“Nobody is trying to make these things more efficient, or at the very least nobody has succeeded in doing so.”
Our reading, the sentence that was checked:
Nobody has succeeded in making LLMs more efficient.
Assessment: Contradicted Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, The Subprime AI Crisis, paragraph 69.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-subprimeai-86c3bd-a1
We recorded 50 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 2024-09-16. 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)
From the speaker
- Nobody is trying
- at the very least nobody has succeeded
- make these things more efficient
- Large Language Models
Our interpretation
- The ordinary reading is that no one had demonstrated greater efficiency in operating LLMs by publication.
- Efficiency means using fewer computing resources for comparable model outputs and workloads. A lower retail price alone is insufficient.
- The surrounding discussion starts in November 2022. I use that month through publication as the observation period; this is a cumulative claim about achievements, not a quarterly financial observation.
Counts as supported if: Credible, sufficiently comprehensive evidence establishes that no demonstrated LLM efficiency improvement existed during the period.
Counts as contradicted if: At least one documented implementation performs comparable LLM work with fewer computing resources during the period.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Was it true for the period described?
Outcome: Contradicted
The dated evidence goes against 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
A documented counterexample defeats the claim that nobody had succeeded. In June 2023, the vLLM developers reported that LMSYS could serve its LLM traffic with 50% fewer GPUs. This concerns computing resources, not merely lower API prices.
Limitations
- The deployment result is reported by the developers.
- It does not establish that every model became more efficient or that any provider became profitable.
Evidence (1 item)
Goes against the claim: vLLM project, UC Berkeley researchers,
the above traffic by 50%.
Why it matters here: The developers report serving the existing LLM traffic with half as many GPUs after changing the serving software. This is a concrete resource-efficiency improvement before publication.
- Publisher
- vLLM project, UC Berkeley researchers
- Kind of source
- Demonstration
- Describes events of
- Public from
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, The Subprime AI Crisis
Nobody is trying to make these things more efficient, or at the very least nobody has succeeded in doing so.
- Where
- paragraph 69
- Published
- 2024-09-16
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-subprimeai-86c3bd-a1: Current. Outcome: Contradicted. 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.