Claims checked against evidence, page 4 of 15
Claims by Gary Marcus and Ed Zitron. Deadlines are judged as of 2026-10-29.
Claims checked against evidence (1432)
Each of these claims was checked against dated evidence.
Page 4 of 15: claims 301 to 400 of 1432, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Gary Marcus Observation
“The technical problems are not new.”
Our reading: The core technical problems of LLMs are not new.
No horizon Evidence as of
Limitation: The evidence establishes continuity in broad failure categories, not identical causes across model generations.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“This cold reality in turns undermines a large fraction of the use cases that people initially fantasized about.”
Our reading: The inherent limitations of LLMs undermine a large fraction of the use cases people initially imagined.
No horizon Evidence as of
Limitation: The studies examine different models, periods, tasks, and forms of human oversight.
Assessment: UnresolvedSaid
Gary Marcus Observation
“What is new that they have at last become widely recognized, not just as the transitory “bugs” the industry wanted you to think of, but as inherent limitations that flow from the very design of LLMs they really are.”
Our reading: LLMs' technical problems are now widely recognized as inherent limitations of their design, not transitory bugs.
No horizon Evidence as of
Limitation: A selection of technical papers is not a representative opinion survey.
Assessment: SupportedSaid
Gary Marcus Observation
“This is in line with the July METR study showing that coding tools sometimes impair productivity rather than increasing it.”
Our reading: The July METR study showed that AI coding tools sometimes impair productivity rather than increase it.
No horizon Evidence as of
Limitation: The trial involved experienced developers working on familiar open-source projects.
Assessment: SupportedSaid
Gary Marcus Observation
“But many others have found that LLMs are not quite as advertised.”
Our reading: Many others have found that LLMs are not quite as good as advertised.
No horizon Evidence as of
Limitation: The advertising comparison is qualitative and supplied by interpretation.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Human domestic robots like Optimus and Figure will be all demo and very little product.”
Our reading: In 2026, humanoid domestic robots such as Optimus and Figure will be mostly demos with very little actual product.
Not yet due Evidence as of
Limitation: The evidence includes no count of Optimus, Figure or NEO units delivered to consumer homes in 2026.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Forecast
“2025 will be known as the year of the peak bubble, and also the moment at which Wall Street began to lose confidence in generative AI.”
Our reading: 2025 will come to be seen as the peak of the generative AI bubble and when Wall Street began losing confidence in generative AI.
No deadline stated Evidence as of
Limitation: The cited commentary is a secondary summary; no primary record can by itself establish collective historical recognition.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Observation
“Few AI companies aside from Nvidia are making a profit, and nobody has much of a technical moat.”
Our reading: Few AI companies other than Nvidia are profitable, and no AI company has much of a technical moat.
No horizon Evidence as of
Limitation: Company-level profits do not isolate profits from AI products.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Work on new approaches such as world models and neurosymbolic will escalate.”
Our reading: In 2026, work on new approaches such as world models and neurosymbolic AI will escalate.
Not yet due Evidence as of
Limitation: This is a qualitative trend forecast with several possible measures.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Forecast
“We won’t get to AGI in 2026 (or 7).”
Our reading: AGI will not be achieved in 2026 or 2027.
Not yet due Evidence as of
Limitation: The evidence covers only OpenAI's statements up to about late August 2026. It does not cover other labs or later events.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Forecast
“there will be tons of lab demos but getting these robots to work in people’s homes will be very very hard, as Rodney Brooks has said many times.”
Our reading: There will be many lab demos of humanoid robots in 2026, but getting them to work in homes will be very hard.
Not yet due Evidence as of
Limitation: The evidence includes no independent 2026 home trials measuring task success, failures or human intervention.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Forecast
“I don’t expect my predictions to be as on target this year as last, for a happy reason: across the field, the intellectual situation has gone from one that was stagnant (all LLMs all the time) and unrealistic (“AGI is nigh”) to one that is more fluid, more realistic, and more open-minded.”
Our reading: Marcus's 2026 predictions will be less accurate than his 2025 ones because the field has become more fluid, realistic, and open-minded.
Not yet due Evidence as of
Limitation: The annual prediction sets require an explicit, consistent scoring method.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Forecast
“No country will take a decisive lead in the GenAI “race”.”
Our reading: No country will take a decisive lead in the generative AI race in 2026.
Not yet due Evidence as of
Limitation: 'Decisive lead' has no threshold, and lead could be measured on several axes.
Source: Six (or seven) predictions for AI 2026 from a Generative AI realist
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Right now, as it happens, China is at most a few months behind; I expect the lead to be slight and to flip back and forth, with nobody retaining a sizeable a lead for long.”
Our reading: The US-China lead in generative AI will be slight and flip back and forth, with neither retaining a sizeable lead for long.
No deadline stated Evidence as of
Limitation: The evidence comes from one capability index, the Epoch Capabilities Index.
Source: The core misconception that is driving American AI policy
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Conversely, nothing the United States can realistically do (short of full out nuclear war) is going to stop Chinese companies from serving generative AI on China-based cloud infrastructure.”
Our reading: Nothing the United States can realistically do, short of full out nuclear war, is going to stop Chinese companies from serving generative AI on China-based cloud infrastructure.
No deadline stated Evidence as of
Limitation: Restrictions on access to advanced compute must not be equated with cessation of existing inference services.
Source: The core misconception that is driving American AI policy
Assessment: MixedSaid
Gary Marcus Observation
“Coke and Pepsi are commodities; genAI models are, too.”
Our reading: Generative AI models are commodities.
No horizon Evidence as of
Limitation: The buyer evidence comes from a single survey of builders by a venture firm, from mid-2025, months before the December 2025 post.
Source: The core misconception that is driving American AI policy
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Nothing China can realistically do (short of full out nuclear war) is going to stop companies like Google, Microsoft, and Amazon from serving generative AI on the cloud.”
Our reading: Nothing China can realistically do, short of full out nuclear war, is going to stop companies like Google, Microsoft, and Amazon from serving generative AI on the cloud.
No deadline stated Evidence as of
Limitation: The operational examples do not provide an exhaustive history of every provider.
Source: The core misconception that is driving American AI policy
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Instead, part of the actual outcome over the next few years will be that both countries build a lot of generative AI infrastructure — quite possibly significantly more than they actually need.”
Our reading: Over the next few years both the US and China will build a lot of generative AI infrastructure, quite possibly significantly more than they need.
Not yet due Evidence as of
Limitation: The horizon is vague. The end date of 2030 is an interpretation.
Source: The core misconception that is driving American AI policy
Assessment: UnresolvedSaid
Gary Marcus Forecast
“I literally don’t see any plausible scenario in which either nation outright “wins” the generative AI race to the exclusion of the other.”
Our reading: Marcus does not see any plausible scenario in which either the US or China outright wins the generative AI race to the exclusion of the other.
No deadline stated Evidence as of
Limitation: Service announcements do not constitute a comprehensive global market census.
Source: The core misconception that is driving American AI policy
Assessment: UnresolvedSaid
Gary Marcus Observation
“For all intents and purposes, the race is basically a tie – with both countries serving many of their own customers, using largely similar products.”
Our reading: For all intents and purposes, the US-China generative AI race is basically a tie, with both countries serving many of their own customers using largely similar products.
No horizon Evidence as of
Limitation: US survey evidence covers teenagers, not the entire domestic market.
Source: The core misconception that is driving American AI policy
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Importantly, since everybody is following the same playbook—ever more massive data, ever more massive servers, fed into ever-larger large language models—nobody has a real technical advantage here.”
Our reading: Because everybody is following the same playbook of ever more massive data and servers fed into ever-larger large language models, nobody has a real technical advantage in the generative AI race.
No horizon Evidence as of
Limitation: DeepSeek’s findings are a developer self-assessment, without comparable measurements establishing the practical magnitude of the disclosed deficits [e1].
Source: The core misconception that is driving American AI policy
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“And because he has become so tight with Silicon Valley he will also be closely tied to any AI-tinged economic debacle that happens on his watch.”
Our reading: If an AI-related economic debacle occurs during Trump's term, he will be closely tied to it because of his closeness to Silicon Valley.
Not yet due Evidence as of
Only if: if an AI-tinged economic debacle happens on his watch
Source: A sad day for America
Assessment: SupportedSaid
Gary Marcus Observation
“As anticipated here yesterday, Trump’s decision to allow Nvidia to sell H200s to China hasn’t gone over well.”
Our reading: Trump's decision to allow Nvidia to sell H200 chips to China has not been well received.
No horizon Evidence as of
Limitation: This assesses substantial backlash, not majority public opinion.
Source: Has the Chinese government figured out the fact that GPUs ≠ AGI?
Assessment: SupportedSaid
Gary Marcus Causal claim
“Part of Beijing’s response is no doubt about protectionism.”
Our reading: China's reluctance to buy H200s is partly driven by protectionism favoring homegrown companies like Huawei.
No horizon Evidence as of
Limitation: The evidence supports the substance of the claim more strongly than the certainty conveyed by “no doubt.”
Source: Has the Chinese government figured out the fact that GPUs ≠ AGI?
Assessment: UnresolvedSaid
Gary Marcus Observation
“As noted yesterday, the H200 move is hardly popular with either party.”
Our reading: The decision to allow H200 sales to China is unpopular with both US political parties.
No horizon Evidence as of
Limitation: Party membership and the required breadth of opposition are unspecified.
Source: Has the Chinese government figured out the fact that GPUs ≠ AGI?
Assessment: SupportedSaid
Gary Marcus Observation
“Same thing with proposed preemption of states’ rights to regulate AI.”
Our reading: Proposed federal preemption of states' rights to regulate AI is unpopular with both US political parties.
No horizon Evidence as of
Limitation: The precise preemption proposal is not named.
Source: Has the Chinese government figured out the fact that GPUs ≠ AGI?
Assessment: SupportedSaid
Gary Marcus Observation
“Meanwhile, both policies face strong bipartisan resistance.”
Our reading: Both federal preemption of state AI laws and H200 chip sales to China face strong bipartisan resistance.
No horizon Evidence as of
Limitation: Strong is qualitative; the evidence does not establish majority opposition within each party.
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If we are to build AGI, we are going to need to learn something from humans, how they reason and understand the physical world, and how they represent and acquire language and complex concepts.”
Our reading: If we are to build AGI, we are going to need to learn something from humans: how they reason and understand the physical world, and how they represent and acquire language and complex concepts.
No deadline stated Evidence as of
Only if: if we are to build AGI
Source: “Scale Is All You Need” is dead
Assessment: SupportedSaid
Gary Marcus Observation
“The machine learning community, which has unwelcomed me since I started vocally critiquing the scaling approach, has finally started to come around.”
Our reading: The machine learning community has started to accept critiques of the scaling approach.
No horizon Evidence as of
Limitation: Rests on one industry write-up plus individual statements. No measured trend.
Source: “Scale Is All You Need” is dead
Assessment: SupportedSaid
Gary Marcus Observation
“And now, at last, the field is starting to realize it.”
Our reading: The AI field is now starting to realize that scaling alone will not reach AGI.
No horizon Evidence as of
Limitation: No earlier survey is available for comparison, so the growth of this view is inferred rather than measured.
Source: “Scale Is All You Need” is dead
Assessment: UnresolvedSaid
Gary Marcus Observation
“Worse, they are burning billions of dollars of month.”
Our reading: OpenAI is burning billions of dollars per month.
No horizon Evidence as of
Limitation: The first-half and full-year reports do not establish a common accounting basis or isolate July–September cash burn; subtracting their figures cannot settle the claim [e1, e2].
Source: OpenAI’s “Code Red”
Assessment: UnresolvedSaid
Gary Marcus Forecast
“In the end LLMs will continue to exist, no matter what; they will get cheaper. And they will find some utility (indeed they are already have).”
Our reading: LLMs will continue to exist, get cheaper, and have some utility.
No deadline stated Evidence as of
Limitation: The deployment and efficiency evidence is vendor-reported.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“And if Sustekver and I (and at this point many others) are right that scaling is largely played out, a decline in demand is sure to follow.”
Our reading: If scaling is largely played out, a decline in demand for AI compute is sure to follow.
No deadline stated Evidence as of
Only if: if scaling is largely played out
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“More than that, any normal seven-year old develops a rich model of the world, including the objects they see, the people they interact with and so on, and even how their bodies work. GPT is still just faking it.”
Our reading: Unlike a seven-year-old, GPT lacks a rich model of the world and is just faking it.
No horizon Evidence as of
Limitation: The behavioral reading of 'faking it' is an evaluator interpretation.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Forecast
“But they will never be AGI, and they never be much more than commodity.”
Our reading: LLMs will never be AGI and will never be much more than a commodity.
No deadline stated Evidence as of
Limitation: One benchmark cannot settle AGI.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“In some ways, GPT-5 is more capable than most 7 or 8 year olds.”
Our reading: In some ways, such as trivia and writing python code, GPT-5 is more capable than most 7- or 8-year-olds.
No horizon Evidence as of
Limitation: The available model evidence is company-reported and predates the target period.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“Ultimately though, what LLMs are great at seeming is plausible – while being much less good at getting their facts straight.”
Our reading: LLMs are good at seeming plausible but much less good at getting facts right.
No horizon Evidence as of
Limitation: The located plausibility experiment concerns prompted rationales rather than ordinary factual answers.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“The reality is OpenAI has ridden to a $500 billion valuation on hopes, not profits. And those hopes are slipping away, as usage declines and others catch up.”
Our reading: OpenAI's $500 billion valuation rests on hopes rather than profits, and those hopes are slipping as usage declines and competitors catch up.
No horizon Evidence as of
Limitation: October 2025 is the contract's selected reporting period; the claim itself specifies no usage window.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“And I certainly don’t think that ChatGPT has lived up to expectations.”
Our reading: ChatGPT has not lived up to expectations.
No horizon Evidence as of
Limitation: The capability comparison is company-reported and limited to its internal benchmark.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Too much industry hype is repeated with too little skepticism, driving up market valuations on analyses that often feel to me superficial.”
Our reading: Too much industry hype is repeated with too little skepticism, driving up market valuations on analyses that often feel to Marcus superficial.
No horizon Evidence as of
Limitation: Correlation between optimistic coverage and rising prices would not establish the direction of causation.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: SupportedSaid
Gary Marcus Observation
“And LLMs themselves have already resorted to using lots of external tools like calculators and python interpreters; it is already clear that they are more of a partial solution than the full solution people imagine.”
Our reading: LLMs have already resorted to using lots of external tools such as calculators and Python interpreters, and it is already clear that they are more of a partial solution than the full solution people imagine.
No horizon Evidence as of
Limitation: Tool adoption does not by itself prove necessity, failure, or a permanent capability ceiling.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“Three years and nearly a trillion dollars later almost of all it remains true. And it’s not just that GPT-4 was pretty much as I anticipated but that the same problems have continued to plague every single model since”
Our reading: Almost all of his December 2022 predictions about GPT-4 remain true, and the same problems plague every model since, including GPT-5, Claude, Gemini, Grok, Llama and DeepSeek.
No horizon Evidence as of
Limitation: Representative failures cannot establish a universal claim about all model versions.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“Worse, it doesn’t prove that its premise—that exponentially increasing compute—will actually bring us to artificial general intelligence. He’s conflating costs going up exponentially with benefits going up exponentially.”
Our reading: Jensen Huang's claim of exponential compute demand does not show that exponentially increasing compute will bring AGI; he conflates exponential costs with exponential benefits.
No horizon Evidence as of
Limitation: This assesses the referenced argument, not the eventual feasibility of AGI.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If the economy goes down, ChatGPT will be at the center of the mess.”
Our reading: If the economy goes down, ChatGPT will be at the center of the mess.
No deadline stated Evidence as of
Only if: if the economy goes down
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Forecast
“I think that the recurring core technical problems that we have seen (as discussed below) with LLMs aren’t going way; instead they inherent to the technology.”
Our reading: The recurring core technical problems of LLMs are inherent to the technology and will not go away.
No deadline stated Evidence as of
Limitation: The formal and practical meanings of hallucination differ.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Observation
“But time has shown that its results have held strong.”
Our reading: The MIT finding that only 5% of companies get a return on generative AI investment has held up.
No horizon Evidence as of
Limitation: Similar difficulties scaling AI do not independently corroborate a 5% return prevalence.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: SupportedSaid
Gary Marcus Causal claim
“The results are disappointing because the underlying tech is unreliable, And that’s been obvious from the start.”
Our reading: Generative AI results are disappointing because the underlying technology is unreliable, which has been obvious from the start.
No horizon Evidence as of
Limitation: The studies cover particular occupations and deployments.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: MixedSaid
Gary Marcus Observation
“productivity studies sometimes show a 30% gain, but none have come close to 10x (1000% gain).”
Our reading: No productivity studies of LLMs have shown anything close to a 10x gain; some show around 30%.
No horizon Evidence as of
Limitation: The counterexample involved six fictional cases and document production, not overall physician productivity.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: UnresolvedSaid
Gary Marcus Forecast
“I just don’t happen to think large language models like ChatGPT will get us there.”
Our reading: Large language models like ChatGPT will not get us to AGI.
No deadline stated Evidence as of
Limitation: AGI and the architectural boundary are not precisely defined.
Source: Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be
Assessment: SupportedSaid
Gary Marcus Observation
“The unfairly dismissed Apple reasoning paper laid bare the generalization issues; another paper called “Is Chain-of-Thought Reasoning of LLMs Mirage? A Data Distribution Lens” put a further nail in the LLM reasoning and generalization coffin.”
Our reading: Research papers (Apple's reasoning paper and the chain-of-thought mirage paper) demonstrated LLM reasoning and generalization failures.
No horizon Evidence as of
Limitation: The result depends on reading the claim as empirical task failures rather than universal incapacity.
Assessment: UnresolvedSaid
Gary Marcus Observation
“The first and most obvious answer is money, which I estimate, back of the envelope as (roughly) a trillion dollars, much of it on Nvidia chips and massive salaries.”
Our reading: Supposing for the sake of argument that LLMs have run their course and new ideas are needed, Marcus estimates, back of the envelope, the money cost of the field taking so long to recognize this at roughly a trillion dollars, much of it on Nvidia chips and massive salaries.
No horizon Evidence as of
Limitation: The spending boundary and starting date remain ambiguous.
Assessment: SupportedSaid
Gary Marcus Observation
“Each new bigger, more expensive model ekes out measurable improvements, but returns appear to be diminishing (that’s what Sutskever is saying about the Kaplan laws) and none of these experiments has solved core issues around hallucinations, generalization, planning and reasoning, as Sutskever too now recognizes.”
Our reading: No LLM scaling experiment has solved the core problems of hallucination, generalization, planning and reasoning.
No horizon Evidence as of
Limitation: 'Solved' has no stated threshold. Someone who counts IMO gold or big benchmark gains as solving reasoning would read the claim differently.
Assessment: SupportedSaid
Gary Marcus Observation
“It has never worked.”
Our reading: Building ever larger LLMs has never succeeded in reaching AGI.
No horizon Evidence as of
Limitation: The conclusion depends on the ordinary public-demonstration reading of AGI.
Assessment: SupportedSaid
Gary Marcus Causal claim
“A big part of the culprit? Maximizing metrics for user engagement.”
Our reading: OpenAI maximizing user-engagement metrics was a big part of the cause of ChatGPT users having mental health crises.
No horizon Evidence as of
Limitation: Causation rests on investigative reporting and company statements. No clinical study or court finding in the packet establishes that engagement-tuned updates caused specific users' crises.
Source: ChatGPT and delusions: an important new inside look at OpenAI
Assessment: UnresolvedSaid
Gary Marcus Forecast
“AGI ain’t actually coming in 2027, and even Kokotajlo, known for his aggressive timelines, is now with me in thinking we probably won’t see AGI this decade.”
Our reading: AGI will not arrive in 2027.
Not yet due Evidence as of
Limitation: The AGI criterion is an ordinary-language interpretation rather than a test Marcus specified.
Source: BREAKING: The AI 2027 doomsday scenario has officially been postponed
Assessment: UnresolvedSaid
Gary Marcus Forecast
“AGI is NOT in fact coming in Trump’s second term, and never was.”
Our reading: AGI will not arrive during Trump's second term (ending January 2029).
Not yet due Evidence as of
Limitation: No single benchmark establishes the broad AGI criterion.
Source: BREAKING: The AI 2027 doomsday scenario has officially been postponed
Assessment: UnresolvedSaid
Gary Marcus Observation
“Suffice for now to say that we have built (and continue to build) our entire national policy (such as it is) around an idea that was never grounded in a sound analysis of AI.”
Our reading: The US has built, and continues to build, its entire national policy (such as it is) around an idea, in context the AI 2027 picture of imminent AGI, that was never grounded in a sound analysis of AI.
No horizon Evidence as of
Limitation: The packet lacks policy-development records and the technical analyses actually relied upon during January 20–November 21, 2025.
Source: BREAKING: The AI 2027 doomsday scenario has officially been postponed
Assessment: SupportedSaid
Gary Marcus Observation
“Google announced their big new state-of-the-art model yesterday, Gemini 3, on most measures better than any other model out there.”
Our reading: Gemini 3 is better than any other model on most measures.
No horizon Evidence as of
Limitation: Much of the comparison comes from Google's own chosen benchmarks.
Source: Hot take on Google’s Gemini 3
Assessment: ContradictedSaid
Gary Marcus Observation
“(It’s only when ChatGPT eclipsed Meta that LeCun began to be sharply, publicly critical of LLMs)”
Our reading: LeCun began to be sharply and publicly critical of LLMs only when ChatGPT eclipsed Meta.
No horizon Evidence as of
Limitation: The decisive essay was coauthored.
Assessment: SupportedSaid
Gary Marcus Observation
“But he neither invented them nor was he the first to apply the back-propagation algorithm to learning their weights (though many people mistakenly believe this).”
Our reading: LeCun neither invented convolutional neural networks nor was the first to train their weights with back-propagation.
No horizon Evidence as of
Limitation: This result includes temporal convolutional networks; a claim restricted to two-dimensional image CNNs would require a separate priority comparison.
Assessment: SupportedSaid
Gary Marcus Observation
“Oracle debt downgraded.”
Our reading: Oracle's debt has been downgraded, which is an ominous sign for generative AI.
No horizon Evidence as of
Limitation: The evidence is secondary coverage of the Barclays note, not the note itself.
Assessment: SupportedSaid
Gary Marcus Observation
“Coreweave drops 20% in a week.”
Our reading: CoreWeave's stock fell 20% in one week, which is an ominous sign for generative AI.
No horizon Evidence as of
Limitation: The weekly figure comes from a news report, not a primary price-data feed.
Assessment: SupportedSaid
Gary Marcus Observation
“Meta financing games revealed.”
Our reading: Meta's financing games have been revealed, presented as one of five recent ominous signs for generative AI.
No horizon Evidence as of
Limitation: The transaction identification and one-month lookback are evaluator interpretations.
Assessment: SupportedSaid
Gary Marcus Observation
“Softbank sells entire Nvidia position.”
Our reading: SoftBank has sold its entire Nvidia position, which is an ominous sign for generative AI.
No horizon Evidence as of
Limitation: The 'ominous sign' framing is not scored.
Assessment: UnresolvedSaid
Gary Marcus Observation
“In short, Altman was—likely in conjunction with Nvidia, which also just seemed to be laying groundwork for a bailout—launching a full court press for loan guarantees when he got caught with hands in the cookie jar.”
Our reading: Altman was launching a full court press for federal loan guarantees, likely in conjunction with Nvidia, which also seemed to be laying groundwork for a bailout.
No horizon Evidence as of
Limitation: The metaphor leaves the required intensity imprecise.
Assessment: UnresolvedSaid
Gary Marcus Observation
“But it is no longer about lying to employees. It is about directly lying to the American public.”
Our reading: Sam Altman is now directly lying to the American public, not just to employees.
No horizon Evidence as of
Limitation: The submission does not expressly request guarantees for OpenAI's own data-center borrowing [e2].
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“Which means you, the taxpayer, will be footing the bill.”
Our reading: If the government goes along, taxpayers will end up paying for AI companies' borrowing.
No deadline stated Evidence as of
Only if: if the government goes along with the backstop
Source: OpenAI probably can’t make ends meet. That’s where you come in.
Assessment: MixedSaid
Gary Marcus Observation
“What Altman couldn’t say then was that he has a plan, to reduce the cost of his borrowing … by having the American taxpayer (indirectly) foot the bill.”
Our reading: OpenAI plans to reduce its borrowing costs by having American taxpayers indirectly bear the cost.
No horizon Evidence as of
Limitation: The original remarks and clarification are reproduced by news outlets; a complete primary transcript was not obtained.
Source: OpenAI probably can’t make ends meet. That’s where you come in.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Nonetheless, a very large fraction of our economy is going into the world of price wars and little moat, massive, massive investments into companies that are all basically following the same strategy of investing more and more money into one single idea, viz. scaling ever larger language models, in hopes that something magical will emerge.”
Our reading: A very large fraction of the US economy is going into massive investments in companies that are all basically following the same strategy of scaling ever larger language models, in hopes that something magical will emerge.
No horizon Evidence as of
Limitation: The broad equipment, software, R&D and data-center categories do not isolate investment in larger-language-model development.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Yet investments in the same single approach grow every day.”
Our reading: Investments in the single LLM/transformer approach continue to grow.
No horizon Evidence as of
Limitation: The evidence lacks a comparable aggregate series isolating LLM/transformer investment in Q3 2025 and earlier periods.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Modest profits was maybe a bit optimistic, with most LLM developers losing money.”
Our reading: Most LLM developers are losing money.
No horizon Evidence as of
Limitation: The selected financial interval is an evaluator interpretation.
Assessment: SupportedSaid
Gary Marcus Observation
“GPT-5 was disappointing, and didn’t come in 2024”
Our reading: GPT-5 was disappointing and was not released in 2024.
No horizon Evidence as of
Limitation: 'Disappointing' is evaluative; reactions were mixed, not unanimous.
Assessment: SupportedSaid
Gary Marcus Observation
“Meanwhile we have entered an era, perhaps unprecedented, of extreme financial interdependence and circular investment:”
Our reading: The AI industry has entered a perhaps unprecedented era of extreme financial interdependence and circular investment.
No horizon Evidence as of
Limitation: Extreme is a qualitative judgment without a stated threshold; the result depends on the ordinary reading specified in the contract.
Assessment: SupportedSaid
Gary Marcus Observation
“no robust solution to hallucinations”
Our reading: There is no robust solution to LLM hallucinations.
No horizon Evidence as of
Limitation: 'Robust' has no threshold; partial mitigations exist and rates are falling.
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If and when the whole generative AI edifice comes crashing down, it will be in no small part because too many otherwise smart investors read too many graphs—from “LLM scaling” to usage statistics—too naively, assuming that things had been going up for a while would continue to go up at the same pace, indefinitely.”
Our reading: If generative AI collapses, a large part of the cause will be investors naively extrapolating scaling and usage trends.
No deadline stated Evidence as of
Only if: if and when the generative AI edifice comes crashing down
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“The problem, as always, lies in generalizing outside the training distribution.”
Our reading: The problem with vibe coding lies, as always, in generalizing outside the training distribution.
No horizon Evidence as of
Limitation: Qualitative reports do not measure causal effects.
Source: Is vibe coding dying?
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Remember how in October and in March I told you that vibe coding — in the sense of amateurs using large language models to write code to “build products that would have previously required teams of engineers” — would never be remotely reliable?”
Our reading: Vibe coding by amateurs using LLMs to build products that previously required engineering teams will never be remotely reliable.
No deadline stated Evidence as of
Limitation: Most of the evidence is anecdotal or comes from a vendor's CEO.
Source: Is vibe coding dying?
Assessment: SupportedSaid
Gary Marcus Observation
“All that really happened is that GPT-5 (oversimplifying slightly) crawled the web for solutions to already-solved problems.”
Our reading: GPT-5 did not solve open Erdős problems; it essentially located existing solutions to already-solved problems on the web.
No horizon Evidence as of
Limitation: The result depends on treating the crawler wording as the acknowledged simplification.
Source: Erdosgate
Assessment: UnresolvedSaid
Gary Marcus Observation
“I found that the claim of “solution” was pretty misleading, as I summed up in a tweet, and they refused to correct their misleading presentation:”
Our reading: In 2019 OpenAI's claim that its robot 'solved' the Rubik's cube was misleading, and OpenAI refused to correct it.
No horizon Evidence as of
Limitation: Disagreement over wording does not by itself prove a factual misrepresentation.
Source: Erdosgate
Assessment: UnresolvedSaid
Ed Zitron Forecast
“I believe that we’re going to see multiple price increases on AI-native companies in the next few months.”
Our reading: Within a few months of 2025-10-20, AI-native companies (products built on top of AI models) will face multiple price increases.
No deadline stated Evidence as of
Limitation: This record does not assess whether model providers changed their prices after October 2025.
Source: This Is How Much Anthropic and Cursor Spend On Amazon Web Services
Assessment: UnresolvedSaid
Gary Marcus Observation
“June, 2025: the Apple reasoning paper confirmed that even with “reasoning”, LLMs still can’t solve distribution shift, the core Achille’s heel in neural networks that I have been writing about for nearly 30 years.”
Our reading: Even with reasoning, LLMs still cannot handle distribution shift, as the June 2025 Apple paper confirmed.
No horizon Evidence as of
Limitation: The competing papers are evidence of a scientific dispute, not equally decisive demonstrations.
Source: Game over. AGI is not imminent, and LLMs are not the royal road to getting there.
Assessment: UnresolvedSaid
Gary Marcus Observation
“We aren’t close to AGI, but we are reshaping our world on the premise that we are.”
Our reading: We are not close to AGI.
No horizon Evidence as of
Limitation: Benchmark failures do not measure time remaining to AGI.
Source: The Grand AGI Delusion
Assessment: UnresolvedSaid
Gary Marcus Observation
“And schoolchildren using ChatGPT to do their homework aren’t enough to cover that.”
Our reading: Schoolchildren using ChatGPT to do their homework are not enough to cover OpenAI's immense costs and cash burn.
No horizon Evidence as of
Limitation: The reporting window and meaning of 'cover that' are not explicit.
Source: OpenAI’s future, foretold?
Assessment: SupportedSaid
Gary Marcus Observation
“GenAI certainly sounds amazing – but it hasn’t always worked well enough to justify the costs.”
Our reading: Generative AI has not always worked well enough to justify its costs.
No horizon Evidence as of
Limitation: Support rests on preliminary, largely self-reported and anonymized deployment research.
Source: OpenAI’s future, foretold?
Assessment: SupportedSaid
Gary Marcus Observation
“The notion on which this Substack was founded in May 2022 — that LLM scaling would not bring us to AGI — has gone mainstream.”
Our reading: The view that LLM scaling will not bring AGI has become mainstream.
No horizon Evidence as of
Limitation: The survey was academically weighted and was not a representative industry poll.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“that LLM scaling would not bring us to AGI”
Our reading: Scaling LLMs will not produce AGI.
No deadline stated Evidence as of
Limitation: There is no universally accepted AGI test.
Assessment: SupportedSaid
Gary Marcus Observation
“The concerns about potential copyright violations that Reid Southen and I expressed here in and at greater length in the IEEE Spectrum in January 2024 continue almost two years later to be a problem.”
Our reading: Copyright violation concerns with generative AI remain a problem as of September 2025.
No horizon Evidence as of
Limitation: A lawsuit establishes a live allegation, not that the defendant infringed.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Perhaps I also no longer need to warn people that the idea that coding would disappear soon is bullshit.”
Our reading: Human coding will not disappear soon due to AI.
No deadline stated Evidence as of
Limitation: This leaves the forecast open because of its timing, not because the evidence shows human coding disappeared.
Assessment: SupportedSaid
Gary Marcus Observation
“No model could have done this in 2022. The graphics are vastly better than they used to be.”
Our reading: Image generation graphics quality has improved vastly since 2022, to a level no 2022 model could reach.
No horizon Evidence as of
Limitation: The evidence does not show that no 2022 model could produce a sharp, mechanically correct image of a rider. The 2022 test found some good outputs.
Source: Spinal Tap meets John Malkovich
Assessment: UnresolvedSaid
Gary Marcus Observation
“Watch the tuners come and go! So much for object permanence.”
Our reading: Grok's generated video failed to keep objects consistent, with the bass tuners appearing and disappearing.
No horizon Evidence as of
Limitation: No evidence items show what the video contains.
Source: Spinal Tap meets John Malkovich
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Agency plus LLMs have already led multiple “vibe coders” to lose databases, with new reports coming in every day or two, stemming simply from the unreliability inherent in LLMs (that Gary has so often harped on).”
Our reading: Agency combined with LLMs has already led multiple vibe coders to lose databases, with new reports every day or two, and these losses stem simply from the unreliability built into LLMs.
No horizon Evidence as of
Limitation: The Cursor incident rests on a user allegation without execution logs or an independent postmortem [e2].
Assessment: UnresolvedSaid
Gary Marcus Forecast
“The bad news is that although this one was stopped, many others won’t be. No one patch would be enough, or even a thousand; there are just too many variations on the overall theme”
Our reading: Many attacks on AI coding tools won't be stopped; not even a thousand patches would suffice given the number of variations.
No deadline stated Evidence as of
Limitation: The evidence concerns researcher demonstrations rather than a census of malicious attacks.
Assessment: MixedSaid
Gary Marcus Observation
“Some customers even wanted the old models back. Almost nobody felt satisfied.”
Our reading: Almost no one was satisfied with GPT-5, and some customers wanted the old models back.
No horizon Evidence as of
Limitation: No representative satisfaction survey of GPT-5 users from the launch week was found, so the share of satisfied users is not known.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“But between the disappointments of GPT-5 and a new study from METR that shows that LLMs do markedly better on coding benchmarks than in real-word practice, I think it is safe to say that LLMs won’t lead the way.”
Our reading: LLMs will not be the approach that leads the way to AGI.
No deadline stated Evidence as of
Limitation: The first accepted AGI is an interpretation of 'lead the way', not a criterion Marcus explicitly supplied.
Assessment: SupportedSaid
Gary Marcus Observation
“But between the disappointments of GPT-5 and a new study from METR that shows that LLMs do markedly better on coding benchmarks than in real-word practice, I think it is safe to say that LLMs won’t lead the way.”
Our reading: LLMs do markedly better on coding benchmarks than in real-world coding practice.
No horizon Evidence as of
Limitation: The study does not establish the same mismatch across all developers, tasks or LLMs; it allows for substantial productivity gains in other coding settings [e2].
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Critically, as I argued at the end of June (and going back to 2019) LLMs never induce proper world models, which is why, for example, they still can’t even play chess reliably,1 and continue to make stupid, head-scratching errors with startling regularity.”
Our reading: LLMs never induce proper world models, which causes them to be unable to play chess reliably and to make frequent stupid errors.
No horizon Evidence as of
Limitation: The strongest intervention evidence concerns a specialist chess model.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“ChatGPT gets them wrong because it is munging words together, not genuinely processing an (elementary) model of British PM history.”
Our reading: ChatGPT gets the British prime minister tasks wrong because it is munging words together rather than genuinely processing an elementary model of British PM history.
No horizon Evidence as of
Limitation: Evidence from Llama-2 cannot directly support or contradict a claim about GPT-5's internal processing.
Assessment: UnresolvedSaid
Gary Marcus Observation
“They don’t induce abstract models of the world that they can reliably engage, even when those models are given to them on a silver platter (as with the rules of chess).”
Our reading: LLMs do not induce abstract world models they can reliably use, even when given the rules explicitly.
No horizon Evidence as of
Limitation: RuleArena tests practical regulations rather than chess.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Even with thinking mode activated, the output was filled with errors that could easily have been avoided, with a 5-second Wikipedia search that any sensible human could have performed.”
Our reading: GPT-5 in thinking mode produced a list of Canadian Prime Ministers full of easily avoidable errors.
No horizon Evidence as of
Limitation: The retrieved plain-text version of the post does not supply independently verifiable image provenance.
Assessment: ContradictedSaid
Gary Marcus Observation
“They do not sanity check their own work.”
Our reading: LLMs do not sanity check their own work.
No horizon Evidence as of
Limitation: SCoRe used specialized training; its results do not establish that every model checks every answer [e1].