The Programmable Data Center — Evan Conrad, SF Compute

NVIDIA is obviously like the 800-pound gorilla in the room. Yeah. Um, why don't they-- 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. [upbeat music] Um, anyway, Evan, welcome back to Leadspace. Thank you so much. Welcome back to AIE.

Actually, first time at AIE. I think so, yeah. Um, 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. Uh, Jump, uh, basically announced that they are getting into compute markets, which is why I'm e-excited to sort of cluster people. You met Oliver from SemiAnalysis- Yeah...

who is basically initiating coverage on compute, compute markets- Yeah... is, is my read on this. Um, I, I-- So people are gonna watch your talk. Mm-hmm. Uh, I think it's very good, uh, explanation of, like, y- both your origin story and why you need to exist and what the hard problems you work on. Yeah. Uh, done in a very simple, engaging manner. Mm-hmm.

I'm very jealous of your writing skills. [chuckles] Thank you. 'Cause you showed the flyer for-- that you showed at NeurIPS. Yeah. And I was like, it's pretty straightforward and, like, simple and, like- Yeah... it's, it's good. [laughs] Yeah. So anyway, congrats. Thank you. I appreciate that. [laughs] Um, and also congrats on AIE. It, it seems to, um... y'all are rolling. We're rolling.

I just came back from the site tour for next year's- Okay... AIE, and then for- Is there a hint? Uh- At Javits Center. Oh. Uh, it's the same place they do Comic-Con. Okay. Wow. Uh, which [laughs]- Wow. Wow! [laughs] Damn. But, um, I think the finance focus is really hitting for folks. Yeah. Um, so we're going to try to split up. Like, this is-- Here, we just lump everything in finance. Like, your finance, their finance.

Like, insurance is finance. Yeah. But, like, they're all super different. [laughs] Yeah. So we'll probably break out things more f-minutely in, in future- Yeah... because, uh, people wanna go deep on hedge fund stuff or- Yeah... banking or personal wealth or- Yeah... um- Especially in, in New York. Yeah. Yeah, like Anthropic was like, "Yeah, we're, we're all about RIAs right now."

Yeah. Uh, RIA is, like, registered investment advisors. Yes. Like, so different from you. [laughs] Yep. No, no, no, I'm, uh, aware of this. Yeah, yeah. Yes. Um, okay. So for listeners who don't know, uh, we did a podcast two years ago- Mm-hmm... where you talked about your, your, uh, you know, where S- SF Compute was. Mm-hmm.

Um, today you're, you're much more in the sort of physical settlements. You're talk-- You talk about- Yeah... your, your back fabric, or what do you call it? Uh, the backplane. Um- Backplane. Yeah. Which, uh, obviously, like, like, 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. Um, and w-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 gonna clean my room?"

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, um, security, and so you get access to the security closet, and you get this, and you do that, and you do this.

And at a certain point, you're just running the whole hotel. Um, and the moment you're running the whole hotel, you say, "Oh, wait a second." Um, instead, my supplier on this market, um, is somebody that looks closer to a fund, um, or a cloud that has access, in our case, a cloud that has access to the data center, and we're gonna level up.

And so what we've spent the last several years doing is building the entire operating capacity of a cloud, um, in order to offer-- in order to actually deliver, uh, the compute at a reliable level. Um, we call that physical settlement. We're taking a term from- The futures market... from futures markets, from other markets, um, where in, you know, oil or soybeans or something, physical settlement means you get delivered the oil and the soybeans. Yes. Um, but, um, in some markets, um, like in oil and also in power, um, there's, like, a delivery network.

In oil, you have to have pipelines. If you would like the oil to be delivered to you, there has to be a way for it to get to you. Yes. Um, in finan-- And for people interested in financial history, during COVID, oil went negative- Yeah... because, uh, so, like, I will pay you to take oil from me- Mm-hmm... because it is sitting there in the, the port.

Uh, and, like, I can't pay for this. Yes. And so I will pay you to take my oil. Yes. [laughs] Uh- That, that is, like, the weirdness of physical settlement. Yes. There's a bunch of weirdnesses like this in other markets as well. So in the power markets and the grid, um, there's parts of the orders that you fill out, the buyers and sellers, that, um, are there because the boiler takes a while to cool off.

Um, and you can, you know, stop the boiler, and then it will-- you can start it back up again within some period of time, and so that has to get factored into the order book. Um, this genre thing is the same thing that happens in compute markets. So we co-designed physical settlement and the order book at the same time, and that meant that we get to actually handle the real constraints of compute, which might include something like, "Oh, I'm okay with being interrupted, but only if there's, like, storage on the thing that I can recover from really quickly."

Um, very similar types of problems. Yeah. So, uh, people can refer to your talk, and you've done a couple other podcasts already. Sure. Um, so I wanna be, like, the sort of 201 level instead of the 101 level. Sure. Uh, and also because I, I am, I have been a derivatives trader- Yes... so I can give people a bit more detail that you normally don't get into. Okay.

Um, so for example, well, how, how mature or deep or liquid is the compute market today? Uh, not very mature or deep or liquid. Okay. Um, so what we've been doing is first we had to, we had to go get a bunch of supply online in the first place, which just takes a while, and then we had to build the entire platform in order to do this.

Um, and we just now got that working, and so now we move, um, capacity over onto the order book, um, which makes it liquid. Um, and so 5% of our total capacity is on the order book. Okay. And we're consistently moving more and more, and then we're bringing on more capacity that's all on the order book.

Um, the nice part is that, um, uh, maybe I should have said the, the volume on the actual order book is small, but the, um, the liquidity on the order book is high. So for that small amount of capacity that you have, um, you have, um, like, very concentrated liquidity. So Um, [lip smack] a sort of standard metric, I would say, for someone who is using this is a customer who buys capacity from us, and they spend $100 to buy that capacity, will make back $25 on average from selling it back to somebody else.

Um, and that's the number that we... Or this is one of the metrics that we try to track because, um, w-we want the liquidity to be useful to the commercial participant, and you can measure liquidity in a bunch of different ways. You can say like, "Oh, I just want tons of makers or something." Um, and you can measure it in, like, high-minded ways. We try to measure it by, like, is this solving the problem, um, for the customer? Yeah. Got it.

Mm-hmm. And so, uh, last time we talked as well, there was a lot of contr- uh, conversation about standardizing the contracts. Yes. Um, talking with, like, uh, the C- CFTC or whatever. Yeah. Uh, what's the status on those things? Um, so I think standardizing the contracts in general is still pretty far away. Yeah. Um, and the-- What we were trying to do at the time was, um, [lip smack] or basically what we learned over the last several years was w-we just needed to operate the whole thing- [laughs]... otherwise you cannot standardize the thing.

And so to get closer to something that can be standardized, you first need to make all the operations of the thing the same. Right. To give an example of what can g- be different is a single operator can offer two different types of virtual machines. One type of virtual machine, um, might have a f- 40% performance, um, degradation versus another types of virtual machine, and 40% is a lot.

Um- When you say performance degradation, are you talking, like, MFU type things, or, like, amount of burn-in that you've experienced or what? It will affect... So in this particular case, I'm talking about GPU kernel launches and how long it takes for that to happen, um, which is a process that happens quite frequently when you're doing sort of either training or inference.

Okay. Um, the um, [lip smack] uh, that will basically-- The end result is that you get a worse MFU, but, um, like- Is the intermediate level- Yeah, it's the intermediate level, basically. Yeah, yeah. So, so what I'm trying to... Uh, so for anyone who's traded commodities before- Yeah... there's different grades of commodity. Yes. Right? There's like... It's not oil, it's light sweet crude- Yeah...

versus, like, whatever- Exactly... the opposite of light sweet crude is. Or it's like, you know, uh, corn level A or corn B or whatever. Yeah. Uh, and so, like, people are trying to... Well, well, you have to establish the equivalence of those things. Correct. Yes. And I'm wondering if they, they have been established.

So what we did instead, um, was we created an order book that had multiple different constraints, and you can say, "I wanna have, um, you know, something that's between this and that. Um, it runs from this time to this time. Um, it's like, uh, X to Y." It's a bunch of different properties that you can attach to your order, and then we match that against sell orders that have those properties- Mm-hmm... um, in a big matching engine.

Um, and the reason why we did it this way is it allows us to generate liquidity without having to standardize before you know what the technology can do. Um, and because the technology was shifting so quickly, um, we wanted to basically be able to do that. The trade-off of this is that you have less concentrated liquidity, and so the prices are, um, going to ha- There's gonna be more spreads between the, the buy and the sell. Yeah. Um- It, it sucks. Yeah.

But on the other hand, it means there's lots of arbitrage to make, um, if you can do that correctly. So if you have a product that can use lots of different types of capacity, um, then you might really like SF Compute because you will be able to use lots of different types of capacity and you'll make more money.

Um, you can take the, the spread. Okay. What dimensions matter in this grid of things? Um, [exhales] so a bunch of different things. Um, one is dimensions, categories that I will say is, like, is there a workload running on it right now or is it capacity in the future? So is it capacity- Sure... right now?

Um, [lip smack] capacity right now, in order for you to sell that, um, you have to interrupt your current workload, so you have to think, "Do I care if my current workload is interrupted or not?" Capacity that's sold that's starting a month from now, um, you can buy and sell that all the time, and so you get, um, liquidity on that really easily.

Um, there is some concern like, "Oh, no, will I have capacity next month?" But it's very different than if you're interrupting a workload right now. Um, the way that the interruption happens matters. So an example is if you're on a cluster and the cluster doesn't ha- have as amazing storage as you want it to have. Um, so an example is, like, do you have a WEC or a VAST system under the hood?

Um, and do you offer that? And, like, what's the size of that? Um, [lip smack] uh, this changes how you respond to, um, being preempted or removed off the cluster. Um, meanwhile, the location of that storage matters. So, um, if you're able to move that data from one cluster to another, um, quickly, then maybe you might be able to spread across multiple different clusters.

Um, and you can be interrupted over here and then pop back up over there. Um, to make that work, you also need, um, like a, a fast transit path between one cluster and another. Typically, this is implemented with a WAN, but most of these clusters don't necessarily have, um, like- Wi- Wide area network? Yeah, a wide area network. So, um, basically cables that connect the clusters together.

A private internet is another way to think about it. Um, a lot of clusters that are being spun up today are like, uh, they're not attached to, um... [lip smack] Traditional CPU clusters were, um, next to the customer for latency reasons. In GPUs, they're next to the power, um, wherever the, like, you know... You put the data center next to the power rather than next to the customer.

Um, this means that a lot of stuff is getting put in places that previously they weren't expecting data centers there. And so you may not have, um, as, um, [lip smack] excellent of a transit path, um, from one to the other. So this, this starts to matter. Um, other examples are, um, weird shit where we theoretically support but don't actually, um, uh... Like this is a thing that we're thinking about but haven't done basically.

Um, the early GB200s broke, the early H100s broke. Um, and so if you were buying H100s or you were buying GB200s, you might really want the ones that were deployed after a certain time because you're not getting the first shitty ones off the rack. And so if you're a super sophisticated customer, you may be willing to pay more, um, because for that- Oh, so it's, it's about, like, what cohort They were, they were produced- Yeah... and they get better over time?

So, like, uh, the first ones off the rack maybe had some fault or some problem. Right. And so if you were the ones who got the first ones, um, or, you know, first ones from, um, the, the first shipment of them, um, then yeah, they would, they would be broken or, um, in some way. I assume the sort of process is, like, look, there's defects everywhere. Like, it, it just happens, right? Totally. And so, like, you get a, you get a new shipment off of, from TSMC, whatever- Mm...

and you just kind of run your tests and, like, you, you figure out which ones, which ones are bad, and you throw them out. And isn't that how it works? I, I, I didn't know about this element of cohorting where, like, let's say Q4 2026, uh, B- B300s are- Mm... way worse than 2027 B300s. Yeah. So- [laughs]... um, I'll, I'll give an example. Um, you're describing burn-in.

Yes. Um, so when you get a cluster, um, a good cloud- Yeah... what it does is it tries to kill all the GPUs, um, before they ever get in the hands of the customer. Um, and then, um, the GPUs that are broken or the nodes that are broken, um, you get RMA'd. Yes. You basically go and request a new- You return it, yeah...

you return it, and you get a new one, and there's a lead time to that, but whatever. You'll get- Yeah... you'll get some back. Um, and then the customer never has to experience this. Um, the, um, thing I'm referring to is that, um, like, in early H100s there were, like, firmware bugs that may not even be related to the specific... Like, they may not be Nvidia.

They might be like Supermicro has a firmware bug in the, um, in the box that you bought, um, and it applies to all of them. [laughs] And it doesn't matter. Like, um, the solution to that was you need to get them to update their firmware, um, or they need to ship a new thing, um, in every new box. Yeah. Well, con- And so it happens...

cognition for, for, like, the, the B300s that, that, that we got, um- Yeah... and, like, you had to use Devin to go fix it. [laughs] Yes. Right. So, like, different types of problems like this can happen. Um- Interesting. Okay. So I'm, like... Okay, look, that seems small compared to the overall... Like, I just wanna, like... I want people to understand the, the shape of this market.

Um, you mentioned, like, preemption, you know- Mm-hmm... and, and, you know, we talked about spot instances and how, how that, uh, uh, the analogy is. Yeah. And then there's also, you know, booking w- a month out. Mm. How far out do those, do these markets go? And is, is real time or sort of preemption more interesting to you versus, uh, far, be- being further, further out? Um, to answer your first question- Yeah...

um, three to five years, um- Wow... we're starting to see- Like, in, like, month by month or quarter by quarter, or how is this? Are you asking how the cash flow works? Like, the, the, the contracts, like, the fu- the futures contracts, right? Like, a- again, like, I'm a former con- contracts, uh, derivatives trader. In the, in the market itself- Yeah...

um, like as in the broader market- Yeah... um, clusters are financed three to five years. Okay. Um, so the, the, the payments are- Totally get it... over that period of time. Yeah. Um, there is also, like, blocks of capacity in, in our order book, um, which can extend any arbitrary time. Yeah. Um, there's some stuff that we have to do, um, for, um, the cash flow on that.

Right now what we do is we just ask people to pay entirely upfront for a block. Um, we don't ask them to do that for a lease for a longer term contract. This gets into something that I definitely need a whiteboard to explain. [laughs] Um, but, um, you had another question I was about to answer, and I forgot what it was. Uh, do you want real time or do you want- Yeah...

like, do you want more, uh... What's more interesting to you, real time or long- longer out? So we care more about longer out because we're trying to build a forwards curve that has a lot of liquidity on it. Um, the, um, these are kind of the same thing and kind of not. Um, so what we do is we can say you can buy an hour out.

You can buy right now to the next hour, or you can buy right now to the next three years, and anywhere in between. That's wild. I know. Um, and so the, um, you know, or five years, technically. Um, the... There's a certain difference that happens that's dependent for each customer. Basically, like, if I have a workload that is, um, uh, that's batch inference, basically, where I can be interrupted at any point, I don't really care, um, then my ability to be cut off, um, is probably, like, I need an hour of time to know, um, that I'm gonna be cut off, and then I'm good. Or I can even n- be n- notified in, you know, a minute or two.

Um, but if I have, um, live inference customers who are expecting me to be online, um, then you can interrupt me, but in a month, maybe two months- Mm... maybe three months. Yeah. Um, and then I can adapt to that, um, capacity over that period of time. Um, but you cannot interrupt me in a minute. Um, and so those two things are priced differently, and the reason why they're priced differently is because what is the price you would pay, um, in order to interrupt your batch job, it's very different than the price you would pay to interrupt your, um, live job.

If you have a live job, you're, you are probably paying an SLA when you break for your customer. Um, does that make sense? Yeah, I see. So it's, this is actually the market for breaking leases. These are the market for breaking leases, basically. Oh, my God. And then there's also the market for future leases or, like, for leases that are, um- Oh, I thought this was, like, a subletting situation.

It's, this is like- Oh, the-... a lease break... so one way to think about it, um, is in power markets there's actually two markets. Um, there's, like, the day ahead market and there's the live market. Compute kind of has the same thing, um, where it has capacity that is coming online, um, or capacity that's about to, um, arrive to you, um, and then capacity that is currently in use, and these have different price dynamics.

Okay. Interesting. Um, backwardation or contango? [laughs] Backwardation or contango? Do you... Uh, so do, uh, is compute further out priced higher than spot or lower than spot? Um, is compute f- further out priced- Does it, does it go like this or does it go like that over, uh, you know, over, like, one year, one year out, two year out? Do- Oh, this is an ex- both at times...

I, I, I find it fascinating that you don't know. Yeah. I mean, both. It, it depends on who you are. Um- Okay... so, uh, I'll make the case Um, wait, so you're asking is compute like right now? So the compute, like, the same unit of compute today- Yes... uh, maybe not today, maybe a month out versus one year out versus two year out- Mm... is that, is that curve going up or going down? We see a, like, a hump.

Oh, my God. Um, okay. So- Explain. Um, uh, like I think this will make intuitive sense. It's basically, like, if I have milk and it is about to expire, I will lower the price on it. Yes. Um, and so that's basically what we see. So it's, it's the Nvidia release cycle, basically, right? Uh, well, we're more saying, like, if there's a block of compute and it's approaching now, um, it can't be sold in the past, and so it has to drop its price in order to- Yeah... clear at some price. Yeah.

And so it- initially you see this, like, drop where, um, stuff that's sold right now, anything that's on the market right now, um, is something that hasn't been sold, and so therefore the price will drop. Mm-hmm. Um, and then it goes up, um, to where it wants to be. Mm-hmm. Um, and then maybe compute in the future is worth less, um, because people don't want...

Like, people would like the compute now, and so they're sort of paying for the shorter lead time. Mm. Um, and they pay less for the compute that's off into the future. This is what I think a normal, healthy curve looks like. Yeah. The place where I'm like, "Oh, man, no shit," um, is that, um, right now if you took a B300 and you tried to run inference on it, um, you can make $40 an hour or so, um, selling that as inference. Any model. Yeah.

Any model, basically. Um- [laughs] And so that's if you use it at 100%, and maybe if you only have, like, 50% utilization or something, so like $25 an hour. Yeah. So $25 an hour, meanwhile, you can buy B300s at, like, $5 to $6 an hour. O- one of these numbers has to change [laughs]. Yeah. Um- It's a huge arbitrage. It's a huge arbitrage.

And you, you mentioned this in your, your talks a lot. Yeah. So, um, a bunch of people are basically looking at this and thinking, um, "If I think prices are going to go up, I might be willing to pay more than what I previously thought in the future, um, because I might think, 'Okay, I can buy a five right now, but if I can't buy any more, then maybe I will be willing to buy even more in the future just to lock it in if it's gonna come online later.'" So we see the other side as well. Um, kind of depends on who you are. Cool. Uh, so, um, is it... Okay, and, and just, I just really wanna make super clear on this. Of course.

Do you care about the different generations of chips and... or do you just care about overall flops and you spread it among whatever chips you want? W- uh, we care about different generations of chips because- Yeah, you care. Yeah, yeah. I think, like, I o- only, I guess, do your customers care? Yes. Right? Okay. Oh, wait.

Uh, when you say, uh, different generation chips, do you mean, like- So, like, B300s versus-... B300 versus B200s? Yeah, yeah, exactly. Yeah. Um- Or, or, like, even AMD or whatever, right? Uh, yes, I think people totally care about the generation of chips. Um, uh, I think part of this is because of things like vRAM, um, and part of this is because of, like, um, uh, larger ND link domains- Yeah...

and things like that. So there's just no way to abstract all this to, like, some generalized unit of compute that you can sort of mush around or whatever you like. I think, like, when you were saying oil, even oil isn't oil. Yeah. Oil is actually just, like, a bunch of different types of oil. Yeah, like jet fuel or car fuel, whatever. I think it's the same thing.

Um- You're gonna get a bunch of different types. It is, but you have oil refineries- Yeah... that you can s- yeah, effectively are black boxes. You can put oil in and get it, like, whatever out. Uh-huh. Um, i- if compute it is so, uh, heterogeneous, I, I guess is the- Uh-huh... is the word, is that you just have to expose the underlying complexity to your end customer, which is bad for you- Yes... as a market maker.

[laughs] Maybe. Like, you want to identify the one, like, choke point or, like, the, I, I don't know what the better word for choke point is. Like a sh- shelling point- Yeah... where everyone agrees, like, okay, like this is kind of the standard, and we'll- Yeah... we'll all agree to this standard and, uh, you know, it'll be somewhat inefficient. Yes.

Uh, so we've been able to reduce the, um, the, the problems to a certain level, but they still exist. Yeah. Y- y- like your job is, like, dimensionality reduction almost. Yes. W- we reduce a lot of the dimensions, but nonetheless, there are still dimensions. Yeah. Like, it's still gonna exist. Um- Do, do you know how many SKUs you have? [laughs] So, um- Like five times a hundred times, like, five thousand. That equals... I don't know. Like, what's... [laughs] W- we actually made SKUs, like, a, a concept, um, that...

And the goal is to make it so that somebody else can make their own SKU. So if I wanna buy Blackwells, I can say, "I'm willing to buy Blackwells at $6 an hour or less." Uh-huh. "And you can match me against GB300s, or you can match me against B300s, and I don't care which Blackwell, as long as it's less than $6 an hour." Um- But you just said you care about memory and other stuff. Totally.

And CPUs and- But not every customer does. So some customers, like, um, just can work on Blackwells, whatever it is. Some customers are super hyper-specific, and they need this exact thing, and they're gonna pay more for that because the market's gonna charge them more. Okay. Um, and that's what we do.

Um- So, and so people use your APIs to programmatically define what they want, and then y- you match that with whatever composition of sellers, uh, they might, they might, they might be a- able to fulfill. Yes. Um, uh, with the caveat that having a named query is the new thing we're doing. Um, but the query system as a concept is how the order book works right now. Okay. Um. Interesting. Um, all right.

So, uh, y- you know, I, I think that's, like, a, a lot of, like, I guess, nitty-gritty. Have you, have you... Are there anything about, like, I guess, that you wanna educate people on, whether it's financial people or engineers, about the state of the compute trading market as, as you've- Mm... developed it?

Because e- effectively, you, you guys are defining it. Um, mainly just the requirement for physical settlement. Yeah. Um, we think that if you... We s- we spent three years learning why you need physical settlement. [laughs] Um, and, um, our entire conversation today is, um, that it is, these things are not as fundable as you think. We're gonna try and make them fundable. That's the industry's job to do that.

Um, but without that, you run a lot of risks. Um, if you try to build, um, too much financialization before you've, the industry has had time to build out the, um, the physical settlement, then I think what you have is you have, um- A market that's very easy to manipulate, and a market that has a lot of basis risk, where, um, the price you may be hedging against actually has nothing to do with the price that, um, the market, um- Yeah. Yeah, yeah...

you can actually buy the capacity for. Yeah, so for, for listeners, uh, we, we talked about this briefly last time, but, um, uh, f- people are sh- often shocked to find out that, let's say the market for credit default swaps is 12 times the m- the, the size of the bonds that they insure. Mm-hmm. Uh, something like that. Or, like, uh, the, the markets for mortgage, um, sort of reinsurance or, uh, all that is, is, is larger than the underlying physical settlement.

So th- that's why physical versus cash settled. Yeah. And, and, like, basically cash settlements are, like, exactly the kind of, uh, the side bet that you see in The Big Short. Mm-hmm. Um, where, uh, they, they may, uh, may or may not have anything to do with the underlying thing itself. Yeah. And it may, may be used as a proxy for overall other stuff. Yes. That...

And you don't want to- that to go out of control. Yeah. So to be clear, we think, um, cash settlement as a future concept is a great idea. Yeah. Um, we just think it should be derived from physical settlement. Yeah. Um, because as you said, it's a side bet. Um, and it would be kind of like if you... I mean, it'd be kind of like if you made a cash settle in the future before you made the physical settlement.

Um, it- Which I think people are kind of doing already. Uh, yeah, totally. Yeah. People are trying to make cash settlement before you make the physical settlement. And people do that for all sorts of things, including- Yeah... startup stock. Yes. Right? Like, you can buy and sell a sh- a made-up share of OpenAI, but it's- Yeah... nothing to do with the actual share. Yes.

[laughs] Um, and lots of those go very poorly. [laughs] Um, like, it would be kind of bad if you made a, um... It would be bad if you started selling, um, the s- the derivative on a stock that doesn't exist yet. Um... [laughs] Yeah. Yeah. Well, so I mean, one thing that we do... Uh, so I used to be a currency trader, right? Yeah. And, and interest rates as well.

And one, one thing we do is you have a, a board that gets together and says, like, "This is the London Interbank Offered Rate, LIBOR." Yes. "And we all agree, like, this is the, the, the monthly fixing, and we all trade to that fixing." Yes. Uh, i- is that even meaningful at some point in three years from now? [laughs] So critically, LIBOR was manipulated.

Yes. Um, our concern at the moment is- 'Cause it's 12 banks. Yeah. Yes. Um, well, our, our concern is there's a very similar problem, um, where you can manipulate an index. Um, our impression is why you can manipulate that index is because a lot of the indexes are based on a very small amount of data that has, um, uh, not, not a lot of correlation with the actual price of buying a new reserve contract.

Um, that's what we're concerned about. [laughs] Okay. Interesting. Um, all right. I, I think, I think that's the, the, the rough state of things. Uh-huh. Uh, um, I think the, the other part of just, just today is, uh, you, you, you, you talked with Oliver, you, you and, uh, you're doing an event with Jump, and your, your friends with Silicon Data.

Uh, what are you learning from all these other players? Like, who, who are the major players that you would wanna call out as, like, doing interesting things? Ooh. Um, uh, so we're big fans of Silicon Data and Compute Change. Um, I think they were also very early to this. Um, and I think they are doing, like, a reasonable shot at the, a path that we think, um, that we'll likely end up being partners with. Um- And can you dist- distinguish, like, your, your different paths?

So, um, the general gist is they're doing cash settlement, we're doing physical settlement. Um, we think both can exist and should exist. Mm-hmm. Um, and that they're sort of mutually beneficial to each other. Um, and so totally fine. Um, so I think they are one of the earliest in the space, and I think they are, um, very clearly took the bet, um, well before anybody else did, and I think that's, like, very honorable to do.

Um... Um, you mentioned, uh, Jump or- Semi Analysis, Jump. Like, these are the players emerging. Like, as far- Yeah... like, you were my first introduction to this. Yeah. And then, and I see all these other guys coming in, and I'm like, "Okay, like it's, something's happening here." I don't know the landscape as clearly as you do. Yeah.

So, um, the categories of folks that we've seen is there's folks that are trying to set better standards. So Semi Analysis, I think, is working to try and create better standards. Um, there's folks that are trying to do, um, residual value insurance, and so on. Um, there's a, a number of different companies in the space.

I don't know any of them super well, but, um, I know, know this as a concept, and I think that's important. Um, there are folks that are trying to do, um, a deposit, um, uh, lending where, like, they'll front your credit risk for the cloud. There's an ecosystem of companies that are forming around how do we resolve the financial bottlenecks of compute. Um, and it is cool to see. I think e- effectively what you want in this ecosystem is buyers and sellers.

You want an analyst, you want, uh... We said insurance or, uh, what, what's the- Uh, residual value insurance. Res- residual, yeah. Yeah. Um, and you, we want this, like, healthy ecosystem out there. Mm-hmm. Um, but obviously you guys are owning the problem of physical settlement, which, which is really interesting.

Um, okay, so I mean, we can go into, like, the, the hard tech side because obviously you are also hiring- Yeah... uh, for extremely cracked engineers. Yeah. Uh, describe your stack. Um, we do all the things a cloud does- [laughs]... without being the cloud. Um, as in we don't own the hardware. Um, the reason why we don't own the hardware is because if we did, we would be able to manipulate the forwards market.

So we have to be Switzerland, we have to be independent, um, which creates a lot of sort of strange things about the company. Um, but in order to do physical settlement, you have to do site visits. You have to visit the data center. Um, you have to, um, you have to design the bill of materials and ensure that, um...

You, you have to basically do, like, supply chain consulting, um, where you have relationships with different OEMs that, um, ensure that things get delivered on time. Um, then you have to, um, do cluster architecture. Um, uh, so you're sort of designing the cluster itself. Um, then you're, um, rolling out racks, or you're working with partners to roll out racks and supervising that.

Um, then you're doing, uh, backplane management, so you're taking over the, um, uh, PDUs and the switches and the DPUs and, um, UFM and BMC, and you're operating, um, the whole, um, harness around the nodes that you're selling. When you say you, as in SF Compute employees? Yeah. Wow. All right. Um, so, uh- When you buy a node from a cloud, um, there's the bare metal. Yeah. But there's all this shit around the bare metal. Yeah, yeah.

Um, and in order to make that bare metal node work, you have to operate everything else. Then we- Mm. Because I just like, traditionally the job of the data center is to extract some, some of this away from you. Yeah. We're, we're the ones that do that. You're, you're [laughs] Okay. Yeah, right. [laughs] Um, but you're not, you're not in the data center business. You're not the, sort of Equinixes of the world.

We're, we're not in the data center business, sort of. Yeah. Um, what we do work with is... So we offer supercomputing as a service. Um, and what this means is folks come to us and say, "You know, I've got X," where X is I've got, you know, X megawatts here, and maybe sometimes they mean I've got a data center, um, that's, like, I would like to turn into a GPU cluster.

Like, my options are I could sell the GPU clust- or I could sell the data center to somebody, or I could become the cloud. And then we go to them and say, "Great, um, we will turn you into the cloud. We're the supercomputing part. You're the data center cl- Let's partner together, and then we will sell, and all make money." Um, sometimes it's people who have something that could turn into a data center, in which case we're gonna basically project manage that up to the layer.

Um, and then we will work with those far- We'll work with those folks to turn their powered land, um, or whatever they have, um, into a data center by working with partners. Um, and then we take over at- Okay... the supercomputing part. Okay. This requires us to have some sort of data center expertise in-house, but we don't self-perform builds. Um, so yeah. Yeah.

So it's, uh, somewhat modular because you- you have different scenarios you need to deploy under. Yeah. Yeah. Uh, someone once described us as like, um, uh, like Palantir for compute, um- [laughs]... because it's like these, each time it's this new rodeo. Um- Yeah... but-- And we like, we, we deeply partner with folks on both sides. Yeah. Uh, in primarily the US? Primarily the US, but we, uh, operate around the world. Yeah.

Uh, at some point, does SF Compute become a misnomer [laughs]? Uh, so when- [laughs] So we're deploying clusters all around the world. Yeah. And when we do that, we're working with people who themselves are trying to be clouds all around the world. Um, they look like sovereign compute players. Um, and our pitch to sovereign compute is amazing.

Uh, let's help you set up a cloud in your own country. Um, and we will bring the San Francisco offtake. We'll bring the San Francisco expertise. Ah. We'll set up the clusters. Um, that's our whole business. Ah. Um, we're gonna make it financially- Who are, who are your main, uh, your biggest announced partners that you can name? Um, nobody I can name- Really?... basically. Wow. Okay.

We will have some announcements, um, in a week or two, um, that we expect to come on. Yeah. All, all things sort of, uh- Sure, sure. Good luck. Yeah. Uh- No, I mean, uh, I, I think that's really interesting for every country to like, who's, who's like investing in sovereign AI to- Yeah... uh, figure this out.

Um, and it- it's interesting that like SF is, is still sort of like that brand name and, uh- Mm-hmm... that, that is, that is very marketable. Uh, so, um, I think one of the interesting things is just thinking through this market. Mm-hmm. Um, NVIDIA is obviously like the 800-pound gorilla in the room. Yeah. Um, why don't they-- Why didn't they make SF Compute?

And again, like I, I, you know, the answer's gonna be market manipulation, but like let's just really get into the incentives of the multiple players. Uh, y- yeah. 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.

If you don't trade on it, um, then you don't have enough liquidity for lenders to finance against the forwards market. Yeah. This is also why Crusoe and CoreWeave and Lambda, why the hyperscalers can't make it, why Base10 and Fireworks and Modal can't make it, 'cause they have too much demand on that side.

Um, this is a thing that benefits the entire industry because it makes the financing costs go down. But nobody can make it unless they give up the ability to do all the big inference money and all the big cloud deals. Um, you have to kill your business in order to make this. Mm. Yeah. That's why nobody does it. It's like structurally they're not set up to do that. Yeah.

Uh, and you're, you're make, you're basically reducing friction, and that's all your revenue. Mm-hmm. Um, you've announced some benchmark- uh, some milestones. Actually, I forgot to s- say that up front. Uh, can you repeat what they are and, uh, you know, just take a victory lap a little bit? Uh- [laughs]... close to several hundred million dollars of volume. Um, we have a significant number of new clusters on the way.

Yeah. Um, and then the things that I think I'm most excited about, um, is we've, um, shipped a whole bunch of new features, um, that make SF Compute very viable for platform as a service companies. Um, an example is, um, we allow customers to subdivide their InfiniBand fabric, um, without having to ask us. So many clouds, when you go to buy them, they will give you one InfiniBand fabric.

Um, and then if you want to re- redraw the lines of that InfiniBand fabric, uh, you need to contact them, and they will then do something for you. Um, we now can do that on the fly for customers, um, which I think is really great, um, because it interfaces with the market really well, um, where you want to be able to redraw things in order to scale up and down your clusters.

Um, we don't- Would you describe this as like you're virtualizing the data center and making it programmable? Yeah. We're virtualizing the data center and making it programmable. Yeah. So all the stuff that you would have gotten if you had owned the hardware, we're trying to virtualize that. Yeah. Um, which I think is, um, exciting to us. It's funny, like the last time this happened was like the cloud.

Yeah. Like the [laughs] Yeah. Uh, which is, which is really cool. Uh- And then f- uh, uh, finally, uh, you know, I think, uh, what is your call to action for, for people, um, you know? Is it, is it engineers? Is it like, "Give me your supply"? Like what, what is it? Um, if you would like to own a GPU cluster and you want the residual value from it, you wanna have it for your portfolio, um, we would love to work with you in order to build that GPU cluster.

We will take over, and we will, uh, do the supercomputing portions of that and ensure that things go right. We own the SLA. And then if it turns out that that was a bad idea, that you need out of that cluster, we can immediately sell that cluster to somebody else so it doesn't hit your balance sheet. Very simple pitch. Mm-hmm.

Well, thank you so much for your time. You're, you're doing a great service to the industry, and I just am happy to be your friend. [laughs] I appreciate it. All right. Thanks. That's it. [upbeat music]

The Programmable Data Center — Evan Conrad, SF Compute

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