Claims checked against evidence, page 8 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 8 of 15: claims 701 to 800 of 1432, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Less than 10% of the work force will be replaced by AI. Probably less than 5%.”
Our reading: By end of 2025 less than 10%, probably less than 5%, of the workforce will be replaced by AI.
Deadline passed Evidence as of
Limitation: Exposure, announced cuts and firm employment effects do not measure realized gross worker replacement [e1, e2, e3].
Assessment: UnresolvedSaid
Gary Marcus Observation
“AI-generated misinformation became increasingly widespread, as I warned in December 2022, but the effects have – thus far — been less than I feared. I continue to be quite concerned, and think vigilance continues to be required.”
Our reading: AI-generated misinformation became increasingly widespread, but its effects have thus far been less than Marcus feared; he remains quite concerned and thinks vigilance continues to be required.
No horizon Evidence as of
Limitation: Site discovery counts do not establish audience exposure, the proportion of content that is false, or how much growth reflects monitoring coverage [e6, e7].
Assessment: UnresolvedSaid
Gary Marcus Observation
“The company has not returned any profits, and is not expecting to do so until at least 2029, but has a burn rate in the billions, and still lacks a clear technical moat.”
Our reading: OpenAI has no profits, does not expect any until at least 2029, burns billions, and lacks a clear technical moat.
No horizon Evidence as of
Limitation: The financial evidence concerns estimates and expectations, not audited realized results.
Assessment: SupportedSaid
Gary Marcus Observation
“There is still no robust solution to hallucinations.”
Our reading: There is still no robust solution to LLM hallucinations.
No horizon Evidence as of
Limitation: The main source is a lab paper describing its own models' limits. Independent leaderboard figures for late 2024 could not be read.
Assessment: SupportedSaid
Gary Marcus Forecast
“Hallucinations (which should really be called confabulations) will continue to haunt generative AI.”
Our reading: Through 2025 hallucinations will continue to plague generative AI.
Deadline passed Evidence as of
Limitation: 'Haunt' has no threshold. The verdict rests on the plain reading that the problem stayed material and recognized.
Assessment: SupportedSaid
Gary Marcus Observation
“There are many such models now; there is a price war. There is very little moat.”
Our reading: There are many GPT-4 level models, a price war, and very little moat among LLM providers.
No horizon Evidence as of
Limitation: 'Moat' is read as a lasting performance lead. Brand, distribution or user base, which are other possible moats, were not measured.
Assessment: SupportedSaid
Gary Marcus Forecast
“AI “Agents” will be endlessly hyped throughout 2025 but far from reliable, except possibly in very narrow use cases.”
Our reading: AI agents will be heavily hyped in 2025 but far from reliable except possibly in very narrow use cases.
Deadline passed Evidence as of
Limitation: The result partly depends on reading coding agents as a 'narrow use case'. A reader who treats coding as broad could call this mixed.
Assessment: SupportedSaid
Gary Marcus Forecast
“OpenAI will continue to preview products months and perhaps years before they are solid and widely available at an accessible price.”
Our reading: By the end of 2025 OpenAI will have continued to preview products months, and perhaps years, before they are solid and widely available at an accessible price.
Deadline passed Evidence as of
Limitation: The result depends on treating premium Pro-only previews as short of ordinary broad consumer access.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Corporate adoption is far more limited than most people expected, and total profits across all companies (except of course hardware companies like NVidia, which profits from chips rather than models) have been modest at best.”
Our reading: Corporate adoption of AI is far more limited than most people expected, and total profits across all companies, except hardware companies like Nvidia that profit from chips rather than models, have been modest at best.
No horizon Evidence as of
Limitation: US broad-AI adoption is only context for corporate generative-AI adoption.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Truly driverless cars, in which no human is required to attend to traffic, will continue to see usage limited to a modest number of cities, mainly in the West, mainly in good weather. Human drivers will still make up a large part of the economy.”
Our reading: By the end of 2025 truly driverless cars, in which no human is required to attend to traffic, will continue to see usage limited to a modest number of cities, mainly in the West and mainly in good weather, and human drivers will still make up a large part of the economy.
Deadline passed Evidence as of
Limitation: Operator announcements are not a global deployment census.
Assessment: UnresolvedSaid
Gary Marcus Observation
“My predictions about the technical and economic limits of generative AI have largely been on target, but I was flat out wrong about investors.”
Our reading: His predictions about generative AI's technical and economic limits were largely correct, but he was wrong about investor behavior.
No horizon Evidence as of
Limitation: Only the financing component is independently established here.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“If there exist AI systems that can perform 8 of the 10 tasks below by the end of 2027, as determined by our panel of judges, Gary will donate $2,000 to a charity of Miles’ choice; if AI can do fewer than 8, Miles will donate $20,000 to a charity of Gary’s choice.”
Our reading: Marcus, in a bet co-signed with Miles Brundage, takes the side that by the end of 2027 AI systems will not be able to perform 8 of the 10 listed tasks, as determined by a panel of judges: he pays $2,000 to Brundage's chosen charity if they can, and Brundage pays $20,000 to Marcus's chosen charity if AI can do fewer than 8.
Not yet due Evidence as of
Limitation: Failure to locate a ruling does not prove that none exists.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Such pretraining is not uncommon in the field, but was not made clear in the video.”
Our reading: Such pretraining is not uncommon in the field, but OpenAI's video did not make clear that o3 had been pretrained on ARC examples.
No horizon Evidence as of
Limitation: Clarity requires considering slides as well as spoken words.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: UnresolvedSaid
Gary Marcus Observation
“What we saw is not AGI.”
Our reading: o3 as demonstrated is not AGI.
No horizon Evidence as of
Limitation: This assesses the categorical wording, not the narrower claim that ARC performance alone did not prove AGI.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: SupportedSaid
Gary Marcus Observation
“At *most* the test is necessary for AGI; it certainly isn’t sufficient.”
Our reading: Passing ARC is at most necessary for AGI and certainly not sufficient.
No horizon Evidence as of
Limitation: The contract notes that AGI is not defined, so 'sufficient' is judged by the designers' stated view.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: MixedSaid
Gary Marcus Observation
“Critical things like factuality, compositionality, and common sense aren’t even addressed.”
Our reading: The ARC test does not address factuality, compositionality, or common sense.
No horizon Evidence as of
Limitation: The result depends on how 'common sense' and 'compositionality' are defined. A narrower reading, such as broad everyday world knowledge, would favor Marcus.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Same was true of the openAI graph: the MIT work (halfway in between o1 and o3) and many others results weren’t shown, making the breakthrough relative to the field seem far bigger than it really was.”
Our reading: OpenAI's graph left out the MIT work (halfway between o1 and o3) and many other results, which made o3's breakthrough relative to the field seem far bigger than it really was.
No horizon Evidence as of
Limitation: The identified MIT figure is an ensemble result, not simply the standalone test-time-training model.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Because the wrong experiment was performed, and key data weren’t given, we can’t compare directly with humans. (And best humans still outperformed the model).”
Our reading: Because the wrong experiment was performed and key data were not given, o3 cannot be compared directly with humans on ARC, and the best humans still outperformed the model.
No horizon Evidence as of
Limitation: No matched evaluation of December o3 and top individual human solvers is supplied.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: SupportedSaid
Gary Marcus Observation
“What was actually done - pretraining on what I believe was hundreds of public examples - is NOT comparable to what humans require.”
Our reading: What was done for o3 on ARC, pretraining on what Marcus believes was hundreds of public examples, is not comparable to what humans require.
No horizon Evidence as of
Limitation: The conclusion depends on reading comparable as referring to ARC-specific preparation.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: SupportedSaid
Gary Marcus Observation
“We still don’t have a solid test of what o3 does without the pretraining, in the case that would be more comparable to humans”
Our reading: There is no solid test yet of o3's ARC performance without pretraining.
No horizon Evidence as of
Limitation: The evaluator page has later updates, and the training note's precise insertion date is not separately archived in this record.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: UnresolvedSaid
Gary Marcus Observation
“Two graphs, one presented by OpenAI and one by Chollet were misleading.”
Our reading: The o3 ARC graphs presented by OpenAI and by Chollet were misleading.
No horizon Evidence as of
Limitation: An image's absence from extracted text cannot establish an omission within that image.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: UnresolvedSaid
Gary Marcus Observation
“Unfortunately the key test was not done.”
Our reading: The key test of o3's base model on ARC without pretraining was not done.
No horizon Evidence as of
Limitation: A narrower reading confined to ARC Prize's own evaluation would be easier to support.
Source: 𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀
Assessment: SupportedSaid
Gary Marcus Observation
“I am saying that many basic problems haven’t been solved.”
Our reading: Many basic problems on the path to AGI remain unsolved.
No horizon Evidence as of
Limitation: The scope and meaning of 'many' are inferred and materially affect the assessment.
Source: o3, AGI, the art of the demo, and what you can expect in 2025
Assessment: SupportedSaid
Gary Marcus Observation
“The product was not yet released to the general public; it’s not been vetted by the scientific community.”
Our reading: As of the demo, o3 had not been released to the public nor vetted by the scientific community.
No horizon Evidence as of
Limitation: 'Vetted by the scientific community' has no precise test. ARC Prize's evaluation is a partial independent check, which he acknowledged.
Source: o3, AGI, the art of the demo, and what you can expect in 2025
Assessment: SupportedSaid
Gary Marcus Observation
“The best results were truly impressive, such as major advances on Francois Chollet’s ARC task and on an important mathematical benchmark FrontierMath.”
Our reading: o3's best demo results showed truly impressive major advances on ARC and FrontierMath.
No horizon Evidence as of
Limitation: 'Truly impressive' is a judgment and was not scored.
Source: o3, AGI, the art of the demo, and what you can expect in 2025
Assessment: SupportedSaid
Gary Marcus Observation
“(It certainly was not the long-awaited GPT-5).”
Our reading: OpenAI's o3 is not the long-awaited GPT-5.
No horizon Evidence as of
Limitation: The core evidence for the demo day is a news report, not an OpenAI page from that day.
Source: o3, AGI, the art of the demo, and what you can expect in 2025
Assessment: UnresolvedSaid
Gary Marcus Forecast
“In 2025, a lot of AI influencers and maybe some companies are going to claim we have reached AGI.”
Our reading: During 2025, many AI influencers and possibly some companies will claim AGI has been reached.
Deadline passed Evidence as of
Limitation: The evidence is a selected set of statements, not a census.
Source: o3, AGI, the art of the demo, and what you can expect in 2025
Assessment: UnresolvedSaid
Gary Marcus Observation
“Even setting aside the 1000x costs, not one person outside of OpenAI has evaluated o3’s robustness across different types of problems.”
Our reading: No one outside OpenAI has evaluated o3's robustness across different problem types.
No horizon Evidence as of
Limitation: A broader reading of problem types could count ARC tasks and produce a different assessment.
Source: o3, AGI, the art of the demo, and what you can expect in 2025
Assessment: UnresolvedSaid
Gary Marcus Observation
“Most commentary has ignored one or both of these ideas.”
Our reading: Most commentary on o3 has ignored one or both of two ideas: that passing ARC may be necessary but not sufficient for AGI, and that a mechanism can be effective in some domains but not others.
No horizon Evidence as of
Limitation: The packet provides no representative sampling frame, denominator, systematic coding, or uncertainty estimate for commentary published between the December 20 announcement and Marcus’s December 21 post.
Source: o3, AGI, the art of the demo, and what you can expect in 2025
Assessment: SupportedSaid
Gary Marcus Observation
“OpenAI’s problems with text and copyright started to become especially clear a year ago, when the New York Times sued them, and showed how close some of the outputs could be to some of the stories on their website.”
Our reading: OpenAI's problems with text and copyright started to become especially clear a year ago, when the New York Times sued OpenAI and showed how close some of the outputs could be to some of the stories on its website.
No horizon Evidence as of
Limitation: A complaint establishes allegations and presented evidence, not a judgment of infringement.
Source: Generative AI’s Continuing Copyright Problems, an Essay in Memory of Suchir Balaji, 1998 - 2024
Assessment: MixedSaid
Gary Marcus Observation
“In January, in IEEE Spectrum, Reid Southen and I demonstrated similar issues in video generation software, such as Midjourney and Dall-E:”
Our reading: In January, in IEEE Spectrum, Marcus and Reid Southen demonstrated issues similar to the New York Times findings in what he called video generation software, such as Midjourney and Dall-E.
No horizon Evidence as of
Limitation: The mixed result turns on retaining the explicit word 'video'.
Source: Generative AI’s Continuing Copyright Problems, an Essay in Memory of Suchir Balaji, 1998 - 2024
Assessment: UnresolvedSaid
Gary Marcus Observation
“OpenAI has yet to make a profit, and doesn’t anticipate making one before 2029.”
Our reading: OpenAI has not yet made a profit and does not expect to before 2029.
No horizon Evidence as of
Limitation: The original financial reporting could not be read with the required tool.
Source: Casey (Newton) Strikes Out
Assessment: UnresolvedSaid
Gary Marcus Observation
“Newton is also conveniently ignoring an absolutely fundamental distinction, between early period of reliable, rapid progress from 2019-2022 (when training for GPT-4) was completed, and a more recent regime in which by many measures, progress has (reportedly) slowed.”
Our reading: After rapid progress in 2019-2022, LLM progress has by many measures reportedly slowed.
No horizon Evidence as of
Limitation: A narrower assessment asking only whether slowdown reports existed would be supported.
Source: Casey (Newton) Strikes Out
Assessment: UnresolvedSaid
Gary Marcus Observation
“Diminishing returns doesn’t mean zero improvement, it means that the “laws” of scaling no longer hold, and that the ROI on adding more data and compute are no longer are predictive in the way that they used to be, hence the need for new approaches.”
Our reading: The diminishing returns he describes in AI do not mean zero improvement; they mean the scaling "laws" no longer hold and the returns on adding more data and compute are no longer predictive in the way they used to be, which is why new approaches are needed.
No horizon Evidence as of
Limitation: Important frontier training results were private.
Source: Casey (Newton) Strikes Out
Assessment: SupportedSaid
Gary Marcus Observation
“In 2022 I warned that there would be chatbot related deaths such as suicides. This began to happen in 2023 and continued in 2024.”
Our reading: Chatbot-related deaths such as suicides occurred in 2023 and 2024.
No horizon Evidence as of
Limitation: Causation is not established. The Belgian link rests on the widow's account and chat logs she supplied. The Setzer link rests on lawsuit allegations.
Source: Casey (Newton) Strikes Out
Assessment: SupportedSaid
Gary Marcus Observation
“Part of the value in skepticism is correctly anticipating road blocks, and suggesting alternative directions (as I have for decades with neurosymbolic AI, which is at last coming into vogue, as noted in Fortune earlier this week).”
Our reading: Neurosymbolic AI is finally coming into vogue.
No horizon Evidence as of
Limitation: The result depends on reading vogue as growing attention.
Source: Casey (Newton) Strikes Out
Assessment: UnresolvedSaid
Gary Marcus Observation
“We still don’t have driverless cars in 97% of the world after a decade’s promises; hallucinations haven’t gone away. Home humanoid robots are nowhere in sight.”
Our reading: Driverless cars are absent from 97% of the world, hallucinations persist, and home humanoid robots are nowhere in sight.
No horizon Evidence as of
Limitation: The percentage is not reproducible without a denominator.
Source: Casey (Newton) Strikes Out
Assessment: UnresolvedSaid
Gary Marcus Forecast
“In February, the day after Sora came out, I warned that it didn’t really understand physics—and wouldn’t — in essay called Sora’s Surreal Physics:”
Our reading: Sora does not really understand physics and will not come to understand it.
No deadline stated Evidence as of
Limitation: Understanding has no speaker-specified test or threshold.
Assessment: SupportedSaid
Gary Marcus Observation
“That gross violation of spatiotemporal continuity/failure of object permanence is not a one-off, either; it’s something general.”
Our reading: Sora's failures of object permanence and spatiotemporal continuity are general rather than isolated.
No horizon Evidence as of
Limitation: Support rests on contemporaneous developer documentation rather than a representative independent audit.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Sora is never really getting it, even with mountains and mountains of data.”
Our reading: Sora will never really grasp object permanence, even with vast amounts of data.
No deadline stated Evidence as of
Limitation: The cited aggregate sentence does not give a Sora-only success rate.
Assessment: UnresolvedSaid
Gary Marcus Observation
“The third illustrates that it cannot reliably eliminate an object part.”
Our reading: ChatGPT with DALL-E 3 cannot reliably eliminate a part of an object from an image.
No horizon Evidence as of
Limitation: The packet contains no independently verified November 2024 omission outputs or directly relevant contemporary evaluation.
Source: Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts
Assessment: ContradictedSaid
Gary Marcus Observation
“The remaining thirteen it got wrong in all three attempts.”
Our reading: In the authors' 17 part-specification experiments, ChatGPT with DALL-E3 got 13 wrong in all three attempts.
No horizon Evidence as of
Limitation: This is a counting finding, not an independent visual assessment of every output.
Source: Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts
Assessment: UnresolvedSaid
Gary Marcus Observation
“But current technology has proven quite poor at such sanity checks. New technology may be needed.”
Our reading: Current AI technology is quite poor at checking whether its own image output satisfies the prompt and feedback, and new technology may be needed.
No horizon Evidence as of
Limitation: The evidence concerns related checking tasks, not an exact replication of the claimed workflow.
Source: Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts
Assessment: UnresolvedSaid
Gary Marcus Observation
“The first example illustrates that ChatGPT with DALL-E 3 is unable to reliably associate labels correctly with the parts of an object.”
Our reading: ChatGPT with DALL-E 3 cannot reliably associate labels correctly with the parts of an object.
No horizon Evidence as of
Limitation: Unresolved does not mean the bicycle example was correct.
Source: Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts
Assessment: UnresolvedSaid
Gary Marcus Observation
“In particular, DALL-E2 was very poor at following specifications about parts of objects.”
Our reading: As of October 2022, DALL-E2 was very poor at following prompt specifications about parts of objects.
No horizon Evidence as of
Limitation: This is an evidence gap, not evidence that DALL-E2 performed well.
Source: Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts
Assessment: UnresolvedSaid
Gary Marcus Observation
“We did not see anything like the kind of improvement that ChatGPT has exhibited over math word problems, over the last two years.”
Our reading: In the authors' body-parts image experiments, they did not see anything like the kind of improvement that ChatGPT has shown on math word problems over the last two years.
No horizon Evidence as of
Limitation: Related benchmark gains cannot substitute for the complete comparison.
Source: Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts
Assessment: UnresolvedSaid
Gary Marcus Observation
“The second example illustrates that it cannot reliably produce a non-standard number of body parts.”
Our reading: ChatGPT with DALL-E 3 cannot reliably produce images with a non-standard number of body parts.
No horizon Evidence as of
Limitation: No verified contemporary prompt-and-image evaluation directly measures requested nonstandard anatomical counts.
Source: Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts
Assessment: UnresolvedSaid
Gary Marcus Observation
“This just in, right on the heels of Satya Nadella also publicly acknowledging diminishing returns:”
Our reading: Satya Nadella has publicly acknowledged diminishing returns.
No horizon Evidence as of
Limitation: Acknowledging possible limits could support a weaker interpretation of Marcus's wording.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Two years on, on the most important question of all – factuality and reliability — we are still pretty much where we were when ChatGPT first came out: wishing and hoping.”
Our reading: Two years after ChatGPT came out, on factuality and reliability, which he calls the most important question, we are still pretty much where we were at its release: wishing and hoping.
No horizon Evidence as of
Limitation: Neither study establishes aggregate progress from November 2022 to November 2024 across representative factuality and reliability tasks.
Source: ChatGPT, at Age Two
Assessment: UnresolvedSaid
Gary Marcus Observation
“There has been a ton of experimentation, but relatively few well-documented success stories, and some fairly negative stories like a recent one at Business Insider on GPT-powered CoPilot,”
Our reading: There has been much experimentation with ChatGPT-style AI but relatively few well-documented success stories.
No horizon Evidence as of
Limitation: The survey measures deployment and measurement practices, not the number of public success stories.
Source: ChatGPT, at Age Two
Assessment: UnresolvedSaid
Gary Marcus Observation
“But a lot of of theories about how ChatGPT would be used in practice have fizzled out.”
Our reading: Many theories about how ChatGPT would be used in practice have fizzled out.
No horizon Evidence as of
Limitation: The population of predictions is undefined.
Source: ChatGPT, at Age Two
Assessment: SupportedSaid
Gary Marcus Observation
“Two years later Google’s search share hasn’t diminished all that much.”
Our reading: Two years after ChatGPT's launch, Google's search share has not diminished much.
No horizon Evidence as of
Limitation: The figures come from secondary reporting of Statcounter because historical primary data were not retrievable.
Source: ChatGPT, at Age Two
Assessment: UnresolvedSaid
Gary Marcus Observation
“Yet hundreds of billions more have been invested on further speculation that scaling would somehow magically cure problems that actually appear to be inherent with the technology.”
Our reading: Hundreds of billions more have been invested on further speculation that scaling would somehow magically cure problems that appear to be inherent to the technology.
No horizon Evidence as of
Limitation: The spending categories cannot be added without checking overlap and accounting treatment.
Source: ChatGPT, at Age Two
Assessment: SupportedSaid
Gary Marcus Observation
“GPT-4 was better than GPT-3 in almost every way; o1 is a far less uniform improvement.”
Our reading: GPT-4 was better than GPT-3 in almost every way, whereas o1 is a far less uniform improvement.
No horizon Evidence as of
Limitation: The result depends on reading the comparison qualitatively across capability categories.
Assessment: UnresolvedSaid
Gary Marcus Observation
“But things have been changing rapidly, and the great scaling retrenchment has already begun.”
Our reading: As of November 2024, a major retreat from scaling claims in the AI field has already begun.
No horizon Evidence as of
Limitation: Great retrenchment has no stated threshold.
Assessment: SupportedSaid
Gary Marcus Observation
“(In some cases o1 doesn’t even do better than GPT-4).”
Our reading: In some cases o1 does not perform better than GPT-4.
No horizon Evidence as of
Limitation: The supported comparison is principally against GPT-4o, not specifically the original GPT-4.
Assessment: UnresolvedSaid
Gary Marcus Observation
“The new version is that there is not one scaling law, but three: scaling with how long you train a model (which isn’t really holding anymore), scaling with how long you post-train a model, and scaling with how long you let a given model wrestle with a given problem (or what Satya Nadella called scaling with “inference time compute”).”
Our reading: Scaling performance by training models longer is no longer really holding.
No horizon Evidence as of
Limitation: The strongest experimental counterevidence concerns an earlier model and is context only.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Even if you look at OpenAI’s own research post on o1, which introduced the newest putative law, it is immediately apparent that scaling test time compute works for some problems and not others.”
Our reading: OpenAI's own o1 research shows that scaling test-time compute helps on some problems but not others.
No horizon Evidence as of
Limitation: The conclusion depends on distinguishing model comparisons from compute-scaling experiments.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Like the first generalization, the new ones will eventually run out.”
Our reading: The new scaling laws (post-training and inference-time) will eventually stop holding.
No deadline stated Evidence as of
Limitation: A narrower reading limited to particular scaling recipes would be easier to support.
Assessment: SupportedSaid
Gary Marcus Observation
“Many other Tech CEOs and others, including Marc Andreeseen (A16Z), Alexandr Wang (ScaleAI), have also used the phrase “hitting a wall” and the idea of diminishing returns in the last couple weeks.”
Our reading: In the last couple of weeks before November 23, 2024, many other tech CEOs and others, including Marc Andreessen and Alexandr Wang, used the phrase "hitting a wall" and the idea of diminishing returns.
No horizon Evidence as of
Limitation: Andreessen did not use the exact phrase 'hitting a wall'; the claim is scored on the group reading.
Source: Satya Nadella and the three stages of scientific truth
Assessment: UnresolvedSaid
Gary Marcus Observation
“Hundreds of billions of dollars were invested on the assumption that I was wrong, largely to the exclusion of investment in other approaches.”
Our reading: Hundreds of billions of dollars went into LLM scaling on the assumption that scaling would keep working, and alternative approaches got little investment.
No horizon Evidence as of
Limitation: The capex figure is a projection made in November 2024 for the full year, not a realized total.
Source: Satya Nadella and the three stages of scientific truth
Assessment: SupportedSaid
Gary Marcus Observation
“Scaling was always a hypothesis.”
Our reading: Scaling (improving AI by adding more data and compute) was always a hypothesis.
No horizon Evidence as of
Limitation: Hypothesis does not mean unsupported speculation.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Crucially, there has never been any principled argument that they would solve hallucinations or reasoning, or edge cases in open-ended worlds —or that synthetic data would suffice indefinitely to keep feeding the machine.”
Our reading: There has never been a principled argument that scaling would solve hallucinations, reasoning, or open-world edge cases, or that synthetic data would suffice indefinitely.
No horizon Evidence as of
Limitation: The claim concerns the existence of arguments, not whether those arguments ultimately proved correct.
Assessment: SupportedSaid
Gary Marcus Observation
“They are merely empirical generalizations that held for a certain period time, when there was enough fresh data and compute.”
Our reading: Scaling laws are merely empirical generalizations that held for a limited period while enough fresh data and compute were available.
No horizon Evidence as of
Limitation: The result depends on reading the sentence in light of the surrounding hedges about scaling having stopped.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Meanwhile, precious little investment has been made in other approaches.”
Our reading: Very little investment has gone into AI approaches other than LLMs.
No horizon Evidence as of
Limitation: The observation period is an explicit annual-reporting interpretation.
Source: CONFIRMED: LLMs have indeed reached a point of diminishing returns
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Given the cost of chips, profits will be elusive.”
Our reading: Because chips are costly, LLM companies will struggle to make profits.
No deadline stated Evidence as of
Limitation: The cited financial evidence concerns one developer.
Source: CONFIRMED: LLMs have indeed reached a point of diminishing returns
Assessment: UnresolvedSaid
Gary Marcus Observation
“Just as I argued here in April 2024, LLMs have reached a point of diminishing returns.”
Our reading: LLMs have reached a point of diminishing returns from scaling.
No horizon Evidence as of
Limitation: The evidence lacks a comparable series of frontier training runs measuring capability gains against proportional increases in compute and data [e1, e2].
Source: CONFIRMED: LLMs have indeed reached a point of diminishing returns
Assessment: UnresolvedSaid
Gary Marcus Forecast
“LLMs such as they are, will become a commodity; price wars will keep revenue low.”
Our reading: LLMs such as they are will become a commodity, and price wars will keep revenue low.
No deadline stated Evidence as of
Limitation: The market study is a preprint.
Source: CONFIRMED: LLMs have indeed reached a point of diminishing returns
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“If enthusiasm for GenAI dwindles and market valuations plummet, AI won’t disappear, and LLMs won’t disappear; they will still have their place as tools for statistical approximation.”
Our reading: If enthusiasm for GenAI dwindles and market valuations plummet, AI and LLMs will not disappear, and LLMs will still have their place as tools for statistical approximation.
No deadline stated Evidence as of
Only if: if enthusiasm for GenAI dwindles and market valuations plummet
Source: CONFIRMED: LLMs have indeed reached a point of diminishing returns
Assessment: SupportedSaid
Gary Marcus Observation
“Many of us, including Melanie Mitchell, Subbarao Kambahapati, Emily Bender, Ernie Davis, etc. have been emphasizing for ages that there are principled limits with LLMs.”
Our reading: Marcus and many others, including Melanie Mitchell, Subbarao Kambhampati, Emily Bender and Ernie Davis, have long emphasized that there are principled limits with LLMs.
No horizon Evidence as of
Limitation: 'For ages' is colloquial, and the evidence does not establish an identical duration for every researcher.
Source: CONFIRMED: LLMs have indeed reached a point of diminishing returns
Assessment: SupportedSaid
Gary Marcus Observation
“People aren’t ignoring GenAI; they are waiting to see if it will work.”
Our reading: People are not ignoring generative AI; they are waiting to see whether it will work.
No horizon Evidence as of
Limitation: 'People' is undefined; the strongest evidence covers businesses.
Source: AI influencer hype gone wild
Assessment: UnresolvedSaid
Gary Marcus Observation
“He then went on to extrapolate that it would be 10,000 times better in 4 years, never considering potential bottlenecks around data, or compute, nor inherent problems with LLMs.”
Our reading: When Musk extrapolated that AI would be 10,000 times better in 4 years, he did not consider potential bottlenecks around data or compute, or inherent problems with LLMs.
No horizon Evidence as of
Limitation: The only item is one news report. It covers parts of Musk's remarks, not the full session.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“(Side bonus: GenAI uses a massive amount of power, to the point of keeping fossil fuels in vogue, and that’s good for oil-rich countries, even if GenAI never becomes reliable.)”
Our reading: Generative AI uses so much power that it keeps fossil fuels in demand, which benefits oil-rich countries whether or not GenAI ever becomes reliable.
No horizon Evidence as of
Limitation: The evidence covers data-centre demand in general, not generative AI specifically.
Assessment: SupportedSaid
Gary Marcus Observation
“(is OpenAI’s o1 100 times better than GPT4? Certainly not by their own data)”
Our reading: By OpenAI's own data, o1 is certainly not 100 times better than GPT-4.
No horizon Evidence as of
Limitation: OpenAI's baseline is GPT-4o, not the original GPT-4.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“My offer to bet him a million dollars on this nonsense stands.”
Our reading: AI will not be able to do anything any human can do within the next one to two years, and Marcus would bet a million dollars on it.
Not yet due Evidence as of
Limitation: The deadline falls after the evaluation date.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“It’s just fantasy to imagine selling 200 times as many humanoid robots in the nearish term when nobody knows how to build a single safe, reliable, generally useful humanoid right now, at any price.””
Our reading: Humanoid robots will not sell 200 times Roomba's roughly 50 million units in the near term.
No deadline stated Evidence as of
Limitation: The threshold inherits the approximation in Marcus's Roomba baseline.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“The same will be true for humanoid robots, and we aren’t going to see six humanoid robots for every car anytime soon.”
Our reading: Many people will not be able to afford or want humanoid robots, and there will not be six humanoid robots per car anytime soon.
No deadline stated Evidence as of
Limitation: Willingness to pay is different from inability to afford a purchase.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Meanwhile GPT-5 still hasn’t arrived, and nor has the much ballyhooed Sora.”
Our reading: As of October 2024, neither GPT-5 nor Sora has been released.
No horizon Evidence as of
Limitation: The required event-date restriction prevents the later launch announcements from serving as decisive evidence.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Those products have not by a longshot lived up to everyone’s expectations, but they do actually ship, and customers can try those products out and decide for themselves. It’s hard to call that smoke and mirrors.”
Our reading: OpenAI's products have not by a longshot lived up to everyone's expectations, but they do actually ship and customers can try them, so it is hard to call OpenAI smoke and mirrors.
No horizon Evidence as of
Limitation: Scoring only product access would omit an explicit part of the listed proposition.
Assessment: ContradictedSaid
Gary Marcus Observation
“The difference between GPT-3 and GPT-4 was by all accounts massive; the difference between GPT-4 and o1 is by all accounts modest.”
Our reading: By all accounts the difference between GPT-3 and GPT-4 was massive, while by all accounts the difference between GPT-4 and o1 is modest.
No horizon Evidence as of
Limitation: The later comparisons use GPT-4o and o1-preview, rather than original GPT-4 and a later o1 release [e1–e3].
Assessment: UnresolvedSaid
Gary Marcus Observation
“But then again, their products have always been served with large side order of bullshit, like when Sam said, apparently jokingly, that “AGI has been achieved internally”. None of the robot stuff they promised in the Rubik’s cube campaign ever shipped.”
Our reading: OpenAI's products have always come with lots of hype, and none of the robotics promised in its Rubik's cube campaign ever shipped.
No horizon Evidence as of
Limitation: The distinction between research ambitions and product promises materially affects the assessment.
Assessment: SupportedSaid
Gary Marcus Observation
“Fall off on increasingly large multiplication problems has repeatedly been observed, both in older models and newer models.”
Our reading: Both older and newer LLMs have repeatedly been observed to get worse as multiplication problems get larger.
No horizon Evidence as of
Limitation: The Deng results are a researcher's social-media post plus a news report, not a peer-reviewed paper.
Source: LLMs don’t do formal reasoning - and that is a HUGE problem
Assessment: MixedSaid
Gary Marcus Observation
“I strongly believe the current results are robust.”
Our reading: Marcus strongly believes that the current results on LLMs' reasoning failures, including the Apple study and the other work he cites, are robust.
No horizon Evidence as of
Limitation: 'I strongly believe' is a statement of belief; only the robustness of the results is graded.
Source: LLMs don’t do formal reasoning - and that is a HUGE problem
Assessment: SupportedSaid
Gary Marcus Observation
“Performance is ok on small problems, but quickly tails off.”
Our reading: o1 does acceptably on small planning problems, but its performance drops quickly as problems get larger.
No horizon Evidence as of
Limitation: The model tested was o1-preview.
Source: LLMs don’t do formal reasoning - and that is a HUGE problem
Assessment: SupportedSaid
Gary Marcus Observation
“Failure to follow the rules of chess is another continuing failure of formal reasoning:”
Our reading: LLMs still fail to follow the rules of chess, which is an ongoing failure of formal reasoning.
No horizon Evidence as of
Limitation: The evidence is one puzzle benchmark with a single prompt format. Other formats or full games may give different illegal-move rates.
Source: LLMs don’t do formal reasoning - and that is a HUGE problem
Assessment: UnresolvedSaid
Gary Marcus Observation
“But the anti-neurosymbolic tradition is limited; its most visible manifestation, LLMs, has brought fame and money, but no robust solution to solving any particular problem with great reliability.”
Our reading: The anti-neurosymbolic tradition is limited. Its most visible manifestation, LLMs, has brought fame and money but no robust solution to solving any particular problem with great reliability.
No horizon Evidence as of
Limitation: High accuracy is evidence of useful capability and should not be dismissed merely because errors remain.
Assessment: ContradictedSaid
Gary Marcus Observation
“We have been told that AI will “solve physics” (per Sam Altman), make major breakthroughs in medicine, battery technology, etc, but so far none of that has materialized.”
Our reading: None of the promised AI advances, such as 'solving physics' (per Sam Altman) or major breakthroughs in medicine and battery technology, has materialized so far.
No horizon Evidence as of
Limitation: The contradiction depends on including basic medical research within medicine; the evidence does not establish transformative clinical benefits [e1, e2].
Assessment: UnresolvedSaid
Gary Marcus Observation
“Almost every putative virtue of AI (aside from making a small number of people rich) has been promissory.”
Our reading: Almost every claimed benefit of AI, other than enriching a few people, has so far been only a promise.
No horizon Evidence as of
Limitation: The search identified examples and a clinical review, not a representative survey of AI promises.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Even though some of the drawbacks, which inherently are less dependent on reliably and tight causal reasoning, such as misinformation use, nonconsensual deepfake porn, ans deepfake kidnapping scams, are already here to say).”
Our reading: Some of AI's drawbacks, which are inherently less dependent on reliable and tight causal reasoning, such as misinformation, nonconsensual deepfake porn, and deepfake kidnapping scams, are already here to stay.
No horizon Evidence as of
Limitation: Reported voice impersonation is not equivalent to forensic confirmation of AI use.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Other forms of AI, that are more domain-specific and specialized might help; those aren’t actually the ones getting the massive funding – or doing the massive harm.”
Our reading: Specialized, domain-specific AI might help with climate change, but it is not the kind getting massive funding or causing massive harm.
No horizon Evidence as of
Limitation: The annual target period is an explicit interpretation of an ongoing observation.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Altman had, much to my surprise, just echoed my longstanding position that current techniques alone would not be enough to get to AGI”
Our reading: Current AI techniques alone will not be enough to reach AGI.
No deadline stated Evidence as of
Limitation: There is no generally accepted empirical test for AGI [e1].
Assessment: SupportedSaid
Gary Marcus Observation
“I rushed out a hot take on X, my usual cautions about how LLMs aren’t really AGI:”
Our reading: LLMs, including OpenAI's new o1, are not really AGI.
No horizon Evidence as of
Limitation: The system card tests several model checkpoints and configurations; it is not an exhaustive evaluation of all contemporary LLMs [e3].
Assessment: SupportedSaid
Gary Marcus Observation
“Almost exactly what I predicted we would see with GPT-4, back on Christmas Day 2022.”
Our reading: Altman's description of o1 as 'still flawed, still limited, seem more impressive on first use' is almost exactly what Marcus predicted we would see with GPT-4 on Christmas Day 2022.
No horizon Evidence as of
Limitation: The agreement is thematic, not verbatim.
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“No such governance board ever materialized; if it does, you can bet it won’t have any teeth, unless there is a government mandate that it does.”
Our reading: The governance board Altman spoke of to The New Yorker in 2016 never materialized, and Marcus bets that if one does, it won't have any teeth unless a government mandate requires it to.
No deadline stated Evidence as of
Only if: if such a board materializes
Source: Scoop: What former employees of OpenAI are worried about
Assessment: UnresolvedSaid
Gary Marcus Observation
“I stressed the same in my Senate testimony, but nothing thus far in existing US law addresses this need.”
Our reading: So far, nothing in existing US law addresses the need for external governance under which companies cannot make decisions of potentially enormous magnitude on their own and external advisory bodies have a strong say in weighing risks against benefits.
No horizon Evidence as of
Limitation: A federal-only assessment would narrow the speaker's wording.
Source: Scoop: What former employees of OpenAI are worried about
Assessment: SupportedSaid
Gary Marcus Observation
“California’s pending AI bill SB-1047 was supposed to be a tiny step in that direction, and even that has gotten watered down.”
Our reading: California's SB-1047, already only a small step toward external AI governance, has been watered down.
No horizon Evidence as of
Limitation: The evidence is the author's dated amendment explanation, not a complete comparison of all bill versions.
Source: Scoop: What former employees of OpenAI are worried about
Assessment: SupportedSaid
Gary Marcus Observation
“When Sam Altman stood next to me in the Senate, he told the Senate that he strongly favored AI regulation. But behind the scenes his team fought to water down the EU AI Act.”
Our reading: Sam Altman told the Senate, standing next to Marcus, that he strongly favored AI regulation, but behind the scenes his team fought to water down the EU AI Act.
No horizon Evidence as of
Limitation: The lobbying finding relies on reputable reporting because the available primary PDF extraction is corrupted.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Companies like OpenAI that have talked big about investing in safety research and then stinted on their promises”
Our reading: OpenAI talked big about investing in safety research and then failed to deliver on those promises.
No horizon Evidence as of
Limitation: The packet does not establish which particular promise Marcus meant.
Assessment: SupportedSaid
Gary Marcus Observation
“It also provides important whistleblower protections, which as many ex OpenAI employees have made clear, are critical.”
Our reading: SB-1047 provides important whistleblower protections, which, as many ex-OpenAI employees have made clear, are critical.
No horizon Evidence as of
Limitation: The assessment concerns a proposed bill, not protections already in force.
Assessment: ContradictedSaid
Gary Marcus Observation
“In the new form, SB 1047 can basically only be used only after something really bad happens, as a tool to hold companies liable. It can no longer protect us against obvious negligence that might likely lead to great harm.”
Our reading: In its new form, SB-1047 can basically only be used after something really bad happens, as a tool to hold companies liable, and it can no longer protect against obvious negligence that might likely lead to great harm.
No horizon Evidence as of
Limitation: The decisive source is the bill sponsor's explanation, rather than a clean statutory transcription.