The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:13:42 · Harry Stebbings, Erik Bernhardsson
[00:13:42] S1: Can I ask you, we've spoken about the three different providers there? Inference as a market has many different speculators commenting on it. When you look at the next five years, how does the inference market play out? [00:13:55] S0: Of I I think inference is gonna be an enormously large market. It's like the new cloud, basically. And... 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 this for the last five years. Think that to me is, you know, not a moat, but it's a very hard product to build, that infrastructure. Why do you need [00:14:25] S1: to have the breadth of that product set? Otherwise, you're just in a compression layer. Yeah, because otherwise you're just selling commodity product.
00:14:25 · Harry Stebbings, Erik Bernhardsson
[00:14:25] S1: to have the breadth of that product set? Otherwise, you're just in a compression layer. Yeah, because otherwise you're just selling commodity product. [00:14:32] S0: And, like, if you're just selling, like, tokens for dollars, there's no switching cost, you know? People can just switch to 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, one wants to build a, you know, buy a single trick pony. People want to buy a portfolio of different things. Like, they want to buy, you know, a whole suite of tools that basically lets them build amazing AI applications on top of it. Right? And the infrastructure underneath is a lot of differentiation, a lot of, you staying power in the markets. It's very hard to copy like the infrastructure required to build all of these products. [00:15:09] S1: What does no one see about the inference market, or you don't see commented on much, that is very obvious to you? [00:15:15] S0: 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. 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.
00:15:15 · Erik Bernhardsson, Harry Stebbings
[00:15:15] S0: 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. 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. [00:15:32] S1: Is it a market that has software margins? Because we're looking at margins now in the 25 to 40% range. That's kind of traditional players. I don't know if that's yours, but that's kind of what inference providers are generally. Do they go to 70 as you earn more
00:15:32 · Harry Stebbings, Erik Bernhardsson
[00:15:32] S1: Is it a market that has software margins? Because we're looking at margins now in the 25 to 40% range. That's kind of traditional players. I don't know if that's yours, but that's kind of what inference providers are generally. Do they go to 70 as you earn more [00:15:45] S0: and more of the stuff? I think basically almost no company today has software margins anymore. Like what you used to see in Silicon Valley ten years ago with this 90% margins plus, I think that's basically gone. And the reason is actually almost topological. It's like you have this like stack of vendors, right? Like today you have applications, you have models, you have infrastructure, you have cloud, you have Nvidia. And so when a customer pays a dollar at the top of the stack, it has to flow through a lot of different every layer, there's going to be, you know, 20%, 30% gross margins. So I think there's a lot of margin opportunity, and there's a lot of opportunity to make a lot of money. I mean, the space is so big, but you're also splitting it across a lot of layers in the stack. So, I mean, you know, like the 90% software margin businesses are gone. 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. [00:16:35] S1: What's a wild prediction you have on the future of the inference market that you think many people would disagree with?
00:16:35 · Harry Stebbings, Erik Bernhardsson
[00:16:35] S1: What's a wild prediction you have on the future of the inference market that you think many people would disagree with? [00:16:40] S0: I one prediction about the inference market is gonna split into a lot of different things because the market got so big in the last... I don't think anyone expected, me included, I don't think anyone expected it to be, you know, so big. It's like 10 x or a 100 x bigger than I ever thought. And that to me means there's gonna be so much room for specialization, low latency, high latency, like various types of fine tuning and post training. I think there's gonna be some wacky new technologies that let you do it with way less resources, new hardware, like all kinds of different things. [00:17:07] S1: I'm really sorry. Does that go against what you just said, though, about the need to own it all and have that breadth of product? If it's so much larger that you need real specialization
00:17:07 · Harry Stebbings, Erik Bernhardsson
[00:17:07] S1: I'm really sorry. Does that go against what you just said, though, about the need to own it all and have that breadth of product? If it's so much larger that you need real specialization [00:17:15] S0: Specialization by product. I think it's gonna fragment into a lot of different products. Now we can build a lot of products and host it all under Modal. And that to me is very exciting because a lot of different, you know, there's gonna be a lot of different products that companies are gonna need. For one product, they're gonna need low latency inference. For one product, they're gonna need batch inference. For one product, they're gonna need sandboxes, which we have. For one product, they're gonna do pre training their own models. You know, maybe it's, you know, certain specialized, you know, audio models or whatever. Like, do a lot of stuff besides LM inference. And so that to me, that 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. [00:17:55] S1: Earlier, you said about building capacity with GPUs. Yep. What's it like doing that today? Is it as crazy as reports suggest? So it is pretty crazy.