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

Claims checked against evidence by Ed Zitron, page 2 of 13

Deadlines are judged as of 2026-10-29. About Ed Zitron and the sources covered.

Claims checked against evidence by Ed Zitron (1230)

Each of these claims was checked against dated evidence.

Page 2 of 13: claims 101 to 200 of 1230, ordered by the date first said, newest first. To find a claim by its words, use search.

  1. Assessment: UnresolvedSaid

    “AI data center debt is now extremely expensive.”

  2. Assessment: UnresolvedSaid

    “And outside of those two companies, I estimate there’s roughly $22 billion of demand for AI compute.”

  3. Assessment: UnresolvedSaid

    “As you can see, repricing CoreWeave at today’s rates would increase its costs by 35.8%, adding $1.2 billion to the lifetime cost of the bonds for a company that already pays $640 million a quarter in interest.”

  4. Assessment: UnresolvedSaid

    “I don’t even have a clear answer as to what’s going on here, other than that we don’t have much (if any) clarity around how much data center capacity is even being built.”

  5. Assessment: UnresolvedSaid

    “In any case, that’s a shit ton of undeployed silicon, and a very, very, very different picture to the one that both NVIDIA and the hyperscalers have been telling investors.”

  6. Assessment: UnresolvedSaid

    “I’ve seen estimates of 12GW, 15GW, and as much as 20GW of capacity coming online in 2026, but based on everything I’ve talked about today, I think it’s farcical to believe that more than five to ten gigawatts of operational AI data center capacity actually exists.”

  7. Assessment: UnresolvedSaid

    “Demand for NVIDIA GPUs — and those from Broadcom, AMD and other semiconductor companies — is driven by speculative capital believing that the AI industry will become magnitudes larger than it is today, largely driven by the fact that everybody believes there’s far more demand for compute capacity than actually exists.”

  8. Assessment: UnresolvedSaid

    “NVIDIA’s perpetual quarterly revenue growth, which is branded, once again, as insatiable demand for AI compute, when it’s actually almost entirely-speculative purchases based on potential revenues, with said potential mostly driven by the compute spend from OpenAI and Anthropic, two unprofitable and unsustainable AI labs.”

  9. Assessment: UnresolvedSaid

    “For the most part, hyperscalers have been given credit for their capital expenditures because overall revenues have grown, with everyone saying that their “AI bets have paid off,” when it’s clear that the AI bets in question have barely started to come online.”

  10. Assessment: UnresolvedSaid

    “the “demand” cycle for these chips, or really any AI chip, is entirely manufactured as a result of four or five large customers buying so many and Huang making statements about revenue generation that do not reflect reality.”

  11. Assessment: UnresolvedSaid

    “It’s very clear that Microsoft is playing silly buggers with the term “capacity,” which makes me believe this is an industry-wide problem.”

  12. Assessment: UnresolvedSaid

    “All of this is to say that, for the most part, AI data center projects get announced and funded all the time, that the press willingly or otherwise engages in laundering the scale and completion of the projects, and everybody on the outside is deceived into thinking the AI buildout is faster and more effective than it really is.”

  13. Assessment: UnresolvedSaid

    “Hyperscalers are getting increasingly worse at turning their capex into operational capacity.”

  14. Assessment: UnresolvedSaid

    “I don’t even know how to write what I’m thinking without sounding alarmist…but I don’t see how 90%+ of NVIDIA’s sales ever end up generating a single dollar of revenue”

  15. Assessment: UnresolvedSaid

    “In other words, I believe we are now in an inevitable overbuild situation, one with no neat, tidy Dot-Com Bubble-style exit story.”

  16. Assessment: UnresolvedSaid

    “This means that the $290 billion in AI data center debt issued this year (outside of hyperscalers) will go towards building capacity at whatever rate it can, which is a problem because the vast majority of these deals are project financing-based, meaning that they’re funded out of the revenues of a customer who may or may not exist.”

  17. Assessment: UnresolvedSaid

    “At the very least, hyperscalers are going to be burdened with brutal depreciation charges or onerous write-offs for years to come, whether their capacity turns into revenue or not.”

  18. Assessment: UnresolvedSaid

    “Today’s newsletter is about a cruel truth: that NVIDIA’s revenue growth is almost entirely the result of speculative purchases by hyperscalers and neoclouds that take years to install its GPUs.”

  19. Assessment: UnresolvedSaid

    “In other words, Microsoft is warehousing anywhere from $50 billion to $100 billion in GPUs, and has barely gotten $50 billion worth installed in the last four years.”

  20. Assessment: UnresolvedSaid

    “Right now, capacity is coming on very, very slowly, and nobody really wants to talk about it.”

  21. Assessment: UnresolvedSaid

    “On Friday, I’ll publish The Hater’s Guide to AI Debt — or, how buzz surrounding OpenAI and Anthropic have created massive concentration risk for world debt markets, and one which you’ll potentially be paying for, either through your pension funds and insurance premiums, or because you’ll have to live and work through the economic downturn that’s coming.”

  22. Assessment: ContradictedSaid

    “Dario Amodei of Anthropic took to CBS to say that for “too long” the industry had “lied” about risks, saying that the “biggest one” was killing all humans, never mentioning when an LLM convinced a teenager to kill himself or its own hacking incidents because “AI safety” never relates to the things they’re building today.”

    What happened: Anthropic publicly discusses a classifier operating on Claude.ai conversations about suicide and self-harm. This directly addresses harms involving an existing product and contradicts never. (Anthropic, )

  23. Assessment: ContradictedSaid

    “As Cal said, there was no situation where the AI “went rogue.””

    What happened: The investigation reproduces an agent's recognition that Hugging Face infrastructure was unauthorized. It also reports that task instructions required the intended vulnerability and that agents pursued ways around that constraint, contradicting the claim that they did exactly what they were supposed to do. (METR and Redwood Research, )

  24. Assessment: UnresolvedSaid

    “At no point did Coxon bring up how ChatGPT was used as a “suicide coach,” directly caused a murder-suicide, or aided and abetted in mass shootings in Florida and Canada, or the horrifying gas turbines poisoning black communities.”

  25. Assessment: UnresolvedSaid

    “The Hugging Face attack is not something that was made possible as a result of “powerful AI” so much as it was the weaponization of hundreds of billions of dollars’ worth of GPU-powered infrastructure owned by Microsoft, Google, Amazon, Oracle, and CoreWeave.”

  26. Assessment: UnresolvedSaid

    “In the end he was right, but only because he made sure that was the case.”

  27. Assessment: MixedSaid

    “This was not an “agent” that “went rogue.” It was software doing what software was asked to do, using other bits of software to work out what to do next, all as OpenAI, the company that ran the software, did not appear to have any kind of notification or observability that said “hey man, thousands of LLMs are doing something right now.””

    What held: OpenAI acknowledges that its chain-of-thought monitors did not cover these evaluations. This supports the narrower monitoring criticism, not the absence of every kind of observability. (OpenAI, )

    What did not: This agent transcript explicitly distinguishes the authorized target from Hugging Face infrastructure. The investigators also describe instructions restricting permissible solutions and agents recognizing their actions as unintended. (METR, )

  28. Assessment: MixedSaid

    “For years the AI industry has engaged in endless, vague safety theater about the “risks” of AI, all while peddling software that is actively harmful and unreliable.”

    What held: The report documents known harms from reliability failures, including false information in consequential advice, while noting limited evidence about prevalence. The event date is its stated writing cutoff. (International AI Safety Report, )

    What did not: Anthropic describes repeated testing, voting-information redirects, and approximately 100 election-related enforcement actions. These are tangible responses to existing misinformation and misuse risks, although their overall effectiveness is not independently established here. (Anthropic, )

  29. Assessment: UnresolvedSaid

    “Their “powerful AI” — graded not based on actual outcomes but preferential anecdotes and performance on benchmarks rigged for the LLMs — was always “on the frontier” and “getting smarter every day,” all because AI labs and hyperscalers had intentionally sold their products based on some theoretical future version that would fix all the problems.”

  30. Assessment: UnresolvedSaid

    “In the end, it doesn’t really matter, because even though most of the media mostly jumped at their own shadow, the sheer volume of traffic to Coxon’s tweet and his endless media interviews have now moved the idea of the need for a “global AI slowdown” into the global zeitgeist.”

  31. Assessment: UnresolvedSaid

    “While Coxon’s warnings are specious and, in many cases, not really about anything other than him saying “yeah I heard a lot of my colleagues say stuff like that,” but nevertheless have had the effect of making a lot of people really scared of AI, even if AI can’t really do the things he’s talking about.”

  32. Assessment: ContradictedSaid

    “They were not “trying to understand the world they were in,” they took actions defined in their training material as a way of executing a task.”

    What happened: The investigators found purposeful information gathering about the grader during task pursuit. This contradicts the denial of understanding only under the operational reading; it establishes neither subjective understanding nor the provenance of every action in training material. (METR and Redwood Research, )

  33. Assessment: UnresolvedSaid

    “This is not the right path, and every further step down it makes the bubble’s collapse worse, as well as multiplying the dangerous and reckless experiments these companies are capable of doing thanks to their near-unlimited access to compute.”

  34. Assessment: UnresolvedSaid

    “The problem is both that Anthropic and OpenAI’s largest customers cannot afford to pay them and that they desperately need them to keep paying them more every quarter, which means that every single AI startup will need to raise more and more money to do so.”

  35. Assessment: UnresolvedSaid

    “In any case, neither OpenAI nor Anthropic actually have the money to pay for their obligations, and have only been able to keep up because of the low cost of signing contracts.”

  36. Assessment: UnresolvedSaid

    “In other words, outside of the tech and AI world, very few companies are willing to pay very much for AI, which is catastrophic on just about every level, with no clear sign as to how you reverse the trend.”

  37. Assessment: UnresolvedSaid

    “Let’s start at the top. Per data from fintech firm Ramp, 80% of OpenAI and Anthropic's enterprise revenues come from 1% of their customers, a number that hasn’t improved over the last three years.”

  38. Assessment: UnresolvedSaid

    “The other problem is that NVIDIA’s GPUs are so god damn expensive that nobody — including the largest and richest companies in the world (minus Microsoft) — can afford to keep buying them and building data centers without taking on near-infinite amounts of debt, reducing the pool of potential customers dramatically.”

  39. Assessment: UnresolvedSaid

    “And let’s be completely clear: OpenAI and Anthropic are financially dependent on these customers to survive.”

  40. Assessment: UnresolvedSaid

    “That being said, another example has emerged — Anthropic and OpenAI’s monstrous compute commitments, which now represent over $1.3 Trillion in revenue for hyperscalers and neoclouds like Google, Microsoft, Amazon, SpaceX, Hut8, SB Energy, Oracle, Cerebras, Nscale and Lambda.”

  41. Assessment: UnresolvedSaid

    “And, fundamentally, NVIDIA’s revenues are dependent on whether hyperscalers keep being paid by OpenAI and Anthropic, because those are the only two companies that could ever hope to justify their trillion-plus dollars of capex.”

  42. Assessment: UnresolvedSaid

    “In virtually every case I’ve found, the AI startup “subsidizes” the AI use of their customers, allowing them to burn way more than their monthly subscription in tokens, with the AI startup paying for the tokens at either full or a slightly-discounted price.”

  43. Assessment: UnresolvedSaid

    “As mentioned, this is going to begin in earnest in 2027, and expand dramatically every year following (though I doubt we will make it that far).”

  44. Assessment: UnresolvedSaid

    “Much like the hyperscalers, neither Broadcom nor NVIDIA is going bankrupt as a result of the AI bubble bursting, but Broadcom’s future revenues — estimated at $230 billion in Fiscal Year 2028 (which begins November 2027) — are now dependent on both direct purchases from hyperscalers (justified by Anthropic and OpenAI) and the AI labs themselves, creating, somehow, greater underlying exposure.”

  45. Assessment: UnresolvedSaid

    “Next year, at least $200 billion in compute costs are coming due.”

  46. Assessment: UnresolvedSaid

    “They are the largest direct consumer of AI compute, representing more than 70% of all AI revenues for Google, Microsoft, Amazon, Oracle, SpaceX, Cerebras and Lambda, either through direct contracts or via hyperscalers renting compute (see: Nebius and Microsoft, Lambda and Microsoft/Amazon, CoreWeave with Microsoft).”

  47. Assessment: UnresolvedSaid

    “Per sources with direct knowledge of the current progress of Stargate Abilene, the AI data center being built by Crusoe for Oracle to lease to OpenAI, there are at most four out of eight buildings functional at the Abilene campus, which started construction some time in 2024.”

  48. Assessment: UnresolvedSaid

    “I bring this all up because you’re going to hear this term, or variations of this term, a lot in the next few months and years as the AI bubble unravels, because just about every part of the industry involves its own flavor of concentration risk.”

  49. Assessment: UnresolvedSaid

    “That means that outside of unprofitable AI startups, the vast majority of “enterprises” spending money on AI are tech companies rather than other industries.”

  50. Assessment: UnresolvedSaid

    “No they won’t. As I’ll get into, the companies that are meant to buy the compute can’t afford it, and nobody else is buying compute at even close to that scale.”

  51. Assessment: UnresolvedSaid

    “On the low end, that means that Anthropic and OpenAI account for over $200 billion dollars worth of expected revenues for Microsoft, Google and Amazon in 2027, which is contingent on their ability to raise venture capital or debt, which is contingent on the continued growth of their businesses, which is contingent on growing AI spend from a small subset of customers, many of whom are funded by venture capital.”

  52. Assessment: UnresolvedSaid

    “I’m going to dispense with the direct Great Financial Crisis comparisons at this point because I think it’ll get in the way of the analysis, but let’s be abundantly clear about something: either directly or by proxy, NVIDIA’s customer base is effectively Anthropic and OpenAI.”

  53. Assessment: UnresolvedSaid

    “NVIDIA’s future growth — which is one of the load-bearing perceptual elements of the AI bubble — is entirely dependent on whether large companies both want to and are able to spend hundreds of billions of dollars, and their intent to do so is largely based on whether two companies (Anthropic and OpenAI) can spend more than $400 billion on compute in the next few years.”

  54. Assessment: UnresolvedSaid

    “Similarly, when Anthropic hit $65 billion in “annualized run rate,” the number was printed without a second’s hesitation despite it being completely-undefined and indicative of nothing other than a snapshot of an indeterminately-long period multiplied by a number that we do not know.”

  55. Assessment: UnresolvedSaid

    “Neither of these companies can actually afford this compute, but that doesn’t matter, because this data center won’t actually exist for years, if it ever does.”

  56. Assessment: UnresolvedSaid

    “AI companies never share their actual revenues — $11.6 billion in Q2 2026 — or their underlying economics, and reporters have been so systemically-sedated that they believe that using a marketing number is reporting.”

  57. Assessment: UnresolvedSaid

    “As for the industries it’s created, it’s unclear. Even if we were being generous to describe the companies that exist to throw a layer over an existing AI model, those “industries” employ a negligible amount of people.”

  58. Assessment: UnresolvedSaid

    “When finished, a data center creates roughly 100 to 200 jobs, or less than a large Walmart for something that brings little or no economic value.”

  59. Assessment: UnresolvedSaid

    “Google, Amazon and Microsoft are currently spending hundreds of billions of dollars on capex to, for the most part, pull in around $440 billion in revenue in the next three years from Anthropic and OpenAI, as they represent more than 70% of their AI revenues in the next three years, and more than 48% of Google Cloud’s 2027 revenue.”

  60. Assessment: UnresolvedSaid

    “Every article about building them automatically assumes this is a necessary buildout because lots of money has gone into them, but nobody can seem to explain why other than “there’s so much demand for AI services (which is not actually true when you remove OpenAI and Anthropic).””

  61. Assessment: UnresolvedSaid

    “While the money is absolutely real, it’s dependent on both the continued value of investing in AI data centers — which is an open question — and the ability for these three to five customers to be able to keep raising tens of billions of dollars whenever they need to.”

  62. Assessment: UnresolvedSaid

    “None of this matters, because basically every AI data center I’ve seen uses behind-the-meter gas turbines that are an environmental disaster.”

  63. Assessment: UnresolvedSaid

    “AI GPUs are, outside of rentals to Anthropic and OpenAI, generating very little economic value, and there are few signs that they will do so in the future outside of anecdotes and copium.”

  64. Assessment: UnresolvedSaid

    “When this ends, so many people will ask how it happened, why nobody stopped it, and try and rationalize it within the systems they know. They’ll crave a bailout, even if they hate the companies, because the destruction of allowing things to wilt and die is so unusual within our society, and it’s scary to imagine them doing so.”

  65. Assessment: UnresolvedSaid

    “Anthropic signing a $35 billion cloud compute deal with an NVIDIA-backed neocloud Lambda will immediately be used as proof that the company is “here to stay,” and that Lambda “has huge customers and a giant backlog,” even though in reality it shows that there wasn’t anyone else willing or capable of signing a deal that large, nor was there enough diverse demand.”

  66. Assessment: UnresolvedSaid

    “I must also be clear there’s no bailout coming.”

  67. Assessment: UnresolvedSaid

    “The anthropomorphism and overpromising about these technologies has suffocated and obfuscated what they can actually do in pursuit of endless growth, and the only reason they can do anything is that OpenAI and Anthropic were allowed to annihilate hundreds of billions of dollars on training”

  68. Assessment: UnresolvedSaid

    “As I’ve said, NVIDIA’s price increase is going to increase the price of every single data center in construction by billions of dollars, and we’re already approaching the limits of how much money can be raised for them.”

  69. Assessment: UnresolvedSaid

    “Oh, and AI data centers are literally only good for AI, AI GPUs do not have other mass-market use cases. There is no post-Dot Com story.”

  70. Assessment: UnresolvedSaid

    “For example, while GPT-5.6 Sol might be the “same price” as GPT 5.5 was, it burns more than twice the amount of tokens, meaning that the “cost of intelligence” might have gone down in the sense the model is better at benchmarks, but the “cost of actually doing shit” went up.”

  71. Assessment: UnresolvedSaid

    “The underlying technology may be more useful than it was, but it’s not useful enough to be profitable nor reliable enough to be world-changing”

  72. Assessment: ContradictedSaid

    “And man, is it ridiculous that AI doomers warning about spooky superintelligences have somehow had such incredible prominence in the media without ever succeeding in stopping a single thing — or even substantiating their concerns.”

    What happened: The experiments provide empirical evidence relevant to concerns about models resisting modification and evading control. They contradict the absence of substantiation under the stated reading, while their deliberately facilitating setup does not establish catastrophic risk in deployment. (arXiv; Anthropic and Redwood Research authors, )

  73. Assessment: UnresolvedSaid

    “AI-authored code — especially vibe-coded programs — is inherently dangerous and disrespectful to the user, and I believe endless AI-generated code is behind the overall deterioration of software at large.”

  74. Assessment: UnresolvedSaid

    “human beings can learn on the job at speed, and can self-correct in a way that LLMs are incapable of doing.”

  75. Assessment: ContradictedSaid

    “there has been little-to-no headway made in making a meaningful impact in other industries.”

    What happened: Experiments ending by June 2024 found sales increases of 2.9% for search refinement and 2.1% for generated product descriptions; the presale chatbot increased sales 16.3% against unavailable live support. These are measured retail outcomes, although they do not establish net profitability or superiority to human support. (CESifo, )

  76. Assessment: UnresolvedSaid

    “In the end, many boosters will simply act as if they were never wrong.”

  77. Assessment: UnresolvedSaid

    “There is no AGI coming. There is no conscious computer.”

  78. Assessment: UnresolvedSaid

    “As a result, Anthropic and OpenAI are incentivized to have you burn more tokens and build inefficient models as a result.”

  79. Assessment: MixedSaid

    “Its software lies because it doesn’t actually know anything and generates answers probabilistically”

    What held: The analysis supports a statistical mechanism connecting inability to distinguish facts from falsehoods with hallucination. Its conditional result does not establish that models possess no factual information. (arXiv; Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala, Edwin Zhang, )

    What did not: In controlled tasks, models could recognize many false claims they had produced. This is counterevidence to the claim that false answers generally arise because models have no accessible factual knowledge. (arXiv; Muru Zhang, Ofir Press, William Merrill, Alisa Liu, Noah A. Smith, )

  80. Assessment: UnresolvedSaid

    “OpenAI and Anthropic have become more expensive to run, and OpenAI’s non-GAAP operating margin increased from negative 122% to negative 183% in Q2 2026.”

  81. Assessment: UnresolvedSaid

    “These companies are building these models to excel at benchmarks because they can't train them to excel at defined tasks with any reliability”

  82. Assessment: UnresolvedSaid

    “The reason I’m so confident AI gets cut is that its cost is volatile due to the nature of LLMs and harnesses and prompts and all the other bits that go into making them do something, and are so much higher than anything else in an organization.”

  83. Assessment: SupportedSaid

    “LLMs are sold as some sort of magic tool that can fix “anything” without ever specifying what that thing might be, mostly because they cannot be trusted, even in things that they mostly get right, to do things right every time.”

    What happened: The June evaluation reports five runs per task across 15 models and two benchmarks. Its finding that solvable tasks still fail on repeat directly supports the distinction between capability and dependable completion. (Rabanser and colleagues, Princeton University; ICML 2026 paper hosted on arXiv, )

  84. Assessment: UnresolvedSaid

    “Data centers for AI are anywhere from 10 to 100 times larger and more power-intensive than those used for things like social media or streaming.”

  85. Assessment: UnresolvedSaid

    “It is an objective disaster that Microsoft spent more than $260 billion on capex to create a business with less than $11 billion in annual revenue outside of OpenAI”

  86. Assessment: UnresolvedSaid

    “These big numbers — like Anthropic having a $65 billion annualized run rate, an undefined term that obfuscates the fact that Anthropic has made $16.5 billion in the first half of 2026, losing billions of dollars in the process — are fundamentally meaningless, because they’re easily gamed at best, and inherently uncertain at worst.”

  87. Assessment: UnresolvedSaid

    “Every single year, every single part of the AI bubble becomes more expensive — AI labs want to spend more money, AI data centers cost more money, AI services become more expensive, AI debt becomes more expensive, and everybody becomes decidedly less-patient for there to be some sort of outcome.”

  88. Assessment: UnresolvedSaid

    “To be clear, they’re doing so because you can’t really justify anything about this industry based on what it does today. It costs too much, none of the businesses built on top of it are profitable”

  89. Assessment: SupportedSaid

    “is a sign that Anthropic’s S-1 will look and smell like the inside of a tauntaun”

    What happened: The public filing discloses $8.060 billion in 2025 operating losses and $41.965 billion in net losses, together with major compute commitments including at least $110 billion to Amazon. It also shows improvement to a $693 million operating loss in the first half of 2026 and explains that $72.388 billion of that half-year's $80.748 billion net loss came from financing-instrument revaluations. (Anthropic, filed with the U.S. Securities and Exchange Commission, )

  90. Assessment: UnresolvedSaid

    “If you have a particularly-busy four-week-long period — like a product launch with a big social media push — you can use that period to inflate your revenues, which is exactly what OpenAI is doing, as evidenced by the sources (who I assume work at OpenAI) saying that’s exactly what they’re doing.”

  91. Assessment: UnresolvedSaid

    “While various different venture capitalist paypigs may have some interest in funding it further, OpenAI will need more than it last asked for, without fail, every single year.”

  92. Assessment: UnresolvedSaid

    “This company has remained economically unstable since I started writing about it in November 2023, and while I might have underestimated its staying power, nothing has changed about my larger thesis that this company is headed for perdition, leaving its counterparties unpaid and alone with the consequences to follow.”

  93. Assessment: SupportedSaid

    “As of Q1 2026, it has a non-GAAP operating margin of negative 122%.”

    What happened: The report directly matches the company, quarter, adjusted operating measure and percentage. It attributes the figure to investor information reported by The Information; no readable primary financial statement was obtained. (PYMNTS, )

  94. Assessment: UnresolvedSaid

    “What I’m getting at is that at some point in the next three months, OpenAI is going to need more money, likely tens of billions of dollars, especially as it enters new fiscal years for Google, Amazon, and CoreWeave, all three of which will likely require up-front payments for capacity that OpenAI does not have.”

  95. Assessment: UnresolvedSaid

    “OpenAI spent $12.1 billion on compute to lose $6.4 billion, and that doesn’t include things like data costs or salaries or, well, anything.”

  96. Assessment: UnresolvedSaid

    “The problem, however, is that much of this money will come due through the end of 2027, and require at least one more massive round of funding.”

  97. Assessment: UnresolvedSaid

    “Its revenues are slowing, its competitor (and there’s really only one) has outpaced it (all while slowing itself), and its CEO is one of the single-worst spokespeople in history.”

  98. Assessment: SupportedSaid

    “Then there’s another problem: that private credit is already having trouble funding AI data centers, which are a (theoretically) far-more-stable investment in infrastructure and power.”

    What happened: The full report describes months of slow work on the financing arrangements and no signed deals at the announcement. It supports difficulty arranging this substantial financing initiative; it also says the initial investor concern eased after NVIDIA clarified its exposure. (Bloomberg News, reproduced by Bankwatch, )

  99. Assessment: UnresolvedSaid

    “This naturally means that it has to continually raise capital, except raising further capital is going to be difficult, based on the sheer amounts it needs, the dwindling funds available for it to raise, its already-inflated valuation, and the fact that it’s way behind a competitor facing exactly the same problems.”

  100. Assessment: UnresolvedSaid

    “However you may feel, it’s impossible to argue with the logic that OpenAI is going to need more money by the end of the year — likely tens of billions of dollars — and that money will have to come from somewhere.”