“In reality, LLMs can generate code, and do some software engineering-adjacent tasks, but, like all Large Language Models, break down and go totally insane, hallucinating more as the tasks get more complex.”
Our reading, the sentence that was checked:
Coding LLMs can generate code but break down and hallucinate more as tasks get more complex.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, The Case Against Generative AI, paragraph 258.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-the-case-against-generative-ai-54f160-a2
We recorded 46 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 2025-09-29. 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
- as tasks get more complex (qualifier, stated by the speaker)
From the speaker
- LLMs can generate code
- break down
- hallucinating more as the tasks get more complex
Our interpretation
- Read the rhetorical description of breakdown as declining functional reliability, and hallucination as invented or unsupported code elements or factual assertions.
- The ordinary reading predicts increasing failure and hallucination rates as coding tasks become more demanding.
- No reporting period is specified. Use September 1–29, 2025, the latest calendar month observable before publication, for the ongoing capability claim; earlier studies provide context.
- Task complexity must be measured independently of whether the model failed. Task duration, dependencies and required changes are possible measures, not definitions supplied by Zitron.
Counts as supported if: Comparable evaluations of contemporary coding models show that both failures and separately measured hallucinations increase across independently defined levels of task complexity.
Counts as contradicted if: Comparable evaluations show reliable performance and stable or declining hallucination rates as complexity increases across a representative task range.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Was it true for the period described?
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
The preprint submitted September 26, 2025, reports library hallucinations in generated code and examines their sensitivity to prompt variations [e1]. Its supplied abstract does not jointly compare functional failures and hallucination rates across independently measured levels of coding-task complexity [e1]. It establishes that library hallucinations occur, but does not settle the claimed increase in failures and hallucinations as tasks become more complex [e1].
Limitations
- Prompt variations are not established here as a measure of engineering-task complexity [e1].
- The supplied record does not specify experimental dates, so publication in September does not establish performance during September 1–29, 2025 [e1].
- The library-hallucination results do not establish a corresponding trend in functional failures [e1].
Evidence (1 item)
Context only: Twist et al., arXiv,
inventing non-existent libraries.
Why it matters here: The study documents library hallucinations and sensitivity to prompt variations. It does not establish that increasing engineering-task complexity increases hallucinations; the retrieved record also does not specify exact experimental dates.
- Publisher
- Twist et al., arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
What would settle it
Comparable evaluations of coding models relevant to September 1–29, 2025, measuring task complexity independently and measuring functional failures and hallucinations separately. With model versions, tools and testing conditions held comparable, the results must establish whether both rates increase with complexity or whether the contrary pattern holds.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, The Case Against Generative AI
In reality, LLMs can generate code, and do some software engineering-adjacent tasks, but, like all Large Language Models, break down and go totally insane, hallucinating more as the tasks get more complex.
- Where
- paragraph 258
- Published
- 2025-09-29
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-the-case-against-generative-ai-54f160-a2: 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-the-case-against-generative-ai-54f160-a2.
HeldBy this site
The final-text check failed: rationale_supported_by_evidence; overclaims: The located difficulty benchmarks address task success, a different outcome..
About assessment wyea-the-case-against-generative-ai-54f160-a1, which was not published.