Claims checked against evidence, page 7 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 7 of 15: claims 601 to 700 of 1432, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: SupportedSaid
Gary Marcus Observation
“And of course, there is still no GPT-5, even after two years, of hype, bullshit, and cryptic hints.”
Our reading: OpenAI still has not released GPT-5 after two years of hype.
No horizon Evidence as of
Limitation: The assessment concerns public availability, not internal prototypes.
Source: OpenAI, in deep trouble
Assessment: UnresolvedSaid
Gary Marcus Observation
“Even Altman himself was uncharacteristically tepid in his product announcement.”
Our reading: Sam Altman was unusually tepid in announcing GPT-4.5, not mentioning AGI.
No horizon Evidence as of
Limitation: The packet provides only one earlier product announcement, which cannot establish Altman's usual tone. [e3]
Assessment: SupportedSaid
Gary Marcus Observation
“So now GPT 4.5 has dropped, and — breaking news — I was right. Hallucinations didn’t disappear today, and nor did stupid errors.”
Our reading: GPT-4.5 did not eliminate hallucinations or stupid errors.
No horizon Evidence as of
Limitation: The evidence is developer-reported.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Gary Marcus was right all along that pure scaling would eventually hit a wall, or at least reach a point of massively diminishing returns.”
Our reading: Pure scaling of LLMs has hit a wall or at least massively diminishing returns.
No horizon Evidence as of
Limitation: The launch comparisons do not isolate the contribution of pretraining scale or provide a controlled measure of returns to additional resources. [e1]
Assessment: UnresolvedSaid
Gary Marcus Observation
“Right now, the math for profitability doesn’t work, influence on productivity of GenAI has been minimal, and prospects for pure scaling aren’t looking great.”
Our reading: The math for generative AI profitability currently doesn't work.
No horizon Evidence as of
Limitation: Available loss reporting is not an industry financial statement.
Source: GPT 4.5 is no GPT-5
Assessment: UnresolvedSaid
Gary Marcus Observation
“Right now, the math for profitability doesn’t work, influence on productivity of GenAI has been minimal, and prospects for pure scaling aren’t looking great.”
Our reading: Generative AI's influence on productivity has been minimal.
No horizon Evidence as of
Limitation: The original survey estimates were subsequently revised.
Source: GPT 4.5 is no GPT-5
Assessment: UnresolvedSaid
Gary Marcus Observation
“It’s not just OpenAI either; it’s the whole field, as I discussed in my own recent Fortune article:”
Our reading: Diminishing returns from scaling affect the whole AI field, not just OpenAI.
No horizon Evidence as of
Limitation: Important underlying experiments were confidential.
Source: GPT 4.5 is no GPT-5
Assessment: SupportedSaid
Gary Marcus Forecast
“Actually, at any moment, we will finally get GPT 4.5, which most certainly wouldn’t live up to expectations around GPT-5 — hence its rather tentative name.”
Our reading: GPT 4.5 will not live up to the expectations that had been set for GPT-5.
Settled before its deadline Evidence as of
Limitation: The decisive measurements are company-reported.
Source: GPT 4.5 is no GPT-5
Assessment: UnresolvedSaid
Gary Marcus Observation
“The truth is that scaling just isn’t producing the fruit that it once did.”
Our reading: Scaling is no longer producing the gains it once did.
No horizon Evidence as of
Limitation: The available reporting is more specific to particular projects than the claim.
Source: GPT 4.5 is no GPT-5
Assessment: UnresolvedSaid
Gary Marcus Observation
“And for that matter people should be comparing GenAI revenue (on the order of low tens of billions per year, pre price wars) with GenAI expenditures (order half a trillion dollars to date), and doing the math, especially given the price wars induced by DeepSeek.”
Our reading: GenAI revenue is on the order of low tens of billions of dollars per year (before the price wars), while GenAI expenditures are on the order of half a trillion dollars to date.
No horizon Evidence as of
Limitation: The claim combines different periods and incompletely defined industry boundaries.
Source: GPT 4.5 is no GPT-5
Assessment: SupportedSaid
Gary Marcus Observation
“In “Deep learning is hitting a wall”, I warned that problems with reasoning and hallucination wouldn’t go away simply as a function of scaling, and they haven’t.”
Our reading: Problems with reasoning and hallucination in deep learning systems have not gone away as a function of scaling.
No horizon Evidence as of
Limitation: Humanity's Last Exam combines knowledge and reasoning; not every wrong answer identifies a reasoning failure.
Source: GPT 4.5 is no GPT-5
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“I personally noticed Musk’s shutting out of opposing views when he stopped answering my emails when I warned him in July – correctly - that throwing in with Trump would undermine the cause of AI safety (which for a decade, until a few weeks ago, seemed extremely important to Musk).”
Our reading: Musk's alignment with Trump has undermined the cause of AI safety, as Marcus correctly warned in July 2024.
No horizon Evidence as of
Limitation: No admitted evidence establishes Musk's causal contribution to the federal advanced-AI policy rollback.
Source: Elon Musk's inability to listen to others is torching almost everything he touches
Assessment: UnresolvedSaid
Ed Zitron Forecast
“I must repeat that Microsoft will have spent over $200 billion in capital expenditures by the end of 2025.”
Our reading: By the end of 2025, Microsoft will have spent over $200 billion in capital expenditures since 2023.
Deadline passed Evidence as of
Limitation: This record does not assess Microsoft's capital spending in any particular period.
Source: There Is No AI Revolution
Assessment: SupportedSaid
Gary Marcus Observation
“Simply false, in view of the many basic aspects of geometry that AlphaGeometry2 does not cover, as discussed above.”
Our reading: The claim that AlphaGeometry2 'just figured out geometry' is false because it does not cover many basic aspects of geometry.
No horizon Evidence as of
Limitation: Support depends on reading figured out geometry as general mastery.
Source: AlphaGeometry2: Impressive accomplishment, but still a long path ahead
Assessment: UnresolvedSaid
Gary Marcus Observation
“Taking this into account, the fraction of problems where a composite AI system could solve the problem starting with its English language statement is at most 60%.”
Our reading: Of the 50 IMO geometry problems from 2000 to 2024, a composite AI system starting from the English problem statements could solve at most 60%.
No horizon Evidence as of
Limitation: Automatic translation success does not itself establish solving success.
Source: AlphaGeometry2: Impressive accomplishment, but still a long path ahead
Assessment: UnresolvedSaid
Gary Marcus Observation
“DeepMind didn’t make that claim, and it isn’t true.”
Our reading: The TechCrunch headline's claim about AlphaGeometry2 was not made by DeepMind and is not true.
No horizon Evidence as of
Limitation: The packet contains no quoted DeepMind report or matched performance evidence.
Source: AlphaGeometry2: Impressive accomplishment, but still a long path ahead
Assessment: SupportedSaid
Gary Marcus Observation
“Unlike DeepMind’s AlphaZero system which learned to play multiple board games, or OpenAI’s ChatGPT, AlphaGeometry2 is nowhere close to being a single general-purpose learning architecture.”
Our reading: AlphaGeometry2 is a specialized system, nowhere close to a single general-purpose learning architecture.
No horizon Evidence as of
Limitation: The documentation became available after Marcus’s February 17 post.
Source: AlphaGeometry2: Impressive accomplishment, but still a long path ahead
Assessment: UnresolvedSaid
Gary Marcus Conditional forecast
“Even if one were to glom together the best general AIs with AlphaGeometry2, the combination would fall far short of the geometric understanding and problem solving ability of high school math student with a talent for math.”
Our reading: Combining the best general AIs with AlphaGeometry2 would still fall far short of a talented high school math student's geometric understanding and problem-solving.
No deadline stated Evidence as of
Only if: if the best general AIs were combined with AlphaGeometry2
Source: AlphaGeometry2: Impressive accomplishment, but still a long path ahead
Assessment: SupportedSaid
Gary Marcus Observation
“About year ago the City of New York tried this kind of thing, with less than spectacular results:”
Our reading: About a year ago the City of New York tried this kind of thing (deploying problematic, unreliable AI to the public), with less than spectacular results.
No horizon Evidence as of
Limitation: The documented tests do not establish a representative overall error rate.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Over the last couple days I have been asking version 2 to draw images identifying and tagging parts of images. The results are consistently awful:”
Our reading: When Marcus asked Grok 2 over the last couple of days to draw images identifying and tagging parts of images, the results were consistently awful.
No horizon Evidence as of
Limitation: Text extraction cannot establish what the embedded images show.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“And it will be the citizens of the United States who bear the consequences, while the guy who stood to get a $400M armored T̶e̶s̶l̶a̶ EV government contract (until the media squawked) stands to collect a big slice of the profits, unemploying thousands of lifelong government employees.”
Our reading: US citizens will bear the consequences, while Musk stands to collect a big slice of the profits, putting thousands of lifelong government employees out of work.
No deadline stated Evidence as of
Limitation: Employment totals combine different reasons for departure and do not establish AI causation.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Yet Musk wants to use shove problematic AI, both biased and unreliable, down our throats, immediately, as reported by The New York Times, and noted in this Bluesky comment:”
Our reading: Musk wants to deploy biased and unreliable AI in government immediately.
No horizon Evidence as of
Limitation: The usable evidence is news reporting based on sources describing an internal meeting; no primary meeting record was successfully read.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Soon such unreliable stuff, with butt-covering warning messages that most people will ignore, will be everywhere.”
Our reading: Soon such unreliable AI, with butt-covering warning messages that most people will ignore, will be everywhere.
No deadline stated Evidence as of
Limitation: The search does not establish that qualifying evidence cannot exist.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“For nearly three years I have been saying that the pure scaling of LLMs – adding more data and more parameters – would eventually run out, and that it would fail to solve hallucinations and boneheaded errors and that scaling laws were merely empirical generalizations, rather than physical laws.”
Our reading: Marcus restates his long-held prediction that pure scaling of LLMs (adding more data and more parameters) would eventually run out and would fail to solve hallucinations and boneheaded errors, and that scaling laws are merely empirical generalizations rather than physical laws.
No deadline stated Evidence as of
Limitation: Available model comparisons change several factors simultaneously.
Source: Breaking: OpenAI's efforts at pure scaling have hit a wall.
Assessment: SupportedSaid
Gary Marcus Forecast
“Second, pure scaling will no longer be the means of attack.”
Our reading: OpenAI will no longer rely on pure scaling as its means of building GPT-5.
Settled before its deadline Evidence as of
Limitation: The disclosure establishes the additional mechanisms, not their precise share of capability gains.
Source: Breaking: OpenAI's efforts at pure scaling have hit a wall.
Assessment: UnresolvedSaid
Gary Marcus Observation
“They were overhyping LLMS as AGI, even as problems like hallucinations and boneheaded persisted.”
Our reading: As a premise of Taming Silicon Valley, tech oligarchs were overhyping LLMs as AGI even as problems like hallucinations and boneheaded errors persisted.
No horizon Evidence as of
Limitation: The packet lacks a specific executive AGI claim matched to contemporary evaluations of the corresponding systems and capabilities.
Source: Everything I warned about in Taming Silicon Valley is rapidly becoming our reality
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“(Also, having our own massive LLMs will do nothing to stop China from using theirs in authoritarian pursuits.)”
Our reading: US possession of massive LLMs will do nothing to stop China from using its LLMs for authoritarian purposes.
No horizon Evidence as of
Limitation: Public records provide limited visibility into covert deployments and their outcomes.
Source: Everything I warned about in Taming Silicon Valley is rapidly becoming our reality
Assessment: ContradictedSaid
Gary Marcus Forecast
“The new administration will do everything in its power to protect businesses, and nothing to protect individuals.”
Our reading: The new administration will do everything in its power to protect businesses, and nothing to protect individuals.
Settled before its deadline Evidence as of
Limitation: The enactment records do not measure enforcement or effectiveness.
Source: Everything I warned about in Taming Silicon Valley is rapidly becoming our reality
Assessment: UnresolvedSaid
Gary Marcus Observation
“LLMs, once novel, are largely a commodity.”
Our reading: LLMs have largely become a commodity.
No horizon Evidence as of
Limitation: The survey’s 2024 findings cannot be isolated to the target window [e1].
Assessment: SupportedSaid
Gary Marcus Observation
“Even the latest systems like Deep Research are still struggling in a few ways – and those ways pretty much correspond exactly to the places that I warned would be LLM’s Achilles’ Heels: hallucinations and reasoning errors.”
Our reading: The latest systems such as OpenAI's Deep Research still suffer from hallucinations and reasoning errors.
No horizon Evidence as of
Limitation: Supports the existence of both error classes, not any particular frequency or severity.
Assessment: UnresolvedSaid
Gary Marcus Observation
“(Note that test-time compute systems are not across the board improvements like GPT-4 was relative to GPT-3 or GPT-3 was relative to GPT-2, but rather improvements in certain domains such as coding and math.)”
Our reading: Test-time compute systems improve only certain domains like coding and math, not across the board as GPT-4 did over GPT-3.
No horizon Evidence as of
Limitation: The decisive model evaluations are developer-reported.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“A key claim of the paper was that pure scaling of LLMs – just adding more data and compute to extant LLM architectures – would not bring us to AGI, and that so-called scaling laws were merely empirical generalizations rather than physical laws.”
Our reading: Pure scaling of LLMs by adding data and compute to existing architectures will not reach AGI; scaling laws are empirical generalizations, not physical laws.
No deadline stated Evidence as of
Limitation: Public model documentation may not disclose enough to establish scaling-only provenance.
Assessment: SupportedSaid
Gary Marcus Observation
“To some extent newer models are doing that.”
Our reading: Newer models such as DeepSeek R1, with a rule-based reward system, partly use neurosymbolic techniques.
No horizon Evidence as of
Limitation: The outcome depends on the broad, training-inclusive meaning supplied by the speaker's example.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“What changed here was the AI landscape. It’s all about money, of course.”
Our reading: Google's shift toward military and surveillance AI was caused by financial pressure in the changed AI landscape.
No horizon Evidence as of
Limitation: Public explanations can omit commercial considerations.
Source: Google, 2001: Don’t Be Evil
Assessment: UnresolvedSaid
Gary Marcus Forecast
“If they were already making zillions from AI, they would have mentioned that on the investor call. But presumably they are not, and that cannot continue indefinitely.”
Our reading: Google is presumably not already making zillions from AI (or it would have said so on the investor call), and that situation cannot continue indefinitely.
No deadline stated Evidence as of
Limitation: The baseline premise remains unverified.
Source: Google, 2001: Don’t Be Evil
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Google has to find a way to make back the money back. Bye bye corporate motto, hello military and surveillance.”
Our reading: Pressure to recoup AI investment is driving Google to abandon its motto and pursue military and surveillance applications.
No horizon Evidence as of
Limitation: The packet contains no decision records or attributable testimony establishing whether pressure to recover AI investment materially influenced the change.
Source: Google, 2001: Don’t Be Evil
Assessment: UnresolvedSaid
Gary Marcus Observation
“Reading between the lines, Google is locked in a epic arms race for AI supremacy that is not so far paying off.”
Our reading: Judging by its earnings call omissions, Google's AI arms race spending has not paid off so far.
No horizon Evidence as of
Limitation: A narrower claim that Google reported no AI revenue would face contrary evidence, but that is not the proposition being graded.
Source: Google, 2001: Don’t Be Evil
Assessment: UnresolvedSaid
Gary Marcus Observation
“failed, multiple times, to include a full list of all US states”
Our reading: In Marcus's test, the free version of ChatGPT failed several times to produce a complete list of all 50 US states.
No horizon Evidence as of
Limitation: The shared transcript linked in Marcus's post was not found, so none of his tables could be checked against the list of 50 states.
Source: ChatGPT in Shambles
Assessment: UnresolvedSaid
Gary Marcus Observation
“issued numerous corrections that were wrong, never acknowledging uncertainty until after its errors were called out.”
Our reading: In Marcus's test, ChatGPT issued many wrong corrections and acknowledged no uncertainty until its errors were pointed out.
No horizon Evidence as of
Limitation: No substantive conversation messages were available as admissible outcome evidence.
Source: ChatGPT in Shambles
Assessment: UnresolvedSaid
Gary Marcus Observation
“failed, multiple times, to properly count to 50”
Our reading: In Marcus's test, ChatGPT failed several times to count to 50 correctly.
No horizon Evidence as of
Limitation: The shared transcript linked in Marcus's post was not found, so the rows in his tables could not be counted.
Source: ChatGPT in Shambles
Assessment: UnresolvedSaid
Gary Marcus Observation
“reported that the letter h could be a vowel, at least when it appeared in the word Northwest”
Our reading: In Marcus's test, ChatGPT reported that the letter h could be a vowel, at least in the word Northwest.
No horizon Evidence as of
Limitation: The shared transcript and the screenshots of the Northwest Territories answer were not found.
Source: ChatGPT in Shambles
Assessment: UnresolvedSaid
Gary Marcus Forecast
“We can be sure that DeepResearch will get heavily used.”
Our reading: Deep Research will be heavily used.
No deadline stated Evidence as of
Limitation: The disclosure does not quantify Deep Research's absolute scale or frequency of use.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Deep Research, because it works faster and expands the reach of what can be automatically generated, is going to make that problem worse.”
Our reading: Deep Research is going to make the problem of the internet being polluted with AI-generated garbage content worse, because it works faster and expands the reach of what can be automatically generated.
No deadline stated Evidence as of
Limitation: Missing product attribution is not evidence that no contribution occurred.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Unfortunately, virtually everything that Deep Research produces will pass a LGTM test ( “it ‘looks good to me’”); people will assume it’s legit, even when it is isn’t.”
Our reading: Virtually all Deep Research output will superficially look good and people will assume it is legitimate even when it is not.
No deadline stated Evidence as of
Limitation: The absence of a located study is an evidence gap, not proof that the predicted behavior does not occur.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: SupportedSaid
Gary Marcus Observation
“And that’s just not realistic given the current state of AI.”
Our reading: Current AI cannot reliably get synthesis of online information and multi-step research right.
No horizon Evidence as of
Limitation: The evidence directly concerns Deep Research, not every contemporary AI system.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“The output of DeepResearch will inevitably feed further models; we will wind up with more of this:”
Our reading: Deep Research output will inevitably be used to train further models, contributing to model collapse.
No deadline stated Evidence as of
Limitation: Training datasets may not disclose product-level provenance.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Few people will fact-check its output carefully, since it will all superficially look ok.”
Our reading: Few people will carefully fact-check Deep Research's output, since it will all superficially look ok.
No deadline stated Evidence as of
Limitation: Checking behavior may vary substantially with expertise and task stakes.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Scammers will use it, for example, to write “medical reports” that are half-baked (in order to sell ads on the webistes that report such “research”).”
Our reading: Scammers will use Deep Research to write half-baked 'medical reports' to sell ads on websites.
No deadline stated Evidence as of
Limitation: The search cannot establish that no qualifying case exists.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Because Deep Research lowers the effort, making it trivial to write legit-sounding articles, paper mill factories (there is already such a thing, designed to game citation counts, “an illegitimate, yet lucrative business“ that is growing) will massively ramp up production, choking the peer review process and saturating the market with hard-to-find, hard-to-rectify errors.”
Our reading: Because Deep Research lowers the effort and makes it trivial to write legit-sounding articles, paper mills will massively ramp up production, choking the peer review process and saturating the market with hard-to-find, hard-to-rectify errors.
No deadline stated Evidence as of
Limitation: The search cannot establish that no undisclosed paper-mill use exists.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“And it’s not just the models that will be infected, but the scientific literature itself.”
Our reading: The scientific literature itself, not just models, will be infected by Deep Research output.
No deadline stated Evidence as of
Limitation: This assessment uses the contextual meaning of infected as erroneous contamination.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: SupportedSaid
Gary Marcus Observation
“The basic conjecture then — which has already been confirmed to some degree – was that LLMs would be used to write garbage content undermining the value of the internet itself.”
Our reading: His 2023 conjecture that LLMs would be used to write garbage content undermining the internet's value has already been confirmed to some degree.
No horizon Evidence as of
Limitation: The Wikipedia study was a preprint and relied on imperfect detectors.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: SupportedSaid
Gary Marcus Observation
“Sam Altman just released a fancier remake Deep Research, which, like Meta’s predecessor, can write science-sounding articles on demand, on any topic of your choice.”
Our reading: OpenAI's Deep Research is essentially a fancier remake of Meta's Galactica that can write science-sounding articles on any topic on demand.
No horizon Evidence as of
Limitation: The evidence supports a shared writing function, not technical equivalence or equal performance.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: UnresolvedSaid
Gary Marcus Forecast
“When — not if — Deep Research begins to flood the medical zone with shite, we are all in trouble.”
Our reading: Deep Research will flood the medical field with garbage content, causing serious trouble.
No deadline stated Evidence as of
Limitation: Benchmark errors establish capability limitations, not the prevalence of harmful medical content in circulation.
Source: Deep Research, Deep Bullshit, and the potential (model) collapse of science
Assessment: SupportedSaid
Gary Marcus Observation
“DeepSeek r1 is not smarter than earlier models, just trained more cheaply”
Our reading: DeepSeek r1 is not smarter than earlier models.
No horizon Evidence as of
Limitation: The result depends on interpreting earlier models as the preceding frontier.
Source: Five things most people don't seem to understand about DeepSeek
Assessment: UnresolvedSaid
Gary Marcus Observation
“DeepSeek r1 is not smarter than earlier models, just trained more cheaply”
Our reading: DeepSeek r1 was trained more cheaply than earlier models.
No horizon Evidence as of
Limitation: Unresolved does not mean the cheaper-training claim is false.
Source: Five things most people don't seem to understand about DeepSeek
Assessment: UnresolvedSaid
Gary Marcus Observation
“It is still expensive to operate ("inference"), especially if you want to make it "think" longer like o3.”
Our reading: DeepSeek r1 is still expensive to run at inference, especially when made to reason longer like o3.
No horizon Evidence as of
Limitation: The claim has a meaningful ordinary reading, but its cost level remains unmeasured.
Source: Five things most people don't seem to understand about DeepSeek
Assessment: SupportedSaid
Gary Marcus Observation
“It doesn't solve hallucinations or problems with reliability.”
Our reading: DeepSeek r1 does not solve hallucinations or reliability problems.
No horizon Evidence as of
Limitation: The hallucination example comes from a reported reasoning trace, rather than a systematic release-period benchmark.
Source: Five things most people don't seem to understand about DeepSeek
Assessment: UnresolvedSaid
Gary Marcus Forecast
“The biggest threat isn't to Nvidia (people will still need GPUs, albeit fewer higher end ones), but rather to OpenAI and Anthropic (because price wars will undercut their hopes of making a profit).”
Our reading: The biggest threat from DeepSeek is not to Nvidia, because people will still need GPUs, albeit fewer higher-end ones.
No deadline stated Evidence as of
Limitation: Shipment mix is not a count of GPU requirements.
Source: Five things most people don't seem to understand about DeepSeek
Assessment: UnresolvedSaid
Gary Marcus Forecast
“The biggest threat isn't to Nvidia (people will still need GPUs, albeit fewer higher end ones), but rather to OpenAI and Anthropic (because price wars will undercut their hopes of making a profit).”
Our reading: The biggest threat from DeepSeek is to OpenAI and Anthropic, because price wars will undercut their hopes of making a profit.
No deadline stated Evidence as of
Limitation: Pricing and product-margin statements are not company-wide net-profit measurements.
Source: Five things most people don't seem to understand about DeepSeek
Assessment: UnresolvedSaid
Gary Marcus Forecast
“The biggest threat isn't to Nvidia (people will still need GPUs, albeit fewer higher end ones), but rather to OpenAI and Anthropic (because price wars will undercut their hopes of making a profit).”
Our reading: People will still need GPUs, though fewer higher-end ones, after DeepSeek.
No deadline stated Evidence as of
Limitation: Actual shipment mix and projected shipment growth measure different things.
Source: Five things most people don't seem to understand about DeepSeek
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“We accidentally upped their technical game.”
Our reading: US export controls accidentally improved China's AI technical capabilities by forcing efficiency innovations.
No horizon Evidence as of
Limitation: Technical development accounts come primarily from DeepSeek.
Source: The race for "AI Supremacy" is over — at least for now.
Assessment: SupportedSaid
Gary Marcus Forecast
“None of this means however that China won the AI race or even took the lead. American companies will incorporate these new results and continue to produce new results of their own.”
Our reading: The recent Chinese results do not mean China has won the AI race or even taken the lead; American companies will incorporate these results and keep producing new results of their own.
Settled before its deadline Evidence as of
Limitation: The decisive examples involve model checkpoints and generated training data, rather than proof that every named technique was adopted.
Source: The race for "AI Supremacy" is over — at least for now.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Meanwhile, revenue (order of $5B) has been modest, compared to outlays that have been much greater; no actual killer app (relative to the costs) has emerged.”
Our reading: OpenAI's revenue (on the order of $5B) has been modest compared with much greater outlays, and no actual killer app, relative to the costs, has emerged.
No horizon Evidence as of
Limitation: Contemporary annual figures were projections [e1, e2].
Source: The race for "AI Supremacy" is over — at least for now.
Assessment: UnresolvedSaid
Gary Marcus Observation
“In late December, a company called DeepSeek, apparently initially built for quantitative trading rather than LLMs, produced a nearly state-of-the-art model that required only roughly 1/50th of the training costs of previous models, — instantly putting them in the big leagues with American companies like OpenAI, Google, and Anthropic, both in terms of performance and innovation.”
Our reading: In late December, DeepSeek, apparently first built for quantitative trading, produced a nearly state-of-the-art model that needed roughly 1/50th of the training costs of previous models, putting it in the big leagues with American companies such as OpenAI, Google and Anthropic in both performance and innovation.
No horizon Evidence as of
Limitation: Unknown accounting basis prevents a supported or contradicted financial verdict.
Source: The race for "AI Supremacy" is over — at least for now.
Assessment: SupportedSaid
Gary Marcus Observation
“Today he walked it back.”
Our reading: On 2025-01-20 Sam Altman walked back his earlier hint that OpenAI was close to building AGI.
No horizon Evidence as of
Limitation: Relies on news reports quoting the X post, not the post itself.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“I do genuinely think we will all have our own AI agents, and companies will have armies of them.”
Our reading: In the long term, everyone will have their own AI agents and companies will have armies of them.
No deadline stated Evidence as of
Limitation: The packet has no population-level data on personal AI agent use.
Assessment: ContradictedSaid
Gary Marcus Forecast
“All that we will have this year are demos.”
Our reading: In 2025, AI agents will exist only as demos.
Deadline passed Evidence as of
Limitation: The result depends on reading 'demos' as covering all AI agent products, not only general-purpose ones.
Assessment: SupportedSaid
Gary Marcus Observation
“That is in part because you can’t build reliable agents unless you can solve the hallucination problem, and there is still no principled trustworthy answer to that problem.”
Our reading: As of January 2025 there is no principled, trustworthy solution to the hallucination problem.
No horizon Evidence as of
Limitation: 'Principled trustworthy answer' has no stated threshold. The assessment uses the contract's reading: a method that reliably prevents hallucinations in general use.
Assessment: UnresolvedSaid
Gary Marcus Forecast
“And they will be worth trillions, since eventually (no time soon) they will do a huge fraction of all human knowledge work, and maybe physical labor too.”
Our reading: Eventually, though not soon, AI agents will do a huge fraction of human knowledge work, perhaps physical labor too, and be worth trillions.
No deadline stated Evidence as of
Limitation: The only evidence is one benchmark from late 2025. It measures what agents can do on a set of projects, not what share of real knowledge work they actually do.
Assessment: SupportedSaid
Gary Marcus Forecast
“The thing about scheduling is that it needs to work reliably. And it won’t.”
Our reading: OpenAI's ChatGPT task-scheduling agent will not work reliably.
Settled before its deadline Evidence as of
Limitation: The evidence is anecdotal. There is no measured failure rate.
Assessment: UnresolvedSaid
Gary Marcus Observation
“No system has been shown to be robust across all the variations (though specific ones vary across replications, perhaps in response to publicly shared examples).”
Our reading: No system has been shown to be robust across all the variations of the posted LLM failure examples under discussion (such as those from Mahabal and Fraser), though which specific examples fail varies across replications, perhaps in response to publicly shared examples.
No horizon Evidence as of
Limitation: Search failure cannot establish that no qualifying demonstration existed.
Assessment: UnresolvedSaid
Gary Marcus Observation
“But they remain superficial, in that their responses invariably have the right kind of language for any given problem, but (unlike the hypothetical Star Trek computer) rarely trustworthy.”
Our reading: LLMs remain superficial: their responses invariably have the right kind of language for any given problem, but they are rarely trustworthy.
No horizon Evidence as of
Limitation: The cited evaluations predate the target snapshot and are context only.
Assessment: SupportedSaid
Gary Marcus Observation
“Despite the daily shouts of “exponential progress” for nearly two years, GPT-5 hasn’t come.”
Our reading: As of January 2025, GPT-5 has not been released despite nearly two years of claims of exponential progress.
No horizon Evidence as of
Limitation: The in-period evidence is a news summary of a paywalled WSJ report.
Source: AGI isn’t coming in 2025, and GPT-5 probably isn’t either.
Assessment: SupportedSaid
Gary Marcus Observation
“But it’s becoming increasingly clear that they are struggling in building a model worthy of the GPT-5 name.”
Our reading: OpenAI is struggling to build a model worthy of the GPT-5 name.
No horizon Evidence as of
Limitation: Rests on anonymous-source news reports summarized by secondary outlets.
Source: AGI isn’t coming in 2025, and GPT-5 probably isn’t either.
Assessment: SupportedSaid
Gary Marcus Observation
“The hypothesis I have been floating since 2022 that scaling data alone would not be enough for robust AI has become increasingly prevalent.”
Our reading: Marcus's hypothesis, which he has been floating since 2022, that scaling data alone would not be enough for robust AI has become increasingly prevalent.
No horizon Evidence as of
Limitation: The evidence does not measure prevalence in a defined population or establish majority agreement.
Source: AGI isn’t coming in 2025, and GPT-5 probably isn’t either.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Some people shift their goalposts; others try to duck specifying goalposts altogether.”
Our reading: In AGI discussions, some people shift their goalposts while others, including Altman, avoid specifying any.
No horizon Evidence as of
Limitation: Omission, inconsistency, and deliberate evasion are different propositions.
Source: The Five Stages of AGI Grief
Assessment: SupportedSaid
Gary Marcus Forecast
“No doubt the next generation of systems will have greater abilities and more extensive applications and will somewhat mitigate the weaknesses.”
Our reading: The next generation of AI systems will have greater abilities and broader applications and will somewhat mitigate current weaknesses.
Settled before its deadline Evidence as of
Limitation: Support uses the ordinary directional reading, not a claim that every successor or application improved.
Source: The Five Stages of AGI Grief
Assessment: UnresolvedSaid
Gary Marcus Observation
“That’s just obviously not so, e.g., “write a biography of X individual without making stuff up” remains pretty unreliable; same for frontier models if you ask them to “play grandmaster level chess without making up illegal moves”.”
Our reading: It is obviously not true that frontier language models can perform competently at pretty much any information task that humans can do, that can be posed and answered in natural language, and that has quantifiable performance; for example, writing a biography of an individual without making things up remains pretty unreliable, as does playing grandmaster-level chess without making illegal moves.
No horizon Evidence as of
Limitation: The unresolved result reflects missing matched evidence, not evidence that the models were reliable.
Source: The Five Stages of AGI Grief
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“As I noted in 2001, the lack of distinct, accessible, reliable database-style records leads to hallucinations.”
Our reading: The lack of distinct, accessible, reliable database-style records leads to hallucinations, as Marcus noted in 2001.
No horizon Evidence as of
Limitation: The historical search was not an exhaustive examination of every 2001 publication.
Source: Why I don’t share Sam Altman’s confidence that AGI is basically a solved problem
Assessment: UnresolvedSaid
Gary Marcus Observation
“Even o1 is losing money; o3 isn’t even available, and perhaps cannot be run at a profit.]”
Our reading: OpenAI's o1 is losing money.
No horizon Evidence as of
Limitation: The evidence covers only the ChatGPT Pro tier. It does not cover o1 sold through other channels, such as ChatGPT Plus or the API.
Source: Why I don’t share Sam Altman’s confidence that AGI is basically a solved problem
Assessment: SupportedSaid
Gary Marcus Forecast
“They will be a major focus of attention in the field in 2025.”
Our reading: AI agents will be a major focus of attention in the AI field in 2025.
Deadline passed Evidence as of
Limitation: 'Major focus' has no numeric threshold. The evidence shows agents were prominent but does not rank them against other themes in the field.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Any one example can be fixed, but examples keep rolling in because no robust solution has been found.”
Our reading: LLM commonsense failure examples keep appearing because no robust solution has been found, even though individual examples can be fixed.
No horizon Evidence as of
Limitation: Spatial reasoning studies cover only part of commonsense reasoning.
Assessment: UnresolvedSaid
Gary Marcus Observation
“To date, though simulation has proved extremely valuable for all kinds of scientific and engineering applications, as well as video games and in creating CGI for films, it has proven much less effective for commonsense reasoning for AI.”
Our reading: Physical simulation has proven much less effective for AI commonsense reasoning than for scientific, engineering, and entertainment applications.
No horizon Evidence as of
Limitation: The evidence supplies no common effectiveness measure or broad comparison across the named domains.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Three approaches to commonsense reasoning have become popular in the last fifteen years; none has proved fully satisfactory.”
Our reading: None of the three popular approaches to commonsense reasoning (simulation, LLM emergence, video generation) has proved fully satisfactory.
No horizon Evidence as of
Limitation: Partial support for two approaches is insufficient to score the entire conjunction supported.
Assessment: SupportedSaid
Gary Marcus Observation
“But what was most striking to us was not the unsurprising fact that these systems are not close to ready for prime time (an employee that bad would probably be immediately fired), but the examples from the list of what went wrong:”
Our reading: Current LLM agents (Claude, Gemini, GPT-4 etc.) are not close to ready for prime time on real-world workplace tasks.
No horizon Evidence as of
Limitation: 'Ready for prime time' has no explicit threshold and is a judgment.
Assessment: SupportedSaid
Gary Marcus Observation
“Ten months later, Sora is finally available to the public, and it’s apparent that it too is struggling with physical reasoning.”
Our reading: Sora, now publicly available, struggles with physical reasoning.
No horizon Evidence as of
Limitation: The announcement does not quantify the physical-error rate or independently reproduce the examples in the post.
Assessment: SupportedSaid
Gary Marcus Observation
“But one important component of physical and conceptual understanding is reasoning about entities and their properties, and current approaches have consistently fallen short on this.”
Our reading: Current AI approaches have consistently fallen short on reasoning about entities and their properties.
No horizon Evidence as of
Limitation: This outcome depends on reading consistently as recurring gaps, not failure on every task.
Assessment: SupportedSaid
Gary Marcus Observation
“Depressingly, despite all of the apparent progress in the last few years, much of what we raised in 2015 and re-emphasized in 2019 remains unsolved.”
Our reading: Much of the commonsense reasoning problem the authors raised in 2015 and 2019 remains unsolved in AI as of January 2025.
No horizon Evidence as of
Limitation: The result depends on interpreting 'unsolved' as lacking broad reliability.
Assessment: UnresolvedSaid
Gary Marcus Observation
“Two and a half years later, more recent models no longer makes the same specific mistakes that GPT-3 did, but still lack a robust understanding of the real external world.”
Our reading: Recent LLMs no longer make GPT-3's specific mistakes but still lack a robust understanding of the real external world.
No horizon Evidence as of
Limitation: The packet contains no independent matched retests of the cited GPT-3 failure cases.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“For example, OpenAI’s o1 is able to create and execute Python code that can do geometric calculation, but because it does not understand how objects are embedded in space and interact in space or, indeed, what spatial relationships actually mean, it often makes very basic mistakes.”
Our reading: OpenAI's o1 often makes very basic spatial mistakes because it does not understand how objects are embedded and interact in space.
No horizon Evidence as of
Limitation: The cited studies do not establish the behavior of released o1 during the target period.
Assessment: SupportedSaid
Gary Marcus Observation
“If general purpose domestic robots are a long way away, “AI agents” —AI systems that act in the world on user’s behalf—are already here to a limited degree.”
Our reading: AI agents, meaning AI systems that act in the world on users' behalf, are already here to a limited degree, even though general-purpose domestic robots are a long way away.
No horizon Evidence as of
Limitation: This establishes limited availability and functionality, not general reliability or widespread adoption.
Assessment: SupportedSaid
Gary Marcus Observation
“Hoping that reliable commonsense will simply “emerge” from large databases and video has proven unrealistic, again and again.”
Our reading: The hope that reliable commonsense will emerge from large text and video datasets has repeatedly proven unrealistic.
No horizon Evidence as of
Limitation: Reliable commonsense remains an interpretive standard.
Assessment: UnresolvedSaid
Gary Marcus Causal claim
“Rosie the Robot is not doing our housework, and that is in part because the kind of physical reasoning a household robot would need to do can’t be done purely by running simulations.”
Our reading: General-purpose household robots like Rosie the Robot are not doing our housework, and this is in part because the physical reasoning such a robot would need cannot be done purely by running simulations.
No horizon Evidence as of
Limitation: The available experiments cover selected manipulation tasks.
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: Throughout 2025, AI agents will be endlessly hyped but far from reliable, except possibly in very narrow use cases.
Deadline passed Evidence as of
Limitation: The promotion shown comes from one company, OpenAI. It does not measure how much agents were promoted across the industry.
Assessment: UnresolvedSaid
Gary Marcus Observation
“My seven December 2022 predictions about GPT-4 largely held true (number seven still pending) for every model that was released in 2024.”
Our reading: His seven December 2022 predictions about GPT-4 largely held true for every model released in 2024, with prediction number seven still pending.
No horizon Evidence as of
Limitation: Selected benchmarks cannot establish a universal claim.
Assessment: SupportedSaid
Gary Marcus Forecast
“Copyright lawsuits over generative AI will continue throughout the year.”
Our reading: Copyright lawsuits over generative AI will continue throughout 2025.
Deadline passed Evidence as of
Limitation: The records establish continuing litigation, not the merits of infringement allegations.
Assessment: SupportedSaid
Gary Marcus Forecast
“Reasoning flubs will continue to haunt generative AI.”
Our reading: Through 2025 reasoning errors will continue to plague generative AI.
Deadline passed Evidence as of
Limitation: 'Flubs' and 'haunt' set no threshold, so the verdict rests on a plain reading.
Assessment: SupportedSaid
Gary Marcus Observation
“Immediately after the February announcement of Sora, I noted Sora’s troubles with physics and predicted that they wouldn’t go away. They haven’t.”
Our reading: Sora's troubles with physics have not gone away since its February 2024 announcement.
No horizon Evidence as of
Limitation: The disclosures do not quantify failure rates or establish that physics performance had not improved [e1, e2].
Assessment: UnresolvedSaid
Gary Marcus Forecast
“Few if any radiologists will be replaced by AI (contra Hinton’s infamous 2016 prediction).”
Our reading: By end of 2025 few if any radiologists will be replaced by AI.
Deadline passed Evidence as of
Limitation: The strongest retrieved workforce evidence concerns the UK.
Assessment: SupportedSaid
Gary Marcus Observation
“My prediction that 2024 would be seen as the year of AI disillusionment was overstated but not entirely off the mark, holding true for some folks but not all, and as such perhaps deserve partial credit.”
Our reading: 2024 was a year of AI disillusionment for some but not all; his prediction was overstated.
No horizon Evidence as of
Limitation: The surveys cover business leaders and employees rather than a representative global public.