Pundit ClaimsAI claims by Gary Marcus and Ed Zitron, with the evidence

Ed Zitron Where's Your Ed At Published

The Case Against Generative AI

We recorded 46 claims from this post. 31 were checked against evidence. 15 can’t be checked. 35 more are held. Read the post at its source.

Claims from this post (46)

In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.

  1. Assessment: UnresolvedSaid

    “This is my comprehensive case that yes, we’re in a bubble, one that will inevitably (and violently) collapse in the near future.”

    What we tested: The AI industry is in a bubble that will inevitably and violently collapse in the near future.

  2. Assessment: Can’t be checkedSaid

    “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 product.””

    What we tested: AI "agents" are a marketing term for what are really broken digital products.

  3. Assessment: UnresolvedSaid

    “Can large language models actually replace coders? Not really, no, and I’ll get into why later in the piece.”

    What we tested: Large language models cannot really replace coders.

  4. Assessment: UnresolvedSaid

    “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.”

    What we tested: OpenAI's Sora cannot replace actors or animators.

  5. Assessment: ContradictedSaid

    “To be clear, some people have lost jobs to AI, just not the white collar workers, software engineers, or really any of the career paths that the mainstream media and AI investors would have you believe.”

    What we tested: Some people have lost jobs to AI, but not white collar workers or software engineers as media and investors claim.

    What happened: The employee representatives describe executed reductions concentrated in support functions, with colleagues already leaving. This provides primary corroboration of actual white-collar displacement linked to AI and automation; the source specifies the first quarter, while the required event-date format permits only year precision here. (IBM European Works Council, employee representatives, )

  6. Assessment: Can’t be checkedSaid

    “It’s often 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 and believable.”

    What we tested: AI cannot replicate emotive good writing in an authentic, believable way.

  7. Assessment: Can’t be checkedSaid

    “The only thing “powerful” about generative AI is its mythology.”

    What we tested: The only powerful thing about generative AI is its mythology.

  8. Assessment: Can’t be checkedSaid

    “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 of shares.”

    What we tested: The OpenAI-Oracle and NVIDIA-OpenAI 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 of shares.

  9. Assessment: UnresolvedSaid

    “Generative AI is nothing like Uber, with OpenAI and Anthropic’s true costs coming in at about $159 billion in the past two years, approaching five times Uber’s $30 billion all-time burn.”

    What we tested: OpenAI and Anthropic's true costs were about $159 billion over the past two years, nearly five times Uber's all-time burn.

  10. Assessment: UnresolvedSaid

    “Which is to say that Microsoft clearly doesn’t have any good news to share, and as I’ll reveal later, they can’t even get 3% of their 440 million Microsoft 365 subscribers to pay for Microsoft 365 Copilot.”

    What we tested: Microsoft cannot get even 3% of its 440 million Microsoft 365 subscribers to pay for Microsoft 365 Copilot.

  11. Assessment: UnresolvedSaid

    “OpenAI is now, at this point, on the hook for over a trillion dollars, an egregious sum for a company that already forecast billions in losses, with no clear explanation as to how it’ll afford any of this beyond “we need more money” and the vague hope that there’s another Softbank or Microsoft waiting in the wings to swoop in and save the day.”

    What we tested: OpenAI is committed to over a trillion dollars of spending with no clear explanation of how it will pay.

  12. Assessment: UnresolvedSaid

    “I’m going to walk you through where I see this industry today, and why I see no future for it beyond a fiery apocalypse.”

    What we tested: He sees no future for the generative AI industry beyond a fiery apocalypse.

  13. Assessment: SupportedSaid

    “You cannot rely on a large language model to do what you want.”

    What we tested: Large language models cannot be relied on to do what users want.

    What happened: The September benchmark report found that leading models completed only a small minority of complex private-codebase tasks under the tested setup. This supports the warning against assuming correct autonomous completion of such work. (Scale AI Research, )

  14. Assessment: UnresolvedSaid

    “You do not know what you’re going to get each time, and hallucinations multiply with the complexity of the thing you’re asking for, or whether a task contains multiple steps (which is a fatal blow to the idea of “agents.””

    What we tested: LLM hallucinations multiply with task complexity and multi-step tasks, which is fatal to the idea of agents.

  15. Assessment: SupportedSaid

    “You can add as many levels of intrigue and “reasoning” as you want, but Large Language Models cannot be trusted to do something correctly, or even consistently, every time.”

    What we tested: Regardless of added reasoning, LLMs cannot be trusted to do things correctly or consistently every time.

    What happened: The company's reported success rate for its new reasoning-capable model is below perfect performance. The same page includes a customer's zero-error result on a narrow internal editing benchmark, which does not establish general reliability. (Anthropic, )

  16. Assessment: Can’t be checkedSaid

    “The bubble has entered its most pornographic, aggressive and destructive stage, where the more obvious it becomes that they’re cooked, the more ridiculous the generative AI industry will act — a dark juxtaposition against every new study that says “generative AI does not work” or new story about ChatGPT’s uncanny ability to activate mental illness in people.”

    What we tested: The bubble has entered its most pornographic, aggressive and destructive stage, where the more obvious it becomes that they're cooked, the more ridiculous the generative AI industry will act.

  17. Assessment: Can’t be checkedSaid

    “Generative AI exists for two reasons: to cost money, and to make executives look busy.”

    What we tested: Generative AI exists only to cost money and make executives look busy.

  18. Assessment: Can’t be checkedSaid

    “Generative AI was always set up to fail, because it was meant to be everything, was talked about like it was everything, is still sold like it’s everything, yet for all the fucking hype, it all comes down to two companies: OpenAI, and, of course, NVIDIA.”

    What we tested: Generative AI was always set up to fail because it was oversold, and it all comes down to OpenAI and NVIDIA.

  19. Assessment: Can’t be checkedSaid

    “OpenAI is the generative AI industry — and it’s a dog of a company.”

    What we tested: OpenAI is the generative AI industry, and it is a dog of a company.

  20. Assessment: UnresolvedSaid

    “As a reminder, OpenAI has leaked that it’ll burn $115 billion in the next four years, and based on my estimates, it needs to raise more than $290 billion in the next four years based on its $300 billion deal with Oracle alone.”

    What we tested: By his estimates, based on its $300 billion Oracle deal alone, OpenAI needs to raise more than $290 billion in the next four years.

  21. Assessment: UnresolvedSaid

    “Even Anthropic and OpenAI seem a little lethargic, both burning billions of dollars while making, by my estimates, no more than $2 billion and $6.26 billion in 2025 so far, despite projections of $5 billion and $13 billion respectively.”

    What we tested: Anthropic and OpenAI seem a little lethargic, both burning billions while making, by his estimates, no more than $2 billion and $6.26 billion respectively in 2025 so far, against full-year projections of $5 billion and $13 billion.

  22. Assessment: UnresolvedSaid

    “In fact, every user loses you money in generative AI, because it’s impossible to do cost control in a consistent manner.”

    What we tested: Every generative AI user loses the provider money because consistent cost control is impossible.

  23. Assessment: ContradictedSaid

    “AI does not have a “profit lever,” because the raw costs of providing access to AI models are so high (and they’re only increasing) that the basic economics of how the tech industry sells software don’t make sense.”

    What we tested: AI businesses have no profit lever because model access costs are high and rising.

    What happened: The second-largest model developer reached GAAP operating income because revenue outgrew costs. This is the 'profit lever' the claim says AI lacks. (Anthropic, PBC (SEC Form S-1), )

  24. Assessment: Can’t be checkedSaid

    “Members of the media, I am begging you, stop doing this.”

    What we tested: The media should stop repeating claims that AI is replacing software engineers without checking them.

  25. Assessment: UnresolvedSaid

    “To Summarize: Coding LLMs Don’t Actually Replace Software Engineers, and Never Will, Due To The Inherent Unreliability Of Large Language Models”

    What we tested: Coding LLMs do not and never will replace software engineers because LLMs are inherently unreliable.

  26. Assessment: UnresolvedSaid

    “This is, by the way, why we are still yet to get any tangible proof that AI is replacing software engineers…because it can’t.”

    What we tested: There is still no tangible proof that AI is replacing software engineers because AI can't replace them.

  27. Assessment: UnresolvedSaid

    “In reality, LLMs can generate code, and do some software engineering-adjacent tasks, but, like all Large Language Models, break down and go totally insane, hallucinating more as the tasks get more complex.”

    What we tested: Coding LLMs can generate code but break down and hallucinate more as tasks get more complex.

  28. Assessment: UnresolvedSaid

    “Generative AI can’t do the job of a software engineer, and it fails while also costing abominable amounts of money.”

    What we tested: Generative AI cannot do a software engineer's job, and it fails while costing huge amounts.

  29. Assessment: MixedSaid

    “They don’t even speed up engineers — they actually make them slower!”

    What we tested: Coding LLMs do not speed up engineers and actually make them slower.

    What held: The randomized trial covered 16 experienced developers completing 246 tasks in familiar mature projects using February–June 2025 tools. It directly supports a slowdown in this setting. (Becker et al., METR; arXiv, )

    What did not: A randomized trial with 96 Google engineers on a complex enterprise task estimated about a 21% reduction in completion time. This contradicts the unconditional assertion that coding AI does not speed engineers up. (Paradis et al., Google; arXiv, )

  30. Assessment: UnresolvedSaid

    “A source that has seen materials related to sales has confirmed that, as of August 2025, Microsoft has around eight million active licensed users of Microsoft 365 Copilot, amounting to a 1.81% conversion rate across the 440 million Microsoft 365 subscribers.”

    What we tested: As of August 2025, Microsoft 365 Copilot had about 8 million active licensed users, a 1.81% conversion of 440 million subscribers.

  31. Assessment: Can’t be checkedSaid

    “This is nothing like anything you’ve seen before, because this is the dumbest shit that the tech industry has ever done.”

    What we tested: The AI bubble is unlike previous bubbles and is the dumbest thing the tech industry has done.

  32. Assessment: UnresolvedSaid

    “AI data centers are nothing like fiber, because there are very few actual use cases for these GPUs outside of AI, and none of them are remotely hyperscale revenue drivers.”

    What we tested: AI data center GPUs have very few use cases outside AI, none of which drive hyperscale revenue, unlike fiber.

  33. Assessment: ContradictedSaid

    “As I discussed a month or so ago, data center development accounted for more of America’s GDP growth than all consumer spending combined, and there really isn’t any demand for AI in general, let alone at the scale that these hundreds of billions of dollars are being sunk into.”

    What we tested: There really isn't any demand for AI in general, let alone at the scale of the hundreds of billions of dollars being sunk into it.

    What happened: The company reported substantial actual usage during the target month. This contradicts an absence of meaningful demand, but does not establish paid demand or profitability. (OpenAI, )

  34. Assessment: Can’t be checkedSaid

    “The conservative estimate of capital expenditures related to data centers is around $400 billion, but given the $50 billion a quarter in private credit, I’m going to guess it breaks $500 billion, all to build capacity for an industry yet to prove itself.”

    What we tested: Given $50 billion a quarter in private credit, he guesses that data-center-related capital expenditures break $500 billion, above a conservative estimate of around $400 billion, all to build capacity for an industry yet to prove itself.

  35. Assessment: UnresolvedSaid

    “If the $50bn-per-gigawatt figure cited by OpenAI’s execs is accurate, OpenAI will need to spend $500bn to create the 10GW of capacity required to unlock the promised $100bn from Nvidia.”

    What we tested: If a gigawatt costs $50bn, OpenAI must spend $500bn on 10GW of capacity to unlock NVIDIA's $100bn.

  36. Assessment: Can’t be checkedSaid

    “In a very real sense, OpenAI threatens the future of available capital for the tech industry.”

    What we tested: OpenAI threatens the future availability of capital for the tech industry.

  37. Assessment: UnresolvedSaid

    “There is simply no space in these projections to absorb that $300 billion, and from what I can tell, by 2029, OpenAI will have actually burned more than $290 billion, assuming that it survives that long, which I do not believe it will.”

    What we tested: If OpenAI survives until 2029, it will have burned more than $290 billion by then.

  38. Assessment: UnresolvedSaid

    “There is simply no space in these projections to absorb that $300 billion, and from what I can tell, by 2029, OpenAI will have actually burned more than $290 billion, assuming that it survives that long, which I do not believe it will.”

    What we tested: OpenAI will not survive until 2029.

  39. Assessment: UnresolvedSaid

    “I need to be blunter: OpenAI has signed multiple different deals and contracts for amounts it cannot afford to pay, that it cannot hope to raise the money to pay for, that defy the amounts of venture capital and private credit available, all to sustain a company that will burn $300 billion and has no path to profitability of any kind.”

    What we tested: OpenAI has signed deals it cannot afford or raise money for, will burn $300 billion, and has no path to profitability.

  40. Assessment: Can’t be checkedSaid

    “Anyway, I cannot be clear enough how unlikely it is that (as NVIDIA has said) “the first gigawatt of NVIDIA systems will be deployed in the second half of 2026,” and that’s if it has bought the land and got the permits and ordered the construction, none of which has happened yet.”

    What we tested: It is very unlikely that the first gigawatt of NVIDIA systems for OpenAI will be deployed in the second half of 2026.

  41. Assessment: UnresolvedSaid

    “In the current relationship, NVIDIA Is Not Sending OpenAI $100 Billion, nor will it send it that much money, because 90% of OpenAI’s funding is gated behind building 9 or 10 gigawatts of data center capacity.”

    What we tested: Under the current deal NVIDIA will not send OpenAI $100 billion, because 90% depends on building 9-10GW of capacity.

  42. Assessment: UnresolvedSaid

    “I am telling you, as I have been telling you for years, again and again and again, that the demand is not there for generative AI, and the demand is never, ever arriving.”

    What we tested: Demand for generative AI is not there and will never arrive.

  43. Assessment: Can’t be checkedSaid

    “The only reason anyone humours any of this crap is the endless hoarding of GPUs to build capacity for a revolution that will never arrive.”

    What we tested: The AI revolution that GPU hoarding is building capacity for will never arrive.

  44. Assessment: Can’t be checkedSaid

    “This NVIDIA deal is a farce — an obvious attempt by the largest company on the American stock market to prop up the one significant revenue-generator in the entire industry, knowing that time is running out for it to create new avenues for eternal growth.”

    What we tested: The NVIDIA-OpenAI deal is a farce, an obvious attempt by NVIDIA to prop up OpenAI, the industry's one significant revenue-generator, knowing that time is running out for it to create new avenues for eternal growth.

  45. Assessment: UnresolvedSaid

    “And while there will be — and have been — disastrous economic consequences, they won’t be as systemically catastrophic as that of the pandemic, or the global financial crisis.”

    What we tested: If OpenAI fails, there will be disastrous economic consequences (some have already occurred), but they will not be as systemically catastrophic as those of the pandemic or the global financial crisis.

  46. Assessment: UnresolvedSaid

    “It is now inevitable that this bubble bursts.”

    What we tested: The AI bubble will inevitably burst.

Held from this post (35)

These records did not pass every check, so nothing about them is published. Held is not a finding.