“As I've written about many, many, many times, the Large Language Models run by companies like OpenAI, Anthropic, Google and Meta are unprofitable and unsustainable, and the transformer-based architecture they run on has peaked.”
Our reading, the sentence that was checked:
LLMs run by OpenAI, Anthropic, Google and Meta are unprofitable and unsustainable.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, Deep Impact, paragraph 56.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-deep-impact-9b9bed-a2
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
- 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
- Large Language Models
- OpenAI, Anthropic, Google and Meta
- unprofitable and unsustainable
- in 2024
Our interpretation
- The nearby explanation expressly cites 2024 losses, so calendar 2024 is the target period.
- The claim concerns the economics of the named companies' LLM services, rather than consolidated corporate profitability.
- The ordinary reading of unprofitable is that attributable revenue does not cover full development and operating costs.
- The minimum testable reading of unsustainable is dependence on continuing financial support outside the services' own earnings. No bankruptcy deadline is supplied.
- The nearby reported private-company figures imply an estimate basis; they do not establish GAAP or another consistent accounting standard for all four businesses.
Counts as supported if: Comparable 2024 revenue and full-cost evidence establishes losses and dependence on outside financial support for the named LLM businesses.
Counts as contradicted if: Comparable evidence establishes that a named LLM business covered its full costs and could sustain its operations from its own earnings during 2024.
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
On September 27, 2024, CNBC reported that OpenAI expected about $5 billion in company-wide losses on $3.7 billion in revenue for 2024; this was a projection, not a realized full-cost LLM-service result [e1]. Alphabet's fourth-quarter 2024 disclosure reports broad segments and places general-model development among shared costs [e2]. Meta's 2024 disclosure reports Family of Apps and Reality Labs without separate LLM accounts [e3]. These sources establish neither losses and continuing dependence on financial support outside service earnings across all four named businesses nor a service that covered its full costs from its own earnings [e1, e2, e3].
Limitations
- CNBC had not seen OpenAI's accounts, and the reported loss estimate excluded stock compensation and some other expenses [e1].
- The packet contains no usable Anthropic financial evidence.
- Alphabet's and Meta's broader results cannot establish standalone LLM-service profitability or dependence on financial support [e2, e3].
Evidence (3 items)
Context only: CNBC,
expects about $5 billion
Why it matters here: This supports concern about OpenAI's economics, but reports expected company-wide losses rather than realized LLM-service results. CNBC says it had not seen the accounts, and reports that the estimate excludes stock compensation and other incompletely explained expenses.
- Publisher
- CNBC
- Kind of source
- News report
- Measures
- OpenAI, Projected company-wide annual loss excluding stock-based compensation and some other expenses, USD billions of dollars, estimate, 2024-01-01 to 2024-12-31
- Describes events of
- Public from
Context only: Alphabet,
Alphabet-level activities
Why it matters here: The release places general-model development among shared costs and reports broader business segments. Those disclosures cannot establish a standalone profit or loss for Google's LLM services.
- Publisher
- Alphabet
- Kind of source
- Company disclosure: the company describing itself, not an independent finding, unaudited
- Measures
- Alphabet, Segment operating income and allocation of shared costs, USD millions of dollars, gaap, 2024-10-01 to 2024-12-31
- Describes events of
- Public from
Context only: Meta,
two reportable segments
Why it matters here: Meta's reported segments do not separate LLM revenue and costs. Its consolidated or segment profitability therefore cannot settle the claim about LLM services.
- Publisher
- Meta
- Kind of source
- Company disclosure: the company describing itself, not an independent finding, unaudited
- Measures
- Meta, Family of Apps and Reality Labs segment operating income, USD millions of dollars, gaap, 2024-01-01 to 2024-12-31
- Describes events of
- Public from
What would settle it
Comparable realized calendar-2024 LLM-service revenue and full attributable costs for all four businesses, plus evidence of whether each required continuing financial support outside service earnings. A named service covering its full costs and sustaining operations from its own earnings would provide a counterexample.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, Deep Impact
As I've written about many, many, many times, the Large Language Models run by companies like OpenAI, Anthropic, Google and Meta are unprofitable and unsustainable, and the transformer-based architecture they run on has peaked.
- Where
- paragraph 56
- Published
- 2025-01-29
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-deep-impact-9b9bed-a2: 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.