Ep. 037 - Who's Funding the $11 Trillion AI Buildout? (Capital Markets)

0:01 for our first ever advertisement. We're going to be doing a lot more of this on future semi- analysis weekly episodes. Semi- analysis is a readerfunded publication. Over to Dan. What are we advertising this week? >> Well, well, we we noticed that 90% of our viewers aren't subscribed. So, if you could hit the like button and subscribe and be sure to hit the alerts. Seriously though, the real ad we're we're hiring for a new group which is called compute capital markets and we're going to look at the the loan demand how the financing markets are evolving for financing GPUs and we'll look at markets which talks about the tools that lenders and everyone in the market need to evaluate how GPU financing is going, how the GPU industry is going.

0:44 That's rental pricing, residual value, all those things that come with it. and so we're looking for credit analysts. We're going to hire four slots. We're looking for Singapore and New York City. we're looking for a junior credit analyst. about, you know, perhaps one to three years of experience in the credit space, investment banking in in Singapore or the US. We're also looking for a credit specialist. That could be someone who's been a little more experienced, probably four or five years credit trading, credit sales, or credit investing. you know, IG high yield would be great. and also a credit analyst.

1:21 Anyone who's been a desk analyst, buy side, sell side. you know, we're looking for people are credit minded. I think that if you enjoy this episode that's going to come and all these structures and, you know, all these Z spreads and T-spreads, I I think you're going to really like this job. So, you know, click click the apply button right here. >> All right, roll the credits. Hello everyone, welcome back to semi analysis weekly. In this episode, we got Dan. we are going to talk about everything to do with capital and markets. How we are funding the buildout right now. Dan has been digging in to everything credit capital markets compute. specifically we put out an article I mean called Nvidia back up universe heads I win tail where we talked about the 11 trillion AI buildout Nvidia's backstock economics and the limits of Nvidia's balance sheet. So, you know, with that as a backdrop, we're we're going to talk through the contents of that article.

2:26 But at the pace things are moving right now, that's like already a month old. And so, yeah, without further ado, Dan, why don't you lead us in talking a little bit about Nvidia's backstop universe, where you came to set up this, you know, article and why you dug into this expanded view of Nvidia's balance sheet. >> Yeah. Yeah. So let me show you a bit about Nvidia's backs stop universe and indeed it it was just a month ago but but things are are moving so fast at this point fund and announced is around about like a hundred billion each month and and this is how we looked at the balance sheet and the whole the whole premise is Nvidia's been giving a lot of back stops and the question is is why right there hasn't a lot of visibility on how much issuance is going to come or how much funding needs there is. so we defined that for the first time. and what we did is we looked at the funding needs and we talked about whether Nvidia's balance sheet would be strong enough to support backto stopping a lot of these funding needs. and we'll talk about that much more later, but I think it's worth going through quickly the constraints and and what what sort of sort of led us to this point. so if I go back to if we think about the constraints, right, Jordan? And you know you you asked me earlier about the the trinity right? You remember that?

3:52 >> Yeah. >> Yeah. We used to talk a lot about the trinity and the trinity the AI project trinity is basically capital offtake and data center. and and Jordan earlier he was kind of asking me like well what what about compute right? Isn't that the fourth leg? which which is a good point because it used to be a pretty big short a pretty big pinch point and it used to be kind of a worsening pinch point but what's happened since then right memory was approximately one of one of the the hugest pinch points right so so let's talk about constraints and the constraints have been moving they've been moving from compute constraints to data center constraints and now financing is a new constraint so so so why why you know why why are those more acute right so what's happened with memory memory has been been been a shortage you can see that AI's percent DM wafers was going to be 70% and then you can see that on the logic side accelerator is is an ever higher share but what happened recently is there's been despecking so let me show you what what what happened so if you look at accelerator perspective this used to be the pattern if you look at GPU ASP it had been going up because of memory and it was set to go up in Ruben Ultra but that that pattern actually stopped in its tracks and what we got instead was dspecing.

5:18 >> Why? for really two reasons, right? Number one, ROI and I'll just kind of flick to a quick a quick bridge chart, right? And so with this one, what we saw is the cost was increasing for the VRNBL72. and what they did to offset that is is decrease soam. But it's not just about price, it's about availability. The issue here is that there just simply hasn't been enough memory to go around to actually ship racks. so the dspec was necessary to to deconstrain that.

5:54 so that's that's the chip constraint. Let's move on to the data center constraint. That was that was one of the first data concern the first constraints that that people worried about. but we've found a lot of creative solutions around that. We've mustered a lot of capital to build data centers. We've activated more powered sites. We refurbished old sites. We've done a lot of behind the meter and to the point where by 2027 28 it's much more balanced. and this is no longer an obstacle. So what are the new constraints? So what what we talk a lot about now is funding. And that's a constraint people have learned about in just in just the last few months.

6:35 I'll show you how we calculated the overall constraint. Let's take a look. So this is the Camille of total capex. It's 11 trillion of capex that needs to be spent by 2029. >> Yeah. Dan, explain this chart just because a lot of people are throwing around the term of 1 trillion of hyperscaler capex. How do you get to 11.3 trillion on this chart? >> Yeah. So 11 11.3 trillion is commumulative capex from 2028 sorry from 2024 through 2029 and so it's escalating each year. The way we did this because we track all the GPUs, we track all shipments, we track total cost of ownership of the racks and the build materials and that's not just across GPUs also XPUs, it's TPUs, AWS, tranium and if you take the sum product of the shipments across the total cost which includes the server, includes networking, includes CPUs, includes everything you actually get to that 11.3 trillion. And by the way you also mentioned hypers scale capex so that's actually a subset of this if you look at the legend this is AI it capex and data center capex and a lot of that capex is not incurred by the hypers sales themselves it's incurred by independent data center companies so that's part of the equation of of why 1 trillion in a year of of hyperscaler capex becomes 11 trillion because it's hypers scale neocloud and then it's it data center >> yeah and this is not covering necessarily other things in the supply chain which we do cover which Dylan has talked about which is you know power plants, industrial equipment stuff in the fabs that is you know a precursor to AI it in this measurement here like you know equipment or just new >> there's a lot of stuff going into the buildout right now you said that there's a constraint related to funding talk me through how people are actually funding this buildout and you just how is where's where is everybody finding the capital?

8:38 >> Yeah. So, so it's a great question. and what we what we said is there's going to be 7 trillion of debt outstanding by 2029. and so what does that work out to? It works out to what we did here is I put the the net additions, the net new funding. So it works out to 1 trillion this year. It's a little flat because whenever we forecast stuff forward, we tend not to add capacity and and deliver it till it's confirmed, right? But and then there'll still be there'll be some debt that rolls off because all amortizing, but you know, I would expect that this estimate will probably creep up. So the green the the blue triangles are our estimates and if you add this up, that gets you your your seven trillion.

9:19 but but Jordan, like where are they getting the money from? Right? To answer that question, let's look at how much money is put to work in the space. So when the investment grade market, it's about 3 trillion of issuance. That's the the the the the red box here. and investment grade is just anyone with a credit rating of triple B triple B minus or higher. and this is typically going to be like hyperscalers. SpaceX is also investment grade. you know typically like great companies, Nvidia's investment grade and high yield would be companies are a little more risky. you know Cororee is technically high yield and you know various other companies have a lot of debt or a little bit more of a nent business model and that high yield issuance is denoted down here it's about 400 billion and leverage loans which are loans that are of a kind of riskier nature or often used for acquisition private equity is is also 400 billion by the way this this is IG bonds this is high yield bonds and then I know loans are kind of separate is not.

10:25 >> Yeah. So, everything's growing. Both high yield and investment grade is both growing. But take me through again where is the where where are some of the individual projects that are that are responsible for >> this much debt being raised? >> Which project? Yeah, absolutely. So, I can give you a few examples. so you know this like just since June since June alone right I mean so to put things in context right so we're saying one trillion this year and we think about probably 500 600 billion was was probably maybe around 500 billion was raised through June so we're pretty much on track but just since June since June 1 until today is October 7 in Asia at least there's been about 400 billion of capital like loans owns bonds of AI you know either raised or announced. So there's been about call it 260 billion worth of major deals that have been raised another 150 billion that are reported but not yet documented. You know some some large examples blue sky funding which is a 24 billion deal which comes with a 6 billion delayed draw term loan as well.

11:43 this is part of the Broadcom Google Anthropic TPU deal. >> Yeah. >> Soapia is another it's a Meta Black Rockck JV for El Paso facility was residual value guarantee for Meta. It's 12.5 billion. Hut 8 raised 4.25 billion of paper. >> Yeah. >> you know Core like all all the NeoClouds have been added. So the the the the ones you just mentioned are just hypers scale. So now we're on a neocloud. So we've raised a 4.2 billion convert. They raised 2.6 billion of delayed draw term loan in August.

12:19 NBIS just raised a 3.5 billion convert. Zancor in Asia did a three billion term loan, one of the first in Asia. Anthropic did a 15 billion and Pakio Revolver. I mean I I could I could go on and I haven't even gotten to the hyperscalar bots, right? yeah. Does that does that help Jordan? I should talk about hyperscar bonds because that's what kind of kicked off this whole firestorm, >> but I could go through I have pages and pages of this.

12:45 >> Yeah. Well, I mean that's a nice little teaser taste there. Maybe we I'd love to talk about some of the specifics both in terms of the actual projects but also in terms of what they are backstopping or what they are raising debt for, which is to say this is not like strictly for GPUs. I think a lot of people might get confused by this and think like, >> you know, this is strictly just people raising money to buy Nvidia GPUs, but there's so much more that goes into actually building a data center that can accept the GPUs and means that Nvidia as well as all the hyperscalers are motivated to backs stop people across the whole industry.

13:26 >> Yeah. Yeah. It's it's it's the data center too. And even though when when you know those who are used to looking at total cost of ownership you know let me show you one of the total cost of ownership charts here. So this is the full stack and and what you notice we always say that most of the total cost ownership is the capital cost versus the data center cost. So if you look here the operating cost per hour is 66 and the the capital cost is is $2 out of out of 280 right? So if I do 213 out of 280 that's around 76% opex. So you might you might think the data center is not a large part right capex is around for a GB300 is around 31 million per megawatt 31 33 million per megawatt and then for data center it's about maybe 10 million plus right so if it's if it's you know if it's 30 mill if it's like 30 billion 10 billion that sounds you know kind of kind of mismatched right And and and the thing is right the the kind of trick here is that the the capital side is a shorter term project right it's a 5-year depreciation period whereas your cap your data center is a 15 year right so on a on a cost of ownership it's like 75 it's it's 7525 but on a capex basis it is more like 6040 right so the data center is even more because we're building more upfront and this data center is going to last 15 years it's going to do three projects of the AIT capex. Does that help put in context like how much the need for data centers?

15:09 >> 100%. Yeah. I think a lot of people don't really understand the breakdown in terms of like the chips are expensive. They make up 3/4 of the capital cost of the project. you know if you're if you're depending how you model them, right? Which I think maybe is the critical question that's outstanding. namely the residual value of some of these GPUs that are coming off their 5year life cycle that people are expecting warranties expired. Do they keep running them for year 6, 7, 8? And how does that how do you view the cash flows for GPUs after the residual value period or the the the modeled lifespan is expired in terms of how it's enabling them to fund future projects?

15:56 >> Yeah. Yeah. So I think that's a question people ask a lot and and I'll go to the you know we've gone through this many times but you know this this one way to think about it right to directly answer your question many people that are doing these projects they actually underwrite it to make money on a 5 to six year period. So they're not actually banking on the residual value at all. so so and this gets into the the lay of the land now because most of the clusters are actually you know built on long-term optakes because that's the only way you can get funding right remember the trendy you need uptake capital and data centers. So, if you don't have a 5-year offtake, you're not getting financing. And that's one of the biggest constraints. And so, because of that constraint, because you don't get financing without a long-term contract, everything's typically like pre pre-ordained. So, typically on a 5-year deal, you're going to get an IR of about 20 to 30% at least. It's been higher in the past. and what that means is by the time your project is over in 5 years, you'll have made on your on your capital investment 25% yield per year without banking on residual value, right? So, so most most things are underwritten actually interestingly without regards to what happens after.

17:24 It's like a bonus. >> Yeah. So, can we back up a second and and talk a little bit about the broader industry? like we just keep saying data center and >> and we you know we kind of talk about GPUs but when it comes to actually analyzing what is being backed up by Nvidia for example >> you have this awesome chart that I've got on screen now Nvidia's offbalance sheet commitments where you summarize the $497 billion of offbalance sheet backs stop commitments that they've made at this And you go through seven categories here. AI cloud agreements where they are, you know, signing deals directly with NeoClouds, data center leases where they're signing deals with, you know, providers that are building data centers. guarantees and LPS guarantees again with more on the energy side, cloud services agreements which are not the same as necessarily a neocloud agreement in this measurement.

18:33 basically an accounting difference there. >> DS center releases for self use different than the previous >> supply and capacity commitments which are primarily around component suppliers like memory and then finally this residual value guarantee where we haven't assigned any value to it but you know it's a category of something that they're guaranteeing for people. So just give me a lay of the land of all of this like it's it's such a mess. There's so much going on.

19:06 >> Yeah. Yeah. So, yeah, I'm happy happy to do that. we can go through each of them. I've got some some nice diagrams I can tell you about, you know, why each of them and how we modeled it and you know what what the point is. you know, good thing this is going to be on YouTube because you're going to want to slow this down to 0.75x when I go through it for all you listeners. But but before I do that, let me talk about like the why, right? like like why why all this backs stop? Because the problem is a lot of people just kind of skip the why and and they go to the what, right?

19:36 and then they they they look at the what and they're like, "Okay, this is circular financing." now now let's talk about the why. why are we going to hit an obstacle? Why is it a constraint? other than rates going up, which is the market telling you that that it's going to be tough, right? And and the numbers I just showed you. but there's something more. like I said, Jordan, most of the optakes are five-year. Most of them are hyperscaler backs stop. And what that means is, let's say you've got a a a Microsoft Nscale OpenAI deal. What's happening is NScale is selling capacity to Microsoft, which is then selling it to OpenAI. So Microsoft is effectively backstopping that sale because they're guaranteeing that that capacity will be used right and some hypers scalers also do that directly like meta is directly offtaken from neopods for instance there are many deals like that >> and scale nscale needs a guarantee to go out and take out a construction loan to buy and to plug them in and turn them on >> and the guarantee is not good if it comes from open AAI because they don't have an ID rating And so instead of relying on OpenAI to pay the bill, they're going to say if OpenAI doesn't pay the bill, Microsoft's on the hook.

21:00 >> Exactly. >> So why does Microsoft sign up for this, Dan? >> Well, it's it's a good question. I think there's internal and external use, right? I think they also take a cut of it. it allows them to expand and access capital without directly using their balance sheet. because it'll show up and and you know as an upcoming work right a lot of it shows up as services of like contract which is under a certain accounting rule I forgot which one is it Jordan ASC 606 >> 606 not 842 yeah >> okay 606 is the yeah I was going about to be ASC 404 if I didn't remember that correctly there's >> yeah there's a number of different designations the accounting standards committee or commission or something anyway the it's it's it's very material to these deals because the definition of a managed service is different than the definition of an operating lease in that a managed service does not end up on your balance sheet. An operating lease does. And so >> the the >> yeah the technical treatment of who's responsible for what in the data center is different than the accounting treatment. And you know who can log into what or enter what you know room in the site or touch what or you know this is less relevant than how the accountants want to track or keep track of where these assets sit and how they depreciate. Right.

22:33 >> Yeah. I think Jordan it's kind of like if it talks like a duck quacks like a duck. It's a duck. So and I don't think rage is like no one's stupid right. but it takes a little more work, right? Because the problem is, they don't do it for you. They don't say like, oh, they add up all this like like Nvidia discloses their obligations. And I think in some of the filings, they will disclose it, but it's not in the balance sheet, right? You have to go looking for it, right? And that's part of what we're going to do at at Compute Capital Market, CCM, our new group, who we're hiring for, by the way, we'll have a PSA later. so if you love all things credit and like and we'll show you some charts, like if you like complicated charts, you're gonna love this job. But anyway, to Jordan's point, it it's it's kind of like, you know, it's something that that's going to be hard for them to walk away from, but my point is even that even if it's not debt, like that is not unlimited, right? It's there's going to be a point where rating agencies where investors say, "Hey, you know, this is just too much for you to backs stop." Like, you know, how are you going to backs stop a three trillion like no one has a crystal ball, right?

23:34 >> Yeah. you specifically use the term balance sheet as a service. So >> like right now it seems that all of the hyperscalers when we talk about this one trillion of capex are effectively putting their balance sheet to work as a service that has limits right. >> Yeah. >> Well, yeah. Yeah. So if you look at you you you look at Amazon, right, for instance. so so they did a 25 billion deal across six. This is back in July, right? And and it priced, you know, kind of wellwide of where their benchmarks were, right? And this was kind of like the canary in the coal mine. I mean, people were definitely aware, but you you are seeing the the hyperscaler spreads blow up by quite a bit and the CDS spreads blow up by quite a bit. I'll actually check for you right now. We can maybe we can edit this a little bit. But let me because I want to say a number if that's okay. You guys don't mind. Give me a sec.

24:32 Model directory. And I can fire up Bloomberg as well. Oops. >> Take your time, dude. No rush. We can just clip this section. >> Yeah. >> Yeah. >> Okay. So, Amazon So, Amazon spread. So, if I look at if I look at July, I'm just looking at July, right? And their 10-year you know, Z spread. >> Sorry. Restart, dude. I'm I'm only seeing the financing market obstacles.

25:02 >> Well, I'm I'm not I'm not sharing it. I'm not sharing it. >> Oh, I saw your cursor moving like you were sharing something. >> I know. No, no, no. It's because it's over it's overlapping the deck. So, another thing I interrupted you. >> Sure. Sure. Sure. Sure. Sure. Yeah. So, so if I look at Amazon, right, for instance, remember when they issued that bond, their 10-year zspread would have been like around 100 basis points. So, roughly, if you have treasuries, it used to be like four four and a half%. and then you add 100 base points or 1% then that means the yield on Amazon debt is about 5 you know 5.5 or 5.75 as the case may be and for 5year that spread was roughly around his 65 basis points. and then you know what what what sort of happened right after is it is it is it blew out probably by like in the summer it blew out by about probably 10 20 basis points from that from that issue which is kind of a lot of a move for IG. I mean things are relatively under control for now but you know you've definitely seen like Oracle has moved up quite a bit since the summer their entire curve probably moved up like 50 basis point 30 50 basis points so you know you are seeing the debt market take note and and and you know rating agencies like will see through this stuff.

26:27 Yeah, walk me through this Oracle increase of 105 basis points and then compare that to Corewave which is well above investment grade at almost 400 basis points higher. >> Yeah. Yeah. So, so, so Oracle their spread is going to be around like looking currently 10 year I've got them down for about 250 Z spread versus an Amazon I've got them only at a spread of 100 110 for for about 10 years.

27:03 but but this chart is a little bit about like as you said the relative you know like how do we compare credit risk? So what what what this is illustrating is a different type of structures. So what I'm showing you here is is let's look at the structure where coreweave is selling an optay contract to meta and the question is how much am I going to charge to lend to coreweave for that. So someone who's lending money to core will say actually this is meta credit risk plus execution. So the meta meta curve at the time we did this for 5 year is around 97 basis points zspread.

27:42 so now now the actual lending to corby for this project was around 200 right it was 225 so far plus 225 which you know when you do a little bit of math comes in slightly under a zpre of 200. and so the difference between that 97 base points and the 200 is actually the execution risk because even though meta is is is there right opt or Microsoft is there you still have to stand up the cluster right which is why you know I we should actually rename this to the cluster max risk right because if they don't have a platinum right you know in fact like there's going to come a day Jordan when when it's like this is your SAS rating and this is your cluster max rating right >> it's not And everybody wants to do make stock picks from cluster max. Now you want to raid people's debt from cluster max. Okay. It is one data point.

28:33 >> Don't tell Ethan he's going to shut us down. >> God then no. We're not we're not branding Neo cloud debt product pricing as cluster backst. >> Sorry. Sorry audience. Jordan does not give investment advice. >> There's a Okay, look there. There's a lot of things that go into building a good quality cluster. I think execution risk on standing up a given data center on a given timeline and then maintaining a contract such that there isn't a cancellation or termination of the contract is typically bare metal and it's typically a very different sort of product that is being sold to people than the managed clusters that we're rating. But actually, I'm thinking this through and I'm I'm not completely convinced in what I'm saying, which is that look, a lot of the experience that applies for building a great bare metal service certainly applies to the quality of the cluster. Like people who build reliable clusters are generally able to maintain and deliver on their contract.

29:34 So, all right, fair enough. We can use this as a rating of some something towards execution risk. >> Well, you you know, Jordan, you say that, but but I've been on a road show lenders. I did New York. I did Singapore and a lot of them have said, "Yeah, this this has been incredibly helpful to their process." So, I think it's like, you know, I mean, I think it's it's look, you know, if someone's made it onto cluster max, go to platinum, like they're unlikely less likely to mess up, right? Like on execution generally, it's just a stamp of approval, right? Just because, you know, you got good grades, you know, you got good doesn't mean you're a genius, but like, you know, probably is more likely than not, right?

30:10 >> I think it's a a signal and a data point, but managed clusters and large bare metal data centers are very different products. And I think just because somebody's in platinum does not necessarily mean that all their data centers are going to be on time and vice versa. Just because somebody's down the list or maybe ranked as like underperforming or something like that does not mean that their data centers will be late or that they will be low quality in the future. So >> I think that there's if if anything I would be >> asking people to assess a given project or a given site. I mean, we've seen people structure every single site within a NeoCloud's portfolio as an SPV.

30:49 And I think that's the right way to think about it where there is a different in some cases very different level of execution risk as you're describing it for a site in one location on one continent that happens to be you know owned and operated by a given neocloud even though they can go to a completely different continent for a different project the next week the next month and raise debt in a totally different way have a totally different team working on it and you know therefore things are a little bit different. Dan, let's move on to >> Well, well, Jordan, I I mean that that's an interesting point, right? Because, you know, a lot of the a lot of the a lot of the clusters, a lot of the NEOCs we talked to that are unavailable, they're not unavailable. It's because they're busy doing bare metal, right?

31:34 You know, like, you know, it's your point, it's not their focus, right? And so, like, yet they've landed large deals. So, it's not like, you know, it's just one data point and some people just don't focus on it. like some some guys who are doing great have landed lots of deals just like hey sorry guys you know we just don't have the product for you to test right now >> and and the whole market has changed I mean we're we're constantly going back to this but when cluster started buyers had five different providers offering them twoe PC's on 256 GPUs so that they could make their decision in an informed manner and sign up now you got to pick a dance partner 6 months before the GPU get delivered and you got to make a prepayment to these people that's worth 30 50% of your contract value in some cases. I mean, you said this earlier on on this episode, effectively the buyers are just paying for people's cost of capital directly.

32:32 And so you're you're you're taking money that you've raised which has a cost of capital and then you're giving to somebody else and you have to pay for their cost of capital. I mean, it's you know, it's it's stacking on top, which is which is driving a lot of people to try to do self-build work, and I think is what it's it's almost like this oscillation. It's like you start out early and you do rental because you don't want it on your balance sheet. You want to move fast. You want the biggest cluster possible. And then you start to grow and you realize, hey, this is a bad deal. Like, I'm kind of getting ripped off. I should own this stuff myself. And then you start doing these self-built sites. And then you reach this limit of what your balance sheet can handle, how much money you can raise. And then you start going, the Frontier Labs go, "Hey guys, I need help. I need to basically mortgage my future right now, and I I need you to build some stuff, and I need to guarantee you some returns on this work because like, hey, I I don't have the balance sheet to do it, and I I need to put your balance sheet to risk." So, it's an interesting curve where the Frontier Labs, the way they're doing deals right now and trying to get everything off their balance sheet, and even the Hyperscalers are trying to get stuff off their balance sheet. It kind of looks like an early stage startup, right? They want the most that they can get right now.

33:39 >> And I think you you've touched on like the the why, right? Which is where we we kind of got here, right? Like why is there all this back stop? And you described the problems, right? Like you you can't get you got to book six months in advance. You can't get get the compute you want. You got to put 50% down. and and and that's that's you know, kind of why they've done the back stop. I mean, let me share I I did up this slide that describes some of the why. and and it's what you said, broaden compute availability, what you just talked about. now supporting the financing market is another thing because we just talked about how unless you have that IG backs stop, you can't do anything. So, it's very hard for like a neoc it there's no incentive as a business model to say like, hey, I'm going to fill my book with half of like various inference as a service.

34:26 because your lenders will will go like hey you know I'm not going to if it's not IG credit like I don't understand this business well enough right which which which kind of you know goes back to this earlier slide that I had on the the willing the the the obstacles right they're still on the learning curve they don't understand the business well enough to underwrite anything other than IG and then they don't have the tools right this is like a lot of what Jordan and I are working on you know Jordan on the tools to assess the operational quality and and myself unassessing like what's happening in the market, right? That's why we have the GPU index. That's that's that's why we're trying to, you know, create tools that lenders need to monitor risk. And you mentioned residual value. This is one of the huge asks we had in in our road show. because banks think in terms of residual value that back they're lending its assets. They're used to lending against property, cars, and they have to know residual value, which is very very hard with with GPUs. It's not like you straight line depreciate. I think the residual value is what you can earn on tokens or what you can easily rent to that.

35:26 >> And it's so distorted right now because I think in a in a normal market where you have like this this contention between supply and demand where people produce enough GPUs based on the demand for them and they can always go a little bit higher then you have real pricing and you have real like settlement. but what what's happening right now is nobody has an understanding of what you know the true theoretical H100 depreciation curve looks like as GB300 comes online as Vera Rubin comes online next year and it outperforms that and then you say how can I run an H100 profitably well we already have models with inference X that show that you really can't if if a GB300 is outperforming your hoppers by like seven times why would anybody want to buy a marginal hopper over a marginal GB300, you you'd never want it. You you'd want the latest and greatest. But this isn't the question people are asking about Hopper. Instead, the question is, I have no option to purchase a B300 GPU because it's all sold out or I have to wait 6 months. I want to do research right now.

36:29 What option do I have to buy? And the answer is renew my H100 contract with four-year-old GPUs for double the price of what I signed it for previously. And this is on screen. And Dan's got our our pricing chart for the one-year term length H100 deals that we know about. I mean, they're approaching the prices that they were signed in the first half of 2023 today, like three bucks an hour for H100 GPUs on a one-year term. And look, if you believe in AG, frankly, I talk to all of these startups a lot and I basically give them the coaching of if you believe in your startup being successful, you have to imagine that somebody's going to do something OpenAI Anthropic can't and it's going to be you. And so you have to, you know, flippantly you have to like believe in AGI here, right? And if you don't, >> you know, Jordan, but like I I had a question because you said you said that no one doing inference would ever rent an H100. So like why are people why is the price going up then?

37:25 >> Well, because I think there's so much demand for it that the price goes up strictly because of it's the only option for a lot of people to do. Second of all, there there are some researchers who prefer H100 because it's familiar. They are more productive by the metric of jobs per day rather than, you know, tokens per second. It's like I don't need to spend time debugging. I don't need to port stuff from BF-16 to FP8 or FP4. I'm doing training and, you know, I need numerical stability. I don't care about these low precision data types that are used for inference. I'm doing some new model where again I care about numerical stability. I don't want this risk. So there's there's reasons why people want to do research with hoppers.

38:05 H100 and H200, but I think the biggest reason why the prices are increasing today is just because GB300 sold out. So people go to the B200, B300, right? Those are sold cascades down. >> Exactly. And I mean, if your answer is wait 6 months and have nothing or get started with H100s today, a startup that just raised money has a pretty easy decision. they go for the H100s they can get access to right now. >> Yeah. Yeah. That makes so much sense.

38:36 And that's kind of like why when we do this index like we we we do it survey based because like you know there's a story behind every data point, right? This is not just you know going like you know scraping a bunch like you know there is a story and and and those matter and you know what I did Jordan I actually decomposed. Oh do you want to go or I can talk about this? >> Yeah sorry. Well, just one last thing.

38:55 If you go back to the previous one, like we would have a more academic theoretical way to forecast the appreciation in the H100 pricing curve. If you look at this chart and sometime around September or October of last year, it just kept going down below $180, below a $140. And we actually I remember some deals being signed at that time for like a $130 for an H100. People were offering stuff that low. >> Yeah, that was the bottom. And but those were not great providers though >> for sellers.

39:25 >> well no the guys who were selling at 130 were not great were not like great providers right >> that fair enough. Yeah. Yeah. Exactly. but okay but the the point is like I know some sellers at that time who were evaluating $130 and going should I do a one year at a$130 or should I do a two-year at a$125 or something like that. And man, those guys are kicking themselves for not doing a two-year then because they just renewed at like literally some of them at 250. Like way way more than they were paying this time last year. And so anyway, the point is like we we would have a real understanding for H100 res residual value if we had seen the pricing curve depreciate like some other asset like a car or something, right? But four-year-old H100s going into next year are going to be selling for for likely higher prices than they are today if the trend continues. And I mean I expect it to because I expect demand to continue to rise. and let me make two more cases for this that are not based on like an AGI future. You don't need to believe in that. You need to believe in data center delays or cancellations due to political posturing and moratoriums and execution risk as you were describing which we see a lot of and and you also have to believe in more demand from consumers on you know anthropic and open API endpoints which I think you can believe in even if you don't believe the models are getting better. You just need to look at things like instinct growing or muse from meta and just imagine a world where more people use AI driven products even if the models stop getting better. Right?

41:05 So the combination of like data centers might take longer to build politicians may get in the way and stop them that will constrain supply more demand for the existing models and the models get better are all a recipe for increased prices over time. I mean that's that's generally the case that I've been making to a lot of people. >> Yeah. Yeah. And and and the thing is right if you did the scientific version we we started this in 2024 we we actually have a forecast and that actually looked like you know downward sloping it actually started detaching. we were like within 2% for like a you know a good year and a half right good 18 months but then in it started dislocating for September because the way we calculate the the theoretical depreciation curve is the relative power the relative compute power of new new GPUs so for instance a GB300 had seven times more inference speed and three times more flops right so it would basically those new capacity would push down the price of your old capacity in theory that's what should happen but what we always said is is you know we understand supply we track price but it is hard to track in real time demand. So what we do is we actually flip it on its head and we say let's observe price and let it derive demand.

42:19 And so you know in this slide this is exactly what I did. what I did is I said you know if if you have a balanced if you have a balanced neutral supply demand then what you see is it depreciating in value or rental price going down you know in a scientific way right in line with new capacity right but what happened is in October right that that that dislocated and what that implies right because if your demand is higher than supply then your price surprised the upside right and so what I did is I I I reverse it and I said Hey, what does this imply for demand supply balance based on price? And what we saw is it blew out completely in late 2025 and it's never looked back. Does this kind of like tie in with what what you've seen, Jordan? Like I assume so. Like maybe not the theoretical stuff, but like what you have started the market.

43:09 Well, a couple of things happened around that time last year. I think basically models stopped getting bigger like the it took a while for the Chinese models to catch up but up until around this time last year when you know reasoning models were were taking off people kind of discovered we can trade flops for parameter count and continue to improve the quality of the model with a lot of innovations around RL and post- training and they've just continued to exploit that where we may have gotten bigger models. I think we we likely did get bigger models, you know, with the Astra and the Fable generation or the Mythos generation, but we didn't get significantly bigger models that outpaced the improvement in hardware that came when the GB300 NVL72 came online, which is to say significantly more memory, memory bandwidth, and FP4 performance were able to keep up with the increased size of the models. And so what that meant is that these these companies could continue increasing price per token on their APIs without necessarily increasing their costs to serve the model significantly.

44:19 And they're you know that like the result is just they're way more profitable like way way more profitable. And you you don't even need to know anything about the anthropic open AAI models to to make this case. You just need to look at the Chinese models and you need to look at what the performance of a Deep Seek V3 was on like an H100, H200 or even a GB200 when you know those were being brought online last year and then you need to compare it to Deep Seek V4 or 4.1 Pro or 4.1 Flash or GLM 5.3 or Kimmy K3 where you just look at all the innovations on sparse attention. You look at the parameter count growing a bit like around two times or three times in some cases but you know flops not increasing significantly and performance increasing so much and so they can command a a pricing premium over the previous tokens because the tokens are more valuable but it's cheaper to serve. you get more throughput per GPU with these. And so people serving Chinese models are enjoying gross margins of 80% right now on a GB300 NVL72.

45:25 And so it's it's just like it's like look obvious that there's going to be more demand for this compute this asset. Now the the the question or or what gets confusing to me here in some cases and maybe you have an answer for this is how nobody forecasted this like we did our best we understood right and I think even the frontier labs who really deeply understand how these models work did not expect themselves to be this profitable and to be able to serve this many users and to grow this past and so even they were caught by surprise and had to do crazy things and sign big contracts at very high premiums just to get access to GPUs tomorrow to serve their users and to go for the bigger research jobs. I don't view this as like an AI fast takeoff, but it seems to be like a revenue and profitability fast takeoff for a lot of these labs.

46:27 >> Yeah. Yeah. And and and I think you know we we've kind of in our discussion we've really hit on what what is the debate going forward and and it's the willingness and the ability and I think the ability is the profitability. like a lot of people who are new to this are are are don't realize just how profitable it is. and that's one of the things that will determine whether we we we overcome this obstacle this kind of you know funding wall. Well, I think yeah, it's also confusing which layers of the cake are profitable, which is to say, >> right? Yeah. Yeah. Look at that. Yeah.

47:02 >> Nvidia clearly had durable profit margins for a long long time, but >> Mhm. >> your Yeah. your layer cake chart is is great to explain like clearly there has been an increase in the profitability of the neoclouds because the amount that they can rent the GPUs for to people has increased recently when the GPU has not there's nothing changed on the operational costs or anything right the people are selling tokens with more efficient models or higher prices and so therefore their profitability is increasing and then my personal experience and I think a lot of other people's experience is value or utility that you're able to derive from using these AI applications is also increasing over time as they get higher quality. And so I don't look at this and say, you know, we're taking from one to give to another. It seems like every layer of the stack in some ways is increasing profitability except for like the server OEMs where it's still a pricing war and they can't 9% margins.

48:01 >> No, no, no. There there's still a there's there's a bit for everyone, but but >> the tin box and the people to plug it in. >> Well, I I can get into that, but but I want to I want to you know, just so everyone stays organized, right? I think again like everything we've talked like Jordan and I have talked to so far, you can you can fit into two buckets. The willingness and the ability. What we're just talking about now is the willingness. You know, what is the return? Will the labs remain high? Like how high are the margins? You know, can end users? And earlier on we were talking about the ability, right? We talked about the balance sheet as a service. We talked about Nvidia backs stop which is trying to address the ability. you know, and who's going to fund them. We talked a lot about that. But let's, you know, why don't we why don't we dip into the the willingness, right? Because that's what we were just talking about. and we've done analysis where you can actually earn up to 100 million per megawatt generating Frontier tokens off of GB300. It it may be a little, you know, optimistic in terms of utilization, but when this thing costs about 40 million per megawatt to stand up, it it's like, you know, if you're if you're force to sell fable, you make this back in half a year. so it's incredibly profitable. and let's compare that against what NeoClouds are typically renting for a GB3 at least a few months ago. about about 12 million per megawatt or 12 million per gigawatt was how much you can how much neoconsor contract. But if you're doing frontier inference, like I said, you can do 100 million per megawatt. And people were were were were shocked, right, when they saw SpaceX and Google renting at 48 million per megawatt. But but but you know that that makes sense, right? Like because they're buying here, selling here. So of course they would, but people were used to seeing this. so so the the profit is there. and and I think Jordan, you mentioned, you know, where where does the profit stay in each layer? So what we did here is we actually organized the entire kind of business as an income statement.

49:55 so you can think about this you can imagine you're you're an anthropic right? So this is your revenue and we put in a couple different ways. So million token about 185 per million token which is a good blend of of of set opus with cash hits. This is actually our own blended spending. even if Opus is is five five dollar input the the cash hits will take it down. so about 185 per million token which is equivalent if you're generating I think we put around I want to say 9,000 tokens per second. I can't remember what we used but it's equivalent to $38 per hour right and then we t we already talked about the cost per hour of of 280 and then a Neocloud. So the cost to own the system is 280, but the cost to rent from a NeoCloud is around it's probably around $4 north of $4 by now. $450 almost $5. So if we just simplistically look at that, what is a gross margin?

50:52 It's 90%, isn't it? Right. Because you're selling at 38, your cost is 380 and this is your profit. So it's incredibly profitable. >> Yeah. >> At least for Frontiers. by the way, this was really cool. Everyone everyone should should read this like drop everything and read this now cuz cuz you know Max and team >> we recorded a pod earlier today you don't know. Yeah this is this is my third pod of the day. I've been cheating on you Dan but yeah Max and Andrew went through >> ligamist.

51:24 >> Come on. >> Okay. Well anyway Max and Andrew went through this on the previous episode. Maybe for people who who missed it, do this one again, which is like for the all-in consumption plans on some of these on some of these these Yeah. these these businesses, you can get a lot of value, but it kind of depitable at this layer or not. Kind of depends on how much people actually use the plans that they let people have access to.

51:58 Yeah, we peg 90% API margin and then I think in the mall we we use like 50 60 but but the question I have is is how much of these people are really using 10%. So I'm kind of you there's a whole cottage industry in in China, right, that like takes apples and makes applesauce, right? They take these $20 plans and then just sell them as API, right, on the gray market. And then, you know, me, I'm on a $20 plan. Like, I I I actually plan my sessions around like, you know, utilizing I have loops so that, you know, I I I use it even when I'm not there. so I'm definitely on on on this bucket, you know, but I don't know where everyone else is.

52:35 >> Yeah. I mean, Max made the case earlier that I think there's a lot of people who maybe get these through their business or, you know, like they work for and they're just like not really worried about the value trade. Exactly. and you just kind of blend it over a bunch of users and it works out. And the loud people are the ones pushing for more limits who are the ones you're losing money on. But well, you keep them around because you don't know how to separate the wheat from the chaff and only only sell the all-in plants to the most profitable segment of users, which is the ones that don't use it. like you know the whole thing about Planet Fitness is marketing it to people marketing gym memberships to people who come once in January for the new year and then never come back that a profitable gym is the one that's empty they don't want anybody to show up I mean kind of cynical there but that's the incentive structure for these guys to do all-in plans whereas the exact opposite is true for the for token pricing where they're just trying to get people to consume as much as possible.

53:39 So >> yeah. Yeah. And and then the next question becomes like okay it's so so profitable now but like what people ask is is that going to be the case in the in the future right let me find our token pricing >> oh gosh where'd I put the token pricing and people say like how fast is token price dropping >> sorry >> I mean >> not to cut you off but let me Yeah. Okay. Yeah. Yeah. Do this one. Yeah.

54:06 >> Yeah. Because because my question for you for you for you Jordan is like people ask me like how fast is token price falling like what do you mean it's falling it's going up right and and in fact even if it's going flat that's gross margin going up because while token price been staying constant they've been generating more tokens right and then they're like okay well great so I see frontier malls been holding up but like how fast are open malls falling I'm like well they're going up right and and the other thing is when you look at you know when you look at token expenditure It's like a changing basket. So, so these indexes that track token expenditure like they don't they don't adjust for that in my opinion. I don't know how they work but they don't adjust for that and they don't adjust for increasing you know with AENTIC it's been all about effective pricing cash but I don't know what do you think of what do you think where do you think token pricing is going in the future Jordan?

54:55 >> Yeah I mean I had a little tweet thread where I went at David Friedberg from the all-in podcast. He didn't respond of course but a lot of people are saying that the there's like this overtaking from open models overtaking closed models and I think the dynamic is just like people are consuming more of everything and they're consuming it at higher prices but you've got the price of input on screen here which I think is a decent metric but you know that little drop down in the middle you >> do the effective right well I you could it's just different approach >> yeah you can do it for cash read which I think is the bulk of our spend is actually on the cash read tier because you Aenic coding is just so much cache it hit and then output is like the most expensive tier even though you know you don't actually generate a bunch of output that is what takes up all the time on the GPU when you're memory bandwidth constrained. Anyway, the the point is like I totally agree that model pricing is going up over time on a blended dollar per million token basis.

55:51 I think that token efficiency is a question mark because you have to trade off efficient ability to solve simple problems with people's more grandiose expectations as they try to solve more complicated problems that weren't possible before with unlimited tokens and now are possible with a lot of tokens. I don't know how you measure that trade-off, but look, none of these dynamics to me point to anybody deciding, I've had enough of AI, I'm going to use less of it. I've had enough of these models, let's make them worse and less efficient. I've had enough of these new GPUs, let's make the next one worse and and less performant.

56:31 It all seems to be going the opposite direction, right? They're going to raise prices over time, more demand for it. The GPUs are going to be more efficient, and the models are going to get higher performance. So, we'll see. there's a lot of ways in which this can break if people go and decide to yolo for the 20 trillion parameter model with the new Nv72 and really use it if they start introducing but you know ultra fast mode and and some of these low batch size optimizations and stuff that can jack prices up. But I think all of that is just like artificial constraints on what to me feels like continued overwhelming demand for these products that drive the usage of the GPUs underlying it.

57:12 >> Yeah. And and we talked a bit about the the income statement, the explicit one, but what about the income statement above, right? What about the users? Right? That's part of the equation, too. What value are they getting? So, I don't see why anyone would step back when these are the kind of efficiencies they're getting. like we built an entire database of you know thousands thousands of debt facilities and all these relationships you know pure public domain and and you know it's been myself Oliver Terrence you know have been doing this and and you know we're all part of current groups because you know we're hiring like I mentioned PSA but you know it would have taken me four months and a team of four to do like half as good of a job as as we've done so far in building up this this this database and the you know relationship table so far.

57:59 and so these are you know we we we've showed this slide a lot. We we had a pot on this but you know again I don't see why anyone who who is who is paying $6 to initiate a company would ever go back you know. No >> would you ever go back Jordan like or would you just stop using fast mode? Like what would you >> I don't use fast mode. >> me neither. >> You'd have to pry this stuff out of my cold dead hands man. Even in my personal life now with some of these like support agents, >> it's like pick a wine. Pick a wine to bring to dinner with my friends. It's like what I'm saying.

58:36 >> Okay. Mom's for $3. I still have a personal touch, Dan. Okay. Like I'm not not writing birthday cards. >> Speak for yourself. >> I'm not that far off the deep end. But yeah, like I I just got this refund on a flight where I was delayed on for 4 hours a few, you know, there's no way I would sit on the phone trying to get this refund ever again, right? I'm like checking, you know, times at the gym or I'm checking like like booking a flight or looking for hotels or checking on the grocery list or just check my email, man. Like what came in last night. I mean it's it's a little weird but I talked to some friends who are not enlightened to the possibilities that having a personal assistant can provide and it does feel like everybody can never go back once they get the experience of the amount of time that you save and and the feeling of like how much wasted time.

59:43 >> Yes. Yes. It's it's it's and I think Muse is going to be eye opening. It's like why did I ever sit there for two hours and browse all these like airline booking sites? Why did I ever you know like >> I I think there's going to be that moment, >> right? >> It gets a little egotistical when you talk about it this way. I think because you know you kind of anthropomorphize the agent then you you view people who have personal assistance today as kind of exploiting like this thing. But anyway, my personal view is kind of like it's it's shocking how much time we waste on this monotonous stuff. And it's it's shocking how, you know, some people that I'm close with do not value their own time.

60:31 you know, I like Yeah, I'm not going to go into all the details there, but you can >> Well, I think I think we're sold on willingness, right? We're sold on the, you know, AI is like useful, right? >> Yeah. >> Well, look, I felt like we're I felt since working at SM analysis, I felt like we've been at the tip of the spear for adopting a lot of this stuff and just like feeling the ability to use it, which both makes our research better and informs the underlying research that motivates some of the data that we see.

61:00 And it's really nice to see other people that we work with and in my personal life adopt these things and then start to like regain control over a lot of things in their life. It's it's it's really cool really cool to see. And I I yeah I just I get really happy when I see people have these good experiences using it and start to get motivated to like build a more positive and you know high quality future together. I don't know how to rap, but yeah, talk to me about the willingness to finance. Do you think >> Yeah. Yeah. Well, we we just we just talked about that. I think we're sold.

61:37 Like, what I got to convince you on is, you know, again, it's willingness and ability, right? And and I got to I got to sell you on ability, right? Like I I I might have scared you with this, but >> talk Yeah. Talk to me about the human element here, right? Because I think it's one thing I've also realized working on SM analysis is how much of the this stuff that seems so data driven all comes down to some human being making a decision like Satcha said pause therefore pause or Jensen said build therefore build or backs stop or whatever right so where does this break down to >> genuinely one of the leaders of these companies having this experience using a personal assistant or an AI or just like having that like personal moment where you're like oh my god I can see the math discovery before it comes out and gets published or I can see this progress we're making on this cancer drug or whatever and like I just really want to bet the company on this and I want to do this work because it means something to me and this is like my life's work as opposed to rationalizing all of the financial details to make it work like like can you even contend with explaining that balance between those two things?

62:41 Yeah, but you know like I I I that that is one of the big questions because we we we covered this is still under willingness, right? Because we covered we're we're sold, right? And I think what you're saying is are other people sold too? And and that's kind of what this chart is a little bit about, right? And I think the question like I I would I would make one that's slightly different because what I would do is I stratified consumer like API tip of the spear people like AI natives and then the rest of the enterprise, right? So I think we're convinced on the sphere tip of the spear. I feel like Muse is going to prove the case in consumer but I don't know in enterprise because I sit at these t you know I I I do like a lot of these events and I meet a lot of you know folks you know like we do talks and dinners on AI and then you know kind of fortune 500 they're like oh like what would I use it for like just summarizing stuff like no it's so much so you know I feel like there's just just a lack of discovery in Fortune 500 and lack of imagination and a lot of people they actually kind of don't come out this with an open mind they come out this like oh great AI like how do I fire people, right? You know, like that's that's all they think about, right? And it's and it's like so I just think the enterprise is it's it's it's of the three it's it's the one I don't know the most. I don't know because it's it's it's so much to like does the C say hey let's embrace it let's trust Amazon let's go full blast or they go like no AI in your work like like what do you have you talked to fortune like where do you think they are on this? Well, I think the dynamic that I imagine happens with the Fortune 500 is that the Fortune 500 just slowly gets replaced with companies that are all in on AI.

64:16 >> Well, look, look at the top 10. >> Possibly true. >> Look at the top 10 companies in the world by market cap, right? I mean, these are all like nine out of 10 of them are AI or semiconductor related or data center related at this point, right? Like when we say hypers scale, all five of them are in there. Then you've got Nvidia, Broadcom, TSMC, the memory guys are are getting close to the top 10. So Fortune 500, I think we use that term to describe if you if you actually mean the 500 biggest businesses in the world measured by revenue. I think a lot of them are just going to >> maybe I should say corporate America, you know, >> legacy businesses, corporate America, >> legacy business. Yeah.

64:54 >> Government, just like people that are slow to adopt AI and and fair enough. I think there are lots of people who will be a lot slower to adopt AI than the biggest companies in the world which I would lump in all the hyperscalers into their soon to be the AI labs if anthropic goes and IPOs at $2 trillion they're going to be you know a top 25 company in the world in market cap or something like that right so I mean that's that's the the proof point right there but you have to make this case of like going out two three years do you expect that this next crop of NEO labs that pursuing all of these new innovations just like completely all get swallowed up and acquired and none of them pan out. I I actually expect many of them to do quite well across a number of different industries continue to grow and if they do get acquired to probably help those businesses grow and and those you know take over and so I you know I don't view like a future where Visa and Bank of America and AT&T are just like so critical to the success or failure of the AI native companies that are trying to grow. Maybe what I'm most interested in is the unlock of capital that the companies that may have been funding those companies in the in the past may turn and shift over and say, "Hey, I I work in infrastructure finance. I work in credit and for the next round of this, I'm not actually going to go do another you know, project for another bridge or for you know, some debt for some credit card payments provider or something like that. I'm actually going to go into this AI thing because I think that's upstream of all of those other businesses I've done and and it actually is infrastructure and I'm going to get these returns and I'm going to have these guarantees and things like that and and that's where I ask you because you know these guys better than me about the human element of like for people who really just don't understand this and don't have the experience of using AI who represent so much you know capital that's still sitting on the sidelines.

67:00 We we joke about stuff like Nvidia's revenue at if it hits $500 billion is going to be like 2.5% of the M2 money supply in the US, right? just unbelievable numbers. We're playing with trillions of dollars of capex we talked about earlier in this episode. But there's still so much left over like what's the other 97.5% of the M2 money supply? So, >> well, like let me, yeah, I can I can share some some stuff because now we're on kind of the the ability, right?

67:34 and and and again, one of the obstacles I talked about earlier was you know, getting people like I guess I'll show you. Oops, it's up here. let me go. One of the key things on the ability is is like understanding and one of the key obstacles is is lenders on the learning curve like so you know the people making decisions on lending like right now they're doing it because like okay it's Nvidia credit risk it's Microsoft credit risk like I don't have to know but that's going to change and that's why all these backups are there so they have time to understand this industry but but let me let me size the problem for you a little bit you know let's let's talk about you know what where where where this is going right like Obviously, it's going to be the second biggest debt financing market, right? It's already surpassed auto loans, surpassed everything >> this year. but everyone's done it without having to have residual value model for GPUs because it's in back stop and it's only second to the US mortgage back market.

68:33 >> so move on from that chart. We've got some audio only listeners, but I I just want to describe it. it's such a compelling chart. auto loans, student loans, credit cards, non- agency, CNBS, HELOC, these US assetbacked credit markets are just like flat lines like slowly going up to the right as people >> you can't you can't even see them going up actually. >> Yeah. And it it they're just five flat lines at the bottom of the chart and then it's just this massive exponential line up to the right. more of a straight line, but yeah, straight line up to the right called AI debt financing. And just explain to me the implications of AI debt financing in 2026 crossing over auto loans and student loans on this chart.

69:20 the implication is and we already see it. We already see long long end we already see 10ear rates going up. We already see credit spreads going up. we already see an industry and and the reason the credit market it it's going to be hard because they don't have the tools to understand like because it's it's all coming from like different fronts. It's a war on so many fronts because you're getting hit by hypers scale or neocloud data center everyone at the same time and it's like it's like a zombie apocalypse but you don't know how many waves of zombies are coming right you know it's like a terminator you know how many terminators give me your money anyway they're money zombies coming right and and you don't know whether whether you're on wave five or wave 50 right so So, so, you know, and and to put things in in in context as well, I've got a slide here that shows you, what this is, like I told you, I showed you what it is in terms of like the corporate bond market, right? And that's that's just up here. but on the next slide, let's talk about overall US issuance. so it's 1 trillion.

70:29 and it's going to cross 1 trillion. So, so if you think about it, right, the total US corp, the US issuance, this is federal, which is in the blue. you know, for those of you listening, that's about five or six trillion a year. You got MBS, RMBBS. just looking ballparking from that, it's looks like 3 or 4 trillion a year of issuance. maybe two to three corporate bonds, we already talked about that about 3 to 4 trillion. total 12.7 trillion. and and and 2026 is the last year that AI debt is going to be less than 10% of all issuance, right? All issuance in the US.

71:04 so so it's going to take believers. and and how how do we handicap that, right? you know, well, this is maybe what we can do is we can we can jump into like, you know, back to the question you asked earlier. It's like why why is Nvidia doing these back stops, right? and let's just look at well, sorry, I'm going to go. Okay, here. Yeah, like why do they have all these different different back stops? And again, it's it's they see this gap coming and and and they're figuring out how to plug it. let's talk a bit about their AICP, right? So, by the way, you know, to start off, we projected Nvidia's balance sheet and we were looking at their ability to actually continue doing these back stop and how much they can do. but let's talk about like an example of one of the backs stops and how it accomplishes those goals. This is the AICP program.

71:53 you know this actually started in Asia. The first one was firm well actually first was sharing AI then firm has followed with another one. you know there's been Alani there's been many others. GMI did one. And the whole idea here is the way this works is Nvidia goes to Neocon and says, "Hey, I'll give you a guarantee." Like if you can't rent your cluster, I'll rent it from you. and I'll give you that that guarantee for 6 years on on 325 on average. it's the the actual table is different. Each year is a different minimum, but if you can't find clients, you can always go to Nvidia and rent to them at at this price. Now, the intention is not for that to actually happen. The idea is you use this as a guarantee to then get financing.

72:37 Remember the trendy right? So now you have an offtake and now you can get financing and then with that you can get data center. with this in mind lenders lenders you see this this little thing that it's priceless Nvidia risk. So it says hey you know I'm really lending to Nvidia plus execution risk. and then what that allows allows the NeoCloud to do is, oh, now I don't have to rent a five-year offtakes to whoever. I can do you know, tokens of service. I can do all these things. It really opens up the market. That's the whole idea. and they don't need to be IG. That's the important part. but this is only like an intermediate intermediate step because what it does is lenders have to understand that they're they're also taking the platform risk whether neo can build a good book clients and they'll get to see it as a lenders neoc. like, hey, can they build an independent business outside the optic? They get to understand the customer profile, the churn rates, the cancellation rates, and they get to, you know, kind of do like a free look or a dry run, practice run until the real thing. And that's why this is kind of really important why they're doing it.

73:40 Does that make sense, Jordan? Any questions? >> Makes perfect sense. Yeah, man. balance sheet as a service. Maybe the the one thing to talk about is like they at some point introduced a a take on the percentage of revenue that the NeoCloud >> I forgot about that >> above the minimum that they promised >> and then they took that away and >> you know >> No, no, no. It's still there, right? >> Okay. Explain that. Yeah.

74:04 >> Yeah. Yeah. So, so it it's also a revenue model as well. and and it's fair, you know, we're not communists after all. so in exchange for the backs stop, Nvidia gets a 50% revenue share above the backs stop. and so for example, let's say you rent at $6 or let's make it easy, $668. Then you the difference is $3. So you got to pay 150 to Nvidia out of out of the six in exchange for that. So it's actually a meaningful revenue share for NVIDIA. And and if you look at the other structures they've done, this is the Lambda structure. they're also taking a revenue share out of Lambda in exchange for that that that guarantee.

74:43 so you know and another third reason Jordan by the way one of these back stops do is is not just facilitate these deals happen and allow you know in this instance they're allowing lambda to to get financing because anthropics not yet IPO is not IG right but with Nvidia supporting the lease for the data center they can actually get this done but there's another reason right let's look at ports pike this is a little complicated but the the bottom line here is Nvidia is is giving 105 billion dollars of residual value guarantees to the ports Pike data center complex in order to support this.

75:19 But the catch is it's going to so what they've done is they've guaranteed that 4.25 gigawatts or up to nine almost nine across two phases will be running Nvidia GPUs. So it also locks in. It's like it's like a kind of a land grab is another another reason for it. and so we looked at we looked at all these different models and I'll talk about one last one and this is the kind of the most interesting one is a 500 billion capital partnership. because this gets into how can we actually you know to your point right you talked about lenders as as one but it's actually very different. There's bank lenders, insurance lenders, they don't want to take a lot of risks. You've got private equity they want to take a lot of risk.

75:58 You got private credit they're in the middle. >> They all syndicate into these things as well. Right. Well, well, it's creating different products for different u different types of investors. So, so the 500 billion capital partnership, this is what we think it could look like. And what you do is you can have an AI lab. They don't have to be IG, but what you do is you trunch the risk. So, you have equity and then you've got a B piece which which doesn't have any residual value guarantee and then you have an A1 A2. You can put one senior to the other and then Nvidia is giving a 25% residual value guarantee in this 500 billion partnership. maybe you can attach this 25% to this trunch or this trunch but because let's say there are losses in this program you know in this business then the equity will take the loss first then the BPS and then part of the A2 and so it will have to go all the way down to to to this the loss have to be so severe to affect the senior trunch which is how you can create different risk profiles and open up the market but anyway all this depends right on Nvidia having the capacity to do this so we analyze the balance sheet you know, and and you know, if you you kind of love talking about this, you know, this is all we all we're going to do. but you know, here's the kind of conclusion of this, Jordan, right?

77:11 they we think they have the capacity, this is not our forecast, but we think they have the capacity to support up to 46 gawatt of capacity. We haven't done it for the hyperscalers yet, but that's still not enough. There's 240 gawatts is going to come in. And so what I'm saying here is is the lending market has to evolve to take risk outside of back stops. And that's what you know we're all about understanding that question and it's it's really the biggest question of our time to be honest which is why we're starting an entire group around this question.

77:44 >> Makes sense man. Yeah. If there's two tangible things I'm taking away from this it's one more education to the lenders on what they're actually >> getting into here. you know what the real deal structures are like and who the end user customers are and why they're structuring things this this way. And then two well I guess two is is twofold. it's it's always incredible to see the size of of these numbers and how much has already been done but also how much bigger it needs to get from here.

78:17 >> this is incremental capacity ads. So >> incremental 60 some odd gigawatts of capacity hats in 2029. Yeah. Let's see. >> When we first start this journey, global data center capacity is like 40 gawatt maybe. And now we're adding like 40 a year. Can you imagine that? >> I can. Yeah. >> Yeah. >> Excited for >> your usage. Yeah. I know. I know. I know. I was looking at the numbers.

78:47 >> Let's let's wrap up one one little funny story. When I got started working at semi analysis, I did not understand like anything about financing and how this stuff works. I was trying to figure out who a lot of these lenders were, what private equity was. I I'd known these names, things like that. And I I remember working on this one deal. I won't say, you know, who was setting up the deal, but it was like private equity.

79:07 >> Do you think DA was like a musical at some point? >> >> Yeah. Yeah. yeah. I still get confused when you talk about project versus workbook IRRa. I I don't know. I just know the project versus project versus equity IRR. >> Equity IR. Yeah. Okay. Okay. I I I just know irr is you spend enough time with Dan, you know, irr is important.

79:37 >> And it doesn't get easier to say IRR. >> IRR. Yeah, >> that's that was the pirate, right? Dan when he started like he needs an eye patch because he's talking about his eye or RR. >> Anyway, >> okay, let me tell this quick story before we wrap. So, this private equity company was setting up this deal. We had to go do this technical DD on it. We had to present to some of the lenders and the lenders were like, you know, from all sorts of different institutions and I remember at the beginning learning like, oh, private equity doesn't have any money for themselves.

80:11 They have lots of ass management, but they set up the deals and then the lenders come in and they actually like fund the deal and private equity like makes carry off of the the top of that, right? Anybody in PE who's listening to this, it's like, yeah, of course that's the way. I don't know this. Anyway, and so I had this meeting with this pension fund to explain like the work that we did and I remember sitting on the call. It was the Ontario Teachers Pension Fund and I was like, "Oh, it's my mom's money."

80:44 this is what's you know getting in here. So anyway, the kind of this whole like loop back to thinking, okay, yeah, yeah, like Canadian pension, Ontario teachers pension, lots of stuff in Quebec. Like I'm Canadian. So anyway, that it it like I ended up feeling like kind of proud of the work that we did. Not like I was helping these people who were just like rent seekers trying to make a little bit of money off of this. Like it actually is pretty hard to set this things these things up. Make people comfortable understanding what they're investing in, explaining the risks, understanding the technology, what's the return, blah blah blah. And yeah, there's going to be more to do in the future because lots of people just like that are probably getting in and evaluating their first AI cloud opportunity or neo cloud deal.

81:27 and it's going to be a big part of the future. >> We're we're on a learning curve and that's, you know, I think I think the more we we help people get up there, you know, talk about everything, I think the more it it helps everyone make make great decisions. >> Yeah. Cool, man. Okay. >> You know, one one last joke. You know, why why it's IRR? >> No. Why? >> Cuz pirate equity. Pirate equity.

82:02 Hey everyone, if you're still listening after this hour and 15 minutee long conversation about credit and markets, you should consider applying to work at semi analysis on credit and markets. Dan is a very fun guy to work with. Dan, who are you hiring for? >> Well, we're we're we're hiring credit analysts in New York and Singapore. So, we're hiring junior credit analysts. So, anyone who's done one to three years of experience could be investment banking e GC well DCM you know anyone who's focused on that. also, we're looking for a more senior credit analyst, anyone who's been, with desk analyst or investment analysts on the buy side. And also a credit specialist, someone who's quite in touch with markets, has a great rolodex, worked in credit trading, credit sales, you know, buy side or sell side. So, love to have you apply. Just click here to find out more. I also need a video editor. If anybody knows about a video editor, shoot me a text.

83:01 Okay, this is your third interruption. We're experimenting with ads on the Semi analysis weekly podcast. We also need a video editor. A cautious hiring. Yeah. So, if you know how to edit videos, please hit me up. We're looking for people. Please hit me up.