The Case Against Generative AI (Part 1)

even three parter, but let's get into him. Over the last few months, we felt the vibes shift downward in an aggressive way, with both Marg Zuckerberg and Clammy Sam Mortman saying that we're in a bubble. In the latter case said, warnings of a bubble are always couched in rank hypocrisy is it's always implied that whoever it is and the companies they represent aren't part of that bubble, but rather it's other people and other companies making unfortunate decisions.

The thing is, there's really no escape for either of these guys, not for Zaken, definitely not for Sam Ortmon. And over the next four episodes, I'm going to make a comprehensive case for the fact that we're in a bubble and condense everything I've been talking about into one series. And I know I've been all over the place, and I get a lot of people saying, oh, well, where did you talk about this? And where'd you talk about that? And that's kind of fair when you put out as

much as I do. But I'm going to break this down in four episodes. I'm going to give you a comprehensive argument against the bubble. Well I mean that for a bubble, I guess, but against generative AI in general. But in this episode, I think it's good to start from the beginning and work our way forward to track the thread from the origins of chat GPT to the billion spurned building data centers all over the world and the weak business justifications for burning and nearly a trillion

dollars to keep this hollow industry alive. Now, in twenty twenty two, a kind of company called open Ai surprise the world with a website called chat GPT. They could generate text that sort of sounded like a person using a technology called large language models LLMS, which can also be used to generate images, video, and computer code, or at least would eventually last. Language models require entire clusters of servers connected with high speed networking or containing this

thing called a GPU. Graphics processing units. These are difference to the GPUs in your xbox or laptop or gaming PC. They cost much much more, and they're good at doing the processes of inference, the creation of an output of any LLM and training, feeding masses of training data to the models, or feeding them information about what a good output might look like so they can later identify a

thing or replicate it. These models showed some immediate promise in their ability to articulate concepts or generate video visuals, audio text, and code. They also immediately had one glaring obvious problem because their propabilistic, meaning that they're just guessing whatever the right output might be. These models can't actually be relied upon to do exactly the same thing every

single time. So if you generated a picture of a person that you wanted to, for example, using this story book, every time you created a new page using the same prompt to describe the protragton, the person would look different, and that difference could be minor of something that a reader could shrug off, or it could make the character look like a completely different person. Now None of this, by the way, is me validating or saying that any

of this stuff is good. I'm just describing him. Moreover, the probabilistic nature of generative AI meant that whenever you asked it a question, it would guess as to the answer, not because it knew the answer, but rather because it was guessing on the right word to add in a sentence based on previous training data. As a result, these models would frequently make mistakes, something which we later referred

to as hallucinations. And that's not even mentioning the cost of training these models, the cost of running them, the vast amounts of computational power they required, the fact that the legality of using materials straight from books in the web without the owner's permission was and remains legally dubious, or the fact that nobody seemed to know how to

use these models that actually create profitable businesses. These problems were overshadowed by something flashy and new, and something that investors and the tech media believed would eventually aut to make jobs that have proven most resistance towardstion knowledge work and the creative economy. The newness and hyper and these expectations sent the market into a frenzy, with every hyper scaler immediately creating the most aggressive market for one supplier

I've ever seen. Nvidia has sold over two hundred billion dollars of GPUs since the beginning of twenty twenty three, becoming the largest company on the American stock market and trading over one hundred and seventy dollars as of writing this sentence, only a few years off to being worth nineteen dollars and fifty two cents a share. Now there's a stock split that happened there, but it works out

that way. Now. While I've talked about some of the propelling factors behind the AI wave, automation and novelty, that's not really the complete picture. A huge reason why everybody decided to do AI was because the software industry's growth was slowing and SaaS software as a service company valuations were stalling or dropping, resulting in the terrifying prospect of companies having to underpromise and over deliver and be efficient,

you know, gross things like running sustainable businesses. Things that normal companies, those whose valuations aren't contingent on ever increase, ever constant growth, don't have to worry about because they're

normal companies. Suddenly there was a new promise of new technology, large language models that were getting exponentially more powerful, which was mostly a lie but hard to disprove because powerful can mean basically anything, and the definition of powerful depended entirely on whoever you asked at any given time and

what that person's motivations were. The media also immediately started tripping over its own feet, mistakenly claiming open ais GPT form model tricked a task grab it into solving a capture. It didn't. This never happened, or saying that, and I quote people who don't know how to code already used

bots to produce full fledged games. And if you weren't wondering what the New York Times was referring to when they said full fledged, there it meant Pong and are coupled together rolling demo of Skyroads game from nineteen ninety three, likely because a bunch of that training data was fed

into the models. Now, the media and investors helped pedal the narrative that AI was always getting better, could basically do anything, and that any problems you saw today would inevitably be solved in a few short months or years. Or that some point. I guess not really sure when that point is, but damn do they think it's coming.

And llms were touted as a kind of digital panacea, and the companies building them off a traditional software companies the chance to plug these models into their software using an API, thus allowing them to write the same generative

AI wave that every other company was riding. The model companies similarly started going after individual and business customers, offering software and subscriptions that promised the world, though this mostly boiled down to chatbots that could generate stuff, and then doubled down with the promise of agents, a marketing term that's meant to make you think autonomous digital worker, but really means broken digital chatbot of some sort or just

broken digital product. It really depends how you're feeling that day. Throughout this era, investors in the media spoke with a sense of inevitability that they never really backed up with data.

It was an era based on confidently asserted vibes. Everything was always getting better and more powerful, even though there was never much proof that this was truly disruptive technology other than in its ability to disrupt apps you were using with AI, making them worse, For example, suggesting questions on every Facebook post that you could ask meta AI, but which METAAI couldn't answer. And I mean on memes, on just random posts. It's really not useful in any way,

shape or form. AI became omnipresent, and it eventually grew to mean everything and nothing. Open AI would see. It's every move loaded over like a gifted child. It's CEO Sam Allman called the Oppenheimer of our age, even if it wasn't really obvious why everybody was impressed. GPT four felt like something a bit different, but was it actually meaningful?

The thing is our official intelligence is built and sold or not just faith, but a series of myths that AI boosters expect us to believe, but the same certainty that we treat things like gravity or the boiling point of water. Can large language models actually replace coders? Not really, No, and I'll get into way later in this series. Consora

Open AI's video creation tool replace actors or animators. No, not at all, But it still fills the air full of tension because you can immediately see who is preregistered to replace everyone that works for them. AI is apparently replacing workers, but nobody seems able to prove it at scale.

But every few weeks a story runs where everybody tries to pretend that AI is replacing workers with some sort of poorly sourced and incomprehensible study, never actually saying somebody's job got replaced by AI, because it isn't happening at scale, and because if you provide real world examples, people can

actually check if they're true. Now, I want to be clear, some people have lost jobs to AI, just not really white collar workers, software engineers, or really any of the career paths that the mainstream media and AI investors would

have you believe. Brian Merchant has done excellent work covering how llms have devoured the work of translators, using cheap, almost good automation to lower already stagnant wages in a field that has already been hurting before the add to generative AI, with some having to abandon the field and others pushed into bankruptcy. I've heard the same for art directors, SEO experts, and copy editors, and Christopher Mims of The

Wall Street Journal covered these last year. These fields all have something in common shitty bosses with little regard for their customers. Who have been eagerly waiting for the opportunity to slash labor to quote Merchant, the drum beat marketing and pop culture of powerful AI encourages and permits management to replace with the great jobs they might not otherwise have. Across the board, the people being replaced by AI are the victims of lazy, incompetent cost cutters who don't care

if they ship poorly translated text to quote Merchant. Again, AI hypers created the cover necessary to justify slashing rates and accepting just good enough automation output for video games and media products. Yet the jobs crisis facing translators speaks to the larger flaws of the large language model era and why other careers aren't seeing this kind of disruption. Generative AI creates outputs, and by extension, defines all labour

as some kind of output creative from a request. In the case translation, it's possible for a company to get by with a shitty version because many customers see translation as what do these words say, even though, as one worker told Brian Merchant, translation is about conveying meaning. Nevertheless, translation workers already started to condense to a world where humans would at times clean up machine generated text, and the same worker warned that the same might come for

other industries. Yet the problem is that translation is a heavily output driven industry, one where idiot bosses can say, oh, yeah, that's fine because they ran an output back through Google Translate and it seemed fine in their native tongue. The problems of a poor translation are obvious, but customers of translation are it seems, often capable of getting by with

a shitty product. The problem is that most jobs are not output driven at all, and what we're buying from a human beings a person's ability to think and do. Every CEO talking about replacing workers with AI is an example of the real problem that most companies are run by people who don't understand or experience the problems they're solving, don't do any real work, don't face any real problems,

and thus can never be trusted to solve them. In the Era of the Business Idio, which is a piece I wrote a while ago, I talked about how this was the result of letting management consultants and neoliberal free market sociopaths take over everything, leaving us with companies run by people who don't know how the companies make money, just that they must always make more without fail and

when you're a big stupid asshole. Every job that you see is condensed to its outputs, and not the stuff that leads up to the output, or the small nuances and conscious decisions that make an output good as opposed to simply acceptable or even bad. What does the software engineer do? They write code right, What does a rater do? They write words right? What does a hairdresser do they can't hear? Yeah, that's of course not actually the case. As I'll get into later in the series, the software

engineer does far more than just code. And when they write code, they're not just saying what would solve this problem with a big smile on their face. They're taking into account their years of experience, what code does, what code could do, and all the things that my break is a result, and all of the things that you can't really tell from just looking at the code, like whether there's a reason things are made in a particle way, And a good coder doesn't just hammer at the keyboard

with the aim of doing a particular task. They factor in questions like how does this functionality fit into the code that's already there? Or if someone has to update this code in the future, how do I make it easy for them to understand what I've written and make changes without breaking a bunch of other stuff. A writer doesn't just write words. They just ideas and ideas and emotions and thoughts and facts and feelings into a condensed

piece of text. They sit up late at night typing thousands and thousands of words, and it drives them in say, it's often quite a motive or at the very least driven who or inspired by a given emotion, which is something that an AI simply can't replicate in a way that's authentic or believable. And a hairdresser doesn't just cut hair.

They cut your hair, which may be wiry, dry, oily long, sure healthy, unhealthy on a scout with particular issues at a time of year, perhaps you want to change length at a time that fits you in the way you like it, which may be impossible to actually write down. But they get it just right, and they make conversation making you feel at ease while they snip and clip away. It tresses with you never having to think for a second. Fuck, does this person know what they're doing? Are they going

to listen to me. This is the true nature of labor. The executives fail to comprehend at scale that the things we do are not units of work, but extrapolations of experience, emotion, and context that cannot be condensed in written meaning or bunches of trading material. Business idiots see our labor as the results of a smart manager saying do this, rather than human ingenuity interpreting both the requests and the shit the manager didn't say. Now, what does the CEO do? Well?

I did look, and a Harvard study said that they spend twenty five percent of their time on people and relationships, twenty five percent on functional and business unit reviews, sixteen percent on organization and culture, and twenty one percent on just strategy, with a few percent here and there for things like professional development. Hmm. That's who runs the vast

majority of companies. People that describe their work predominantly as looking at staff, talking to people, thinking what we do next, and go to lunch. The most highly paid jobs in the world are impossible to describe their labour described in a mishmash of linked inspiration. Yet everybody else's labor is an output that can be automated. As a result, large language models must seem like magic to these dickheads. When you see everything as an outcome, an outcome, you may

or may not understand it. Definitely don't understand the process behind, let alone care about. You've kind of already see your

workers as llms. You create a stratification of the workforce that goes beyond the normal organizational chart, with senior executives those closer to the class level of CEO acting as those who have risen above the doldrums of doing things, to the level of decision making, a fuzzy term that can mean everything from making nuance decisions with input from multiple different subject matter experts to as service now Bill McDermott did in twenty twenty two and I quote make

it clear to everybody in a boardroom of other executives that everything they do must be AIAIAIAIAI. And that's five of those the same extents that some members of the business and tech media that have for the most part gotten by without having to think too hard about the actual things the companies are saying. Look, I realize this sounds a little mean, and it's not a unilateral statement,

and I must must be clear. It doesn't mean that these people know nothing, just that it's been possible that scoot through the world without thinking too hard about whether or not something is true, just because an executive said up. When Salesforce said back in twenty twenty four that it's Einstein Trust Layer and AI would be transformational for jobs, the media dutifully wrote it down and published it without

a second thought. It fully trusted Mark Benioff when he said that Agent Force agents would replace human workers, and then again when he said that AI agents were doing thirty to fifty percent of all the work in Salesforce itself, even though that's an unproven and nakedly ridiculous statement. Salesforce is CFO, by the way, said earlier in this year that AI wouldn't boost sales growth in twenty twenty five.

One would think this would change how Salesforce was covered or how seriously one takes Mark Benioff, But it hasn't because nobody's really paying attention. In fact, nobody seems to want to do their job in this case. And this is how the core myths of jenerit if AI were built by executives saying stuff and the media publishing it without thinking about him. AI is replacing workers. AI is writing entire computer programs. AI is getting exponentially more powerful.

What does powerful mean? Well, it means that the models are getting better on benchmarks that are rigged in their favor. But because nobody fucking explains what the benchmarks are, regular people are regularly told that AI is powerful and getting more powerful every single day. The only thing powerful about generifai is its pathology. The world's executives entirely disconnect different labor and natural production, are doing the only thing they know how to spend a bunch of money and say

vague stuff about AI being the future. There are people journalists, investors, and analysts that have built entire careers on filling in the gaps for the powerful as they splurge billions of dollars in repeat with increasing desperation that the future is here, and then well, absolutely nothing else happens. You've likely seen

a few ridiculous headlines recently, though. One of the most recent and most absurd is that open Ai will pay Oracle three hundred billion dollars over the next four years, closely followed with the claim that Invidia will invest and I put that in air, quotes one hundred billion dollars in open Ai to build ten gigawats of ai data centers, though the deal is structured in a way that means open Ai is paid progressively as each giga what is deployed,

and also apparently open Ai will be leasing the chips rather than buying them out right. I must be clear that these deals are intentionally made to continue the myth of generat ifai to pump in video and to make sure open ai insiders can sell ten point three billion dollars worth of shares, which they're currently trying to do at evaluation of five hundred billion goddamn dollars. I want to be clear about something open Ai cannot afford the

three hundred billion dollars. Open Ai has not received a dollar from Nvidia and won't do so for at least a month when I think they're going to receive ten billion dollars. But the rest of that ninety's only coming when they build those data centers, which open Ai can't

afford to do. In Vidia needs this myth to continue because in truth, all of these data centers are being built for demand that doesn't exist, or that if it did exist, doesn't necessarily translate into business customers paying huge amount amounts for access to open AI's generative AI services.

In Vidia, open Ai, Core Weave, and other AI related companies hope that by announcing theoretical billions of dollars or hundreds of billions of dollars of these strange, vague, and impossible seeming deals, they can keep pretending that the demand is there, because why else would they build all these data centers? Right, Well, there's there's that, and the entire

stock market races on. In video's back. It accounts for seven to eight percent of the value of the S and P five hundred, and Jensen Huang needs to keep selling those fucking GPUs. I intend to explain later how all of this works and how brittle all of this really is. But the intention of these deals is simple to make you think this much money can't be wrong, and I assure you it can. These people need you

to believe this is inevitable. But they are being proven wrong again and again and again, and today I'm going to continue to do so. Underpinning these stories about huge amounts of money and endless opportunity lies a dark secret. The none of this is working, and all of this money has been invested in a technology that doesn't make much revenue and loves to burn millions or billions or

hundreds of billions of dollars. Over half a trillion dollars in fact, has gone into an entire industry without a single profitable company developing models of products built on top of these AI models. By my estimates, there's about forty four billion dollars of revenue in general of AI this year when you add in anthropic and open AIS revenue to the part, along with other stragglers, and most of that number has been gathered from reporting from outlets like

The Information. Because none of these companies share their revenues, all of them lose shit tons of money, and their actual revenues are really, really, really small. Only one member of the Magnificent seven outside of Nvidia, has ever disclosed its AI revenue, Microsoft, which has stopped reporting, in January twenty twenty five, when it reported it would have thirteen billion in aniized revenue this well, I guess that will be for the month because it's month times twelve, about

one point eight three billion a month. I'm I know that sounds like a lot. But Microsoft is a sales machine. It's built specifically to create or exploit software markets, suffocating competitors by using its scale to drive down prices and to leverage the ecosystem it's created over the past few decades. One billion a month in revenue is chump change for an organization that makes over twenty seven billion dollars a quarter in profits. But hey, it's the early days. Did

you get it? Hurt out? God? Thank you, Scott? Did you not listen to my three part series on how to argue with an AI booster? I went over it over there, get out? Okay. This is also nothing like any other tic era. There's never been this kind of cash rush, even in the fiber boom. Over a decade.

Amazon spent about a tenth of the capex that the Magnificent seven spent in the last two years on general AI, building Amazon Web Services, something that now powers a vast chunk of the web and has long been Amazon's most

profitable business generally. If AI is also nothing like Uber, with open Ai and anthropics true costs coming in at around one hundred and fifty nine billion dollars in the past two years, approaching five times Uber's thirty billion dollar all time burn and that's before the bullshit with Nvideo and an Oracle. Microsoft last reported that AI revenue in January. By the way, it's now October. Why did it stop reporting the number? Do you think is it because the

numbers are so good they couldn't possibly let you know? Hmm. As a general rule, publicly traded companies, especially those where the leadership are compensated primarily in equity so stock, they tend to brag about their successes, in part because bragging boosts the value of the thing that the leadership gets paid in. There's no benefit to being shy. Look, Oracle announced they literally filed something saying they had that huge

three hundred billion dollar contract. They did that despite the stock. Why is Microsoft not doing that with their incredible AI revenues? Do you think it's because they're shy? Come on, Satcha, come on out, come on, Satcher. You can show me in the numbers. Been in all seriousness, If Microsoft can't

sell this, nobody can. All right, So I'm explaining this whole thing is if you're brand new and walking up to this relatively unprepared, so I need to introduce another company In twenty twenty, a splinter group jumped off of open ai, funded by Amazon and Google to do much the same thing as open ai, but pretend to be nicer about it until they had to raise money from the Middle East. I am, of course talking about Anthropic, and they've always been a bit better at coding for

some reason, and people really like their clawed models. But like does not mean profit or even much revenue. Both open ai and Anthropic have become the only two companies in generative AI to make any real progress, either in terms of recognition or in sheer commercial terms, accounting for the vast majority of revenue in the AI industry. In a very real sense, the AI industry's revenue is open

ai and Anthropic. In the year where Microsoft recorded thirteen billion dollars in AI revenues, ten billion dollars of that came from open AI's spending on Microsoft Azure, Anthropic burned five point three billion dollars last year, with the vast majority of that going towards compute. Outside of these two companies, there's barely enough revenue to justify a single data center. Where we see today is a time of immense tension. Mark Zuckerberg says we're in a bubble. Sam Mortman says

we're in a bubble. At a barber chairman and billionaire Joe Size says we're in a bubble. Apollo says we're in a bubble. Nobody's making money and nobody knows why they're actually doing this anymore, just that they must do it and must do so immediately. And they've yet to make the case that jenerat Ifai warranted any of the expenditures. Now we're a quarter of the way through this four party,

but this one was necessary. I needed to get you up to speed and kind of give you the lay of the land because we're going to go a little deeper in the next episode and I can't wait for you to hear him see it tomorrow. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Mattasowski. You can check out more of his music and audio projects at Mattasouski dot com, M A T. T O. S O w Ski dot com.

You can email me at easy at Better offline dot com, or visit Better Offline dot com to find more podcast links, and of course my newsletter. I also really recommend you go to chat dot where's youread dot at to visit the discord, and go to our slash Better Offline to check out I'll Reddit. Thank you so much for listening.

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