00:50:59 · Siddhartha Ahluwalia
[00:50:59] S0: Yeah, and Dario doesn't come in a lot of limelight, also.
00:51:02 · Rajkumar Sen
[00:51:02] S1: Yeah, that thing I really like. Anthropic is a great example, actually. You know what? Uh year and a half back, they had an ad in Wall Street on Wall Street, and somebody messaged me, like, I don't know what this company is doing, that looks so bad. I said that is because that is exactly where you're talking to me. That it's not bad, it's just getting your attention. Maybe they will build a stellar product, like sometimes you can kill company uh competition with your product rather than with your advertising, right? Uh, no phenomenal. I mean, the way the way uh they are creating that you know uh business, and then every product is at least as good as their competitor, uh, and they're also trying to build everything which is good and bad for starters about validation, but we'll see, you know. I think um uh but since you brought up the topic, right? Uh I also feel like you know this is not a one-way street. I think uh there will be a fair share of enterprise use cases which will go to OpenAI also. All enterprise business the on the uh on on this thing, and then a lot of the business will also be shared with Databricks and influencing companies on open source. So there's like there is going to be a big you know uh change in the next two years that people will build on anthropic, people will build on open AI, uh, then governance and harnessing will be on data breaks or snowflake, and then influencing maybe on base. So, how do you do all of this together? And uh where is most of the data going to? Is it going to the front-end models of the open source? The closed ones of the open source is is is going to things are going to get very interesting. So we all thought open Aanthropic and Tundal next year, we're gonna see a very different picture.
00:52:50 · Siddhartha Ahluwalia
[00:52:50] S0: Very interesting. And what do you think the the opportunities are still there? Because Databricks is the I think the only company that thinks on data in an AI first way.
00:52:59 · Rajkumar Sen
[00:52:59] S1: Yeah. So I would say for founders, if you are thinking if you want to start a company, assume that you are inside Databricks or like Anthropic or maybe Snowflake, and then think of like getting to talk to people there, and because they're in the best position to figure out what is left in data MA. I think there is a lot of innovation to be done in the space of data MA. I several things come to my mind. It's not that I'm starting to encompass in these areas, but um uh I honestly feel that uh there's a lot of innovation being done right now inside database uh for LakeBase and Superbase and others, but there is also uh a high throughput agent tech database on the high-end enterprise stuff that that that is also needed. See for the new AI apps and new devs and all of that, LakeBase is great, Postgres is great. Uh, but also like there is uh the higher end of the database market, like Oracle, you know, SQL Server, mainframes. Um if you want to run agentic on them, are you going to run it on Oracle? Maybe not. Maybe you know, some of Oracle apps need to run on a modern AI database. I'm certain idea. There's a lot of opportunity in that. Um I think the challenge is like now, with every cycle, people think databases are dead. Databases were not in looked as a good source of investment, but the day neon got acquired for a billion dollars, suddenly databases are hard. So databases always have this, oh my god, there's nothing left in databases. And then there's a billion dollar exit, and then suddenly everybody's valuation is increased, that cycle will come back. I feel um there is a lot still left on the iceberg and AI space. How do you innovate on formats for AI?
00:54:59 · Siddhartha Ahluwalia
[00:54:59] S0: For portfolio community.
00:55:00 · Rajkumar Sen
[00:55:00] S1: Yeah, and then there is like on a formatting side, LanceDB and others, but there are also like on the optimization thing, uh, which is what I think you know um Rowan's company is doing, and hopefully, you know, we'll get a chance to be part of partnering together. Um there are how big this will go, I don't know, but there's still a lot of innovation left. There is also, I believe, a lot of data management related problems on inferencing. Now, will they be performance features within inferencing companies or some inferencing early employees at inferencing companies will come out and start a product? It's like how Confluent came out of LinkedIn, right? They saw the pattern they came out now. Uh that will also be there. And it's no surprise that you know I'm I'm seeing a lot of data bricks and uh anthropic folks joining inferencing companies now with data management background. So I'm seeing the trend also. Um, and yeah, I think there's event management, even you know, uh, somebody, something like Kafka. I know Redpanda is trying to pivot to some uh agentic. So the all of this will be there. Now, do they go inside the database as part of ClickHouse, Databricks, or they're outside to means to be seen? But there is going to be a lot of innovation uh just on data infrastructure for AI. Um, but couple of things that I but I've my personal uh uh interest right now is uh trying to basically applied AI. As much as I'm using AI, like AI is doing very good for coding, yeah, AI is doing reasonably doing good, like I'm a big fan of Glean inside database. I've used it. I think uh you can point AI to you know models to a set of documents and you can ask questions. Uh I always wondered like why Google actually didn't create the question-answer thing in their search. Why did they stop at search? But you know, eventually it's there. Um I was listening to um uh recorded version of uh Nvidia's keynote, Jensen Huang. I think he brought an interesting topic that you know uh whatever uh you know innovation has happened on the frontier model uh big models on internet data. Yeah, a lot of this innovation has to also transform to business data.