Speaker 1: Zone Media, Hello, and welcome to Better Offline. I am,
Speaker 1: of course your host ed zitron what and after a
Speaker 1: few three part episodes, I had an idea, what if
Speaker 1: I did a four parter? In all seriousness, I know
Speaker 1: that this is a little bit long, but the topic
Speaker 1: we're about to explore demand's quite a bit of depth
Speaker 1: and it isn't something I could really do justice to
Speaker 1: in a one part or two parts, or I guess
Speaker 1: even three parter, but let's get into him. Over the
Speaker 1: last few months, we felt the vibes shift downward in
Speaker 1: an aggressive way, with both Marg Zuckerberg and Clammy Sam
Speaker 1: Mortman saying that we're in a bubble. In the latter
Speaker 1: case said, warnings of a bubble are always couched in
Speaker 1: rank hypocrisy is it's always implied that whoever it is
Speaker 1: and the companies they represent aren't part of that bubble,
Speaker 1: but rather it's other people and other companies making unfortunate decisions.
Speaker 1: The thing is, there's really no escape for either of
Speaker 1: these guys, not for Zaken, definitely not for Sam Ortmon.
Speaker 1: And over the next four episodes, I'm going to make
Speaker 1: a comprehensive case for the fact that we're in a
Speaker 1: bubble and condense everything I've been talking about into one series.
Speaker 1: And I know I've been all over the place, and
Speaker 1: I get a lot of people saying, oh, well, where
Speaker 1: did you talk about this? And where'd you talk about that?
Speaker 1: And that's kind of fair when you put out as
Speaker 1: much as I do. But I'm going to break this
Speaker 1: down in four episodes. I'm going to give you a
Speaker 1: comprehensive argument against the bubble. Well I mean that for
Speaker 1: a bubble, I guess, but against generative AI in general.
Speaker 1: But in this episode, I think it's good to start
Speaker 1: from the beginning and work our way forward to track
Speaker 1: the thread from the origins of chat GPT to the
Speaker 1: billion spurned building data centers all over the world and
Speaker 1: the weak business justifications for burning and nearly a trillion
Speaker 1: dollars to keep this hollow industry alive. Now, in twenty
Speaker 1: twenty two, a kind of company called open Ai surprise
Speaker 1: the world with a website called chat GPT. They could
Speaker 1: generate text that sort of sounded like a person using
Speaker 1: a technology called large language models LLMS, which can also
Speaker 1: be used to generate images, video, and computer code, or
Speaker 1: at least would eventually last. Language models require entire clusters
Speaker 1: of servers connected with high speed networking or containing this
Speaker 1: thing called a GPU. Graphics processing units. These are difference
Speaker 1: to the GPUs in your xbox or laptop or gaming PC.
Speaker 1: They cost much much more, and they're good at doing
Speaker 1: the processes of inference, the creation of an output of
Speaker 1: any LLM and training, feeding masses of training data to
Speaker 1: the models, or feeding them information about what a good
Speaker 1: output might look like so they can later identify a
Speaker 1: thing or replicate it. These models showed some immediate promise
Speaker 1: in their ability to articulate concepts or generate video visuals,
Speaker 1: audio text, and code. They also immediately had one glaring
Speaker 1: obvious problem because their propabilistic, meaning that they're just guessing
Speaker 1: whatever the right output might be. These models can't actually
Speaker 1: be relied upon to do exactly the same thing every
Speaker 1: single time. So if you generated a picture of a
Speaker 1: person that you wanted to, for example, using this story book,
Speaker 1: every time you created a new page using the same
Speaker 1: prompt to describe the protragton, the person would look different,
Speaker 1: and that difference could be minor of something that a
Speaker 1: reader could shrug off, or it could make the character
Speaker 1: look like a completely different person. Now None of this,
Speaker 1: by the way, is me validating or saying that any
Speaker 1: of this stuff is good. I'm just describing him. Moreover,
Speaker 1: the probabilistic nature of generative AI meant that whenever you
Speaker 1: asked it a question, it would guess as to the answer,
Speaker 1: not because it knew the answer, but rather because it
Speaker 1: was guessing on the right word to add in a
Speaker 1: sentence based on previous training data. As a result, these
Speaker 1: models would frequently make mistakes, something which we later referred
Speaker 1: to as hallucinations. And that's not even mentioning the cost
Speaker 1: of training these models, the cost of running them, the
Speaker 1: vast amounts of computational power they required, the fact that
Speaker 1: the legality of using materials straight from books in the
Speaker 1: web without the owner's permission was and remains legally dubious,
Speaker 1: or the fact that nobody seemed to know how to
Speaker 1: use these models that actually create profitable businesses. These problems
Speaker 1: were overshadowed by something flashy and new, and something that
Speaker 1: investors and the tech media believed would eventually aut to
Speaker 1: make jobs that have proven most resistance towardstion knowledge work
Speaker 1: and the creative economy. The newness and hyper and these
Speaker 1: expectations sent the market into a frenzy, with every hyper
Speaker 1: scaler immediately creating the most aggressive market for one supplier
Speaker 1: I've ever seen. Nvidia has sold over two hundred billion
Speaker 1: dollars of GPUs since the beginning of twenty twenty three,
Speaker 1: becoming the largest company on the American stock market and
Speaker 1: trading over one hundred and seventy dollars as of writing
Speaker 1: this sentence, only a few years off to being worth
Speaker 1: nineteen dollars and fifty two cents a share. Now there's
Speaker 1: a stock split that happened there, but it works out
Speaker 1: that way. Now. While I've talked about some of the
Speaker 1: propelling factors behind the AI wave, automation and novelty, that's
Speaker 1: not really the complete picture. A huge reason why everybody
Speaker 1: decided to do AI was because the software industry's growth
Speaker 1: was slowing and SaaS software as a service company valuations
Speaker 1: were stalling or dropping, resulting in the terrifying prospect of
Speaker 1: companies having to underpromise and over deliver and be efficient,
Speaker 1: you know, gross things like running sustainable businesses. Things that
Speaker 1: normal companies, those whose valuations aren't contingent on ever increase,
Speaker 1: ever constant growth, don't have to worry about because they're
Speaker 1: normal companies. Suddenly there was a new promise of new technology,
Speaker 1: large language models that were getting exponentially more powerful, which
Speaker 1: was mostly a lie but hard to disprove because powerful
Speaker 1: can mean basically anything, and the definition of powerful depended
Speaker 1: entirely on whoever you asked at any given time and
Speaker 1: what that person's motivations were. The media also immediately started
Speaker 1: tripping over its own feet, mistakenly claiming open ais GPT
Speaker 1: form model tricked a task grab it into solving a capture.
Speaker 1: It didn't. This never happened, or saying that, and I
Speaker 1: quote people who don't know how to code already used
Speaker 1: bots to produce full fledged games. And if you weren't
Speaker 1: wondering what the New York Times was referring to when
Speaker 1: they said full fledged, there it meant Pong and are
Speaker 1: coupled together rolling demo of Skyroads game from nineteen ninety three,
Speaker 1: likely because a bunch of that training data was fed
Speaker 1: into the models. Now, the media and investors helped pedal
Speaker 1: the narrative that AI was always getting better, could basically
Speaker 1: do anything, and that any problems you saw today would
Speaker 1: inevitably be solved in a few short months or years.
Speaker 1: Or that some point. I guess not really sure when
Speaker 1: that point is, but damn do they think it's coming.
Speaker 1: And llms were touted as a kind of digital panacea,
Speaker 1: and the companies building them off a traditional software companies
Speaker 1: the chance to plug these models into their software using
Speaker 1: an API, thus allowing them to write the same generative
Speaker 1: AI wave that every other company was riding. The model
Speaker 1: companies similarly started going after individual and business customers, offering
Speaker 1: software and subscriptions that promised the world, though this mostly
Speaker 1: boiled down to chatbots that could generate stuff, and then
Speaker 1: doubled down with the promise of agents, a marketing term
Speaker 1: that's meant to make you think autonomous digital worker, but
Speaker 1: really means broken digital chatbot of some sort or just
Speaker 1: broken digital product. It really depends how you're feeling that day.
Speaker 1: Throughout this era, investors in the media spoke with a
Speaker 1: sense of inevitability that they never really backed up with data.
Speaker 1: It was an era based on confidently asserted vibes. Everything
Speaker 1: was always getting better and more powerful, even though there
Speaker 1: was never much proof that this was truly disruptive technology
Speaker 1: other than in its ability to disrupt apps you were
Speaker 1: using with AI, making them worse, For example, suggesting questions
Speaker 1: on every Facebook post that you could ask meta AI,
Speaker 1: but which METAAI couldn't answer. And I mean on memes,
Speaker 1: on just random posts. It's really not useful in any way,
Speaker 1: shape or form. AI became omnipresent, and it eventually grew
Speaker 1: to mean everything and nothing. Open AI would see. It's
Speaker 1: every move loaded over like a gifted child. It's CEO
Speaker 1: Sam Allman called the Oppenheimer of our age, even if
Speaker 1: it wasn't really obvious why everybody was impressed. GPT four
Speaker 1: felt like something a bit different, but was it actually meaningful?
Speaker 1: The thing is our official intelligence is built and sold
Speaker 1: or not just faith, but a series of myths that
Speaker 1: AI boosters expect us to believe, but the same certainty
Speaker 1: that we treat things like gravity or the boiling point
Speaker 1: of water. Can large language models actually replace coders? Not really, No,
Speaker 1: and I'll get into way later in this series. Consora
Speaker 1: Open AI's video creation tool replace actors or animators. No,
Speaker 1: not at all, But it still fills the air full
Speaker 1: of tension because you can immediately see who is preregistered
Speaker 1: to replace everyone that works for them. AI is apparently
Speaker 1: replacing workers, but nobody seems able to prove it at scale.
Speaker 1: But every few weeks a story runs where everybody tries
Speaker 1: to pretend that AI is replacing workers with some sort
Speaker 1: of poorly sourced and incomprehensible study, never actually saying somebody's
Speaker 1: job got replaced by AI, because it isn't happening at scale,
Speaker 1: and because if you provide real world examples, people can
Speaker 1: actually check if they're true. Now, I want to be clear,
Speaker 1: some people have lost jobs to AI, just not really
Speaker 1: white collar workers, software engineers, or really any of the
Speaker 1: career paths that the mainstream media and AI investors would
Speaker 1: have you believe. Brian Merchant has done excellent work covering
Speaker 1: how llms have devoured the work of translators, using cheap,
Speaker 1: almost good automation to lower already stagnant wages in a
Speaker 1: field that has already been hurting before the add to
Speaker 1: generative AI, with some having to abandon the field and
Speaker 1: others pushed into bankruptcy. I've heard the same for art directors,
Speaker 1: SEO experts, and copy editors, and Christopher Mims of The
Speaker 1: Wall Street Journal covered these last year. These fields all
Speaker 1: have something in common shitty bosses with little regard for
Speaker 1: their customers. Who have been eagerly waiting for the opportunity
Speaker 1: to slash labor to quote Merchant, the drum beat marketing
Speaker 1: and pop culture of powerful AI encourages and permits management
Speaker 1: to replace with the great jobs they might not otherwise have.
Speaker 1: Across the board, the people being replaced by AI are
Speaker 1: the victims of lazy, incompetent cost cutters who don't care
Speaker 1: if they ship poorly translated text to quote Merchant. Again,
Speaker 1: AI hypers created the cover necessary to justify slashing rates
Speaker 1: and accepting just good enough automation output for video games
Speaker 1: and media products. Yet the jobs crisis facing translators speaks
Speaker 1: to the larger flaws of the large language model era
Speaker 1: and why other careers aren't seeing this kind of disruption.
Speaker 1: Generative AI creates outputs, and by extension, defines all labour
Speaker 1: as some kind of output creative from a request. In
Speaker 1: the case translation, it's possible for a company to get
Speaker 1: by with a shitty version because many customers see translation
Speaker 1: as what do these words say, even though, as one
Speaker 1: worker told Brian Merchant, translation is about conveying meaning. Nevertheless,
Speaker 1: translation workers already started to condense to a world where
Speaker 1: humans would at times clean up machine generated text, and
Speaker 1: the same worker warned that the same might come for
Speaker 1: other industries. Yet the problem is that translation is a
Speaker 1: heavily output driven industry, one where idiot bosses can say, oh, yeah,
Speaker 1: that's fine because they ran an output back through Google
Speaker 1: Translate and it seemed fine in their native tongue. The
Speaker 1: problems of a poor translation are obvious, but customers of
Speaker 1: translation are it seems, often capable of getting by with
Speaker 1: a shitty product. The problem is that most jobs are
Speaker 1: not output driven at all, and what we're buying from
Speaker 1: a human beings a person's ability to think and do.
Speaker 1: Every CEO talking about replacing workers with AI is an
Speaker 1: example of the real problem that most companies are run
Speaker 1: by people who don't understand or experience the problems they're solving,
Speaker 1: don't do any real work, don't face any real problems,
Speaker 1: and thus can never be trusted to solve them. In
Speaker 1: the Era of the Business Idio, which is a piece
Speaker 1: I wrote a while ago, I talked about how this
Speaker 1: was the result of letting management consultants and neoliberal free
Speaker 1: market sociopaths take over everything, leaving us with companies run
Speaker 1: by people who don't know how the companies make money,
Speaker 1: just that they must always make more without fail and
Speaker 1: when you're a big stupid asshole. Every job that you
Speaker 1: see is condensed to its outputs, and not the stuff
Speaker 1: that leads up to the output, or the small nuances
Speaker 1: and conscious decisions that make an output good as opposed
Speaker 1: to simply acceptable or even bad. What does the software
Speaker 1: engineer do? They write code right, What does a rater do?
Speaker 1: They write words right? What does a hairdresser do they
Speaker 1: can't hear? Yeah, that's of course not actually the case.
Speaker 1: As I'll get into later in the series, the software
Speaker 1: engineer does far more than just code. And when they
Speaker 1: write code, they're not just saying what would solve this
Speaker 1: problem with a big smile on their face. They're taking
Speaker 1: into account their years of experience, what code does, what
Speaker 1: code could do, and all the things that my break
Speaker 1: is a result, and all of the things that you
Speaker 1: can't really tell from just looking at the code, like
Speaker 1: whether there's a reason things are made in a particle way,
Speaker 1: And a good coder doesn't just hammer at the keyboard
Speaker 1: with the aim of doing a particular task. They factor
Speaker 1: in questions like how does this functionality fit into the
Speaker 1: code that's already there? Or if someone has to update
Speaker 1: this code in the future, how do I make it
Speaker 1: easy for them to understand what I've written and make
Speaker 1: changes without breaking a bunch of other stuff. A writer
Speaker 1: doesn't just write words. They just ideas and ideas and
Speaker 1: emotions and thoughts and facts and feelings into a condensed
Speaker 1: piece of text. They sit up late at night typing
Speaker 1: thousands and thousands of words, and it drives them in say,
Speaker 1: it's often quite a motive or at the very least
Speaker 1: driven who or inspired by a given emotion, which is
Speaker 1: something that an AI simply can't replicate in a way
Speaker 1: that's authentic or believable. And a hairdresser doesn't just cut hair.
Speaker 1: They cut your hair, which may be wiry, dry, oily long,
Speaker 1: sure healthy, unhealthy on a scout with particular issues at
Speaker 1: a time of year, perhaps you want to change length
Speaker 1: at a time that fits you in the way you
Speaker 1: like it, which may be impossible to actually write down.
Speaker 1: But they get it just right, and they make conversation
Speaker 1: making you feel at ease while they snip and clip away.
Speaker 1: It tresses with you never having to think for a second. Fuck,
Speaker 1: does this person know what they're doing? Are they going
Speaker 1: to listen to me. This is the true nature of labor.
Speaker 1: The executives fail to comprehend at scale that the things
Speaker 1: we do are not units of work, but extrapolations of experience, emotion,
Speaker 1: and context that cannot be condensed in written meaning or
Speaker 1: bunches of trading material. Business idiots see our labor as
Speaker 1: the results of a smart manager saying do this, rather
Speaker 1: than human ingenuity interpreting both the requests and the shit
Speaker 1: the manager didn't say. Now, what does the CEO do? Well?
Speaker 1: I did look, and a Harvard study said that they
Speaker 1: spend twenty five percent of their time on people and relationships,
Speaker 1: twenty five percent on functional and business unit reviews, sixteen
Speaker 1: percent on organization and culture, and twenty one percent on
Speaker 1: just strategy, with a few percent here and there for
Speaker 1: things like professional development. Hmm. That's who runs the vast
Speaker 1: majority of companies. People that describe their work predominantly as
Speaker 1: looking at staff, talking to people, thinking what we do next,
Speaker 1: and go to lunch. The most highly paid jobs in
Speaker 1: the world are impossible to describe their labour described in
Speaker 1: a mishmash of linked inspiration. Yet everybody else's labor is
Speaker 1: an output that can be automated. As a result, large
Speaker 1: language models must seem like magic to these dickheads. When
Speaker 1: you see everything as an outcome, an outcome, you may
Speaker 1: or may not understand it. Definitely don't understand the process behind,
Speaker 1: let alone care about. You've kind of already see your
Speaker 1: workers as llms. You create a stratification of the workforce
Speaker 1: that goes beyond the normal organizational chart, with senior executives
Speaker 1: those closer to the class level of CEO acting as
Speaker 1: those who have risen above the doldrums of doing things,
Speaker 1: to the level of decision making, a fuzzy term that
Speaker 1: can mean everything from making nuance decisions with input from
Speaker 1: multiple different subject matter experts to as service now Bill
Speaker 1: McDermott did in twenty twenty two and I quote make
Speaker 1: it clear to everybody in a boardroom of other executives
Speaker 1: that everything they do must be AIAIAIAIAI. And that's five
Speaker 1: of those the same extents that some members of the
Speaker 1: business and tech media that have for the most part
Speaker 1: gotten by without having to think too hard about the
Speaker 1: actual things the companies are saying. Look, I realize this
Speaker 1: sounds a little mean, and it's not a unilateral statement,
Speaker 1: and I must must be clear. It doesn't mean that
Speaker 1: these people know nothing, just that it's been possible that
Speaker 1: scoot through the world without thinking too hard about whether
Speaker 1: or not something is true, just because an executive said up.
Speaker 1: When Salesforce said back in twenty twenty four that it's
Speaker 1: Einstein Trust Layer and AI would be transformational for jobs,
Speaker 1: the media dutifully wrote it down and published it without
Speaker 1: a second thought. It fully trusted Mark Benioff when he
Speaker 1: said that Agent Force agents would replace human workers, and
Speaker 1: then again when he said that AI agents were doing
Speaker 1: thirty to fifty percent of all the work in Salesforce itself,
Speaker 1: even though that's an unproven and nakedly ridiculous statement. Salesforce
Speaker 1: is CFO, by the way, said earlier in this year
Speaker 1: that AI wouldn't boost sales growth in twenty twenty five.
Speaker 1: One would think this would change how Salesforce was covered
Speaker 1: or how seriously one takes Mark Benioff, But it hasn't
Speaker 1: because nobody's really paying attention. In fact, nobody seems to
Speaker 1: want to do their job in this case. And this
Speaker 1: is how the core myths of jenerit if AI were
Speaker 1: built by executives saying stuff and the media publishing it
Speaker 1: without thinking about him. AI is replacing workers. AI is
Speaker 1: writing entire computer programs. AI is getting exponentially more powerful.
Speaker 1: What does powerful mean? Well, it means that the models
Speaker 1: are getting better on benchmarks that are rigged in their favor.
Speaker 1: But because nobody fucking explains what the benchmarks are, regular
Speaker 1: people are regularly told that AI is powerful and getting
Speaker 1: more powerful every single day. The only thing powerful about
Speaker 1: generifai is its pathology. The world's executives entirely disconnect different
Speaker 1: labor and natural production, are doing the only thing they
Speaker 1: know how to spend a bunch of money and say
Speaker 1: vague stuff about AI being the future. There are people journalists, investors,
Speaker 1: and analysts that have built entire careers on filling in
Speaker 1: the gaps for the powerful as they splurge billions of
Speaker 1: dollars in repeat with increasing desperation that the future is here,
Speaker 1: and then well, absolutely nothing else happens. You've likely seen
Speaker 1: a few ridiculous headlines recently, though. One of the most
Speaker 1: recent and most absurd is that open Ai will pay
Speaker 1: Oracle three hundred billion dollars over the next four years,
Speaker 1: closely followed with the claim that Invidia will invest and
Speaker 1: I put that in air, quotes one hundred billion dollars
Speaker 1: in open Ai to build ten gigawats of ai data centers,
Speaker 1: though the deal is structured in a way that means
Speaker 1: open Ai is paid progressively as each giga what is deployed,
Speaker 1: and also apparently open Ai will be leasing the chips
Speaker 1: rather than buying them out right. I must be clear
Speaker 1: that these deals are intentionally made to continue the myth
Speaker 1: of generat ifai to pump in video and to make
Speaker 1: sure open ai insiders can sell ten point three billion
Speaker 1: dollars worth of shares, which they're currently trying to do
Speaker 1: at evaluation of five hundred billion goddamn dollars. I want
Speaker 1: to be clear about something open Ai cannot afford the
Speaker 1: three hundred billion dollars. Open Ai has not received a
Speaker 1: dollar from Nvidia and won't do so for at least
Speaker 1: a month when I think they're going to receive ten
Speaker 1: billion dollars. But the rest of that ninety's only coming
Speaker 1: when they build those data centers, which open Ai can't
Speaker 1: afford to do. In Vidia needs this myth to continue
Speaker 1: because in truth, all of these data centers are being
Speaker 1: built for demand that doesn't exist, or that if it
Speaker 1: did exist, doesn't necessarily translate into business customers paying huge
Speaker 1: amount amounts for access to open AI's generative AI services.
Speaker 1: In Vidia, open Ai, Core Weave, and other AI related
Speaker 1: companies hope that by announcing theoretical billions of dollars or
Speaker 1: hundreds of billions of dollars of these strange, vague, and
Speaker 1: impossible seeming deals, they can keep pretending that the demand
Speaker 1: is there, because why else would they build all these
Speaker 1: data centers? Right, Well, there's there's that, and the entire
Speaker 1: stock market races on. In video's back. It accounts for
Speaker 1: seven to eight percent of the value of the S
Speaker 1: and P five hundred, and Jensen Huang needs to keep
Speaker 1: selling those fucking GPUs. I intend to explain later how
Speaker 1: all of this works and how brittle all of this
Speaker 1: really is. But the intention of these deals is simple
Speaker 1: to make you think this much money can't be wrong,
Speaker 1: and I assure you it can. These people need you
Speaker 1: to believe this is inevitable. But they are being proven
Speaker 1: wrong again and again and again, and today I'm going
Speaker 1: to continue to do so. Underpinning these stories about huge
Speaker 1: amounts of money and endless opportunity lies a dark secret.
Speaker 1: The none of this is working, and all of this
Speaker 1: money has been invested in a technology that doesn't make
Speaker 1: much revenue and loves to burn millions or billions or
Speaker 1: hundreds of billions of dollars. Over half a trillion dollars
Speaker 1: in fact, has gone into an entire industry without a
Speaker 1: single profitable company developing models of products built on top
Speaker 1: of these AI models. By my estimates, there's about forty
Speaker 1: four billion dollars of revenue in general of AI this
Speaker 1: year when you add in anthropic and open AIS revenue
Speaker 1: to the part, along with other stragglers, and most of
Speaker 1: that number has been gathered from reporting from outlets like
Speaker 1: The Information. Because none of these companies share their revenues,
Speaker 1: all of them lose shit tons of money, and their
Speaker 1: actual revenues are really, really, really small. Only one member
Speaker 1: of the Magnificent seven outside of Nvidia, has ever disclosed
Speaker 1: its AI revenue, Microsoft, which has stopped reporting, in January
Speaker 1: twenty twenty five, when it reported it would have thirteen
Speaker 1: billion in aniized revenue this well, I guess that will
Speaker 1: be for the month because it's month times twelve, about
Speaker 1: one point eight three billion a month. I'm I know
Speaker 1: that sounds like a lot. But Microsoft is a sales machine.
Speaker 1: It's built specifically to create or exploit software markets, suffocating
Speaker 1: competitors by using its scale to drive down prices and
Speaker 1: to leverage the ecosystem it's created over the past few decades.
Speaker 1: One billion a month in revenue is chump change for
Speaker 1: an organization that makes over twenty seven billion dollars a
Speaker 1: quarter in profits. But hey, it's the early days. Did
Speaker 1: you get it? Hurt out? God? Thank you, Scott? Did
Speaker 1: you not listen to my three part series on how
Speaker 1: to argue with an AI booster? I went over it
Speaker 1: over there, get out? Okay. This is also nothing like
Speaker 1: any other tic era. There's never been this kind of
Speaker 1: cash rush, even in the fiber boom. Over a decade.
Speaker 1: Amazon spent about a tenth of the capex that the
Speaker 1: Magnificent seven spent in the last two years on general AI,
Speaker 1: building Amazon Web Services, something that now powers a vast
Speaker 1: chunk of the web and has long been Amazon's most
Speaker 1: profitable business generally. If AI is also nothing like Uber,
Speaker 1: with open Ai and anthropics true costs coming in at
Speaker 1: around one hundred and fifty nine billion dollars in the
Speaker 1: past two years, approaching five times Uber's thirty billion dollar
Speaker 1: all time burn and that's before the bullshit with Nvideo
Speaker 1: and an Oracle. Microsoft last reported that AI revenue in January.
Speaker 1: By the way, it's now October. Why did it stop
Speaker 1: reporting the number? Do you think is it because the
Speaker 1: numbers are so good they couldn't possibly let you know? Hmm.
Speaker 1: As a general rule, publicly traded companies, especially those where
Speaker 1: the leadership are compensated primarily in equity so stock, they
Speaker 1: tend to brag about their successes, in part because bragging
Speaker 1: boosts the value of the thing that the leadership gets
Speaker 1: paid in. There's no benefit to being shy. Look, Oracle
Speaker 1: announced they literally filed something saying they had that huge
Speaker 1: three hundred billion dollar contract. They did that despite the stock.
Speaker 1: Why is Microsoft not doing that with their incredible AI revenues?
Speaker 1: Do you think it's because they're shy? Come on, Satcha,
Speaker 1: come on out, come on, Satcher. You can show me
Speaker 1: in the numbers. Been in all seriousness, If Microsoft can't
Speaker 1: sell this, nobody can. All right, So I'm explaining this
Speaker 1: whole thing is if you're brand new and walking up
Speaker 1: to this relatively unprepared, so I need to introduce another
Speaker 1: company In twenty twenty, a splinter group jumped off of
Speaker 1: open ai, funded by Amazon and Google to do much
Speaker 1: the same thing as open ai, but pretend to be
Speaker 1: nicer about it until they had to raise money from
Speaker 1: the Middle East. I am, of course talking about Anthropic,
Speaker 1: and they've always been a bit better at coding for
Speaker 1: some reason, and people really like their clawed models. But
Speaker 1: like does not mean profit or even much revenue. Both
Speaker 1: open ai and Anthropic have become the only two companies
Speaker 1: in generative AI to make any real progress, either in
Speaker 1: terms of recognition or in sheer commercial terms, accounting for
Speaker 1: the vast majority of revenue in the AI industry. In
Speaker 1: a very real sense, the AI industry's revenue is open
Speaker 1: ai and Anthropic. In the year where Microsoft recorded thirteen
Speaker 1: billion dollars in AI revenues, ten billion dollars of that
Speaker 1: came from open AI's spending on Microsoft Azure, Anthropic burned
Speaker 1: five point three billion dollars last year, with the vast
Speaker 1: majority of that going towards compute. Outside of these two companies,
Speaker 1: there's barely enough revenue to justify a single data center.
Speaker 1: Where we see today is a time of immense tension.
Speaker 1: Mark Zuckerberg says we're in a bubble. Sam Mortman says
Speaker 1: we're in a bubble. At a barber chairman and billionaire
Speaker 1: Joe Size says we're in a bubble. Apollo says we're
Speaker 1: in a bubble. Nobody's making money and nobody knows why
Speaker 1: they're actually doing this anymore, just that they must do
Speaker 1: it and must do so immediately. And they've yet to
Speaker 1: make the case that jenerat Ifai warranted any of the expenditures.
Speaker 1: Now we're a quarter of the way through this four party,
Speaker 1: but this one was necessary. I needed to get you
Speaker 1: up to speed and kind of give you the lay
Speaker 1: of the land because we're going to go a little
Speaker 1: deeper in the next episode and I can't wait for
Speaker 1: you to hear him see it tomorrow. Thank you for
Speaker 1: listening to Better Offline. The editor and composer of the
Speaker 1: Better Offline theme song is Mattasowski. You can check out
Speaker 1: more of his music and audio projects at Mattasouski dot com,
Speaker 1: M A T. T O. S O w Ski dot com.
Speaker 1: You can email me at easy at Better offline dot com,
Speaker 1: or visit Better Offline dot com to find more podcast links,
Speaker 1: and of course my newsletter. I also really recommend you
Speaker 1: go to chat dot where's youread dot at to visit
Speaker 1: the discord, and go to our slash Better Offline to
Speaker 1: check out I'll Reddit. Thank you so much for listening.
Speaker 1: Better Offline is a production of cool Zone Media. For
Speaker 1: more from cool Zone Media, visit our website cool Zonemedia
Speaker 1: dot com, or check us out on the iHeartRadio app,
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