AI Doesn't Have ROI
We recorded 18 claims from this post. 7 were checked against evidence. 11 can’t be checked. 6 more are held. Read the post at its source.
Claims from this post (18)
In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.
Assessment: Can’t be checkedSaid
Causal claim
“Every different prompt and project and interaction can go wrong in a way that is hard to predict or plan for other than having an eternal vigilance that the supposed “intelligence” doesn’t do something catastrophically stupid, because LLMs have no thoughts, consciousness or ability to learn outside of pre and post-training.”
Our reading: Every LLM prompt, project and interaction can go wrong in ways that are hard to predict or plan for, short of constant vigilance against catastrophically stupid behaviour, because LLMs have no thoughts, consciousness or ability to learn outside pre-training and post-training.
No horizon
Qualifier: hard to predict or plan for
Assessment: Can’t be checkedSaid
Observation
“These users — much like the users of effectively every subsidized AI subscription — never really knew how much anything they did cost, because Microsoft intentionally hid the actual cost of prompts and allowed users to spend obscene amounts as a way of boosting growth for GitHub Copilot.”
Our reading: Microsoft intentionally hid the actual cost of GitHub Copilot prompts and let users overspend in order to boost growth.
No horizon
Assessment: UnresolvedSaid
Causal claim
“This is why every AI startup is inherently unprofitable — they’re literally sending every penny of their venture capital money directly to Anthropic and OpenAI to power their unprofitable services.”
Our reading: Every AI startup is inherently unprofitable because they are literally sending every penny of their venture capital directly to Anthropic and OpenAI to power their unprofitable services.
No horizon Evidence as of
Limitation: A founder's undefined profitability statement is weaker than financial accounts.
Assessment: UnresolvedSaid
Observation
“AI labs may be able to run their own infrastructure and save some costs, but we have no evidence that this makes anything “profitable.””
Our reading: There is no evidence that AI labs running their own infrastructure makes anything profitable.
No horizon Evidence as of
Limitation: The result depends on the meaning of profitable and the required causal connection.
Assessment: UnresolvedSaid
Observation
“For example, Anthropic lets you burn anywhere from $8 to $13.50 in tokens for every dollar of subscription cost, and while AI boosters will say that Anthropic is “profitable on inference,” nobody actually has proof outside of theoretical scenarios posed by CEO Dario Amodei.”
Our reading: Anthropic lets subscribers burn $8 to $13.50 in tokens per subscription dollar, and there is no proof beyond Amodei's theoretical scenarios that Anthropic is profitable on inference.
No horizon Evidence as of
Limitation: The January calculation does not verify May allowances, model mix or caching treatment [e1].
Assessment: Can’t be checkedSaid
Causal claim
“Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees.”
Our reading: Anthropic, OpenAI and every other AI company deliberately obscured the costs of AI use because they knew that as soon as users had to pay for a model's mistakes they would scream in protest.
No horizon
Scope: every other AI company
Assessment: MixedSaid
Causal claim
“This issue bubbled to the surface in the last few months because Anthropic and OpenAI both quietly moved all of their enterprise customers to token-based billing in Q1 2026, and because these enterprise customers are run by Business Idiots with no connection to actual work, CEOs encouraged (or actively incentivized) their workers to use AI as much as possible, in some cases even making one’s AI use a KPI that could cost them their job.”
Our reading: The AI cost problem surfaced in the last few months because Anthropic and OpenAI quietly moved all their enterprise customers to token-based billing in Q1 2026, and because CEOs had encouraged or incentivized workers to use AI as much as possible, in some cases making AI use a job-threatening KPI.
No horizon Evidence as of
Limitation: Anthropic's migration record does not establish OpenAI's Q1 customer coverage [e1].
Assessment: UnresolvedSaid
Causal claim
“The overwhelming majority of AI users are completely divorced from and actively trained to ignore the true cost of AI tokens, which means they naturally use these services in a way that’s actively uneconomical.”
Our reading: The overwhelming majority of AI users are completely divorced from, and actively trained to ignore, the true cost of AI tokens, so they naturally use these services in an actively uneconomical way.
No horizon Evidence as of
Limitation: The population and economic perspective are broader than those covered by the located studies.
Assessment: UnresolvedSaid
Observation
“OpenAI, Anthropic and other AI companies have actively conspired to mislead the world about the true costs of AI, and it was working great right up until they decided to try charging what it actually cost.”
Our reading: OpenAI, Anthropic and other AI companies actively conspired to mislead the world about AI's true costs, and this worked well until they decided to try charging what AI actually costs.
No horizon Evidence as of
Limitation: The FTC report does not resolve the specific allegation of coordinated cost deception [e1].
Assessment: UnresolvedSaid
Observation
“I must repeat myself: AI is more expensive today than it was three years ago, and it is not getting cheaper.”
Our reading: AI costs more today than it did three years ago and is not getting cheaper.
No horizon Evidence as of
Limitation: The packet does not isolate matched total task costs at the required endpoints or the immediate prepublication trend.
Assessment: Can’t be checkedSaid
Observation
“Sam Altman’s comments about “intelligence too cheap to meter” were lies.”
Our reading: Sam Altman's claims about 'intelligence too cheap to meter' were lies.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“That fact AI companies backed by the full manufactured consent of the markets and most of the economy still had to subsidize their products shows exactly how flimsy their value truly is.”
Our reading: The fact that AI companies, backed by the full manufactured consent of the markets and most of the economy, still had to subsidize their products shows exactly how flimsy their value is.
No horizon
Qualifier: backed by the full manufactured consent of the markets and most of the economy
Assessment: Can’t be checkedSaid
Causal claim
“The only way to inflate the AI bubble both on a hardware and software level was to mislead the general public and investors on the costs and efficacy of AI models.”
Our reading: The AI bubble could only be inflated by misleading the public and investors about the costs and efficacy of AI models.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“Nevertheless, the threat of AI job loss has been enough to allow both Sam Altman and Dario Amodei to raise hundreds of billions of dollars lying about it”
Our reading: The threat of AI job loss allowed Sam Altman and Dario Amodei to raise hundreds of billions of dollars while lying about it, and both have since walked back their job-loss claims.
No horizon
Assessment: Can’t be checkedSaid
Observation
“In fact, I’d be willing to believe that AI had a negative economic impact, increasing operating expenses across the board and giving some software engineers prompt-based concussions by automating some coding in a way that makes them lazy and bad at writing software”
Our reading: AI may have had a negative economic impact, raising operating expenses and making some software engineers worse at writing software.
No horizon
Assessment: Can’t be checkedSaid
Rhetoric
“I realize I’m painting with a broad brush, so let me get a broader one: I believe anyone who relies on LLMs for anything is a mark.”
Our reading: Anyone who relies on LLMs for anything is a mark.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“They didn’t raise hundreds of billions of dollars by talking about any product currently on the market, and that’s because the current products are not very good products.”
Our reading: AI companies did not raise hundreds of billions of dollars by talking about any product currently on the market, because current products are not very good.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“This shit needs to work every time without fail and be absolutely flawless and autonomous.”
Our reading: AI needs to work every time without fail and be absolutely flawless and autonomous.
No horizon
Held from this post (6)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The two reviews read the claim differently
- The criteria are ambiguous. Our reading, not assessed: Nobody can measure the ROI of AI or standardize the cost of a task, because LLMs inherently hallucinate and harnesses and interfaces keep multiplying.
- The criteria are ambiguous. Our reading, not assessed: Every user of every AI subscription service has their tokens subsidized, and the actual cost of AI is hidden from them.
- The two reviews read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: Although per-million-token prices are falling, models use far more tokens for the same task, so inference cost per task is rising.
- The two reviews read the claim differently