problem you're solving for the stressed out CIO?

A So I think that there's a couple of different points. You talk to every organization. They're like, AI is going to transform it. I want intelligent apps. And by the way, I don't want intelligent apps, just my engineering team, what it's building. I want sales. I want finance. I want my like merchandising team, all of those being able to use it, but I struggle. I can't today. And the main reasons that they say it is like. A, there's way too many things to choose from. Like I have, am I picking one of the different hyperscalers? Is the right answer gonna be, do I pick anthropic? Do I pick, uh, open AI? Do I pick Gemini? Do I pick open source? So one is like, which one of those do I build on to move forward? Second, like my data spread out, help me there. Third is, can you actually talk to me about context? And context is different than data. Think about it as you're onboarding a new employee. How do you explain Everything that goes on in your organization so they can operate effectively. How do you get back? Cause that's what agents are. Um, and then lastly, how do I control this? Cause agents sound great, but my favorite was everybody was like open claws out. So what I did is I took a Mac mini, disconnected it from the internet and put open claw on it. And I was like, great. Congratulations. What does that do? So how do I unleash all of these, but make sure that governance works…

AI assessment note: “And so the Databricks pitch is like... We break down all the different silos.”

Q I know what, but How in 2026, what does that mean to a CIO? What do they, and maybe what that really means, I assume what that means is they want to do more with their data than they could do before, but context is super important for agents, but I want to know what does 95% of the world mean when they want you to help them with context?

A Yeah, so look, I think that there's a couple of different ways to think about it. There is one in some organizations that you have, especially if you're in a regulated industry, like I was meeting with a large bank yesterday, and the answer is if somebody wants to, let's call it talk to their data and ask questions, when you say, hey, show me what does my revenue look like, or what is my loss, like, those are not just a table called revenue. They have very, very clear definitions, right? And so you want to be able to map those somewhere, which is like, these are our core terminologies of what it means to our industry and our world. I think that's one. The second thing is though, there's a lot of different things that are kind of Let's call it shorthand that is not captured somewhere. I'll give you a very simple answer. If I ask, ah, you know, I'm talking to my data and I say, hey, can you show me in the last fiscal year, what basically my top spenders were on the major clouds that we support, ah, at the end of the last, like fiscal quarter in EMEA, what's the answer? Sounds like a simple thing. Anybody at Databricks would know what I mean, but then it's like, what does he mean by clouds? What is fiscal quarter? What is their fiscal quarter? What is inside of EMEA? How do you think about top spenders? Like, These are all ones that you, if you were normally in the company, you're…

AI assessment note: “these are our core terminologies of what it means to our industry and our world.”

Q What are folks exaggerating, getting a little bit wrong? What are you seeing for real from the front lines?

A Look, I think if you sit on X, it's kind of interesting. You'd get this viewpoint that everybody has figured out, you know, AI, everybody's building their own LLM resource agent to do everything. Yep. I think the rest of the world is definitely a little bit different. You know, still how many of them actually properly adopted AI in the way of coding tools or like systems is pretty nascent. But every one of them, we were talking about this backstage is realizing Every CEO says, if we are not adopting AI, we are behind, right? And so now you have all of them telling their employees, if you're not using AI, we are behind. You've got to figure it out. We're going to measure your performance by it. Go use tokens. And so now we have this other problem, which is happening where everybody's like, okay, all my employees are token maxing. My spend on tokens is going up, but I have no idea what I'm getting out for it. Right? So I think everybody realizes how important AI is outside of the We're gonna give them access to pick one of the models, and we're gonna start using coding in our engineering team. It's still early days for many of them, and they're all looking for how do we redesign our processes, and how do we actually think about governance and data to make AI work well, right? So, I mean, that's what we at least see in many of the organizations we work with.

AI assessment note: “You'd get this viewpoint that everybody has figured out... rest of the world is definitely a little bit different”

Q for pre AI, B and SAS and exploding for AI. Databricks is clearly a beneficiary over all of this trend, like unquestionably, but your point backstage is there's a murky middle where it's not clear of all of your workflows and application, which one you really are and how important it is to be on the AI side. So what's your learning advice there in general and working with customers?

A Look, I, it's, uh, it's funny, right? I, I think I, I say that the only question I get asked more than when are you going to IPO now is like, what do you think of the SaaSpocalypse? I think many organizations are looking at this and saying, look, frankly, uh, you're either AI is a wave that's going to crush you or you're riding the wave, right? And people want to spend a lot of money on the latter and they don't want to spend a bunch of money on the former. I think describing exactly why that's the case for folks is a little bit harder, but I do think a lot of folks are realizing I've got to go on the AI journey and I'm struggling to figure out how, and the notion that I'm going to vibe code my own CRM and I'm going to vibe code all my own applications. I think that that it's just not a reality, right? Even if you could build it, maintaining it, evolving it, liability, I think what's going to happen is, um, which is opportunity for many people here. I think any industry that is highly valuable, that has a monopoly today, Will not have a monopoly, 12 to 24 months from now, because.

AI assessment note: “I do think a lot of folks are realizing I've got to go on the AI journey”

Q ask it in plainish English how to access my data. You talked about win rate, loss rates, or whatever you do with the bank. What can I do now in Databricks? I couldn't do 12 months ago. What has changed at the product level and for the buyer? Like what does the buyer want out of you? It doesn't have to be 12 months. It could be 18 or six.

A I think the following is right. How did you take any organization? Um, like how do you run the business? A lot of it is you want to be data driven in some way, right? And so you're looking at it and let's say, uh, as an example, something as simple as you want to make a decision inside of your stores, right? Like I'll take a company that we work with, like You want to make a decision of what do I have to staff in my inventory in my stores? Previously, you would have done something like the following. Okay. What are the questions I want? I have a dashboard. Somebody went out and built me a dashboard, right? And it has a fixed set of questions. When I am a, that pipeline is kind of running. Every time I wanted something new, I have to go ask somebody it's a one week turnaround. And so I get something static that every week it kind of comes up. I look at it. If I want a new question, I'm like. I would really, really care about now. What about X? But it's going to take me a while. I'm just not going to do it. So now inside of Databricks.

AI assessment note: “Previously, you would have done something like the following.”