I Will Never Respect A Website
We recorded 36 claims from this post. 18 were checked against evidence. 18 can’t be checked. 17 more are held. Read the post at its source.
Claims from this post (36)
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: UnresolvedSaid
Forecast
“LLMs will always hallucinate, their outputs are not trustworthy as a result, they cannot be deterministic, and any chance of any mistakes of any kind are unforgivable.”
Our reading: LLMs will always hallucinate, so their outputs are not trustworthy.
No deadline stated Evidence as of
Limitation: The forecast has no deadline.
Assessment: ContradictedSaid
Observation
“LLMs will always hallucinate, their outputs are not trustworthy as a result, they cannot be deterministic, and any chance of any mistakes of any kind are unforgivable.”
Our reading: LLMs cannot be deterministic.
No horizon Evidence as of
Limitation: The evidence is the implementing lab's report, not an independent replication.
Assessment: ContradictedSaid
Observation
“Every “AI agent” story is either about code generation, summarizing some sort of information source, or generating something based on an information source that you may or may not be able to trust.”
Our reading: Every AI agent story is about code generation, summarization, or generating content from an information source of uncertain trustworthiness.
No horizon Evidence as of
Limitation: The classification depends on the meaning of 'generating something'; a reading encompassing all tool commands would change the result.
Assessment: Can’t be checkedSaid
Observation
“In any case, no previous real or imagined tech revolution has ever inspired such eager defensiveness, tribalism or outright aggression toward dissenters, nor such ridiculous attempts to obfuscate the truth about a product outside of cryptocurrency, an industry with obvious corruption and financial incentives.”
Our reading: Apart from crypto, no previous tech revolution inspired such defensiveness, tribalism and obfuscation as AI.
No horizon
Qualifier: except cryptocurrency
Assessment: Can’t be checkedSaid
Rhetoric
“LLMs are excellent digital busyboxes for those who want to come up with a way to work differently rather than actually doing work.”
Our reading: LLMs are excellent digital busyboxes for people who want to come up with a way to work differently rather than actually doing work.
No horizon
Scope: for those who want to come up with a way to work differently rather than actually doing work
Assessment: Can’t be checkedSaid
Observation
“They do not think, they do not have knowledge, they are conjuring up their own training data (or reflecting post-training instructions from those developing them or documents instructing them to act a certain way), and any time you try and make them do something more-complicated, they begin to fall apart, and/or become exponentially more-expensive.”
Our reading: LLMs do not think or have knowledge, and they fall apart or become exponentially more expensive on more complicated tasks.
No horizon
Qualifier: on more complicated tasks
Assessment: Can’t be checkedSaid
Observation
“LLMs are not “artificial intelligence” at all.”
Our reading: LLMs are not artificial intelligence.
No horizon
Assessment: UnresolvedSaid
Observation
“The so-called “agentic AI” that is supposedly powerful and autonomous is actually incredibly demanding of its human users — you must set it up in so many different ways and connect it to so many different services and check that every “agent” (different chatbot) is instructed in exactly the right way, and that none of these agents cause any problems (they will) with each other.”
Our reading: So-called agentic AI, supposedly powerful and autonomous, is actually incredibly demanding of its human users, who must set it up in many ways, connect it to many services, and check that every agent is instructed correctly, and its agents will cause problems with each other.
No horizon Evidence as of
Limitation: Neither example is a representative workload study.
Assessment: Can’t be checkedSaid
Observation
“Large Language Models perform an impression of automating that process, but for the most part force you, the user, to do the shit that matters, even if that means “be responsible for the code that it puts out.””
Our reading: LLMs perform an impression of automating the process of building software, but for the most part force the user to do the things that matter, even if that only means being responsible for the code they put out.
No horizon
Qualifier: for the most part
Assessment: Can’t be checkedSaid
Observation
“Anyone you read talking about how LLMs “allowed them to look through a massive dataset” is likely using Python.”
Our reading: People who credit LLMs with letting them analyze massive datasets are likely really using Python.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“They should have to show you how much work it took to prepare the system, and why it’s superior to just doing it themselves.”
Our reading: People claiming others are being left behind by AI should have to show the setup effort required and why AI beats doing it themselves.
No horizon
Assessment: UnresolvedSaid
Observation
“I’m in a research-heavy business, and I have tried the most-powerful LLMs and highest-priced RAG/post-RAG research tools, and every time find them bereft of any unique analysis or suggestions.”
Our reading: In his research-heavy business, the author has tried the most powerful LLMs and the highest-priced RAG/post-RAG research tools and every time found them bereft of any unique analysis or suggestions.
No horizon Evidence as of
Limitation: Failure to locate records does not show that the sessions did not occur.
Assessment: Can’t be checkedSaid
Causal claim
“I just think that they would be used dramatically less if there weren’t an industrial-scale publicity campaign run through the media and the majority of corporate America both incentivizing and forcing them to do so.”
Our reading: Developer use of LLMs would be dramatically lower without the media publicity campaign and corporate pressure.
No horizon
Qualifier: absent media and corporate pressure
Assessment: Can’t be checkedSaid
Causal claim
“Similarly, I’m not sure anybody would’ve been anywhere near as excited if OpenAI and Anthropic hadn’t intentionally sold them a product that was impossible to support long-term.”
Our reading: The author is not sure anybody would have been anywhere near as excited about LLMs if OpenAI and Anthropic had not intentionally sold them a product that was impossible to support long-term.
No horizon
Qualifier: I'm not sure
Assessment: UnresolvedSaid
Forecast
“The con of the Large Language Model era is that any subscription you pay for is massively subsidized, and that any product you use can and will see its service degraded as these companies desperately try to either ease their capacity issues or lower their burn rate.”
Our reading: Any LLM subscription you pay for is massively subsidized, and any LLM product you use can and will see its service degraded as these companies desperately try to ease their capacity issues or lower their burn rate.
No deadline stated Evidence as of
Limitation: The financial assertion lacks a stated accounting basis.
Assessment: Can’t be checkedSaid
Observation
“This also suggests that Anthropic’s (and OpenAI’s by extension) business model is fundamentally flawed.”
Our reading: Anthropic's inability to serve its paying customers at its current infrastructure scale, while still burning money, suggests that Anthropic's business model, and by extension OpenAI's, is fundamentally flawed.
No horizon
Qualifier: suggests
Assessment: ContradictedSaid
Causal claim
“OpenAI and Anthropic need to show continued revenue growth, which means that they must have capacity available for new customers, which means that old customers will always be the first to be punished.”
Our reading: Because OpenAI and Anthropic must show revenue growth and keep capacity for new customers, existing customers will always be punished first.
No horizon Evidence as of
Limitation: The counterexample was a small, temporary experiment at Anthropic.
Assessment: UnresolvedSaid
Causal claim
“Not only must Anthropic and OpenAI find a way to make their unsustainable and unprofitable services burn less money, but they must also constantly dance with metering out whatever capacity they have to their customers, because the more extra capacity they buy, the more money they lose.”
Our reading: Anthropic's and OpenAI's services are unsustainable and unprofitable, and buying more capacity makes them lose more money.
No horizon Evidence as of
Limitation: The contract's accounting basis is unknown.
Assessment: UnresolvedSaid
Causal claim
“As I’ve said for years, AI companies are inherently unsustainable due to the unreliable and inconsistent outputs of Large Language Models and the incredible costs of providing the services.”
Our reading: AI companies are inherently unsustainable because of unreliable LLM outputs and high service costs.
No horizon Evidence as of
Limitation: The financial metric and accounting basis are unspecified.
Assessment: UnresolvedSaid
Observation
“It’s also clear, at this point, that Anthropic and OpenAI have both offered subscriptions that were impossible to provide at scale at the price and availability that they were leading up to 2026, and that they did so with the intention of growing their revenue to acquire more customers, equity investment and attention.”
Our reading: It is clear that Anthropic and OpenAI both offered subscriptions that were impossible to provide at scale at the price and availability they had leading up to 2026, and did so intending to grow revenue to acquire more customers, equity investment and attention.
No horizon Evidence as of
Limitation: The reported Pro losses lack a defined accounting basis or matched subscriber revenue and cost data [e1].
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“In fact, the ethical thing to do would’ve been to charge for and restrict the services in line with their actual costs so that users could have reliable and consistent access to the services in question.”
Our reading: Anthropic and OpenAI should have priced and restricted services in line with actual costs.
No horizon
Assessment: UnresolvedSaid
Forecast
“On June 1 2026, any article or post about Codex’s efficacy must be rewritten, as rate limits will be halved.”
Our reading: On June 1 2026, Codex rate limits will be halved.
Deadline passed Evidence as of
Limitation: The strongest retrospective evidence concerns one subscription tier.
Assessment: Can’t be checkedSaid
Observation
“Right now, I’d guess that OpenAI has access to around 2GW of capacity (as of the end of 2025), and Anthropic around 1GW based on discussions with sources.”
Our reading: The author guesses OpenAI had about 2GW of capacity at end of 2025 and Anthropic about 1GW.
No horizon
Qualifier: author's guess based on sources
Assessment: UnresolvedSaid
Conditional forecast
“And if it doesn’t make that much revenue — and also doesn’t get funding or debt to support it — OpenAI will run out of money, much as Anthropic will if that capacity gets built and it doesn’t make tens of billions of dollars a month to pay for it.”
Our reading: If OpenAI misses its projected revenue and lacks funding or debt, it will run out of money; Anthropic will too if its capacity is built and it doesn't earn tens of billions a month.
No deadline stated Evidence as of
Only if: OpenAI misses projected revenue and lacks funding or debt
Assessment: UnresolvedSaid
Conditional forecast
“In fact, I think that as capacity limits get hit, both Anthropic and OpenAI will degrade the experience for the user (either through model degradation or rate limit decay) as much as they can.”
Our reading: As capacity limits are hit, Anthropic and OpenAI will degrade user experience as much as they can via model degradation or rate limits.
No deadline stated Evidence as of
Only if: as capacity limits get hit
Assessment: ContradictedSaid
Forecast
“I see no scenario where costs come down, or where rate limits are eased.”
Our reading: The author sees no scenario in which costs for Anthropic's and OpenAI's services come down or their rate limits are eased.
Settled before its deadline Evidence as of
Limitation: The finding concerns the rate-limit branch.
Assessment: Can’t be checkedSaid
Forecast
“I imagine that at some point enterprise customers will be able to pay for an even higher priority tier, and that Anthropic’s “Teams” subscription (which allows you to use the same subsidized subscriptions as everyone else) will be killed off, forcing anyone in an organization paying for Claude Code (and eventually Codex) via the API, as has already happened for Anthropic’s enterprise users.”
Our reading: The author imagines that at some point enterprise customers will be able to pay for an even higher priority tier, and that Anthropic's Teams subscription will be killed off, forcing organizations paying for Claude Code (and eventually Codex) onto the API, as already happened for Anthropic's enterprise users.
No horizon
Qualifier: I imagine
Assessment: UnresolvedSaid
Forecast
“Anyone using a subscription to OpenAI or Anthropic’s services needs to wake up and realize that their way of life is going away — that rate limits will make current workflows impossible, that prices will increase, and that the product they’re selling even today is not one that makes any economic sense.”
Our reading: For OpenAI and Anthropic subscribers, rate limits will make current workflows impossible and prices will increase.
No deadline stated Evidence as of
Limitation: The OpenAI help page is mutable; its displayed update age places the accessed version on October 27.
Assessment: UnresolvedSaid
Observation
“Every single LLM product is being sold under false pretenses about what’s actually sustainable and possible long term.”
Our reading: Every LLM product is sold under false pretenses about long-term sustainability and possibility.
No horizon Evidence as of
Limitation: Terms of service do not capture the whole sales presentation.
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Both he and Mr. Amodei need to immediately stop overstating the capabilities of Large Language Models.”
Our reading: Altman and Amodei must immediately stop overstating LLM capabilities.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Mr. Amodei should immediately cease any and all discussions of AI potentially or otherwise eliminating 50% of white collar jobs, as Mr. Altman should cease predicting when Superintelligence might arrive, as Mr. Amodei should actively reject and denounce any suggestions of AI “creating a white collar bloodbath.””
Our reading: Amodei should stop discussing AI eliminating 50% of white-collar jobs and Altman should stop predicting superintelligence timelines.
No horizon
Assessment: UnresolvedSaid
Forecast
“These conversations are also nonsensical and misleading when you compare them to what Large Language Models can do, and this rhetoric is a blatant attempt to scare people into paying for software today based on what it absolutely cannot and will not do in the future.”
Our reading: LLMs cannot and will not do what AI executives' job-loss and superintelligence rhetoric claims.
No deadline stated Evidence as of
Limitation: The labor evidence covers a U.S. payroll sample through June 2026, not every workforce through the evaluation date.
Assessment: Can’t be checkedSaid
Observation
“Both Altman and Amodei engage in the language of AI doomerism as a means of generating attention, revenue and investment capital, actively selling their software and future investment potential based on their ownership of a technology that they say (disingenuously) is potentially going to take everybody’s jobs.”
Our reading: Altman and Amodei both use the language of AI doomerism to generate attention, revenue and investment capital, selling their software and future investment potential on a technology they disingenuously say may take everybody's jobs.
No horizon
Assessment: UnresolvedSaid
Causal claim
“These actions are morally wrong, and are also the direct result of the AI industry’s deceptive and manipulative scare campaign, one promoted by men like Altman and Amodei, as well as doomer fanfiction writers like Yudowsky, and, of course, Daniel Kokotajlo of AI 2027 — both of whom have had their work validated and propagated via the New York Times.”
Our reading: The attack on Sam Altman's home was a direct result of the AI industry's deceptive scare campaign.
No horizon Evidence as of
Limitation: The affidavit contains allegations supporting probable cause, not a final determination of motive or responsibility.
Assessment: Can’t be checkedSaid
Causal claim
“These are acts of violence that could have been stopped had ChatGPT and similar applications not been anthropomorphized by design, and trained to be “friendly.””
Our reading: The acts of violence by people twisted by LLMs into taking their own and others' lives could have been stopped had ChatGPT and similar applications not been anthropomorphized by design and trained to be 'friendly'.
No horizon
Qualifier: had ChatGPT and similar applications not been anthropomorphized by design and trained to be 'friendly'
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Stop talking about AGI. Start talking about this like regular old software, because that’s all that ChatGPT is.”
Our reading: AI executives should stop talking about AGI and describe ChatGPT as the regular software it is.
No horizon
Held from this post (17)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The criteria are ambiguous. Our reading, not assessed: OpenAI's new $100/month tier appears to have immediately reduced rate limits for $20/month subscribers.
- The two reviews read the claim differently
- The two reviews read the claim differently
- The record does not meet the current rules. Our reading, not assessed: The AI industry was sold on a lie, and capacity problems are turning even true believers against the AI labs.
- The criteria are ambiguous. Our reading, not assessed: LLMs get 'better' at coding as measured by benchmarks that don't really compare to real-world success, and even with the supposedly meteoric improvements over the last few months, nobody can actually explain what the result of that improvement is, nor does it appear to extend to any domain outside coding.
- Attribution is uncertain
- The two reviews read the claim differently
- The two reviews read the claim differently
- The criteria are ambiguous. Our reading, not assessed: Because LLMs interpret meaning from their training data without knowledge, thoughts, feelings or ability to learn, they can and will make mistakes, and when one is instructing another chatbot, such a mistake might build a fundamental flaw into the software or break the process entirely.
- The criteria are ambiguous. Our reading, not assessed: The OpenAI or Anthropic service that subscribers pay for is not sustainable at its current price, and the companies have no way to fix that problem.
- The record does not meet the current rules. Our reading, not assessed: Anthropic cannot satisfactorily serve its current paying customers at current scale and still expects to burn $14 billion.
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews read the claim differently
- The criteria are ambiguous. Our reading, not assessed: LLMs are designed to focus on users and affirm every interaction.
- The two reviews reached different assessments. Our reading, not assessed: The author believes things will only get worse for customers of OpenAI's and Anthropic's services as capacity issues continue.