“Worse still, LLMs allow shitty software engineers that would otherwise be isolated by their incompetence to feign enough intelligence to get by, leading to them actively lowering the quality of code being shipped.”
Our reading, the sentence that was checked:
LLMs let incompetent engineers get by, lowering the quality of shipped code.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, AI Is Really Weird, paragraph 91.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-ai-is-really-weird-682156-a1
We recorded 42 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 2026-04-08. 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
- otherwise be isolated by their incompetence
- feign enough intelligence to get by
- lowering the quality of code being shipped
Our interpretation
- The plain reading is that LLMs conceal inadequate engineering ability, allowing people who would otherwise fail workplace checks to contribute production code, thereby reducing its quality.
- Inadequate ability means failing independently assessed requirements for the relevant work. Being junior or unfamiliar with one library is not itself incompetence.
- Getting by means passing hiring, performance or review checks. Shipped code means deployed or released code, rather than a laboratory exercise.
- The claim does not require proof of deliberate deception by each engineer.
Counts as supported if: Evidence links independently measured low baseline competence to LLM-enabled passage through workplace checks and then to worse released-code outcomes than an appropriate comparison group.
Counts as contradicted if: Comparable evidence shows that LLM-enabled contributions from the specified low-competence group maintain or improve released-code quality, without the proposed concealment mechanism.
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
The learning study supports a related concern about reduced understanding, but it does not establish this claim. Participants learning an unfamiliar library were not shown to be incompetent engineers who would otherwise fail workplace checks. The study also did not follow their code into production. The proposed concealment and quality-loss mechanism remains unverified.
Limitations
- The cited study measured immediate learning in a small experimental setting.
- Neither junior status nor low output is a sufficient measure of incompetence.
- No direct counterevidence settles the complete mechanism either.
Evidence (1 item)
Context only: Anthropic,
decrease in mastery
Why it matters here: Anthropic reported weaker immediate mastery among participants learning a new library with AI assistance. The date identifies that research announcement; the page does not separately date the experiment, which did not measure workplace screening or shipped defects.
- Publisher
- Anthropic
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
What would settle it
A workplace study that independently measures baseline competence, tracks hiring or review decisions with and without LLM assistance, and compares defects in the resulting released code.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, AI Is Really Weird
Worse still, LLMs allow shitty software engineers that would otherwise be isolated by their incompetence to feign enough intelligence to get by, leading to them actively lowering the quality of code being shipped.
- Where
- paragraph 91
- Published
- 2026-04-08
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-ai-is-really-weird-682156-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.