Erik Bernhardsson

Because I think a lot of the constraint for building a neocloud very quickly is on the financial side. You need to be very good at, you go lock in like a long-term customer contract, then you go raise a bunch of money against it, then you go buy the chips, then you deliver the chips, and then you pay off the debt, like over the next few years. Doing that sort of dance with the financials, like, is kind of an interesting game. And CoreWeave has done it exceptionally well.

I mean, I'm biased, but like we spent the last five years building, you know, an incredibly complex infrastructure stack. I think it's very, very hard to replicate what we built. What was the hardest thing? All the code and, you know, everything around the file system, the container runtime, the scheduler, like building the system that can, you know, place like millions of sandboxes every minute, you know, scale up and down many, many thousands of GPUs, you know, power hundreds of thousands of t

All the sandboxes, all the agentic stuff, workflows, post training, reinforcement learning. If you do all those things, there's actually so much opportunity to make money.

Otherwise you're just selling commodity product. And like, if you're just selling like tokens for dollars, there's no switching cost. You know, people can just switch a different provider. It's just a race to the bottom. But if you couple it with all these other things around it, sandboxes and post training and reinforcement learning and all these things, like you can actually create a whole set of tools. Like no one wants to build a, you know, buy a single chick pony. People want to buy a portf

I think inference is going to be an enormously large market. It's like the new cloud basically. But I don't think inference in itself is maybe like the only thing you need. One of the things that I feel very strongly about is like it has to come with like a lot of other products, it has to come with post training, sandboxes, agentic stuff, all kinds of, you know, other types of inference, like low latency inference, batch inference. So building that platform, by the way, we've been working on th

I think we started with the developer experience. So that was, and we had a very clear sort of PLG product market fit, product led growth in the sense that people came bottoms up. They just sign up on a product. Like I think the consumer background of coming from Spotify is very important in how I thought about building what's now a B2B company selling tech effectively.

I think we have like really, really good vibes. Like I'm terminally online, like people tweet like way more about Modal. I think there's like a little bit more of a community. I think there's like much better vibes around us.

I think the market for inference is so large. And that, you know, when a market is like, you know, a billion dollars, like you're not going to have too many sub markets. But when a market is starting to get 10 billion, 100 billion, whatever, like you're going to have a lot of different sub markets. And Jav does classification and things like that, regression type things very, very well. And there's a large enough market for that.

I think the brand and the community and the movement around, you know, just the sort of everything they're doing is so incredible. And that to me is like a massive, massive asset in this new age. That's a hundred billion dollar company. I think there is an opportunity to create a hundred billion dollar company with a lot of specialized models in the inference.

I think in fact, like we're actually more long than we're short GPUs because, you know, we can charge a higher price to our customers.

And so that to me creates a lot of opportunity to basically kind of rethink what a cloud looks like in the era of AI and kind of deconstruct the cloud and rebuild it for this new era of software.

We had talked to them, I think, a long time ago before they launched, but they launched overnight on modal and we weren't even aware of it. They just like scale up and we gave them a ton of GPUs and it just happened automatically. So that magic, I think, is something that is people really value with modal.

if you pull all of those customers together, you end up with the very sort of smooth overall demand. And that is something we can manage extremely efficiently. And so one of the value props of modal besides like we can scale up with you is also you don't even have to do capacity planning. Just come to us. We'll give you 100 GPUs.

we built a lot of our infrastructure ourselves. Like we don't use Kubernetes and Docker and things like that. We built it on file system. We built it on container runtime. We built it on scheduler. We built it on basically how we manage the capacity. And that enables us to basically treat like all of the world's compute as one big pool of capacity.

part of the value prop of modal is like our customers don't have to think about that. We do the dirty work of doing all the GPU deals under the hood.

Now, we can build a lot of those products and host it all under modal. And that to me is very exciting because there's going to be a lot of different products that companies are going to need.

I think modal has an opportunity long-term to get to 40, 50, 60% margins, but we're never going to get to software margins.

One thing that people don't see about the inference market is I actually think in the long term, there is so much gross margin opportunity in this space because you can build all these things around it. ... If you do all those things, there's actually so much opportunity to make money.

if you couple it with all these other things around it, sandboxes and post training and reinforcement learning and all these things, like you can actually create a whole set of tools. ... A lot of differentiation, a lot of, you know, staying power in the markets.

I think that to me is, you know, not a moat, but it's a very hard product to build that infrastructure.

it has to come with like a lot of other products, it has to come with post trading, sandboxes, agentic stuff, all kinds of, you know, other types of inference, like low latency inference, batch inference. So building that platform, by the way, we've been working on this for the last five years.

I think we started with the developer experience. So that was, and we had a very clear sort of PLG product market fit, product led growth in the sense that people came bottoms up. They just sign up on a product.

I think we have like really, really good vibes. Like I'm terminally online, like people tweet like way more about Modal. I think there's like a little bit more of a community.

I think we have, it's coupled with the developer experience and the product and the website and the visual identity and all these things.

We've been extremely capital efficient. We burn only a few million a month.

It's gone pretty well.

I just saw this big opportunity in the market. I almost thought to myself, like, why is no one else doing this? Such an obvious idea.