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

Checked claims by Ed Zitron, page 14 of 15

Deadlines are judged as of 2026-11-01. About Ed Zitron and the sources covered.

Checked claims by Ed Zitron (1,433)

Claims we checked against evidence.

Claims 1,301 to 1,400 of 1,433, newest first. Page 14 of 15.

  1. Assessment: UnresolvedSaid

    “They both went through yCombinator, and both of them have sold products that don't do what they said it would based on what it might do in the future.”

  2. Assessment: UnresolvedSaid

    “The Rot-Com bubble will inevitably pop, but until it does, billions will be plowed into building hardware and software to inflate the Rot-Com bubble in the hopes that the growth-at-all-costs party will never end.”

  3. Assessment: SupportedSaid

    “Despite being an inherently AI-powered feature, it can’t distinguish between ordinary computer usage and genuinely sensitive information — like passwords, health information, or trade secrets that must be protected at all costs.”

    What happened: The contemporary reproduction of Microsoft's own FAQ explicitly says that the preview did not hide sensitive information. The surrounding FAQ lists manual and application-based exclusions, which do not amount to automatic sensitivity recognition. (Gibson Research Corporation, Security Now!, )

  4. Assessment: UnresolvedSaid

    “Generative AI is not going to change the world, and while artificial intelligence itself might, it isn't going to do so in the hands of Sam Altman and OpenAI, or Anthropic.”

  5. Assessment: UnresolvedSaid

    “The answer is simple: it isn't coming.”

  6. Assessment: UnresolvedSaid

    “I believe, as crazy as it sounds, that this strategy will lead to the contraction and eventual death of Facebook, and anticipate that as things get difficult for the company, Instagram will begin to decay in a similar way.”

  7. Assessment: UnresolvedSaid

    “Zuckerberg has blown over $40 billion dollars on the metaverse so far, and seems to be intent on doing the same with generative AI, sinking further billions into expensive graphics processing units and other assorted hardware to spin up features that nobody asked for or wanted.”

  8. Assessment: UnresolvedSaid

    “Yet by incentivizing as much content and as many interactions as possible rather than curating the experience in any way is slowly killing Facebook.”

  9. Assessment: SupportedSaid

    “By adding a generative AI assistant, Meta gives users more things to play with, more reasons to interact with the platform, and more ways to generate content to put on the platform, which in turn creates more reasons for others to interact with it.”

    What happened: Meta documents an assistant that answers questions and generates images, plus a Messenger and Instagram feature allowing a friend to modify a generated image in the group chat. That directly supplies the additional interaction opportunities described. (Meta, )

  10. Assessment: SupportedSaid

    “Embrace that which actually delivers value to your lives, not the theoretical promises of any number of monotonous billionaires that want to turn every website into fuel for a machine that continually gets things wrong.”

    What happened: The dated snapshot reports hallucination rates of 2.5% for GPT 4 Turbo, 4.6% for Gemini 1.5 Pro and 7.4% for Claude 3 Opus on document summarization. These recurring errors support the ordinary reading, although the automated scoring and restricted task do not establish a universal error rate. (Vectara, )

  11. Assessment: UnresolvedSaid

    “Just so we are abundantly, painfully, earnestly clear here, OpenAI lied to the media multiple times.”

  12. Assessment: UnresolvedSaid

    “AI agents are error-prone, and the likelihood of failure in an automated task like moving a calendar invite or ordering an Uber is exponentially higher and less tolerable in a consumer setting.”

  13. Assessment: UnresolvedSaid

    “and he still can’t get past any of these very obvious problems, partly because he isn’t technical and thus can’t really solve any of the problems himself, and partly because the problems he’s facing are burdened by the laws of mathematics and physics.”

  14. Assessment: SupportedSaid

    “Yet these tiny little problems all lead to one overwhelming issue: that Sora isn’t so much a tool to make movies as it is a slot machine that spits out footage that may or may not be of any use at all.”

    What happened: Cederberg's first-hand account describes inconsistent character details, unwanted features and extensive selection and correction. The same interview also documents a completed film and useful creative applications, so it supports unpredictability rather than total uselessness. (fxguide, )

  15. Assessment: UnresolvedSaid

    “To make Sora kind of useful, OpenAI will have to find a way to dramatically increase the precision of prompts, reduce hallucinations to almost nothing, and vastly increase processing power across the board.”

  16. Assessment: SupportedSaid

    “Like I said, useful. But it isn’t AI.”

    What happened: The versioned README describes browser automation and demonstrates explicit navigation, clicking and script execution. The assessment infers from that documented mechanism that these scripted actions are not themselves an AI model. (Microsoft, Playwright project, )

  17. Assessment: UnresolvedSaid

    “Generative AI is extremely expensive because even the simplest prompts require GPT-4 to run highly-complex mathematical equations on GPUs that cost $10,000 apiece.”

  18. Assessment: ContradictedSaid

    “Since Rabbit is acting as a third-party, and doesn’t use a standard like Oauth or SAML, it needs to keep user credentials in their original form. This is just insanely dangerous.”

    What happened: The report describes inspection during April 29–May 10; the event date marks that testing window's end. It found that service-issued session cookies, rather than entered usernames and passwords, were collected for storage. (Obscurity Labs, )

  19. Assessment: SupportedSaid

    “Worse still, it lacks very, very basic features that you’d expect in a device from a decade ago — timers, reminders, calendars, photos, access to your emails, things that one would assume were obvious for an “AI assistant,””

    What happened: The launch review identifies four of the five omissions. Its May 1 update reports other improvements without changing this feature list. (Tom's Guide, )

  20. Assessment: UnresolvedSaid

    “Sora’s attempts to replace filmmakers are dead on arrival because these are impractical and ineffective solutions to problems that nobody complained about other than Hollywood executives.”

  21. Assessment: MixedSaid

    “OpenAI failed to make a more efficient model for Microsoft late last year, and while GPT-5 is meant to be “materially better,” it isn’t obvious what “better” means when GPT-4 performs worse at some tasks than its predecessor.”

    What held: This supports the substance of the reported efficiency-project failure. It is secondary reporting, not a primary project record or independent corroboration of The Information's sources. (The Indian Express, )

    What did not: The account places cancellation in the middle of 2023, contradicting the literal late-2023 timing while corroborating that a failure occurred. The year-only event date avoids inventing a precise cancellation month. (Synthedia, )

  22. Assessment: SupportedSaid

    “Putting aside the remarkable security concerns, the “Large Action Model” also looks like barely-functioning dogshit.”

    What happened: This is the review's cumulative result as of publication: neither ride booking nor food ordering had succeeded. The surrounding passage describes repeated attempts and errors. (The Verge, David Pierce, )

  23. Assessment: UnresolvedSaid

    “If, say, Uber changes the layout of its website, or changes the flow of certain actions (like booking a cab, or changing the pickup location), it would effectively break that entire part of the service.”

  24. Assessment: SupportedSaid

    “Funnily enough, this is exactly what the R1 is — an Android app shoved into a bright orange box.”

    What happened: The investigator identifies three APKs on MediaTek's Android software and describes extracting the firmware and running the launcher on phones. This supports the ordinary architectural reading, while showing that 'an app' simplifies a small collection of applications. (Rithwik Jayasimha, reproduced by Thread Reader, )

  25. Assessment: SupportedSaid

    “To digress, none of this shit actually works if you’re making an actual movie.”

    What happened: The interview documents missing control over continuity between separately generated shots. It supports the specific reliability concern, but does not establish that every movie use was impractical. (fxguide, )

  26. Assessment: ContradictedSaid

    “Generative AI hallucinates because it doesn’t have a consciousness or any ability to learn or know anything.”

    What happened: The experiments demonstrate usable abilities acquired through training and adaptation to examples. They contradict an absolute absence of learning under the operational reading, without establishing consciousness. (arXiv; Tom B. Brown and colleagues, )

  27. Assessment: UnresolvedSaid

    “While Rabbit has tens of millions of dollars of venture funding, Rabbit’s business is about as functional as the R1, and I would bet money that it finds a way to try to charge its 100,000 or so customers some sort of subscription fee to stay afloat, just after the single most conspicuous tech reviewer called it “barely reviewable.””

  28. Assessment: MixedSaid

    “Every single story about artificial intelligence seems to begin with “well, AI can’t do much yet, but…” and then lean into sloppy science fiction that masks that these “autonomous agents” are effectively chatbots that can answer questions based on a document, tech that’s existed for a decade or more.”

    What held: The authors describe Watson's February 2011 system deriving answers using unstructured text and matching evidence from passages. This establishes the document-question-answering capability more than a decade before the post, without establishing equivalence to modern agent architectures. (Association for Logic Programming; Adam Lally and Paul Fodor, )

    What did not: WebVoyager observes websites, chooses actions and executes them through a browser, with an evaluation reporting 55.7% task success in this original version. Those interactive capabilities exceed answering questions from a supplied document, even though reliability was incomplete. (Hongliang He and colleagues, arXiv, )

  29. Assessment: ContradictedSaid

    “Large Language Models are — and always will be — incapable of reasoning or consciousness”

    What happened: SATBench reports performance well above its 50% chance baseline on generated, solver-checked puzzles, without allowing external tools during model evaluation. Reasoning-trace checks and partial human validation support a limited inference capability, although they also reveal errors. (Anjiang Wei and colleagues, arXiv, )

  30. Assessment: UnresolvedSaid

    “Lyu — and, for that matter, Rabbit/Cyber Manufacture Co. — has failed to be transparent with its customers, the press, and the public writ large. And yet he demands an unbelievable amount of faith from users of the Rabbit R1, who must provide their usernames and passwords for any service they wish to connect to the device by remotely connecting into a virtual machine and typing them.”

  31. Assessment: SupportedSaid

    “And all of this is deeply relevant at a time when Rabbit’s R1 device has launched without several key features, such as “Teach Mode” and a stable, reliable version of its alleged “Large Action Model.””

    What happened: David Pierce's launch-period testing found repeated failures to complete Uber and DoorDash requests, alongside unreliable Spotify behavior. These firsthand results support the ordinary reading that the delivered action system was not dependable. (The Verge, )

  32. Assessment: SupportedSaid

    “The device — which costs $200 — has already started shipping and the reviews are not good.”

    What happened: Rabbit reports actual customer handovers and, elsewhere in the same update, customers beginning to receive devices. Customer distribution had begun. (Rabbit, )

  33. Assessment: ContradictedSaid

    “And it all kind of makes sense why generative AI doesn’t really help anyone other than those who want to sell something.”

    What happened: The dated episode presents the hosts using an AI tour guide for museum descriptions and questions. Access to exhibits during personal recreation is a concrete non-sales benefit. (Eyes On Success, )

  34. Assessment: UnresolvedSaid

    “In many respects, this comparison is eerily prophetic, given the slowdown (and even regression) of improvement we’ve seen in LLM-based services like ChatGPT.”

  35. Assessment: ContradictedSaid

    “As a result, every single LLM-based application — ChatGPT, Google Gemini, Anthropic's Claude, Meta's LLaMA, to name but a few — is trained on an indeterminately-large portion of the internet made up of both Common Crawl (a 250-billion page repository of web content) and what appears to be as much of the rest of the internet as they're able to download, legally or otherwise.”

    What happened: The methods restrict StarCoderBase's training set to The Stack's GitHub repositories; StarCoder adds Python fine-tuning from that corpus. This documented training recipe does not include Common Crawl. (BigCode research collaboration, arXiv, )

  36. Assessment: UnresolvedSaid

    “First, AI can’t handle one of the most essential tasks of software engineering, which is collaboratively discussing the requirements of a project and how to implement the technology.”

  37. Assessment: UnresolvedSaid

    “LLMs also tend to hallucinate, a virtually-unsolvable problem where they authoritatively make incorrect statements that creates horrifying results in generative art and renders them too unreliable for any kind of mission critical work.”

  38. Assessment: UnresolvedSaid

    “As I wrote (and broadcast) recently, LLMs are running out of training data, and the desperation to find more will lead to significant legal consequences when it's revealed exactly how much they stole and from whom they stole it from.”

  39. Assessment: SupportedSaid

    “As I wrote (and broadcast) recently, LLMs are running out of training data, and the desperation to find more will lead to significant legal consequences when it's revealed exactly how much they stole and from whom they stole it from.”

  40. Assessment: UnresolvedSaid

    “Yet I believe generative AI is going to lead several of these companies to ruin.”

  41. Assessment: ContradictedSaid

    “These models are not "learning" or, let's be honest, "training" on this data, because that's not how they work — they're using mathematics to plagiarize it based on the likelihood that somebody else's answer is the correct one.”

    What happened: The documented objective is predictive model training. A probability over next tokens is not a probability that somebody else's complete answer is correct. (OpenAI, arXiv, )

  42. Assessment: UnresolvedSaid

    “As these generative AI companies become more desperate, they'll (intentionally or otherwise) ingest AI-generated content, which causes a degenerative training effect called "model collapse" (also known as Habsburg AI) that destroys the model's ability to deliver useful answers.”

  43. Assessment: UnresolvedSaid

    “OpenAI may make billions, but it has yet to turn any kind of profit, and its operating costs vastly surpass any revenue it gets from licensing its technology or selling premium memberships.”

  44. Assessment: UnresolvedSaid

    “As you'd expect, these lax standards have led to Facebook being flooded with content created with generative AI”

  45. Assessment: ContradictedSaid

    “Artificial intelligence isn't new, these models aren't new, and after just over a year of hype, generative AI is yet to prove it can do anything substantially different other than helping tech company CEOs raise money and create headlines.”

    What happened: The public research demonstration reported practical customer-support benefits beyond fundraising or publicity. The event date records when that demonstration became public, not when the underlying workplace observations occurred. (arXiv; Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, )

  46. Assessment: UnresolvedSaid

    “Google and Amazon have invested billions in ChatGPT competitor Anthropic (and both claim to be Anthropic's primary cloud provider), and in doing so have guaranteed — according to a source of mine — $750 million a year (Google Cloud) and $800 million a year (AWS) in revenue by mandating that Anthropic uses their services to power its "Claude" model.”

  47. Assessment: ContradictedSaid

    “In essence, the AI boom requires more high-quality data than currently exists to progress past the point we're currently at, which is one where the outputs of generative AI are deeply unreliable.”

    What happened: SCoRe uses existing MATH and MBPP problems, generated correction traces, and correctness rewards. Its Gemini 1.0 Pro coding experiments improve self-correction on held-out HumanEval, demonstrating further error-correction progress without requiring an expanded stock of human-written material. (Google DeepMind researchers via arXiv, )

  48. Assessment: UnresolvedSaid

    “Nvidia is likely to be the canary in the coal mine for the AI industry, and if AI fails to drive meaningful profits for both the customer and the provider, orders for Nvidia chips will dramatically slow.”

  49. Assessment: UnresolvedSaid

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

  50. Assessment: UnresolvedSaid

    “That doesn't fix the overall problem that we are running out of data to feed the models, and that the solution in many cases is to use the broken tool to make more data.”

  51. Assessment: UnresolvedSaid

    “If businesses don't adopt AI at scale — not experimentally, but at the core of their operations — the revenue is simply not there to sustain the hype, and once the market turns, it will turn hard, demanding efficiency and cutbacks that will lead to tens of thousands of job cuts.”

  52. Assessment: MixedSaid

    “As I've discussed before, today's models are rife with hallucinations that make integrating them actively dangerous for many businesses, and the only way to have fewer hallucinations is to give the models more compute power to process and analyze data, a dwindling resource that AI is actively helping to destroy.”

    What held: The January version reports frequent hallucinations on verifiable legal questions, supporting a material risk in consequential business workflows. It supports the risk clause within legal tasks, not a measured count of affected businesses or the compute-depletion claim. (arXiv; Matthew Dahl and colleagues, )

    What did not: The paper reports improved Alpaca truthfulness on TruthfulQA through activation intervention and explains how to incorporate it into existing parameters. This counters the necessity of greater deployed processing capacity, although learning the intervention requires offline computation and the result is benchmark-specific. (arXiv; Kenneth Li and colleagues, )

  53. Assessment: UnresolvedSaid

    “Generative video models like OpenAI's Sora are dead on arrival, requiring massive amounts of compute and significantly more training data — which is itself both complex and scarce, and carries with it massive legal implications.”

  54. Assessment: SupportedSaid

    “Models like ChatGPT and Claude are deeply dependent on training data to improve their outputs, and their very existence is actively impeding the creation of the very thing they need to survive.”

    What happened: The study estimates a 25% relative reduction in Stack Overflow activity within six months of ChatGPT's release, using less-exposed comparison platforms. It examines content creation and reports no significant deterioration in post quality that would explain the loss as merely low-quality material. (PNAS Nexus; institutional record at UCL Discovery, )

  55. Assessment: UnresolvedSaid

    “Paying publishers to license their content doesn't actually fix the problem, because it doesn't increase the amount of content that they create, but rather helps line the pockets of executives and shareholders.”

  56. Assessment: UnresolvedSaid

    “In the event that adoption slows — or, more accurately, demand never arrives — Microsoft, Amazon and Google only have so long before the markets sour on their "it's the future, we swear!" messaging, demanding real revenue to match their multi-billion dollar infrastructural bets.”

  57. Assessment: SupportedSaid

    “much of what makes them scary is the assumption that OpenAI will fix hallucinations, something that the company has categorically failed to do.”

    What happened: OpenAI itself said its flagship model still hallucinates facts. That model was still the flagship at the time of the post. (OpenAI, )

  58. Assessment: UnresolvedSaid

    “You simply cannot fix them. Hallucinations are not going away.”

  59. Assessment: UnresolvedSaid

    “I can find no companies that have integrated generative AI in a way that has truly improved their bottom line other than Klarna,”

  60. Assessment: UnresolvedSaid

    “As we speak, there are few if any meaningful improvements to our lives as a result of the last year's artificial intelligence boom.”

  61. Assessment: UnresolvedSaid

    “If the AI bubble pops, the entire tech industry will suffer as venture capitalists are once again washed out through chasing an unprofitable, barely-substantiated trend.”

  62. Assessment: UnresolvedSaid

    “In reality, a Habsburg AI will be one that is increasingly more generic and empty, normalized into a slop of anodyne business-speak as its models are trained on increasingly-identical content.”

  63. Assessment: UnresolvedSaid

    “And because these platforms were built to reward scale and volume far more often than quality, AI naturally rewards those who can find the spammiest ways to manipulate the algorithm.”

  64. Assessment: UnresolvedSaid

    “This is why so many models' responses sound so similar — they're heavily dependent on the stuff they're fed for their outputs, and so much of their "intelligence" comes from the same training data.”

  65. Assessment: UnresolvedSaid

    “Like a plant turning to capture sunlight, parts of the internet have already twisted toward the satisfaction of algorithms, and as others become dependent on generative AI (like Quora, which now promotes ChatGPT-generated answers at the top of results), so will the web become more dependent and dictated by automated systems.”

  66. Assessment: UnresolvedSaid

    “The ultimate problem is that this morass of uselessness will lead companies like Google to force their generative AIs to "fix" the problem by generating answers to sift through the crap.”

  67. Assessment: UnresolvedSaid

    “The numerous lawsuits against OpenAI could break the back of the company, and while Altman and other AI fantasists may pretend that these models are an intractable path to the future of society, any force that controls (or makes them pay for) the data that they use will kneecap the company and force them to come up with a way to make these models ethically.”

  68. Assessment: UnresolvedSaid

    “As more internet content is created, either partially or entirely through generative AI, the models themselves will find themselves increasingly inbred, training themselves on content written by their own models which are, on some level, permanently locked in 2023, before the advent of a tool that is specifically intended to replace content created by human beings.”

  69. Assessment: UnresolvedSaid

    “The only thing that can truly make them better is more stuff, but when the majority of stuff being created isn't good, or interesting, or even written for a human being, ChatGPT or Claude's models will learn the rotten habits of rotten content.”

  70. Assessment: UnresolvedSaid

    “OpenAI's ChatGPT and Anthropic's Claude are dependent on a constant flow of training data to improve their models, to the point that it's effectively impossible for them to operate without violating copyright.”

  71. Assessment: SupportedSaid

    “Generative AI is a perfect tool for soullessly churning out content to match a particular set of instructions — such as those that an algorithm follows — and while an algorithm can theoretically be tuned to evaluate content as "human," so can scaled content be tweaked to make it seem more human.”

    What happened: The researchers generated tens of thousands of instruction-following examples, establishing repeatable production at scale. Their accompanying evaluation and limitations do not establish flawless adherence or universal content quality. (Stanford Center for Research on Foundation Models, )

  72. Assessment: SupportedSaid

    “The algorithms are easily-tricked, and the tools used to trick them are becoming easier to use and scale.”

    What happened: The report describes using Byword to generate 1,800 articles in hours and obtain search traffic, supporting accessible production at scale. It also reports that much of the traffic subsequently disappeared; figures are estimates and campaign claims, not audited platform records. (The Decoder, )

  73. Assessment: UnresolvedSaid

    “I am not saying that user-generated content will disappear, but that human beings cannot create content at the scale that automation can, and when a large chunk of the internet is content for robots, that is the content that will inform tomorrow's models.”

  74. Assessment: SupportedSaid

    “Yet at some point these portals will generate an answer based off of the data they have (or have access to, in the case of Tumblr and Wordpress) rather than linking you to a place where you can find an answer by reading something created by another person.”

    What happened: Google announced public US rollout of AI Mode, describing retrieval across web subtopics and an in-search response experience. The same announcement retains source links, which limits support to direct answers substituting for necessary source visits. (Google, )

  75. Assessment: MixedSaid

    “Amazon already plans to heavily integrate generative AI into Alexa, and Apple is planning the same with Siri, all to show the world that they're "innovative" and "pushing boundaries" without being able to explain how.”

    What held: Amazon publicly described a substantial LLM-based redesign before the post. This supports the Alexa plan component, not the claimed motive. (Amazon, )

    What did not: Amazon explained a concrete intended improvement: creating routines through speech without manual app programming. This contradicts a literal inability to explain the proposed benefit, but does not prove reliable delivery or disprove promotional motives. (Amazon, )

  76. Assessment: UnresolvedSaid

    “When I tried to replicate this in the real world, Copilot made four images with completely incomprehensible text, only one of which said "Mike's."”

  77. Assessment: SupportedSaid

    “While Google has yet to fully make the move, the writing is on the wall, regardless of whether the actual assistant is fit for the task and not, say, hallucinating things that don't exist.”

    What happened: The reporter directly confirmed that Assistant could no longer be selected on the tested Pixel. The article places this observation within Google's mobile migration and reports the removal of the previous switching controls. (Droid Life, )

  78. Assessment: SupportedSaid

    “While Sora and other video generators like Pika may seem like the future (and are capable of some impressive magic tricks), they are not particularly adept — much like a lot of generative AI — at performing a particular task.”

    What happened: The developer admits Sora may confuse spatial details in a prompt and struggle with precise events and camera paths. In other words, it may fail at a specific requested task. (OpenAI, )

  79. Assessment: UnresolvedSaid

    “I believe that once reality catches up with artificial intelligence's marketing, there will be a dramatic knock-on effect that savages the entire tech industry.”

  80. Assessment: ContradictedSaid

    “ChatGPT can spin up piles of anodyne business copy, yet its outputs always require enough editing that it's questionable how much time you've actually saved.”

    What happened: The randomized experiment involved 453 professionals completing occupation-specific writing assignments. Substantial savings on completed work, accompanied by higher assessed quality, contradict the blanket claim under the stated reading of business copy. (Science; abstract reproduced by Stanford SCALE, )

  81. Assessment: UnresolvedSaid

    “Big tech has turned the startup ecosystem into a giant goldmine, one that guarantees that almost every dollar spent on any AI product is eventually shared with one of a few multi-trillion dollar tech firms.”

  82. Assessment: ContradictedSaid

    “we keep running into the problem that these things are interesting, surprising, but not particularly useful for anything.”

    What happened: A randomized experiment found ChatGPT cut time on professional writing tasks by 40% and raised quality by 18%. That is a large, practical benefit, and it contradicts 'not particularly useful for anything'. (Science (Noy & Zhang), abstract listed by Stanford SCALE, )

  83. Assessment: UnresolvedSaid

    “As a result, every interaction with ChatGPT or Claude is, on some level, guaranteed revenue for one of the big tech firms.”

  84. Assessment: SupportedSaid

    “There are cool little app integrations, interesting things like live translation in Samsung devices, but these are features, not applications.”

    What happened: Samsung's launch documentation places Live Translate inside its existing Phone app. That supports the feature-versus-standalone-product distinction for the named example. (Samsung Electronics, )

  85. Assessment: UnresolvedSaid

    “Public companies are pumping their valuations and executive salaries off the back of artificial intelligence hype, yet nobody is saying the blatantly obvious — that this industry is deeply unprofitable and yet to prove its worth.”

  86. Assessment: UnresolvedSaid

    “As the use of artificial intelligence grows, so do these revenue streams, forcing almost every single dollar spent on AI into the hands of a few trillion-dollar tech firms.”

  87. Assessment: UnresolvedSaid

    “Despite the frothy tales and visions of how generative artificial intelligence will automate our entire existence, there's a distinct lack of practical outputs that suggest that it is even capable of doing so.”

  88. Assessment: UnresolvedSaid

    “The assumption is that audiences are stupid, and ignorant, and "just won't care," and I firmly disagree — I think regular people will find this stuff deeply offensive.”

  89. Assessment: UnresolvedSaid

    “As it stands, OpenAI and Anthropic are heavily dependent on companies believing that they have to integrate AI into their products, which will require these companies to be able to find ways to integrate AI that users actually care about.”

  90. Assessment: UnresolvedSaid

    “AI fanatics are currently fantasizing over a world where they can put a few sentences into a prompt and create an entire series of TV, unable to realize that we are rapidly approaching the top of generative AI's S-curve, where after a period of rapid growth things begin to slow down dramatically.”

  91. Assessment: SupportedSaid

    “And there are so many stories about how unreliable this technology is.”

    What happened: The court found that ChatGPT fabricated cases used in the March 1 filing. This is a primary judicial finding of incorrect generated material, rather than an allegation repeated by the speaker. (U.S. District Court for the Southern District of New York, reproduced by FindLaw, )

  92. Assessment: UnresolvedSaid

    “And as I've suggested above, while these models might "improve," the billions of dollars burned by OpenAI, Anthropic and Stability AI's models have found few ways to mitigate the restrictions of an artificial intelligence that doesn't have an intellect.”

  93. Assessment: UnresolvedSaid

    “Moreover, the scale is different, and serving a webpage will always cost less than processing a request sent to a generative AI model.”

  94. Assessment: UnresolvedSaid

    “What these stories don't seem to discuss are whether these companies are making a profit, likely because generative AI is a deeply unprofitable product, demanding massive amounts of cloud computing power”

  95. Assessment: UnresolvedSaid

    “Tech's largest cash cow since the cloud computing boom of the 2000s is based on a technology that is impossibly unreliable, a technology with a potent inverted Midas touch that burns far more money than it makes.”

  96. Assessment: UnresolvedSaid

    “We are beset with spam calls, spam texts, spam messages on social networks, notifications from apps we forgot we installed, search results that are stuffed with results built specifically to manipulate search engines, emails from companies we’ve bought one thing from, popular home pages full of AI-spun misinformation and algorithms that try and hurt and manipulate us.”

  97. Assessment: UnresolvedSaid

    “The podcast has also struggled to retain its initial momentum, with viewership tumbling from 160,000 views on the first episode to around 40,000 an episode for the last 9 months, with the exception of the George Carlin special which sits at 55,000 views at the time of writing.”

  98. Assessment: UnresolvedSaid

    “Already, despite only being available to a handful of users, Grok has fabricated a story about a British MP being expelled from Parliament, presumably confusing the ouster of disgraced New York congressman George Santos with a separate and unrelated story from across the Atlantic.”

  99. Assessment: UnresolvedSaid

    “Musk rushed a Large Language Model to market in a few months, one where bigotry is a feature not a bug, and has potentially trained it on the garbled musings of people that he has incentivized to fill its data source with nonsense.”

  100. Assessment: SupportedSaid

    “And that’s acknowledging the fact that LLMs — even those developed a legion of well-resourced and well-fed PHDs, like GPT-4, and based on a well-curated dataset of high-quality material — have a habit of hallucinating facts, figures, and events.”

    What happened: OpenAI acknowledges factual hallucinations in GPT-4. Its accompanying report of improvement over earlier models does not remove that recurring limitation. (OpenAI, )