The Programmable Data Center — Evan Conrad, SF Compute

0:00

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.

0:26

Anyway, Evan, welcome back to Lanespace. Thank you so much. 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.

0:44

basically announced that they are getting into compute markets, which is why I'm excited to sort of cluster people. You met Oliver from Semi Analysis, who is basically initiating coverage on compute markets, is my read on this.

0:58

So people are gonna watch your talk, 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, done in a very simple, engaging manner. I'm very jealous of your writing skills, because you showed the flyer that you showed at NeurIPS. I was like, it's pretty straightforward and simple, and it's good.

1:19

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. And then for... A hint? At Javits Center. It's the same place they do Comic-Con. Okay. Wow. Which... Wow. Wow. Damn. But I think the finance focus is really hitting for folks.

1:41

Yeah. 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. 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.

2:00

Especially in New York. Yeah, like Anthropic was like, yeah, we're all about RIAs right now. RIAs, like Registered Investment Advisors. Yes. So different from you. 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 where S Compute was.

2:23

Today, you're much more in the sort of physical settlements. You talk about your back fabric, or what do you call it? The backplane. Backplane, which obviously takes a lot of hardcore engineering stuff. Can you give a status update on where is SF Compute versus listeners who last heard you on a podcast two years ago? So when we started, we were this company called Junark, and then we were just trying to sublease our cluster.

2:47

And we started there in the technical stack. 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? And then you say, oh, I guess I probably also need the hotel room as well. Or like I need the... the cleaning closet. And so you go get access to the cleaning closet.

3:07

And then they want room service. And so you get access to the kitchen. And then they want 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. And the moment you're running the whole hotel, you say, oh. Wait a second. Instead, my supplier on this market is somebody that looks closer to a fund or a cloud that has access– in our case, a cloud that has access to the data center, and we're going to level up.

3:36

And so what we've spent the last several years doing is building the entire operating capacity of a cloud in order to offer– in order to actually deliver the compute at a reliable level. We call that physical settlement. We're taking a term from... The futures market. From futures markets, from other markets, where in oil or soybeans or something, physical settlement means you get delivered the oil and the soybeans.

4:02

Yes. But... In some markets, like in oil and also in power, 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. And for people interested in financial history, during COVID, oil went negative. So I will pay you to take oil from me because it is sitting there in the port.

4:31

And I can't pay for this. And so I will pay you to take my oil. Yes. 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, there's parts of the orders that you fill out, the buyers and sellers that are there because the boiler takes a while to cool off.

4:56

And you can stop the boiler, and then you can start it back up again within some period of time. And so that has to get factored into the order book. 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 OK with being interrupted, but only if there's storage on the thing that I can recover from really quickly.

5:25

Very similar types of problems. Yeah. So people can refer to your talk. You've done a couple other podcasts already. So I want to be like the sort of 201 level instead of the 101 level. And also because I have been a derivatives trader, so I can give people a bit more detail that you normally don't get into. So for example, how mature or deep or liquid is the computer market today? Not very mature or deep or liquid.

5:49

So what we've been doing is first 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. And we just now got that working. And so now we move capacity over onto the order book, which makes it liquid. And so 5% of our total capacity is on the order book.

6:10

and we're consistently moving more and more, and then we're bringing on more capacity that's all on the order book. The nice part is that,

6:18

maybe I should have said, the volume on the actual order book is small, but the liquidity on the order book is high. So for that small amount of capacity that you have, you have very concentrated liquidity.

6:31

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. And that's the number that we or this is one of the metrics that we try to track because we want the liquidity to be useful to the commercial participant.

6:52

And you can measure liquidity in a bunch of different ways. You can say like, oh, I just want tons of makers or something. And you can measure it in like high-minded ways. We try to measure it by like, is this solving the problem for the customer? Yeah, got it. And so last time we talked as well, there was a lot of conversation about standardizing the contracts.

7:11

Yes. Talking with like the CFTC or whatever. What's the status of those things? So I think standardizing the contracts in general is still pretty far away. And

7:23

what we were trying to do at the time was...

7:28

Or basically, what we learned over the last several years was we just needed to operate the whole thing. 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. To give an example of what can be different is a single operator can offer two different types of virtual machines.

7:48

One type of virtual machine might have a 40% performance degradation versus another types of virtual machine. And 40% is a lot. 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, which is a process that happens quite frequently when you're doing sort of either training or inference.

8:12

The... That will basically... The end result is that you get a worse MFU, but like... It's the intermediate level. Yeah, it's the intermediate level, basically. Yeah, so for anyone who's traded commodities before, there's different grades of commodity, right? It's not oil, it's light sweet crude versus whatever the opposite of light sweet crude is.

8:33

Or it's like corn level A or corn B or whatever. And so people are trying to, well, you have to establish the equivalence of those things. Correct, yes. And I'm wondering if they have been established. So what we did instead was we created an order book that had multiple different constraints. And you can say, I want to have something that's between this and that.

8:52

It runs from this time to this time. It's like X to Y. It's a bunch of different properties that you can attach to your order. And then we match that against cell orders that have those properties in a big matching engine. 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.

9:10

And because the technology was shifting so quickly, we wanted to basically be able to do that. The tradeoff of this is that you have less concentrated liquidity. And so the prices are going to have there's going to be more spreads between the buy and the sell. Yeah, it sucks. Yeah. But on the other hand, it means there's lots of arbitrage to make if you can do that correctly.

9:30

So if you have a product that can use lots of different types of capacity, 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. You can take the spread. OK. What dimensions matter in this grid of things? So a bunch of different things. 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 right now? Capacity right now, in order for you to sell that, you have to interrupt your current workload.

10:03

So you have to think, do I care if my current workload is interrupted or not? Capacity that's starting a month from now, you can buy and sell that all the time. And so you get liquidity on that really easily. 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.

10:20

The way that the interruption happens matters. So an example is if you're on a cluster and the cluster doesn't have as amazing storage as you want it to have. So an example is like, do you have a Weka or a Vast system under the hood? And do you offer that? And what's the size of that? This changes how you respond to being preempted or removed off the cluster.

10:41

Meanwhile, the location of that storage matters. So if you're able to move that data from one cluster to another quickly, then maybe you might be able to spread across multiple different clusters and you can be interrupted over here and then pop back up over there. To make that work, you also need like 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...

11:08

Wide area network? Yeah, a wide area network. So basically cables that connect the clusters together. A private internet is another way to think about it. A lot of clusters that are being spun up today are like... They're not attached to...

11:21

Traditional CPU clusters were next to the customer for latency reasons. In GPUs, they're next to the power.

11:33

You put the data center next to the power rather than next to the customer.

11:37

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 as excellent of a transit path from one to the other. So this starts to matter. Other examples are... Weird shit where we theoretically support but don't actually,

11:58

like, this is a thing that we're thinking about but haven't done, basically. The early GB200s broke. The early H100s broke. 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 GD1s off the rack. And so if you're a super sophisticated customer, you may be willing to pay more.

12:19

Oh, so it's about what cohort they were produced, and they get better over time? So the first ones off the rack maybe had some faults or some problem. And so if you were the ones who got the first ones from the first shipment of them, then, yeah, they would be broken in some way. I assume the sort of process is, like, look, there's defects everywhere.

12:45

Like, it just happens, right? Totally. And so, like, you get a new shipment from TSMC or whatever, and you just kind of run your tests, and, like, you figure out which ones are bad, and you throw them out. And isn't that how it works? I didn't know about this element of cohorting where, like, let's say Q4 2026 B300s are way worse than 2027 B300s.

13:08

Yeah, so I'll give an example. You're describing burn-in. So when you get a cluster, a good cloud, what it does is it tries to kill all the GPUs before they ever get in the hands of the customer. And then the GPUs that are broken or the nodes that are broken, you get RMA'd. You basically go and you return it and you get a new one. And there's a lead time to that.

13:30

But whatever, you'll get some back. And then the customer never has to experience this. The thing I'm referring to is that

13:42

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 box that you bought and it applies to all of them. And it doesn't matter. The solution to that was you need to get them to update their firmware, or they need to ship a new thing in every new box.

14:07

Yeah. has this for the B300s that we got. And you had to use to go fix it. Yes, correct. So different types of problems like this can happen. Interesting. Okay, so that seems small compared to the overall. I want people to understand the shape of this market. You mentioned preemption, and we talked about spot instances and how the analogy is.

14:31

And then there's also booking a month out. How far out do these markets go? And is real-time or preemption more interesting to you versus

14:43

being further out? To answer your first question, three to five years. Wow. Month by month or quarter by quarter? Are you asking how the cash flow works? The contracts, the futures contracts. Again, I'm a former derivatives trader. In the market itself, as in the broader market, clusters are financed three to five years. Okay.

15:06

The payments are for that period of time. There is also like blocks of capacity in our order book, which can extend any arbitrary time. There's some stuff that we have to do for the cash flow on that. Right now what we do is we just ask people to pay entirely upfront for a block. We don't ask them to do that for a lease for a longer term contract.

15:25

This gets into something that I definitely need a whiteboard to explain. But

15:31

you had another question I was about to answer. I forgot. Do you want real-time? What's more interesting to you, real-time or 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.

15:47

These are kind of the same thing and kind of not. 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. And so the– or five years, technically. The–

16:11

there's a certain difference that happens that's dependent for each customer. Basically, like, if I have a workload that is...

16:20

that's batch inference, basically, where I can be interrupted at any point, I don't really care, then...

16:28

My ability to be cut off is probably like I need an hour of time to know that I'm going to be cut off and then I'm good. Or I can even be notified in a minute or two. But if I have live inference customers who are expecting me to be online, then... You can interrupt me, but in a month, maybe two months, maybe three months. And then I can adapt to that capacity over that period of time.

16:56

But you cannot interrupt me in a minute. 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 in order to interrupt your batch job? It's very different than the price you would pay to interrupt your live job. If you have a live job, you are probably paying an SLA when you break for your customer.

17:19

Does that make sense? Yeah, I see. So 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... Oh, I thought this was like a subletting situation. This is like, oh, the lease break. So one way to think about it is in power markets, there's actually two markets.

17:39

There's like the day ahead market and there's the live market. Compute kind of has the same thing where it has capacity that is coming online or capacity that's about to arrive to you and then capacity that is currently in use. And these have different price dynamics. Okay, interesting. Backward nation or contango? Backward nation or contango? So is compute further out priced higher than spot or lower than spot? Is compute further out priced...

18:11

Does it go like this? Does it go like that over one year out, two year out? Oh, this is both at different times. I find it fascinating that you don't know. Yeah, I mean, both. It depends on who you are. Okay. So I'll make the case... Wait, so you're asking, is Compute sold right now? The same unit of Compute today, maybe not today, maybe a month out versus one year out versus two year out.

18:36

Is that curve going up or going down? We see like a hump. Oh, my God. Explain. 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. And so that's basically what we see. It's the NVIDIA release cycle basically, right? Well, we're more saying like if there's a block of compute and it's approaching now, it can't be sold in the past.

19:00

And so it has to drop its price in order to clear at some price. And so initially you see this like drop where stuff that's sold right now, anything that's on the market right now is something that hasn't been sold. And so therefore the price will drop. And then it goes up to where it wants to be. And then maybe compute in the future is worth less because people would like the compute now.

19:23

And so they're sort of paying for the shorter lead time. 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, yeah, no shit is that right now, if you took a B300 and you tried to run inference on it, you can make $40 an hour or so selling that as inference.

19:46

Any model. Any model, basically. 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. So $25 an hour. Meanwhile, you can buy B300s at like $5 to $6 an hour.

19:59

one of these numbers has to change. Yeah, it's a huge arbitrage. It's a huge arbitrage. You mention this in your talks a lot. Yeah, so a bunch of people are basically looking at this and thinking, if I think prices are going to go up, I might be willing to pay more than what I previously thought in the future 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 going to come online later.

20:24

So we see the other side as well. Yeah. kind of depends on who you are. Cool. And I just really want to make super clear on this. Of course. Do you care about the different generations of chips? Or do you just care about overall flops, and you spread it among whatever chips you want? We care about different generations of chips. You care? I guess, do your customers care? Yes.

20:46

Oh, wait, when you say different generation chips, do you mean like B300s, B300s? Yeah, exactly. Or like even AMD or whatever, right? Yes, I think people totally care about the generation of chips. I think part of this is because of things like VRAM, and part of this is because of

21:03

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

21:18

I think it's the same thing. You're going to get a bunch of different types. It is, but you have oil refineries.

21:24

Effectively, a black box is you can put oil in and get whatever out. If compute, it is so heterogeneous, I guess is the word. You just have to expose the underlying complexity to your end customer, which is bad for you as a market maker.

21:40

Like you want to identify the one like choke point or like, I don't know what the better word for choke point is, like a shelling point where everyone agrees like, okay, like this is kind of the standard and we'll all agree to this standard. And, you know, it'll be somewhat inefficient. Yes. So we've been able to reduce the problems to a certain level.

22:02

But your job is like dimensionality reduction. Yes. We reduce a lot of the dimensions, but nonetheless, there are still dimensions. It's still going to exist. Do you know how many SKUs you have? So...

22:15

Like five times 100 times 5,000 equals... I don't know. We actually made SKUs like a concept that... And the goal is to make it so that somebody else can make their own SKU. So if I want to buy Blackwell's, I can say I'm willing to buy Blackwell's at... $6 an hour or less. And you can match me against GB300s or you can match me against B300s.

22:39

And I don't care which Blackwell, as long as it's less than $6 an hour. But you just said you care about memory and other stuff. Totally. But not every customer does. So some customers just can work on Blackwells, whatever it is. Some customers are super hyper-specific and they need this exact thing. And they're going to pay more for that because the market's going to charge them more.

22:55

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

23:18

Interesting. All right. So, you know, I think that's like a lot of, I guess, nitty gritty. Are there anything about, I guess, that you want to educate people on, whether it's financial people or engineers, about the state of the compute trading market as you've developed it? Because effectively, you guys are defining it.

23:38

Mainly just the requirement for physical settlement. We spent three years learning why you need physical settlement. And our entire conversation today is that it is... These things are not as fungible as you think. We're going to try and make them fungible. That's the industry's job to do that. But without that, you run a lot of risks. If you try to build too much financialization before the industry has had time to build out the physical settlement, then I think what you have is you have...

24:15

A market that's very easy to manipulate and a market that has a lot of basis risk where the price you may be hedging against actually has nothing to do with the price that the market

24:26

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

24:48

So that's why physical versus cash settled. And like basically cash settlements are like exactly the kind of the side bet that you see in the big short where they may or may not have anything to do with the underlying thing itself. And it may be used as a proxy for overall other stuff. And you don't want that to go out of control. Yeah. So to be clear, we think cash settlement as a future concept is a great idea.

25:15

We just think it should be derived from physical settlement because, as you said, it's a side bet. And it would be kind of like if you made a cash settlement future before you made the physical settlement, which I think people are kind of doing already. Yeah, totally. People are trying to make cash settlement before you make the physical settlement.

25:33

And people do that for all sorts of things, including startup stock. You can buy and sell a made-up share of OpenAI, but it's nothing to do with the actual share. Yes. And lots of those go very poorly. It would be kind of bad if you started selling the

25:54

derivative on a stock that doesn't exist yet. Yeah. So, I mean, one thing that we do, so I used to be a currency trader, right? And interest rates as well. And one thing we do is you have a board that gets together and says, like, this is the London interbank offered rate, LIBOR. And we all agree, like, this is the monthly fixing. And we all trade to that fixing.

26:14

Yes. Is that even meaningful at some point in three years from now? So, critically, LIBOR was manipulated. Yes. Our concern at the moment is... Because there's 12 banks. Yes. Yes. Well, our concern is there's a very similar problem where you can manipulate an index. 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

26:39

not a lot of correlation with the actual price of buying a new reserve contract.

26:45

That's what we're concerned about. Okay. Interesting. All right. I think that's the rough state of things.

26:53

I think the other part of just today is you talked with Oliver and you're doing an event with Jump and you're friends with Silicon Data. What are you learning from all these other players? Who are the major players that you would want to call out as doing interesting things?

27:11

So we're big fans of Silicon Data and Compute Exchange. I think they were also very early to this, and I think they are doing a reasonable shot at a path that we think that we'll likely end up being partners with. Can you distinguish your different paths? Yes. So the generalist is they're doing cash settlement. We're doing physical settlement.

27:31

We think both can exist and should exist and that they're sort of mutually beneficial to each other. And so totally fine. So I think they are one of the earliest in the space and I think they are very clearly took the bet well before anybody else did. And I think that's like very honorable to do.

27:48

You mentioned jump? Semi-analysis, jump. These are the players emerging. You were my first introduction to this. And then I see all these other guys coming in, and I'm like, OK, something's happening here. I don't know the landscape as clearly as you do. Yeah, so the categories of folks that we've seen is there's folks that are trying to set better standards.

28:07

So Semi Analysis, I think, is working to try and create better standards. There's folks that are trying to do residual value insurance and so on. There's a number of different companies in the space. I don't know any of them super well, but I know this is a concept, and I think that's important. There are folks that are trying to do deposit

28:26

lending, where they'll front your credit risk for the cloud. There's an ecosystem of companies that are reforming around how do we resolve the financial bottlenecks of compute. And it is cool to see. I think effectively what you want in this ecosystem is buyers and sellers. You want an analyst. You want, we said insurance or what's the...

28:46

Residual value insurance. Yeah. And we want this healthy ecosystem out there. But obviously, you guys are owning the problem of physical settlement, which is really interesting. OK, so we can go into the hard tech side, because obviously, you are also hiring for Extremely Cracked Engineers. Describe your stack. We do all the things a cloud does without being the cloud.

29:12

As in, we don't own the hardware. 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, which creates a lot of sort of strange things about the company. But in order to do physical settlement, you have to do site visits. You have to visit the data center.

29:32

You have to... You have to design the bill of materials and ensure that

29:39

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

30:01

Then you're doing backplane management. So you're taking over the PDUs and the switches and the DPUs and UFM and BMC. And you're operating the whole harness around the nodes that you're selling. When you say you as an SF compute employees. Yeah. Wow. So when you buy a node from a cloud, there's the bare metal, but there's all this shit around the bare metal.

30:25

And in order to make that bare metal node work, you have to operate everything else. Then we... Because traditionally the job of the data center is to extract some of this away from you. Yeah. We're the ones that do that. But you're not in the data center business. You're not the sort of Equinix's of the world. We're not in the data center business, sort of.

30:49

What we do work with is... So we offer supercomputing as a service. 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 that's Like, I would like to turn into a GPU cluster. Like, my options are I could sell the GPU cluster, or I could sell the data center to somebody, or I could become the cloud.

31:16

And then we go to them and say, great, we will turn you into the cloud. We're the supercomputing part. You're the data center part. Let's partner together, and then we will sell, and I'll make money.

31:25

Sometimes it's people who have something that could turn into a data center, in which case we're going to basically project manage that up to the layer. And then we will work with those folks to turn their powered land or whatever they have into a data center by working with partners. And then we take over at the supercomputing part. This requires us to have some sort of data center expertise in-house, but we don't self-perform builds.

31:53

So, yeah. Yeah. So it's somewhat modular because you have different scenarios you need to deploy under. Yeah. Someone once described us as like Palantir for compute because it's like each time it's this new rodeo.

32:12

And we deeply partner with folks on both sides. Yeah. And primarily the U.S.? Primarily the US, but we operate around the world. Yeah. At some point, does SF Compute become a misnomer? So we're deploying clusters all around the world. And when we do that, we're working with people who themselves are trying to be clouds all around the world.

32:33

They look like sovereign compute players. And our pitch to sovereign compute is amazing. Let's help you set up a cloud in your own country. And we will bring the San Francisco offtake. We'll bring the San Francisco expertise. We'll set up the clusters. That's our whole business. Ah. We're going to make it. Who are your biggest announced partners that you can name?

32:55

nobody I can name, basically. We will have some announcements in a week or two that we expect to come on. All things sort of good luck. I think this is really interesting for every country who's investing in Sovereign AI to figure this out. It's interesting that SF is still that brand name that is very marketable. I think One of the interesting things is just thinking through this market.

33:26

NVIDIA is obviously like the 800-pound gorilla in the room. Why didn't they make SF Compute? And again, the answer is going to be market manipulation, but let's just really get into the incentives of the multiple players. 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.

33:50

If you don't trade on it, then you don't have enough liquidity for lenders to finance against the forwards market. This is also why Crusoe and Corweave and Lambda, why the hyperscalers can't make it, why Base 10 and Fireworks and Modal can't make it, because they have too much demand on that side. This is a thing that benefits the entire industry because it makes the financing costs go down.

34:11

But... Nobody can make it unless they give up the ability to do all the big inference money and all the big cloud deals. You have to kill your business in order to make this. That's why nobody does it. Structurally, they're not set up to do that. And you're basically reducing friction and that's all your revenue. You've announced some milestones.

34:33

Actually, I forgot to say that up front. Can you repeat what they are and just take a victory lap a little bit? We've closed several hundred million dollars of volume. We have a significant number of new clusters on the way. And then the things that I think I'm most excited about is we've... Shipped a whole bunch of new features that make SF Compute very viable for platform as a service companies.

35:03

An example is we allow customers to subdivide their InfiniBand fabric without having to ask us. So many clouds, when you go to buy them, they will give you one InfiniBand fabric. And then if you want to redraw the lines of that InfiniBand fabric, you need to contact them and they will then do something for you. We now can do that on the fly for customers, which I think is really great because it interfaces with the market really well, where you want to be able to redraw things in order to scale up and down your clusters.

35:36

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. So all the stuff that you would have gotten if you had owned the hardware, we're trying to virtualize that, which I think is

35:49

exciting to us. It's funny. The last time this happened was the cloud, which is really cool. And then finally, I think, what is your call to action for people? Is it engineers? Is it give me your supply? What is it? If you would like to own a GPU cluster and you want the residual value from it, you want to have it for your portfolio, we would love to work with you in order to build that GPU cluster.

36:14

We will take over and we will do the supercomputing portions of that. and ensure the 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. Well, thank you so much for your time.

36:30

You're doing a great service to the industry and I just am happy to be your friend. I appreciate it. All right.