“The basic finding was: loads of things scale, but not at all ( as foreseen both by Kurenkov in his essay last year and in my much lampooned but basically accurate essay, Deep learning is hitting a wall).”
Our reading, the sentence that was checked:
BIG-bench shows many capabilities improve with scale but not all, which bears out Marcus's 'Deep learning is hitting a wall' essay.
Assessment: Supported Evidence as of
Where and when Gary Marcus wrote it
Marcus on AI, Does AI really need a paradigm shift?, paragraph 36.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment marcus-does-ai-really-need-a-paradigm-shift-282b4f-a2
We recorded 14 claims from this post. See every claim from this post.
The wording above is the version this site observed on 2026-10-29. The source may have been edited after 2022-06-11. This page does not claim the wording was captured then.
What the claim commits to
- Kind
- Observation
- Deadline, as the speaker wrote it
- None stated. This site does not add one.
- Deadline state when assessed
- No horizon
- Deadline state on 2026-10-29
- No horizon (the release’s reference date)
Conditions
- per BIG-bench findings (qualifier, stated by the speaker)
From the speaker
- The basic finding was
- loads of things scale, but not at all
- as foreseen
- Deep learning is hitting a wall
Our interpretation
- The surrounding sentence about progress on some measures but not others makes not all the intended reading of the apparent typo not at all.
- The test concerns the original June 2022 BIG-bench paper and its evaluated model sizes.
- The reference to the earlier essay is assessed as corroboration of its limited contention that improvement was uneven across measures. It is not treated as proof of every argument in that essay or of a permanent scaling barrier.
Counts as supported if: The original BIG-bench report finds broad improvements with model scale alongside tasks or measures that do not improve over the tested range.
Counts as contradicted if: The original report finds improvement on all tested measures, or fails to report the claimed coexistence of improving and non-improving performance.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Was it true for the period described?
Outcome: Supported
The dated evidence matches what the claim committed to, under the criteria frozen before grading.
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 horizon
- Assessment recorded
- Policy version
- 2026-10-28.7
Rationale
The June 9, 2022 BIG-bench report explicitly states that some tasks improve with scale while others show no improvement over the tested range [e1]. This supports the limited claim of uneven measured progress across tasks [e1].
Limitations
- The finding does not establish a permanent scaling barrier or what happens beyond the tested model sizes [e1].
- It corroborates uneven measured progress, not every argument in Marcus's earlier essay [e1].
Evidence (1 item)
Supports the claim: Srivastava et al.; arXiv,
no improvement with scale
Why it matters here: The original report directly states the coexistence Marcus describes. Sections 3.4.2 and 6.1 also explain that metric choice can conceal gradual progress and that some limitations may change with larger models or improved data.
- Publisher
- Srivastava et al.; arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Where it was said (1)
Said once in the sources this register covers.
: Marcus on AI, Does AI really need a paradigm shift?
The basic finding was: loads of things scale, but not at all ( as foreseen both by Kurenkov in his essay last year and in my much lampooned but basically accurate essay, Deep learning is hitting a wall).
- Where
- paragraph 36
- Published
- 2022-06-11
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment marcus-does-ai-really-need-a-paradigm-shift-282b4f-a2: Current. Outcome: Supported. 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.
About assessment marcus-does-ai-really-need-a-paradigm-shift-282b4f-a2.