“What these stories don't seem to discuss are whether these companies are making a profit, likely because generative AI is a deeply unprofitable product, demanding massive amounts of cloud computing power”
Our reading, the sentence that was checked:
Generative AI is a deeply unprofitable product that demands massive amounts of cloud computing power.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, Subprime Intelligence, paragraph 22.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-sam-altman-fried-dda545-a1
We recorded 26 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 2024-02-19. 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
- generative AI is a deeply unprofitable product
- demanding massive amounts of cloud computing power
Our interpretation
- The ordinary reading is that selling generative AI generally produces substantial overall losses, with computing a major expense. This does not mean every provider loses money.
- Use calendar 2023, the latest completed year before publication and the year used for revenues in the surrounding passage.
- Overall profitability includes development and operating expenses. Positive gross margins alone would not contradict this reading.
- The surrounding financial figures are attributed to press reports, so the implied accounting basis is estimate.
Counts as supported if: Representative evidence for 2023 establishes substantial losses across generative AI providers and substantial computing requirements.
Counts as contradicted if: Representative evidence for 2023 establishes that generative AI provision was generally profitable, or that computing requirements were modest.
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 evidence supports substantial computing requirements and losses at particular providers. It does not establish the broader claim that generative AI was generally deeply unprofitable in 2023. Anthropic's accounts are company-specific, and the cited OpenAI loss concerns 2022. Reported profitability at Midjourney also matters. Neither extrapolating from two companies nor treating one profitable exception as a sector-wide rebuttal would be fair.
Limitations
- The financial sources use different scopes and profit definitions.
- Some accessible reporting summarizes sources whose underlying accounts were unavailable.
Evidence (4 items)
Context only: Anthropic, PBC / U.S. Securities and Exchange Commission,
history of net losses
Why it matters here: The accompanying accounts report a 2023 net loss of $1,531 million. This supports the concern about Anthropic, but its company scope and GAAP basis differ from the sector-wide estimate.
- Publisher
- Anthropic, PBC / U.S. Securities and Exchange Commission
- Kind of source
- Regulatory filing, audited
- Measures
- Anthropic, Net loss, USD millions, gaap, 2023-01-01 to 2023-12-31
- Describes events of
- Public from
Context only: Anthropic,
extensive resources including compute power
Why it matters here: Anthropic directly describes substantial training requirements. It does not quantify cloud expenses or establish profitability across generative AI products.
- Where
- Final paragraph
- Publisher
- Anthropic
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
Context only: The Decoder,
lost $540 million
Why it matters here: This corroborates the reported historical loss discussed in the post. It cannot establish OpenAI's 2023 result.
- Publisher
- The Decoder
- Kind of source
- Secondary summary
- Measures
- OpenAI, Reported loss; net versus operating classification unspecified, USD millions, estimate, 2022-01-01 to 2022-12-31
- Describes events of
- Public from
Context only: The Decoder,
reportedly profitable
Why it matters here: Reported profitability at Midjourney is a material exception to a blanket reading. One profitable company does not establish profitability for the category as a whole.
- Where
- Third body paragraph
- Publisher
- The Decoder
- Kind of source
- Secondary summary
- Measures
- Midjourney, Reported profitability; profit definition unspecified qualitative, estimate, ? to ?
- Describes events of
- Public from
What would settle it
Comparable 2023 financial and computing-cost records covering a representative share of generative AI provision.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, Subprime Intelligence
What these stories don't seem to discuss are whether these companies are making a profit, likely because generative AI is a deeply unprofitable product, demanding massive amounts of cloud computing power
- Where
- paragraph 22
- Published
- 2024-02-19
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-sam-altman-fried-dda545-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.