Speaker 1: Media.
Speaker 2: Hi, I'm ed Zitron, and welcome back to Better Offline
Speaker 2: And this is our third and final part of our
Speaker 2: Better Offline and Vidia special, where we're talking about well,
Speaker 2: the shakiness behind its growth and how the company, despite
Speaker 2: being on incredibly infirm ground, is definitely not Enron or
Speaker 2: Nortail or Wolcom or Lucien or any other dot com
Speaker 2: bubble era affirm that imploded under its own way and
Speaker 2: well quite dodgy accounting. The thing is, even if Enron
Speaker 2: is nothing like them, there's still quite a few causes
Speaker 2: for concern, and that's largely driven from the fact that
Speaker 2: in Vidio makes the majority of its money selling GPUs
Speaker 2: to a handful of customers, and so well, some of
Speaker 2: those also look to be on some of their own
Speaker 2: incredibly shaky ground. And yeah, I'm talking about Oracle now.
Speaker 2: In Vidia's health saying nothing of its growth isn't just
Speaker 2: tied to these customers. It's also tied to whether these
Speaker 2: customers can actually turn a profit from their capex spending.
Speaker 2: And even that's not even certain so due to the
Speaker 2: fact that so much money has been piled into building
Speaker 2: AI infrastructure and big tech has promised to spend hundreds
Speaker 2: of billions of dollars more in the next year. Big
Speaker 2: tech has found itself in a bit of a hole.
Speaker 2: How big of a hole? Well, by the end of
Speaker 2: the year, Microsoft, Amazon, Google, and Meta will have spent
Speaker 2: over four hundred billion dollars in capital expenditures, much of
Speaker 2: it focused on building AI infrastructure, on top of two
Speaker 2: hundred and twenty eight point four billion dollars in capex
Speaker 2: in twenty twenty four and around one hundred and forty
Speaker 2: eight billion in capital expenditures in twenty twenty three, for
Speaker 2: a total of seven hundred and seventy six billion in
Speaker 2: the space of three years, and they expect to spend
Speaker 2: more than four hundred billion dollars more in twenty twenty six.
Speaker 2: Every time I read these numbers, I feel a little crazy.
Speaker 2: As a result, based on my own analysis, big tech
Speaker 2: needs to make two trillion dollars in brand new, brand
Speaker 2: spanking new revenue, specifically from AI by twenty thirty. All
Speaker 2: of this was effectively for nothing. Now, I go into
Speaker 2: detail about this in the premium newsletter I did on
Speaker 2: October thirty first, but I'm going to give you a
Speaker 2: short explanation here. First, though, we have to talk about depreciation,
Speaker 2: and because I'm lazy, I'm going to quote myself in
Speaker 2: that newsletter I just mentioned a couple of seconds ago
Speaker 2: at hem. So when Microsoft buyers say one hundred million
Speaker 2: dollars worth of GPUs, it immediately comes out of its
Speaker 2: capital expenditures, which is when a company uses money to
Speaker 2: invest in either buying or upgrading something. It then adds
Speaker 2: to its property, plants and Equipment assets PPE for sure,
Speaker 2: although some companies list this on their annual and quarterly
Speaker 2: financials as property and equipment PPE sits on the balance sheet,
Speaker 2: it's an asset as it's stuff for the company that
Speaker 2: it owns or as least, GPUs depreciate, meaning they lose
Speaker 2: value or over time, and this depreciation is represented on
Speaker 2: a balance sheet and the income statement. Essentially, the goal
Speaker 2: is to represent the value of an asset that a
Speaker 2: company has on the income statement, and we see how
Speaker 2: much the assets have declined during the reporting period, whether
Speaker 2: that be a year or a quarter or something else,
Speaker 2: Whereas the balance sheet shows the cumulative depreciation of every
Speaker 2: asset currently in play. Depreciation does two things. And I
Speaker 2: know this sounds like a lot, but I'll break it
Speaker 2: down for you First, it allows a company to accurately
Speaker 2: to an extent, represent the value of things it owns
Speaker 2: over their useful life. Secondly, it allows a company to
Speaker 2: deduct the value of an asset across said useful life
Speaker 2: right up until its eventual removal, versus having to take
Speaker 2: a big hit up front. The way this depreciation is
Speaker 2: actually calculated can vary. There are several different methods available,
Speaker 2: with some allowing for greater deductions at the start of
Speaker 2: the term, which is useful for those items that will
Speaker 2: experience the biggest drop in value right after buying them
Speaker 2: and their initial use. An example you're probably familiar with
Speaker 2: is a new car which loses a significant chunk of
Speaker 2: its value the moment is driven off a dealership. Long
Speaker 2: creation has become a big ugly problem with GPUs specifically
Speaker 2: because of that useful life to find either as how
Speaker 2: long the thing is able to be run before it
Speaker 2: dies or how long before it becomes obsolete, and nobody
Speaker 2: seems to be able to come up with a consensus
Speaker 2: about how long this should be. In Microsoft's case, the
Speaker 2: appreciation for its service is spread over six years, a
Speaker 2: convenient change it made in August twenty twenty two, A
Speaker 2: few months before the launch of chat GPT and before
Speaker 2: it bought a bunch of fucking GPUs. This means that
Speaker 2: Microsoft can spread the cost of tens of thousands of
Speaker 2: a one hundred GPUs brought in twenty twenty or the
Speaker 2: four hundred and fifty thousand, h one hundred gupus it
Speaker 2: bought in twenty twenty four across six years, regardless of
Speaker 2: whether those are the years they'll be generating revenue or
Speaker 2: naturally functioning. Corwy for what It's worth says the same thing,
Speaker 2: but largely because it's betting that it'll still be able
Speaker 2: to find users for older silicon after its initial contracts
Speaker 2: with the companies like OpenAI expire. The problem is is
Speaker 2: that aigpus are fairly new concepts, and thus all of
Speaker 2: this is pretty much untested ground. Whereas we know how long,
Speaker 2: say a truck or a piece of heavy machinery can
Speaker 2: last and how long it can deliver value to an organization,
Speaker 2: we don't know the same thing about the kind of
Speaker 2: data center GPUs that hyperscalers are spending tens of billions
Speaker 2: of dollars on each year. Any kind of depreciation schedule
Speaker 2: is based on at best assumptions and at worst hope.
Speaker 2: Now this is important. The concept of an AI data
Speaker 2: center is super new. We maybe saw the first ones
Speaker 2: in twenty ninety. In question, it's kind of hard to say,
Speaker 2: but even at the scale we're seeing today a Gigawa
Speaker 2: data center pretty much brand new, maybe a couple years old.
Speaker 2: I don't even think they've even built any but we'll
Speaker 2: get to that in a bit. There are a lot
Speaker 2: of assumptions at play. There's the assumption that the cards
Speaker 2: won't degrade with heavy usage, or the assumption that future
Speaker 2: generations of GPUs won't be so powerful and impressive that
Speaker 2: they'll render the previous ones more obsolete than expected, kind
Speaker 2: of like how the first jet powered planes of the
Speaker 2: nineteen fifties did to those manufactured just a decade prior.
Speaker 2: The assumption that there will be in fact a market
Speaker 2: for Alder cards, than that there'll be a way to
Speaker 2: lease them profitably. What if those assumptions are I don't know, wrong.
Speaker 2: What if that hope is ultimately irrational. So there's a
Speaker 2: quote from the Center for Information Technology Policy framing this problem, well,
Speaker 2: that'll link to in the notes. Here is the puzzle.
Speaker 2: The chips at the heart of the infrastructure build out
Speaker 2: have a useful lifespan of one to three years due
Speaker 2: to rapid technological obsolescence and physical wear, but companies appreciate
Speaker 2: them over five or six years. In other words, they
Speaker 2: spread out the cost of their massive capital expenditures over
Speaker 2: a longer period than the facts warrant what the economist
Speaker 2: is referred to as the four trillion dollar accounting puzzle
Speaker 2: at the heart of the AIICLOUD. This is why Michael
Speaker 2: Burry brought it up recently because spreading out these costs
Speaker 2: allowed spigtech to make their net income i e. Their
Speaker 2: profits look better in simple terms. By spreading out the
Speaker 2: costs over six years rather than three, hyperscalers are able
Speaker 2: to reduce the line item that eats into their earnings,
Speaker 2: which makes their companies look better to the markets. So
Speaker 2: why does this create an artificial time limit? Well, let's
Speaker 2: start with a horrible fact. It takes it's two point
Speaker 2: five years of construction time in about fifty billion dollars
Speaker 2: per gigawa of data center capacity. No matter when the
Speaker 2: GPUs for a giga what data center are bought one
Speaker 2: way or another, these GPUs are depreciating in value either
Speaker 2: through death or reduced efficacy through wear and tear, or
Speaker 2: becoming obsolete, which is very likely as in Video is
Speaker 2: committed to releasing a new GPU every single year. Newer
Speaker 2: generation GPUs, like in videos Blackwell and Verra Reuben require
Speaker 2: entirely new data center architecture, meaning that one as to
Speaker 2: why they build a brand new data center a retrofit
Speaker 2: an old one. Essentially, we have facilities that are being
Speaker 2: built around a GPU design or product that may change
Speaker 2: in a year or two. Now I hear that in
Speaker 2: the Oberon racks that they use for the Blackwells will
Speaker 2: be used with some Verra Rubin. But even then there's
Speaker 2: going to be an even bigger, more huger Vera Ruben
Speaker 2: that comes that might even that I read somewhere that
Speaker 2: there might even be like killer what level ones just
Speaker 2: in like one hundred killer what ones that this company's insane. Nevertheless,
Speaker 2: at some point Wall Street is going to need to
Speaker 2: see some sort of return on this investment, and right
Speaker 2: now that return is negative dollars. I break it down
Speaker 2: on my October thirty first premium piece, but for your sake,
Speaker 2: I'll just say it. I estimate the big techniques to
Speaker 2: make two dollars for every dollar of CAPEX they've spent,
Speaker 2: and this revenue must be new, brand new, As this
Speaker 2: CAPEX is only for AI. This CAPEX is useless for
Speaker 2: everything else. It does not help it. And no, it
Speaker 2: doesn't help that they bolted copilot onto fucking everything that
Speaker 2: is not working, and in fact, the Australian Competition Commission
Speaker 2: is suing them. Maybe I mentioned that later, but whatever, Meta, Amazon,
Speaker 2: Google and Microsoft are already years and hundreds of billions
Speaker 2: of dollars in and are yet to see a dollar
Speaker 2: of profit, creating a one point two to one trillion
Speaker 2: dollar hole just to justify the expenses. So around six
Speaker 2: hundred and five billion dollars of CAPEX all told at
Speaker 2: the time I calculated it. Much of this CAPEX has
Speaker 2: been committed or spent before they've even turned on a
Speaker 2: single goddamn GPU. You might argue that there's a scenario
Speaker 2: here where say, an A one hundred GPU is useful
Speaker 2: past the three or six year shelf life. Even if
Speaker 2: that were the case, the average rental price of an
Speaker 2: A one hundred is ninety nine cents an hour. This
Speaker 2: is a four or five year old GPU, and customers
Speaker 2: are paying for it like they would a five year
Speaker 2: old piece of hardware. The same fate awaits the H
Speaker 2: one hundred, which was released in twenty twenty two but
Speaker 2: was still sold in great volume through twenty twenty four,
Speaker 2: and I hear the H two hundred of the same
Speaker 2: generation is still selling to this day. Every year in
Speaker 2: Vidia releases a new GPU, lowering the value of all
Speaker 2: the other GPUs in the process, making it harder to
Speaker 2: fill in the holes created by all the other GPUs
Speaker 2: is capex and costs. This whole time, nobody appears to
Speaker 2: have found a way to make a profit, meaning that
Speaker 2: the hole created by these GPUs remains unfilled, all while
Speaker 2: big tech firms by more GPUs, creating more holes to fill.
Speaker 2: So now that you know this, there's a fairly obvious
Speaker 2: question to ask, why in the hell are they still
Speaker 2: buying GPUs? Well, so, where the fuck are these GPUs going?
Speaker 2: So a few weeks ago I wrote a piece Premium
Speaker 2: one called the Hater's Guide to Nvidia, and I asked
Speaker 2: the basic question in there, where have all the GPUs
Speaker 2: that Invidia has sold actually gone? In particular, the six
Speaker 2: million Blackwell GPUs that Jensen one keeps banging on about
Speaker 2: Now there's little evidence that these are being used in
Speaker 2: the volume which they're sold, suggesting that they're either languishing
Speaker 2: in the supply chain or being warehoused by hyperscalers, or
Speaker 2: even in Nvidia themselves. Now there's the argument that this
Speaker 2: could be and this is wanky Nvidian bullshit, this could
Speaker 2: actually be two GPUs per GPU sold because there's two
Speaker 2: chips on each GPU. Even if that was the case,
Speaker 2: three billion GPUs Blackwell, specifically the brand new ones, they're
Speaker 2: not in service.
Speaker 1: Now.
Speaker 2: While I'm not going to go and copy paste an
Speaker 2: entire premium piece into this script, I am, however, going
Speaker 2: to go into detail about what I found. And the
Speaker 2: truth is, I can only really see in this inclodes,
Speaker 2: looking over like bunches of data center maps, reading hundreds
Speaker 2: of press releases, documents, earning statements. I've only been able
Speaker 2: to find maybe a couple hundred thousand Blackwell GPUs in existence,
Speaker 2: maybe half a million to seven hundred and fifty thousand
Speaker 2: if you include the stuff that hasn't even been built yet.
Speaker 2: Let's go into it. So Stargate Apilene allegedly four hundred
Speaker 2: thousand Blackweld gps A, going there now Oracle CEO Coco,
Speaker 2: I should say, Clay McGurk, that's probably not how you
Speaker 2: say that. He claimed very recently there were ninety six
Speaker 2: thousand of them in stored, so not great. There's theoretically
Speaker 2: in one hundred and thirty one thousand Blackwell GPU cluster
Speaker 2: owned by Oracle that they announced in March twenty twenty five,
Speaker 2: so that should be online. Never five thousand blackweld GPUs
Speaker 2: at the University of Texas Austin, which sound like they're online,
Speaker 2: more than fifteen hundred in a Lambda data center in Columbus, Ohio.
Speaker 2: Those are online. The Department of Energy is still in
Speaker 2: development one hundred thousand GPU supercluster as well as ten
Speaker 2: thousand in Vidia blackweld GPUs that are expected to be
Speaker 2: available in twenty twenty six, and it's Equinox cluster. Really
Speaker 2: can't establish how many of those are actually in operation.
Speaker 2: Fifty thousand of these blackweld GPUs going into the still
Speaker 2: unbuilt musk Run Colossus to supercluster, Corby's largest GB two
Speaker 2: hundred Blackwell cluster of two four hundred and ninety six
Speaker 2: Blackwell GPUs, tens of thousands of them deployed globally by Microsoft,
Speaker 2: including forty six hundred Blackwell Ultra GPUs and two hundred
Speaker 2: and sixty thousand of them, these Blackwell GPUs going into
Speaker 2: five AI data centers for the South Korean government, and yeah,
Speaker 2: I just want to be clear that that is also
Speaker 2: fairly recently announced, so probably not even not even built,
Speaker 2: let alone powered on. I goan to be honest, I'm
Speaker 2: genuinely unable to find one million Blackwell GPUs like inexistence. Now,
Speaker 2: some of you might say, oh, there's a bunch of
Speaker 2: secret ones. There's a bunch of them. They don't announce
Speaker 2: every single one. Here's the thing. Three million of these
Speaker 2: fucking things have allegedly been shipped. I can't find a
Speaker 2: million of them. And considering everybody always talks about their
Speaker 2: GPU purchases, I'm kind of shocked at calm. Now. I
Speaker 2: do not know where these six million black Weld GPUs
Speaker 2: have gone, but they certainly haven't gone into data centers
Speaker 2: that are powered and turned on. In fact, power has
Speaker 2: become one of the biggest issues of building these things,
Speaker 2: and the fact it's really difficult and maybe impossible to
Speaker 2: get the amount of power these things need to the
Speaker 2: goddamn data centers. In really simple terms, there isn't enough
Speaker 2: power or built data centers for those black world GPUs
Speaker 2: to run, in part because the data centers aren't built,
Speaker 2: and in part because there isn't enough power for the
Speaker 2: ones that are. Microsoft CEO Sacha Nadella recently said in
Speaker 2: a podcast that his company and I quote didn't have
Speaker 2: the warm shells to plug into, meaning buildings with sufficient power,
Speaker 2: and heavily suggested that Microsoft may actually have a bunch
Speaker 2: of chips sitting in inventory that they couldn't plug in now.
Speaker 2: Just to give you an estimate here, even if we
Speaker 2: say three million GPUs, even if we're going with the moonmath,
Speaker 2: the Vinvidia, if we're going into the make believe world,
Speaker 2: the twisted mind of Jensen Wang, still three million GPUs.
Speaker 2: We'll look at still like five or six gigawatts of capacity.
Speaker 2: It's not being built. I don't even think two gigawats
Speaker 2: of data center capacity have been built. And I swear
Speaker 2: to fucking God, if one of you emails me and said,
Speaker 2: and Eric is built, and twenty gigawatts or something, power
Speaker 2: can get built. Power can get built. You can build power,
Speaker 2: getting it to the data center and actually powering the
Speaker 2: data center correctly, as in things turn on, everything works,
Speaker 2: nothing overloads, nothing blacks out, and the power is consistently done.
Speaker 2: Takes what's just months of surveys and scientific stuff and
Speaker 2: then years to just get it done. Stargate Appleine only
Speaker 2: has two hundred megawatts. They're gonna need over one point
Speaker 2: four gigawats just to turn the fucking thing on. I'm
Speaker 2: so tired of He's God damn GPUs bo. With all
Speaker 2: this said, why pray tell? Is Jensen Huang of Nvidia
Speaker 2: saying that he has twenty million Blackwell and Vera Rubin
Speaker 2: GPUs ordered through the end of twenty twenty six. Where
Speaker 2: are they fucking going? Jensen? Now, I think that number
Speaker 2: also includes the six million. And also, to be clear,
Speaker 2: I know a lot of you aren't technical, which is awesome.
Speaker 2: I love. I want you all to know about this.
Speaker 2: You need to know that this is part of the
Speaker 2: course within Nvidia in video loves schmushing accountancy things together
Speaker 2: and coming up with random numbers. Credit the case could
Speaker 2: go our friend of the show for telling me the story.
Speaker 2: But during the early twenty twenties, so that there is
Speaker 2: twenty twenty two during the Big Crypto rush. In Vidia
Speaker 2: classified gaming GPUs that were sold to bitcoin miners as
Speaker 2: gaming revenue. They got digged by the SEC. Wasn't fraud,
Speaker 2: but just so you know, in video will move shit around.
Speaker 2: And I truly do not know where these GPUs are.
Speaker 2: I do not know even why anyone is still buying GPUs.
Speaker 1: Now.
Speaker 2: AI bulls will tell you that there's this insatiable demand
Speaker 2: for AI and these massive amounts of orders are proof
Speaker 2: of something or rather, and you know what, I'll give
Speaker 2: them that it's proof that people are buying a lot
Speaker 2: of GPUs. I just don't know why nobody has made
Speaker 2: a profit from AI, and those making revenue aren't really
Speaker 2: making that much. Let me give you an example. My
Speaker 2: reporting on open ai from November twelfth suggests that the
Speaker 2: company I only made four point three two nine billion
Speaker 2: dollars in revenue for the end of September, extrapolated from
Speaker 2: the twenty percent revenue share that Microsoft receives in the company.
Speaker 2: And now some people who write really shit our substacks
Speaker 2: have argued with the figures, claiming that they're either delayed
Speaker 2: or are not inclusive of the revenue that open Ai
Speaker 2: is paid from Microsoft as part of being's ai integration
Speaker 2: and sales of open AI's models throughout Microsoft as ure.
Speaker 2: So I want to be clear of two things. Some
Speaker 2: a deeply bitter person. This is a crual accounting, meaning
Speaker 2: that these numbers are revenue booked in the quarter I
Speaker 2: reported them. Any comments about quarter long delays or naive approaches,
Speaker 2: and you know who I'm fucking talking about, if you're listening,
Speaker 2: are incorrect and a riboso. Also, Microsoft's revenue share payments
Speaker 2: to open ai kind of pathetic, totally based on documents
Speaker 2: reviewed by this newsletter publication whatever you call me, media
Speaker 2: entity floating blob in the podcast, for sixty nine point
Speaker 2: one million dollars in counting the year Q three, twenty
Speaker 2: twenty five. And by the way, the actual number for
Speaker 2: that three month period, including all royalties, is about four
Speaker 2: point five two seven billion dollars of revenue. I just
Speaker 2: want to be clear about something with open Ai. I'm
Speaker 2: not saying they're misrepresenting their numbers to anyone. I hope
Speaker 2: that open ai is being honest with their revenues. But
Speaker 2: if it comes out, I'm right, if it comes out
Speaker 2: that it turns out that they've been telling investors completely
Speaker 2: different numbers, I'm gonna be absolutely fucking insufferable. I'm going
Speaker 2: to bring in I'm going to be playing Tommy Trumpets
Speaker 2: a walk around cheering that you can get five minutes
Speaker 2: of monologue about that. Also in the same period, Open
Speaker 2: Ai spent eight point six seven billion dollars on inference,
Speaker 2: which is the process in which an l them creates
Speaker 2: its output. This is the biggest company in the generative
Speaker 2: AI space, with eight hundred million weekly active users in
Speaker 2: the Mandate of Heaven in the eyes of the media, Anthropic,
Speaker 2: it's largest competitor, Allegacy, will make eight hundred and thirty
Speaker 2: three million dollars in revenue in December tween twenty five,
Speaker 2: and based on my estimates, will end up having about
Speaker 2: four and a half to five billion dollars if revenue
Speaker 2: by the end of the year. Based on my reporting
Speaker 2: from October Andthropic spent two point six six billion dollars
Speaker 2: on Amazon Web Services through the end of September, meaning
Speaker 2: that it, based on my own analysis of reported revenues,
Speaker 2: spent one hundred and four percent of its revenue up
Speaker 2: to that point just on AWS likely spent as much
Speaker 2: on Google Cloud. Now, the reason I'm bringing up these
Speaker 2: numbers is these are the champions, the champions of the
Speaker 2: AI boom, yet their revenues kind of fucking stink. Wow.
Speaker 2: Even if open ai made thirteen billion dollars this year,
Speaker 2: even if Anthropic made five billion dollars, okay, wow, so
Speaker 2: that's not even twenty billion dollars. That's like nineteen billion
Speaker 2: dollars less than Microsoft spent on GPU's and other capex
Speaker 2: in the last quarter. That's dogshit. I'm sorry, I'm just
Speaker 2: tired of I am tired of humoring this. I'm sure
Speaker 2: all of you are too. I find it loathsome that
Speaker 2: we have to pretend these people are gifted somehow, they
Speaker 2: have shit. Ask businesses that burn billions of dollars, and
Speaker 2: you know what. Another thing I'm tired about is everybody
Speaker 2: telling this story about Anthropic being more efficient and only
Speaker 2: burning two point eight billion dollars this year. Now one
Speaker 2: has to ask a question about why this company that's
Speaker 2: allegedly reducing costs had to raise thirteen billion dollars in
Speaker 2: September twenty twenty five, after raising three point five billion
Speaker 2: dollars in March twenty twenty five, after raising four billion
Speaker 2: dollars in November twenty twenty four. Am I really meant
Speaker 2: to read stories about Anthropic hitting break even in twenty
Speaker 2: twenty eight with a straight face, especially as other stories
Speaker 2: say that we cash flow positive as soon as twenty
Speaker 2: twenty seven. This company's as big a pile of shit
Speaker 2: as open Ai. Open Ai raised eighteen point three billion
Speaker 2: dollars this year. That's less than two billion dollars more
Speaker 2: than Anthropic, who makes a bunch less revenue. Can't believe
Speaker 2: I'm defending open Ai. But these companies are the two
Speaker 2: two largest ones in the generative of AI space, and
Speaker 2: by extension, the two largest consumers of GPU COMPUW Both
Speaker 2: companies burn billions of dollars and require an infinite amount
Speaker 2: of venture capital to keep them alive. At a time
Speaker 2: when the Saudi Public Investment Fund is struggling and the
Speaker 2: US venture capital system is set to run out of
Speaker 2: cash in the next year and a half, The two
Speaker 2: largest sources of actual revenue for selling AI compute are
Speaker 2: subsidized by venture capital and debt. What happens if these
Speaker 2: sources dry up. They're not paying out of cash flow,
Speaker 2: and in all seriousness, who else is buying AI compute?
Speaker 2: What are they doing with them? Hyperscalers other than Microsoft,
Speaker 2: which chose to stop reporting its AI revenue back in
Speaker 2: January when it claimed it made about a billion dollars
Speaker 2: a month in revenue, don't disclose anything about their AI revenue,
Speaker 2: which in turn means that we have no real idea
Speaker 2: of how much real actual money is coming to justify
Speaker 2: these GPUs core We've made one point three six billion
Speaker 2: dollars in revenue and lost one hundred and ten million
Speaker 2: dollars doing so in the last quarter. And if that's
Speaker 2: indicative of the kind of actual real demand for AI compume,
Speaker 2: I think it's time to start panicking about whether all
Speaker 2: of this was for nothing. Corweave has a backlog of
Speaker 2: over fifty billion dollars in compute, and twenty two billion
Speaker 2: dollars of that is open AI company that burns billions
Speaker 2: of dollars a year and lives on venture subsidies. Fourteen
Speaker 2: billion dollars of that is Meta, which is yet to
Speaker 2: work out how to make any kind of real money
Speaker 2: from generative AI. And no, it's generative AI ads are
Speaker 2: not the future four or four media. I love you,
Speaker 2: but that story was bunk and the rest of it
Speaker 2: is likely a mixture of Microsoft and Video, which agreed
Speaker 2: to buy six point three billion dollars of any unused
Speaker 2: compute from Corewave through twenty thirty two. Should also be clear,
Speaker 2: I do pay and subscribe to four or four I
Speaker 2: love it. Just the AI ads story was wank, I
Speaker 2: love you, I love you, Joe, I love I love
Speaker 2: I love the publication. Sorry. I also forgot Google, by
Speaker 2: the way, which is renting capacity from Corewave to rent
Speaker 2: to open AI, and I'm not shitting you. Oh fuck Sorry.
Speaker 2: I also forgot to mention that Corwy's backlog problem stems
Speaker 2: from data center construction delays. That and Corewave has fourteen
Speaker 2: billion dollars in debt, mostly from buying GPUs, which is
Speaker 2: able to raise by using GPUs as collateral, and then
Speaker 2: it had contracts and customers willing to pay for it,
Speaker 2: such as in Video, who is also selling it the GPUs.
Speaker 2: I also left something out of this script, which is
Speaker 2: that just the last week, core We've just raised another
Speaker 2: two billion dollars of debt. When this all ends, I
Speaker 2: am going to be a little insufferable. But let's just
Speaker 2: be abundantly clear. Core Weaver has bought all those GPUs
Speaker 2: to rent open AI, Microsoft for open Ai, Meta Google
Speaker 2: for open Ai, and in Video, which is the company
Speaker 2: that benefits from core Weave's continuum ability to buy GPUs? Otherwise,
Speaker 2: where's the fucking business? Exactly? Who are the customers, who
Speaker 2: are the people renting the GPUs? And what is the
Speaker 2: purpose for which they're being rented? How much money is
Speaker 2: renting those gps? Can you? Can you tell me? Can
Speaker 2: anyone tell me? Can anyone tell me anything? You can
Speaker 2: sit and wank and waffle on about the supposed glorious
Speaker 2: AI revolution all you want, but where's the goddamn money?
Speaker 2: And why exactly are we still buying GPUs? What are
Speaker 2: they doing, who are they being rented for what purpose?
Speaker 2: And why isn't it creating the kind of revenue that's
Speaker 2: actually worth sharing or products that are actually worth using.
Speaker 2: Is it because the products suck? Is it because the
Speaker 2: revenue sucks? Is it because it's unprofitable to make the revenue?
Speaker 2: And why at this point in history do we not
Speaker 2: know hundreds of billions of dollars that have made in
Speaker 2: Vidia the biggest company on the stock market, and we
Speaker 2: still do not know why people buy these fucking things,
Speaker 2: nor do we know what they fucking cost imagine if
Speaker 2: we sold cars and we didn't have a milesber gallon rating.
Speaker 2: I'm serious. That's effectively where we are. Oh God, and
Speaker 2: Video is currently making hundreds of billions of dollars in
Speaker 2: revenue selling GPUs to companies that either plug them in
Speaker 2: and start losing money or I assume put them in
Speaker 2: a warehouse for safety. And those companies increasingly a raking
Speaker 2: up mountains of debt to do so, and billions more
Speaker 2: in long term lease payments. And this brings me to
Speaker 2: my core anxiety. Why exactly a company's pre ordering GPUs?
Speaker 2: What benefit is there in doing so? Blackwell does not
Speaker 2: appear to be more efficient in a way that actually
Speaker 2: makes anybody a profit. And we're potentially years from seeing
Speaker 2: these GPUs in operation in data centers at the scale
Speaker 2: they're being shipped, So why is anyone buying more? I
Speaker 2: just want to be really specific about something, because I
Speaker 2: don't feel like I nailed this down two and a
Speaker 2: half years. Fifty billion dollars per gigabot of data centers.
Speaker 2: You may be thinking, what black Wells, You'll just shove
Speaker 2: them in the old data centers, right, No, they use
Speaker 2: these Oberon racks specific new racks. They take a bunch
Speaker 2: more power, and they need a bunch of liquid cooling.
Speaker 2: You can't just retrofit easily. You have to bulldoze shit
Speaker 2: and rebuild well, remove all the housing and then add
Speaker 2: HVAC stuff. It's very expensive and takes a long time.
Speaker 2: And look, I just don't know what's happening with these GPUs,
Speaker 2: and I'm a little bit concerned, And I doubt these
Speaker 2: are new customers. They're likely hyperscalers, neo clouds like core Weave,
Speaker 2: and resellers like Dell and supermicro, who also both sell
Speaker 2: to Gorewave. Because the only companies that can actually afford
Speaker 2: to buy GPUs are those with massive amounts of cash
Speaker 2: or debt, to the point that even Google, Amazon, Meta,
Speaker 2: and Oracle are taken on massive amounts of new debt
Speaker 2: or without a plan to make a profit. Oracle is
Speaker 2: looking potentially at fifty six billion dollars of debt. It's
Speaker 2: completely bonkers. In Video's largest customers are increasingly unable to
Speaker 2: afford its GPUs, which appear to be increasing in price
Speaker 2: with every subsequent generation. In Video's GPUs are so expensive
Speaker 2: that the only way you can buy them is by
Speaker 2: already having billions of dollars or being able to raise
Speaker 2: billions of dollars, which means in a very real sense
Speaker 2: that in Video is dependent not on its customers, but
Speaker 2: on its customers credit ratings and financial backers, and the
Speaker 2: larger private credit institutions, which I'm eventually going to have
Speaker 2: to do a newsletter on and a podcast an because honestly,
Speaker 2: every time I read about the private credit situation with
Speaker 2: blue out again, I'll begin here in the wa bit
Speaker 2: from kill bill it's not good. And to make matters worse,
Speaker 2: the key reason that one would buy a GPU is
Speaker 2: to either run AI services using it, or rent it
Speaker 2: to somebody else to run AI services, and the two
Speaker 2: largest parties spending money on these services are open Ai
Speaker 2: and Anthropic, both of whom lose billions of dollars and
Speaker 2: thus are much like the people buying the GPUs depending
Speaker 2: on venture capital and debt. Now remember open ai and
Speaker 2: Anthropic both have lines of credit, four billion dollars for
Speaker 2: open Ai and two and a half billy for Aanthropic.
Speaker 2: In simple terms, in Nvidia's customers rely on debt to
Speaker 2: buy as GPUs, and in Vidia's customers customers rely on
Speaker 2: debt to pay to rent them. Yeah, it's not great
Speaker 2: yet it actually gets worse from there. Who after all
Speaker 2: of the biggest customers paying the company's renting GPUs to
Speaker 2: sell their AI bottles. That's right, AI startups, all of
Speaker 2: which are deeply unprofitable. Cusser. Anthropic's largest customer and now
Speaker 2: it's biggest competitor in the AI coding sphere, raised two
Speaker 2: point three billion dollars in November after raising nine hundred
Speaker 2: million dollars in June. But plex D, one of the
Speaker 2: most popular right put that in their quotes, raised two
Speaker 2: hundred million dollars in September after raising one hundred million
Speaker 2: dollars in July, after seeming to fail to raise half
Speaker 2: a billion dollars in May. After raising five hundred million
Speaker 2: dollars in December twenty twenty four, Cognition raised four hundred
Speaker 2: million dollars in September after raising three hundred million dollars
Speaker 2: in March, and Coher raised one hundred million dollars in September,
Speaker 2: a month after it raised five hundred million dollars. None
Speaker 2: of these companies are profitable, not even close. I read
Speaker 2: a story in Newcomer by Tom Datan that said the
Speaker 2: cursor cents one hundred percent of its revenue to Anthropic
Speaker 2: to pay for its models. Very cool. So I really
Speaker 2: want to lay this out for you because it's very
Speaker 2: bad when you think about So venture capital is feeding
Speaker 2: money to startups. They fund the startups AI startups, and
Speaker 2: then they pay either or both open ai or Anthropic
Speaker 2: to use their models. Now open Ai and Anthropic need
Speaker 2: to serve those models, right, So they then raised venture
Speaker 2: capital or there to pay hypersu scalers or neoclouds to
Speaker 2: rent Nvidia GPUs. At that point, hyper scalers and neoclouds
Speaker 2: then use either debt or exist in cash flow in
Speaker 2: the case of Hyperscalers, though not for long, to buy
Speaker 2: more in Vidia GPUs. Only one company appears to make
Speaker 2: profit here, and it's in Vidia. Well in video and
Speaker 2: its resellers like Dell and super Micro, which buy in
Speaker 2: Vidia GPUs, put them in service and sell them to
Speaker 2: neo clouds like Lamter or Core with at some point
Speaker 2: to link in. This debt back chain breaks because very
Speaker 2: little cash flow exists to prop it up. At some point,
Speaker 2: venture capitalists will be forced to stop funneling money into unprofitable,
Speaker 2: unsustainable AI companies which will make those companies unable to
Speaker 2: funnel money into the pockets of anthropic and ope and
Speaker 2: AI who rent the GPUs will then not be able
Speaker 2: to funnel money into the pockets of those buying GPUs,
Speaker 2: which will make it harder for those companies to justify
Speaker 2: buying GPUs. And at that point some of this comes
Speaker 2: to InVideo and Invidia doesn't make so make so much money.
Speaker 2: I'm honest, none of Nvidia's success really makes any sense.
Speaker 2: Who's buying so many GPUs and where are they going?
Speaker 2: Why are in Vidia's inventories increasing? Is it really just
Speaker 2: pre buying parts for future orders? Why are their accounts
Speaker 2: receivable climbing? And how much product is Nvidia shipping before
Speaker 2: it gets paid? While these are both explainable as this
Speaker 2: is a big company and this is how companies do business,
Speaker 2: and that's true, why do receivables not seem to be
Speaker 2: coming down? And how long, realistically can the largest company
Speaker 2: on the stock market continue to grow revenues selling assets
Speaker 2: that only seem to lose its customers money and don't
Speaker 2: seem to even be in use for years. I worry
Speaker 2: about it in Nvidia, not because I think there's a
Speaker 2: massive scandal, but because so much rights and its success
Speaker 2: and its success rights on the back of dwindling amounts
Speaker 2: of venture capital, and there because nobody is actually making
Speaker 2: money to pay for these GPUs, let alone running them.
Speaker 2: In fact, I'm not even saying in video goes tits up.
Speaker 2: I want to be clear about that. I think they
Speaker 2: may even have another good quarter or two in them.
Speaker 2: It really just comes down to how long people are
Speaker 2: willing to be stupid and how long Jensen Wong is
Speaker 2: able to call up Sachin Adella and Co. At three
Speaker 2: in the morning and say, buy one billion dollars of GPUs,
Speaker 2: you pig Finnom style baby. But really, I think much
Speaker 2: of the US stock market's growth is held up by
Speaker 2: how long everybody is willing to be gas lit by
Speaker 2: Jensen Wong into believing that they need more GPUs. At
Speaker 2: this point, it's barely about AI anymore, as AI revenue
Speaker 2: real cash made from selling services run on those GPUs,
Speaker 2: doesn't even cover the costs, let alone create the cash
Speaker 2: flow necessary to buy more seventy thousand dollars GPUs thousands
Speaker 2: at a time. It's not like any actual innovational progress
Speaker 2: is driving this bullshit. In any case, the market's crave
Speaker 2: are healthy in video has so many hundreds of billions
Speaker 2: of dollars of invidious stock sits in the hands of
Speaker 2: retail investors and people's four O one ks, and its
Speaker 2: endless growth has helped paper over the pallid growth of
Speaker 2: the US stock market and by extension, the decay of
Speaker 2: the tech industry's ability to innovate. Once this pops, and
Speaker 2: it will pop because there's simply not enough money to
Speaker 2: do this forever, there must be a referendum on those
Speaker 2: that chose to ignore the naked instability of this era
Speaker 2: and the endless lies that inflate the AI bubble. I
Speaker 2: will be walking around with a gavel. I am going
Speaker 2: to be taking heads. I am fucking sick of this era.
Speaker 2: And what I'm most sick of is that so few
Speaker 2: people are still to this day willing to admit how
Speaker 2: bad this is. And I know in the next few
Speaker 2: months we're going to get articles some major media outlets
Speaker 2: that say, how could we have seen this coming? And
Speaker 2: like I said in the previous episode, they could have
Speaker 2: fucking looked. All of them could have looked, and they
Speaker 2: could have looked a year ago. The incredible support I
Speaker 2: get from all of you truly makes this show a
Speaker 2: joy to make, even though I've done way too many
Speaker 2: retakes on this and apologies to Mattasowski for the noises
Speaker 2: I make. But I think in the next few months
Speaker 2: we're all going to be validated. It's going to be
Speaker 2: the great vindication. But until then, everybody is betting billions
Speaker 2: on the eye idea that wily coyote won't look down.
Speaker 2: He's gonna have to at some point, won't it. Thank
Speaker 2: you for listening to Better Offline. The editor and composer
Speaker 2: of the Better Offline theme song is Matasowski. You can
Speaker 2: check out more of his music and audio projects at
Speaker 2: Mattasowski dot com, m A T T O. S O
Speaker 2: W s ki dot com. You can email me at
Speaker 2: easy at Better offline dot com or visit Better Offline
Speaker 2: dot com to find more podcast links and of course,
Speaker 2: my newsletter. I also really recommend you go to chat
Speaker 2: dot Where's Youreed dot at to visit the discord, and
Speaker 2: go to our slash Better Offline to check out our reddit.
Speaker 2: Thank you so much for listening.
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