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

Claims checked against evidence by Ed Zitron, page 11 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 11 of 13: claims 1001 to 1100 of 1230, ordered by the date first said, newest first. To find a claim by its words, use search.

  1. Assessment: UnresolvedSaid

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

  2. Assessment: UnresolvedSaid

    “effectively meaning that every dollar of revenue is immediately eaten by the costs of acquiring it.”

  3. Assessment: UnresolvedSaid

    “And, fundamentally, I can find no compelling evidence that suggests that OpenAI will be able to sustain this growth. In fact, I can find no historical comparison, and believe that OpenAI's growth is already stumbling.”

  4. Assessment: UnresolvedSaid

    “And the fact these "annualized" figures have changed so little since June means that growth has slowed, because otherwise said numbers would have increased (as "annualized" is based on projections).”

  5. Assessment: MixedSaid

    “ChatGPT Plus has no real moat, little product differentiation (outside of its advanced voice mode, which Meta is already working on a competitor to), and increasing commoditization from other models, open source platforms, and even on-device models (like on Copilot+-powered PCs).”

    What held: The voice section announces spoken conversations with Meta AI across its messaging products. This supports the existence of a voice competitor, without establishing equal quality. (Meta, )

    What did not: Reasoning-model access was already part of Plus before the post. Together with e4, this establishes a capability difference beyond the voice exception. (OpenAI, )

  6. Assessment: UnresolvedSaid

    “Scenario 1 is obviously dangerous, offering a tool that costs a great deal of money to provide only a little value to customers for free, meaning that at best you'll have customers that lose you money every single time they interact with the product.”

  7. Assessment: UnresolvedSaid

    “These "agents" are branded to sound like intelligent lifeforms that can make intelligent decisions, but are really just trumped-up automations that require enterprise customers to invest time programming them.”

  8. Assessment: UnresolvedSaid

    “Every company promising "AI agents" is really just trying to rebrand the concept of chatbots and provide a front-end to let you connect them to data sources and give them guardrails so they don't scream curse words at your customers or write poetry about how bad the company is.”

  9. Assessment: UnresolvedSaid

    “The answer is simple: the tech industry's desperate attachment to artificial intelligence is largely fueled by the SaaS industry, because AI is the first meaningful new "thing" they've had to flog to customers in quite some time”

  10. Assessment: UnresolvedSaid

    “While these companies had costs before, generative AI is multitudes higher than regular cloud compute costs, meaning that any new revenue growth from this software will be burdened by leveraging an increasingly-expensive solution to a problem that most of them have trouble describing.”

  11. Assessment: UnresolvedSaid

    “These products almost always include some sort of half-hearted generative addition too — creating presentations or drafting emails or creating boilerplate content, all things that you can do for free with ChatGPT, Claude or whatever other Large Language Model you can find.”

  12. Assessment: UnresolvedSaid

    “It doesn't really matter that these agents are basically the same "chatbot-that-connects-to-large-database-of-information" thing we’ve seen a million times, but this time connected to a Large Language Model.”

  13. Assessment: ContradictedSaid

    “Almost every one of these companies is claiming that these systems "reduce drudgery" or "enhances productivity," yet there's no real explanation as to how it might do so or what the drudgery is that's being relieved, nor any unassailable metrics on how productivity improves.”

    What happened: This identifies both the work involved—writing replies—and the proposed mechanism—generating suggested responses and revisions. It directly contradicts the absence of a concrete explanation, without proving the size of any productivity gain. (ServiceNow, )

  14. Assessment: UnresolvedSaid

    “And the reason I've so agonizingly explained it is that I believe the current generative AI boom is almost entirely fueled by the hunger of the Software As A Service parasite for growth...and the slow collapse of growth in tech's favorite business model.”

  15. Assessment: UnresolvedSaid

    “While this sounds like a lot, it’s actually a staggering reversal from the previous 30% quarter-over-quarter growth and suggests that momentum has rapidly slowed.”

  16. Assessment: SupportedSaid

    “In essence, Amazon is benefitting from AI in much the same way NVIDIA is.”

    What happened: The account documents Slack using AWS instances, including GPU instances, to host models for its AI features. This directly establishes AWS's role as a computing supplier for another company's AI product. (Amazon Web Services, co-authored with Slack, )

  17. Assessment: UnresolvedSaid

    “As The Information reported a couple of weeks ago, Microsoft has categorically failed to turn Copilot for Microsoft 365 (a product line that makes at least $15 billion of revenue a year) into a meaningful revenue driver, with “between 0.1% and 1% of the 440 million existing users of Microsoft 365...paying for the new AI features" according to an equity analyst's estimation.”

  18. Assessment: UnresolvedSaid

    “And, more worryingly, they're building all of these tools on top of generative AI, which is extremely expensive and, as I've argued, unsustainable at the rates being charged.”

  19. Assessment: ContradictedSaid

    “That being said, we're yet to see this actually happen, outside of OpenAI's alleged $3.4 billion of yearly revenue (it still loses $5 billion a year), and generative AI's multi-billion dollar contribution to Amazon Web Services revenue over an indeterminate amount of time.”

    What happened: Microsoft publicly attributed eight percentage points of Azure growth to AI services before the post. The same call describes organizations using Azure's model services, making this a counterexample under the ordinary reading of customer AI integrations. (Microsoft, )

  20. Assessment: UnresolvedSaid

    “These are all signs that things are beginning to collapse.”

  21. Assessment: UnresolvedSaid

    “My concern is that I believe we’re in the midst of a subprime AI crisis, where thousands of companies have integrated generative AI at prices that are far from stable, and even further from profitable.”

  22. Assessment: UnresolvedSaid

    “In short, OpenAI is burning money, will only burn more money, and to continue burning more money it will have to raise money from investors that are signing a document that says "we may never make a profit."”

  23. Assessment: UnresolvedSaid

    “As I've explained in agonizing detail in the past, OpenAI will have to continue to raise more money than any startup has ever raised in history, in perpetuity, to survive.”

  24. Assessment: UnresolvedSaid

    “Given OpenAI’s existing profit-sharing deals with Microsoft — not to mention its deep, deep unprofitability — any such returns are, at the best, theoretical.”

  25. Assessment: UnresolvedSaid

    “At the beginning of August, Microsoft, Amazon, and Google all took a beating from the markets for their massive capital expenditures related to AI, and all three of them will face the wheel next quarter if they can't show a significant increase in revenue from the combined $150bn (if not more) that they've put into new data centers and NVIDIA GPUs.”

  26. Assessment: UnresolvedSaid

    “Even though Big Tech could keep funding this boom — after all, they’re pretty much entirely responsible for its existence — that won’t help the startups that won't be able to afford to run their companies, after getting used to discounted rates.”

  27. Assessment: UnresolvedSaid

    “At some point, the incredible, toxic burn-rate of generative AI is going to catch up with them, which in turn will lead to price increases, or companies releasing new products and features with wildly onerous rates — like the egregious $2-a-conversation rate for Salesforce’s “Agentforce” product — that will make even stalwart enterprise customers with budget to burn unable to justify the expense.”

  28. Assessment: UnresolvedSaid

    “This is because, despite there being a model that checks its work, the work-checking part of the model is still capable of hallucinations.”

  29. Assessment: ContradictedSaid

    “Nobody is trying to make these things more efficient, or at the very least nobody has succeeded in doing so.”

    What happened: The developers report serving the existing LLM traffic with half as many GPUs after changing the serving software. This is a concrete resource-efficiency improvement before publication. (vLLM project, UC Berkeley researchers, )

  30. Assessment: UnresolvedSaid

    “Putting aside terms like "performance" — as they are largely used as a means of generating things "accurately" or "faster" rather than being good at stuff — Large Language Models have effectively plateaued.”

  31. Assessment: UnresolvedSaid

    “These models are built by two companies that are deeply unprofitable (Anthropic is on course to lose $2.7 billion this year), and that have pricing designed to get more customers rather than make any kind of profit.”

  32. Assessment: SupportedSaid

    “I believe that, at the very least, Microsoft will begin reducing costs in other areas of its business as a means of helping sustain the AI boom.”

    What happened: Reporting based on an interview with Microsoft president Brad Smith links operating-cost pressure to the infrastructure investment discussed in the article, alongside implemented workforce cuts. Together with the Gaming memo, this supports AI investment as a contributing reason for reductions elsewhere. (GeekWire, )

  33. Assessment: UnresolvedSaid

    “If any of these lawsuits prevail, it would be calamitous for OpenAI and Anthropic, and even more so for Google and Meta, whose Gemini and Llama models use datasets including millions of artists' work, mostly because it's "virtually impossible" for AI models to forget training data, meaning that they'd need to be retrained from scratch, which would cost billions of dollars and dramatically reduce their efficacy at tasks they're already not particularly good at.”

  34. Assessment: ContradictedSaid

    “The prices are not decreasing, the software is not becoming more useful, and the "next generation" model that we’ve been hearing about since last November has turned out to be a dud.”

    What happened: OpenAI announced lower input and output token rates for the updated GPT-3.5 Turbo offering. This directly contradicts the broad claim that model prices were not decreasing. (OpenAI, )

  35. Assessment: UnresolvedSaid

    “People fear Sam Altman of OpenAI. He's grown big because people fear his existence and what he may say about them.”

  36. Assessment: UnresolvedSaid

    “This master plan only falls apart when the “thing” that needs to be trained using hardware that they monopolize doesn’t actually provide the business returns that they need to justify its existence.”

  37. Assessment: ContradictedSaid

    “No private startup can actually afford to build and scale a Large Language Model without the help of Google, Amazon or Microsoft — and thus they would, assuming they actually became useful, effectively control every single product involving this technology.”

    What happened: The paper documents DeepSeek-V2's 236-billion-parameter model, substantial pretraining, and deployed inference on H800 hardware. Together with the independently identified High-Flyer resources in e2 and e3, this supplies a counterexample already operating before the post. (DeepSeek-AI, arXiv, )

  38. Assessment: UnresolvedSaid

    “No private startup can actually afford to build and scale a Large Language Model without the help of Google, Amazon or Microsoft — and thus they would, assuming they actually became useful, effectively control every single product involving this technology.”

  39. Assessment: UnresolvedSaid

    “Microsoft, Google, Amazon and Meta also monopolize the infrastructure itself, both in the data centers they can build (or already own) and in their access to the specialized GPUs necessary to run these models.”

  40. Assessment: UnresolvedSaid

    “While this might seem like a competitive marketplace, because these models all require incredibly large amounts of cloud compute and cash to both train and maintain, most companies can’t really compete at scale.”

  41. Assessment: SupportedSaid

    “That $32.5 is now $195. If it wanted to have the same margins, it would need to make $105 in profit from each user. So, $300. $300 a user for a product it hasn’t even built yet.”

    What happened: Applying the independent margin definition to the supplied assumptions gives $195 cost, $300 revenue, and $105 profit per user annually. The dates identify when the hypothetical inputs and their mathematical implication were public; OpenStax supplies the definition, not an OpenAI financial measurement. (OpenStax, )

  42. Assessment: SupportedSaid

    “For reasons I’ve alluded to at the start of this article, investors are getting impatient with the AI hype boom — and, more specifically, with the giant tech companies that are bankrolling it.”

    What happened: Morgan explicitly connects impatience to large expenditures and investors wanting corresponding revenue. Reuters identifies his firm as a Microsoft shareholder; no separate primary investor communication was located. (Reuters, republished by AOL, )

  43. Assessment: SupportedSaid

    “In the event that Anthropic or OpenAI is able to raise, we’ll see the cycle continue another quarter, at which point something crazy needs to happen to make any of this worth it. If these companies can raise, they’ll keep the bubble inflated, though not forever.”

    What happened: OpenAI reported that it had raised new funding. This satisfies the stated antecedent. (OpenAI, )

  44. Assessment: UnresolvedSaid

    “There’s the obvious one — OpenAI or Anthropic shutting down — but I think that’s very unlikely based on their corporate structure. OpenAI or Anthropic “collapsing” will be more like the Character.ai deal — an attempt to hide the ugly mess where a big tech firm absorbs them.”

  45. Assessment: UnresolvedSaid

    “So far — and this is napkin math — I’d estimate that a total of $200 billion has been spent to get generative AI to this point, in infrastructure, in funding, in energy, in so many different meaningless ways, all to get us to the point that we have a tool that’s really good at generating things that aren’t as good as what a human could make.”

  46. Assessment: UnresolvedSaid

    “Any price increases by OpenAI or Anthropic: If they start needing to make more money, they could get desperate. This will be a sign their unit economics are no longer working out with their investors.”

  47. Assessment: UnresolvedSaid

    “Yet, generative AI doesn’t actually do these things.”

  48. Assessment: UnresolvedSaid

    “OpenAI may be able to raise money, but investors have no real path to liquidity, as OpenAI most assuredly won’t be able to go public, and in the process of investing will find out exactly how bad things are there.”

  49. Assessment: SupportedSaid

    “Regardless, this article has put it very plainly: OpenAI only gets access to the latest technology as fast as Microsoft permits.”

    What happened: The contemporary report establishes the exclusive cloud relationship underlying dependence on Microsoft for advanced compute. The specific primary-company passage could not be retrieved with read_page, so this report supplies the dated outcome evidence. (TechCrunch, )

  50. Assessment: UnresolvedSaid

    “To be clear, there is nothing wrong, on its face, with generative AI. It does interesting things with documents, and I fully believe there will be some low-fi version of generative AI that plugs along silently once the hyper scalers flee.”

  51. Assessment: ContradictedSaid

    “The promises of artificial intelligence will not be kept through the pursuit of generative AI, as generative AI is a dead-end technology that has peaked, one that costs too much — both financially and socially — to keep it going.”

    What happened: The benchmark operator reports verified scores of 37.6% for Opus 4.5 and 54% for a Gemini-based refinement, alongside disclosed methods and open-source solutions. These material advances contradict a categorical capability ceiling, while the report also identifies costs, narrow task coverage and possible overfitting. (ARC Prize Foundation, )

  52. Assessment: UnresolvedSaid

    “The two most prevalent Large Language Models (outside Meta’s Llama) — OpenAI’s GPT and Anthropic’s Claude — are effectively big tech’s welfare recipients, receiving billions in cloud credits to run their extremely-expensive models without having to build out their own infrastructure.”

  53. Assessment: UnresolvedSaid

    “Venture capital isn’t really what’s propping up generative AI — it’s Google or Microsoft. If either of them decide it’s time to slow down investment, the boom is done, as referenced above.”

  54. Assessment: SupportedSaid

    “We’re starting to see a shift — or, more accurately, a centralization — in the point of failure for AI.”

    What happened: The report identifies increasing integration and partnerships involving Google, Amazon, Microsoft, Meta, Apple and Nvidia, alongside developers' reliance on cloud computing. This supports the ordinary dependence reading, while the report also recognizes that partnerships can help independent developers and improve competition. (UK Competition and Markets Authority, )

  55. Assessment: MixedSaid

    “OpenAI has historically, based on reporting, failed to make its models more efficient, failing to deliver their more efficient "Arrakis" model to Microsoft in late 2023.”

    What held: The report describes cancellation of Arrakis after inadequate performance and says Microsoft had hoped it would reduce costs. It supports the specific setback, but does not establish a late-2023 delivery deadline. (The Decoder, )

    What did not: OpenAI explicitly reported achieved efficiency savings before the post. This contradicts a broad claim of failure to improve operating efficiency without inferring savings merely from lower API prices. (OpenAI, )

  56. Assessment: UnresolvedSaid

    “And because OpenAI (and the competition) are so deep in the hole with transformer-based models, I believe they will continue to drive billions into them, burning money on training them using data that may or may not have been legally acquired”

  57. Assessment: ContradictedSaid

    “Along with $3.5 billion from Saudi Arabia’s Public Investment fund and over $8 billion from Softbank, the latter a secondary market sale, it also raised (equity and debt financing respectively) from Goldman Sachs and Morgan Stanley — two parties I do not believe are going to be willing to subsidize generative AI.”

    What happened: Both named banks participated in OpenAI's executed credit facility, demonstrating willingness to finance the business. The report's commercial interest terms do not establish a concessionary subsidy. (CNBC, )

  58. Assessment: UnresolvedSaid

    “It would require a way of expanding what OpenAI can sell to address the entirety of corporate America, which would require use cases that I do not believe generative AI is capable of meeting, like automating chunks of the economy rather than bankrupting freelance designers and copy-editors.”

  59. Assessment: UnresolvedSaid

    “As a result, expectations are higher of what future generations of GPT might do, making it inevitable that the company will disappoint investors and customers.”

  60. Assessment: UnresolvedSaid

    “here’s the crux of the matter: generative AI is a product with no mass-market utility - at least on the scale of truly revolutionary movements like the original cloud computing and smartphone booms - and it’s one that costs an eye-watering amount to build and run.”

  61. Assessment: SupportedSaid

    “Pre-AGI in this case refers to anything OpenAI has ever developed, as it has yet to develop AGI and has yet to get past the initial "chatbot" stage of its own 5-level system of evaluating artificial intelligence.”

    What happened: The report places OpenAI just below level 2. Approaching that level does not establish that it had passed the initial chatbot stage. (The Decoder, )

  62. Assessment: UnresolvedSaid

    “I am simply saying that for OpenAI to grow to the $10+ billion revenue a year it needs to survive, it would need to replace entire chunks of the labor force.”

  63. Assessment: UnresolvedSaid

    “Raise either multiple rounds, or the largest funding round ever raised by any company, and then have to keep doing so in perpetuity.”

  64. Assessment: SupportedSaid

    “And OpenAI must grow, because $3.5 billion to $4.5 billion a year in revenue is simply not enough to keep this company going.”

    What happened: The article reports estimated 2024 operating costs of $8.5 billion against estimated revenue of $3.5–4.5 billion. Both ends of the revenue range fall below the estimated costs, supporting the operating-coverage reading. (The Decoder, )

  65. Assessment: UnresolvedSaid

    “I see no signs that the transformer-based architecture can do significantly more than it currently does.”

  66. Assessment: UnresolvedSaid

    “If it succeeds — which I add would require potentially entirely new branches of psychology and mathematics to do so, as we humans barely understand our brains — that would be a huge technological achievement that still wouldn't turn things around.”

  67. Assessment: UnresolvedSaid

    “though I believe OpenAI's continued existence is necessary to keep companies interested/invested in the industry at all.”

  68. Assessment: UnresolvedSaid

    “Conversely, OpenAI has devoured no monopoly, and the product category it created — the only one it is really part of — is one entirely subsidized by venture capital and Microsoft.”

  69. Assessment: ContradictedSaid

    “Even though it doesn't really automate anything.”

    What happened: Intercom reported that deployed Fin systems resolved customer questions directly. The existence of successful bounded tasks contradicts 'anything', although the reported percentage requires qualification. (Intercom, )

  70. Assessment: ContradictedSaid

    “Here's the thing, I get that that might be what they're pushing toward, but generative AI isn't even automation at this point.”

    What happened: Intercom reported actual customer deployments resolving queries directly through Fin. This supports the existence of bounded task automation, although the reported percentage is not an independently verified success rate. (Intercom, )

  71. Assessment: UnresolvedSaid

    “Because right now the thing I've been on about with generative AI is on top of not being super useful, it's so unprofitable.”

  72. Assessment: UnresolvedSaid

    “As these organizations push their engineers harder, said engineers will turn to AI-generated code, poisoning codebases with insecure and buggy code as companies shed staff to keep up with Wall Street’s demands in ways that I’m not sure people are capable of understanding.”

  73. Assessment: UnresolvedSaid

    “they're allowed to get away from the very obvious problem: that Large Language Models are peaking, will not solve the kind of complex problems that actually matter, and OpenAI (and other LLM companies) are being allowed to accumulate money and power in a way that's allowed them to do actual damage in broad daylight.”

  74. Assessment: UnresolvedSaid

    “The media seems nigh-on incapable of accepting that generative AI is a big, stupid, costly and environmentally-destructive bubble, to the point that they'll happily accept marketing slop and vague platitudes about how big something that's already here will be in the future based on a remarkable lack of proof.”

  75. Assessment: UnresolvedSaid

    “The problem with stories like this is that they suggest that OpenAI is working on Artificial General Intelligence, or something that left unchecked could somehow destroy society, as opposed to what it’s actually working on — increasingly faster iterations of a Large Language Model that's absolutely not going to do that.”

  76. Assessment: UnresolvedSaid

    “The problem is that these pieces aren’t about breakthroughs — they’re marketing collateral dressed up as technological progress, superficially-exciting yet hollow on the inside, which almost feels a little too on the nose.”

  77. Assessment: UnresolvedSaid

    “I guarantee you Anthropic, Google and Meta are all working on something doing exactly the same thing, because what they are describing is something that limits every single Large Language Model due to the very nature of how they’re built, something Reuters even acknowledges by adding that “this is something that has eluded AI models to date.””

  78. Assessment: ContradictedSaid

    “To reach level 3 (whatever “Agents, systems that can take actions” means) you’d need a level of sentience dependent on entirely new forms of mathematics.”

    What happened: The account describes Claude Sonnet 3.7 using search, notes, Slack, and price-setting tools over a month, with humans providing physical services. I infer from this documented implementation that sustained action-taking did not require the asserted new mathematical foundations. (Anthropic, )

  79. Assessment: UnresolvedSaid

    “While there's tons of evidence that they're impossible to fix, surely a safety-minded culture would be one that sought to fix or mitigate the most obvious and dangerous problem of Large Language Models.”

  80. Assessment: UnresolvedSaid

    “In essence, on top of generative AI not having any killer apps, not meaningfully increasing productivity or GDP, not generating any revenue, not creating new jobs or massively changing existing industries, it also requires America to totally rebuild its power grid, which Janous regrettably adds the US has kind of forgotten how to do.”

  81. Assessment: UnresolvedSaid

    “I see no situation where OpenAI and Anthropic continue to iterate on Large Language Models in perpetuity, as at some point Microsoft, Amazon and Google decide (or are forced to decide) that cloud compute welfare isn't a business model.”

  82. Assessment: ContradictedSaid

    “And yes, that sound you hear is the slow deflation of the bubble I've been warning you about since March.”

    What happened: Their account of PitchBook data reports record generative-AI investment in Q2. The same article describes weaker US seed activity and cooler enthusiasm, so the contradiction concerns broad financial contraction. (Aspiring for Intelligence — Sabrina Albert and Vivek Ramaswami, )

  83. Assessment: UnresolvedSaid

    “Basically, there's going to be a moment that spooks a venture capital firm into pushing one of its startups to sell, or the sudden, unexpected yet very obvious collapse of a major player.”

  84. Assessment: UnresolvedSaid

    “All of this excitement, every second of breathless hype has been built on this idea that the artificial intelligence industry – led by generative AI – will somehow revolutionize everything from robotics to the supply chain, despite the fact that generative AI is not actually going to solve these problems because it isn't built to do so.”

  85. Assessment: UnresolvedSaid

    “The reason I'm suddenly bringing up superintelligences — or AGI (artificial general intelligence) — is because throughout every defense of generative AI is a deliberate attempt to get around the problem that generative AI doesn't really automate many tasks.”

  86. Assessment: UnresolvedSaid

    “Generative AI, as I said back in March, is peaking, if it hasn't already peaked.”

  87. Assessment: ContradictedSaid

    “It cannot do much more than it is currently doing, other than doing more of it faster with some new inputs.”

    What happened: The public evaluation reported 97.8% success on basic Blocksworld and 52.8% on its obfuscated version, where earlier tested LLMs remained below 5%. These are substantive planning gains; the event date denotes publication of the demonstration, since exact test-run dates are not supplied. (Valmeekam, Stechly, Gundawar and Kambhampati / arXiv, )

  88. Assessment: UnresolvedSaid

    “The jobs market isn't going to change because of generative AI, because generative AI can't actually do many jobs, and it's mediocre at the few things that it's capable of doing.”

  89. Assessment: UnresolvedSaid

    “Or, put another way, each additional step becomes increasingly (and exponentially) more expensive to take.”

  90. Assessment: SupportedSaid

    “For Goldman to suddenly turn on the AI movement suggests that it’s extremely anxious about the future of generative AI, with almost everybody agreeing on one core point: that the longer this tech takes to make people money, the more money it's going to need to make.”

    What happened: The investor's model illustrates the recurring recovery burden from continued AI infrastructure spending. Its numerical estimate is not treated as audited industry profit or as a figure claimed by Zitron. (Sequoia Capital — David Cahn, )

  91. Assessment: UnresolvedSaid

    “It's been eighteen months since this bubble inflated, and since then very little has actually happened involving technology doing new stuff, just an iterative exploration of the very clear limits of what an AI model that generates answers can produce, with the answer being "something that is, at times, sort of good."”

  92. Assessment: UnresolvedSaid

    “Yet, I have a counterpoint: no it doesn't.”

  93. Assessment: ContradictedSaid

    “It isn’t getting much more efficient.”

    What happened: The paper reports stronger model performance with 93.3% less attention-cache memory than DeepSeek 67B. Its deployment tests report 5.76 times the generation throughput on an eight-H800 node using the earlier service's workload distribution, demonstrating substantial resource-efficiency gains. (DeepSeek-AI / arXiv, )

  94. Assessment: UnresolvedSaid

    “While it's a useful efficiency tool, said efficiency is based off of a technology that is extremely expensive, and I believe that at some point AI companies like Anthropic and OpenAI will have to increase prices — or begin to collapse under the weight of a technology that has no path to profitability.”

  95. Assessment: MixedSaid

    “Behind the scenes, generative AI isn’t moving the needle”

    What held: Named executives described inaccurate answers and substantial preparation work, with Juniper not ready for production deployment. This supports the narrower contention that some enterprises were struggling to obtain useful results. (The Wall Street Journal, reproduced by AI Incident Database, )

    What did not: Klarna reported a material reduction in repeat inquiries from an operating generative AI assistant. This is a realized operational benefit, although company-reported and not a measure of net financial return. (Klarna, )

  96. Assessment: UnresolvedSaid

    “even if their models are all extremely similar, as they’re all trained on the same quickly-dwindling amount of training data.”

  97. Assessment: UnresolvedSaid

    “They don’t care that the output is mediocre, that the product is unprofitable, that there is a quickly-approaching wall that generative AI can’t leap over as it runs out of training data, or that the transformer-based architecture of Large Language Models has hard limitations that are impossible to overcome.”

  98. Assessment: UnresolvedSaid

    “The deep poison of the Shareholder Supremacy — the nihilistic intention of contorting companies to please analysts and investors — is one that will uniquely punish the tech industry, just like it did to General Electric.”

  99. Assessment: UnresolvedSaid

    “Google, Microsoft and Meta have bet their futures on generative AI, cramming it into their products with little regard for utility, all to show the markets that they’re futuristic rocket ship growth companies”

  100. Assessment: ContradictedSaid

    “Generative AI is hitting a wall, and any future improvements will be marginal, and cost billions to achieve.”

    What happened: ARC Prize reports 75.7% and 87.5% for the o3 preview configurations and contrasts these with GPT-4o's 5% performance in 2024. The evaluator describes a substantial advance, while disclosing benchmark-specific training and increased inference computation. (ARC Prize, )