Erik Bernhardsson on Creating Tools That Make AI Feel Effortless

**Elad Gil** (0:05)
Today, I'm chatting with Erik Bernhardsson, founder and CEO of Modal. Modal developed a serverless cloud platform tailored for AI, machine learning and data applications. And before that, Erik worked at better.com and Spotify, where he led Spotify's machine learning efforts and built the recommender system. Well, Erik, thanks so much for joining me today on No Priors.

**Erik Bernhardsson** (0:21)
Yeah, thanks, it's great to be here.

**Elad Gil** (0:23)
So if I remember correctly, you worked at Spotify and helped build out their ML team and recommender system, and then we're also at better.com. What inspired you to start Modal, and what problem were you hoping to solve?

**Erik Bernhardsson** (0:33)
Yeah, so I started at Spotify a long time ago, 2008, and it's been seven years there. And yeah, I built the music recommendation system. And back then, there was like nothing really in terms of data infrastructure. Hadoop was like the most modern thing. And so I spent a lot of time building a lot of infrastructure. In particular, I built a workflow schedule called Luigi that basically no one uses today. I built a vector database called Innoi that, you know, for a brief period people used, but no one really uses today. So I spent a lot of time building a lot of that stuff.
And then later at Better, I was the CTO and thinking a lot about like developer productivity and stuff. And then during the pandemic, I took some time off and started hacking on stuff. And I realized I always wanted to build basically a better infrastructure for these types of things, like data AI, machine learning. So pretty quickly I realized like this is what I wanted to do and that was sort of the genesis of Modal.

**Elad Gil** (1:22)
That's cool. How did that approach evolve? Or what are the main areas of the company focuses on today?

**Erik Bernhardsson** (1:28)
So I started looking into, first of all, just like what are the challenges with data AI, machine learning infrastructure and starting to think about from like a developer productivity point of view, what's a tool I want to have? And I realized like a big sort of challenge is like working with the cloud is arguably kind of annoying. Like as much as like I love the cloud for it powered it, that gives me, and I've used the cloud since way back 2009 or so, it's actually pretty frustrating to work with. And so in my head, I had this idea of like, what if you make cloud development feel almost like as good as local development, right? Like how does this like fast feedback loops? And so I started thinking about like, how do we build that and realize pretty quickly like, well, actually, we can't really use Docker and Kubernetes, so we're going to have to throw that out and probably going to have to build their own file system, which we did pretty early and build our own scheduler, build our own container runtime. So that was like basically the two first years of Modal, which is like laying all that like foundational infrastructure layer in place.

**Elad Gil** (2:21)
Yeah, and then in terms of the things that you offer today for your customers, what are the main services or products or...?

**Erik Bernhardsson** (2:28)
Yeah, so we're infrastructure as a service, so which means like on one side, we run a very big compute pool, like thousands of GPUs and CPUs, and we make it very easy to get, you know, if you need 100 GPUs, we can typically get you that within seconds.
So sort of one big multi-tenant pool, which means like capacity planning is something we kind of take, you know, it's something we solve for customers. They don't really need to think about reservations. We always provide a lot of on-demand GPUs. On the other side, there's a Python SDK that makes it very easy to build applications. So the idea is like you write code, basically like functions in Python, and then we take those functions, turn them into serverless functions in the cloud. We handle all the containerization and all the infrastructure stuff, so you don't have to think about all the sort of Kubernetes and Docker and stuff. The real killer app, as it turns out, like we started this company pre-Gen.AI, but as it turns out, the main thing that really started driving all the traction was when Stable Diffusion came out. A bunch of people came to us and were like, hey, actually, this looks kind of cool. You have GPU access. It's very easy to have to think about spinning up machines and provisioning them. So that was our first killer app. It was just doing Gen.AI in a serverless way with the focus of diffusion models. Now we have a lot more of different modalities. A lot of users still like text to image, but we also see a lot of audio and music. So one example of a customer I think is super cool, building really amazing stuff is Suno, which does AI-generated music. So they run all the inference on Modal, very large scale. There's a lot of customers like that, sort of dealing with building cool gen AI models. In particular, I would say in the Modal is like audio, video, image and music, stuff like that.