“The anthropomorphism and overpromising about these technologies has suffocated and obfuscated what they can actually do in pursuit of endless growth, and the only reason they can do anything is that OpenAI and Anthropic were allowed to annihilate hundreds of billions of dollars on training”
Our reading, the sentence that was checked:
Anthropomorphism and overpromising about LLMs have suffocated and obscured what they can actually do in pursuit of endless growth, and the only reason they can do anything is that OpenAI and Anthropic were allowed to spend hundreds of billions of dollars on training.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, The AI Hater's Manifesto, paragraph 31.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-the-ai-haters-manifesto-14ba8d-a1
We recorded 42 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 2026-08-25. 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
- anthropomorphism and overpromising
- suffocated and obfuscated what they can actually do
- in pursuit of endless growth
- the only reason they can do anything
- OpenAI and Anthropic
- hundreds of billions of dollars on training
Our interpretation
- The spending assertion concerns cumulative expenditure already incurred by the post date, not future commitments, fundraising, valuation, or industry-wide infrastructure investment.
- I read the two companies jointly and interpret hundreds of billions as at least USD 200 billion. The wording does not specify whether the threshold applies jointly or individually.
- The ordinary causal reading is that this expenditure was necessary for meaningful LLM capabilities and that humanlike presentation and exaggerated promises materially impaired understanding of those capabilities.
- The passage supplies no accounting basis or attribution for its spending figure. Its accounting basis therefore remains unknown.
- Suffocated and endless growth are rhetorical descriptions. The assessable component is material distortion of public understanding, not the rhetoric itself.
Counts as supported if: Comparable records establish the stated cumulative training expenditure, and credible causal evidence establishes both its necessity for meaningful capabilities and the asserted distortion of public understanding.
Counts as contradicted if: Comparable cumulative training accounts fall below the threshold, or credible evidence defeats the claimed necessity or material distortion. Evidence about another company's model can challenge necessity but cannot settle the named companies' expenditure.
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 does not establish the compound claim. Annual compute figures do not measure cumulative training expenditure. DeepSeek reports a much smaller training-run estimate, but that is another company and excludes prior research. A controlled framing experiment found negligible immediate effects, while leaving longer-term effects open. These findings challenge parts of the argument without settling its spending assertion or broad causal explanation.
Limitations
- The financial component has an unspecified accounting basis.
- The causal language combines several propositions with different evidence requirements.
- Experimental framing results cannot be generalized to all marketing or prolonged exposure.
Evidence (3 items)
Context only: Stanford HAI,
reported compute spend
Why it matters here: The report separates research compute from inference and describes annual spending. It does not establish cumulative training expenditure through August 2026.
- Publisher
- Stanford HAI
- Kind of source
- Secondary summary
- Measures
- OpenAI and Anthropic, Annual compute spending, including research and inference, USD billions, estimate, 2025-01-01 to 2025-12-31
- Describes events of
- Public from
Context only: DeepSeek-AI via arXiv,
$5.576M.
Why it matters here: This challenges the suggestion that useful models necessarily require hundreds of billions in direct training costs. It is a rental-price estimate for one other company's model and explicitly excludes prior research and ablation experiments.
- Publisher
- DeepSeek-AI via arXiv
- Kind of source
- Preprint, not peer reviewed
- Measures
- DeepSeek, Estimated compute rental cost of the official DeepSeek-V3 training, USD millions, estimate, ? to 2024-12
- Describes events of
- Public from
Context only: Hou, Hao, Park and Linzen via arXiv,
negligible immediate effects.
Why it matters here: The experiment provides counterevidence to a simple immediate framing effect. It does not rule out cumulative exposure effects or establish the consequences of commercial overpromising.
- Publisher
- Hou, Hao, Park and Linzen via arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
What would settle it
Comparable training-only expenditure records through August 25, 2026, together with evidence testing investment necessity and the effect of marketing on public understanding.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, The AI Hater's Manifesto
The anthropomorphism and overpromising about these technologies has suffocated and obfuscated what they can actually do in pursuit of endless growth, and the only reason they can do anything is that OpenAI and Anthropic were allowed to annihilate hundreds of billions of dollars on training
- Where
- paragraph 31
- Published
- 2026-08-25
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-the-ai-haters-manifesto-14ba8d-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.