Speaker 1: Zone Media, Hello im ed Zetron, and this, of course
Speaker 1: is better offline what Welcome to the second part of
Speaker 1: our four part series, where I give you my most comprehensive,
Speaker 1: most up to day explanation of why we're in a
Speaker 1: bubble and what that even means. The reason why I'm
Speaker 1: taking my time to be descriptive and comprehensive is because
Speaker 1: I want this to make sense to those who listen
Speaker 1: to it. Having written hundreds of thousands of words this
Speaker 1: year about the AI bubble, so many of the arguments
Speaker 1: I've made and the secrets I've exposed are contained in
Speaker 1: their own discreet little episodes or newsletters. This is my
Speaker 1: series to consolidate all of the information I've put out
Speaker 1: there in one place, and I want to make it
Speaker 1: makes sense to anyone who listens to him. I want anyone,
Speaker 1: even who someone who doesn't even know that much about AI,
Speaker 1: to listen to the audience I've been making for the
Speaker 1: past three years, to understand why things are dire and
Speaker 1: to feel the same alarm I'm feeling, or at least
Speaker 1: understand why I'm alarmed. Because I don't like to tell
Speaker 1: you how you feel. Old school bit of FEEDBACKERD got
Speaker 1: from a listener once, and I appreciate that to this day.
Speaker 1: Now today I'll make the case the generative AI's fundamental
Speaker 1: growth story is flawed and explain why we're in the
Speaker 1: midst of an egregious bubble. This industry is sold by
Speaker 1: keeping things vague and knowing that most people don't dig
Speaker 1: much deeper than a headliner problem I simply do not have.
Speaker 1: This industry is effectively in service of two companies, open
Speaker 1: Ai and Nvidia, who pump headlines out through endless contracts
Speaker 1: between them or subsidiaries or investments to give the illusion
Speaker 1: of activity. Open Ai has now promised over four hundred
Speaker 1: billion dollars in the next four years, though honestly they
Speaker 1: might owe about a trillion dollars with all the data
Speaker 1: centers they're signed up for. All of these are egregious
Speaker 1: sums for a company that have already forecasted billions in
Speaker 1: losses with no clear explanation as to how it will
Speaker 1: afford any of this beyond we need more money and
Speaker 1: they're vague hope that there's another soft bank or Microsoft
Speaker 1: waiting in the wings to swoop in and save the day. Now,
Speaker 1: I'm going to walk you through where I see this
Speaker 1: industry today and why I see no future for it
Speaker 1: beyond a horrible, fiery car rack. While everybody reasonably harps
Speaker 1: on about hallucinations, which to remind you, is when a
Speaker 1: model authoritativety states something that isn't true, the truth of
Speaker 1: why that's bad is far more complex and actually far
Speaker 1: worse than it seems. You cannot rely on a large
Speaker 1: language model to do what you want, even though its
Speaker 1: highly tuned models on the most expensive and intricate platforms
Speaker 1: can't actually be relied upon to do exactly what you want.
Speaker 1: And I know some people might say, well, yes, they
Speaker 1: do every time, one hundred percent of the time. A
Speaker 1: hallucination isn't just when these models say something that isn't true.
Speaker 1: It's when they decide to do something wrong because it
Speaker 1: seems the most likely thing to do, or when a
Speaker 1: coding model decides to go on a wild goose chase,
Speaker 1: failing the user and burning a ton of money in
Speaker 1: the process. The advent of reasoning models those engineered to
Speaker 1: think through problems in the way reminiscent of a human.
Speaker 1: But it's not thinking. They don't think. They have no consciousness.
Speaker 1: They literally you ask them something and they break down
Speaker 1: what the prompt might mean and then choose it's not thinking,
Speaker 1: and the expansion of what people are trying to use
Speaker 1: llms for demands that the definition of an AI hallucination
Speaker 1: be widened, not merely referring to factual errors, but fundamental
Speaker 1: errors in understanding the user's request or intent or what
Speaker 1: constitutes a task, in part because these models, as I said,
Speaker 1: cannot think and do not know anything. However successful a
Speaker 1: model might be in generating something good once, it will
Speaker 1: also often generate something bad, or it will generate the
Speaker 1: right thing, but in an inefficient and over verbos fashion,
Speaker 1: you do not know what you're going to get each time,
Speaker 1: and hallucinations multiply with the complexity of the thing you're
Speaker 1: asking for or whether a task contains multiple steps, which
Speaker 1: is a fatal blow to the idea of agents. You
Speaker 1: can add as many levels of intrigue and reasoning as
Speaker 1: you want, but large language models cannot be trusted to
Speaker 1: do something correctly or even consistently, let alone every time.
Speaker 1: Model companies have successfully convinced everybody that the issue is
Speaker 1: that users are prompting the models wrong and that the
Speaker 1: people need to be trained to use AI. But what
Speaker 1: they're doing is training people to explain away the inconsistencies
Speaker 1: of large language models and to assume individual responsibility for
Speaker 1: what is an innate floor in how these fucking things work.
Speaker 1: Large language models are also uniquely expensive. Many mistakenly try
Speaker 1: and claim that this is like the dot com boom
Speaker 1: or uber, but the basic unique economics of generative AI
Speaker 1: are insane. Providers must purchase tens or hundreds of thousands
Speaker 1: of GPUs, each costing fifty thousand to seventy thousand apiece,
Speaker 1: and the hundreds of millions or billions of dollars of
Speaker 1: infrastructure that goes around them are so expensive and hard
Speaker 1: to install, and that's without mentioning things like staffing or construction,
Speaker 1: or power or water or even permitting. Then you turn
Speaker 1: them on and immediately they start losing new money. Despite
Speaker 1: hundreds of billions of GPUs sold, nobody seems to actually
Speaker 1: make any of it, other than Video of course, the
Speaker 1: company that makes them, and resellers like Dell and Supermicro
Speaker 1: who buy the GPUs put them in servers and sell
Speaker 1: them to other people. Now, if you're an eager listener,
Speaker 1: I would love to hear from you on one question,
Speaker 1: then this is just something that's been bouncing around my head.
Speaker 1: Super Micro is in Video a customer of super Micro.
Speaker 1: They're a custom in super Micro is a huge customer
Speaker 1: of Video. I've read something like seventy percent of the
Speaker 1: cost of goods sold is buying GPUs. But I read
Speaker 1: that in Video as a customer of them. But I
Speaker 1: can't find anything else. Reach out easy at better Offline
Speaker 1: dot com if you've got any thoughts there anyway, But
Speaker 1: back to those resourers. This arrangement works out great for
Speaker 1: Jensen Wang, the CEO of in Video, and terribly for
Speaker 1: everybody else. Today, I'm gonna explain the insanity of the
Speaker 1: situation we'll find ourselves in and why I continue to
Speaker 1: do this work underterred. The bubble has entered its most pornographic,
Speaker 1: aggressive and destructive stage, where the more obvious it becomes
Speaker 1: that we're all cooked here in AI land, the more
Speaker 1: ridiculous the GENERATIVAI industry will act a dark juxtaposition against
Speaker 1: every new study that says generative AI does not work,
Speaker 1: or new story about chat gpd's uncannyability to activate mental
Speaker 1: illness in people. And we're going to start looking at
Speaker 1: one company in Video which now dominates the stock market
Speaker 1: and has taken extraordinary and dangerous measures to sustain growth.
Speaker 1: That is, to any sane person, completely unsustainable and unrealistic
Speaker 1: on every level. But let's start simple. Nvidia is a
Speaker 1: hardware company that sells GPUs, including consumer GPUs that you'd
Speaker 1: see in a modern gaming PC. But when you read
Speaker 1: someone say GPU within the context of AI, they mean
Speaker 1: enterprise focused GPUs like the A one hundred, h one hundred,
Speaker 1: H two hundred, and more modern GPUs like the Blackwell
Speaker 1: Series B two hundred and GB two hundred, which combines
Speaker 1: two GPUs with an Nvidia CPU. This is all complex sounding,
Speaker 1: but I want you to have the groundwork. These GPUs
Speaker 1: cost anywhere from fifty to seventy thousand dollars and require
Speaker 1: tens of thousands of dollars more of infrastructure networking to
Speaker 1: cluster these server acts of GPUs together to provide compute
Speaker 1: and massive cooling systems to deal with the massive amounts
Speaker 1: of heat they produce, as well as servers themselves that
Speaker 1: they run on, which typically use top of the line
Speaker 1: data center CPUs and contain vast quantities of high speed
Speaker 1: memory and storage. GPUs itself is likely the most expensive
Speaker 1: single it and within an AI server the other costs.
Speaker 1: And I'm not even factoring in the actual physical building
Speaker 1: that the server lives in, or the water or electricity
Speaker 1: that uses. Well, all this crap adds up. I've mentioned
Speaker 1: in Video because it has a virtual monopoly in this space,
Speaker 1: Genera IVAI effectively requires in video GPUs, in part because
Speaker 1: it's the only company really making the kinds of high
Speaker 1: powered cards that GENERAIVAI demands, and because in Video created
Speaker 1: something called Kuda. Cuda a collection of software tools that
Speaker 1: lets programmers write software that runs on GPUs, which were
Speaker 1: traditionally used primarily for rendering graphics in games. While there
Speaker 1: are some open source alternatives as well as alternatives from
Speaker 1: Intel with its Arc GPUs and AMD, in Vidia's main
Speaker 1: rival in the consumer space, these aren't nearly as mature
Speaker 1: or feature Richkuda's been around for ten fifteen years now,
Speaker 1: they really knew what they were doing. They also have
Speaker 1: bought a company called Melanox, which did the high speed
Speaker 1: networking back in twenty nineteen, I think, for six billion dollars. Anyway,
Speaker 1: due to the complexities of AI models, one cannot just
Speaker 1: stand up a few of these GPUs either you need
Speaker 1: clusters of thousands, tens of thousands, or hundreds of thousands
Speaker 1: of them for it to be worthwhile making any investment
Speaker 1: in GPUs and the hundreds of millions or billions of dollars,
Speaker 1: especially considering they require completely different data center architecture to
Speaker 1: make them run. You've probably read a bunch of stuff
Speaker 1: about cryptominers turning into AI data center providers. These crypto
Speaker 1: data centers have to be knocked down and replaced. They
Speaker 1: you can't just put the same GPUs in isn't going
Speaker 1: to work, and with the new Blackwell ones, the brand
Speaker 1: new ones, and then the Rubens following them, same deal.
Speaker 1: A common request like asking a generative AI model to
Speaker 1: pass through thousands of lines of code and make a
Speaker 1: change or in addition, may use multiples of these fifty
Speaker 1: thousand dollars GPUs at the same time. And so if
Speaker 1: you aspire to serve thousands or millions of concurrent users,
Speaker 1: you need to spend big, really really really big. It's
Speaker 1: these factors, the vendor lock in the ecosystem and the
Speaker 1: fact that generative AI really only works when you're buying
Speaker 1: GPUs at scale that underpin the rise of Nvidia. But
Speaker 1: Beyond the economic and technical factors, there are human ones too.
Speaker 1: To understand the AI bubble is to understand why CEOs
Speaker 1: do the things they do. Because the executive job is
Speaker 1: so vague, they can telegraph the value of their labour
Speaker 1: by spending money on initiatives and partnerships and stratagem. AI
Speaker 1: gave hyperscalers the excuse to spend hundreds of billions of
Speaker 1: dollars on data centers and buy a bunch of GPUs
Speaker 1: to go in them, because that, to the markets, looks
Speaker 1: like they're doing something. By virtue of spending a lot
Speaker 1: of money in a frighteningly short amount of time, Satchnadella
Speaker 1: received multiple glossy profiles, all without having to prove that
Speaker 1: AI can really do anything, be it a job or
Speaker 1: make Microsoft money. Nevertheless, AI allowed CEOs to look busy,
Speaker 1: and once the markets and journalists had agreed on the
Speaker 1: consensus opinion that AI would be big, all that these
Speaker 1: executives had to do was buy GPUs and do AI
Speaker 1: or plug AI within their own software profitts. But really
Speaker 1: it was just jump on the big stupid asshole train.
Speaker 1: We are in the midst of one of the darkest
Speaker 1: forms of software in history, described by many as unwanted guest,
Speaker 1: invading their products, their social media feeds, their bosses, empty minds,
Speaker 1: and resting in the hands of monsters. Every story of
Speaker 1: AI's success feels bereft of any real triumph, with every
Speaker 1: literal description of its abilities involving multiple caveats about the
Speaker 1: mistakes it makes or the incredible costs of running in
Speaker 1: generator of AI really exists for two reasons, to cost
Speaker 1: money and to make executives look busy. It was meant
Speaker 1: to be the new enterprise software and the new I
Speaker 1: phone and the new Netflix all at once, a panacea
Speaker 1: where the software guys pay one hardware guy for GPUs
Speaker 1: to unlock the incredible value creation of the future. In
Speaker 1: many ways, Jenera iv AI was always set up to
Speaker 1: fail because it was meant to be Everything. Was talked
Speaker 1: about like it was everything. It's still sold like it's everything.
Speaker 1: Yet for all the fucking hype, it comes down to
Speaker 1: two companies, Open Ai and Nvidia, and in Video was
Speaker 1: for a while living high on the hog. All CEO
Speaker 1: Jensen Huang had to do every three months would say,
Speaker 1: check out these numbers and the markets and business journalist
Speaker 1: would squeer with glee, even as he said stuff like
Speaker 1: the more you buy, the more you save, in part
Speaker 1: tipping his head to the very real and sensible idea
Speaker 1: of accelerated computing, but frame within the context of the
Speaker 1: cash inferna that's generated AI, and it all seems kind
Speaker 1: of fucking ludicrous. Huang showmanship worked really well for Invidia
Speaker 1: for a while because for a while the growth was easy.
Speaker 1: Everybody was buying GPUs, Meta, Microsoft, Amazon, Google, and to
Speaker 1: a lesser extent, Apple and Tesla made up forty two
Speaker 1: percent of Invidia's revenue, creating at least for the first
Speaker 1: four a degree of shared mania where everybody justified buying
Speaker 1: tens of billions of dollars of GPUs by saying the
Speaker 1: other guy's doing it. This is one of the major
Speaker 1: reasons the AI bubble is happening, because people conflated in
Speaker 1: Vidia's incredible sales with interest in AI rather than everybody
Speaker 1: buying GPUs at once. Don't worry, I'll explain the revenue
Speaker 1: side a little bit later. We're here for the long haul.
Speaker 1: Sit down, get comfort. You're gonna need to be anyway,
Speaker 1: Invidia is now facing a big problem that the only
Speaker 1: thing that grows forever is cancer. On September ninth, twenty
Speaker 1: twenty five, The Wall Street Journal said that in Vidio's
Speaker 1: wow factor was fading, going from beating analyst estimates by
Speaker 1: nearly twenty one percent in its fiscal year Q two
Speaker 1: twenty twenty four earnings to scraping by with a pathetic
Speaker 1: measly one point five to two percent beat in its
Speaker 1: most recent earnings, something that for any other company would
Speaker 1: be a good thing because they made so much money,
Speaker 1: But framed against the delusional expectations that generative AI has inspired, well,
Speaker 1: the figure looks nothing short of ominous. I quote the
Speaker 1: Wall Street Journal. Already, in Vidia's fifty six percent annual
Speaker 1: revenue growth rate in its latest quarter was its slowest
Speaker 1: in more than two years. If analyst projections hold, growth
Speaker 1: will slow further in the current quarter. In any other scenario,
Speaker 1: fifty six percent year of a year growth would lead
Speaker 1: to an abundance of dom perignon and one signing hundreds
Speaker 1: of boobs. But this is in video, and that's just
Speaker 1: not good enough. Back in February twenty twenty four, in
Speaker 1: Video was booking two hundred and sixty five percent year
Speaker 1: of a year growth but in its February twenty twenty
Speaker 1: five earnings in Video only grew by a measly per
Speaker 1: thetic disgusting seventy eight percent year of a year. I'm
Speaker 1: being sarcastic, of course. It isn't so much that in
Speaker 1: Vidia isn't growing, but to grow year over year at
Speaker 1: the rates that people expect is insane. Life was a
Speaker 1: lot easier when in Video went from six point oh
Speaker 1: five billion dollars in revenue in Q four fiscal year
Speaker 1: twenty twenty five to twenty two billion dollars in revenue
Speaker 1: in Q four fiscal year twenty twenty four. But for
Speaker 1: it to grow even fifty five percent year of a
Speaker 1: year from Q two FY twenty twenty six, I'm just
Speaker 1: going to truncate that now, which is forty six point
Speaker 1: seven billion dollars to Q two twenty twenty seven. That
Speaker 1: would require them to make seventy two point three eight
Speaker 1: five billion dollars in revenue in the space of three months,
Speaker 1: mostly from selling GPUs, which make up about eighty eight
Speaker 1: percent of its revenue. Just want to be clear that
Speaker 1: in a year they would have to make seventy two
Speaker 1: billion dollars just selling pretty much GPUs and the associated
Speaker 1: hardware in the space of three months. It's it's insane.
Speaker 1: This is this is really that it's too much. It's
Speaker 1: too much to expect. And this, by the way, we
Speaker 1: would put in video in the ballpark of Microsoft, who
Speaker 1: made seventy six billion dollars in their last quarterly earnings,
Speaker 1: and within the neighborhood of Apple, who made ninety four
Speaker 1: billion dollars in their last quarter of earnings. And they
Speaker 1: would do this predominantly making money in an industry that
Speaker 1: a year and a half ago barely made the company
Speaker 1: six billion dollars in a quarter. And the market needs
Speaker 1: in Vidia to perform. They must, they must, as the
Speaker 1: company makes up seven to eight percent of the value
Speaker 1: of the S and P five hundred. It's not enough
Speaker 1: for Invidia to be wildly profitable, or to having a
Speaker 1: monopsony on selling GPUs, or for it to have effectively
Speaker 1: ten x their stock in a few years. No, no, no, more, more, more,
Speaker 1: always more. Number must go up. It must continue to
Speaker 1: grow at the fastest rate of anything ever, making more
Speaker 1: and more money, selling more and more of these GPUs
Speaker 1: to a small group of companies that immediately start losing money.
Speaker 1: The moment they plugged them in. It's not brilliant, is it.
Speaker 1: While a few members of the Magnificent Seven could be
Speaker 1: depended on to funnel tens of billions of dollars into
Speaker 1: a furnace each quarter, there were limits even for companies
Speaker 1: like Microsoft, which had brought over four hundred and eighty
Speaker 1: five thousand g us in twenty twenty four alone. To
Speaker 1: take a step back about how people actually make money
Speaker 1: from buying these GPUs, companies like Microsoft, Google, and Amazon
Speaker 1: make their money by either selling access to large language
Speaker 1: models that people incorporate into their products, or by renting
Speaker 1: out servers full of those GPUs to run influence the thing,
Speaker 1: to generate the output, or train AI models for companies
Speaker 1: that develop and market their models themselves, namely Anthropic and
Speaker 1: Open AI, with some smaller competitors that don't really matter
Speaker 1: that latter revenue stream. Renting out GPUs is where Jensen
Speaker 1: Wong found a solution to that horrible eternal growth problem
Speaker 1: the neo cloud, namely companies like core Weave, Lambda, and Nebius. Now,
Speaker 1: these businesses are fairly straightforward. They own or lease data
Speaker 1: centers that they then feel full of servers that are
Speaker 1: full of Nvidio GPUs, which they then rent out on
Speaker 1: an hourly basis to customers either on a per GPU basis,
Speaker 1: are in large badges for large customers who guarantee they'll
Speaker 1: use a certain amount of compute and sign up for
Speaker 1: a long term agreement for moderan an era a time
Speaker 1: couple years perhaps these larger commitments. A neocloud is a
Speaker 1: specialist cloud compute company that exists only to provide access
Speaker 1: to GPUs for AI, Unlike Amazon Web Services, Microsoft Azure,
Speaker 1: and Google Cloud, all of which to have healthy businesses
Speaker 1: selling other kinds of compute with AI, as I'll get
Speaker 1: into later, failing to provide much of a return on
Speaker 1: investment at all. It's not just the fact that these
Speaker 1: companies are more specialized than say AWS or Azure, as
Speaker 1: you've gathered from the name. These are new, young, and
Speaker 1: in almost all cases incredibly precarious businesses, each with financial
Speaker 1: circumstances that will make a Greek finance minister blush. That's
Speaker 1: because setting up a neocloud is expensive, even if the
Speaker 1: company in question already has data centers as core We've
Speaker 1: did with its cryptocurrency mining operation, AI requires, as I said,
Speaker 1: completely new data center infrastructure to run and call the GPUs,
Speaker 1: and those GPUs also need paying for. And then there's
Speaker 1: the other stuff I mentioned earlier like power, water and
Speaker 1: the other bits of the computer. CPU are the bah
Speaker 1: blah blah blah blah blah. As a result, these neoclouds
Speaker 1: are forced to raise billions of dollars in debt, which
Speaker 1: they can lateralized using the GPUs they already have, along
Speaker 1: with contracts from customers, which they then use to buy
Speaker 1: more GPUs. That's right, they buy GPUs from Nvidia. They
Speaker 1: raised dead on those GPUs and then they used that
Speaker 1: debt to my more GPUs from Nvidia. It's enough to
Speaker 1: drive a man insane. Corwave, for example, has twenty five
Speaker 1: billion dollars in debt on an estimated five point three
Speaker 1: five billion dollars of revenue in twenty twenty five, losing
Speaker 1: hundreds of billions of dollars per quarter. Now you know
Speaker 1: who also invests in these neoclouds. You'll never guess. It's
Speaker 1: in video. Invidia is also one of Corweed's largest customers,
Speaker 1: accounting for fifteen percent of its revenue in twenty twenty four,
Speaker 1: and just signed a deal to buy six point three
Speaker 1: billion dollars of any capacity that core Weave can't otherwise
Speaker 1: sell to someone else through twenty thirty two, an extension
Speaker 1: of a one point three billion dollar twenty twenty three
Speaker 1: deal reported by the Information. It was also the anchor
Speaker 1: investment in Corewey's IPO about two hundred and fifty million dollars.
Speaker 1: In Vidia is currently doing the same thing with Lambda,
Speaker 1: another neocloud that Invidia invested in, which also plans to
Speaker 1: go public next year. In Vidia is also one of
Speaker 1: Landa's largest customers, signing a deal with it this summer
Speaker 1: to rent ten thousand GPUs for one point three billion
Speaker 1: dollars over four years in the UK. And Video has
Speaker 1: also just invested seven hundred million dollars in n Scale,
Speaker 1: a former cryptomner that has never built an AI data
Speaker 1: center that that has despite having no experience, committed one
Speaker 1: billion dollars and or one hundred thousand GPUs to an
Speaker 1: open AI data center in Norway. On Thursday, September twenty fifth,
Speaker 1: Nscale announced that it closed another funding round within Video
Speaker 1: listed as the main backer. Although it's unclear how much
Speaker 1: money it put in, it would be a safe to
Speaker 1: assume it's probably at least one hundred million dollars. In
Speaker 1: Vidia also invested in Nebius, an outgrowth of Russian conglomerate Yandex,
Speaker 1: and Nebbeus provides, through their partnership with Nvidia, tens of
Speaker 1: thousands of dollars of compute credits to companies in Videa's
Speaker 1: Inception startup program. Look, in Vidia's plan is simple, fund
Speaker 1: these neoclouds. Let these neoclouds load themselves up with debt,
Speaker 1: with which point they buy bunches of GPUs from Video,
Speaker 1: which can be used as collateral for loans along with
Speaker 1: contracts and customers, allowing the neoclouds to buy even more
Speaker 1: GPUs from Nvidia. It is just that simple. It's infinite money, right,
Speaker 1: just money, me money. Now, you fund the company, the
Speaker 1: company buys from you, you fund them again. They've used
Speaker 1: the thing they bought to buy from more from you,
Speaker 1: unlimited money. Except that is for ones more problem. These
Speaker 1: companies don't They don't really appear to have that many customers,
Speaker 1: and they don't appear to be making much money. As
Speaker 1: I went into a recent premium newsletter, and Video funds
Speaker 1: and sustains neoclouds as a way of funneling revenue to itself,
Speaker 1: as well as partners like super Micro and Dell resellers
Speaker 1: that take in video GPUs like I mentioned and put
Speaker 1: them in service to sell pre built to customers. These
Speaker 1: two companies made up thirty nine percent of in video
Speaker 1: revenues last quarter. Yet when you remove hyperscalar revenue Microsoft, Amazon, Google,
Speaker 1: Open Ai, and n Video from the revenues of these neoclouds,
Speaker 1: there's barely one billion dollars in revenue can bind across
Speaker 1: core Weave, Nebius and Lambda. Corwev's five point three five
Speaker 1: billion dollars in revenue is predominantly made up with its
Speaker 1: contracts with Nvidia, Microsoft, who are offering that compute to
Speaker 1: open Ai, Google who have hired Corewave to offer compute
Speaker 1: to open Ai. And I'm not kidding, and of course
Speaker 1: open Ai itself, which has now promised core Weave twenty
Speaker 1: two point four billion dollars in business over the next
Speaker 1: five years. This is all a lot of stuff, So
Speaker 1: I'll make it really simple. There's no real money in
Speaker 1: offering AI compute, but that isn't Jensen Huong's problem, So
Speaker 1: we simply will force in Nvidia to hand money to
Speaker 1: these companies that they have contracts to point at so
Speaker 1: they can raise debt to buy more of those GPUs,
Speaker 1: so that in Vidia can give them more contracts they
Speaker 1: can use that to raise more money. It's it's really bad,
Speaker 1: all right, It's really bad. When I read this stuff
Speaker 1: out loud, I feel a little crazy because it's so
Speaker 1: obviously unsustainable. Neil clouds are effectively giant private equity vehicles
Speaker 1: that exist to raise money to buy GPUs from Nvidia
Speaker 1: or for hyperscalers to move money around so they don't
Speaker 1: have to increase their capital expenditures and can, as Microsoft
Speaker 1: did earlier in the year, simply walk away from deals
Speaker 1: they don't like, with the masses of data center at
Speaker 1: least as they walk from. Nebius recently signed a seventeen
Speaker 1: point four billion dollar deal with Microsoft, which even included
Speaker 1: the clause in its six K filing and official filing
Speaker 1: with the government the Microsoft can terminate the deal in
Speaker 1: the event that the capacity isn't built by the delivery dates.
Speaker 1: And by the way, ah Nebius already used the contract
Speaker 1: that Microsoft gave them to raise three billion dollars to
Speaker 1: I'm not shitting you here, build the data center to
Speaker 1: actually to actually provide the compute for the for the contract.
Speaker 1: They don't have it. Yeah, now they don't have them.
Speaker 1: They don't have the fucking compute. They they haven't fucking
Speaker 1: built up. No one built up. They haven't got the compute. Mate,
Speaker 1: These fucking companies are right anyway. Anyway, sorry, sorry, I'll
Speaker 1: stop spiraling. Let me just break down these numbers. Let's
Speaker 1: look at cort we First, Microsoft, they're sixty percent of
Speaker 1: their revenue twenty twenty four and they're providing compute mostly
Speaker 1: for open Ai. Fifteen percent of their revenue last year
Speaker 1: was in VideA, and then the rest was Meta and
Speaker 1: then open Ai and then Google. Lambda half of their
Speaker 1: revenue comes from Amazon and Microsoft, and now one point
Speaker 1: five billion dollars of their revenue comes from in VideA,
Speaker 1: which are their current revenue by the way, and that
Speaker 1: one point five billion dollars over four years. So the
Speaker 1: current revenue, it's two hundred and fifty billion dollars. Well,
Speaker 1: that would make in video the largest customer. I realize
Speaker 1: I'm just saying numbers here, but for real, with that contract,
Speaker 1: because Lambda only made two hundred and fifty million dollars
Speaker 1: in the first half of this year, and in Video
Speaker 1: is spreading one point five billion dollars across four years.
Speaker 1: In Video is the largest customer now Now Nebutus has
Speaker 1: got similar revenue to Lambda, but their largest customer is
Speaker 1: now is It's it's fucking Microsoft. It's just they don't
Speaker 1: have real customers. They just have hyperscalers or in VideA themselves.
Speaker 1: And from my analysis, it appears that Core we've despited
Speaker 1: expectations to make that five point three five billion dollars
Speaker 1: this year, has only around five hundred million dollars of
Speaker 1: non Magnificent seven or open AI revenue in twenty twenty five,
Speaker 1: with Lambda estimated to have maybe a round of one
Speaker 1: hundred million dollars in AI revenue. Otherwise, Nebius only around
Speaker 1: two hundred and fifty million dollars, and that's being generous.
Speaker 1: In much simpler terms, the Magnificent seven is the AI bubble,
Speaker 1: and the AI bubble exists to buy more GPUs because,
Speaker 1: as I'll talk about, there's no real money or growth
Speaker 1: coming out of this other than the amount that private
Speaker 1: credit is investing. And this really is quite worrying. By
Speaker 1: the way, I had a quote here for analyst that
Speaker 1: says is about fifty billion dollars a quarter for the
Speaker 1: low end for the past three quarters. So why is
Speaker 1: this bad?
Speaker 2: All right?
Speaker 1: I don't know. Let's start simple. Fifty billion dollars a
Speaker 1: quarter of data center funding is going into an industry
Speaker 1: that has less revenue than three to play mobile game
Speaker 1: gensin impact. That feels pretty bad. Who's going to use
Speaker 1: these data centers? How are they even going to make
Speaker 1: money on them? Firms don't typically hold on to assets.
Speaker 1: They sell them or they take them public. That doesn't
Speaker 1: seem great to me anyway. If AI was truly the
Speaker 1: next big growth vehicle, neoclouds would be swimming in diverse
Speaker 1: global revenue streams. Instead, they're heavily centralized around the same
Speaker 1: few names, one of which in Vidia, directly benefits from
Speaker 1: their existence not as a company doing business, but as
Speaker 1: an entity that can accrue debt and spend money on GPUs.
Speaker 1: These neoclouds are entirely dependent on a continual flow of
Speaker 1: private credit from firms like Gold and Sachs, Who's becked,
Speaker 1: Nebbia's Corweave and Lambda, JP Morgan, Lambda Crusoe Building Applene,
Speaker 1: Texas As Open AI Data Center, and of course Corwave
Speaker 1: and Blackstone Lambda and corwav, who have in a very
Speaker 1: real sense created an entirely debt based infrastructure to feed
Speaker 1: billions of dollars directly to Nvidia, all in the name
Speaker 1: of an AI revolution that's yet to arrive. The fact
Speaker 1: that the rest of the neoclo revenue stream is effectively
Speaker 1: either a hyperscaler or open ai is also concerning. Hyperscalers
Speaker 1: are at this point the majority of data center capital
Speaker 1: expenditures and have yet to prove an any kind of
Speaker 1: success from building out this capacity, outside of course, Microsoft's
Speaker 1: investment in open Ai, which has succeeded in generating revenue
Speaker 1: while burning billions of dollars of revenue on well, I mean,
Speaker 1: it's not really any profit, is they have just burning money.
Speaker 1: It's also insane when you say this stuff. I've got
Speaker 1: two more goddamn episodes of this and when I read
Speaker 1: these scripts, I'm just like, how is nobody else more
Speaker 1: freaked out? Oh well, hyperscaler revenue is also capricious. But
Speaker 1: even if it isn't, why are there no other major customers?
Speaker 1: Why across all of these companies does there not seem
Speaker 1: to be one major customer who isn't open Ai Well,
Speaker 1: the answers are quite obvious. Nobody that wants it can
Speaker 1: afford it, and those that can afford it don't need it.
Speaker 1: It's also unclear what exactly hyperscalers are doing with this compute,
Speaker 1: because it sure isn't making money. While Microsoft makes ten
Speaker 1: billion dollars in revenue from renting compute to open Ai
Speaker 1: via their Microsoft as Your Cloud, it does so at cost,
Speaker 1: and was charging open Ai one dollars and thirty cents
Speaker 1: per hour for each a one hundred AIGPU at a
Speaker 1: loss of two point two dollars an hour per GPU,
Speaker 1: meaning that it is likely losing money on this compute,
Speaker 1: especially as semi analysis as the total cost per hour
Speaker 1: per GPU around one dollars and forty six cents with
Speaker 1: the cost of capital and debt associated for a hyperscaler,
Speaker 1: though it's unclear that whether that's for an H one
Speaker 1: hundred or an A one hundred GPU. In any case,
Speaker 1: how do these neoclouds pay for their debt if the
Speaker 1: hyperscalers give up or in video doesn't send them money,
Speaker 1: or more likely private credit begins to notice that there's
Speaker 1: no real revenue growth outside of circular compute deals with
Speaker 1: neocloud's largest suppliers, investors and customers. Don't know why I
Speaker 1: said plural there, because it's just one in video, And
Speaker 1: the answer is they don't. In fact, I have serious
Speaker 1: concerns that they can't even build the capacity necessary to
Speaker 1: fulfill these deals, but nobody seems to worry or think
Speaker 1: about them. But really, though it appears to be taking
Speaker 1: Oracle and Cruso around two point five years per gigawatt
Speaker 1: of compute capacity, how exactly are any of these neoclouds,
Speaker 1: or indeed Oracle itself able to expand to capture this revenue.
Speaker 1: Who knows, but I assume somebody is going to say
Speaker 1: open Ai. Here's an insane statistic for you. By the way,
Speaker 1: open ai will account for in both its revenue projected
Speaker 1: thirteen billion dollars and in its own compute cost ten dollars,
Speaker 1: somewhere in the region of forty to fifty percent of
Speaker 1: all AI revenues in twenty twenty five. As a reminder,
Speaker 1: open ai is leaked that it will burn one hundred
Speaker 1: and fifteen billion dollars in the next four years, and
Speaker 1: based on my estimates, it actually needs to raise I
Speaker 1: mean upwards are four hundred billion dollars in the next
Speaker 1: four years based on its three hundred billion dollar deal
Speaker 1: with Oracle and some recently announced one hundred billion dollar
Speaker 1: compute purchases for backup. And that alone is a very
Speaker 1: bad sign, very very bad indeed. Especially it's with three
Speaker 1: years and five hundred billion dollars or more into this
Speaker 1: hype cycle, with few signs of life outside of well
Speaker 1: open AI promising people money, and that's not healthy or
Speaker 1: sane or normal, and it's certainly not stable, and it's
Speaker 1: going to get bad real fast. Gatget tomorrow. Thank you
Speaker 1: for listening to Better Offline. The editor and composer of
Speaker 1: the Better Offline theme song is Matasowski. You can check
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Speaker 1: dot com, M A T T O S O W
Speaker 1: s ki dot com. You can email me at easy
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Speaker 1: you so much for listening better.
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