The Case Against Generative AI (Part 1)

Summary

Better Offline — "The Case Against Generative AI (Part 1)"

Host: Ed Zitron
Date: September 30, 2025
Podcast: Cool Zone Media and iHeartPodcasts

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Episode Overview

In this first of a four-part series, tech industry veteran Ed Zitron embarks on a deep investigation into the generative AI bubble: how it began, the myths that sustain it, and the precarious reality beneath the surface. By tracing the rise of large language models and the tidal wave of industry hype and investment, Zitron lays the groundwork for a comprehensive critique of generative AI—an industry that he argues is burning through vast sums of money without ever delivering meaningful or profitable technological breakthroughs.

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Key Discussion Points & Insights

1. Setting the Scene: The AI Bubble's Origins

• Zitron recounts the pivotal moment in 2022 when OpenAI released ChatGPT, surprising the world with a website that "could generate text that sort of sounded like a person" using large language models (LLMs) ([03:33]).
• He describes the enormous server infrastructure and the vast computational expense such models require, noting this was all for outputs that were essentially guesses—sometimes impressive, often error-prone or nonsensical.
• Quote:

  “These models can't actually be relied upon to do exactly the same thing every single time.”
  — Ed Zitron ([04:36])

2. Hype Meets Reality: The Disconnect

• AI's rapid ascendance is attributed not only to technological leaps, but also to a software industry desperate for growth as SaaS valuations stalled.
• The media and investors propagated the myth of exponential improvement—promoting stories (sometimes fabricated or mischaracterized, like GPT-4 tricking a TaskRabbit) that painted AI as an unstoppable force ([06:59]).
• Zitron exposes how terms like "powerful" became slippery, used as catch-alls to justify investment and maintain the industry's narrative of inevitability.

3. The Myth of Job Replacement

• AI, particularly LLMs, are often said to be automating jobs, but Zitron contends these claims are largely unfounded outside a handful of output-driven roles (e.g., translators, some content moderation) ([13:29]).
• Real-world displacement is limited, and typically where "shitty bosses" are willing to sacrifice quality for cost-cutting, often resulting in worse products.
• Quote:

  “The people being replaced by AI are the victims of lazy, incompetent cost-cutters who don’t care if they ship poorly translated text.”
  — Ed Zitron ([14:22])

4. Labor as More Than "Outputs"

• A central failure among executives and AI boosters: misunderstanding the complexity and context of most human labor ([16:24]). Jobs are not simply bundling outputs; they draw on deep experience, emotion, and context that cannot be reduced to prompts or datasets.
• Zitron vividly contrasts management’s "output obsession" with the reality of what software engineers, writers, and even hairdressers actually do ([17:20]).
• Quote:

  “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... 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.”
  — Ed Zitron ([16:45])

5. The Media’s Complicity in the Bubble

• Media outlets’ uncritical amplification of executive claims (like Salesforce’s “AI will be transformational for jobs”) perpetuate myths without challenge ([19:55]).
• Zitron lampoons this credulity:
  • Quote:

    "It fully trusted Mark Benioff when he said that Agent Force agents would replace human workers, and then again when he said AI agents were doing 30 to 50% of all the work..."
    — Ed Zitron ([20:53])

6. The Hollow Industry: Where’s the Revenue?

• Mega-deals (e.g., OpenAI’s supposed $300B contract with Oracle, Nvidia’s $100B datacenter investments) are dissected as smoke and mirrors designed to prolong the illusion of booming demand ([23:53]).
• Zitron points out AI’s staggering capital burn: “Over half a trillion dollars in fact, has gone into an entire industry without a single profitable company developing models or products built on top of these AI models.” ([24:40])
• Despite massive spending, actual revenue remains meager—Microsoft’s briefly reported $13B in annualized AI revenue is “chump change” for the company ([26:59]).
• Quote:

  “If Microsoft can’t sell this, nobody can.”
  — Ed Zitron ([29:33])

7. OpenAI, Anthropic, and the House of Cards

• Zitron positions OpenAI and Anthropic as the only real revenue drivers in the space, with even their fortunes built largely on deals and compute spending rather than real, sustainable customer demand ([30:12]).
• He underscores a sense of farce:
  • "Where we sit today is a time of immense tension. Mark Zuckerberg says we're in a bubble. Sam Altman says we're in a bubble... 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." ([31:17])

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Notable Quotes & Memorable Moments

• “Artificial intelligence is built and sold on not just faith, but a series of myths that AI boosters expect us to believe with the same certainty that we treat things like gravity or the boiling point of water.”
  — Ed Zitron ([10:38])

• “The only thing powerful about generative AI is its pathology. The world’s executives … are doing the only thing they know how to do: spend a bunch of money and say vague stuff about AI being the future.”
  — Ed Zitron ([24:13])

• On the absurdity of market announcements:

  “These deals are intentionally made to continue the myth of generative AI, to pump Nvidia, and to make sure OpenAI insiders can sell $10.3 billion worth of shares...”
  — Ed Zitron ([24:41])

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Timestamps for Important Segments

• 03:33 — Introduction to the four-part case against generative AI; contextualizing the AI bubble.
• 04:30 – 08:00 — The rise of LLMs, their technical reality, and immediate problems.
• 13:29 – 16:45 — The myth of worker replacement and who actually faces job loss.
• 16:45 – 19:55 — The complexity of labor beyond output; executives’ misapprehensions.
• 19:55 – 21:35 — The media’s role in spreading (and not questioning) executive hype.
• 23:53 – 26:59 — The reality of revenue: big deals, bigger losses, meager actual returns.
• 29:33 – 31:17 — The AI industry as a house of cards, with the so-called “Magnificent Seven” and the true nature of the bubble.

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Final Thoughts / Tone

Ed Zitron approaches the subject with sharp wit, palpable frustration, and a deep skepticism earned from years in the tech trenches. His language is often blunt—sometimes profane—and he’s unafraid of calling out industry figures, media complicity, and executive ignorance.
This part one episode lays the structural groundwork: exposing how an industry grounded in probability and persuasion, rather than genuine capability or utility, became the fastest-moving tech bubble of the modern era.

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What’s Next?

Zitron teases deeper dives in upcoming episodes, promising to unpack more industry myths and challenge the notion that generative AI’s underlying technology or business model justifies its unprecedented spending—and the cultural transformation so many tech leaders insist is inevitable.

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Transcript

[Ed Zitron]

This is an iHeart podcast.

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[Ed Zitron]

Hello and welcome to Better Offline. I am of course your host, Ed Zitron.

[Las Culturistas Hosts (Matt Rogers and Bowen Yang)]

Better Offline.

[Ed Zitron]

And after a few three part episodes I had an idea. What if I did a four parter? In all seriousness, I know that this is a little bit long, but the topic we're about to explore demands quite a bit of depth and it isn't something I could really do justice to in a one parter or two parter, or I guess even three parter. But let's get into it. Over the last few months, we've felt the vibe shift downward in an aggressive way, with both Mark Zuckerberg and clammy Sam Altman saying that we're in a bubble. In the latter case, said warnings of a bubble are always couched in rank hypocrisy, as 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 Zark and definitely not for Sam Altman. 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 did you talk about that? And that's kind of fair when you put out as much as I do. But so 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 the. 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 ChatGPT to the billions burned building data centers all over the world, and the weak business justifications for burning in nearly a trillion dollars to keep this hollow industry Alive. Now in 2022, a kind of company called OpenAI surprised the world with a website called ChatGPT that could generate te that sort of sounded like a person. Using a technology called large language models, which can also be used to generate images, video and computer code, or at least would eventually. Large language models require entire clusters of servers connected with high speed networking, all containing this thing called a GPU. Graphics processing units. These are different 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 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 they're probabilistic, 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 a storybook, every time you create a new page using the the same prompt to describe the protagonist, the person would look different and that difference could be minor, 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 it. 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 on 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 automate jobs that have proven most resistant towards nation knowledge, work and the creative economy. The newness and hype and these expectations sent the market into a frenzy, with every hyperscaler immediately creating the most aggressive market for one supplier I've ever seen, Nvidia has sold over $200 billion of GPUs since the beginning of 2023, becoming the largest company on the American stock market and trading at over $170 as of writing this sentence, only a few years after being worth $19.52 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 under promise 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 increasing, 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 OpenAI's GPT4 model tricked a TaskRabbit into solving a captcha. 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 were wondering what the New York Times was referring to when they said full fledged there it meant Pong and a cobbled together rolling demo of Sky Roads, a game from 1993, likely cause a bunch of that training data was fed into the models. Now the media and investors helped peddle 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 at 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 offered 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 meta AI 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 eventually grew to mean everything and nothing. OpenAI would see its every move lorded over like a gifted child its CEO Sam Altman called the Oppenheimer of our age, Even if it wasn't really obvious why Everybody was impressed. GPT4 felt like something a bit different, but was it actually meaningful? The thing is, artificial intelligence is built and sold on not just faith, but a series of myths that AI boosters expect us to believe with 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 why later in this series.

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[Ed Zitron]

Can Sora OpenAI'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 pre registered 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 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, not 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 advent of 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 drumbeat marketing and pop culture of powerful AI encourages and permits management to replace or degrade 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 hype has created the COVID 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 labor as some kind of output created from a request. In the case of 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 one worker told Brian Merchant, translation is about conveying meaning. Nevertheless, translation work has 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 being is 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 Idiot, 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 a software engineer do? They write code. Right? What does a writer do? They write words. Right? What does a hairdresser do? They cut hair. Yeah, that's of course not actually the case. As I'll get into later in the series, a 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 might break as 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 particular 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 jostle 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 insane. It's often quite emotive, or at the very least, driven 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, short, healthy, unhealthy, on a scalp with particular issues at a time of year when perhaps you want to change length at a time that fits you and 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 at your 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, that 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 is the results of a smart manager saying, do this, rather than human ingenuity interpreting both a request and the shit the manager didn't say. Now, what does a CEO do? Well, I did look. And a Harvard study said that they spend 25% of their time on people and relationships, 25% on functional and business unit reviews, 16% on organization and culture, and 21% 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 stuff, talking to people, thinking what we do next, and going to lunch. The most highly paid jobs in the world are impossible to describe. Their labor described in a mishmash of linkedinspiration. 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 and definitely don't understand the process behind, let alone care about. You 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 nuanced decisions with input from multiple different subject matter Experts to as ServiceNow Bill McDermott did in 2022, and I quote, make it clear to everybody in a boardroom of other executives that everything they do must be AI, AI, AI, AI, AI. And that's five of those. The same extends to 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 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 to scoot through the world without thinking too hard about whether or not something is true just because an executive said it. When Salesforce said back in 2024 that its 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 30 to 50% of all the work in Salesforce itself, even though that's an unproven and nakedly ridiculous statement. Salesforce's CFO, by the way, said earlier in this year that AI wouldn't boost sales growth in 2025. One would think this would change how Salesforce was covered or how seriously one takes Mark Benny off. But it hasn't, because nobody's really paying attention. In fact, nobody seems to want to do their job in this case.

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This week on a very special episode of Health Discovered, we're taking a closer look at a condition that affects hundreds of thousands of men each year. Prostate cancer.

[Ed Zitron]

I first found out about my cancer at the age of 45. Anything with cancer, you just think death sentence.

[Health Discovered Host]

In this episode, we'll explore the science behind detection along with the practical steps men can take to protect their health. Listen to health discovered on America's number one podcast network, iHeart. Open your free iHeart app search health Discovered and start listening.

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[Ed Zitron]

Come on.

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[Ed Zitron]

And this is how the core myths of generative AI were built. By executives saying stuff and the media publishing it without thinking about it. 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 generative AI is its pathology. The the world's executives, entirely disconnected from 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 and 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 OpenAI will pay Oracle 300 dol billion over the next four years, closely followed with the claim that Nvidia will invest, and I put that in Air quotes, $100 billion in OpenAI to build 10 gigawatts of AI data centers. Though the deal is structured in a way that means OpenAI is paid progressively as each gigawatt is deployed. And also apparently OpenAI will be leasing the chips rather than buying them outright. I must be clear that these deals are intentionally made to continue the myth of generative AI, to pump Nvidia and to make sure OpenAI insiders can sell $10.3 billion worth of shares, which they're currently trying to do at a of 500 billion goddamn dollars. I want to be clear about something. OpenAI cannot afford the $300 billion. OpenAI 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 $10 billion. But the rest of that 90, that's only coming when they build those data centers, which OpenAI can't afford to do. Nvidia 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 OpenAI's generative AI services. Nvidia, OpenAI, 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 that. And the entire stock market rests on Nvidia's back. It accounts for 7 to 8% of the value of the S&P 500. 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 sea secret that 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 or products built on top of these AI models. By my estimates, there's about $44 billion of revenue in generative AI this year. When you add in Anthropic and OpenAI's 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 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 2025 when it reported it would have 13 billion in annualized revenue. This. Well, I guess that would be for the month because it's month times 12. About 1.083 billion a month. I know that sounds like a lot, but Microsoft is a sales machine. It's built specific to 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 $27 billion a quarter in profits. But Ed, it's the early days. Did you get in here? Ow. I think he's gone. 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 tech 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 generative AI building Amazon Web Services, something that now powers a vast chunk of the web and has long been Amazon's most profitable business unit. Generative AI is also nothing like Uber, with OpenAI and Anthropic's true costs coming in at around $159 billion in the past two years, approaching five times Uber's $30 billion all time burn. And that's before the bullshit with Nvidia and 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? 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 leadership gets paid in. There's no benefit to being shy. Look, Oracle announced. They literally filed something saying they had that huge $300 billion contract. They did that despite the stock. Why is Microsoft not doing that with their incredible AI revenues? Do you. Do you think it's because they're. They're shy? Come on, Satya. Come on out. Come on, Satya. You can show me the numbers, but in all seriousness, if Microsoft can't sell this, nobody can. All right? So I'm explaining this whole thing as if you're brand new and walking up to this relatively unprepared. So I need to introduce another company. In 2020, a splinter group jumped off of OpenAI, funded by Amazon and Google. Google to do much the same thing as OpenAI, but pretend to be nicer about it until they had to raise money from the Middle East. I'm of course talking about Anthropic, and they've always been a bit better at coding for some reason, and people really like their Claude models. But like does not mean profit or even much revenue. Both OpenAI 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 OpenAI and Anthropic. In the year where Microsoft recorded $13 billion in AI revenues, $10 billion of that came from OpenAI's spending on Microsoft Azure. Anthropic burned $5.3 billion 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 sit today is a time of immense tension. Mark Zuckerberg says we're in a bubble. Sam Altman says we're in a bubble. Alibaba chairman and billionaire Joe Tsai says we're in a bubble. 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 generative AI warranted any of the expenditures. Now we're a quarter of the way through this four parter, 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 gonna go a little deeper in the next episode and I can't wait for you to hear him. See you tomor. 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 [e-mail] m a t t o s o W-K-I.com youm can email me at ezetteroffline.com or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat where's your ed at? To visit the Discord Discord and go to R betteroffline to check out our Reddit thank you so much for listening.

[Health Discovered Host]

Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, Visit our website coolzonemedia.com or check us out on the iHeartRadio app, Apple Podcasts or wherever you get your podcast.

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Ugh. Come on.

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