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

Ed Zitron Where's Your Ed At Published

AI Is Too Expensive

We recorded 41 claims from this post. 18 were checked against evidence. 23 can’t be checked. 31 more are held. Read the post at its source.

Claims from this post (41)

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 week, I’ll publish the second part to my ongoing series (“What If…We’re In An AI Bubble?”) about the factors and events that will cause the AI bubble to finally pop.”

    What we tested: The AI bubble will eventually pop, driven by identifiable factors and events.

  2. Assessment: UnresolvedSaid

    “Every AI startup loses millions or billions of dollars a year, and nobody appears to have worked out a way to stop hemorrhaging cash.”

    What we tested: Every AI startup loses millions or billions of dollars a year and none appears to have found a way to stop.

  3. Assessment: UnresolvedSaid

    “None of them will talk about their actual AI revenues.”

    What we tested: None of Microsoft, Meta, Amazon or Google disclose their actual AI revenues.

  4. Assessment: UnresolvedSaid

    “This means that around 30% of Microsoft’s capex ($87 billion) went to building OpenAI’s infrastructure.”

    What we tested: Around 30% (~$87B) of Microsoft's capex since FY2023 went to building OpenAI's infrastructure.

  5. Assessment: UnresolvedSaid

    “Okay, fine. Microsoft also has 20 million Microsoft 365 Copilot subscribers for an absolute maximum revenue of $7.2 billion…if every single one were paying $30 a month, which they are most assuredly not as Microsoft has been offering discounts on it for years.”

    What we tested: Microsoft 365 Copilot's 20 million subscribers yield at most $7.2B a year, and actually less due to discounts.

  6. Assessment: UnresolvedSaid

    “Based on my reporting from last year, Microsoft made around $7.5 billion from OpenAI’s inference spend and $761 million from its revenue share in Fiscal Year 2025, a year when it invested (either spent or allocated) around $88.2 billion in capital expenditures.”

    What we tested: In FY2025 Microsoft made ~$7.5B from OpenAI inference and $761M from revenue share while investing ~$88.2B in capex.

  7. Assessment: Can’t be checkedSaid

    “If we assume that Microsoft’s other AI services grew 10% quarter-over-quarter, I estimate that Microsoft likely made around $17.9 billion in AI revenue in FY2025, or a little under a fifth of its capex.”

    What we tested: Assuming Microsoft's other AI services grew 10% quarter-over-quarter, Zitron estimates Microsoft likely made around $17.9 billion in AI revenue in FY2025, a little under a fifth of its capex.

  8. Assessment: UnresolvedSaid

    “You can argue that “actually GPUs are profitable to run” (I disagree!), but for any of this to make sense, four things have to happen:”

    What we tested: GPUs are not profitable to run.

  9. Assessment: UnresolvedSaid

    “Sidenote: In all honesty, I have no idea how Meta makes this make sense, as it plans to invest at least $125 billion in capex in 2026 and has, to this point, not shown any actual, real growth in its revenue from AI, and no, those increases in conversion don’t mean actual revenue.”

    What we tested: Meta has not shown real revenue growth from AI despite planning at least $125B of 2026 capex.

  10. Assessment: Can’t be checkedSaid

    “I also cannot find an economic scenario where this pays itself off.”

    What we tested: Zitron says he cannot find an economic scenario in which hyperscalers' AI capex pays itself off.

  11. Assessment: UnresolvedSaid

    “Based on Microsoft’s own disclosures, without Anthropic and OpenAI’s additions, RPO would have been effectively flat, as evidenced by the fact that in Q3FY26, remaining performance obligations sat at $627 billion.”

    What we tested: Excluding OpenAI and Anthropic deals, Microsoft's RPO would have been effectively flat.

  12. Assessment: UnresolvedSaid

    “Doubling that won’t be enough. Tripling it won’t be enough. In fact, to pay this off, these companies will need to be making over $100 billion each in AI revenue in the next year, because otherwise there is no covering these losses.”

    What we tested: To pay off investments now totaling over $900 billion, Microsoft, Google and Amazon will each need to be making over $100 billion in AI revenue in the next year, and doubling or tripling their best-case combined $70 billion in annual AI revenue won't be enough.

  13. Assessment: Can’t be checkedSaid

    “If the margins were good, they’d be sharing the margins. If the revenues were good, they’d be sharing the revenues (and no, run rates aren’t revenues).”

    What we tested: Hyperscalers' failure to disclose AI margins and revenues indicates they are not good.

  14. Assessment: Can’t be checkedSaid

    “And it all comes back to a very simple point: AI is too expensive.”

    What we tested: AI is too expensive.

  15. Assessment: UnresolvedSaid

    “You are not recovering these investments without either a massive new product line that doesn’t exist today or three or four Anthropic or OpenAI-sized compute contracts.”

    What we tested: Hyperscalers will not recover their AI investments without a massive new product line or three or four more Anthropic/OpenAI-sized compute contracts.

  16. Assessment: Can’t be checkedSaid

    “(Oracle is trying to compete and may die as a result)”

    What we tested: Oracle may die as a result of trying to compete as an AI cloud provider.

  17. Assessment: Can’t be checkedSaid

    “Sidenote: I have absolutely no idea what Meta does, and my chaos bet is that it starts renting out its compute to Anthropic or OpenAI when things get rough.”

    What we tested: As a speculative bet, Meta may start renting its compute to Anthropic or OpenAI when things get rough.

  18. Assessment: Can’t be checkedSaid

    “Anthropic has done a great deal of work to obfuscate how much it actually makes or spends, but I think it’s likely it burns even more than OpenAI, given the fact that it’s had to raise $75 billion in the last 6 months”

    What we tested: Anthropic obfuscates its finances and likely burns even more money than OpenAI.

  19. Assessment: UnresolvedSaid

    “I’d estimate it needs $5 billion a year for its compute deal xAI (so $20 billion over the total period) and an estimated $30 billion to cover its deal with CoreWeave. That brings us to a total of $380 billion.”

    What we tested: By Zitron's partly estimated tally, Anthropic needs about $380 billion over the next four years: $330 billion in cloud obligations to Amazon, Google and Microsoft, an estimated $20 billion for its xAI compute deal, and an estimated $30 billion for its CoreWeave deal.

  20. Assessment: Can’t be checkedSaid

    “Numerous reports about how Anthropic will turn “cash flow positive” in either 2027 or 2028 are fantastical, illogical, entirely driven by ridiculous projections, and should never have been reported as anything other than an attempt by companies to mislead their investors.”

    What we tested: Numerous reports that Anthropic will turn cash-flow positive in 2027 or 2028 are fantastical, illogical and driven entirely by ridiculous projections, and should never have been reported as anything other than an attempt by companies to mislead their investors.

  21. Assessment: Can’t be checkedSaid

    “Because even if Anthropic were making $100 billion in annual revenue, it would probably be losing $300 billion or more to get there.”

    What we tested: If Anthropic reached $100B annual revenue it would probably be losing $300B or more to get there.

  22. Assessment: Can’t be checkedSaid

    “The fact it had to raise $30 billion in February, $15 billion in April, and now $30 billion more in May all while allegedly pulling in more than $3 billion a month in revenue suggests that its COGS are fucking horrendous, and its growth is coming at a terrible financial cost.”

    What we tested: Anthropic's raising $30 billion in February, $15 billion in April and $30 billion in May while allegedly bringing in more than $3 billion a month suggests its COGS are horrendous and its growth is coming at a terrible financial cost.

  23. Assessment: Can’t be checkedSaid

    “I think the answer is simple: their CFOs know that doing so would reveal their actual margins, which are hot dogshit with sprinkles on top.”

    What we tested: Zitron thinks Anthropic's and OpenAI's CFOs know that going public would reveal their actual margins, which he calls extremely bad, and that this is why neither appears to be seriously considering an IPO.

  24. Assessment: Can’t be checkedSaid

    “One important detail to keep in mind here is that as of a month or two ago, Anthropic moved all enterprise customers to token-based-billing, which will begin, I believe, a true stress-test of the true “value” of AI as costs skyrocket.”

    What we tested: Zitron believes Anthropic's move of all enterprise customers to token-based billing will begin a true stress-test of AI's value as costs skyrocket.

  25. Assessment: UnresolvedSaid

    “Earlier in the week, carnival barker and Salesforce CEO Marc Benioff said his company would spend $300 million on Anthropic tokens in 2026, and as I discussed in my premium from Friday, unrestrained AI spending is inflating the revenues of Anthropic and OpenAI in a way that isn’t sustainable for anybody involved:”

    What we tested: Unrestrained AI spending is inflating the revenues of Anthropic and OpenAI in a way that isn't sustainable for anybody involved.

  26. Assessment: UnresolvedSaid

    “The problem is simple: nobody actually knows how much AI is going to cost them in any given quarter. This means that the current token spend you’re seeing is entirely experimental, which is why organizations keep burning through their tokens so fast.”

    What we tested: Nobody actually knows how much AI is going to cost them in any given quarter, so current token spend is entirely experimental, which is why organizations keep burning through their tokens so fast.

  27. Assessment: Can’t be checkedSaid

    “Executives have, across the board, given their engineers free reign to burn as many tokens as they’d like, and while I severely doubt that Anthropic actually hit $50 billion in annualized revenue outside of not-quite-fraudulent non-GAAP measurements, I believe its revenue growth has come from an artificial boost from a tech industry searching for a reason to pay somebody money.”

    What we tested: Executives across the board have given engineers free rein to burn tokens, Zitron severely doubts Anthropic actually hit $50 billion in annualized revenue outside of not-quite-fraudulent non-GAAP measurements, and he believes its revenue growth has come from an artificial boost from a tech industry searching for a reason to pay somebody money.

  28. Assessment: Can’t be checkedSaid

    “To be very clear about what I mean, I think there is currently an AI token binge across both Anthropic and OpenAI.”

    What we tested: Zitron thinks there is currently an AI token binge across both Anthropic and OpenAI.

  29. Assessment: UnresolvedSaid

    “In some cases, the pressure to use AI for everything is turning companies’ software stacks into slop.”

    What we tested: In some cases, pressure to use AI for everything is degrading companies' software stacks.

  30. Assessment: UnresolvedSaid

    “Every organization I’ve talked to has exceeded or is nearing the edge of their annual token budget barely five months into the year, which means that everybody has suddenly given themselves an extra few million dollars’ worth of operating expenses for reasons that escape effectively everybody I’ve talked to.”

    What we tested: Every organization the author spoke to has exceeded or nearly exhausted its annual AI token budget five months into 2026.

  31. Assessment: Can’t be checkedSaid

    “This might be because Anthropic obfuscates the data that might tell customers the real costs.”

    What we tested: Executives pushing AI use without knowing what it costs might be because Anthropic obfuscates the data that might tell customers the real costs.

  32. Assessment: Can’t be checkedSaid

    “While I’m not accusing Anthropic of anything untoward, massive, multi-million dollar contracts that involve individuals burning thousands or tens of thousands of dollars’ worth of tokens with no service level agreement, transparency or true granularity into the burn is a perfect setup for a company — not saying it’s Anthropic! — to do something dastardly with those numbers.”

    What we tested: Multi-million-dollar token contracts with no service level agreement, transparency or true granularity into usage are a perfect setup for a company to do something dastardly with the numbers, though Zitron explicitly says he is not accusing Anthropic.

  33. Assessment: Can’t be checkedSaid

    “Unless, of course, you can’t actually measure how many tokens a particular task can take with much accuracy, in which case every single AI token budget is bullshit.”

    What we tested: If you can't measure with much accuracy how many tokens a particular task takes, then every AI token budget is bullshit.

  34. Assessment: Can’t be checkedSaid

    “Before AI, this wasn’t as much of a problem, in the sense that inefficiencies and wasted hours weren’t directly connected to a chatbot that is specifically designed to burn money.”

    What we tested: AI chatbots are designed to burn money, tying workplace inefficiency directly to a growing cost.

  35. Assessment: Can’t be checkedSaid

    “Organizations aren’t burning millions or hundreds of millions of dollars a year on AI because it’s good, they’re doing it because they are run by people who do not know what the fuck they’re doing.”

    What we tested: Organizations spend heavily on AI because of incompetent leadership, not because AI is good.

  36. Assessment: UnresolvedSaid

    “Every time you defend generative AI you defend a machine of capital that has burned $1 trillion and created one of the most-wasteful products in history.”

    What we tested: Generative AI is a machine of capital that has burned $1 trillion and created one of the most wasteful products in history.

  37. Assessment: Can’t be checkedSaid

    “Anthropic is a company purpose-built to con people into giving it by money by making people feel smart.”

    What we tested: Anthropic is a company purpose-built to con people into giving it money by making them feel smart.

  38. Assessment: Can’t be checkedSaid

    “Nobody wants this, nobody wanted it since the beginning, it was forced upon everyone, and to pretend otherwise is laughable and offensive.”

    What we tested: There is no genuine demand for generative AI; it was forced on everyone.

  39. Assessment: Can’t be checkedSaid

    “Generative AI was purpose-built to grift an economy run by executives and managers who don’t actually do any work.”

    What we tested: Generative AI was built to grift executives and managers who don't do real work.

  40. Assessment: UnresolvedSaid

    “Yet even a Business Idiot eventually realizes that too much money is being spent, and the first one of these dimwits to cut their token budget will send the rest of them running for the doors.”

    What we tested: Executives will eventually see they are overspending, and once one cuts their AI token budget the others will rush to follow.

  41. Assessment: Can’t be checkedSaid

    “We should make everybody who obsessed over theoretical ideas about what AI can or will do ashamed for their intellectual deceit or constant ignorance.”

    What we tested: AI boosters who obsessed over theoretical AI capabilities should be shamed for their deceit or ignorance.

Held from this post (31)

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