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

Claims checked against evidence, page 20 of 31

Claims by Gary Marcus and Ed Zitron. Deadlines are judged as of 2026-10-29.

Claims checked against evidence (3100)

Each of these claims was checked against dated evidence.

Page 20 of 31: claims 1901 to 2000 of 3100, ordered by the date first said, newest first. To find a claim by its words, use search.

  1. Assessment: UnresolvedSaid

    Gary Marcus

    “The second example illustrates that it cannot reliably produce a non-standard number of body parts.”

  2. Assessment: UnresolvedSaid

    Gary Marcus

    “The whole scaling debate (and the discussion around economics that Ed Zitron and I each often raise) is really about whether generative AI in particular can be made into AGI just using more data and compute; a system like AlphaFold 3 that combines classical AI with generative AI is on a different axis that has seen far less investment.”

  3. Assessment: UnresolvedSaid

    Gary Marcus

    “Even if the AI acceleration we saw from 2019-2023 slows down for five years, as I have often speculated, it won’t slow down forever.”

  4. Assessment: SupportedSaid

    Gary Marcus

    “LLMs are already dangerous, despite their limits.”

    What happened: The researchers demonstrated unfavorable job assignments and harsher hypothetical criminal judgments associated with African American English. This establishes a concrete discriminatory risk, while the experiment does not establish that employers or courts actually imposed these outcomes. (arXiv, )

  5. Assessment: UnresolvedSaid

    Gary Marcus

    “This just in, right on the heels of Satya Nadella also publicly acknowledging diminishing returns:”

  6. Assessment: UnresolvedSaid

    Ed Zitron

    “Generative AI is incredibly unprofitable.”

  7. Assessment: ContradictedSaid

    Ed Zitron

    “The products aren't coming.”

    What happened: The disclosure dates general availability to May 2025 and reports continued growth in weekly users and business subscriptions in February 2026. Its reported commercial scale supplies evidence that adoption extended beyond an experimental launch. (Anthropic, )

  8. Assessment: ContradictedSaid

    Ed Zitron

    “I also want to be clear that none of these companies ever had a plan.”

    What happened: Microsoft specified concrete generative AI functions inside Word and other work applications. That is a product and deployment strategy beyond purchasing GPUs, regardless of whether the promised benefits were achieved. (Microsoft, )

  9. Assessment: SupportedSaid

    Ed Zitron

    “OpenAI currently spends $2.35 to make $1, and Microsoft CFO Amy Hood said that OpenAI would cut into Microsoft's profits this October, losing it a remarkable $1.5 billion, "mainly because of an expected loss from OpenAI" according to CNBC.”

    What happened: These estimates imply $8.7 billion of expenses and a ratio rounding to $2.35. CNBC also reports exclusions, including equity-based compensation, so this supports the cited estimate rather than comprehensive accounting expenses. (CNBC, )

  10. Assessment: UnresolvedSaid

    Ed Zitron

    “"Orion," OpenAI's next model, will underwhelm, as will its competitors' models, and at some point somebody is going to blink in one of the hyperscalers, and the AI era will be over.”

  11. Assessment: UnresolvedSaid

    Ed Zitron

    “If we're truly at the diminishing returns stage of transformer-based models, it will be extremely difficult to justify buying further iterations of NVIDIA GPUs past Blackwell.”

  12. Assessment: UnresolvedSaid

    Ed Zitron

    “Once the AI bubble pops, there are no other hyper-growth markets left, which will in turn lead to a bloodbath in big tech stocks as they realize that they’re out of big ideas to convince the street that they’re going to grow forever.”

  13. Assessment: UnresolvedSaid

    Ed Zitron

    “The constant refrain I hear from VCs and AI fantasists is that "chips will bring down the cost of inference," yet I don't see any proof of that happening, nor do I think it'll happen quickly enough for these companies to turn things around.”

  14. Assessment: UnresolvedSaid

    Ed Zitron

    “Remember, the capital expenditures on generative AI and the associated infrastructure have gone over $200 billion in the last year.”

  15. Assessment: UnresolvedSaid

    Ed Zitron

    “And before then, you’re going to get a lot of desperate stories about how “AI gains can be found outside of training new models” to try and keep the party going, despite reality flicking the lights on and off and threatening to call the police.”

  16. Assessment: UnresolvedSaid

    Ed Zitron

    “I believe all of this is going nowhere, and that at some point Google, Microsoft, or Meta is going to blink and pull back on their capital expenditures.”

  17. Assessment: UnresolvedSaid

    Ed Zitron

    “There is no iPhone moment coming, I'm afraid.”

  18. Assessment: ContradictedSaid

    Ed Zitron

    “The entire tech industry has become oriented around a dead-end technology that requires burning billions of dollars to provide inessential products that cost them more money to serve than anybody would ever pay.”

    What happened: The preceding sentence identifies 2024 revenue of $4 billion and inference compute and hosting costs of $2.4 billion. This reported positive serving margin supplies a counterexample to the categorical assertion, without establishing company-wide profitability. (Epoch AI, )

  19. Assessment: UnresolvedSaid

    Ed Zitron

    “Instead, big tech has found itself peddling increasingly-more-expensive iterations of near-identical Large Language Models — a direct result of them all having to use the same training data, which it’s now running out of.”

  20. Assessment: UnresolvedSaid

    Ed Zitron

    “Putting that aside, OpenAI is also a terrible business that has to burn $5 billion to make $3.4 billion, with no proof that it’s capable of bringing down costs.”

  21. Assessment: UnresolvedSaid

    Ed Zitron

    “Even if OpenAI listens to its better angels, the reality still stands: ads won’t dam the rising tide of red ink that promises to eventually drown the company.”

  22. Assessment: UnresolvedSaid

    Ed Zitron

    “If NVIDIA doesn’t solve its cooling problem — and solve it well — its customers will undoubtedly encounter thermal throttling, where the chip reduces speed in order to avoid causing permanent damage.”

  23. Assessment: UnresolvedSaid

    Ed Zitron

    “The revenue isn't coming.”

  24. Assessment: UnresolvedSaid

    Ed Zitron

    “Throughout these pieces I have repeatedly made the point that — separate to any lack of a core value proposition, training data drought, or unsustainable economics — generative AI is a dead end due to the limitations of probabilistic models that hallucinate, where they authoritatively state things that aren't true.”

  25. Assessment: ContradictedSaid

    Ed Zitron

    “Generative AI's products have effectively been trapped in amber for over a year.”

    What happened: Developers could use the API to have Claude inspect screens, move a cursor, click and type. This materially extends possible workflows, although Anthropic describes the release as experimental and error-prone. (Anthropic, )

  26. Assessment: UnresolvedSaid

    Ed Zitron

    “OpenAI, the biggest player in the industry, is on course to lose more than $5 billion this year, with competitor Anthropic (which also makes its own transformer-based model, Claude) on course to lose more than $2.7 billion this year.”

  27. Assessment: MixedSaid

    Ed Zitron

    “The hallucination problem is one that is nowhere closer to being solved — and, at least with the current technology — may never go away, and it makes it a non-starter for a great many business tasks, where you need a high level of reliability.”

    What held: Testing on May 23–27 found frequent hallucinations in a specialized legal research product. This supports the barrier to unverified, high-reliability legal work under the stated interpretation, but does not establish the number of business tasks excluded. (arXiv, )

    What did not: Table 2 reports SimpleQA hallucination rates of 0.61 for GPT-4o and 0.44 for o1-preview, with improvements on the other listed tests. The same paragraph reports contrary anecdotal feedback and cautions against generalizing beyond these evaluations. (OpenAI, )

  28. Assessment: ContradictedSaid

    Ed Zitron

    “The models aren't going to get better.”

    What happened: The report identifies MAI-Thinking-1's base-model training on 8K GB200 GPUs and reports 84.2% GPQA Diamond pass@1, exceeding the pre-post results in e3. It uses the same 198-question Diamond subset; its large output budget and grading procedure limit exact comparability. (Microsoft AI, )

  29. Assessment: UnresolvedSaid

    Gary Marcus

    “Two years on, on the most important question of all – factuality and reliability — we are still pretty much where we were when ChatGPT first came out: wishing and hoping.”

  30. Assessment: UnresolvedSaid

    Gary Marcus

    “There has been a ton of experimentation, but relatively few well-documented success stories, and some fairly negative stories like a recent one at Business Insider on GPT-powered CoPilot,”

  31. Assessment: UnresolvedSaid

    Gary Marcus

    “But a lot of of theories about how ChatGPT would be used in practice have fizzled out.”

  32. Assessment: SupportedSaid

    Gary Marcus

    “Two years later Google’s search share hasn’t diminished all that much.”

    What happened: This supplies the launch-era worldwide baseline. Together with e2, it supports a modest decline; historical primary figures were not retrievable through the page reader. (Local Dialog, )

  33. Assessment: UnresolvedSaid

    Gary Marcus

    “Yet hundreds of billions more have been invested on further speculation that scaling would somehow magically cure problems that actually appear to be inherent with the technology.”

  34. Assessment: UnresolvedSaid

    Gary Marcus

    “Be on your guard; it’s going to happen a lot.”

  35. Assessment: UnresolvedSaid

    Gary Marcus

    “Every big tech company is desperate for training data, and we can expect more companies following the same sly opt-out playbook, hiding what is going in obscure settings and Terms of Service nobody reads.”

  36. Assessment: UnresolvedSaid

    Gary Marcus

    “This is certainly logically possible, though not well-explained elsewhere in Microsoft’s documentation, such as this which says (vaguely) that ”connected experiences [may] use machine learning services to … Analyze Data…” among others things.”

  37. Assessment: SupportedSaid

    Gary Marcus

    “GPT-4 was better than GPT-3 in almost every way; o1 is a far less uniform improvement.”

    What happened: The study evaluates legacy GPT-3 models and GPT-4 under aligned settings across more than twenty benchmarks and seven capability categories. Its overall analysis reports broad improvements across those categories from GPT-3 to GPT-4. (Zheng et al., ByteDance and University of Illinois Urbana-Champaign, arXiv, )

  38. Assessment: UnresolvedSaid

    Gary Marcus

    “But things have been changing rapidly, and the great scaling retrenchment has already begun.”

  39. Assessment: SupportedSaid

    Gary Marcus

    “(In some cases o1 doesn’t even do better than GPT-4).”

    What happened: The stated count implies that o1 did not outperform GPT-4o in the remaining three subcategories. It supports the limited 'some cases' claim under the contextual reading of GPT-4. (OpenAI, )

  40. Assessment: UnresolvedSaid

    Gary Marcus

    “The new version is that there is not one scaling law, but three: scaling with how long you train a model (which isn’t really holding anymore), scaling with how long you post-train a model, and scaling with how long you let a given model wrestle with a given problem (or what Satya Nadella called scaling with “inference time compute”).”

  41. Assessment: UnresolvedSaid

    Gary Marcus

    “Even if you look at OpenAI’s own research post on o1, which introduced the newest putative law, it is immediately apparent that scaling test time compute works for some problems and not others.”

  42. Assessment: UnresolvedSaid

    Gary Marcus

    “Like the first generalization, the new ones will eventually run out.”

  43. Assessment: SupportedSaid

    Gary Marcus

    “Many other Tech CEOs and others, including Marc Andreeseen (A16Z), Alexandr Wang (ScaleAI), have also used the phrase “hitting a wall” and the idea of diminishing returns in the last couple weeks.”

    What happened: Wang publicly used the 'wall' framing on Nov 20, 2024, three days before the post. He partly agreed that a wall had been hit, and the report links it to diminishing returns from training. (The Verge, )

  44. Assessment: UnresolvedSaid

    Gary Marcus

    “Hundreds of billions of dollars were invested on the assumption that I was wrong, largely to the exclusion of investment in other approaches.”

  45. Assessment: SupportedSaid

    Gary Marcus

    “Scaling was always a hypothesis.”

    What happened: Early scaling research built its claims from empirical measurements across several tasks. This supports the distinction between observed regularities and guarantees about their indefinite continuation. (arXiv; Hestness et al., )

  46. Assessment: UnresolvedSaid

    Gary Marcus

    “Crucially, there has never been any principled argument that they would solve hallucinations or reasoning, or edge cases in open-ended worlds —or that synthetic data would suffice indefinitely to keep feeding the machine.”

  47. Assessment: SupportedSaid

    Gary Marcus

    “They are merely empirical generalizations that held for a certain period time, when there was enough fresh data and compute.”

    What happened: The authors explicitly characterize their loss relationships as empirical. Their reported measurements cover finite ranges of model size, data and training compute. (arXiv; Kaplan et al., )

  48. Assessment: UnresolvedSaid

    Gary Marcus

    “Meanwhile, precious little investment has been made in other approaches.”

  49. Assessment: UnresolvedSaid

    Gary Marcus

    “Given the cost of chips, profits will be elusive.”

  50. Assessment: UnresolvedSaid

    Gary Marcus

    “Just as I argued here in April 2024, LLMs have reached a point of diminishing returns.”

  51. Assessment: UnresolvedSaid

    Gary Marcus

    “LLMs such as they are, will become a commodity; price wars will keep revenue low.”

  52. Assessment: UnresolvedSaid

    Gary Marcus

    “If enthusiasm for GenAI dwindles and market valuations plummet, AI won’t disappear, and LLMs won’t disappear; they will still have their place as tools for statistical approximation.”

  53. Assessment: SupportedSaid

    Gary Marcus

    “Many of us, including Melanie Mitchell, Subbarao Kambahapati, Emily Bender, Ernie Davis, etc. have been emphasizing for ages that there are principled limits with LLMs.”

    What happened: Bender and Koller's paper makes an explicit conceptual-limit argument more than four years before the post. The metadata gives July 2020; July 31 is used as a conservative availability bound. (Association for Computational Linguistics, )

  54. Assessment: UnresolvedSaid

    Ed Zitron

    “At some point, these ugly messes will collapse as it becomes clear that their entire infrastructure is written upon increasingly-automated levels of crap, rife with hallucinations and devoid of any human touch.”

  55. Assessment: SupportedSaid

    Gary Marcus

    “People aren’t ignoring GenAI; they are waiting to see if it will work.”

    What happened: A third of the firms were still testing. That fits 'waiting to see if it will work.' (CNBC (syndicated by NBC San Diego), )

  56. Assessment: SupportedSaid

    Gary Marcus

    “But only 25% of the correspondents thought it was worth it.”

    What happened: Matches his 25% yes, 25% no, 50% too-soon breakdown exactly. (CNBC (syndicated by NBC San Diego), )

  57. Assessment: UnresolvedSaid

    Gary Marcus

    “He then went on to extrapolate that it would be 10,000 times better in 4 years, never considering potential bottlenecks around data, or compute, nor inherent problems with LLMs.”

  58. Assessment: UnresolvedSaid

    Gary Marcus

    “(Side bonus: GenAI uses a massive amount of power, to the point of keeping fossil fuels in vogue, and that’s good for oil-rich countries, even if GenAI never becomes reliable.)”

  59. Assessment: SupportedSaid

    Gary Marcus

    “(is OpenAI’s o1 100 times better than GPT4? Certainly not by their own data)”

    What happened: On AIME, one of OpenAI's best showcases for o1, the gain is about sixfold (74% vs 12%), far below 100x. (OpenAI, )

  60. Assessment: UnresolvedSaid

    Gary Marcus

    “My offer to bet him a million dollars on this nonsense stands.”

  61. Assessment: UnresolvedSaid

    Gary Marcus

    “It’s just fantasy to imagine selling 200 times as many humanoid robots in the nearish term when nobody knows how to build a single safe, reliable, generally useful humanoid right now, at any price.””

  62. Assessment: SupportedSaid

    Gary Marcus

    “It’s just fantasy to imagine selling 200 times as many humanoid robots in the nearish term when nobody knows how to build a single safe, reliable, generally useful humanoid right now, at any price.””

    What happened: The accompanying expert discussion says simple tasks still require substantial training and performance remains strongest in controlled, repetitive settings. This supports the ordinary reading that dependable general usefulness had not been established. (IEEE Transmitter, )

  63. Assessment: UnresolvedSaid

    Gary Marcus

    “The same will be true for humanoid robots, and we aren’t going to see six humanoid robots for every car anytime soon.”

  64. Assessment: UnresolvedSaid

    Ed Zitron

    “Google is exactly the monster that Sundar Pichai and Prabhakar Raghavan wanted it to be — a lumbering private equity vehicle that uses its crooked money machine to demolish smaller players, except there are no more hyper-growth markets left for it to throw billions at, leaving it with Generative AI, a technology that lacks mass-market utility and burns cash with every prompt.”

  65. Assessment: UnresolvedSaid

    Gary Marcus

    “Meanwhile GPT-5 still hasn’t arrived, and nor has the much ballyhooed Sora.”

  66. Assessment: UnresolvedSaid

    Gary Marcus

    “Those products have not by a longshot lived up to everyone’s expectations, but they do actually ship, and customers can try those products out and decide for themselves. It’s hard to call that smoke and mirrors.”

  67. Assessment: ContradictedSaid

    Gary Marcus

    “The difference between GPT-3 and GPT-4 was by all accounts massive; the difference between GPT-4 and o1 is by all accounts modest.”

    What happened: The company reported substantial preference gains across three important reasoning domains for the actual preview model. This challenges the asserted agreement about only modest improvement, while remaining a company-run evaluation. (OpenAI, )

  68. Assessment: UnresolvedSaid

    Gary Marcus

    “But then again, their products have always been served with large side order of bullshit, like when Sam said, apparently jokingly, that “AGI has been achieved internally”. None of the robot stuff they promised in the Rubik’s cube campaign ever shipped.”

  69. Assessment: SupportedSaid

    Gary Marcus

    “Jailbreaks aren’t new, but even after years of them, the tech industry has nothing like a robust response.”

    What happened: The study evaluates four defenses and reports substantial vulnerability to human multi-turn attacks despite strong results against automated single-turn attacks. This supports the ordinary reading that established defenses lacked broad robustness. (arXiv; Scale AI and UC Berkeley researchers, )

  70. Assessment: UnresolvedSaid

    Gary Marcus

    “The more people use LLMs, the more trouble we are going to be in.”

  71. Assessment: UnresolvedSaid

    Gary Marcus

    “And, bonus, there is little if any government regulation in place to do much about this.”

  72. Assessment: SupportedSaid

    Ed Zitron

    “It isn’t. You saw a prototype and heard a speech.”

    What happened: Explicitly identifies the unveiled Cybercab as a prototype. The article's headline uses arrival language, but its description supports Zitron's distinction. (The Verge, )

  73. Assessment: UnresolvedSaid

    Ed Zitron

    “Generative AI's destructive, larcenous growth exists in large part because people buy Sam Altman's shit, and because they believe that he's somebody special, a genius operator that can do the impossible, which in turn helps them platform his half-truths and outright lies.”

  74. Assessment: UnresolvedSaid

    Ed Zitron

    “Sam Altman's rise to power came, in part, from members of the media propping him up as a genius”

  75. Assessment: UnresolvedSaid

    Ed Zitron

    “Yet the media still takes the idea that he’d launch a robotaxi network seriously, even though Musk has been lying about this shit since 2019.”

  76. Assessment: MixedSaid

    Ed Zitron

    “far too many simply ran exactly what Musk said and acted as if he’d proven that these were actual robots walking around and doing things on their own accord, as opposed to what was blatantly obvious: that they were controlled remotely by human operators.”

    What held: Reports a bartender robot's acknowledgment of assistance, alongside accounts of teleoperated guest interactions. (Ars Technica, )

    What did not: Contradicts including walking in a blanket account of remote control. The reported operation combined autonomous locomotion with human-assisted interactions. (Ars Technica, )

  77. Assessment: SupportedSaid

    Gary Marcus

    “Fall off on increasingly large multiplication problems has repeatedly been observed, both in older models and newer models.”

    What happened: For older models (GPT-3, ChatGPT, GPT-4), multi-digit multiplication accuracy falls from near perfect to zero as problem size grows. (arXiv (Dziri et al., NeurIPS 2023), )

  78. Assessment: MixedSaid

    Gary Marcus

    “I strongly believe the current results are robust.”

    What held: An independent team built a controlled benchmark with irrelevant context (IC) and found LLMs significantly sensitive to it. This reproduces the core GSM-NoOp-style finding. (arXiv (Yang et al.), )

    What did not: A peer-reviewed re-analysis found the GSM-Symbolic drop significant for only 8 of 20 models, and partly explained by larger numbers in the new dataset. That cuts against the robustness of that headline result. (arXiv (Długosz, Oliveira, Díaz-Rodríguez; accepted EMNLP 2026), )

  79. Assessment: SupportedSaid

    Gary Marcus

    “Performance is ok on small problems, but quickly tails off.”

    What happened: o1-preview scored 97.8% on small problems but 23.63% on 6-20 block problems, and its correct answers on the larger set were mostly the shorter plans. That is good performance on small problems with a quick fall-off. (arXiv (Valmeekam, Stechly, Kambhampati), )

  80. Assessment: SupportedSaid

    Gary Marcus

    “Failure to follow the rules of chess is another continuing failure of formal reasoning:”

    What happened: On a 1,000-puzzle benchmark in mid-2024, the strongest model tested (GPT-4o) made illegal moves 12.7% of the time, and most other LLMs made more illegal moves than legal ones. That matches a continuing failure to follow chess rules before the post. (The Register, )

  81. Assessment: UnresolvedSaid

    Gary Marcus

    “But the anti-neurosymbolic tradition is limited; its most visible manifestation, LLMs, has brought fame and money, but no robust solution to solving any particular problem with great reliability.”

  82. Assessment: ContradictedSaid

    Gary Marcus

    “We have been told that AI will “solve physics” (per Sam Altman), make major breakthroughs in medicine, battery technology, etc, but so far none of that has materialized.”

    What happened: The paper reports demonstrated accuracy in the CASP14 assessment conducted in May–July 2020. Together with e2, it establishes an achieved major research advance rather than an untested promise. (Nature, )

  83. Assessment: UnresolvedSaid

    Gary Marcus

    “Almost every putative virtue of AI (aside from making a small number of people rich) has been promissory.”

  84. Assessment: UnresolvedSaid

    Gary Marcus

    “Even though some of the drawbacks, which inherently are less dependent on reliably and tight causal reasoning, such as misinformation use, nonconsensual deepfake porn, ans deepfake kidnapping scams, are already here to say).”

  85. Assessment: UnresolvedSaid

    Gary Marcus

    “Other forms of AI, that are more domain-specific and specialized might help; those aren’t actually the ones getting the massive funding – or doing the massive harm.”

  86. Assessment: UnresolvedSaid

    Ed Zitron

    “Even a $2 price increase (the first expected price hike for ChatGPT Plus, as the company reportedly works towards charging $44 per month) and similar price hikes on the Teams and Enterprise plans won’t do much to stem the flow of red ink on its balance sheet.”

  87. Assessment: ContradictedSaid

    Ed Zitron

    “That product is extremely commoditized. The free version of ChatGPT is effectively identical to the free version of Anthropic's Claude, Meta's AI assistant, Microsoft's Copilot, and even Twitter's "Grok."”

    What happened: The launch announcement places access in premium subscription tiers during the target quarter. The explicit premium-only history in e2 corroborates that this was not an ordinary free consumer offering. (xAI, )

  88. Assessment: UnresolvedSaid

    Ed Zitron

    “At the current cost of revenue, it will cost OpenAI more than $27 billion to hit that revenue target.”

  89. Assessment: UnresolvedSaid

    Ed Zitron

    “Unlike Facebook and Google, ChatGPT’s most frequent free users actually become less valuable over time, and become a burden on a system that already burns money.”

  90. Assessment: UnresolvedSaid

    Ed Zitron

    “Yet, once you get past these problems, you run head-first into the largest one: that generative AI is deeply unprofitable to run.”

  91. Assessment: UnresolvedSaid

    Ed Zitron

    “As I've said before, OpenAI is unprofitable, unsustainable and untenable in its current form”

  92. Assessment: UnresolvedSaid

    Ed Zitron

    “Because access to its APIs isn’t a particularly big business, OpenAI’s hopes of hitting its revenue goals is almost entirely contingent on ChatGPT Plus continuing to grow.”

  93. Assessment: SupportedSaid

    Ed Zitron

    “All of this is happening at a time when an astronomical amount of talent is leaving the company — the talent that OpenAI desperately needs to build products that people will actually pay for.”

    What happened: The simultaneous departure announcements cover the chief technology officer, chief research officer and a research vice president. Those roles support the ordinary reading of a substantial concentration of relevant leadership departures. (The Register, )

  94. Assessment: ContradictedSaid

    Ed Zitron

    “To hit a revenue target of $11.6 billion in 2025, OpenAI would need to increase revenue from ChatGPT customers by 310%.”

    What happened: The derived counterexample reaches the target with a 213.5% increase in the stipulated ChatGPT baseline, assuming the stated mix and a full year at the resulting rate. This is a calculation from reported premises, not a measured revenue outcome. (CNBC, )

  95. Assessment: SupportedSaid

    Ed Zitron

    “To be abundantly clear, as it stands, OpenAI currently spends $2.35 to make $1.”

    What happened: The reported estimates imply $8.7 billion of expenses on their loss basis and an expense-to-revenue ratio of 2.35135. This supports the arithmetic reading, not a claim about realized expenditure. (CNBC, )

  96. Assessment: UnresolvedSaid

    Ed Zitron

    “Also, at some point, OpenAI will have to work out a way to go public, because otherwise there is absolutely no reason to invest in this company, unless you are doing so with the belief that you will be able to offload your shares in a future secondary sale.”

  97. Assessment: SupportedSaid

    Ed Zitron

    “OpenAI's products are increasingly-commoditized, with Google, Meta, Amazon and even Microsoft building generative AI models to compete.”

    What happened: The retrospective comparison identifies capability convergence by Llama 3.1, released during the target quarter. It supports increasing model substitutability while also documenting historical lags behind closed models. (Epoch AI, )

  98. Assessment: UnresolvedSaid

    Ed Zitron

    “Cutting prices, therefore, only serves to slash whatever meager margins exist on those customers, or to turn those potentially-profitable customers into a loss center.”

  99. Assessment: UnresolvedSaid

    Ed Zitron

    “OpenAI lives and dies on the revenue gained from ChatGPT, a product that hasn't meaningfully changed since it launched beyond adding new models that do, for most users, exactly the same thing.”

  100. Assessment: UnresolvedSaid

    Ed Zitron

    “And, after that, OpenAI will still need to raise more rounds of funding.”