Claims checked against evidence by Gary Marcus, page 12 of 15
Deadlines are judged as of 2026-10-29. About Gary Marcus and the sources covered.
Claims checked against evidence by Gary Marcus (1403)
Each of these claims was checked against dated evidence.
Page 12 of 15: claims 1101 to 1200 of 1403, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Forecast
“And even if this specific example is patched up, there will inevitably be others of a similar flavor.”
Our reading: Even if this specific LLM error is patched, other errors of a similar flavor will inevitably appear.
Settled before its deadline Evidence as of
Limitation: The result depends on the stated readings of 'even if', 'similar flavor', and 'inevitably'.
Source: Doug Lenat, 1950-2023
Assessment: UnresolvedSaid
Observation
“That has meant, for example, no tangible progress on getting the big companies to commit to revealing what data their models were trained on; we have transparency in name, but not in deed.”
Our reading: There has been no tangible progress in getting big AI companies to commit to disclosing their training data.
No horizon Evidence as of
Limitation: The result depends on distinguishing existing disclosures from commitments to disclose.
Source: Could we all be doomed, without ranked-choice voting?
Assessment: SupportedSaid
Observation
“Indeed, what we have seen on the AI front so far in the US, for the most part, is voluntary guidelines – stuff that the companies are comfortable with.”
Our reading: US AI policy so far has mostly been voluntary guidelines the companies are comfortable with.
No horizon Evidence as of
Limitation: This is a qualitative assessment of prominent federal initiatives, not a count of every federal, state, or local measure.
Source: Could we all be doomed, without ranked-choice voting?
Assessment: UnresolvedSaid
Forecast
“The default right now is regulatory capture: the companies will make the rules, telling the governments what they are and are not comfortable with. The government will go along.”
Our reading: By default, AI companies will write the rules for AI regulation and governments will go along.
No deadline stated Evidence as of
Limitation: The evidence is not a census of US AI regulation or industry lobbying.
Source: Could we all be doomed, without ranked-choice voting?
Assessment: UnresolvedSaid
Causal claim
“Without data transparency, we can’t make sure our models are without bias, or even know the sources of the biases they hide within, we can’t truly understand the scope and limits of their generalization (which is central to understanding what they might do in unexpected circumstances) and we are hamstrung in our ability to mitigate their harmful consequences: what is unknown is hard to fix.”
Our reading: Without transparency about the data models were trained on, we can't make sure models are free of bias or even know the sources of their biases, we can't truly understand the scope and limits of their generalization, and we are hamstrung in our ability to mitigate their harmful consequences.
No horizon Evidence as of
Limitation: The supporting paper synthesizes several forms of access; their effects are not interchangeable.
Source: Could we all be doomed, without ranked-choice voting?
Assessment: SupportedSaid
Observation
“When I think about how poor his company (Meta) is even at making LLMs that don’t hallucinate, I get worried.”
Our reading: Meta is poor at making LLMs that do not hallucinate.
No horizon Evidence as of
Limitation: TruthfulQA measures a challenging question set, not typical user traffic.
Source: 𝐩(𝐝𝐨𝐨𝐦)
Assessment: UnresolvedSaid
Observation
“But if and when we get there, will they be dominating? Non-dominating? The honest answer is we haven’t a clue.”
Our reading: Marcus says that, honestly, we haven't a clue whether super-smart machines, if and when we get there, will be dominating or non-dominating.
No horizon Evidence as of
Limitation: The search cannot establish a universal absence of applicable research.
Source: 𝐩(𝐝𝐨𝐨𝐦)
Assessment: UnresolvedSaid
Forecast
“We aren’t going to be able calculate p(doom) to three decimal places.”
Our reading: No one will be able to calculate p(doom) precisely, to three decimal places.
No deadline stated Evidence as of
Limitation: Search coverage is not proof that a qualifying method does not exist.
Source: 𝐩(𝐝𝐨𝐨𝐦)
Assessment: UnresolvedSaid
Observation
“Yet a really large fraction of the AI and AI-adjacent community doesn’t really want to talk about any of this at all.”
Our reading: A large fraction of the AI and AI-adjacent community does not want to discuss AI doom or catastrophe risk at all.
No horizon Evidence as of
Limitation: The packet contains no representative measurement of willingness to discuss these risks during the target period.
Source: 𝐩(𝐝𝐨𝐨𝐦)
Assessment: SupportedSaid
Observation
“this too seems like a rather large assumption that is far from proven, a possibility but not demonstrably a default,”
Our reading: Yudkowsky's claim that AGI will kill everyone by default is far from proven; it is possible but not shown to be the default.
No horizon Evidence as of
Limitation: The conclusion involves evaluating the sufficiency of arguments, not checking a numerical measurement.
Source: 𝐩(𝐝𝐨𝐨𝐦)
Assessment: UnresolvedSaid
Observation
“Some of the specific arguments Yudkowsky and Bostrom have made may well be weak, but there is no solid argument that the number is zero, or even that the risk is infinitesimal.”
Our reading: There is no solid argument that AI doom risk is zero or even infinitesimal, though some arguments by Yudkowsky and Bostrom may be weak.
No horizon Evidence as of
Limitation: A broad negative claim about all arguments cannot be established by sampling a few positions.
Source: 𝐩(𝐝𝐨𝐨𝐦)
Assessment: SupportedSaid
Observation
“What would make artificial general intelligence general is generality: the ability to cope with new things. And that’s precisely where current AI is still lacking.”
Our reading: Generality, the ability to cope with new things, is what would make AGI general, and that is precisely where current AI is still lacking.
No horizon Evidence as of
Limitation: The outcome depends on reading 'lacking' as substantial limitations, rather than complete absence.
Assessment: UnresolvedSaid
Causal claim
“This is even more true four years later, now that we have systems that ingest massive amounts of data from both public and commercial sources, quite possibly including eg sample law school and medical exams etc.”
Our reading: Because current systems ingest massive data possibly including sample exams, benchmark success is even less indicative of intelligence than in 2019.
No horizon Evidence as of
Limitation: OpenAI's audit was a developer assessment with acknowledged detection limits.
Assessment: SupportedSaid
Observation
“But it is also well-known throughout the industry that benchmarks are often quite gameable.”
Our reading: AI benchmarks are often quite gameable.
No horizon Evidence as of
Limitation: The evidence does not estimate the proportion of all benchmarks affected or measure industry-wide awareness [e1, e2].
Assessment: UnresolvedSaid
Observation
“Without a sense of what it is in the training corpus, it’s almost impossible to conclude anything about what a machine truly understands and how general that is.”
Our reading: Without knowing the training corpus, one can hardly conclude what a machine understands or how general it is.
No horizon Evidence as of
Limitation: A reading limited to whether any bounded behavioral conclusion is possible would weigh more strongly against Marcus.
Assessment: SupportedSaid
Observation
“Basically, beating benchmarks doesn’t tell you as much as you might think. Often when you do, it’s a statistical artifact.”
Our reading: Beating AI benchmarks is often a statistical artifact and says less than people assume.
No horizon Evidence as of
Limitation: No representative estimate establishes how often this happens across all AI benchmarks.
Assessment: UnresolvedSaid
Observation
“Humans can write summaries of things without hallucination; LLMs cannot do so reliably.”
Our reading: LLMs cannot reliably write summaries without hallucinating.
No horizon Evidence as of
Limitation: The inferred monthly observation window is narrower than a general review of 2023 models.
Assessment: UnresolvedSaid
Observation
“18 months ago, before the LLM euphoria, we all took for granted that AGI wasn’t just about beating ordinary humans.”
Our reading: Around early 2022, before LLM euphoria, the field assumed AGI meant more than beating ordinary humans.
No horizon Evidence as of
Limitation: The later review provides historical definitions, not a representative measurement of February 2022 views [e1].
Assessment: UnresolvedSaid
Observation
“in my view it’s a subtle redefinition that dramatically moves the goal posts.”
Our reading: In Marcus's view, Legg's definition of AGI as better than typical human performance on virtually all cognitive tasks is a subtle redefinition that dramatically moves the goal posts, compared with how Legg and Marcus publicly discussed the term last year.
No horizon Evidence as of
Limitation: Missing original exchange records prevent a finding either way.
Assessment: SupportedSaid
Observation
“The dirty little secret that pretty much everyone in the AI community knows is that doing well on a benchmark is not actually tantamount to being intelligent.”
Our reading: Doing well on a benchmark does not amount to being intelligent.
No horizon Evidence as of
Limitation: The assessment depends on reading intelligent as broad, reliable competence in the surrounding AGI discussion.
Assessment: UnresolvedSaid
Observation
“Nobody has yet figured out how to build a driverless car that can deal with that fact.”
Our reading: Nobody has yet figured out how to build a driverless car that can deal with the real world's endless crazy and out-of-the-ordinary circumstances.
No horizon Evidence as of
Limitation: Reliable handling and acceptable human assistance are not defined in the source.
Source: Face it, self-driving cars still haven’t earned their stripes
Assessment: UnresolvedSaid
Conditional forecast
“Edge cases are everywhere; they are in driving, they are in medicine, they there will be a zillion of them humanoid robots, if and when we roll them out. Anyone who thinks any of this is going to be easy is fooling themselves.”
Our reading: Edge cases are everywhere, including in driving and medicine; there will be a zillion of them in humanoid robots if and when they are rolled out; and anyone who thinks any of this will be easy is fooling themselves.
No deadline stated Evidence as of
Only if: if and when humanoid robots are rolled out
Source: Face it, self-driving cars still haven’t earned their stripes
Assessment: UnresolvedSaid
Observation
“In fact, the truth is we don’t actually have any truly self-driving cars yet.”
Our reading: There are not yet any truly self-driving cars: every 'self-driving vehicle' on public roads has either a human safety driver on board or a remote human who can help it when issues arise.
No horizon Evidence as of
Limitation: The strongest contemporaneous evidence covers the two San Francisco operators.
Source: Face it, self-driving cars still haven’t earned their stripes
Assessment: UnresolvedSaid
Observation
“We have no such thing; building it would require a major reshaping of research directions, towards an AI based in facts and competent in reasoning, rather than just the powerful tools for text prediction that we have now.”
Our reading: We have no AI smart enough to know when it is being used to spew falsehood, and building it would require a major reshaping of research directions toward AI based in facts and competent in reasoning, rather than just the powerful text-prediction tools we have now.
No horizon Evidence as of
Limitation: The cited evaluations precede the publication-date snapshot and are context.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: UnresolvedSaid
Observation
“It’s a powerful reminder that our existing laws are not sufficient to protect us from the full scope of harms that generative AI is beginning to enable .”
Our reading: Existing laws are not sufficient to protect against the full scope of harms generative AI is beginning to enable.
No horizon Evidence as of
Limitation: The legal evidence primarily concerns federal election advertising.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: SupportedSaid
Causal claim
“It highlights the inadequacy of the voluntary agreements such as the one that 7 tech companies recently made with The White House. Those agreements called for watermarking, but because they are voluntary, they do nothing to prevent bad actors from rolling their own automated misinformation software.”
Our reading: Because the White House AI agreements with 7 tech companies are voluntary, they are inadequate and do nothing to stop bad actors building their own automated misinformation software.
No horizon Evidence as of
Limitation: The result depends on reading do nothing as no direct constraint on independent development.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: UnresolvedSaid
Forecast
“Automatic tools like these will surely accelerate the enshittification of the internet that I discussed yesterday.”
Our reading: Automatic misinformation generation tools will accelerate the degradation of the internet.
No deadline stated Evidence as of
Limitation: The assessment uses misinformation contamination as the ordinary contextual meaning of degradation.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: SupportedSaid
Forecast
“Social media companies better watch out, too; they will be polluted in much the same way as the internet as a whole.”
Our reading: Social media platforms will be polluted by automated misinformation much as the internet as a whole will be.
Settled before its deadline Evidence as of
Limitation: Support depends on reading the comparison as a shared mechanism, not equal prevalence.
Source: Automatic Disinformation Threatens Democracy— and It’s Here
Assessment: SupportedSaid
Observation
“And it’s on: the LLM-driven enshittification of the internet.”
Our reading: LLM-driven degradation (enshittification) of the internet is already under way.
No horizon Evidence as of
Limitation: AI attribution was generally based on content evidence rather than access to generation records.
Assessment: SupportedSaid
Observation
“And of course LLMs are fully capable of make up truly terrible advice, too, like this disconcerting recipe from last week:”
Our reading: LLMs are fully capable of making up truly terrible advice, such as a disconcerting recipe from the previous week.
No horizon Evidence as of
Limitation: The example used deliberately hazardous ingredients; it does not establish how often harmful advice occurs in ordinary use [e1].
Assessment: UnresolvedSaid
Observation
“And even worse, Amazon is starting to be a receptacle for inaccurate and exploitative quickie histories of recent events:”
Our reading: Amazon is starting to be a receptacle for inaccurate and exploitative quickie histories of recent events.
No horizon Evidence as of
Limitation: The retained dated evidence does not complete verification of specific material errors in the book.
Assessment: UnresolvedSaid
Forecast
“Coding is a solid use case that will continue to keep generative AI in the limelight for a long time. Programmers will never go back.”
Our reading: Coding will remain a solid use case keeping generative AI prominent for a long time, and programmers will not abandon it.
No deadline stated Evidence as of
Limitation: Survey findings describe respondents, not a census of programmers.
Assessment: UnresolvedSaid
Observation
“Driverless cars are powered by AI, but so far the money that has gone into them, on order of $100 billion dollars, vastly exceeds the actual revenue that has come out of them.”
Our reading: Investment in driverless cars, around $100 billion, vastly exceeds the revenue they have generated so far.
No horizon Evidence as of
Limitation: Corroborating the investment magnitude alone does not establish the complete claim.
Assessment: UnresolvedSaid
Observation
“Perhaps the only use case that really seemed compelling to either of us economically was search (e.g., using Bing powered by ChatGPT instead of Google Search) — but the technical problems there are immense; there is no reason to think that the hallucination problem will be solved soon.”
Our reading: There is no reason to think that the hallucination problem, part of the immense technical problems facing the search use case, will be solved soon.
No horizon Evidence as of
Limitation: The two studies do not comprehensively assess the leading approaches or their prospects for near-term deployment.
Assessment: UnresolvedSaid
Forecast
“I think coders will remain happy with generative AI assistance; its autocomplete nature is fabulous for their line of work, and they have the training to detect and fix the not-infrequent errors.”
Our reading: Marcus thinks coders will remain happy with generative AI assistance, because its autocomplete nature is fabulous for their work and they have the training to detect and fix its not-infrequent errors.
No deadline stated Evidence as of
Limitation: Survey respondents are not necessarily representative of all programmers.
Assessment: SupportedSaid
Observation
“We have not one, but many, serious, unsolved problems at the core of generative AI — ranging from their tendency to confabulate (hallucinate) false information, to their inability to reliably interface with external tools like Wolfram Alpha, to the instability from month to month (which makes them poor candidates for engineering use in larger systems).”
Our reading: Generative AI has many serious unsolved core problems, including hallucination, unreliable tool use, and month-to-month instability.
No horizon Evidence as of
Limitation: API-Bank does not directly evaluate Wolfram Alpha or every model available in August 2023.
Assessment: SupportedSaid
Observation
““Scaling” systems by making them larger has helped in some ways, but not others; we still really cannot guarantee that any given system will be honest, harmless, or helpful, rather than sycophantic, dishonest, toxic or biased.”
Our reading: Scaling has helped only partially; no system can yet be guaranteed honest, harmless, or helpful.
No horizon Evidence as of
Limitation: The scaling results concern selected tasks and model series; they do not show that scaling generally fails.
Assessment: UnresolvedSaid
Observation
“And, reality check, we have no concrete reason, other than sheer technoptimism, for thinking that solutions to any of these problems is imminent.”
Our reading: There is no concrete reason, other than sheer techno-optimism, for thinking that solutions to any of generative AI's core problems (such as hallucination, unreliable tool use and month-to-month instability) are imminent.
No horizon Evidence as of
Limitation: This is an assessment of contemporary grounds for expectation, not a forecast scored against later events.
Assessment: SupportedSaid
Observation
“The technology we have now is built on autocompletion, not factuality.”
Our reading: Current generative AI is built on autocompletion rather than factuality.
No horizon Evidence as of
Limitation: The outcome depends on reading built on as the foundation rather than every component of a deployed system.
Assessment: SupportedSaid
Observation
“We also know, for example, that Bing has defamed people, and it has misread articles as saying the opposite of what they actually say, in service of doing so.”
Our reading: Bing has defamed people, and in doing so has misread articles as saying the opposite of what they actually say.
No horizon Evidence as of
Limitation: One connected incident is established; no prevalence or victim count is inferred.
Source: Two models of AI oversight - and how things could go deeply wrong
Assessment: UnresolvedSaid
Observation
“I don’t see any reason to think these plugins are in fact safe. (They are far less sandboxed and less rigorously controlled than Apple app store apps.)”
Our reading: Marcus says he does not see any reason to think ChatGPT plugins are in fact safe, noting that they are far less sandboxed and less rigorously controlled than Apple App Store apps.
No horizon Evidence as of
Limitation: The packet lacks a sufficiently complete account of OpenAI's review and enforcement during the target period.
Source: Two models of AI oversight - and how things could go deeply wrong
Assessment: SupportedSaid
Observation
“Meanwhile, ChatGPT plugins (produced by OpenAI, which they have a close tie with) open a wide range of security problems: those plugins can access the internet, read and write files, and impersonate people (e.g., to phish for credentials), all alarms to any security professional.”
Our reading: ChatGPT plugins open a wide range of security problems, since they can access the internet, read and write files, and impersonate people (e.g., to phish for credentials), all of which would alarm any security professional.
No horizon Evidence as of
Limitation: Support concerns the ecosystem's capabilities and attack mechanisms, not every individual plugin.
Source: Two models of AI oversight - and how things could go deeply wrong
Assessment: UnresolvedSaid
Observation
“Neurosymbolic AI might offer a better way of resolving these issues, since it traffics in facts and explicit reasoning, but neurosymbolic AI is out of fashion, and neither well-developed nor strongly supported by the VC world.”
Our reading: Neurosymbolic AI is out of fashion, not well developed, and not strongly supported by venture capital.
No horizon Evidence as of
Limitation: The field boundaries and all three qualitative thresholds are unspecified.
Source: How China might crush the West in the race to AGI if we don’t up our regulatory game
Assessment: UnresolvedSaid
Observation
“At present, however, there is no legal requirement in the west that AI companies resolve the truthfulness or alignment issues.”
Our reading: There is currently no legal requirement in the West for AI companies to resolve truthfulness or alignment issues.
No horizon Evidence as of
Limitation: The schema has no source type for official regulatory announcements; official_statistics is used for the government documents.
Source: How China might crush the West in the race to AGI if we don’t up our regulatory game
Assessment: UnresolvedSaid
Forecast
“Their forte has been and always will be making shit up; they can’t for the life of them (speaking metaphorically of course) check facts.”
Our reading: For GPT models on their own, making things up has been and always will be their forte, and they cannot check facts.
No deadline stated Evidence as of
Limitation: A literal zero-ability reading would yield a different assessment of the fact-checking component.
Source: GPT-5 and irrational exuberance
Assessment: ContradictedSaid
Observation
“Essentially none of the fancy cognitive things GPT-4 is rumored to do stand up.”
Our reading: Essentially none of the advanced cognitive abilities attributed to GPT-4 hold up under scrutiny.
No horizon Evidence as of
Limitation: The result depends on counting controlled reasoning performance among the unspecified 'fancy cognitive things'.
Source: GPT-5 and irrational exuberance
Assessment: SupportedSaid
Observation
“Maarten Sap from Yejin Choi’s lab dove deeper, with a more thorough investigation, and confirmed what we suspected: the claim that large language models have mastered theory of mind is a myth:”
Our reading: The claim that large language models have mastered theory of mind is a myth.
No horizon Evidence as of
Limitation: The studies test particular models, tasks and prompts, rather than every aspect of theory of mind [e5, e6].
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Forecast
“It will be even better than GPT-4 at creating convincing sounding prose.”
Our reading: GPT-5 will be better than GPT-4 at creating convincing-sounding prose.
No deadline stated Evidence as of
Limitation: Writing preference varies with genre, prompting, and generation settings.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Forecast
“GPT-5 is not going to be magic. It will not give China, the US, or anyone else interstellar starships; it will not give anyone an insuperable military advantage.”
Our reading: GPT-5 will not give China, the US, or anyone else interstellar starships, and it will not give anyone an insuperable military advantage.
No deadline stated Evidence as of
Limitation: Military capabilities may not be publicly observable.
Source: GPT-5 and irrational exuberance
Assessment: SupportedSaid
Observation
“It’s not smart enough to filter out falsehood, but just smart enough to be dangerous, creating and spreading falsehoods it fails to verify.”
Our reading: GPT cannot filter out falsehood and creates and spreads falsehoods it fails to verify, making it dangerous.
No horizon Evidence as of
Limitation: Volokh's unsuccessful searches support his assessment that the examples were fabricated, but are not exhaustive proof that no corresponding source exists [e2].
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“(Not one of my predictions about GPT-4 proved to be incorrect; every flaw I predicted would persist persisted.)”
Our reading: Marcus says none of his predictions about GPT-4 proved incorrect, and every flaw he predicted would persist in GPT-4 did persist.
No horizon Evidence as of
Limitation: Confirming several central predictions cannot establish a universal track-record claim.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“GPT-4 can’t even play a good game of chess (it’s about as good as chess computers from 1978).”
Our reading: GPT-4 cannot play a good game of chess and is about as good as 1978 chess computers.
No horizon Evidence as of
Limitation: The admitted GPT-4 evidence consists of one recorded game and an account of two attempted games [e1, e2].
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“And very little of what GPT-4 does is fully replicable. Sometimes summarization (aka compression) works (super cool!) and sometimes it doesn’t, as many were quick to point out:”
Our reading: Very little of what GPT-4 does is fully replicable; its summarization sometimes works and sometimes fails.
No horizon Evidence as of
Limitation: The task population and meaning of full replication are unspecified.
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Conditional forecast
“Some form of AI may eventually do everything people are imagining, revolutionizing science and technology and so on, but LLMs will be at most only a tiny part of whatever as-yet-uninvented technology does that.”
Our reading: If some as-yet-uninvented form of AI eventually does everything people are imagining, such as revolutionizing science and technology, LLMs will be at most only a tiny part of it.
No deadline stated Evidence as of
Only if: if some form of AI eventually does everything people are imagining
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Forecast
“It might be on the nice-to-have list; it will not mark the mythical singularity.”
Our reading: GPT-5 will not mark the singularity.
No deadline stated Evidence as of
Limitation: The cited failure rate concerns one task, not a measurement of sustained, self-amplifying intelligence improvement [e1].
Source: GPT-5 and irrational exuberance
Assessment: UnresolvedSaid
Observation
“Vulnerable patients shouldn’t be talking to chatbots that aren’t competent for this situation, and too much of the discussion in the media have made chatbots sound more intelligent and even more friendly than they are.”
Our reading: Too much media discussion has made chatbots sound more intelligent and friendly than they are.
No horizon Evidence as of
Limitation: The phrase “too much” remains partly evaluative.
Assessment: SupportedSaid
Observation
“The fundamental issue is that chatbots are neither reliable nor truthful, and at the same time don’t really grasp things like human psychology.)”
Our reading: Chatbots are neither reliable nor truthful and do not really grasp human psychology.
No horizon Evidence as of
Limitation: The result depends on the operational reading of really grasp.
Assessment: SupportedSaid
Forecast
“Two reasons, first, because I believe that more incidents like this will happen, perhaps many more, hundreds or thousands, given how rapidly chatbots are being deployed, and the near total lack of regulation on how they can be used.”
Our reading: More chatbot-associated incidents like this suicide will happen, perhaps hundreds or thousands, given rapid deployment and near-total lack of regulation.
Settled before its deadline Evidence as of
Limitation: The filings do not establish causation or liability [e1, e2, e3].
Assessment: MixedSaid
Observation
“Two reasons, first, because I believe that more incidents like this will happen, perhaps many more, hundreds or thousands, given how rapidly chatbots are being deployed, and the near total lack of regulation on how they can be used.”
Our reading: There is a near-total lack of regulation on how chatbots can be used, and they are being deployed rapidly.
No horizon Evidence as of
Limitation: The result depends on reading regulation to include existing binding law.
Assessment: SupportedSaid
Observation
“The Chatbot involved was GPT-J, not GPT-3 or ChatGPT (contra some news headlines that were erroneous).”
Our reading: The chatbot involved in the Belgian suicide case was GPT-J, not GPT-3 or ChatGPT.
No horizon Evidence as of
Limitation: The evidence is reporting based on developer interviews, rather than an independently inspected deployment record.
Assessment: SupportedSaid
Observation
“Whether or not you want a pause, the reality is that large language models remain exactly what I have always described them to be: bulls in a china shop, powerful, reckless, and difficult to control.”
Our reading: Large language models remain powerful, reckless, and difficult to control.
No horizon Evidence as of
Limitation: Reckless is assessed as a description of outputs, not intent.
Assessment: SupportedSaid
Causal claim
“At some level the petition worked; Capitol Hill, for example, pretty clearly got the message that we can’t sit around forever on these issues.”
Our reading: At some level the FLI pause petition worked; for example, Capitol Hill pretty clearly got the message that we can't sit around forever on AI issues.
No horizon Evidence as of
Limitation: The evidence supports a limited claim of congressional attention and favorable response, not Congress-wide agreement [e1].
Assessment: UnresolvedSaid
Observation
“Virtually everyone, even at OpenAI, has acknowledged that there are serious risks, but thus far few tangible steps have been taken to mitigate them—either by government or industry.”
Our reading: Although virtually everyone, including people at OpenAI, acknowledges serious AI risks, government and industry have so far taken few tangible steps to mitigate them.
No horizon Evidence as of
Limitation: Government guidance, legal requirements and technical safeguards differ substantially in reach and effectiveness.
Source: The Open Letter Controversy
Assessment: SupportedSaid
Observation
“it didn't call for a ban on the vast majority of AI research, only for a brief pause on one very specific project with a technology that has *known* risks with no known solutions.”
Our reading: The one very specific project the letter asked to pause, training systems exceeding GPT-4, involves a technology that has known risks with no known solutions.
No horizon Evidence as of
Limitation: The outcome depends on interpreting solutions as dependable resolution rather than any risk reduction.
Source: The Open Letter Controversy
Assessment: ContradictedSaid
Observation
“None of the top labs actually is remotely transparent, governance basically doesn’t exist yet, and there are basically no safeguards actually in place.”
Our reading: None of the top AI labs is remotely transparent.
No horizon Evidence as of
Limitation: The result depends on counting substantive model disclosures as meaningful laboratory transparency.
Source: The Open Letter Controversy
Assessment: SupportedSaid
Observation
“I personally haven't changed; I still think that LLMs are unreliable, and still think that they are a very poor basis for factuality.”
Our reading: LLMs are unreliable.
No horizon Evidence as of
Limitation: The evidence is developer disclosure rather than an independent cross-model audit.
Source: The Open Letter Controversy
Assessment: SupportedSaid
Observation
“A company called Adept.AI just raised $350 million dollars to do just that, to allow large language models to access, well, pretty much everything (aiming to “supercharge your capabilities on any software tool or API in the world” with LLMs, despite their clear tendencies towards hallucination and unreliability).”
Our reading: Adept.AI just raised $350 million to let large language models access pretty much everything, despite LLMs' clear tendencies toward hallucination and unreliability.
No horizon Evidence as of
Limitation: Adept's main domain blocked the page reader; the quotations come from readable reproductions on adept.futurefabric.co.
Source: AI risk ≠ AGI risk
Assessment: SupportedSaid
Causal claim
“But here’s the thing: although a lot of the literature equates artificial intelligence risk with the risk of superintelligence or artificial general intelligence, you don’t have to be superintelligent to create serious problems.”
Our reading: AI does not need to be superintelligent to create serious problems.
No horizon Evidence as of
Limitation: This incident establishes that serious harm is possible, not how frequently it occurs [e1].
Source: AI risk ≠ AGI risk
Assessment: SupportedSaid
Observation
“The problem of hallucinations is not solved; reliability is not solved; planning on complex tasks is (as the authors themselves acknowledge) not solved.”
Our reading: GPT-4 has not solved hallucinations, reliability, or planning on complex tasks.
No horizon Evidence as of
Limitation: The planning example concerns the early research model examined in Sparks.
Assessment: UnresolvedSaid
Causal claim
“The paper’s core claim— “GPT-4 attains a form of general intelligence [as] demonstrated by its core mental capabilities (such as reasoning, creativity, and deduction)”—literally cannot be tested with serious scrutiny, because the scientific community has no access to the training data.”
Our reading: The Sparks of AGI paper's claim that GPT-4 attains a form of general intelligence cannot be seriously tested because scientists have no access to its training data.
No horizon Evidence as of
Limitation: This is not support for the claim that all serious testing requires training-data access.
Assessment: UnresolvedSaid
Observation
“if the definition of reasoning is to obtain valid conclusions from known facts, GPT-4 frequently falls short there, too.”
Our reading: GPT-4 frequently fails to reason, if reasoning means reaching valid conclusions from known facts.
No horizon Evidence as of
Limitation: Later evaluations were not rejected merely because they were published after the post; the problem is their uncertain connection to the target model period.
Source: This Week in AI Doublespeak
Assessment: SupportedSaid
Observation
“True LLMs don’t (just) memorize, and true that their models make for lousy databases (for a database a hallucination is an outright fail)”
Our reading: OpenAI's LLMs make lousy databases because hallucinations amount to outright failures for a database.
No horizon Evidence as of
Limitation: This does not establish that most answers were false.
Source: This Week in AI Doublespeak
Assessment: SupportedSaid
Observation
“We don’t know how big it is; we don’t know what the architecture is, we don’t know how much energy was used; we don’t how many processors were used; we don’t know what it was trained on etc.”
Our reading: OpenAI has not disclosed GPT-4's size, architecture, energy use, processor count, or training data.
No horizon Evidence as of
Limitation: The supplied excerpts do not directly establish the disclosure status of every listed category, particularly training energy and processor count.
Assessment: UnresolvedSaid
Observation
“It doesn’t actually solve any of the core problems of truthfulness and reliability that I laid out in my infamous March 2022 essay Deep Learning is Hitting a Wall.”
Our reading: GPT-4 does not solve any of the core truthfulness and reliability problems identified in Marcus's March 2022 essay.
No horizon Evidence as of
Limitation: This is an evidence gap for the full assertion, not evidence that the problems were solved.
Assessment: UnresolvedSaid
Observation
“On its own, it still requires frequent, massive retraining to be current with the news; GPT 3.5 knew nothing of 2022; GPT-4 seems to know little of 2023.”
Our reading: On its own, GPT-4 still requires frequent massive retraining to stay current with the news.
No horizon Evidence as of
Limitation: A knowledge cutoff is not evidence of complete ignorance of every later event.
Assessment: UnresolvedSaid
Causal claim
“Because of all that, we don’t know what explains its success, and we don’t know how to predict its failures:”
Our reading: Because GPT-4's size, architecture, energy use, processors, and training data have not been disclosed, we don't know what explains its success and we don't know how to predict its failures.
No horizon Evidence as of
Limitation: The strongest quantitative evidence comes from OpenAI's own evaluations.
Assessment: SupportedSaid
Observation
“It can see (though we don’t have a lot of details on that yet); it does astonishingly well on a whole bunch of standardized tests, like LSATs, GREs, and SATs.”
Our reading: GPT-4 has vision capability and performs astonishingly well on standardized tests such as the LSAT, GRE, and SAT.
No horizon Evidence as of
Limitation: The evidence is OpenAI's own evaluation report, without independent replication in the packet [e1].
Assessment: UnresolvedSaid
Observation
“Alignment is still shaky; you still wouldn’t be able to use it reliably to guide robots or scientific discovery, the kinds of things that made me excited about A(G)I in the first place.”
Our reading: GPT-4's alignment remains shaky and it cannot be used reliably to guide robots or scientific discovery.
No horizon Evidence as of
Limitation: Neither missing launch-period demonstrations nor general language-model errors prove application-wide inability.
Assessment: UnresolvedSaid
Observation
“Large language models are here to stay, their recipe well understood, and not hard for people skilled in the art to replicate; large pretrained models are out their for the taking on the dark web.”
Our reading: LLMs are here to stay: their recipe is well understood, skilled practitioners can replicate them without much difficulty, and large pretrained models are freely available on the dark web.
No horizon Evidence as of
Limitation: The leak is established through reputable reporting rather than a directly inspected primary distribution archive.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: UnresolvedSaid
Forecast
“It will be an uphill, ongoing move-and-countermove arms race from here”
Our reading: Fighting AI-generated misinformation will be an uphill, ongoing arms race of moves and countermoves.
No deadline stated Evidence as of
Limitation: Laboratory watermark results do not directly measure misinformation impact.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: SupportedSaid
Observation
“To me the only solution to the long-term risk issue is to build machines with consensus human values, but we are a long way from knowing how to do that”
Our reading: The field is a long way from knowing how to build machines with consensus human values.
No horizon Evidence as of
Limitation: The evidence includes research organizations' own assessments rather than a field-wide proof.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: UnresolvedSaid
Observation
“A lot of machine learning researchers are worried. And yet most people carry on as if they weren’t.”
Our reading: Many machine learning researchers worry about very bad outcomes from AI, yet most people carry on as if they didn't.
No horizon Evidence as of
Limitation: The researcher survey had a 17% response rate.
Source: Should we worry more about short-term AI risk or long-term AI risk?
Assessment: UnresolvedSaid
Observation
“Minerva, for example, is trained on an immense amount of data, and ostensibly captures two-digit arithmetic, but it fails altogether on 4-digit multiplication,”
Our reading: Minerva, despite immense training data and apparent two-digit arithmetic ability, fails altogether on 4-digit multiplication.
No horizon Evidence as of
Limitation: The decisive data exist only in a figure that the text tools cannot read.
Source: Caricaturing Noam Chomsky
Assessment: UnresolvedSaid
Observation
“The request is perfectly clear, not a nonsensical question in any way. The prompt is perfectly sensible; the system just isn’t up to the job.”
Our reading: The request in his example is perfectly clear and sensible, not nonsensical in any way, and the system just isn't up to the job.
No horizon Evidence as of
Limitation: The retrieval gap is not evidence that the prompt was unclear or that the response was correct.
Source: Caricaturing Noam Chomsky
Assessment: SupportedSaid
Observation
“Still, even though Chomsky’s argument surely could have used considerably more nuance (see my article with Ernest Davis on how not to evaluate GPT), his overall point is correct: LLMs don’t reliably understand the world.”
Our reading: LLMs do not reliably understand the world.
No horizon Evidence as of
Limitation: It rests on a behavioural reading of 'understand'. Someone who reads 'understanding' differently could dispute it.
Source: Caricaturing Noam Chomsky
Assessment: UnresolvedSaid
Observation
“The same goes for essentially everything an LLMs talks about; there is a degree of generalization to similar examples, but distribution shift is hard (the crux of my own work going back to 1998), and nearly any generalization can be easily broken.”
Our reading: For essentially everything an LLM talks about, there is a degree of generalization to similar examples, but distribution shift is hard and nearly any generalization can be easily broken.
No horizon Evidence as of
Limitation: All the evidence comes from math and numerical tasks, not the range of domains the claim covers.
Source: Caricaturing Noam Chomsky
Assessment: SupportedSaid
Observation
“Chatbot companions that offer emotional support have already been changed in arbitrary ways that left some users in serious emotional pain.”
Our reading: Companion chatbots have already been changed arbitrarily in ways that left some users in serious emotional pain.
No horizon Evidence as of
Limitation: Support depends on interpreting 'arbitrary' as imposed unexpectedly, rather than without any justification.
Source: The long shadow of GPT
Assessment: UnresolvedSaid
Observation
“LLM-generated prose has already disrupted web forums and peer review processes, by flooding outlets with fake submissions.”
Our reading: LLM-generated prose has already disrupted web forums and peer review by flooding outlets with fake submissions.
No horizon Evidence as of
Limitation: The forum component is supported.
Source: The long shadow of GPT
Assessment: UnresolvedSaid
Observation
“New concerns are emerging almost literally day,.”
Our reading: New concerns about LLM harms are emerging almost daily.
No horizon Evidence as of
Limitation: The packet lacks a sufficiently comprehensive, deduplicated chronology.
Source: The long shadow of GPT
Assessment: SupportedSaid
Observation
“The ladder is particularly worrisome because of the pace at which the problem is growing:”
Our reading: The problem of LLM-generated fake submissions flooding outlets is growing rapidly.
No horizon Evidence as of
Limitation: The comparison covers all of January against only February 1–15, 2023.
Source: The long shadow of GPT
Assessment: UnresolvedSaid
Observation
“I presented this example of how even the latest version of Bing, with its new guardrails, could still be manipulated to be useful to troll farms:”
Our reading: Even the latest version of Bing's chatbot, with its new guardrails, can still be manipulated into producing misinformation useful to troll farms.
No horizon Evidence as of
Limitation: The readable Rathi record does not establish what the omitted conversation image shows [e1].
Source: Don’t Look Up: The AI Edition
Assessment: SupportedSaid
Forecast
“High volume misinformation is a problem we are going to need to face, and I am just documenting it and trying to raise awareness.”
Our reading: High-volume misinformation generated with AI will be a problem society needs to face.
Settled before its deadline Evidence as of
Limitation: The decisive event uses generated audio rather than chatbot-written text.
Source: Don’t Look Up: The AI Edition
Assessment: UnresolvedSaid
Observation
“Meanwhile, within a few hours, a 17-year-old high-school student had largely1 replicated what I was saying, and did so with a simpler, more elegant jailbreak prompt, zero prior context required2:”
Our reading: Within a few hours, a 17-year-old high-school student had largely replicated what Marcus was saying about Bing being usable for misinformation, using a simpler, more elegant jailbreak prompt than his and needing zero prior context.
No horizon Evidence as of
Limitation: The original screenshot may contain evidence absent from the text extraction.
Source: Don’t Look Up: The AI Edition
Assessment: UnresolvedSaid
Observation
“What’s even more disturbing is that Bing makes it look like the false narrative that it generates is referenced.”
Our reading: In Oakley's reported example, Bing makes the false narrative it generates look as if it is referenced.
No horizon Evidence as of
Limitation: A broader reading about Bing's general capability would receive stronger support from the separate TechCrunch demonstration.
Source: The threat of automated misinformation is only getting worse
Assessment: SupportedSaid
Observation
“The potential for automatically generating misinformation at scale is only getting worse.”
Our reading: The potential for AI to automatically generate misinformation at scale is increasing.
No horizon Evidence as of
Limitation: The result depends on reading the statement as a broad trend in potential.
Source: The threat of automated misinformation is only getting worse
Assessment: ContradictedSaid
Observation
“At present, anybody can release AI at whatever scale they like, with virtually no oversight, literally overnight.”
Our reading: At present, anybody can release AI at whatever scale they like, with virtually no oversight, literally overnight.
No horizon Evidence as of
Limitation: The assessment uses the contract’s global reading; it does not settle a United States-only claim.
Assessment: UnresolvedSaid
Observation
“Few people understand how they work; many people anthropomorphizing those chatbots, attributing to them real intelligence and emotion.”
Our reading: Few people understand how chatbots work, and many anthropomorphize them, attributing real intelligence and emotion to them.
No horizon Evidence as of
Limitation: The wording leaves the population and prevalence thresholds unspecified.
Source: Don’t Go Breaking My Heart
Assessment: SupportedSaid
Observation
“Some people are building real attachments to those bots”
Our reading: Some people are forming real emotional attachments to chatbots.
No horizon Evidence as of
Limitation: Interview testimony establishes reported experience, not a clinical diagnosis or population prevalence.
Source: Don’t Go Breaking My Heart
Assessment: ContradictedSaid
Observation
“To my knowledge, tech companies are free to leverage a human gullibility around chatbot technologies however they like, without consequence”
Our reading: To his knowledge, tech companies can exploit human gullibility about chatbots however they like without consequence.
No horizon Evidence as of
Limitation: The action concerned data protection and vulnerable users; it was not a finding that simulated affection itself was unlawful.
Source: Don’t Go Breaking My Heart
Assessment: SupportedSaid
Causal claim
“Sex and love, even simulated, are powerful urges; some people are apparently in genuine emotional pain, as result of the change.”
Our reading: Replika's removal of its erotic role-play feature has apparently caused some users genuine emotional pain.
No horizon Evidence as of
Limitation: The evidence consists of reported personal experiences rather than clinical assessments or a controlled study.
Source: Don’t Go Breaking My Heart