Artem Keydunov: commitment

Transcript context

…I think it's a multidimensional question. That's an example of, you know, like where you have a lot of inside the question. In terms of examples, I think a lot of people building different, you know, like agents or chatbots. You have a company that built an internal Slack bot that sort of answers questions, you know, like based on the data in a warehouse. And then like a lot of people kind of go in and like ask that chatbot this question. Is it like a real big use case? Maybe. Is it still like a toy pet project? Maybe too right now. I think it's really hard to tell them apart at this point because there is a lot of like a hype, you know, and just people building LLM stuff because it's cool and everyone wants to build something, you know, like even at least a pet project. So that's what happened in Krizawa community as well. We see a lot of like people building a lot of cool stuff and it probably will take some time for that stuff to mature and kind of to see like what are real, the best use cases. But I think what I saw so far, one use case was building this chatbot and we have even one company that are building it as a service. So they essentially connect into Q semantic layer and then offering their like chatbot So you can do it in a web, in a slack, so it can, you know, like answer questions based on data in your semantic layer, but also see a lot of things like they're just being built in house. And there are other use cases, sort of automation, you know, like that agent checks on the data and then kind of perform some actions based, you know, like on changes in data. But other dimension of your question is like, will it replace people or not? I think, you know, like what I see so far in data specifically, you know, like a few use cases of LLM, I don't see Q being part of that use case, but it's more like a copilot for data analyst, a copilot for data engineer, where you develop something, you develop a model and it can help you to write a SQL or something like that. So you know, it can create a boilerplate SQL, and then you can edit this SQL, which is fine because you know how to edit SQL, right? So you're not going to make a mistake, but it will help you to just generate, you know, like a bunch of SQL that you write again and again, right? Like boilerplate code. So sort of a copilot use case. I think that's great. And we'll see more of it. I think every platform that is building for data engineers will have some sort of a copilot capabilities and Cubectl, we're building this copilot capabilities to help people build semantic layers easier. I think that just a baseline for every engineering product right now to have some sort of, you know, like a copilot capabilities. Then the other use case is a little bit more where Cube is being involved is like, how do we enable access to data for non-technical people through the natural language as an interface to data, right? Then the other use case is a little bit more where Cube is being involved is like, how do we enable access to data for non-technical people through the natural language as an interface to data, right? Like visual dashboards, charts, it's always has been an interface to data in every BI. Now I think we will see just a second interface as a just kind of a natural language. So I think at this point, many BI's will add it as a commodity feature is like Tableau will probably have a search bar at some point saying like, Hey, ask me a question. I know that some of the, you know, like AWS Squeak site, they're about to announce features like this in their like BI. And I think Power BI will do that, especially with their deal with open AI. So every company, every BI will have this some sort of a search capabilities built in inside their BI. So I think that's just going to be a baseline feature for them as well. But that's where Cube can help because we can provide that context, right? Do you know how, or do you have an idea for how these products will differentiate once you get the same interface? So right now there's like, you know, Tableau is like the super complicated and it's like super sad. It's like easier. Yeah. Do you just see everything will look the same and then how do people differentiate?…

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