“In any case, the massive expense of running generative models hasn’t been the limiter on their deployment or success — you can blame that on the fact that they, as a piece of technology, are neither artificial intelligence nor capable of providing the kind of meaningful outcomes that would make them the next Smartphone.”
Our reading, the sentence that was checked:
The massive expense of running generative models hasn't been what limits their deployment or success; the limit is that they are neither artificial intelligence nor capable of the meaningful outcomes that would make them the next smartphone.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, Deep Impact, paragraph 126.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-deep-impact-9c375e-a1
We recorded 49 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 2025-01-29. 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
- the massive expense of running generative models
- hasn’t been the limiter on their deployment or success
- neither artificial intelligence
- nor capable of providing the kind of meaningful outcomes
- the next Smartphone
Our interpretation
- The testable causal reading is that operating expense was not a material binding constraint on deployment or success, while capability and reliability limitations were.
- The statement is retrospective through January 29, 2025. It supplies no starting date, so none is imposed.
- I do not turn the philosophical label artificial intelligence or the smartphone comparison into an invented performance threshold.
- This concerns technology-wide deployment constraints, not a stated financial metric for a particular company.
Counts as supported if: Comparative deployment evidence shows that lowering operating costs did little to remove deployment constraints, while capability or reliability improvements materially removed them.
Counts as contradicted if: Credible deployment evidence isolates model operating expense as a material binding constraint, or otherwise establishes that the proposed capability-based explanation is materially wrong.
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 evidence establishes both cost concerns and real reliability problems. It does not establish their relative causal effects. Lucidworks discusses implementation costs, which extend beyond running models. The legal-tool evaluation measures errors, not adoption responses to prices. Neither warrants a supported or contradicted verdict on the claimed industry-wide explanation.
Limitations
- The deployment survey was commissioned by an AI vendor and measures reported experiences.
- The reliability study covers one professional domain.
- The broader assertions about intelligence and smartphone-like importance remain undefined.
Evidence (2 items)
Context only: Lucidworks, distributed by GlobeNewswire,
businesses face cost and security hurdles.
Why it matters here: The survey announcement identifies costs alongside other deployment barriers, challenging a simple dismissal of cost. However, implementation costs and reported concerns do not isolate model-running expense or establish its effect relative to capabilities.
- Publisher
- Lucidworks, distributed by GlobeNewswire
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
Context only: Magesh and colleagues, arXiv,
differences between systems in responsiveness and accuracy.
Why it matters here: The preregistered evaluation found hallucinations in commercial legal research tools, supporting the existence of consequential reliability limitations. It did not compare the effects of those limitations and operating expense on adoption.
- Where
- Abstract
- Publisher
- Magesh and colleagues, arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
What would settle it
Contemporaneous customer or deployment studies that separate inference-price changes from capability changes and document which constraint prevented or enabled adoption.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, Deep Impact
In any case, the massive expense of running generative models hasn’t been the limiter on their deployment or success — you can blame that on the fact that they, as a piece of technology, are neither artificial intelligence nor capable of providing the kind of meaningful outcomes that would make them the next Smartphone.
- Where
- paragraph 126
- Published
- 2025-01-29
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-deep-impact-9c375e-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.