The $500 Billion AI Debt Machine & The Great CapEx Test

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Speaker B: to the Market Maker, uh, podcast and Piers. I was going through some of the analytics and looking at the performance of some of the recent episodes and um, just as you might expect, anything with AI in it seems to be outperforming. And I don't want to be one who's like chasing clicks, but if that's what the people want, then who am I to deny them of that privilege?

Speaker C: So give the masses what they crave.

Speaker B: So in this episode, uh, we would normally do a bit of review of the week. So what I've tried to do here from the research perspective for this episode is blend together some of the news that's been happening this week with the dominant kind of narratives, that of AI and on macro, on inflation, US cpi, which came out yesterday from when we're recording this. So it's kind of framed around this shift we've spoken about a few times in recent episodes between who builds the best AI model to now, who has the trillions of dollars needed to build the data centers to run them? And that's distinctly tied to a headline of which we'll cover shortly.

Speaker C: Yep, absolutely. Um, and like, just to look at the numbers hitting the tape this week. So as a pure play AI cloud provider, who you'll know the name of when we get to it. Um, but basically they've been showing a staggering $104.2 billion revenue backlog. Um, I know that's a bit of a mouthful, um, but the order book, well, I don't think in the history of mankind there's ever been an order book that looks like this. Um, anyway, we'll get onto that. The next up is then, well, Nvidia teaming up with six of Wall Street's heaviest hitters, um, to basically mobilize a new $500 billion financing machine. And then we've got global AI capital expenditures pacing towards $1 trillion annually. That is, um, in terms of the, if you like, the hyperscaler, ah, uh, spending splurge that seems to be continuing to accelerate.

Speaker B: Yeah. So we'll try and go through this in the next kind of 30, 40 minutes. Been loving the comments by the way. Recent videos has really taken off. People sharing their ideas, agreeing, criticizing. Actually if you do like the conversations in the show, do that please because it really helps uh, the show perform on the different platforms. So we'd love to hear from you. So really dissecting then the financial plumbing we can call it behind this AI super cycle when it comes to that company you mentioned, that company is coreweave and Core Weave had an earnings blowout. I think their shares went up like almost 20% uh, when I was watching Bloomberg yesterday. Brings up the question then, what are Neo clouds and why their revenue is doubling and how they manage their massive debt loads? I mean the numbers are quite staggering actually, which I'm sure you're going to unpack in a moment with Nvidia, their 500 billion Wall street consortium. So the names involved in this, the ones that everyone likes to hear about, the Blackstones, the Apollos, and I guess trying to explain how they're transforming GPUs into a brand new financial asset class, uh, which we were just talking offline about, which is, which is really interesting and I think a good point to know, particularly if you're a student going into some of these conversations. Application season coming up and then the CapEx, of course, ongoing tug of war. This massive hardware spending is triggering volatility in tech stocks and driving a bit of a rotation some of the broader market sectors. So let's dive into this first theme. Core Weave's Q2 earnings report. Uh, I went on the about section of Core Weave and I was like, okay, what's the easiest way to try and explain this business? So here's the best, um, way of doing it and the reason why a lot of people look at it. It's become like this ultimate bellwether for AI hardware demand. And we'll talk about circular financing in a moment. But it's really important within that ecosystem. So what is it? Specialized cloud computing provider that builds data centers packed with these Nvidia graphics processing units to rent high performance compute power to AI developers and major tech companies. So that's what it is. Top line revenue surged 112% year over year to 2.58 billion. They also raised their full year guidance range to around 12 and a half to 13.2 billion. So 112% year over year revenue, you can't top that. Surely you haven't gotten any numbers that can get better than that?

Speaker C: Well, I can because that wasn't the most impressive thing in the report. Um, even though that in of itself is just outrageous, the most crazy, crazy number was as I mentioned, this 104.2 billion revenue, um, backlog. So I mean, what is that? So firstly, right, so they made 2.58 billion. That was their revenue. That's for the quarter that's behind us. Okay. They've given guidance for the full year that we're in to say that on this full year that we're currently in, we might get to 13 billion. Okay, so think about those for some numbers, even if you take the 13 billion and they hit that this year, 13 across a whole 12 months, their revenue backlog, that is basically book, their order book, that is orders committed is 104 billion,

Speaker B: which, what's the legal, um, what's the legal tie in for that? Are they, surely that could change though. That number could flex or I guess they'd probably have to pay a penalty, right, if you pull the order.

Speaker C: Yeah, absolutely. Look, some, the bears out there will talk of this circular financing or put in a easier way to understand the house of cards and whether or not that's going to come collapsing down. We're going to talk about that in a minute. All right, but let's just, let's just bask in the glow of this unbelievable number. So look, that, that order book, um, backlog, 104 billion, right, that's up just the backlog now has increased by 246% year over year. Okay, so this is all it's like in the last 12 months and. Well, it's been in the last 24 months. But the exponentiality of the way this is ramping is just staggering. Right, and by the way, then that being that, right, right after the quarter ended, so not in these figures, they just landed another $25 billion in net new customer commitments. Another 20, another 25. So that their backlogs now basically 130 billion, which is 10 times their forward looking revenue for the, for the year that we're in now. I um, mean, words just failed me at this point. But look, you know, so here we're talking about the long term contracts from the big guns. All right, so it's your metas, your open AIs, your Microsoft's. Okay? And obviously you can split and you might say, well okay, that sounds all amazing, but when might this revenue, you know, when, when are they committing to this revenue? And actually so 40% of that 104 billion is committed in the next 24 months. You then got another 39% that's committed 25 to 48 months and then 21%, the remainder is committed for 48 months plus. So we're obviously talking about a very long, you know, Runway, um, of commitments here, which is awesome for this business. I mean look, you know, we're talking about valuations and so on, like your order, but your forward looking order book is everything. Right? Because if you're buying shares in this company now, well of course you're buying future growth. And I mean they've got committed growth that's just in insane.

Speaker B: Maybe we could take a step back for a second and you know this essentially is cloud, but when people think of that, a lot of people think traditional cloud providers. So Azure from uh, Microsoft or Amazon aws, Google cloud. So maybe we could just explain for a moment what is a uh, what is this? What is a NEO cloud rather than traditional cloud providers.

Speaker C: Okay, so your traditional hyperscalers so that, you know those big, the big three as you've mentioned. Right. So Amazon's AWS cloud, you've got Microsoft's Azure and you've got Google cloud. So they're built, I mean they've been building those platforms for decades and they are general purpose computing. All right, we're talking web servers, whatever enterprise databases, app backends. I mean we are a big user of aws, like Amplify, um, ourselves. That's our cloud provider that we use. Right, but it's general purpose, the NEO cloud. Well we're talking specialization here. These are specialized providers built ground up exclusively for artificial intelligence. They are optimized for AI. Right. So core Weave uses things like liquid cooled racks, custom high speed interconnect networks. Um, they use bare metal GPU deployment.

Speaker B: So outside of computing, you can kind of think of it as in Volkswagen, uh, and Tesla both make cars, but Volkswagen will try and use the traditional uh, manufacturing plant and tweak it a little bit to make the electric vehicles. Whereas Tesla's built graphics ground up. Is that be a similar kind of

Speaker C: comparable top level you might say. Yeah, Volkswagen's mass market trying to build cars for everyone in the entire system. And um, maybe Ferrari, who trying to build a car that can race around a track the fastest. And so it's that specialization that has attracted all the big guns because they're, they're very specific spend from these hyperscalers. They're very, very, very specific spend is for this AI.

Speaker B: Is that why people buy Ferraris?

Speaker C: Apparently.

Speaker B: I don't think many people buy a Ferrari to race it round tracks.

Speaker C: No, but from an engineering point of

Speaker B: view of Course, I know you're such an engineering purist and that's what attracts

Speaker C: people to buy them because they're buying into that specialization and the thought of it.

Speaker B: Yeah, Anyway, okay, so let, let's talk about the flip side of the, the hypergrowth, then the cost structure of this because giving you a couple more numbers from their earnings release that we had this week. So they reported an adjusted net loss of $567 million for the quarter. Their GAAP net loss was $14 per share. Check this figure out though, because everyone goes a bit capex crazy when they're talking about the fragility of the sort of stock market rally we've had based on the spend. So the capital expenditures hit 9.4 billion in Q2 alone. So the company's spending 9.4 billion in the quarter. Their adjusted net loss of half a billion on the capex spend. What I thought I'd do is look for a comparable. And I know there's a bit of a, uh, defensive nature of Apple and they're not the biggest spenders on capex, but I just thought from a magnitude of company given that not many people even know core weave exists in the kind of public domain. So that 9.4 billion, Apple's CapEx in Q2, 2.5 billion. I mean that's just insane.

Speaker C: Yeah. So look, whilst those revenue figures, the revenue growth, awesome, the revenue backlog that uh, is the order book looking forward is disgustingly unbelievable in a positive way. They are racing to try and keep up with demand to the point where they're having to spend and spend and spend, uh, to try and keep up. But the spending rate is right now obviously much Greater. Like that 9.4 billion spend in Q2, their revenue was only 2.5, so they're spending three and a half, whatever that multiple is, three and a half, nearly four times their revenues. Right. So obviously they're making a loss. But the point is that they can't capture that 104 billion order book, they can't capture the money that's been committed without investing in their uh, growth now. Right. So it's about rapid expansion. So they're taking tens of billions in debt to buy Nvidia chips and build out mega, uh, facilities. And in fact, you know, quarterly interest expense alone, ah, surged to $640 million. So just to service the interest on the debt they're having to take on to try and build out capacity to try and capture this revenue commitment in the future. Right, So a couple of more stats then. Power Capacity. Um, so core weaves active power footprint at the moment is 1.5 gigawatts. Right? Well, that's what they're monetizing. Um, they've got contracted power, so what they've committed to, to build out is to reach 4.2 gigawatts at the moment. So they're at 1.5 gig and they've got a pathway to 4.2. Right. Just to put that in perspective, 1 gigawatt powers about 750,000 homes. Right. So if you think about core weaves contracted power pipeline, it basically matches the electricity consumption of a city of 3 million households. So that's the kind of power roadmap that they're trying to fund. And right now the revenues haven't quite caught up, so they're still losing money.

Speaker B: So if you want to know why you're sat in England right now and it's 38 degrees outside. Uh, you could probably pin some of the blame on these guys contributing, uh, to that. But so the demand pipeline definitely is legit. I mean, looking at those percentage splits and it seems very weighted to the near term, um, which is a good thing. So, 104 billion backlog is this, like you often talk about this when you and I talk about more traditional markets, um, this priced for perfection idea. So is this an execution risk scenario?

Speaker C: Um, for sure it's an execution risk, but I would say more than anything, it's a funding risk. Um, and we'll come onto it. It's the house of cards risk. Or maybe it's a solid foundation. It's kind of all interwoven and people use this word circular financing, but I would say it's a funding risk above everything else.

Speaker B: Okay, well on that point then, look, that ties into that other big story we've had of the week. Uh, and that was Nvidia announced strategic partnerships with six of the world's premier private capital institutions. Those being Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, KKR. They've got all of their hands in on this.

Speaker C: It's the big guns. But basically, what's the mandate? Basically, they're mobilizing over $500 billion of third party capital over time to underwrite this, this whole AI infrastructure building.

Speaker B: Yeah, and m thinking about the sheer scale of this. Historically, chip makers sold hardware to companies that funded purchases out of corporate cash flow or standard corporate bonds. But to quote the main man in the center of all of this action, uh, Jensen Huang of Nvidia, the CEO, he said in AI compute is revenue in his Words, we're helping create a new class of productive investable infrastructure, I. E. These AI factories. So I thought that's quite a good then segue from what he said as to trying to then unpack this idea of the business model and this advent of a new asset class. In terms of how these big institutional pockets of money, Blackstones and Apollos and kkrs, how are they engineering this? These are clever people. There's definite risk here. So how have they done it in a way to mitigate that risk to maximize on the opportunity?

Speaker C: Well, I mean, let's try and break it down. Uh, you know, how is this financial engineering actually working? So like historically tech, I mean think back pre AI tech hardware used to depreciate very quickly. That's just because you know the new version is going to be out uh, next year. Right. So in terms of, in terms of what value can you extract from an asset? You know, what's its lifetime worth to you as a business? And if you buy it this year and if you're going to buy something else and replace it next year, well obviously you've got a super short one year timeframe. And so you can't really, you know, you have to write off all that cost in this year number one. And you can't use that as a tangible asset to borrow against because next year it won't have any value. Right, that's how it used to work. But the game in town now is basically Nvidia's cuda, right? That's their Compute Unified Device Architecture. Um, their kind of software EcoSystem gives these GPUs a much longer flexible software life across different models and operators. Okay, so there's this thing called the value cascade concept that they've come up with to try and give this a kind of basically a new category. But basically the core argument is that the GPU stays cutting edge for six years. So even though think about Nvidia and we talked about it on previous episodes, their engineering kind of cycle is 12 months. So each 12 months they're trying to bring out the latest chip. And the latest chip is always, you know, several X factor, uh, better than the previous one. So isn't it the same if you bought a chip last year? Well, it's worth nothing this year. Well, the answer is no. So they're basically in this CUDA system. These older chips stay valuable. So chips basically move down a hierarchy of workloads as it ages and at each stage it still generates revenue. So years three to four, we call this supporting the Secondary life, real time inference, right? And then you've got years, five to six there. You support tertiary life. It's more like batch inference and analytics workloads. I mean, what does that mean? You know, when you're on um, Claude at the moment or ChatGPT or whatever, you can choose which of its systems to use, right? Depending on the job you want it to have. Um, and um, basically think about it like that. I don't want to use the Ferrari every single day, you know, just to drive to Sainsbury's, okay? So I want to use my, my Volkswagen Golf to go to Sainsbury's. So even though the Ferrari is much, much better, I'm still going to use the Golf and it's going to give me value. Basically. These companies can continue to generate revenue for 5, 6, I don't know, people are talking 10 years, right? And obviously we don't know yet about the 10, but do we know about the 5 or 6? What I will say is Michael Burry, the famous dude from the big short who nailed the financial crisis and made an absolute fortune, he's closed his hedge fund. Closed it, I um, think. Was it the end of last year? Closed it. Why? Performance is shocking.

Speaker B: Why?

Speaker C: Because he was taking the other side of this argument, this precise argument. He was saying these chips only have a 12 month lifetime. These companies are buying them and then they're spreading and capitalizing that cost and spreading the cost over six, seven years. And this m artificially inflates the, their profit. And so you are trading these stocks at valuations on the idea they're making these profits. He was saying they're false. However, Michael Burry's wrong and he's now out of business. Because we're already seeing this, because we were talking, when was it? Last week. OpenAI launched its kind of consumer facing product in 2022. We're almost four years in now and these chips are still producing revenue. So it looks like the companies are right and Michael Burry is wrong.

Speaker B: I'm reading a book at the moment called Superforecasting, which is this research theory about trying to determine really who has an ability to forecast things in the future. And why this is particularly relevant in a financial context is like trying to identify, you know, who can predict what's going to happen in a certain event. And so therefore you have to think about multiple different layers in order to arrive at uh, that decision. One of the qualifying factors early in the chapters is about when you have these people, particularly in finance, talking heads, people who go on Bloomberg, sell side institutions. Michael Burry, they um, never ever will explicitly put a timeline on the forecast and that null and voids the forecast in which they're making. Because if you're going to say this is an AI bubble, that's a house of cards, but you don't define the timeline well then basically it's an open ended statement that has zero commitment and zero predictability power behind it. So if it does fail, you can expect Michael Burry to be like, right Netflix, where's my, you know, the big short part two. Because I want to get paid out now. Because I was right about everything like I'm right about before.

Speaker C: Well of course that's what happened in the financial crisis. He was so early on uh, that trade, the trade being that the housing market's gonna collapse. He was so early, he was almost too early. He was like starting to put These trades on 2005, 2006 we got into 2007 and it was still going against him and against him. And he was getting margin calls from the banks who he'd done over the counter options and kind of derivatives deals with to kind of position himself for this strategy. And he almost killed him before then just, just in time. He was right and fine, made a fortune. So maybe there's, maybe there's an argument there that he's right but not for five years. Yeah, maybe he's just not right.

Speaker B: The other one was Ray Dalio. There's a book called the Fund, if you read that one. And he was back in the 80s talking about all this negative stuff and very Doom and Glo. But yeah, I mean he, he ends up being right but a decade or two off the, off the uh, off the needle. But okay, so look, summarizing then what you've said. So because these GPU clusters generate predictable long duration cache flows via these, you know, when you're dealing with these hyperscalers, these are long term contracts off of very established deep capital pocketed companies who are spending big time. So when you, even when you think about the others like anthropic, but then the traditional ones like meta. So Wall street is treating them right to say like real estate or aircraft fleets or, or probably the more correct definition infrastructure assets. So how you'd see it.

Speaker C: Yeah, absolutely. Yeah, it's that classic, you know, if you want to lend someone money, well then as the lender, uh, you know you obviously got to assess the credit risk and you've got to assess, well, what's the opportunity for the person I'm Borrowing, sorry, lending to here. And is there any tangible asset that I can under or they can underwrite, um, the loan with? So if it goes wrong and the wheels come off, what am I left with as the lender of value that I can extract at least some of my money and get it back. And yeah, so you know, a very old school way of financing like a manufacturing business was you would lease your, you would basically sell your machinery, you know, on your production line and uh, basically lease it back or you would borrow money with the machinery being the collateral to underwrite the loan. You know, it's these physical, tangible things from which you are able to generate value and generate the revenue that you do. Right. And so that's it. These chips or these data centers, if you want to kind of just zoom out a bit, these are the infrastructure plays of our time.

Speaker B: M. It's interesting you said that because I know your, your brother's in a bit of this space with the manufacturing plants. I didn't think of it like that. Uh, so actually this isn't a new model. It's a different type of machinery that uh, is the technology chips, but the model is a traditional one.

Speaker A: This episode is brought to you by Accenture. When your advertising operations fall out of sync, everything else follows. Spotify and Accenture are working together to reinvent the rhythm of ad sales using automation, analytics and smarter workflows to simplify campaign delivery, delivery and access better data across the business. The result, less time spent on operations, more time connecting brands with the moments and fandoms that matter most. Learn more@accenture.com Spotify this model is.

Speaker C: Oh, uh, my God, it's decades, decades, decades. Decades old.

Speaker B: M. Okay, well look, that leads us on then to the somewhat elephant in the room, which is that word, the circular financing. When you go on YouTube, it's like that's the buzzword because everyone wants the house of cards because we're, we love to see the world burn. It seems to be the way that people like to look at these things. So yeah, let's dive into a little bit about when what happened to Nvidia, when some of this news was breaking with this funding because you would think, hang about, they're all, they're forming a consortium, they're going to give you half a trillion dollars in cash to fuel the dream and then your shares fall. How does that work?

Speaker C: Well, so on the news that broke earlier this week about this $500 billion debt deal that Nvidia have spun up, their stock, their share price dropped 2.9%. And like for them, that's a lot. When you kind of spin it into market cap terms, that's 60 billion market cap just vanishes, right, just in that 2.9% drop. Uh, and this is because critics point to the circular nature of these deals. Right. So Nvidia invests in or helps structure debt for its clients, the Neo Clouds, Core Weave, for example. Right. Those clients, they then use that borrowed cash to buy Nvidia GPUs. Nvidia records that as revenue. That's your circular bit.

Speaker A: Right?

Speaker C: Let's kind of, let's like go on.

Speaker B: Hold on. Uh, given what you also previously said, are uh, they not also like a seed investor in OpenAI and therefore telling OpenAI, you should have multiple series of models that users can deploy so that we can extend the lifetime value of the chip clusters.

Speaker C: So this is it, right? This is the house of cards. So basically Nvidia, they're using their balance sheet strength, obviously, monster balance sheet. They've got cash flow that's just, uh, unbelievable. Okay. Massively strong balance sheet and they're using that and their reputation to back its key customers. So how are they doing this? Like core Weave, they'll go and buy an equity stake, right? And then in return they're going to get purchase commitments from CoreWeave. And um, basically from the lender's point of view, you know, if you're Apollo, why would I lend into this circular machine? It's because basically Nvidia's the backstop guarantor for the loan, right? It's not a straightforward loan, this. You've got the payer of last resort is Nvidia, the biggest company on the planet who's got hundreds of billions of cash, uh, on their balance sheet. Right. So these arrangements that typically come with strings though. So as We've alluded to CoreWeave commits to buying and deploying Nvidia's latest chips. So when you think about Coreweave's 104 billion revenue backlog, well, some of that is from obviously Nvidia on this loan. But anyway, we'll come back to that. So the actual debt financing, um, that these build outs comes mainly from the private credit, uh, and the kind of banks and the insurers. Right? So Nvidia's backing is part of what makes that debt investment grade. So investment grade is important here because Core Weave have issued their own debt. They issued an 8.5 billion. This is separate to the Nvidia 500 bill thing. Right. They issued a corporate bond for 8.5 billion. Um, and it was rated a 3 by Moody's. This is March of this year. Um, and that is the first GPU backed loan to reach investment grade status. And, uh, the only reason it's investment grade. So investment grade status just means it's cheaper for coreweave to borrow because the credit risk is lower, if you like, the risk to the lender is lower and it's all lower, uh, and a lower interest rate as a result. Because Nvidia and their role in that Dex Stack is basically. So it's less lender and it's more the entity whose backstop makes the debt investment grade in the first place. So because Nvidia are, uh, propping this all up, it makes it cheaper for their customers to borrow to then buy Nvidia chips. That's your, that's your kind of circular nature. Right. But to kind of finish this off, as long as coreweave can build out compute, which is dependent on hyperscalers keeping their, uh, commitments to buy that commute, then fine, revenues flow and the debt gets serviced. Okay, the moment that basically the moment the hyperscalers stop spending or they're not going to stop, currently it's an exponential acceleration in spending. The moment we get evidence of a deceleration in spending, it only needs to be a deceleration. Worse would be flattening. So we're going to carry on spending, but only at the same rate. Even worse would be we're going to start to reduce our spending. You've got different grades of problem here. Even worse. I mean, the absolute Armageddon is we're going to stop spending. Right. Which isn't really going to happen. But just to finish, if they stop spending, then obviously there's multiple Nvidia exposures here. There's revenue like Nvidia's revenue drops their equity stakes in all of the pies they've got their fingers in. Well, that devalues and basically then the backstop obligations become mount up and become maybe unserviceable and you get hit simultaneously, which is then that systemic concentration risk that analysts are flagging.

Speaker B: Yeah. And just on that point of the concentration risk, uh, I read a research paper from Columbia University and I know there's just a neat way of just summarizing that in three categories. So one that you've just been talking about is Nvidia's near, um, monopolistic position in AI chips compounds the concentration risk. I mean, it blows my mind that the authorities can let this happen. I mean, it's just kind of cops and robbers I guess. When there's money to be made you can move as fast as you can. The uh, regulators are too busy trying to work out who's got power rather than sort out this sort of stuff. But the entire ecosystem stability depending on one company's financial, health, strategic decisions. So monopoly that Nvidia have, they're like the center of the universe, they're the sun if you like. In this instance then you've got the geographic and infrastructure concentration. So you remember several months ago, this is the additional layered risk. If you remember the word stargate, that was the one you remember when all of the AI bros were with Trump in the Oval Office and they were like right, yeah. And then there was the softbank were there as well. Oracle and they were talking about Sam Altman. Oh yeah, they're all there. And they were talking about a multiple hundred billion S in cumulative spending across five US sites. So that in itself creates a concentrated exposure to specific data center locations and power infrastructure. I mean such as everything diversification is key and this is not that. And then you've got the customer concentration which you've kind of alluded to amplifying compounding the systemic nature of the risk. So your Microsoft alphabets, your Amazon, your Metis.

Speaker C: So yeah, and maybe, maybe like Nvidia's the sun, your hyperscalers, that's the fuel that's powering that sun. Right. As long as Microsoft and Alphabet and Amazon and Meta carry on spending, shoveling in the fuel, then we're fine. Okay. There is another risk which is more of a regulatory risk for the U.S. i would say because you've got those massive commitments. Oh, we're going to build data centers here and there and whatever and all these states, there's a real regulatory, lots of regulatory hurdles slowing everything down. So actually in the end, so you've got a uh, you've got a compute capacity problem. You just can't build the stuff quick enough partially because the regulators get in the way and block it. Then you've got a power problem which is are there enough electrons in the world to actually power all of this kind of planned build out. And that's also back to regulatory stuff in the US trying to build more nuclear reactors. I mean it basically takes 10 to 15 years to from start to finish to get a, to spin up a nuclear reactor. Right. I think China do it in 12 months. So I think from a, from a geopolitical race perspective, yeah, the U.S. they want to be careful they don't score an own goal here by regulation regulating their way, you know, out of the front position that they're currently in.

Speaker B: Well uh, let's step back a bit and let's tie this into some of the macro context firstly of how does this fit then within the investment thesis or strategy so to speak.

Speaker C: So the fuel that they're shoveling into the sun, so AI hyperscalers, their capex, this is an estimate for 2026 so a 12 month period they'll spend 730 to 800 billion. That's just the four hypers or the four or so biggest companies. Right. If you take everything, it's thought that we're going to break a trillion dollars this year. Right. Um so like if you think about Goldman's and JP so their projected global AI so if you, if trying to forecast this out as you said is very difficult. Um but Morgan Stanley estimates that the total cumulative hyperscaler spending will cross 3.5 trillion over the next three years. 3.5 trillion. So uh, if Morgan Stanley are right, if these hyperscalers carry on stepping up their spend, which they will only do if they can prove a return on investment for this spend, then this engine, this circular financing engine will carry on firing like super hot. But if at any point there's not an ROI sort of evidence then this is where your kind of wheels come off this circular financing machine

Speaker B: in order to sort of hedge yourself. Then in this scenario is this where Stephen and I were talking about this uh a few episodes ago. So this is where you what uh diversify by moving into the other parts of the supply chain. Namely as you were just talking about energy is the key ingredient. Energy and utilities. So data centers requiring power, driving demand for nuclear nat gas, grid infrastructure. Then you've got the industrial material orientated category. So electrical transformers, cooling systems, construction, so forth. Interestingly I was, I was with my friend from uni the uh weekend and he's a surveyor and he was saying it's, it's crazy where Google has these like secret entities that it goes around just buying up swabs of like real estate so they don't get basically charged a ton of money and they're just trying to find any location in proximity to natural water where they could just pulled out some of these centers and it's going crazy. And then there's the value and defensive so high dividend sectors providing that stability while the mega cap tech digests its capex uh investment. So maybe we could to kind of finish this section off then. Um, one other thing that we did see was anthropic. And the FT just broke a few hours before we were recording this. The valuation of 2 trillion. I thought we were at 1. What have I missed? I've blinked, I've missed it.

Speaker C: Revenue growth, that's what you've missed. We're a little bit in the dark, right? If you go back to the start of the year, we know that at the end of last year they were on, um, a $10 billion revenue run rate. So in that month of December, if you just took what they did in that month, multiplied it by 12, then that's 10 billion. They were at 1 billion at, uh, the end of 2024. So that's a 10x growth right now. At the start of this year, everyone was going, they can 10x again. So 1 billion to 10 billion to 100 billion. They can 10x again here, right? And everyone's going, no way. Not possible. Not possible. In May, which is the last official revenue kind of news we got there. Uh, they're already at 80 billion now. They're in a blackout phase because they've filed for their ipo. They go, uh, dark. So we actually haven't heard anything much from them. But you're now people who were in the know, they're talking about, they're talking about by the end of this year. Forget 100. They're, look, they're 110, 120 billion, which would be a 12x, so that the growth rate is accelerating. And then you're talking about people saying, well, it's going to 10x again the year after. So people are saying they'll do a trillion dollars of revenue, or the run rate at least will be a trillion dollars by the end of 2027. Bear in mind, the biggest companies on the planet do what, uh, 400 billion. Like the hyperscalers. They do 400, 500 billion revenue a year. We're talking a company that didn't exist five years ago. Didn't exist, doing a double that trillion dollars by the end of 2027. Look, that's, that's the, that's the bull case, right? Um, so the point about valuation then. Well, because it's hard, right? How do you compare it, uh, to, you know, what's, what's the kind of market comp here? And you could look to people like Palantir, for example, or Nebius, right? These are obviously much smaller, but they're trading at, uh, 55 times revenue in the open market now, right? So if Anthropic's growing, basically was Like a thousand percent a year. Um, then you would think, you know, even at the low end, they'd be getting 30 times revenue. Like low end, 30 times revenue would be 3 trillion, not 2. Some are saying 2 trillion is an absolute steal.

Speaker B: So, slight caveat though. When I was reading this report in the ft, the person quoted in saying these numbers, you're right, there's, let's say, a blackout period, not allowed to speak, just so happens to be an investor, uh, in the company talking their book up. I mean, I don't think they're wrong, don't get me wrong, but I think it's, uh, yeah, so funny. This is like the marketing. Ah, the marketing person. Just juicing. Just, you know, we haven't heard from Anthropic in a while. It feels like it's been a couple of weeks. Hang about, get back front and center, please. All this core weave, core weave, go away. Come on, this is Anthropic story. This is October3trillion. Here we go. So, yeah, interesting. Um, so one thing then to wrap up this bit, then we'll talk to close on US CPI and tie this to the macro, because as I said, the two major narratives for markets definitely are this AI infrastructure and where it might go and the spend around it. Given the magnitude of the companies involved in it lifting the stock market to these record highs, but also the macro climate in regards to inflation and interest rates. Before we talk, US CPI is Wall Street's $500 billion private credit pool. Uh, a masterstroke then in scaling this global compute infrastructure? Or is it too much leverage before the RRI has even begun to be proven? I don't want your answer, Piers. I want anyone listening because this is quite divisive. What do you think? Yeah, I've got a feeling everyone's going to be slightly bearish here, but I'd love to see the thesis behind the bullish or bearishness that people have.

Speaker C: Um, all right, so look, let's hear

Speaker B: let's talk about the US CPI report then. Cause lo and behold, stocks did rise on the back of this. And it was, uh, a bit of a surprise there were the one that Bloomberg and the rest of the financial media were latching onto, rightly or wrongly, was the core CPI that came in at, uh, 0.2% month to month, 2.5% year on year, the slowest annual pace since March of 2021. So how do you, how does that fit in then to where the market is at with its thinking with the new Fed chair, Walsh and interest Rate expectations going out for 2026.

Speaker C: Yeah. So it's a good report. Um, 2.5%, you're right. It's the lowest since March 2020. However, it matches January and February 2026 numbers. So we have been here.

Speaker B: This isn't sound effective. You don't. Why did you have to go and mention that?

Speaker C: Uh, so look, we had, I think obviously it's tied all to straights, uh, of four moves and what goes on and energy prices and how that filters down through the system. But look, we're back to pre, straight to four moves, right? That's the point here. January and February, before things kicked off in the Gulf, we were at 2.5 and we were expecting the downward trend of the previous couple of years to continue. And um, back then, remember we were expecting a couple of rate cuts this year if that trend were to continue. It obviously didn't. We bucked higher. So we went 2.6 March, we went 2.8 April, we went 2.9 May, but now it's toppled back down. June 2.6 July 2.5. So it's like, all right, that uh, inflation concern maybe's over. So it just means Walsh doesn't have to hike. I think you forget, uh. Oh, we've got one more. So this was inflation for July, right. We will actually have the August inflation data announced before the next Fed meeting. But based on this.

Speaker B: Yeah, the Fed Watch tool which allows us to look at ah, short term interest rate futures. So it basically gives us an implied probability of how the markets are expecting what for when. And so that now sees a September rate hike, odds down to 38%.

Speaker C: I think that's too high. I'd be selling that myself. I'm a seller. Um, there is the one thing in the mix in the basket that's causing concern. Computer software and accessories. That part of the basket's up 21% year on year. Why? Because of everything we just spoken about. Right. The demand or the lack the demand supply imbalance for everything. AI has meant the cost of these semiconductors chips for example, has just gone through the roof. So obviously that's feeding through into inflation, but it's not enough. That component's currently not big enough or I mean you could argue the basket isn't set up appropriately for the modern day's expenditure. So I don't know, there's two sides to that argument. Right. But for now, the way the inflation basket is measured at the moment, that computer software and accessories classification isn't enough to kind of take the Whole inflation number back higher. Right. Um, so there is one thing in the basket. Just as a quick aside, biggest down, biggest faller in the basket, lettuce. The price of lettuce dropped 16.4%.

Speaker B: There you go. I can, I can stack my burger now, uh, with tomato and lettuce.

Speaker C: There's a disease, there's a parasite or disease in the Midwest, in the US in lettuce. People aren't buying it because they don't want to get stomach M bug. So you've got now oversupply because the demand has dropped. So you got lettuce down 16.4%. So there's a supply and demand case study if you want to go and grab uh, hold of that.

Speaker B: And then one thing on the timeline also to be aware of next month is Jackson Hole M. Jackson Hole Symposium is one of those platforms where the Fed chair gets to give a keynote speech that's happening at the end of August, I think 27th through to 29th. He's normally like the main headline act like the Glastonbury headline. And it's definitely outside of the fixed set schedule of Fed events that happen eight times a year. That's the other one where if there is a little signal to be issue to the market about what's going to happen in September, the likelihood he might give some, some, some things or not, given what he's got, I don't know.

Speaker C: Don't hold your breath.

Speaker B: Come up and go, hi, Nice mountain scenery. Thanks very much.

Speaker C: He's going to say nothing. I think this. Yeah, I know historically that Jack's not,

Speaker B: not a thing anymore.

Speaker C: I don't think. Well, I think it's uh, definitely going to become less and less a thing. Whether it's. He's only just got into the seat, so maybe it's still a thing. But I doubt he would use that platform to signal forward guidance, given that everything he's about is not giving forward guidance. Right.

Speaker B: So it's good nightlife though, out in Wyoming. He might have a few beers the night before and then the genuine Kevin comes out to play. And then we can have some trading activity then keep it. Uh, okay, so that concludes the episode. As I said, love to hear your thoughts and what you think about a lot of the. Particularly the AI investing side of things. Uh, let us know. But thanks very much, Piers, and thanks everyone for listening.

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