Claims checked against evidence, page 21 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 21 of 31: claims 2001 to 2100 of 3100, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: SupportedSaid
“Two recent studies show nothing like “10x” improvement :”
What happened: The primary announcement identifies approximately 800 developers and reports no significant improvement in its efficiency measures. That is plainly below tenfold. (Uplevel, )
Assessment: SupportedSaid
“The data are coming in – and it’s not.”
What happened: The published study reported no significant improvement in pull-request cycle time or throughput. Its results support the claim about the contemporary research record, although they cannot establish a universal absence of benefits. (Uplevel, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: SupportedSaid
“many lawsuits pending”
What happened: The order addresses six actions: three consolidated author actions, the Times action, Daily News, and the Center for Investigative Reporting. Ongoing discovery and future deadlines establish pending status; grouping the author actions and the newly consolidated newspaper actions leaves three proceedings. (U.S. District Court, Southern District of New York; copy hosted by Chat GPT Is Eating the World, )
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“From left to right that’s Ilya Sutskever (now gone, less than a year later), Greg Brockman (on leave, at least until the end of the year), CTO Mira Murati (departure just announced) and Sam Altman (fired, and then rehired).”
What happened: OpenAI announced Sutskever's departure in May, less than a year after the September 2023 reference point. (OpenAI, )
Assessment: SupportedSaid
“Co-founders John Schulman also left, so did Andrej Karpathy and Jan Leike, and perhaps a dozen others.”
What happened: The report establishes Schulman's departure before the post and identifies him as a co-founder. (TechCrunch, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“I rushed out a hot take on X, my usual cautions about how LLMs aren’t really AGI:”
What happened: The launch-era card documents failures in computer-mediated tasks normally performed by human lab assistants; its METR section also reports difficulties using tools and environmental feedback. These are substantive capability gaps supporting the ordinary human-level reading, rather than evidence based merely on occasional errors. (OpenAI, )
Assessment: SupportedSaid
“Almost exactly what I predicted we would see with GPT-4, back on Christmas Day 2022.”
What happened: Altman's original statement contains all three elements of the comparison: continuing flaws, continuing limitations, and a stronger first impression than sustained use supports. (Sam Altman / X, )
Assessment: SupportedSaid
“You should be watching to see what the candidates plan to do about fraud and disinformation, as AI contributes to escalation in both.”
What happened: This primary agency release connects AI to greater attack scale and describes increasing use of cloned voices and videos to induce fraudulent transactions. It provides a mechanism and an operational assessment, but no estimate of AI-attributable losses. (Federal Bureau of Investigation, San Francisco, )
Assessment: SupportedSaid
“And you should be watching to see what they plan to do about the growing problem of nonconsensual deep fake porn, and especially how that might affect the already vulnerable population of teenage girls, who have been hard by social media.”
What happened: The Korean passage says the threat from sexual deepfakes of acquaintances and celebrities is substantially increasing. The accompanying police series records 297 cases through July 2024, compared with 180 in all of 2023, directly supporting growth in recorded cases. (Korean National Police Agency; copy hosted by the National Youth Policy Institute, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“California’s pending AI bill SB-1047 was supposed to be a tiny step in that direction, and even that has gotten watered down.”
What happened: The author's official amendment announcement explicitly describes replacing criminal penalties with civil penalties. That is a concrete reduction in enforcement severity before publication, while preserving civil enforcement. (Office of California Senator Scott Wiener, )
Assessment: SupportedSaid
“When Sam Altman stood next to me in the Senate, he told the Senate that he strongly favored AI regulation. But behind the scenes his team fought to water down the EU AI Act.”
What happened: Altman enthusiastically endorsed a proposed oversight agency and also endorsed licensing. The same exchange includes Marcus, establishing their participation in the hearing. (U.S. Senate Committee on the Judiciary / Government Publishing Office, )
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“It also provides important whistleblower protections, which as many ex OpenAI employees have made clear, are critical.”
What happened: The official analysis confirms whistleblower protections in the relevant version. Its major provisions cover disclosures about noncompliance and unreasonably dangerous models to the Attorney General or Labor Commissioner. (California Legislature, Senate Third Reading analysis, )
Assessment: ContradictedSaid
“In the new form, SB 1047 can basically only be used only after something really bad happens, as a tool to hold companies liable. It can no longer protect us against obvious negligence that might likely lead to great harm.”
What happened: The sponsor's official description expressly retains enforcement before harm where public safety faces an imminent threat. It also confirms the narrower point that enforcement was restricted. (Office of California Senator Scott Wiener, )
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“Most or all of the major big tech companies joined a lobbying organization that fought SB-1047, tooth and nail, despite broad public support for the bill.”
What happened: The report names four of the five companies in the stated ordinary Big Tech grouping. Contemporary reporting supplements the earlier primary partnership disclosure; affiliation does not establish that each company directed this particular campaign. (The Register, )
Assessment: SupportedSaid
“California’s SB-1047, not yet signed into law, but on its way to being one of the first really substantive AI bills in the US, primarily addressed to liability around catastrophic risks, was significantly weakened in last-minute negotiations.”
What happened: The author's firsthand amendment summary confirms that civil penalties replaced criminal perjury penalties. It also identifies a reduced attestation standard and narrower access to civil penalties before harm, supporting a material reduction in safeguards. (Office of California Senator Scott Wiener, )
Assessment: SupportedSaid
“As I emphasized last year in front of the Senate, there is not one risk but many: bias, disinformation, defamation, toxicity, invasion of privacy, and so much more, to say nothing of risks to employment, intellectual property, and the environment, precisely because recent techniques can be applied in so many ways, still poorly understood.”
What happened: The report identifies bias, harmful content, disinformation, privacy, intellectual-property and environmental risks, and discusses labor-market effects. It links estimation difficulties to diverse applications, limited training-data visibility, and immature safety measurement. (National Institute of Standards and Technology, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“Even then developers are required only to implement "reasonable safeguards" against these severe risks--not to fully mitigate them.”
What happened: The July 3 analysis describes reasonable safeguards and a prohibition on release where unreasonable critical-harm risk remains. Together these support risk reduction without a zero-risk guarantee. (Foley & Lardner LLP, )
Assessment: SupportedSaid
“Lastly, asking for standards is not unique to AI; it's common across many industries to ask that companies evaluate the safety of their products according to set standards: just look at the pharmaceutical industry, or aviation, automobiles, etc.”
What happened: FDA describes manufacturers testing drugs and submitting evidence against a defined safety-and-effectiveness requirement. The event date denotes this standing regulatory practice at the post date, not the document's publication. (U.S. Food and Drug Administration, )
Assessment: SupportedSaid
“And much of what would be required is already something companies committed to voluntarily, in discussions at the White House and in Seoul.”
What happened: The announcement records pre-release testing, cybersecurity safeguards and sharing risk information. These overlap with central safety functions identified in the bill analysis. (The White House, announcing participating companies' commitments, )
Assessment: SupportedSaid
“But you don't make clear that much of the bill's requirements are limited to models with training runs of $100 million+. Companies that can afford that, presumably valued in the billions, are not exactly "little-tech."”
What happened: The article expressly discusses the July 3 amendment and confirms that the $100 million training-cost threshold remained central. Its current page also contains a later veto notice, which is not used here. (Foley & Lardner LLP, )
Assessment: SupportedSaid
“OpenAI recently put Paul Nakasone (ex NSA) on the board”
What happened: OpenAI directly announces Nakasone's appointment within the period described. Its concluding biography also identifies his former leadership of the NSA. (OpenAI, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“An awful lot of key players have left the island. Most voluntarily.”
What happened: Sutskever presents the departure as his decision. This supports classifying it as voluntary on the public record. (Ilya Sutskever, via X, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“Nvidia dropped 6%, 20% over the last month”
What happened: The table's Close column gives $125.83 on July 5, $107.27 on August 2 and $100.45 on August 5. These produce declines of 20.17% over the month and 6.36% for the day; the separate Adj Close column is not used. (Yahoo Finance, )
Assessment: SupportedSaid
“OpenAI tempered expectations for its next event, and said we wouldn’t see GPT-5 then.”
What happened: The direct spokesperson statement supports the claimed expectation-setting. (TechCrunch, )
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, )
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, )
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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: SupportedSaid
“Over a quarter century later, handling outliers is still the Achilles’ Heel of neural networks.”
What happened: The evaluation reports failures across several unfamiliar visual domains, supporting a recurring weakness in contemporary generative neural systems. It also finds improvements from in-context learning, while reporting that this approach remains vulnerable to distribution shifts. (arXiv; Zhang and colleagues, )
Assessment: MixedSaid
“Symbolic systems have always been good for outliers; neural networks have always struggled with them. For 30 years, this has been a constant.”
What held: The gSCAN experiments report substantial failures when neural models must apply compositional rules to unfamiliar combinations. This supports the recurring neural-difficulty part of the claim, but does not establish symbolic superiority across the entire period. (arXiv; Ruis and colleagues, )
What did not: In a comparison of seven models, the meta-trained neural approach combined systematicity and flexibility that the symbolic and ordinary neural comparators did not jointly achieve. This contradicts a stable categorical division between successful symbolic generalization and unsuccessful neural generalization. (Nature, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
Assessment: SupportedSaid
“Another GenAI monetization scheme bites the dust.”
What happened: The report establishes discontinuation of the celebrity-persona offering. It also contains direct confirmation from Meta, making this stronger than an unconfirmed report of a cancellation. (The Verge, )
Assessment: UnresolvedSaid
Assessment: UnresolvedSaid
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, )
Assessment: UnresolvedSaid