Business Breakdowns
· Alan Tu
[00:00:00] S1: Sure. For any given credit card company, the implementation might be different than another. But to bring your use case to life, it is very accurate to think about that core value proposition that Databricks provides, which is that you can imagine the amount of data that goes into making a decision of whether or not a transaction is worthy of a fraud alert. There's a tremendous amount of input that can go into that. And there's probably never enough; you could always add more data to that analysis. And that hits on the core thing that Databricks provides, which is the pipelines to bring in all that data, process that data because all the different types of data are going to be in different formats, and feed that into a machine learning model that gets fine-tuned by the data scientists and tweaked and will constantly be updated based off of the facts on the ground and the empirical data of also evaluating these models: are they actually accurate after the fact? And then tweaking those models again. All of that is core Databricks value proposition. Now, once Databricks helps a company come to that decision, who is actually sending the fraud alert. Again, there can be different architectures here, but typically, companies will build another application that actually takes the action, and the model output from Databricks will inform that action, is maybe a classic way to think about the architecture.
· Matt Reustle
[00:00:00] S0: That makes sense. The application that sits atop it's ultimately informed by the Databricks models that are analyzing all those things. And I can imagine it being rules-based: if a transaction meets these criteria and it gets sent up, then that triggers the fraud alert warning. Where I was going with that is, I'm trying to grasp again, with you'll have companies that are using multiple different vendors, obviously, there's so many different use cases. The one I just brought up is a cost savings. We've already talked about revenue growth and how this can be used. But in terms of ways to measure stickiness and ramping with customers, what does that look like? It just assumes the more you would use the model, the more ingrained it would be in your business, therefore less likely to churn.
· Alan Tu
[00:00:00] S1: Totally. They've disclosed that their net dollar expansion rates are greater than 140%. So, there is embedded in that a high level of stickiness and also embedded growth. So, quantitatively, that's the numbers that they've disclosed. But I think qualitatively, the right way to think about it is that many of these use cases that we've discussed are very core to the fundamental product that businesses are selling. Sometimes in the world of data analytics, you can just envision a data scientist or data analyst in the back office running an analysis for the strategy team that may or may not feel sticky. But when you think about these use cases where this is a content streamer that is suggesting the next movie you should watch after you finish a movie, that is core to the product, and that can oftentimes be revenue generating and very mission critical. So, there is that level of stickiness in terms of once you get embedded into use cases in production. And then there is also the added layer of stickiness around just the fact that there's a concept of data gravity. And once you put in the work to store and catalog data within a data platform, that becomes very sticky. So, there's a lot of different dimensions with which Databricks becomes very embedded within a company. I think the last thing I would mention is, if you've done the work to process data once, you could potentially use that for multiple use cases. You can then again imagine how that becomes very hard for, even if a certain product at sunset, but there's another product that's still leveraging the same data, that's very sticky as well.
· Matt Reustle
[00:00:00] S0: You've made the allusion, and it's the elephant in every room now, of AI. I guess I'll just start with the very highest of level of what are the impacts of AI on a business like Databricks, to the extent that they're beneficiaries, to the extent that there's risks associated with it. I'll let you wax poetic.
· Alan Tu
[00:00:00] S1: Well, maybe just to start with some quantitative framing. So, Databricks, they're now over $4 billion of ARR. They have disclosed that about a quarter of it, $1 billion, is AI-related revenue. And so, AI has already become a very large part of the business. But I think beneath that, there are different ways to perhaps slice and dice the impact of AI on Databricks. And I think for me, one of the things that I've really liked about Databricks as an investment is that there are multiple ways to win. Starting with the core data processing piece, there's almost a consensus understanding within enterprises now that you don't have an AI strategy without a data strategy. Everyone recognizes, of course, the model providers are doing what they're doing, and every generation of models is getting smarter and smarter. But at the end of the day, if you don't have good, clean, well-cataloged data, the models can only do so much. One of the ways that AI has really benefited Databricks's business is that it's just created a tremendous amount of prioritization and awareness of the importance of the core product that Databricks has always provided. In my mind, that is a durable tailwind. That companies will need to do, have always needed to do anyway, that is actually not dependent on whether we achieve AGI or not, or what does the next OpenAI model do. The reality is that as long as there is a general belief and understanding that AI is important, there will be a driver towards more data engineering and data processing. That's a general tailwind for Databricks that I think, when you think about Databricks the business as an investor, it actually paints a picture of a more durable growth trajectory that is perhaps not as spiky on the upside, but also not as volatile on the downside either in the case that sentiment around AI changes.
· Matt Reustle
[00:00:00] S0: That makes a lot of sense just in terms of a heuristic at a high level for thinking about AI within the business.