“But between the disappointments of GPT-5 and a new study from METR that shows that LLMs do markedly better on coding benchmarks than in real-word practice, I think it is safe to say that LLMs won’t lead the way.”
Our reading, the sentence that was checked:
LLMs will not be the approach that leads the way to AGI.
Assessment: Unresolved No deadline stated on 2026-10-29 Evidence as of
Where and when Gary Marcus wrote it
Marcus on AI, OpenAI’s Waterloo? [with corrections], paragraph 34.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment marcus-openais-waterloo-b64a45-a1
We recorded 8 claims from this post. See every claim from this post.
The wording above is the version this site observed on 2026-10-28. The source may have been edited after 2025-08-14. This page does not claim the wording was captured then.
What the claim commits to
- Kind
- Forecast
- Deadline, as the speaker wrote it
- None stated. This site does not add one.
- Deadline state when assessed
- No deadline stated
- Deadline state on 2026-10-29
- No deadline stated (the release’s reference date)
From the speaker
- LLMs won’t lead the way
- AGI
- new paths
Our interpretation
- In context, 'lead the way' means provide the principal technological approach behind the first broadly accepted AGI systems.
- An LLM-based system may include tools, search, reinforcement learning and other components. Their presence alone does not make it a non-LLM approach.
- The preceding discussion of GPT-5 and METR supplies reasons for the prediction, not conditional antecedents.
- There is no stated deadline. I do not substitute ordinary useful or trustworthy software for the contextual destination of AGI.
Counts as supported if: The first independently demonstrated, broadly accepted AGI systems principally depend on a non-LLM approach, with technical evidence showing LLMs were not the leading mechanism.
Counts as contradicted if: The first independently demonstrated, broadly accepted AGI systems principally depend on LLMs.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Did the forecast come true?
Outcome: Unresolved
The record was checked, and the evidence available by the cutoff does not settle it.
Scored on a plain reading. The speaker named no measure for this claim, so it was judged on the reading an informed reader would take, stated below under our interpretation. Both reviews had to find that reading fair.
- Evidence as of
- Deadline state when assessed
- No deadline stated
- Assessment recorded
- Policy version
- 2026-10-28.7
Rationale
The forecast concerns the approach that leads to AGI, not whether later models improve on benchmarks. The subsequent capability announcement is material context, but it does not meet the decisive-event rule. Neither current progress nor current limitations establish which approach will deliver the first accepted AGI. With no deadline, the forecast remains open.
Limitations
- The first accepted AGI is an interpretation of 'lead the way', not a criterion Marcus explicitly supplied.
- A hybrid architecture could make attribution disputed even after an accepted AGI demonstration.
Evidence (1 item)
Context only: OpenAI,
99.9% score
Why it matters here: This later capability announcement is relevant contrary context to a prediction against the LLM research path. The reported scores establish neither broadly accepted AGI nor the architectural attribution needed to contradict the forecast.
- Publisher
- OpenAI
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
What would settle it
Independent recognition of the first AGI systems, accompanied by technical evidence showing whether LLMs or a different approach provided their principal capabilities.
Where it was said (1)
Said once in the sources this register covers.
: Marcus on AI, OpenAI’s Waterloo? [with corrections]
But between the disappointments of GPT-5 and a new study from METR that shows that LLMs do markedly better on coding benchmarks than in real-word practice, I think it is safe to say that LLMs won’t lead the way.
- Where
- paragraph 34
- Published
- 2025-08-14
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment marcus-openais-waterloo-b64a45-a1: Current. Outcome: Unresolved. Evidence as of 2026-10-28. Public date not recorded.
Recorded events
Assessment publishedBy this site
First assessment published after both reviews and the final-text check passed.