In conversation with Erik Bernhardsson, CEO and Founder of Modal

In this episode of the Tractable podcast, Kshitij Grover, the cofounder and CTO of Orb, interviews Eric, the founder and CEO of Modal. They discuss Eric's technical background, his experience building a music recommendation system at Spotify, and his journey to founding Modal. The conversation delves into the challenges faced in the early days of Spotify, the evolution of serverless technology, and the unique approach taken by Modal in simplifying cloud deployments, especially focusing on data, AI, and machine learning workloads.

Transcript:

Kshitij [00:00:04]: Hello everyone welcome to another episode of the Tractable podcast. I'm Kshitij, cofounder and CTO here at Orb. And today, I have with me Erik. Erik is the founder and CEO of Modal, which is a serverless platform that makes deploying your code to the cloud and specifically, Modal's cloud really easy. Modal is known for being simple to get started with and super scalable even at really large workloads, both on CPUs and GPUs. Companies big and small use Modal, including Ramp, Substack, and Suno. So excited to dive in with Erik today.

Kshitij [00:00:36]: Erik, welcome.

Erik [00:00:37]:Thank you. And, yeah, it's great to be here. By the way, that was a phenomenal pitch. I'm just gonna steal that.

Kshitij [00:00:44]: Well, glad to hear that. Look, I think I wanna dive right into the technical conversation. So I wanna talk a lot about Modal. Before we get there, tell me a little bit about your technical background before starting Modal. What sort of problems have you historically been interested in? And then maybe you can kind of pave the path from that to Modal and what you're doing now.

Erik [00:01:03]: Yeah. So kind of rewinding the clock a little bit. I grew up in Sweden. I started coding more than 30 years ago. I did a lot of programming competitions in high school and and university. And then when I graduated, I ended up joining this then obscure music streaming company, called Spotify, was employee number 30 or something that. This is back in Sweden and in particular, I for whatever reason, was able to convince them to hire me to build a music recognition system despite, barely having any experience with and started working on that and I started working on that and spent 7 years at Spotify. I did a little bit of everything, but, in particular, I built a music recommendation system, but I also did a lot of other sorts of data, AI, and machine learning stuff. Later left, and I was the CTO of a Fintech company called Better, which has its own sort of, you know, interesting kind of roller coaster story. I left 3 years ago and started Modal early 2021 roughly.

Kshitij [00:01:57]: And that's interesting that you worked on the recommendation system at Spotify. What was the kind of primary challenges there? Was it, having to come up with a lot of things from scratch because this technology wasn't super hot at the time? I mean, it sounds pretty early in those days.

Erik [00:02:13]: Yeah. I mean, this is do you remember, Netflix prize?  That was kinda cool. And so, I took a lot of inspiration from that. It's a slightly different problem because they had ratings, Spotify didn't have ratings. But in many ways, it was quite, you know, quite inspired by that, and a lot of Spotify had a lot of data we looked at, you know, who listens to what, and we don't necessarily know, whether they it or not. But, just the fact that they listen to something pretty strong signal. And it turns out, roughly what you can do is, you take all that data and, put it in a big matrix and then you factorize that matrix.

Erik [00:02:45]: And a lot of NLP models actually worked really well. So it's also so so we started looking at, you know, a lot of NLP models. Because you think of, what they listen to as a token essentially and it's a sequence prediction if you incorporate time. So I spent a lot of time, building that and I mean back then it was, 2008 - 2009.

Erik [00:03:03]: There was Hadoop, there was stuff like that.

Erik [00:03:08]: And it wasn't very nice to work with, but, yeah, we had to scale it out. And I spent a lot of time, you know, building distributed, you know, machine learning, large scale methods, and built a lot of data pipelines. I ended up open-sourcing, I think called Luigi to do these, because we had very complex data workflows. And then I ended up building a vector database because there was nothing like that, I needed it. And so I ended up building my own vector database called Annoy, which is also open source but has no use today. Yeah. I mean, it was, an amazing problem.

Full transcript here.