“They don’t care that the output is mediocre, that the product is unprofitable, that there is a quickly-approaching wall that generative AI can’t leap over as it runs out of training data, or that the transformer-based architecture of Large Language Models has hard limitations that are impossible to overcome.”
Our reading, the sentence that was checked:
Generative AI's output is mediocre and the product is unprofitable.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, The Shareholder Supremacy, paragraph 113.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-tss-2b682f-a1
We recorded 22 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 2024-07-01. 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
- the output is mediocre
- the product is unprofitable
Our interpretation
- Assess only the normalized quality-and-profitability claim, not the adjacent assertions about training data or architectural limits.
- The ordinary reading is that generative AI generally produces middling practical results and fails to cover its full development and operating costs.
- Use January–March 2024, the latest normally reportable financial quarter before publication, as a common observation period for this combined claim.
- Mediocre would require comparison with ordinary competent performance on representative practical tasks. No particular model, task mixture or quality threshold is supplied.
- The financial clause names no company, currency or accounting basis.
Counts as supported if: Representative evaluations show broadly middling practical output, and comparable financial records show negative full-cost profitability for the relevant products in the target period.
Counts as contradicted if: Representative evaluations establish consistently strong practical output, or comparable financial records establish profitability for the relevant product population in the target period.
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
Neither component is established at the stated breadth. Research documents particular planning failures, but those earlier tests cannot classify all generative AI output. Microsoft's broader segment results do not reveal product profitability. Without a representative quality standard and matching financial records, the combined claim cannot fairly receive a supported, contradicted or mixed verdict.
Limitations
- Mediocre is testable only after specifying a practical baseline and task population.
- The common reporting quarter is an evaluator interpretation.
- Documented limitations do not establish generally poor performance, and broader company profits do not establish product profits.
Evidence (2 items)
Context only: Microsoft,
Operating income increased
Why it matters here: The disclosure shows rising operating income for a broader segment. It cannot establish whether generative AI products themselves made or lost money.
- Publisher
- Microsoft
- Kind of source
- Company disclosure: the company describing itself, not an independent finding, unaudited
- Measures
- Microsoft Intelligent Cloud segment, Year-over-year increase in operating income, USD billions, company reported, 2024-01-01 to 2024-03-31
- Describes events of
- Public from
Context only: arXiv; Subbarao Kambhampati,
generating executable plans autonomously
Why it matters here: The paper describes concrete limitations in earlier GPT-4 planning experiments. Those tests concern a narrow task and earlier period, so they cannot establish that generative AI output generally was mediocre in the target quarter.
- Publisher
- arXiv; Subbarao Kambhampati
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
What would settle it
A defined product and task population, representative contemporaneous quality evaluations, and product financial records using a specified accounting basis.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, The Shareholder Supremacy
They don’t care that the output is mediocre, that the product is unprofitable, that there is a quickly-approaching wall that generative AI can’t leap over as it runs out of training data, or that the transformer-based architecture of Large Language Models has hard limitations that are impossible to overcome.
- Where
- paragraph 113
- Published
- 2024-07-01
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-tss-2b682f-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.