Business Breakdowns
· Alan Tu
[00:00:00] S1: Yeah. And there's probably a couple other ways to think about AI's impact on Databricks. Another way is actually the fact that there is this huge cohort of AI-native companies, including the largest AI labs, that can and do use Databricks as well for themselves internally. This is something that companies in the public market also talk about, is that there's, "How is AI actually impacting product and use cases?" And there's also the fact that, "Are you part of the stack that AI-native companies are utilizing?" And Databricks is very much there. And then I think the final part of AI's implication on Databricks as business is actually product. One of the big picture bets that Databricks is making, going back to this idea that they have a DNA of having an opinion about where the world is going and where can Databricks add the most value, it's really around the idea that in the future, AI and LLMs have already proven, even if the models don't get any better than where they are today, the ability to automate more work. And when you think about how big of a TAM that is, it's probably just as infinite as the TAM that we talked about initially around data. So, what Databricks is doing is building products that, in the same way that they came out with MLflow that helped that whole process of a data scientist building a machine learning model, they're creating an entire stack with products called Agent Bricks and LakeBase that all together will help enterprises build their own agentic applications to automate specific use cases and actually automate labor and work, which is just tremendous amounts of ROI.
· Matt Reustle
[00:00:00] S0: Yeah. And I'll reframe it maybe, and you can tell me how accurate this is versus inaccurate. Most of what we talk about with agentic is oftentimes just shrinking the context, giving it very deep context in specific tasks, and therefore the quality of the response is going to be much stronger. It's not going to be pulling random places on the web. Databricks obviously has the most rich data to use in terms of informing those models and therefore can partner with businesses who might want to develop those agents.
· Alan Tu
[00:00:00] S1: Yeah. So, to your point, part of what the industry is realizing is that there are techniques that are important to be leveraged to build effective agentic applications. For example, RAG, which is retrieval-augmented generation. There's a whole set of processes around enabling that, with vector databases, for example, embeddings. So, Databricks' offerings there. There's also a very important part of building agentic applications is around model evaluation. So, because of the very unpredictable nature of the large language models, it's not quite as easy to always know exactly, "Is our application or agent acting the way that we think it should?" And so, there's a whole set of technologies around model evaluation, around being able to actually quantify, " How are these models behaving? Are they doing what we think that they should be doing?" And Databricks again is building products around that. In the same way that it's parallel going back to the machine learning era where you would go through a similar process around evaluating models there. You can get a sense of the fact that while there's a lot of focus on the core large language models, if you actually want to build applications in production, there's so much around and beyond just the model that is really where I think Databricks has a strong right to win.
· Matt Reustle
[00:00:00] S0: Yes, the infamous question of where the value might accrue within the layers of the AI ecosystem is quite interesting. On the opposite side of the equation, there's been this interesting, solid, good relationship with the cloud infrastructure providers. AWS is there. Does that change at all with AI? Because to your earlier point, the data cleanup and all of that becomes even more important. I have personally been able to clean up unstructured data into structured data much more cleanly with AI. Different story when it comes to me versus doing this at cloud scale, but what's your view on where that relationship, where the cloud providers haven't necessarily moved into this category, does that change at all? Is that a risk? Is that something that you think about at all?
· Alan Tu
[00:00:00] S1: Well, it's funny you mentioned that cloud providers haven't really moved into Databricks as a market because they actually do have offerings there. And I think it's actually more reflection of the fact that Databricks has done such a good job of both executing on product but also just market positioning that you sort of have that, yeah, but I would say that for as long as I've followed Databricks, and I first met Ali over 10 years ago, they had just signed their first strategic partnership with Microsoft, that was actually Databricks branded under Azure, as Azure Databricks. And that was an incredibly important partnership to jumpstart Databricks's monetization. I bring that up because Databricks, from day one, Ali as a business leader has always been extremely pragmatic and strategic about how they operate vis-à-vis the hyperscalers. And so, what I would say is that what has always been the case is that there is "coopetition" with the hyperscalers. The reality is that for customers that are using Databricks, they will also be consuming infrastructure, compute, and storage of the hyperscalers. So, there is a benefit for the hyperscaler clouds when customers are using Databricks on top of their infrastructure. And that coopetition dynamic, I totally expect to continue in the world of AI. But I do think it's a very important question because this is a big enough market that the hyperscalers do care about it. And that it's been another, I believe, underappreciated strength of Databricks, which is their ability to align themselves with the hyperscalers. There are a lot of examples of companies that came out with a great product, had a tremendous amount of momentum, and then Microsoft decided that this was too strategic for them to lose, and so they were going to put all of their weight behind killing that product. I think we can all think of different examples here. And that is oftentimes been a real challenge for growth-stage software companies. I think Databricks has just done a very good job of never positioning themselves in such a way that the hyperscalers are 100% incented to kill them. There's enough alignment, there's enough opportunity for partnership and mutual growth, that that relationship with the hyperscalers has generally been relatively synergistic despite the fact that they do represent very real competition.
· Matt Reustle
[00:00:00] S0: I bring it up all the time, but Amazon with something like FedEx and UPS, where they were relying on them, and then FedEx and UPS couldn't deliver during the holidays, and it was a big problem. To the extent that Amazon built out a network and then eventually started to compete with it. But there is something to the coopetition being of high enough quality where it's not creating a problem. There are other dynamics that get involved, but that's very useful framing. I did want to get a little bit more into just some of the financial dynamics. You mentioned the $4 billion in ARR at this point. How does it work from a customer perspective? Is it a simple usage-based revenue model?