“This is why so many models' responses sound so similar — they're heavily dependent on the stuff they're fed for their outputs, and so much of their "intelligence" comes from the same training data.”
Our reading, the sentence that was checked:
Many models' responses sound alike because they depend heavily on the same training data.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, Are We Watching The Internet Die?, paragraph 35.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-are-we-watching-the-internet-die-3b4ed8-a1
We recorded 29 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 2024-03-11. This page does not claim the wording was captured then.
What the claim commits to
- Kind
- Causal claim
- 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)
From the speaker
- so many models' responses sound so similar
- heavily dependent on the stuff they're fed
- so much of their "intelligence" comes from the same training data
Our interpretation
- The statement explains perceived similarities among models available around March 11, 2024.
- Sound similar means recurring wording, style, or semantic patterns under comparable prompts.
- Shared training data is asserted to be a substantial cause, not necessarily the sole cause.
- Common subject matter or use of the same broad source category does not alone establish substantial overlap in actual training examples.
Counts as supported if: Comparative output measurements establish substantial similarity, and controlled comparisons or reliable training records show that shared training examples materially cause it after accounting for prompts, decoding, architecture, and alignment.
Counts as contradicted if: Representative controlled comparisons find no substantial similarity, or isolate substantial training overlap and show it does not materially account for the observed similarity.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Does the evidence support the cause?
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 horizon
- Assessment recorded
- Policy version
- 2026-10-28.7
Rationale
Later research documents cross-model repetition, but explicitly leaves its causes unresolved. A reanalysis disputes how strongly some measurements establish homogeneity, and separate research shows that alignment changes response distributions. None of this demonstrates that shared training examples substantially caused the similarities Zitron described in March 2024. Equally, an additional influence such as alignment does not rule out shared data as a contributing cause. The causal explanation remains unverified.
Limitations
- Later studies cannot directly establish the behavior of the unspecified March 2024 model population.
- Shared data and shared alignment practices may operate together.
- The methodological reanalysis is a preprint and does not claim to disprove all homogeneity.
Evidence (3 items)
Context only: Jiang et al., NeurIPS 2025 paper, revised arXiv version,
high syntactic repetition across different models.
Why it matters here: The study supports output repetition in its later model sample. The surrounding passage explicitly leaves open whether pretraining data, alignment, memorization, contamination, or generalization causes it; it does not establish the causal claim about March 2024 models.
- Publisher
- Jiang et al., NeurIPS 2025 paper, revised arXiv version
- Kind of source
- Peer-reviewed paper
- Describes events of
- Public from
Context only: Schaeffer et al., arXiv,
We do not resolve whether the Artificial Hivemind is real.
Why it matters here: This reanalysis challenges the strength of the similarity evidence, including its comparison baselines, while acknowledging a residual effect. It neither disproves ordinary response similarity nor establishes its cause.
- Publisher
- Schaeffer et al., arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Context only: Lake, Choi, and Durrett, NAACL 2025 paper, arXiv,
The alignment process changes several properties
Why it matters here: The analysis identifies alignment-related changes in response diversity and information aggregation, which a causal test must account for. This is an additional mechanism, not proof that shared pretraining data has no effect.
- Publisher
- Lake, Choi, and Durrett, NAACL 2025 paper, arXiv
- Kind of source
- Peer-reviewed paper
- Describes events of
- Public from
What would settle it
A controlled study of relevant models that varies training-data overlap while holding other major influences fixed, or sufficiently detailed training records combined with a credible causal comparison.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, Are We Watching The Internet Die?
This is why so many models' responses sound so similar — they're heavily dependent on the stuff they're fed for their outputs, and so much of their "intelligence" comes from the same training data.
- Where
- paragraph 35
- Published
- 2024-03-11
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-are-we-watching-the-internet-die-3b4ed8-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.
About assessment wyea-are-we-watching-the-internet-die-3b4ed8-a1.