00:28:27 · Rajkumar Sen
[00:28:27] S1: I think given the opportunities around data and AI, I also I believe that even Snowflake will grow significantly uh because they have a great product. Uh, as I said, right? Um, if you have a product that can handle structured data very well, uh, I think uh they have a solid data warehousing product. I feel like they're also going to grow. They're gonna like it's not going to be one way. Uh there is going to be tremendous amount of innovation from Snowflake also. Uh, but I don't have so much visibility because I have not worked there and I can only see it from outside as a hopefully as a you know, if I start a company tomorrow as a partner of theirs, yeah. But um there are top, you know, with I think a lot of innovation is going to happen in every vertical, and most of the compute money and infrastructure money is going to end up at Databricks and Snowflake. Where will people go, right? I think AWS is great, but I think uh the app side is completely shifting over to Databricks and Snowflake now. Uh this was not the case earlier, you know, like uh they were dominating in databases and all of this. A lot of that shift has happened. Again, if you went and asked somebody, you know, um, will somebody will take on AWS? Yeah, you wouldn't say no one. Then Snowflake took on AWS. If somebody told you in 2020 somebody is going to take on Snowflake, you would have been okay. No, no, Databricks took on. So, you know, this is the beauty of thing, right? Nothing is constant. Uh, so you know, remains to be seen. But um for Arcion, uh we, you know, uh at some point we we had an offer from Databricks, and obviously, you know, um, they know how to value team and company, which is I already knew it uh from the mosaic.
00:30:19 · Siddhartha Ahluwalia, Rajkumar Sen, S3, S2
[00:30:19] S0: So they so they valued it properly, though you didn't have to negotiate on the offer and anything. [00:30:23] S1: I don't think we had too many negotiations, and they were very generous also in uh uh employee uh compensation. Okay. So, in addition to X amount of money for the cap table, they were super generous in uh handing out amazing RSU packages for everybody in the company. I can't name numbers, but uh I think uh Jyoti Bansal once gave a famous statement. You know, uh I met X millionaires as part of AppDynamics. I could claim the same actually for 90% of the Arcion employees. Okay, or maybe as of today, everybody given the stock price if they have not sold, uh, because uh database was very, very generous. Yeah, they appreciated the talent we are bringing in. Uh and uh that's the beauty of that company, you know, like uh more than valuing the product or whatever, like you know, the team is also important because you know what you can build inside is more valuable, right? Because with Arcion, how many customers you would have got, right? [00:31:24] S3: 20. [00:31:25] S1: 20. If you're part of Databricks, that number is probably thousand. [00:31:29] S2: Okay.
00:31:30 · Rajkumar Sen
[00:31:30] S1: But for when you left Databricks, the number was thousand for Arcion? We transitioned the product to something called LakeFlow Connect. Okay, um, I can't name the number, but the number is tremendously high. Okay, uh, it's growing. Um and obviously the porting of the project into you know the infrastructure took a little bit of time, and obviously uh product had to be more mature as a startup. You can have less mature product, and if you're part of Databricks portfolio, you have to be super stable and super mature. So we have to do all of that. Uh I think we did well. Uh products up and running. Now I think the products getting integrated with Genie and all of it, also. So, and then I I think more connectors are coming very soon. Uh, so you know um it's a it's gonna it's gonna be good. I I feel proud of the team and what we achieved. Uh and um the I think being see I one uh interesting aspect of Databricks is and maybe it's true for other companies, I haven't worked in Snowflake, Anthropic, or OpenAI. Maybe it's the same everywhere. It's like um you're always pushed to do more. Okay, you can't like Hogia types in India. There is nothing like that. So as much amount of hours I was putting in in Arcion, like the amount of hours actually increased at data points. So your family would be more and happy. Yeah, they're like, okay, we heard that after exit, life is chill. I said, no, no, no, no, no, this company it's actually like you know, uh, there's a lot of training also to be done. Like instead of three salespeople, you're trying to train to like 500. Yeah, instead of talking to five customers, you're going in front of these are like heavyweight customers, right? So it was phenomenal. Um, I enjoyed uh uh that you know that that journey. And but that at some point, you know, I also felt like you know, I was doing this thing for five years, then I did it again for two and a half years. So after seven and a half years, you know, it's like you know, you're doing only comedy movies, and you know, even Leonardo had to do that one big movie to get an Oscar, you know. I don't know if you really wanted to do it.
00:33:44 · Siddhartha Ahluwalia, Rajkumar Sen, S2
[00:33:44] S0: It was an earth movie or something like that. Exactly. [00:33:47] S1: So I'm like, fine, you know, let's let's let's recharge. And also, I think I was seeing a lot of um, I started to use Claude and all of it, Claude code, and I thought, like, you know, uh, if I I need to take a short break to educate myself in AI. [00:34:02] S2: Sure.
00:34:02 · S2, Rajkumar Sen, Siddhartha Ahluwalia
[00:34:02] S2: Sure. [00:34:03] S1: Uh, what is reinforcement learning? Like, you know, um uh how each front-end model is different. I think when I quit, I think GPT OSS and Llama, a couple of models were. When did you quit database? Uh February. One thing I took up as a challenge is to actually study the source code of Llama because some of the Llama code is in C my favorite language. So um I took a challenge upon myself to because you know data space is awesome, but then I want to see what's what is in AI. Like I always get technologically interested, not as a user. Find a lot of people are using MCPC, et cetera. My I'm like a hardcore systems guy. So my thing was like, okay, let's I have to go to the code and see you know what they're doing. At least I need to know fundamentally how the state transitions happen and all that. And I realized at the end, after doing my thing, that you know, it is not as hard as I thought it is, though now I'm so I had to dig a little bit on uh, you know, how do they do reasoning on what is the state of the art? And obviously, a lot of new models are coming, you know. Yeah, uh, very, very, very interesting. The AI space is changing. Um, it's I I feel like there are a lot of parallels, you know. Like uh when new databases were created, like Mongo was there, like it one new paradigm starts in, then you know, you have to even people in the database space had to learn how MongoDB does things and all. It's similar now, like the data people are having. [00:35:33] S0: You have to learn how databricks does things.
00:35:33 · Siddhartha Ahluwalia, Rajkumar Sen, S2
[00:35:33] S0: You have to learn how databricks does things. [00:35:34] S1: You have to, because that's the future, right? If you are like uh if you're not AI trained now, yeah, you're not gonna have a job. So, and being AI trained, I always tell people not only using like the harness stuff or like them, like if you can write a new application that does reasoning, you can spin up your own text-to-sQL thing, but also try to learn what the models are internally doing. Yeah, how does inferencing, like you know, I'm doing a lot of research on like my ex-boss Samir, he's joined Baseten as head of engineering. So I started learning like you know, why inferencing companies are existing, why Baseten exists, why Together AI exists, why fire you know fire exist, why not anthropic? Uh so you know how about what is your insight? [00:36:19] S0: Why they exist? [00:36:21] S1: See, I think one part is obviously the OSS movement is happening, and then I think um fundamentally not every company can solve all the problems. [00:36:31] S2: Yeah.