“I do not believe that the companies making foundation models (such as OpenAI and Anthropic) have been incentivized to do more with less, and because their chummy relationships with hyperscalers were focused almost entirely on "make the biggest, most hugest models possible, using the biggest, most hugest chips," and because the absence of profitability didn’t stop them from raising more money, efficiency was never a major problem for them.”
Our reading, the sentence that was checked:
He hypothesises that foundation model companies such as OpenAI and Anthropic were not incentivised to do more with less, and that because their hyperscaler relationships focused almost entirely on the biggest models and chips, and because being unprofitable didn't stop them raising money, efficiency was never a major problem for them.
Assessment: Unresolved Evidence as of
Where and when Ed Zitron wrote it
Where's Your Ed At, Deep Impact, paragraph 37.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment wyea-deep-impact-c0a67c-a2
We recorded 50 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
- 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)
Conditions
- stated as his hypothesis / 'I do not believe' (qualifier, stated by the speaker)
- focused almost entirely (qualifier, stated by the speaker)
From the speaker
- I have a hypothesis
- not ... incentivized to do more with less
- focused almost entirely
- absence of profitability didn’t stop them from raising more money
- efficiency was never a major problem
Our interpretation
- The claim is that financing and hyperscaler relationships removed meaningful pressure to economize, not merely that these companies spent heavily.
- The surrounding text acknowledges efficiency teams. Therefore, the existence of an efficiency project alone does not refute the causal hypothesis.
- The relevant period is the companies' history through the January 29, 2025 post; later conduct cannot establish earlier incentives.
Counts as supported if: Financing terms, compute agreements, internal decisions, or credible comparative evidence show that capital access and hyperscaler arrangements materially reduced efficiency pressure and directed priorities overwhelmingly toward scale.
Counts as contradicted if: Comparable evidence shows that these arrangements imposed substantial efficiency pressure or that efficiency materially constrained investment and operating decisions during the claimed period.
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
OpenAI announced GPT-4o mini as a cost-efficient small model on July 18, 2024, and Anthropic described Haiku as fast and affordable on March 13, 2024. [e1, e2] Anthropic's November 22, 2024 announcement paired a new $4 billion Amazon investment with work on computational efficiency. [e3] These disclosures document efficiency initiatives alongside financing, but do not establish whether financing and hyperscaler relationships reduced meaningful pressure to economize through January 29, 2025. [e1, e2, e3]
Limitations
- The announcements do not establish financing terms, internal budget constraints, or the relative priority of scale and efficiency. [e1, e2, e3]
- Product pricing and positioning do not measure underlying resource savings or establish incentives across the wider industry. [e1, e2]
Evidence (3 items)
Context only: OpenAI,
cost-efficient small model
Why it matters here: OpenAI publicly pursued smaller, cheaper models before DeepSeek's releases. This challenges a literal claim of no efficiency effort, but does not establish how financing affected the urgency of that work.
- Where
- Opening paragraph
- Publisher
- OpenAI
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
Context only: Anthropic,
the fastest and most affordable
Why it matters here: Anthropic's product strategy included speed and affordability. This is material counterevidence to an overly literal reading, but product positioning does not reveal the strength or source of internal efficiency pressure.
- Where
- Opening paragraph
- Publisher
- Anthropic
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
Context only: Anthropic,
our primary cloud
Why it matters here: The announcement confirms substantial financing while describing joint work on kernels, software, and computational efficiency. It shows that funding and efficiency collaboration coexisted, without establishing whether funding weakened economic pressure.
- Publisher
- Anthropic
- Kind of source
- Company disclosure: the company describing itself, not an independent finding
- Describes events of
- Public from
What would settle it
Financing and compute agreements, internal budget decisions, or credible comparative evidence from the period through January 29, 2025 showing how those arrangements affected efficiency pressure and whether efficiency materially constrained operating or investment decisions.
Where it was said (1)
Said once in the sources this register covers.
: Where's Your Ed At, Deep Impact
I do not believe that the companies making foundation models (such as OpenAI and Anthropic) have been incentivized to do more with less, and because their chummy relationships with hyperscalers were focused almost entirely on "make the biggest, most hugest models possible, using the biggest, most hugest chips," and because the absence of profitability didn’t stop them from raising more money, efficiency was never a major problem for them.
- Where
- paragraph 37
- Published
- 2025-01-29
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment wyea-deep-impact-c0a67c-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.