Ep. 037 - Who's Funding the $11 Trillion AI Buildout? (Capital Markets) | Dan Nishball, Jordan Nanos

Jordan Nanos (0:01)

For our first ever advertisement, we're going to be doing a lot more of this on future SemiAnalysis Weekly episodes. SemiAnalysis is a reader funded publication. Over to Dan, what are we advertising this week?

Dan Nishball (0:12)

Well, we noticed that 90% of 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 hiring for a new group, which is called Compute Capital Markets. And we're going to look at the loan demand, how the financing markets are evolving for financing GPUs. And we're going to look at markets, which talks about the tools that lenders and everyone in the market need to evaluate how GPU finance is going, how the GPU industry is going, 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 perhaps one to three years of experience in the credit space, investment banking 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. IG, high yield would be great.

And also a credit analyst. Anyone who's been a desk analyst, buy side, sell side. We're looking for people who are credit minded. I think that if you enjoy this episode that's gonna come and all these structures and all these Z spreads and T spreads, I think you're gonna really like this job. So, click the apply button right here.

Jordan Nanos (1:41)

All right, roll the credits.

Hello everyone, welcome back to SemiAnalysis Weekly. In this episode we got Dan. We're gonna talk about everything to do with capital and markets, how we're funding the build out right now. Dan has been digging in to everything, credit, capital markets, compute. Specifically, we put out an article, I mean, called the Video Backstep Universe, heads I went and tailed to, where we talked about the $11 trillion AI build out, Nvidia's backstop economics, and the limits of Nvidia's balance sheet. So, you know, with that as a backdrop, we're gonna talk through the contents of that article, 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. You can set up this, you know, article on why you dug into this expanded view of Nvidia's balance sheet.

Dan Nishball (2:47)

Yeah, yeah. So let me show you a bit about Nvidia's backstop universe. And indeed, it was just a month ago, but things are moving so fast. At this point, funded and announced is around about like a hundred billion each month. And this is how we looked at the balance sheet. And the whole premise is Nvidia has been giving a lot of backstops, and the question is why, right? There hasn't been a lot of visibility on how much issuance is going to come in 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 backstopping 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 what sort of led us to this point.

So, if I go back to...

If we think about the constraints, right, Jordan, and you asked me earlier about the Trinity, right? You remember that?

Jordan Nanos (3:52)

Yeah.

Dan Nishball (3:53)

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 Jordan, earlier, he was kind of asking me, like, well, what about compute, right? Isn't that the fourth leg?

Which is a good point, because it used to be 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 the hugest pinch points, right? So let's talk about constraints. And the constraints have been moving. They've been moving from compute constraints to data science constraints, and now financing is a new constraint.

So why are those more acute, right? So what's happened with memory? Memory has been a shortage. You can see that AI's percentage of DRAM wafers was going to be 70 percent. And then you can see that on the logic side, accelerator is an ever higher share. But what happened recently is there has been de-speccing. So let me show you 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 RUBIN Ultra, but that pattern actually stopped in its tracks. And what we got instead was de-speccing. Why? For really two reasons, right? Number one, ROI. And I'll just flick to a quick bridge chart, right? And so with this one, what we saw is the cost was increasing for the VRN BL72, and what they did to offset that is decreased SOCAM. 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 de-spec was necessary to de-constraint that.