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.
00:36:31 · S2, Rajkumar Sen
[00:36:31] S2: Yeah. [00:36:31] S1: So if you have a very high throughput, this is the classic, like I came back to the Arcion example, right? People are doing batch data movement, but then they want that continuous CDC, and I feel like training and inference is kind of the parallel thing, right? The inferencing, because the continuous process and live operational has to happen at super high throughput, super high uh scale, but also a lot of custom things have to be done per application. Like if you're running an application that does 700, 800 calls per second, that has some bespoke features that can only be done with a combination of base 10 or together with a chemie or one of the OSS deep sick models. Sure, entropy can solve that problem, but it's all about like you know, can you solve all the problems on Earth? [00:37:18] S2: Yeah. [00:37:19] S1: So there is that section of I'm sure people when we use Cloud or OpenAI, or obviously you're doing inferencing there. But if you want to run that kind of scale application where you need that at high throughput, you have to go to another vendor. Maybe one of these vendors will be bought by somebody at some point. But uh same thing when things get big, they will need scale, they will need uh you know, throughput, you will need one customization, which bigger companies may not want to do, but then that's where the startups thrive. And uh, I'm I'm super uh excited on the influencing companies because they feel like with the combination of open source and uh you know, then they can bring that value, right?
00:37:19 · Rajkumar Sen, Siddhartha Ahluwalia
[00:37:19] S1: So there is that section of I'm sure people when we use Cloud or OpenAI, or obviously you're doing inferencing there. But if you want to run that kind of scale application where you need that at high throughput, you have to go to another vendor. Maybe one of these vendors will be bought by somebody at some point. But uh same thing when things get big, they will need scale, they will need uh you know, throughput, you will need one customization, which bigger companies may not want to do, but then that's where the startups thrive. And uh, I'm I'm super uh excited on the influencing companies because they feel like with the combination of open source and uh you know, then they can bring that value, right? [00:38:06] S0: We have about two companies which are in inferencing. One is a company started by the founder of Qure.ai. Qure is one of the leading radiology AI companies, 40 million R valued at 400. So the the hospitals that were using Qure, uh they never relied on models, right? They said we want influencing in-house. The founder came out of it and just built inferencing in-house.