The Programmable Data Center — Evan Conrad, SF Compute

This was recorded on the sidelines of the new Compute Trading track at AIE NYC . Catch us next at AIE CODE , our last flagship for the top agentic engineers in the world. In the early days of cloud, former guest Martin Casado made something so disruptive that Cisco was threatened enough to pay $600m for it, and VMware outbid for $1.3B: @bhorowitz told @martin_casado : “The high offer is a signal that something in the environment has changed. A high offer from an acquirer means you should not take it!”\n\n“Don’t just sell to Cisco. Run a process.”\n\nMartin stopped ...","username":"wisercharlie","name":"Charlie Wiser","profile_image_url":"https://pbs.substack.com/profile_images/2107605878388178944/7eJbSzvJ_normal.jpg","date":"2026-09-20T15:42:00.000Z","photos":[{"img_url":"https://pbs.substack.com/media/HSpBt7ZbIAAlSNt.jpg","link_url":"https://t.co/VvCJSWrSD7"}],"quoted_tweet":{},"reply_count":4,"retweet_count":1,"like_count":29,"impression_count":37073,"expanded_url":null,"video_url":null,"video_preview_media_key":null,"belowTheFold":false}" data-component-name="Twitter2ToDOM"> That concept was software-defined networking , which virtualized the routing network by separating the control plane from the data plane. That revolution is now coming for GPUs - where previously running your own supercompute cluster required $50-100m upfront to get commitments and do the entire stack from hardware to firmware to kernels, SF Compute is now enabling you to not only buy or sell GPU hours now and in the future, but also to programmatically control everything from your network to memory. We last spoke with Evan about commoditizing compute . At AI Engineer New York, we went deeper: what does it take to physically deliver a GPU contract, and what has to be true before a market in compute can become liquid? From selling a room to running the hotel Evan describes SF Compute’s expansion with a hotel analogy. A customer wants a room, then asks for cleaning, room service, and security. Eventually you discover that delivering the room means operating the hotel. SF Compute began by trying to sublease a cluster; the company has since built much of the operating capacity of a cloud to deliver the compute traded on its market. That work is physical settlement: the buyer gets usable compute, rather than simply a cash payment against an index. It brings the physical constraints of a cluster into the market itself. Can a workload be interrupted? How quickly can it recover from storage? Which GPU generation, networking and software configuration does it need? Those details affect both the contract and the price. Making compute tradeable Rather than waiting for every GPU cluster to become interchangeable, SF Compute matches orders with different constraints. Evan argues that this makes trading possible while the technology changes, at the cost of spreading liquidity across more variants. We also discuss spot and forward pricing, batch versus live inference, and why an index can become disconnected from the cost of actually buying compute. Independence is central to Evan’s case for a market operator. He argues that companies with large hardware or inference businesses have incentives that make it harder to run a neutral exchange. We also get into the unglamorous engineering: visiting sites, designing clusters, running the backplane, and making infrastructure programmable. Near the end, he describes the ability to subdivide InfiniBand fabric on the fly as one example. Guest: Evan Conrad, CEO of SF Compute . Show notes Evan’s AI Engineer NYC talk: How Compute Markets Work Our previous SF Compute conversation Why oil futures went negative in 2020 — EIA How SF Compute customers resell unused GPU capacity SF Compute’s $245M compute sale Abel: SF Compute’s settlement ledger CFTC: Barclays and LIBOR manipulation Elastic InfiniBand partitions Timestamps 0:00 — Why can’t NVIDIA build this? 0:25 — Welcome back to Latent Space 2:23 — From selling a room to running the hotel 3:47 — Physical settlement: actually delivering compute 6:00 — Liquidity and the GPU order book 8:42 — Trading nonstandard compute 10:00 — Interrupting and moving workloads 12:20 — Why hardware cohorts matter 14:32 — How far out do compute contracts go? 16:19 — Batch versus live inference leases 17:58 — The shape of compute prices 20:33 — GPU generations, specifications and SKUs 23:30 — Physical versus cash settlement 27:00 — The emerging compute market ecosystem 29:03 — What it takes to deliver a GPU cluster 32:24 — Sovereign compute 33:26 — Why NVIDIA can’t build this market 34:48 — A programmable data center Transcript Automated transcript, lightly formatted; speaker labels have not been verified. 00:00 NVIDIA is obviously like the 800-pound gorilla in the room. Why didn’t they make SF Compute? So our impression is that NVIDIA can’t make it, but benefits tremendously from it existing. The reason why they can’t make it is because they would then not be independent enough for people to trade on it. 00:25 Anyway, Evan, welcome back to Latent Space. Thank you so Welcome back to AIE. Actually, first time at AIE. I think so, yeah. This is a very special one because I feel like you have been working on compute trading, compute markets for a while. Now it’s like feeling of the moment. Yeah. Jump basically announced that they are getting into compute markets, which is why I’m excited to sort of cluster people. You met Oliver from SemiAnalysis, who is basically initiating coverage on compute markets is my read on this. So people are going to watch your talk. 01:00 I think it’s a very good explanation of both your origin story and why you need to exist and all the hard problems you work on. Yeah. Done in a very simple, engaging manner. I’m very jealous of your writing skills because you showed the flyer that you showed at New Reps. Yeah. I was like, it’s pretty straightforward and simple and it’s good. Yeah. So anyway, congrats. Thank you. I appreciate that. And also congrats on AIE. It seems to, y’all are rolling. We’re rolling. I just came back from the site tour for next year’s AIE. then for You have like a hint? At Javits Center. It’s the same place they do Comic Con. Okay. Wow. 01:35 Wow. Wow. Damn. But I think the finance focus is really hitting for folks. Yeah. So we’re going to try to split out. Like this is, here we just lump everything in finance. Like your finance, their finance, like insurance is finance, but like they’re all super different. Yeah. So we’ll probably break out things more minutely in future because people want to go deep on hedge fund stuff or banking or personal wealth. Yeah. Especially in New York. Yeah. Anthropic was like, yeah, we’re all about RIAs right now. Yeah. RIAs like registered investment advisors. Yes. Like so different from you. 02:10 No, no, no. I’m aware of this. Yeah. Yeah. Okay. So for listeners who don’t know, we did a podcast two years ago where you talked about your, you know, where SF Compute was. Um, today you’re, you’re much more in a sort of physical settlement. You’re talking, you’re talking about your, your back fabric or what do call it? Uh, the backplane. Backplane. Yeah. Which obviously that takes a lot of hardcore engineering stuff. Yeah. Um, can you give a status update on like, where is SF Compute versus listeners who last heard you on a podcast two years ago? So, uh, when we started, we were this company called Junark and then we were just trying to sublease our cluster. 02:45 Um, and we started there in the technical stack. Um, and a way to think about it is it’s like you’re in a hotel and you’re trying to sell that hotel room. And then somebody asks you, Hey, like, are you going to clean my room? Um, and then you say, Oh, I guess I probably also need the hotel room as well. Or like I need the, um, the cleaning closet. And so you go get access to the cleaning closet and then they want room service. And so you get access to the kitchen and then, you know, they want, u