In this Worldbuilders interview, Sumeet Singh talks with a representative from SF Compute about why AI GPU compute remains expensive and how SF Compute is trying to change that. The discussion outlines the mismatch between traditional cloud providers’ public‑market‑driven business models—high markup, premium managed services, and long contracts—and what GPU customers actually need: reliability, security, short‑term contracts, and especially low price. The speaker describes two strategic paths for emerging "neo‑clouds": (1) building large GPU clusters to sell volume to big AI labs like OpenAI and Anthropic, focusing on price and letting customers handle their own services; and (2) offering specialized managed services (e.g., Slurm, storage, or platforms like Modal). He argues that clouds will tend to specialize in one path, as trying to excel at both dilutes resources. The conversation then contrasts CPU‑driven SaaS workloads, which enjoy high margins because software can be sold repeatedly, with GPU‑driven AI workloads that follow the "bitter lesson": compute scales with data and model size, leading to thin, volume‑sensitive margins. Consequently, GPU customers are highly price‑sensitive, pushing providers to lower prices. SF Compute positions itself as a cloud that deliberately sacrifices high margins to offer the cheapest possible GPU compute while maintaining reliability and security, betting that lower prices will accelerate AI adoption and benefit the world.
Chapters
One of the things I think that SF Compute does is it reduces prices for compute broadly. Um, the way almost all the clouds have set up their capital structure and the way their company works is we're gonna make great software, we're gonna charge a high markup for it, um, and then we're gonna get really great margins from it. Um, the problem of this is this is divorced from what customers want. It's what the public markets would like them to be like. This is why they will, why they will present this. Um, but it is not what any buyer of GPUs wants. What every buyer of GPUs wants is they want reliability, they want security, and then they want better price, and then they want shorter contracts. Um, and as long as you are reliable and stable, um, and you are secure, Um, you are mainly focused on price.
They view it as a commodity, but the reality is like, it's not as simple as a commodity, I guess.
Yeah. So, um, the caveat to this is there are, there are lots of little details, um, in GPU clouds that matter tremendously. Reliability, security, um, price, contract duration are the key things. On the micro scale, um, it's like little tiny details about the way that your system is implemented.
So now we know If SF Compute doesn't exist, how the story ends. But if SF Compute does exist and we do exist, what do the other clouds do? What should everyone else in the market be doing?
If you're a NeoCloud, you have 2 paths that you could go down, and many clouds today are doing both. One path is you go straight to the top of the market and you deploy large clusters without a lot of services on them for reasonable prices to OpenAI and Anthropic. You get offtake from the biggest buyers, and those biggest buyers are cost-sensitive, and they have internal teams that can do most of the managed services that you offer. And you should totally try and sell your managed services to them, but you should be okay if they don't buy them because you're mainly trying to scale volume for your clusters. The other thing you should do is you should make managed services.
The other path.
The other path. So this is things like offering a great Slurm product, um, or great storage products, or, you know, you can push really high up and offer something that looks closer to like Modal. Modal is an excellent managed service. Um, but I think these things are hard to do in the same company because you end up focusing your, um, your resources on both at once and they kind of don't necessarily overlap with each other. Um, an amazing example of this is Modal's product is amazing and really good. They are an excellent managed service. They do not build clusters.
They're focused exactly on that one thing.
Yes. And as they scale and they raise VC and they make profit, um, their money goes into making their thing even better. And their managed service is leagues ahead of any GPU cloud, any normal cloud. It's way better than anything anyone has ever produced because they have focused directly on that. Um, meanwhile, Modal cannot sell like a big GPU cloud, uh, or GPU cluster to OpenAI. Like, I don't think that's going to happen because, um, the amount of expertise and experience you need, um, in order to cite and land a cluster that it's like signed off by OpenAI, um, is a lot.
Mm-hmm.
And I would suspect that you will see pretty much every cloud, um, like split into doing one of the two or the, um, Probably a lot of them will keep going on, you know, one side or the other, but the winners will be the ones who specialize in one area, is my guess.
Where do you think amongst that sort of, you know, 2 paths does SF Compute ultimately end up sitting in then?
Our goal is to sell to people who look like managed services. Part of what we're doing is trying to make the best cloud provider that would sell to managed services. It is the place in the market that's almost underappreciated Because if you're a big cloud, the big clouds have competing incentives right now. One is the public markets want them to have managed services because they want them to be a high-margin business. But then OpenAI comes along and says, here is a massive contract. We're going to make AGI. Would you like this money? And obviously you should do the OpenAI thing.
Right.
Um, and so they try to do both. Um, and we think the thing we're trying to do is split those, um, and make the greatest thing we can for people who want to do managed services. Um, and the things those folks want are the same things that we cared about in the beginning, which is short-term contracts, um, or contracts you can get out of. It's price. Um, it's specifically price. And SF Compute is not trying to be a thing that captures a massive margin on top of it. We're explicitly trying to make the prices go down, right?
And the price is so important because of that sort of difference between CPUs and GPUs that we spoke about earlier, right? Like, can you double-click on that?
Um, so in CPUs, um, almost by definition, like, your customers are like SaaS companies. It's Gusto and Rippling or something. And Gusto and Rippling does not need more CPUs to make more money. Gusto and Rippling can make great software, um, and then they can, uh, sell it like crazy, and their CPU bill doesn't really go up, not significantly. And they end up with top-of-line margins because they end up with SaaS margins. They end up with 70-plus, 70% margins.
You can build it once and sell it infinite times.
Correct.
That was the beauty of SaaS.
That was the idea. That doesn't exist in AI. It's just never going to exist because of the bitter lesson.
Here at Worldbuild, our model economy thesis, which I mentioned, really was based on the bitter lesson, right? the general models with scale, with more compute, with more data will beat the clever architecture. Or even for me, the, you know, sort of workflows and lightweight applications, you know, sort of built around those models and with those models. Can you walk through, you know, why sort of GPU buyers, people, you know, sort of people that even believe the bitter lesson, like those GPU buyers, like why are they different from CPU buyers? Like what is the, can you explain the economic reality?
In CPUs, the customers of the cloud are SaaS companies. And SaaS companies almost by definition have really fat margins.
Build something once, you could sell it infinite times.
Exactly. And then it gets better over time and so on. The problem of GPUs is that is not how AI works. In AI, you have the bitter lesson. And the way to conceptually think about this is if you're doing some sort of generation, like you're generating a video or something, Your customer's paying you for the video, and then you turn around and you use compute for that video, and you do that every time. And so as your volume scales, your margins stay roughly the same, and they don't look like SaaS margins. They look really thin. You as the customer need to be price sensitive. It is a requirement of your business.
Mm-hmm.
And that means that you push down the prices for your GPU cloud too. And so then your GPU cloud also has lower margins than the CPU clouds do, and you care less about fancy managed services, um, and you care more, um, about, um, like price, as long as reliability and security and, um, so on are there.
Is the why for you here ultimately then, like the why around SF Compute to lower the cost of compute?
Like, yeah, totally.
Price is everything.
Totally. San Francisco Compute is completely distinct in GPU clouds in that we are trying to give you the best possible price. Um, almost every GPU cloud in existence. every cloud, um, is positioning themselves to the public markets as we are the premium experience, we offer these great managed services, um, and therefore you should, um, reward us with a high markup for our GPUs. Um, they tell the public markets that. Um, meanwhile, customers don't agree. Um, customers instead say, if I'm buying a 3-year contract with you, um, I better get a discount. Um, and the GPU clouds behind the scenes do that, um, when they can.
Mm-hmm.
Um, we are explicitly trying to make the, the cheapest possible GPU cloud. Um, that doesn't mean we're sacrificing on reliability. That doesn't mean we're sacrificing on the product. Um, but if you wanted to design a business model that produced the best possible prices, that's, you would end up with SF Compute basically.
Lower prices, the more compute can proliferate.
The world gets better when you make prices lower. The world gets better when things are more affordable. Um, it's almost like a rule of reality. And at the moment, I think a lot of what SF Compute has been doing for a while is implicitly betting on people like lower prices.
