“As a result, Anthropic and OpenAI are incentivized to have you burn more tokens and build inefficient models as a result.”
Our reading, the sentence that was checked:
Token-based pricing incentivizes Anthropic and OpenAI to make users burn more tokens and build inefficient models.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, The AI Hater's Manifesto, paragraph 94.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-the-ai-haters-manifesto-973013-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 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
- Anthropic and OpenAI
- incentivized to have you burn more tokens
- build inefficient models as a result
Our interpretation
- Read as a claim that token billing creates a financial incentive for avoidable token consumption at comparable task quality.
- An incentive does not establish intent, misconduct, or that either company actually follows it.
- Additional billed tokens can increase receipts while also increasing serving costs. Assess the commercial incentive rather than receipts alone.
- The claim concerns incentives around the post date, not an eventual forecast.
Counts as supported if: Applicable pricing and marginal-cost evidence establish that avoidable additional tokens improve expected commercial returns for both named providers, and link that advantage to inefficient model design.
Counts as contradicted if: Evidence establishes that avoidable additional tokens reduce expected commercial returns for both providers, undermining the asserted design incentive.
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
Token billing supplies a plausible revenue mechanism, and research demonstrates a related incentive to misreport tokenization. Neither establishes that building inefficient models improves returns for Anthropic and OpenAI. Both providers also describe efficiency improvements. Those disclosures do not disprove a conflicting incentive, but they prevent inferring inefficient design from the billing structure alone.
Limitations
- Provider efficiency disclosures are not independent verification.
- The research tests different model families and a different mechanism.
- This assessment does not infer intent or misconduct.
Evidence (4 items)
Context only: arXiv,
financial incentive
Why it matters here: The paper establishes a related financial incentive under its model and tests open models. Misreporting tokenization differs from generating unnecessary tokens, and the experiments do not establish either named provider's actual economics.
- Publisher
- arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Context only: Anthropic,
without spending more tokens
Why it matters here: Anthropic publishes a customer report of improved performance without more tokens. This weighs against treating token billing as proof of inefficient design, but does not eliminate a possible conflicting incentive.
- Publisher
- Anthropic
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
Context only: OpenAI,
reducing repeated work
Why it matters here: OpenAI describes engineering measures that reduce repeated computation and context overhead. These are relevant counterevidence to an automatic link from token billing to inefficient design, but are not an independent measure of incentives.
- Publisher
- OpenAI
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
Context only: OpenAI,
more direct path
Why it matters here: The disclosure combines token-metered pricing with claimed efficiency gains and lower customer prices. It does not disclose the marginal profit from unnecessary tokens.
- Publisher
- OpenAI
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
What would settle it
Provider-specific marginal economics or controlled evidence showing whether avoidable token consumption improves commercial returns at comparable quality, and whether that incentive shapes model design.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, The AI Hater's Manifesto
As a result, Anthropic and OpenAI are incentivized to have you burn more tokens and build inefficient models as a result.
- Where
- paragraph 94
- Published
- 2026-08-25
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-the-ai-haters-manifesto-973013-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.