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

Claims checked against evidence, page 18 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 18 of 31: claims 1701 to 1800 of 3100, ordered by the date first said, newest first. To find a claim by its words, use search.

  1. Assessment: SupportedSaid

    Ed Zitron

    “OpenAI burned more than $5 billion last year.”

    What happened: The reported 2024 net loss exceeds the stated threshold. This supports the passage's inclusive loss interpretation, not a claim about cash consumption. (Ars Technica, )

  2. Assessment: UnresolvedSaid

    Ed Zitron

    “In short, most of the coverage you read on artificial intelligence is led by companies that benefit financially from you thinking artificial intelligence is important and by default all of this coverage mentions OpenAI or ChatGPT.”

  3. Assessment: UnresolvedSaid

    Ed Zitron

    “Furthermore, nothing about this product moves OpenAI toward profitability. In fact, I think they're doing the opposite.”

  4. Assessment: UnresolvedSaid

    Ed Zitron

    “Anthropic is currently raising $2 billion at a $60 billion valuation primarily based off of this trumped-up marketing nonsense.”

  5. Assessment: UnresolvedSaid

    Ed Zitron

    “To be clear, OpenAI burned approximately $5 billion on compute alone.”

  6. Assessment: MixedSaid

    Ed Zitron

    “The one "big thing" that they've been able to do is to use "reasoning" to make the Large Language Models "think" (they do not have consciousness, they are not "thinking," this just means using more tokens to answer a particular question and having multiple models check the work), which mostly results in them being a bit more accurate when generating an answer, but at the expense of speed and cost.”

    What held: The developer acknowledges cost and speed limitations in the o1 discussion. This supports that part of the claim, but does not establish a universal tradeoff or predominantly small accuracy gains. (OpenAI, )

    What did not: Appendix A reports AIME pass@1 scores of 44.6 for o1-preview and 9.3 for GPT-4o, showing substantial gains on a demanding reasoning task. These selected evaluations challenge the modest-gain characterization, but cannot determine gains across all ordinary usage. (OpenAI, )

  7. Assessment: UnresolvedSaid

    Ed Zitron

    “The tech industry — and part of our economy — is accelerating at speed into a brick wall, driven by people like Sam Altman, Dario Amodei, Marc Benioff, and Larry Ellison, all men that are incentivized to have you value their companies based on something other than what their businesses actually sell.”

  8. Assessment: SupportedSaid

    Ed Zitron

    “User numbers alone tell you nothing about the sustainability or profitability of a business, or how those people use the product.”

    What happened: The disclosed figure is an aggregate weekly activity count. The inference supporting the claim is limited: this count does not encode profit, retention, purposes, or the distribution of activity across days. (PYMNTS, )

  9. Assessment: UnresolvedSaid

    Ed Zitron

    “Deep Research uses OpenAI's o3 model which can cost as much as $1,000 a query, and while I imagine these prompts aren't that expensive, they are still significantly more so than a regular query from ChatGPT.”

  10. Assessment: UnresolvedSaid

    Ed Zitron

    “There is no "artificial intelligence" part of Microsoft's revenue or earnings. This is literally Microsoft taking anything with "AI" on it and saying "we made money!"”

  11. Assessment: UnresolvedSaid

    Ed Zitron

    “And when this all falls apart — and I believe it will — there will be a very public reckoning for the tech industry.”

  12. Assessment: UnresolvedSaid

    Ed Zitron

    “The damage will be tens of thousands of people fired, long-term environmental and infrastructural chaos, and a profound depression in Silicon Valley that I believe will dwarf the dot-com bust.”

  13. Assessment: UnresolvedSaid

    Ed Zitron

    “There isn't a single god damn startup in the history of anything — other than perhaps Facebook — that has had this level of coverage at such an early stage.”

  14. Assessment: SupportedSaid

    Gary Marcus

    “About year ago the City of New York tried this kind of thing, with less than spectacular results:”

    What happened: The reporters tested the operating city chatbot and documented incorrect guidance about housing vouchers, cash payments, and workers’ tips. Ten staff members received the same incorrect voucher answer, supporting repeated unreliability. (The Markup, )

  15. Assessment: UnresolvedSaid

    Gary Marcus

    “Over the last couple days I have been asking version 2 to draw images identifying and tagging parts of images. The results are consistently awful:”

  16. Assessment: UnresolvedSaid

    Gary Marcus

    “And it will be the citizens of the United States who bear the consequences, while the guy who stood to get a $400M armored T̶e̶s̶l̶a̶ EV government contract (until the media squawked) stands to collect a big slice of the profits, unemploying thousands of lifelong government employees.”

  17. Assessment: UnresolvedSaid

    Gary Marcus

    “Yet Musk wants to use shove problematic AI, both biased and unreliable, down our throats, immediately, as reported by The New York Times, and noted in this Bluesky comment:”

  18. Assessment: UnresolvedSaid

    Gary Marcus

    “Soon such unreliable stuff, with butt-covering warning messages that most people will ignore, will be everywhere.”

  19. Assessment: UnresolvedSaid

    Gary Marcus

    “For nearly three years I have been saying that the pure scaling of LLMs – adding more data and more parameters – would eventually run out, and that it would fail to solve hallucinations and boneheaded errors and that scaling laws were merely empirical generalizations, rather than physical laws.”

  20. Assessment: SupportedSaid

    Gary Marcus

    “Instead, OpenAI will be throwing the kitchen sink at future efforts to build GPT-5 , including “test-time-compute”, a new approach that involves longer, more expensive inference times rather than “constant time inference” of GPT-4-like models, as well as (I suspect) massive amounts of synthetic data”

    What happened: The release documentation describes controllable thinking time in the deployed GPT-5 API. This directly supports the forecast inclusion of additional inference-time reasoning. (OpenAI, )

  21. Assessment: SupportedSaid

    Gary Marcus

    “Second, pure scaling will no longer be the means of attack.”

    What happened: The release documents a system combining fast responses with deeper reasoning and routing. Those mechanisms establish that GPT-5 goes beyond increasing pretraining data and parameters alone. (OpenAI, )

  22. Assessment: UnresolvedSaid

    Gary Marcus

    “They were overhyping LLMS as AGI, even as problems like hallucinations and boneheaded persisted.”

  23. Assessment: UnresolvedSaid

    Gary Marcus

    “(Also, having our own massive LLMs will do nothing to stop China from using theirs in authoritarian pursuits.)”

  24. Assessment: ContradictedSaid

    Gary Marcus

    “The new administration will do everything in its power to protect businesses, and nothing to protect individuals.”

    What happened: Trump signed legislation creating criminal prohibitions and removal obligations protecting people from nonconsensual intimate imagery. This completed action contradicts the prediction of doing nothing for individuals. (The White House, )

  25. Assessment: SupportedSaid

    Ed Zitron

    “There were multiple other companies doing exactly what GPT-3 did months before ChatGPT launched.”

    What happened: The announcement offers Jurassic-1 through an open-beta API and web environment and describes text generation, question answering, and classification. It establishes one other company's comparable functional offering well before the cutoff. (AI21 Labs, )

  26. Assessment: UnresolvedSaid

    Ed Zitron

    “It only caught fire because the media insisted it did so.”

  27. Assessment: UnresolvedSaid

    Gary Marcus

    “LLMs, once novel, are largely a commodity.”

  28. Assessment: SupportedSaid

    Gary Marcus

    “Even the latest systems like Deep Research are still struggling in a few ways – and those ways pretty much correspond exactly to the places that I warned would be LLM’s Achilles’ Heels: hallucinations and reasoning errors.”

    What happened: The paragraph expressly identifies factual hallucinations and incorrect inferences as remaining limitations. It also reports a lower rate than existing ChatGPT models, which qualifies the severity without contradicting their continued presence. (OpenAI, )

  29. Assessment: UnresolvedSaid

    Gary Marcus

    “(Note that test-time compute systems are not across the board improvements like GPT-4 was relative to GPT-3 or GPT-3 was relative to GPT-2, but rather improvements in certain domains such as coding and math.)”

  30. Assessment: UnresolvedSaid

    Gary Marcus

    “A key claim of the paper was that pure scaling of LLMs – just adding more data and compute to extant LLM architectures – would not bring us to AGI, and that so-called scaling laws were merely empirical generalizations rather than physical laws.”

  31. Assessment: SupportedSaid

    Gary Marcus

    “To some extent newer models are doing that.”

    What happened: Section 2.2.2 describes programmed accuracy and formatting rewards for R1-Zero, including code tests. Section 2.3.4 explicitly applies its rule-based reasoning reward methodology to R1, supporting the limited training-time combination described by Marcus. (DeepSeek-AI, arXiv, )

  32. Assessment: SupportedSaid

    Gary Marcus

    “Me, December 2022: The problem with LLMs is that they hallucinate, and their errors can be hard to catch.”

    What happened: Kubacka describes checking plausible scientific citations and discovering nonexistent articles, mismatched identifiers, and invented physics. She reports that an explanation in her own field initially made her reconsider whether the invented phenomenon existed, directly supporting the possibility of difficult-to-detect errors. (Teresa Kubacka, Lookalikes and Meanders, )

  33. Assessment: UnresolvedSaid

    Gary Marcus

    “What changed here was the AI landscape. It’s all about money, of course.”

  34. Assessment: UnresolvedSaid

    Gary Marcus

    “If they were already making zillions from AI, they would have mentioned that on the investor call. But presumably they are not, and that cannot continue indefinitely.”

  35. Assessment: UnresolvedSaid

    Gary Marcus

    “Google has to find a way to make back the money back. Bye bye corporate motto, hello military and surveillance.”

  36. Assessment: UnresolvedSaid

    Gary Marcus

    “Reading between the lines, Google is locked in a epic arms race for AI supremacy that is not so far paying off.”

  37. Assessment: UnresolvedSaid

    Gary Marcus

    “failed, multiple times, to include a full list of all US states”

  38. Assessment: UnresolvedSaid

    Gary Marcus

    “issued numerous corrections that were wrong, never acknowledging uncertainty until after its errors were called out.”

  39. Assessment: UnresolvedSaid

    Gary Marcus

    “failed, multiple times, to properly count to 50”

  40. Assessment: UnresolvedSaid

    Gary Marcus

    “reported that the letter h could be a vowel, at least when it appeared in the word Northwest”

  41. Assessment: UnresolvedSaid

    Gary Marcus

    “We can be sure that DeepResearch will get heavily used.”

  42. Assessment: UnresolvedSaid

    Gary Marcus

    “Deep Research, because it works faster and expands the reach of what can be automatically generated, is going to make that problem worse.”

  43. Assessment: UnresolvedSaid

    Gary Marcus

    “Unfortunately, virtually everything that Deep Research produces will pass a LGTM test ( “it ‘looks good to me’”); people will assume it’s legit, even when it is isn’t.”

  44. Assessment: SupportedSaid

    Gary Marcus

    “And that’s just not realistic given the current state of AI.”

    What happened: The launch disclosure identifies source-discrimination and uncertainty-communication weaknesses directly relevant to dependable research. These support the ordinary reading that the output required substantive checking. (OpenAI, )

  45. Assessment: UnresolvedSaid

    Gary Marcus

    “The output of DeepResearch will inevitably feed further models; we will wind up with more of this:”

  46. Assessment: UnresolvedSaid

    Gary Marcus

    “Few people will fact-check its output carefully, since it will all superficially look ok.”

  47. Assessment: UnresolvedSaid

    Gary Marcus

    “Scammers will use it, for example, to write “medical reports” that are half-baked (in order to sell ads on the webistes that report such “research”).”

  48. Assessment: UnresolvedSaid

    Gary Marcus

    “Because Deep Research lowers the effort, making it trivial to write legit-sounding articles, paper mill factories (there is already such a thing, designed to game citation counts, “an illegitimate, yet lucrative business“ that is growing) will massively ramp up production, choking the peer review process and saturating the market with hard-to-find, hard-to-rectify errors.”

  49. Assessment: UnresolvedSaid

    Gary Marcus

    “And it’s not just the models that will be infected, but the scientific literature itself.”

  50. Assessment: SupportedSaid

    Gary Marcus

    “The basic conjecture then — which has already been confirmed to some degree – was that LLMs would be used to write garbage content undermining the value of the internet itself.”

    What happened: NewsGuard traced a viral false psychiatrist story to a site it classified as AI-generated and documented subsequent dissemination. This is a concrete information-quality harm, although its attribution of this particular rewrite to AI is qualified. (NewsGuard, )

  51. Assessment: SupportedSaid

    Gary Marcus

    “Sam Altman just released a fancier remake Deep Research, which, like Meta’s predecessor, can write science-sounding articles on demand, on any topic of your choice.”

    What happened: The launch documentation describes user prompts producing comprehensive, cited reports across science, finance, policy, engineering, and consumer topics. This establishes the broad report-generation capability in the comparison. (OpenAI, )

  52. Assessment: UnresolvedSaid

    Gary Marcus

    “When — not if — Deep Research begins to flood the medical zone with shite, we are all in trouble.”

  53. Assessment: SupportedSaid

    Gary Marcus

    “The real lesson of DeepSeek is that we can still make vast improvements in efficency.”

    What happened: The report documents the training resources of the December V3 model and compares its performance with leading models. Its resource total covers the official training stages and excludes earlier research and ablation experiments; the comparative magnitude is supplied by e2. (DeepSeek-AI / arXiv, )

  54. Assessment: SupportedSaid

    Ed Zitron

    “DeepSeek's models don't require massive new data centers (they run on the GPUs currently used to run services like ChatGPT, and can even work on more austere hardware), nor do they require an endless supply of bigger, faster NVIDIA GPUs every year to progress.”

    What happened: The report identifies the hardware actually used and describes training-efficiency improvements and deployment on H800s. It supports the existing-hardware route, although it is a company report and does not document the construction history of the facility. (DeepSeek-AI, )

  55. Assessment: UnresolvedSaid

    Ed Zitron

    “Right now, I do not believe that there are companies that are stymied by the pricing that OpenAI and their ilk offer, nor do I think there are many companies or use cases that don’t exist because Large Language Models are too expensive.”

  56. Assessment: UnresolvedSaid

    Ed Zitron

    “He ended with “look forward to bringing you all AGI and beyond” — something which, I add, has always been close on the horizon in Altman’s world, although curiously has yet to materialize, or even come close to materializing.”

  57. Assessment: UnresolvedSaid

    Ed Zitron

    “OpenAI only makes 27% of its revenue from selling access to its models — around a billion dollars in annual recurring revenue — with the rest ($2.7 billion or so) coming from subscriptions to ChatGPT.”

  58. Assessment: UnresolvedSaid

    Ed Zitron

    “There really isn’t anything special about any of these companies anymore — they have no moat, their infrastructural advantage is moot, and their hordes of talent irrelevant.”

  59. Assessment: UnresolvedSaid

    Ed Zitron

    “We also know that OpenAI’s training and inference costs in 2024 were around $7 billion, meaning that either refining current models or building new ones is quite costly.”

  60. Assessment: UnresolvedSaid

    Ed Zitron

    “Once these models are hosted elsewhere, and once DeepSeek's methods (which I'll get to shortly) are recreated (which won't take long), I believe we'll see that these prices are indicative of how cheap these models are to run.”

  61. Assessment: UnresolvedSaid

    Ed Zitron

    “OpenAI and Anthropic no longer have moats to raise capital with.”

  62. Assessment: UnresolvedSaid

    Ed Zitron

    “This isn’t a particularly exciting pitch to investors or the public markets, because there’s no product, innovation or business model to point to, and if they’d actually try and productize it and turn it into a business, it’s quite obvious at this point that there really isn’t a multi-trillion dollar industry for generative AI.”

  63. Assessment: SupportedSaid

    Ed Zitron

    “One thing we do know is that OpenAI and Anthropic will now have to either drop the price of accessing their models, and potentially even the cost of their subscriptions.”

    What happened: The announcement says the reduction is in effect and applies to the same o3 model. This establishes OpenAI's part of the prediction. (OpenAI, )

  64. Assessment: UnresolvedSaid

    Ed Zitron

    “As a result, I don’t really see anything changing, beyond the eventual collapse of the API market for companies like Anthropic and OpenAI.”

  65. Assessment: UnresolvedSaid

    Ed Zitron

    “After all, OpenAI loses money on every transaction — even its $200-a-month "ChatGPT Pro" subscription.”

  66. Assessment: UnresolvedSaid

    Ed Zitron

    “While NVIDIA will find other ways to make money — Jensen Huang always does — it's going to be a hard sell for any hyperscaler to justify spending billions more on GPUs to markets that now know that near-identical models can be built for a fraction of the cost with older hardware.”

  67. Assessment: UnresolvedSaid

    Ed Zitron

    “As I've written about many, many, many times, the Large Language Models run by companies like OpenAI, Anthropic, Google and Meta are unprofitable and unsustainable, and the transformer-based architecture they run on has peaked.”

  68. Assessment: UnresolvedSaid

    Ed Zitron

    “As I've written about many, many, many times, the Large Language Models run by companies like OpenAI, Anthropic, Google and Meta are unprofitable and unsustainable, and the transformer-based architecture they run on has peaked.”

  69. Assessment: UnresolvedSaid

    Ed Zitron

    “In any case, the massive expense of running generative models hasn’t been the limiter on their deployment or success — you can blame that on the fact that they, as a piece of technology, are neither artificial intelligence nor capable of providing the kind of meaningful outcomes that would make them the next Smartphone.”

  70. Assessment: UnresolvedSaid

    Ed Zitron

    “And, as I noted at the time, it was not particularly reliable, failing to accurately count the number of times the letter ‘r’ appeared in the word “strawberry.””

  71. Assessment: UnresolvedSaid

    Ed Zitron

    ““AGI” isn’t coming.”

  72. Assessment: MixedSaid

    Ed Zitron

    “The launch of its "Operator" "agent" was a joke — a barely-functional product that is allegedly meant to control your computer and take distinct actions, but doesn't seem to work.”

    What held: The tester reports five failures involving web research and online tools. These show real launch limitations, but the small, relatively demanding task set is not a representative consumer success-rate estimate. (Zengyi Qin / Hugging Face Community, )

    What did not: Newton describes successful browser research producing walking-tour links and a completed lesson-plan task. His same review documents substantial shopping friction, so these successes limit the broad dismissal without establishing reliable autonomy. (Platformer / Casey Newton, )

  73. Assessment: UnresolvedSaid

    Ed Zitron

    “I do not believe that the companies making foundation models (such as OpenAI and Anthropic) have been incentivized to do more with less, and because their chummy relationships with hyperscalers were focused almost entirely on "make the biggest, most hugest models possible, using the biggest, most hugest chips," and because the absence of profitability didn’t stop them from raising more money, efficiency was never a major problem for them.”

  74. Assessment: ContradictedSaid

    Ed Zitron

    “Large Language Models (and reasoning models) are niche.”

    What happened: The November survey estimated broad public participation, with search, explanation and writing leading uses. Material qualifications in this same source are possible online-panel selection bias and light usage: its conservative weekly-use estimate was about 29%, and most adults did not use LLMs in a typical week. (Rethink Priorities, )

  75. Assessment: UnresolvedSaid

    Ed Zitron

    “And if OpenAI cuts its prices to compete with DeepSeek, its losses will only deepen.”

  76. Assessment: UnresolvedSaid

    Ed Zitron

    “After all, OpenAI lost $5 billion (after $3.7 billion in revenue, too!), and Anthropic just under $3 billion in 2024.”

  77. Assessment: UnresolvedSaid

    Ed Zitron

    “And this, in turn, will make any investor think twice about writing the company a blank check — which, as I’ve said ad nauseum, is potentially fatal, as OpenAI needs to continually raise more money than any startup ever has in history, and it has no path to breaking even.”

  78. Assessment: SupportedSaid

    Ed Zitron

    “Whether or not someone chooses to pay DeepSeek is largely irrelevant — someone else will take what it’s created and build their own, or people will start running their own DeepSeek instances renting GPUs from one of the various cloud computing firms.”

    What happened: Skywork records the release of its final OR1 models on May 13 and explicitly identifies DeepSeek's distilled models as their starting points. This is an actual third-party derivative release after the forecast. (Skywork AI, )

  79. Assessment: MixedSaid

    Ed Zitron

    “They're running out of training data, and the actual capabilities of these models were peaking as far back as March 2024.”

    What held: The estimate supports an approaching constraint on usable human-generated public text under continuing scaling trends. It does not show that data was already exhausted, and the discussion identifies other data sources and ways progress could continue. (Epoch AI, )

    What did not: Using its baseline testing framework, ARC Prize reports public-evaluation scores of 21.2% for o1-preview and 9% for GPT-4o. This is post-March capability progress, although the evaluator also reports substantially greater inference time. (ARC Prize, )

  80. Assessment: ContradictedSaid

    Ed Zitron

    “The current models developed by both the hyperscalers (Gemini, Llama, et. al) and multi-billion-dollar "startups" like OpenAI and Anthropic are horribly inefficient, I had just made the mistake of assuming that they'd actually tried to make them more efficient.”

    What happened: Meta describes concrete changes to parallelization, reliability, storage and training infrastructure, alongside inference optimizations. This is substantive efficiency engineering, although the improvement magnitude is company-reported. (Meta, )

  81. Assessment: SupportedSaid

    Gary Marcus

    “DeepSeek r1 is not smarter than earlier models, just trained more cheaply”

    What happened: The release report characterizes R1 as comparable to o1 overall. Its detailed results also limit that conclusion: R1 slightly exceeds o1 on AIME and exceeds several older models, while trailing o1 on several knowledge benchmarks. (DeepSeek-AI / arXiv, )

  82. Assessment: UnresolvedSaid

    Gary Marcus

    “DeepSeek r1 is not smarter than earlier models, just trained more cheaply”

  83. Assessment: UnresolvedSaid

    Gary Marcus

    “It is still expensive to operate ("inference"), especially if you want to make it "think" longer like o3.”

  84. Assessment: SupportedSaid

    Gary Marcus

    “It doesn't solve hallucinations or problems with reliability.”

    What happened: DeepSeek reports prompt sensitivity in the released R1 model. This establishes a continuing reliability limitation, though it does not itself measure hallucinations. (DeepSeek-AI / arXiv, )

  85. Assessment: UnresolvedSaid

    Gary Marcus

    “The biggest threat isn't to Nvidia (people will still need GPUs, albeit fewer higher end ones), but rather to OpenAI and Anthropic (because price wars will undercut their hopes of making a profit).”

  86. Assessment: UnresolvedSaid

    Gary Marcus

    “The biggest threat isn't to Nvidia (people will still need GPUs, albeit fewer higher end ones), but rather to OpenAI and Anthropic (because price wars will undercut their hopes of making a profit).”

  87. Assessment: UnresolvedSaid

    Gary Marcus

    “The biggest threat isn't to Nvidia (people will still need GPUs, albeit fewer higher end ones), but rather to OpenAI and Anthropic (because price wars will undercut their hopes of making a profit).”

  88. Assessment: SupportedSaid

    Gary Marcus

    “This morning, Nvidia is down more than 10% in pretrading.”

    What happened: The report explicitly places these losses in pre-market trading that morning. Its approximately 12% decline exceeds the claim's 10% threshold. (Associated Press, syndicated by Spectrum News NY1, )

  89. Assessment: UnresolvedSaid

    Gary Marcus

    “We accidentally upped their technical game.”

  90. Assessment: SupportedSaid

    Gary Marcus

    “None of this means however that China won the AI race or even took the lead. American companies will incorporate these new results and continue to produce new results of their own.”

    What happened: NVIDIA explicitly developed a model from an R1-distilled checkpoint. Its report describes additional reinforcement-learning experiments and improved mathematics and coding results, establishing adoption and a further technical contribution. (NVIDIA Research, )

  91. Assessment: UnresolvedSaid

    Gary Marcus

    “Meanwhile, revenue (order of $5B) has been modest, compared to outlays that have been much greater; no actual killer app (relative to the costs) has emerged.”

  92. Assessment: UnresolvedSaid

    Gary Marcus

    “In late December, a company called DeepSeek, apparently initially built for quantitative trading rather than LLMs, produced a nearly state-of-the-art model that required only roughly 1/50th of the training costs of previous models, — instantly putting them in the big leagues with American companies like OpenAI, Google, and Anthropic, both in terms of performance and innovation.”

  93. Assessment: SupportedSaid

    Gary Marcus

    “On LLMs China is already catching up.”

    What happened: The preceding sentence gives end-2023 U.S.–China gaps of 17.5, 13.5, 24.3 and 31.6 percentage points on MMLU, MMMU, MATH and HumanEval. All four narrowed by end-2024, including the language, mathematics and coding measures directly relevant to this claim. (Stanford Institute for Human-Centered Artificial Intelligence, )

  94. Assessment: SupportedSaid

    Gary Marcus

    “Today he walked it back.”

    What happened: Shows that on 20 January 2025 Altman publicly said OpenAI had not built AGI and would not deploy it next month, which plays down the closeness he had suggested. (Decrypt, )

  95. Assessment: UnresolvedSaid

    Ed Zitron

    “Suggesting one is "taking detailed notes on bloggers" is an attempt to intimidate people that are seriously evaluating the fact that OpenAI burns $5 billion a year and has no path to profitability.”

  96. Assessment: UnresolvedSaid

    Ed Zitron

    “I am 100% sure that these chatbots will return, because it's fairly obvious that Meta intends to fill your feeds with content either entirely generated or summarized by generative AI, be it with fake profiles or content itself.”

  97. Assessment: UnresolvedSaid

    Ed Zitron

    “Meta will push as much AI slop as it wants, both created by its generative models and their users, and massively ramp up content that riles up users with little regard for the consequences.”

  98. Assessment: UnresolvedSaid

    Ed Zitron

    “AI-generated slop already dominates the platform, and as discussed earlier, the quality of the platform has fallen into utter disrepair, mostly because Meta's only concern is keeping you on the platform to show you ads or content it’s been paid to show you, even if the reason you're seeing it is because you can't find the stuff you actually want to find.”

  99. Assessment: SupportedSaid

    Gary Marcus

    “And those demos won’t work all that reliably.”

    What happened: In the 2025 revision, the best agent finished only 30% of realistic workplace tasks on its own. That is far from reliable. (arXiv (Carnegie Mellon University et al.), )

  100. Assessment: UnresolvedSaid

    Gary Marcus

    “I do genuinely think we will all have our own AI agents, and companies will have armies of them.”